text,target
"  In these notes we formally describe the functionality of Calculating Valid
Domains from the BDD representing the solution space of valid configurations.
The formalization is largely based on the CLab configuration framework.
",Calculating Valid Domains for BDD-Based Interactive Configuration
"  Motivation: Profile hidden Markov Models (pHMMs) are a popular and very
useful tool in the detection of the remote homologue protein families.
Unfortunately, their performance is not always satisfactory when proteins are
in the 'twilight zone'. We present HMMER-STRUCT, a model construction algorithm
and tool that tries to improve pHMM performance by using structural information
while training pHMMs. As a first step, HMMER-STRUCT constructs a set of pHMMs.
Each pHMM is constructed by weighting each residue in an aligned protein
according to a specific structural property of the residue. Properties used
were primary, secondary and tertiary structures, accessibility and packing.
HMMER-STRUCT then prioritizes the results by voting. Results: We used the SCOP
database to perform our experiments. Throughout, we apply leave-one-family-out
cross-validation over protein superfamilies. First, we used the MAMMOTH-mult
structural aligner to align the training set proteins. Then, we performed two
sets of experiments. In a first experiment, we compared structure weighted
models against standard pHMMs and against each other. In a second experiment,
we compared the voting model against individual pHMMs. We compare method
performance through ROC curves and through Precision/Recall curves, and assess
significance through the paired two tailed t-test. Our results show significant
performance improvements of all structurally weighted models over default
HMMER, and a significant improvement in sensitivity of the combined models over
both the original model and the structurally weighted models.
",A study of structural properties on profiles HMMs
"  This paper proposes an approach to training rough set models using Bayesian
framework trained using Markov Chain Monte Carlo (MCMC) method. The prior
probabilities are constructed from the prior knowledge that good rough set
models have fewer rules. Markov Chain Monte Carlo sampling is conducted through
sampling in the rough set granule space and Metropolis algorithm is used as an
acceptance criteria. The proposed method is tested to estimate the risk of HIV
given demographic data. The results obtained shows that the proposed approach
is able to achieve an average accuracy of 58% with the accuracy varying up to
66%. In addition the Bayesian rough set give the probabilities of the estimated
HIV status as well as the linguistic rules describing how the demographic
parameters drive the risk of HIV.
",Bayesian approach to rough set
"  Noise, corruptions and variations in face images can seriously hurt the
performance of face recognition systems. To make such systems robust,
multiclass neuralnetwork classifiers capable of learning from noisy data have
been suggested. However on large face data sets such systems cannot provide the
robustness at a high level. In this paper we explore a pairwise neural-network
system as an alternative approach to improving the robustness of face
recognition. In our experiments this approach is shown to outperform the
multiclass neural-network system in terms of the predictive accuracy on the
face images corrupted by noise.
","Comparing Robustness of Pairwise and Multiclass Neural-Network Systems
  for Face Recognition"
"  Evolutionary Learning proceeds by evolving a population of classifiers, from
which it generally returns (with some notable exceptions) the single
best-of-run classifier as final result. In the meanwhile, Ensemble Learning,
one of the most efficient approaches in supervised Machine Learning for the
last decade, proceeds by building a population of diverse classifiers. Ensemble
Learning with Evolutionary Computation thus receives increasing attention. The
Evolutionary Ensemble Learning (EEL) approach presented in this paper features
two contributions. First, a new fitness function, inspired by co-evolution and
enforcing the classifier diversity, is presented. Further, a new selection
criterion based on the classification margin is proposed. This criterion is
used to extract the classifier ensemble from the final population only
(Off-line) or incrementally along evolution (On-line). Experiments on a set of
benchmark problems show that Off-line outperforms single-hypothesis
evolutionary learning and state-of-art Boosting and generates smaller
classifier ensembles.
",Ensemble Learning for Free with Evolutionary Algorithms ?
"  Gaussian mixture models (GMM) and support vector machines (SVM) are
introduced to classify faults in a population of cylindrical shells. The
proposed procedures are tested on a population of 20 cylindrical shells and
their performance is compared to the procedure, which uses multi-layer
perceptrons (MLP). The modal properties extracted from vibration data are used
to train the GMM, SVM and MLP. It is observed that the GMM produces 98%, SVM
produces 94% classification accuracy while the MLP produces 88% classification
rates.
","Fault Classification in Cylinders Using Multilayer Perceptrons, Support
  Vector Machines and Guassian Mixture Models"
"  The act of bluffing confounds game designers to this day. The very nature of
bluffing is even open for debate, adding further complication to the process of
creating intelligent virtual players that can bluff, and hence play,
realistically. Through the use of intelligent, learning agents, and carefully
designed agent outlooks, an agent can in fact learn to predict its opponents
reactions based not only on its own cards, but on the actions of those around
it. With this wider scope of understanding, an agent can in learn to bluff its
opponents, with the action representing not an illogical action, as bluffing is
often viewed, but rather as an act of maximising returns through an effective
statistical optimisation. By using a tee dee lambda learning algorithm to
continuously adapt neural network agent intelligence, agents have been shown to
be able to learn to bluff without outside prompting, and even to learn to call
each others bluffs in free, competitive play.
",Learning to Bluff
"  The semiring-based constraint satisfaction problems (semiring CSPs), proposed
by Bistarelli, Montanari and Rossi \cite{BMR97}, is a very general framework of
soft constraints. In this paper we propose an abstraction scheme for soft
constraints that uses semiring homomorphism. To find optimal solutions of the
concrete problem, the idea is, first working in the abstract problem and
finding its optimal solutions, then using them to solve the concrete problem.
  In particular, we show that a mapping preserves optimal solutions if and only
if it is an order-reflecting semiring homomorphism. Moreover, for a semiring
homomorphism $\alpha$ and a problem $P$ over $S$, if $t$ is optimal in
$\alpha(P)$, then there is an optimal solution $\bar{t}$ of $P$ such that
$\bar{t}$ has the same value as $t$ in $\alpha(P)$.
",Soft constraint abstraction based on semiring homomorphism
"  This paper proposes a neuro-rough model based on multi-layered perceptron and
rough set. The neuro-rough model is then tested on modelling the risk of HIV
from demographic data. The model is formulated using Bayesian framework and
trained using Monte Carlo method and Metropolis criterion. When the model was
tested to estimate the risk of HIV infection given the demographic data it was
found to give the accuracy of 62%. The proposed model is able to combine the
accuracy of the Bayesian MLP model and the transparency of Bayesian rough set
model.
",Bayesian Approach to Neuro-Rough Models
"  Water plays a pivotal role in many physical processes, and most importantly
in sustaining human life, animal life and plant life. Water supply entities
therefore have the responsibility to supply clean and safe water at the rate
required by the consumer. It is therefore necessary to implement mechanisms and
systems that can be employed to predict both short-term and long-term water
demands. The increasingly growing field of computational intelligence
techniques has been proposed as an efficient tool in the modelling of dynamic
phenomena. The primary objective of this paper is to compare the efficiency of
two computational intelligence techniques in water demand forecasting. The
techniques under comparison are the Artificial Neural Networks (ANNs) and the
Support Vector Machines (SVMs). In this study it was observed that the ANNs
perform better than the SVMs. This performance is measured against the
generalisation ability of the two.
","Artificial Neural Networks and Support Vector Machines for Water Demand
  Time Series Forecasting"
"  An ensemble based approach for dealing with missing data, without predicting
or imputing the missing values is proposed. This technique is suitable for
online operations of neural networks and as a result, is used for online
condition monitoring. The proposed technique is tested in both classification
and regression problems. An ensemble of Fuzzy-ARTMAPs is used for
classification whereas an ensemble of multi-layer perceptrons is used for the
regression problem. Results obtained using this ensemble-based technique are
compared to those obtained using a combination of auto-associative neural
networks and genetic algorithms and findings show that this method can perform
up to 9% better in regression problems. Another advantage of the proposed
technique is that it eliminates the need for finding the best estimate of the
data, and hence, saves time.
","Fuzzy Artmap and Neural Network Approach to Online Processing of Inputs
  with Missing Values"
"  Militarised conflict is one of the risks that have a significant impact on
society. Militarised Interstate Dispute (MID) is defined as an outcome of
interstate interactions, which result on either peace or conflict. Effective
prediction of the possibility of conflict between states is an important
decision support tool for policy makers. In a previous research, neural
networks (NNs) have been implemented to predict the MID. Support Vector
Machines (SVMs) have proven to be very good prediction techniques and are
introduced for the prediction of MIDs in this study and compared to neural
networks. The results show that SVMs predict MID better than NNs while NNs give
more consistent and easy to interpret sensitivity analysis than SVMs.
",Artificial Intelligence for Conflict Management
"  The idea of symbolic controllers tries to bridge the gap between the top-down
manual design of the controller architecture, as advocated in Brooks'
subsumption architecture, and the bottom-up designer-free approach that is now
standard within the Evolutionary Robotics community. The designer provides a
set of elementary behavior, and evolution is given the goal of assembling them
to solve complex tasks. Two experiments are presented, demonstrating the
efficiency and showing the recursiveness of this approach. In particular, the
sensitivity with respect to the proposed elementary behaviors, and the
robustness w.r.t. generalization of the resulting controllers are studied in
detail.
",Evolving Symbolic Controllers
"  This paper introduces a continuous model for Multi-cellular Developmental
Design. The cells are fixed on a 2D grid and exchange ""chemicals"" with their
neighbors during the growth process. The quantity of chemicals that a cell
produces, as well as the differentiation value of the cell in the phenotype,
are controlled by a Neural Network (the genotype) that takes as inputs the
chemicals produced by the neighboring cells at the previous time step. In the
proposed model, the number of iterations of the growth process is not
pre-determined, but emerges during evolution: only organisms for which the
growth process stabilizes give a phenotype (the stable state), others are
declared nonviable. The optimization of the controller is done using the NEAT
algorithm, that optimizes both the topology and the weights of the Neural
Networks. Though each cell only receives local information from its neighbors,
the experimental results of the proposed approach on the 'flags' problems (the
phenotype must match a given 2D pattern) are almost as good as those of a
direct regression approach using the same model with global information.
Moreover, the resulting multi-cellular organisms exhibit almost perfect
self-healing characteristics.
",Robust Multi-Cellular Developmental Design
"  This paper uses Artificial Neural Network (ANN) models to compute response of
structural system subject to Indian earthquakes at Chamoli and Uttarkashi
ground motion data. The system is first trained for a single real earthquake
data. The trained ANN architecture is then used to simulate earthquakes with
various intensities and it was found that the predicted responses given by ANN
model are accurate for practical purposes. When the ANN is trained by a part of
the ground motion data, it can also identify the responses of the structural
system well. In this way the safeness of the structural systems may be
predicted in case of future earthquakes without waiting for the earthquake to
occur for the lessons. Time period and the corresponding maximum response of
the building for an earthquake has been evaluated, which is again trained to
predict the maximum response of the building at different time periods. The
trained time period versus maximum response ANN model is also tested for real
earthquake data of other place, which was not used in the training and was
found to be in good agreement.
","Response Prediction of Structural System Subject to Earthquake Motions
  using Artificial Neural Network"
"  This paper presents a fault classification method which makes use of a
Takagi-Sugeno neuro-fuzzy model and Pseudomodal energies calculated from the
vibration signals of cylindrical shells. The calculation of Pseudomodal
Energies, for the purposes of condition monitoring, has previously been found
to be an accurate method of extracting features from vibration signals. This
calculation is therefore used to extract features from vibration signals
obtained from a diverse population of cylindrical shells. Some of the cylinders
in the population have faults in different substructures. The pseudomodal
energies calculated from the vibration signals are then used as inputs to a
neuro-fuzzy model. A leave-one-out cross-validation process is used to test the
performance of the model. It is found that the neuro-fuzzy model is able to
classify faults with an accuracy of 91.62%, which is higher than the previously
used multilayer perceptron.
","Fault Classification using Pseudomodal Energies and Neuro-fuzzy
  modelling"
"  This paper presents bushing condition monitoring frameworks that use
multi-layer perceptrons (MLP), radial basis functions (RBF) and support vector
machines (SVM) classifiers. The first level of the framework determines if the
bushing is faulty or not while the second level determines the type of fault.
The diagnostic gases in the bushings are analyzed using the dissolve gas
analysis. MLP gives superior performance in terms of accuracy and training time
than SVM and RBF. In addition, an on-line bushing condition monitoring
approach, which is able to adapt to newly acquired data are introduced. This
approach is able to accommodate new classes that are introduced by incoming
data and is implemented using an incremental learning algorithm that uses MLP.
The testing results improved from 67.5% to 95.8% as new data were introduced
and the testing results improved from 60% to 95.3% as new conditions were
introduced. On average the confidence value of the framework on its decision
was 0.92.
",On-Line Condition Monitoring using Computational Intelligence
"  This paper overviews the basic principles and recent advances in the emerging
field of Quantum Computation (QC), highlighting its potential application to
Artificial Intelligence (AI). The paper provides a very brief introduction to
basic QC issues like quantum registers, quantum gates and quantum algorithms
and then it presents references, ideas and research guidelines on how QC can be
used to deal with some basic AI problems, such as search and pattern matching,
as soon as quantum computers become widely available.
",The Road to Quantum Artificial Intelligence
"  Cluster matching by permuting cluster labels is important in many clustering
contexts such as cluster validation and cluster ensemble techniques. The
classic approach is to minimize the euclidean distance between two cluster
solutions which induces inappropriate stability in certain settings. Therefore,
we present the truematch algorithm that introduces two improvements best
explained in the crisp case. First, instead of maximizing the trace of the
cluster crosstable, we propose to maximize a chi-square transformation of this
crosstable. Thus, the trace will not be dominated by the cells with the largest
counts but by the cells with the most non-random observations, taking into
account the marginals. Second, we suggest a probabilistic component in order to
break ties and to make the matching algorithm truly random on random data. The
truematch algorithm is designed as a building block of the truecluster
framework and scales in polynomial time. First simulation results confirm that
the truematch algorithm gives more consistent truecluster results for unequal
cluster sizes. Free R software is available.
",Truecluster matching
"  Semantic network research has seen a resurgence from its early history in the
cognitive sciences with the inception of the Semantic Web initiative. The
Semantic Web effort has brought forth an array of technologies that support the
encoding, storage, and querying of the semantic network data structure at the
world stage. Currently, the popular conception of the Semantic Web is that of a
data modeling medium where real and conceptual entities are related in
semantically meaningful ways. However, new models have emerged that explicitly
encode procedural information within the semantic network substrate. With these
new technologies, the Semantic Web has evolved from a data modeling medium to a
computational medium. This article provides a classification of existing
computational modeling efforts and the requirements of supporting technologies
that will aid in the further growth of this burgeoning domain.
",Modeling Computations in a Semantic Network
"  This paper describes a system capable of semi-automatically filling an XML
template from free texts in the clinical domain (practice guidelines). The XML
template includes semantic information not explicitly encoded in the text
(pairs of conditions and actions/recommendations). Therefore, there is a need
to compute the exact scope of conditions over text sequences expressing the
required actions. We present a system developed for this task. We show that it
yields good performance when applied to the analysis of French practice
guidelines.
",Automatically Restructuring Practice Guidelines using the GEM DTD
"  Representing and reasoning about qualitative temporal information is an
essential part of many artificial intelligence tasks. Lots of models have been
proposed in the litterature for representing such temporal information. All
derive from a point-based or an interval-based framework. One fundamental
reasoning task that arises in applications of these frameworks is given by the
following scheme: given possibly indefinite and incomplete knowledge of the
binary relationships between some temporal objects, find the consistent
scenarii between all these objects. All these models require transitive tables
-- or similarly inference rules-- for solving such tasks. We have defined an
alternative model, S-languages - to represent qualitative temporal information,
based on the only two relations of \emph{precedence} and \emph{simultaneity}.
In this paper, we show how this model enables to avoid transitive tables or
inference rules to handle this kind of problem.
",Temporal Reasoning without Transitive Tables
"  This paper is a survey of a large number of informal definitions of
``intelligence'' that the authors have collected over the years. Naturally,
compiling a complete list would be impossible as many definitions of
intelligence are buried deep inside articles and books. Nevertheless, the
70-odd definitions presented here are, to the authors' knowledge, the largest
and most well referenced collection there is.
",A Collection of Definitions of Intelligence
"  Web semantic access in specific domains calls for specialized search engines
with enhanced semantic querying and indexing capacities, which pertain both to
information retrieval (IR) and to information extraction (IE). A rich
linguistic analysis is required either to identify the relevant semantic units
to index and weight them according to linguistic specific statistical
distribution, or as the basis of an information extraction process. Recent
developments make Natural Language Processing (NLP) techniques reliable enough
to process large collections of documents and to enrich them with semantic
annotations. This paper focuses on the design and the development of a text
processing platform, Ogmios, which has been developed in the ALVIS project. The
Ogmios platform exploits existing NLP modules and resources, which may be tuned
to specific domains and produces linguistically annotated documents. We show
how the three constraints of genericity, domain semantic awareness and
performance can be handled all together.
","A Robust Linguistic Platform for Efficient and Domain specific Web
  Content Analysis"
"  We consider the problem of finding an n-agent joint-policy for the optimal
finite-horizon control of a decentralized Pomdp (Dec-Pomdp). This is a problem
of very high complexity (NEXP-hard in n >= 2). In this paper, we propose a new
mathematical programming approach for the problem. Our approach is based on two
ideas: First, we represent each agent's policy in the sequence-form and not in
the tree-form, thereby obtaining a very compact representation of the set of
joint-policies. Second, using this compact representation, we solve this
problem as an instance of combinatorial optimization for which we formulate a
mixed integer linear program (MILP). The optimal solution of the MILP directly
yields an optimal joint-policy for the Dec-Pomdp. Computational experience
shows that formulating and solving the MILP requires significantly less time to
solve benchmark Dec-Pomdp problems than existing algorithms. For example, the
multi-agent tiger problem for horizon 4 is solved in 72 secs with the MILP
whereas existing algorithms require several hours to solve it.
","Mixed Integer Linear Programming For Exact Finite-Horizon Planning In
  Decentralized Pomdps"
"  In this paper, we employ Probabilistic Neural Network (PNN) with image and
data processing techniques to implement a general purpose automated leaf
recognition algorithm. 12 leaf features are extracted and orthogonalized into 5
principal variables which consist the input vector of the PNN. The PNN is
trained by 1800 leaves to classify 32 kinds of plants with an accuracy greater
than 90%. Compared with other approaches, our algorithm is an accurate
artificial intelligence approach which is fast in execution and easy in
implementation.
","A Leaf Recognition Algorithm for Plant Classification Using
  Probabilistic Neural Network"
"  When Kurt Goedel layed the foundations of theoretical computer science in
1931, he also introduced essential concepts of the theory of Artificial
Intelligence (AI). Although much of subsequent AI research has focused on
heuristics, which still play a major role in many practical AI applications, in
the new millennium AI theory has finally become a full-fledged formal science,
with important optimality results for embodied agents living in unknown
environments, obtained through a combination of theory a la Goedel and
probability theory. Here we look back at important milestones of AI history,
mention essential recent results, and speculate about what we may expect from
the next 25 years, emphasizing the significance of the ongoing dramatic
hardware speedups, and discussing Goedel-inspired, self-referential,
self-improving universal problem solvers.
","2006: Celebrating 75 years of AI - History and Outlook: the Next 25
  Years"
"  In this paper we extend the new family of (quantitative) Belief Conditioning
Rules (BCR) recently developed in the Dezert-Smarandache Theory (DSmT) to their
qualitative counterpart for belief revision. Since the revision of quantitative
as well as qualitative belief assignment given the occurrence of a new event
(the conditioning constraint) can be done in many possible ways, we present
here only what we consider as the most appealing Qualitative Belief
Conditioning Rules (QBCR) which allow to revise the belief directly with words
and linguistic labels and thus avoids the introduction of ad-hoc translations
of quantitative beliefs into quantitative ones for solving the problem.
",Qualitative Belief Conditioning Rules (QBCR)
"  Many systems can be described in terms of networks of discrete elements and
their various relationships to one another. A semantic network, or
multi-relational network, is a directed labeled graph consisting of a
heterogeneous set of entities connected by a heterogeneous set of
relationships. Semantic networks serve as a promising general-purpose modeling
substrate for complex systems. Various standardized formats and tools are now
available to support practical, large-scale semantic network models. First, the
Resource Description Framework (RDF) offers a standardized semantic network
data model that can be further formalized by ontology modeling languages such
as RDF Schema (RDFS) and the Web Ontology Language (OWL). Second, the recent
introduction of highly performant triple-stores (i.e. semantic network
databases) allows semantic network models on the order of $10^9$ edges to be
efficiently stored and manipulated. RDF and its related technologies are
currently used extensively in the domains of computer science, digital library
science, and the biological sciences. This article will provide an introduction
to RDF/RDFS/OWL and an examination of its suitability to model discrete element
complex systems.
",Using RDF to Model the Structure and Process of Systems
"  This paper deals with enriched qualitative belief functions for reasoning
under uncertainty and for combining information expressed in natural language
through linguistic labels. In this work, two possible enrichments (quantitative
and/or qualitative) of linguistic labels are considered and operators
(addition, multiplication, division, etc) for dealing with them are proposed
and explained. We denote them $qe$-operators, $qe$ standing for
""qualitative-enriched"" operators. These operators can be seen as a direct
extension of the classical qualitative operators ($q$-operators) proposed
recently in the Dezert-Smarandache Theory of plausible and paradoxist reasoning
(DSmT). $q$-operators are also justified in details in this paper. The
quantitative enrichment of linguistic label is a numerical supporting degree in
$[0,\infty)$, while the qualitative enrichment takes its values in a finite
ordered set of linguistic values. Quantitative enrichment is less precise than
qualitative enrichment, but it is expected more close with what human experts
can easily provide when expressing linguistic labels with supporting degrees.
Two simple examples are given to show how the fusion of qualitative-enriched
belief assignments can be done.
",Enrichment of Qualitative Beliefs for Reasoning under Uncertainty
"  We try to perform geometrization of psychology by representing mental states,
<<ideas>>, by points of a metric space, <<mental space>>. Evolution of ideas is
described by dynamical systems in metric mental space. We apply the mental
space approach for modeling of flows of unconscious and conscious information
in the human brain. In a series of models, Models 1-4, we consider cognitive
systems with increasing complexity of psychological behavior determined by
structure of flows of ideas. Since our models are in fact models of the
AI-type, one immediately recognizes that they can be used for creation of
AI-systems, which we call psycho-robots, exhibiting important elements of human
psyche. Creation of such psycho-robots may be useful improvement of domestic
robots. At the moment domestic robots are merely simple working devices (e.g.
vacuum cleaners or lawn mowers) . However, in future one can expect demand in
systems which be able not only perform simple work tasks, but would have
elements of human self-developing psyche. Such AI-psyche could play an
important role both in relations between psycho-robots and their owners as well
as between psycho-robots. Since the presence of a huge numbers of
psycho-complexes is an essential characteristic of human psychology, it would
be interesting to model them in the AI-framework.
",Toward Psycho-robots
"  In this paper we study cellular automata (CAs) that perform the computational
Majority task. This task is a good example of what the phenomenon of emergence
in complex systems is. We take an interest in the reasons that make this
particular fitness landscape a difficult one. The first goal is to study the
landscape as such, and thus it is ideally independent from the actual
heuristics used to search the space. However, a second goal is to understand
the features a good search technique for this particular problem space should
possess. We statistically quantify in various ways the degree of difficulty of
searching this landscape. Due to neutrality, investigations based on sampling
techniques on the whole landscape are difficult to conduct. So, we go exploring
the landscape from the top. Although it has been proved that no CA can perform
the task perfectly, several efficient CAs for this task have been found.
Exploiting similarities between these CAs and symmetries in the landscape, we
define the Olympus landscape which is regarded as the ''heavenly home'' of the
best local optima known (blok). Then we measure several properties of this
subspace. Although it is easier to find relevant CAs in this subspace than in
the overall landscape, there are structural reasons that prevent a searcher
from finding overfitted CAs in the Olympus. Finally, we study dynamics and
performance of genetic algorithms on the Olympus in order to confirm our
analysis and to find efficient CAs for the Majority problem with low
computational cost.
","Fitness landscape of the cellular automata majority problem: View from
  the Olympus"
"  This paper introduces the concept of fitness cloud as an alternative way to
visualize and analyze search spaces than given by the geographic notion of
fitness landscape. It is argued that the fitness cloud concept overcomes
several deficiencies of the landscape representation. Our analysis is based on
the correlation between fitness of solutions and fitnesses of nearest solutions
according to some neighboring. We focus on the behavior of local search
heuristics, such as hill climber, on the well-known NK fitness landscape. In
both cases the fitness vs. fitness correlation is shown to be related to the
epistatic parameter K.
",Local search heuristics: Fitness Cloud versus Fitness Landscape
"  This theoretical work defines the measure of autocorrelation of evolvability
in the context of neutral fitness landscape. This measure has been studied on
the classical MAX-SAT problem. This work highlight a new characteristic of
neutral fitness landscapes which allows to design new adapted metaheuristic.
",Measuring the Evolvability Landscape to study Neutrality
"  This paper describes a system capable of semi-automatically filling an XML
template from free texts in the clinical domain (practice guidelines). The XML
template includes semantic information not explicitly encoded in the text
(pairs of conditions and actions/recommendations). Therefore, there is a need
to compute the exact scope of conditions over text sequences expressing the
required actions. We present in this paper the rules developed for this task.
We show that the system yields good performance when applied to the analysis of
French practice guidelines.
","From Texts to Structured Documents: The Case of Health Practice
  Guidelines"
"  We develop a general framework for MAP estimation in discrete and Gaussian
graphical models using Lagrangian relaxation techniques. The key idea is to
reformulate an intractable estimation problem as one defined on a more
tractable graph, but subject to additional constraints. Relaxing these
constraints gives a tractable dual problem, one defined by a thin graph, which
is then optimized by an iterative procedure. When this iterative optimization
leads to a consistent estimate, one which also satisfies the constraints, then
it corresponds to an optimal MAP estimate of the original model. Otherwise
there is a ``duality gap'', and we obtain a bound on the optimal solution.
Thus, our approach combines convex optimization with dynamic programming
techniques applicable for thin graphs. The popular tree-reweighted max-product
(TRMP) method may be seen as solving a particular class of such relaxations,
where the intractable graph is relaxed to a set of spanning trees. We also
consider relaxations to a set of small induced subgraphs, thin subgraphs (e.g.
loops), and a connected tree obtained by ``unwinding'' cycles. In addition, we
propose a new class of multiscale relaxations that introduce ``summary''
variables. The potential benefits of such generalizations include: reducing or
eliminating the ``duality gap'' in hard problems, reducing the number or
Lagrange multipliers in the dual problem, and accelerating convergence of the
iterative optimization procedure.
",Lagrangian Relaxation for MAP Estimation in Graphical Models
"  This paper addresses a method to analyze the covert social network foundation
hidden behind the terrorism disaster. It is to solve a node discovery problem,
which means to discover a node, which functions relevantly in a social network,
but escaped from monitoring on the presence and mutual relationship of nodes.
The method aims at integrating the expert investigator's prior understanding,
insight on the terrorists' social network nature derived from the complex graph
theory, and computational data processing. The social network responsible for
the 9/11 attack in 2001 is used to execute simulation experiment to evaluate
the performance of the method.
",Analyzing covert social network foundation behind terrorism disaster
"  Methods to solve a node discovery problem for a social network are presented.
Covert nodes refer to the nodes which are not observable directly. They
transmit the influence and affect the resulting collaborative activities among
the persons in a social network, but do not appear in the surveillance logs
which record the participants of the collaborative activities. Discovering the
covert nodes is identifying the suspicious logs where the covert nodes would
appear if the covert nodes became overt. The performance of the methods is
demonstrated with a test dataset generated from computationally synthesized
networks and a real organization.
",Node discovery problem for a social network
"  An empty spot refers to an empty hard-to-fill space which can be found in the
records of the social interaction, and is the clue to the persons in the
underlying social network who do not appear in the records. This contribution
addresses a problem to predict relevant empty spots in social interaction.
Homogeneous and inhomogeneous networks are studied as a model underlying the
social interaction. A heuristic predictor function approach is presented as a
new method to address the problem. Simulation experiment is demonstrated over a
homogeneous network. A test data in the form of baskets is generated from the
simulated communication. Precision to predict the empty spots is calculated to
demonstrate the performance of the presented approach.
",Predicting relevant empty spots in social interaction
"  To appear in Theory and Practice of Logic Programming (TPLP), 2008.
  We are researching the interaction between the rule and the ontology layers
of the Semantic Web, by comparing two options: 1) using OWL and its rule
extension SWRL to develop an integrated ontology/rule language, and 2) layering
rules on top of an ontology with RuleML and OWL. Toward this end, we are
developing the SWORIER system, which enables efficient automated reasoning on
ontologies and rules, by translating all of them into Prolog and adding a set
of general rules that properly capture the semantics of OWL. We have also
enabled the user to make dynamic changes on the fly, at run time. This work
addresses several of the concerns expressed in previous work, such as negation,
complementary classes, disjunctive heads, and cardinality, and it discusses
alternative approaches for dealing with inconsistencies in the knowledge base.
In addition, for efficiency, we implemented techniques called
extensionalization, avoiding reanalysis, and code minimization.
","Translating OWL and Semantic Web Rules into Prolog: Moving Toward
  Description Logic Programs"
"  We consider hexagonal cellular automata with immediate cell neighbourhood and
three cell-states. Every cell calculates its next state depending on the
integral representation of states in its neighbourhood, i.e. how many
neighbours are in each one state. We employ evolutionary algorithms to breed
local transition functions that support mobile localizations (gliders), and
characterize sets of the functions selected in terms of quasi-chemical systems.
Analysis of the set of functions evolved allows to speculate that mobile
localizations are likely to emerge in the quasi-chemical systems with limited
diffusion of one reagent, a small number of molecules is required for
amplification of travelling localizations, and reactions leading to stationary
localizations involve relatively equal amount of quasi-chemical species.
Techniques developed can be applied in cascading signals in nature-inspired
spatially extended computing devices, and phenomenological studies and
classification of non-linear discrete systems.
",Evolving localizations in reaction-diffusion cellular automata
"  We describe decomposition during search (DDS), an integration of And/Or tree
search into propagation-based constraint solvers. The presented search
algorithm dynamically decomposes sub-problems of a constraint satisfaction
problem into independent partial problems, avoiding redundant work.
  The paper discusses how DDS interacts with key features that make
propagation-based solvers successful: constraint propagation, especially for
global constraints, and dynamic search heuristics.
  We have implemented DDS for the Gecode constraint programming library. Two
applications, solution counting in graph coloring and protein structure
prediction, exemplify the benefits of DDS in practice.
",Decomposition During Search for Propagation-Based Constraint Solvers
"  A fundamental problem in artificial intelligence is that nobody really knows
what intelligence is. The problem is especially acute when we need to consider
artificial systems which are significantly different to humans. In this paper
we approach this problem in the following way: We take a number of well known
informal definitions of human intelligence that have been given by experts, and
extract their essential features. These are then mathematically formalised to
produce a general measure of intelligence for arbitrary machines. We believe
that this equation formally captures the concept of machine intelligence in the
broadest reasonable sense. We then show how this formal definition is related
to the theory of universal optimal learning agents. Finally, we survey the many
other tests and definitions of intelligence that have been proposed for
machines.
",Universal Intelligence: A Definition of Machine Intelligence
"  Although the definition and measurement of intelligence is clearly of
fundamental importance to the field of artificial intelligence, no general
survey of definitions and tests of machine intelligence exists. Indeed few
researchers are even aware of alternatives to the Turing test and its many
derivatives. In this paper we fill this gap by providing a short survey of the
many tests of machine intelligence that have been proposed.
",Tests of Machine Intelligence
"  We consider an agent interacting with an unknown environment. The environment
is a function which maps natural numbers to natural numbers; the agent's set of
hypotheses about the environment contains all such functions which are
computable and compatible with a finite set of known input-output pairs, and
the agent assigns a positive probability to each such hypothesis. We do not
require that this probability distribution be computable, but it must be
bounded below by a positive computable function. The agent has a utility
function on outputs from the environment. We show that if this utility function
is bounded below in absolute value by an unbounded computable function, then
the expected utility of any input is undefined. This implies that a computable
utility function will have convergent expected utilities iff that function is
bounded.
","Convergence of Expected Utilities with Algorithmic Probability
  Distributions"
"  Most definitions of ontology, viewed as a ""specification of a
conceptualization"", agree on the fact that if an ontology can take different
forms, it necessarily includes a vocabulary of terms and some specification of
their meaning in relation to the domain's conceptualization. And as domain
knowledge is mainly conveyed through scientific and technical texts, we can
hope to extract some useful information from them for building ontology. But is
it as simple as this? In this article we shall see that the lexical structure,
i.e. the network of words linked by linguistic relationships, does not
necessarily match the domain conceptualization. We have to bear in mind that
writing documents is the concern of textual linguistics, of which one of the
principles is the incompleteness of text, whereas building ontology - viewed as
task-independent knowledge - is concerned with conceptualization based on
formal and not natural languages. Nevertheless, the famous Sapir and Whorf
hypothesis, concerning the interdependence of thought and language, is also
applicable to formal languages. This means that the way an ontology is built
and a concept is defined depends directly on the formal language which is used;
and the results will not be the same. The introduction of the notion of
ontoterminology allows to take into account epistemological principles for
formal ontology building.
",Le terme et le concept : fondements d'une ontoterminologie
"  Stream computing is the use of multiple autonomic and parallel modules
together with integrative processors at a higher level of abstraction to embody
""intelligent"" processing. The biological basis of this computing is sketched
and the matter of learning is examined.
",Stream Computing
"  In this paper we introduce a new selection scheme in cellular genetic
algorithms (cGAs). Anisotropic Selection (AS) promotes diversity and allows
accurate control of the selective pressure. First we compare this new scheme
with the classical rectangular grid shapes solution according to the selective
pressure: we can obtain the same takeover time with the two techniques although
the spreading of the best individual is different. We then give experimental
results that show to what extent AS promotes the emergence of niches that
support low coupling and high cohesion. Finally, using a cGA with anisotropic
selection on a Quadratic Assignment Problem we show the existence of an
anisotropic optimal value for which the best average performance is observed.
Further work will focus on the selective pressure self-adjustment ability
provided by this new selection scheme.
",Anisotropic selection in cellular genetic algorithms
"  This philosophical paper explores the relation between modern scientific
simulations and the future of the universe. We argue that a simulation of an
entire universe will result from future scientific activity. This requires us
to tackle the challenge of simulating open-ended evolution at all levels in a
single simulation. The simulation should encompass not only biological
evolution, but also physical evolution (a level below) and cultural evolution
(a level above). The simulation would allow us to probe what would happen if we
would ""replay the tape of the universe"" with the same or different laws and
initial conditions. We also distinguish between real-world and artificial-world
modelling. Assuming that intelligent life could indeed simulate an entire
universe, this leads to two tentative hypotheses. Some authors have argued that
we may already be in a simulation run by an intelligent entity. Or, if such a
simulation could be made real, this would lead to the production of a new
universe. This last direction is argued with a careful speculative
philosophical approach, emphasizing the imperative to find a solution to the
heat death problem in cosmology. The reader is invited to consult Annex 1 for
an overview of the logical structure of this paper. -- Keywords: far future,
future of science, ALife, simulation, realization, cosmology, heat death,
fine-tuning, physical eschatology, cosmological natural selection, cosmological
artificial selection, artificial cosmogenesis, selfish biocosm hypothesis,
meduso-anthropic principle, developmental singularity hypothesis, role of
intelligent life.
","The Future of Scientific Simulations: from Artificial Life to Artificial
  Cosmogenesis"
"  This paper has been withdrawn.
",Serious Flaws in Korf et al.'s Analysis on Time Complexity of A*
"  In this paper, we describe a new algorithm that consists in combining an
eye-tracker for minimizing the fatigue of a user during the evaluation process
of Interactive Evolutionary Computation. The approach is then applied to the
Interactive One-Max optimization problem.
","Eye-Tracking Evolutionary Algorithm to minimize user's fatigue in IEC
  applied to Interactive One-Max problem"
"  In this paper, I present a method to solve a node discovery problem in a
networked organization. Covert nodes refer to the nodes which are not
observable directly. They affect social interactions, but do not appear in the
surveillance logs which record the participants of the social interactions.
Discovering the covert nodes is defined as identifying the suspicious logs
where the covert nodes would appear if the covert nodes became overt. A
mathematical model is developed for the maximal likelihood estimation of the
network behind the social interactions and for the identification of the
suspicious logs. Precision, recall, and F measure characteristics are
demonstrated with the dataset generated from a real organization and the
computationally synthesized datasets. The performance is close to the
theoretical limit for any covert nodes in the networks of any topologies and
sizes if the ratio of the number of observation to the number of possible
communication patterns is large.
",Node discovery in a networked organization
"  In an emergency situation, the actors need an assistance allowing them to
react swiftly and efficiently. In this prospect, we present in this paper a
decision support system that aims to prepare actors in a crisis situation
thanks to a decision-making support. The global architecture of this system is
presented in the first part. Then we focus on a part of this system which is
designed to represent the information of the current situation. This part is
composed of a multiagent system that is made of factual agents. Each agent
carries a semantic feature and aims to represent a partial part of a situation.
The agents develop thanks to their interactions by comparing their semantic
features using proximity measures and according to specific ontologies.
","Multiagent Approach for the Representation of Information in a Decision
  Support System"
"  A new method is presented, that can help a person become aware of his or her
unconscious preferences, and convey them to others in the form of verbal
explanation. The method combines the concepts of reflection, visualization, and
verbalization. The method was tested in an experiment where the unconscious
preferences of the subjects for various artworks were investigated. In the
experiment, two lessons were learned. The first is that it helps the subjects
become aware of their unconscious preferences to verbalize weak preferences as
compared with strong preferences through discussion over preference diagrams.
The second is that it is effective to introduce an adjustable factor into
visualization to adapt to the differences in the subjects and to foster their
mutual understanding.
",Reflective visualization and verbalization of unconscious preference
"  This paper describes application of rough set theory, on the analysis of
hydrocyclone operation. In this manner, using Self Organizing Map (SOM) as
preprocessing step, best crisp granules of data are obtained. Then, using a
combining of SOM and rough set theory (RST)-called SORST-, the dominant rules
on the information table, obtained from laboratory tests, are extracted. Based
on these rules, an approximate estimation on decision attribute is fulfilled.
Finally, a brief comparison of this method with the SOM-NFIS system (briefly
SONFIS) is highlighted.
",Application of Rough Set Theory to Analysis of Hydrocyclone Operation
"  We are interested in the problem of multiagent systems development for risk
detecting and emergency response in an uncertain and partially perceived
environment. The evaluation of the current situation passes by three stages
inside the multiagent system. In a first time, the situation is represented in
a dynamic way. The second step, consists to characterise the situation and
finally, it is compared with other similar known situations. In this paper, we
present an information modelling of an observed environment, that we have
applied on the RoboCupRescue Simulation System. Information coming from the
environment are formatted according to a taxonomy and using semantic features.
The latter are defined thanks to a fine ontology of the domain and are managed
by factual agents that aim to represent dynamically the current situation.
",Agent-Based Perception of an Environment in an Emergency Situation
"  In this paper, we study the influence of the selective pressure on the
performance of cellular genetic algorithms. Cellular genetic algorithms are
genetic algorithms where the population is embedded on a toroidal grid. This
structure makes the propagation of the best so far individual slow down, and
allows to keep in the population potentially good solutions. We present two
selective pressure reducing strategies in order to slow down even more the best
solution propagation. We experiment these strategies on a hard optimization
problem, the quadratic assignment problem, and we show that there is a value
for of the control parameter for both which gives the best performance. This
optimal value does not find explanation on only the selective pressure,
measured either by take over time and diversity evolution. This study makes us
conclude that we need other tools than the sole selective pressure measures to
explain the performances of cellular genetic algorithms.
","On the Influence of Selection Operators on Performances in Cellular
  Genetic Algorithms"
"  Review of: Brigitte Le Roux and Henry Rouanet, Geometric Data Analysis, From
Correspondence Analysis to Structured Data Analysis, Kluwer, Dordrecht, 2004,
xi+475 pp.
","Geometric Data Analysis, From Correspondence Analysis to Structured Data
  Analysis (book review)"
"  In this study, we introduce general frame of MAny Connected Intelligent
Particles Systems (MACIPS). Connections and interconnections between particles
get a complex behavior of such merely simple system (system in
system).Contribution of natural computing, under information granulation
theory, are the main topics of this spacious skeleton. Upon this clue, we
organize two algorithms involved a few prominent intelligent computing and
approximate reasoning methods: self organizing feature map (SOM), Neuro- Fuzzy
Inference System and Rough Set Theory (RST). Over this, we show how our
algorithms can be taken as a linkage of government-society interaction, where
government catches various fashions of behavior: solid (absolute) or flexible.
So, transition of such society, by changing of connectivity parameters (noise)
from order to disorder is inferred. Add to this, one may find an indirect
mapping among financial systems and eventual market fluctuations with MACIPS.
Keywords: phase transition, SONFIS, SORST, many connected intelligent particles
system, society-government interaction
",Phase transition in SONFIS&SORST
"  Affinity propagation clustering (AP) has two limitations: it is hard to know
what value of parameter 'preference' can yield an optimal clustering solution,
and oscillations cannot be eliminated automatically if occur. The adaptive AP
method is proposed to overcome these limitations, including adaptive scanning
of preferences to search space of the number of clusters for finding the
optimal clustering solution, adaptive adjustment of damping factors to
eliminate oscillations, and adaptive escaping from oscillations when the
damping adjustment technique fails. Experimental results on simulated and real
data sets show that the adaptive AP is effective and can outperform AP in
quality of clustering results.
",Adaptive Affinity Propagation Clustering
"  Approximately more than 90% of all coal production in Iranian underground
mines is derived directly longwall mining method. Out of seam dilution is one
of the essential problems in these mines. Therefore the dilution can impose the
additional cost of mining and milling. As a result, recognition of the
effective parameters on the dilution has a remarkable role in industry. In this
way, this paper has analyzed the influence of 13 parameters (attributed
variables) versus the decision attribute (dilution value), so that using two
approximate reasoning methods, namely Rough Set Theory (RST) and Self
Organizing Neuro- Fuzzy Inference System (SONFIS) the best rules on our
collected data sets has been extracted. The other benefit of later methods is
to predict new unknown cases. So, the reduced sets (reducts) by RST have been
obtained. Therefore the emerged results by utilizing mentioned methods shows
that the high sensitive variables are thickness of layer, length of stope, rate
of advance, number of miners, type of advancing.
","Assessment of effective parameters on dilution using approximate
  reasoning methods in longwall mining method, Iran coal mines"
"  This study, fundamentals of fuzzy block theory, and its application in
assessment of stability in underground openings, has surveyed. Using fuzzy
topics and inserting them in to key block theory, in two ways, fundamentals of
fuzzy block theory has been presented. In indirect combining, by coupling of
adaptive Neuro Fuzzy Inference System (NFIS) and classic block theory, we could
extract possible damage parts around a tunnel. In direct solution, some
principles of block theory, by means of different fuzzy facets theory, were
rewritten.
",Toward Fuzzy block theory
"  This paper describes application of information granulation theory, on the
analysis of hydrocyclone perforamance. In this manner, using a combining of
Self Organizing Map (SOM) and Neuro-Fuzzy Inference System (NFIS), crisp and
fuzzy granules are obtained(briefly called SONFIS). Balancing of crisp granules
and sub fuzzy granules, within non fuzzy information (initial granulation), is
rendered in an open-close iteration. Using two criteria, ""simplicity of rules
""and ""adaptive threoshold error level"", stability of algorithm is guaranteed.
Validation of the proposed method, on the data set of the hydrocyclone is
rendered.
","Analysis of hydrocyclone performance based on information granulation
  theory"
"  In everyday life it happens that a person has to reason about what other
people think and how they behave, in order to achieve his goals. In other
words, an individual may be required to adapt his behaviour by reasoning about
the others' mental state. In this paper we focus on a knowledge representation
language derived from logic programming which both supports the representation
of mental states of individual communities and provides each with the
capability of reasoning about others' mental states and acting accordingly. The
proposed semantics is shown to be translatable into stable model semantics of
logic programs with aggregates.
",Logic programming with social features
"  Many social Web sites allow users to publish content and annotate with
descriptive metadata. In addition to flat tags, some social Web sites have
recently began to allow users to organize their content and metadata
hierarchically. The social photosharing site Flickr, for example, allows users
to group related photos in sets, and related sets in collections. The social
bookmarking site Del.icio.us similarly lets users group related tags into
bundles. Although the sites themselves don't impose any constraints on how
these hierarchies are used, individuals generally use them to capture
relationships between concepts, most commonly the broader/narrower relations.
Collective annotation of content with hierarchical relations may lead to an
emergent classification system, called a folksonomy. While some researchers
have explored using tags as evidence for learning folksonomies, we believe that
hierarchical relations described above offer a high-quality source of evidence
for this task.
  We propose a simple approach to aggregate shallow hierarchies created by many
distinct Flickr users into a common folksonomy. Our approach uses statistics to
determine if a particular relation should be retained or discarded. The
relations are then woven together into larger hierarchies. Although we have not
carried out a detailed quantitative evaluation of the approach, it looks very
promising since it generates very reasonable, non-trivial hierarchies.
",Constructing Folksonomies from User-specified Relations on Flickr
"  We analyze the style and structure of story narrative using the case of film
scripts. The practical importance of this is noted, especially the need to have
support tools for television movie writing. We use the Casablanca film script,
and scripts from six episodes of CSI (Crime Scene Investigation). For analysis
of style and structure, we quantify various central perspectives discussed in
McKee's book, ""Story: Substance, Structure, Style, and the Principles of
Screenwriting"". Film scripts offer a useful point of departure for exploration
of the analysis of more general narratives. Our methodology, using
Correspondence Analysis, and hierarchical clustering, is innovative in a range
of areas that we discuss. In particular this work is groundbreaking in taking
the qualitative analysis of McKee and grounding this analysis in a quantitative
and algorithmic framework.
",The Structure of Narrative: the Case of Film Scripts
"  In the last year more than 70,000 people have been brought to the UK
hospitals with serious injuries. Each time a clinician has to urgently take a
patient through a screening procedure to make a reliable decision on the trauma
treatment. Typically, such procedure comprises around 20 tests; however the
condition of a trauma patient remains very difficult to be tested properly.
What happens if these tests are ambiguously interpreted, and information about
the severity of the injury will come misleading? The mistake in a decision can
be fatal: using a mild treatment can put a patient at risk of dying from
posttraumatic shock, while using an overtreatment can also cause death. How can
we reduce the risk of the death caused by unreliable decisions? It has been
shown that probabilistic reasoning, based on the Bayesian methodology of
averaging over decision models, allows clinicians to evaluate the uncertainty
in decision making. Based on this methodology, in this paper we aim at
selecting the most important screening tests, keeping a high performance. We
assume that the probabilistic reasoning within the Bayesian methodology allows
us to discover new relationships between the screening tests and uncertainty in
decisions. In practice, selection of the most informative tests can also reduce
the cost of a screening procedure in trauma care centers. In our experiments we
use the UK Trauma data to compare the efficiency of the proposed technique in
terms of the performance. We also compare the uncertainty in decisions in terms
of entropy.
",Feature Selection for Bayesian Evaluation of Trauma Death Risk
"  We present in this article a new evaluation method for classification and
segmentation of textured images in uncertain environments. In uncertain
environments, real classes and boundaries are known with only a partial
certainty given by the experts. Most of the time, in many presented papers,
only classification or only segmentation are considered and evaluated. Here, we
propose to take into account both the classification and segmentation results
according to the certainty given by the experts. We present the results of this
method on a fusion of classifiers of sonar images for a seabed
characterization.
",Fusion for Evaluation of Image Classification in Uncertain Environments
"  The investigation of the terrorist attack is a time-critical task. The
investigators have a limited time window to diagnose the organizational
background of the terrorists, to run down and arrest the wire-pullers, and to
take an action to prevent or eradicate the terrorist attack. The intuitive
interface to visualize the intelligence data set stimulates the investigators'
experience and knowledge, and aids them in decision-making for an immediately
effective action. This paper presents a computational method to analyze the
intelligence data set on the collective actions of the perpetrators of the
attack, and to visualize it into the form of a social network diagram which
predicts the positions where the wire-pullers conceals themselves.
","Intuitive visualization of the intelligence for the run-down of
  terrorist wire-pullers"
"  This paper describes application of information granulation theory, on the
design of rock engineering flowcharts. Firstly, an overall flowchart, based on
information granulation theory has been highlighted. Information granulation
theory, in crisp (non-fuzzy) or fuzzy format, can take into account engineering
experiences (especially in fuzzy shape-incomplete information or superfluous),
or engineering judgments, in each step of designing procedure, while the
suitable instruments modeling are employed. In this manner and to extension of
soft modeling instruments, using three combinations of Self Organizing Map
(SOM), Neuro-Fuzzy Inference System (NFIS), and Rough Set Theory (RST) crisp
and fuzzy granules, from monitored data sets are obtained. The main underlined
core of our algorithms are balancing of crisp(rough or non-fuzzy) granules and
sub fuzzy granules, within non fuzzy information (initial granulation) upon the
open-close iterations. Using different criteria on balancing best granules
(information pockets), are obtained. Validations of our proposed methods, on
the data set of in-situ permeability in rock masses in Shivashan dam, Iran have
been highlighted.
",Rock mechanics modeling based on soft granulation theory
"  Knowledge-based economy forces companies in the nation to group together as a
cluster in order to maintain their competitiveness in the world market. The
cluster development relies on two key success factors which are knowledge
sharing and collaboration between the actors in the cluster. Thus, our study
tries to propose knowledge management system to support knowledge management
activities within the cluster. To achieve the objectives of this study,
ontology takes a very important role in knowledge management process in various
ways; such as building reusable and faster knowledge-bases, better way for
representing the knowledge explicitly. However, creating and representing
ontology create difficulties to organization due to the ambiguity and
unstructured of source of knowledge. Therefore, the objectives of this paper
are to propose the methodology to create and represent ontology for the
organization development by using knowledge engineering approach. The
handicraft cluster in Thailand is used as a case study to illustrate our
proposed methodology.
",An Ontology-based Knowledge Management System for Industry Clusters
"  Crisis response poses many of the most difficult information technology in
crisis management. It requires information and communication-intensive efforts,
utilized for reducing uncertainty, calculating and comparing costs and
benefits, and managing resources in a fashion beyond those regularly available
to handle routine problems. In this paper, we explore the benefits of
artificial intelligence technologies in crisis response. This paper discusses
the role of artificial intelligence technologies; namely, robotics, ontology
and semantic web, and multi-agent systems in crisis response.
",The Role of Artificial Intelligence Technologies in Crisis Response
"  We present and discuss a mixed conjunctive and disjunctive rule, a
generalization of conflict repartition rules, and a combination of these two
rules. In the belief functions theory one of the major problem is the conflict
repartition enlightened by the famous Zadeh's example. To date, many
combination rules have been proposed in order to solve a solution to this
problem. Moreover, it can be important to consider the specificity of the
responses of the experts. Since few year some unification rules are proposed.
We have shown in our previous works the interest of the proportional conflict
redistribution rule. We propose here a mixed combination rule following the
proportional conflict redistribution rule modified by a discounting procedure.
This rule generalizes many combination rules.
","Toward a combination rule to deal with partial conflict and specificity
  in belief functions theory"
"  In this chapter, we present and discuss a new generalized proportional
conflict redistribution rule. The Dezert-Smarandache extension of the
Demster-Shafer theory has relaunched the studies on the combination rules
especially for the management of the conflict. Many combination rules have been
proposed in the last few years. We study here different combination rules and
compare them in terms of decision on didactic example and on generated data.
Indeed, in real applications, we need a reliable decision and it is the final
results that matter. This chapter shows that a fine proportional conflict
redistribution rule must be preferred for the combination in the belief
function theory.
","A new generalization of the proportional conflict redistribution rule
  stable in terms of decision"
"  These last years, there were many studies on the problem of the conflict
coming from information combination, especially in evidence theory. We can
summarise the solutions for manage the conflict into three different
approaches: first, we can try to suppress or reduce the conflict before the
combination step, secondly, we can manage the conflict in order to give no
influence of the conflict in the combination step, and then take into account
the conflict in the decision step, thirdly, we can take into account the
conflict in the combination step. The first approach is certainly the better,
but not always feasible. It is difficult to say which approach is the best
between the second and the third. However, the most important is the produced
results in applications. We propose here a new combination rule that
distributes the conflict proportionally on the element given this conflict. We
compare these different combination rules on real data in Sonar imagery and
Radar target classification.
","Une nouvelle r\`egle de combinaison r\'epartissant le conflit -
  Applications en imagerie Sonar et classification de cibles Radar"
"  When implementing a propagator for a constraint, one must decide about
variants: When implementing min, should one also implement max? Should one
implement linear equations both with and without coefficients? Constraint
variants are ubiquitous: implementing them requires considerable (if not
prohibitive) effort and decreases maintainability, but will deliver better
performance.
  This paper shows how to use variable views, previously introduced for an
implementation architecture, to derive perfect propagator variants. A model for
views and derived propagators is introduced. Derived propagators are proved to
be indeed perfect in that they inherit essential properties such as correctness
and domain and bounds consistency. Techniques for systematically deriving
propagators such as transformation, generalization, specialization, and
channeling are developed for several variable domains. We evaluate the massive
impact of derived propagators. Without derived propagators, Gecode would
require 140000 rather than 40000 lines of code for propagators.
",Perfect Derived Propagators
"  A serious defect with the Halpern-Pearl (HP) definition of causality is
repaired by combining a theory of causality with a theory of defaults. In
addition, it is shown that (despite a claim to the contrary) a cause according
to the HP condition need not be a single conjunct. A definition of causality
motivated by Wright's NESS test is shown to always hold for a single conjunct.
Moreover, conditions that hold for all the examples considered by HP are given
that guarantee that causality according to (this version) of the NESS test is
equivalent to the HP definition.
",Defaults and Normality in Causal Structures
"  This paper has been withdrawn by the author due to extremely unscientific
errors.
",The model of quantum evolution
"  The textured images' classification assumes to consider the images in terms
of area with the same texture. In uncertain environment, it could be better to
take an imprecise decision or to reject the area corresponding to an unlearning
class. Moreover, on the areas that are the classification units, we can have
more than one texture. These considerations allows us to develop a belief
decision model permitting to reject an area as unlearning and to decide on
unions and intersections of learning classes. The proposed approach finds all
its justification in an application of seabed characterization from sonar
images, which contributes to an illustration.
",Belief decision support and reject for textured images characterization
"  We study two aspects of information semantics: (i) the collection of all
relationships, (ii) tracking and spotting anomaly and change. The first is
implemented by endowing all relevant information spaces with a Euclidean metric
in a common projected space. The second is modelled by an induced ultrametric.
A very general way to achieve a Euclidean embedding of different information
spaces based on cross-tabulation counts (and from other input data formats) is
provided by Correspondence Analysis. From there, the induced ultrametric that
we are particularly interested in takes a sequential - e.g. temporal - ordering
of the data into account. We employ such a perspective to look at narrative,
""the flow of thought and the flow of language"" (Chafe). In application to
policy decision making, we show how we can focus analysis in a small number of
dimensions.
","The Correspondence Analysis Platform for Uncovering Deep Structure in
  Data and Information"
"  In this paper we extend Inagaki Weighted Operators fusion rule (WO) in
information fusion by doing redistribution of not only the conflicting mass,
but also of masses of non-empty intersections, that we call Double Weighted
Operators (DWO). Then we propose a new fusion rule Class of Proportional
Redistribution of Intersection Masses (CPRIM), which generates many interesting
particular fusion rules in information fusion. Both formulas are presented for
any number of sources of information. An application and comparison with other
fusion rules are given in the last section.
","Extension of Inagaki General Weighted Operators and A New Fusion Rule
  Class of Proportional Redistribution of Intersection Masses"
"  In this chapter, we propose a new practical codification of the elements of
the Venn diagram in order to easily manipulate the focal elements. In order to
reduce the complexity, the eventual constraints must be integrated in the
codification at the beginning. Hence, we only consider a reduced hyper power
set $D_r^\Theta$ that can be $2^\Theta$ or $D^\Theta$. We describe all the
steps of a general belief function framework. The step of decision is
particularly studied, indeed, when we can decide on intersections of the
singletons of the discernment space no actual decision functions are easily to
use. Hence, two approaches are proposed, an extension of previous one and an
approach based on the specificity of the elements on which to decide. The
principal goal of this chapter is to provide practical codes of a general
belief function framework for the researchers and users needing the belief
function theory.
","Implementing general belief function framework with a practical
  codification for low complexity"
"  In this paper, we propose in Dezert-Smarandache Theory (DSmT) framework, a
new probabilistic transformation, called DSmP, in order to build a subjective
probability measure from any basic belief assignment defined on any model of
the frame of discernment. Several examples are given to show how the DSmP
transformation works and we compare it to main existing transformations
proposed in the literature so far. We show the advantages of DSmP over
classical transformations in term of Probabilistic Information Content (PIC).
The direct extension of this transformation for dealing with qualitative belief
assignments is also presented.
",A new probabilistic transformation of belief mass assignment
"  We discuss metacognitive modelling as an enhancement to cognitive modelling
and computing. Metacognitive control mechanisms should enable AI systems to
self-reflect, reason about their actions, and to adapt to new situations. In
this respect, we propose implementation details of a knowledge taxonomy and an
augmented data mining life cycle which supports a live integration of obtained
models.
","On Introspection, Metacognitive Control and Augmented Data Mining Live
  Cycles"
"  Each cognitive science tries to understand a set of cognitive behaviors. The
structuring of knowledge of this nature's aspect is far from what it can be
expected about a science. Until now universal standard consistently describing
the set of cognitive behaviors has not been found, and there are many questions
about the cognitive behaviors for which only there are opinions of members of
the scientific community. This article has three proposals. The first proposal
is to raise to the scientific community the necessity of unified the cognitive
behaviors. The second proposal is claim the application of the Newton's
reasoning rules about nature of his book, Philosophiae Naturalis Principia
Mathematica, to the cognitive behaviors. The third is to propose a scientific
theory, currently developing, that follows the rules established by Newton to
make sense of nature, and could be the theory to explain all the cognitive
behaviors.
",Hacia una teoria de unificacion para los comportamientos cognitivos
"  In this article we review standard null-move pruning and introduce our
extended version of it, which we call verified null-move pruning. In verified
null-move pruning, whenever the shallow null-move search indicates a fail-high,
instead of cutting off the search from the current node, the search is
continued with reduced depth.
  Our experiments with verified null-move pruning show that on average, it
constructs a smaller search tree with greater tactical strength in comparison
to standard null-move pruning. Moreover, unlike standard null-move pruning,
which fails badly in zugzwang positions, verified null-move pruning manages to
detect most zugzwangs and in such cases conducts a re-search to obtain the
correct result. In addition, verified null-move pruning is very easy to
implement, and any standard null-move pruning program can use verified
null-move pruning by modifying only a few lines of code.
",Verified Null-Move Pruning
"  We extend Knuth's 16 Boolean binary logic operators to fuzzy logic and
neutrosophic logic binary operators. Then we generalize them to n-ary fuzzy
logic and neutrosophic logic operators using the smarandache codification of
the Venn diagram and a defined vector neutrosophic law. In such way, new
operators in neutrosophic logic/set/probability are built.
",n-ary Fuzzy Logic and Neutrosophic Logic Operators
"  Various local search approaches have recently been applied to machine
scheduling problems under multiple objectives. Their foremost consideration is
the identification of the set of Pareto optimal alternatives. An important
aspect of successfully solving these problems lies in the definition of an
appropriate neighbourhood structure. Unclear in this context remains, how
interdependencies within the fitness landscape affect the resolution of the
problem.
  The paper presents a study of neighbourhood search operators for multiple
objective flow shop scheduling. Experiments have been carried out with twelve
different combinations of criteria. To derive exact conclusions, small problem
instances, for which the optimal solutions are known, have been chosen.
Statistical tests show that no single neighbourhood operator is able to equally
identify all Pareto optimal alternatives. Significant improvements however have
been obtained by hybridising the solution algorithm using a randomised variable
neighbourhood search technique.
","Randomised Variable Neighbourhood Search for Multi Objective
  Optimisation"
"  The paper describes the proposition and application of a local search
metaheuristic for multi-objective optimization problems. It is based on two
main principles of heuristic search, intensification through variable
neighborhoods, and diversification through perturbations and successive
iterations in favorable regions of the search space. The concept is
successfully tested on permutation flow shop scheduling problems under multiple
objectives. While the obtained results are encouraging in terms of their
quality, another positive attribute of the approach is its' simplicity as it
does require the setting of only very few parameters. The implementation of the
Pareto Iterated Local Search metaheuristic is based on the MOOPPS computer
system of local search heuristics for multi-objective scheduling which has been
awarded the European Academic Software Award 2002 in Ronneby, Sweden
(http://www.easa-award.net/, http://www.bth.se/llab/easa_2002.nsf)
",Foundations of the Pareto Iterated Local Search Metaheuristic
"  The article describes an investigation of the effectiveness of genetic
algorithms for multi-objective combinatorial optimization (MOCO) by presenting
an application for the vehicle routing problem with soft time windows. The work
is motivated by the question, if and how the problem structure influences the
effectiveness of different configurations of the genetic algorithm.
Computational results are presented for different classes of vehicle routing
problems, varying in their coverage with time windows, time window size,
distribution and number of customers. The results are compared with a simple,
but effective local search approach for multi-objective combinatorial
optimization problems.
","A Computational Study of Genetic Crossover Operators for Multi-Objective
  Vehicle Routing Problem with Soft Time Windows"
"  The talk describes a general approach of a genetic algorithm for multiple
objective optimization problems. A particular dominance relation between the
individuals of the population is used to define a fitness operator, enabling
the genetic algorithm to adress even problems with efficient, but
convex-dominated alternatives. The algorithm is implemented in a multilingual
computer program, solving vehicle routing problems with time windows under
multiple objectives. The graphical user interface of the program shows the
progress of the genetic algorithm and the main parameters of the approach can
be easily modified. In addition to that, the program provides powerful decision
support to the decision maker. The software has proved it's excellence at the
finals of the European Academic Software Award EASA, held at the Keble college/
University of Oxford/ Great Britain.
",Genetic Algorithms for multiple objective vehicle routing
"  The article presents a framework for the resolution of rich vehicle routing
problems which are difficult to address with standard optimization techniques.
We use local search on the basis on variable neighborhood search for the
construction of the solutions, but embed the techniques in a flexible framework
that allows the consideration of complex side constraints of the problem such
as time windows, multiple depots, heterogeneous fleets, and, in particular,
multiple optimization criteria. In order to identify a compromise alternative
that meets the requirements of the decision maker, an interactive procedure is
integrated in the resolution of the problem, allowing the modification of the
preference information articulated by the decision maker. The framework is
prototypically implemented in a computer system. First results of test runs on
multiple depot vehicle routing problems with time windows are reported.
","A framework for the interactive resolution of multi-objective vehicle
  routing problems"
"  The integration of fuzzy set theory and fuzzy logic into scheduling is a
rather new aspect with growing importance for manufacturing applications,
resulting in various unsolved aspects. In the current paper, we investigate an
improved local search technique for fuzzy scheduling problems with fitness
plateaus, using a multi criteria formulation of the problem. We especially
address the problem of changing job priorities over time as studied at the
Sherwood Press Ltd, a Nottingham based printing company, who is a collaborator
on the project.
",Improving Local Search for Fuzzy Scheduling Problems
"  The article proposes a heuristic approximation approach to the bin packing
problem under multiple objectives. In addition to the traditional objective of
minimizing the number of bins, the heterogeneousness of the elements in each
bin is minimized, leading to a biobjective formulation of the problem with a
tradeoff between the number of bins and their heterogeneousness. An extension
of the Best-Fit approximation algorithm is presented to solve the problem.
Experimental investigations have been carried out on benchmark instances of
different size, ranging from 100 to 1000 items. Encouraging results have been
obtained, showing the applicability of the heuristic approach to the described
problem.
","Bin Packing Under Multiple Objectives - a Heuristic Approximation
  Approach"
"  The article presents a local search approach for the solution of timetabling
problems in general, with a particular implementation for competition track 3
of the International Timetabling Competition 2007 (ITC 2007). The heuristic
search procedure is based on Threshold Accepting to overcome local optima. A
stochastic neighborhood is proposed and implemented, randomly removing and
reassigning events from the current solution.
  The overall concept has been incrementally obtained from a series of
experiments, which we describe in each (sub)section of the paper. In result, we
successfully derived a potential candidate solution approach for the finals of
track 3 of the ITC 2007.
","An application of the Threshold Accepting metaheuristic for curriculum
  based course timetabling"
"  The paper presents a study of local search heuristics in general and variable
neighborhood search in particular for the resolution of an assignment problem
studied in the practical work of universities. Here, students have to be
assigned to scientific topics which are proposed and supported by members of
staff. The problem involves the optimization under given preferences of
students which may be expressed when applying for certain topics.
  It is possible to observe that variable neighborhood search leads to superior
results for the tested problem instances. One instance is taken from an actual
case, while others have been generated based on the real world data to support
the analysis with a deeper analysis.
  An extension of the problem has been formulated by integrating a second
objective function that simultaneously balances the workload of the members of
staff while maximizing utility of the students. The algorithmic approach has
been prototypically implemented in a computer system. One important aspect in
this context is the application of the research work to problems of other
scientific institutions, and therefore the provision of decision support
functionalities.
","Variable Neighborhood Search for the University Lecturer-Student
  Assignment Problem"
"  We introduce an extended tableau calculus for answer set programming (ASP).
The proof system is based on the ASP tableaux defined in [Gebser&Schaub, ICLP
2006], with an added extension rule. We investigate the power of Extended ASP
Tableaux both theoretically and empirically. We study the relationship of
Extended ASP Tableaux with the Extended Resolution proof system defined by
Tseitin for sets of clauses, and separate Extended ASP Tableaux from ASP
Tableaux by giving a polynomial-length proof for a family of normal logic
programs P_n for which ASP Tableaux has exponential-length minimal proofs with
respect to n. Additionally, Extended ASP Tableaux imply interesting insight
into the effect of program simplification on the lengths of proofs in ASP.
Closely related to Extended ASP Tableaux, we empirically investigate the effect
of redundant rules on the efficiency of ASP solving.
  To appear in Theory and Practice of Logic Programming (TPLP).
",Extended ASP tableaux and rule redundancy in normal logic programs
"  In this paper, a Gaifman-Shapiro-style module architecture is tailored to the
case of Smodels programs under the stable model semantics. The composition of
Smodels program modules is suitably limited by module conditions which ensure
the compatibility of the module system with stable models. Hence the semantics
of an entire Smodels program depends directly on stable models assigned to its
modules. This result is formalized as a module theorem which truly strengthens
Lifschitz and Turner's splitting-set theorem for the class of Smodels programs.
To streamline generalizations in the future, the module theorem is first proved
for normal programs and then extended to cover Smodels programs using a
translation from the latter class of programs to the former class. Moreover,
the respective notion of module-level equivalence, namely modular equivalence,
is shown to be a proper congruence relation: it is preserved under
substitutions of modules that are modularly equivalent. Principles for program
decomposition are also addressed. The strongly connected components of the
respective dependency graph can be exploited in order to extract a module
structure when there is no explicit a priori knowledge about the modules of a
program. The paper includes a practical demonstration of tools that have been
developed for automated (de)composition of Smodels programs.
  To appear in Theory and Practice of Logic Programming.
","Achieving compositionality of the stable model semantics for Smodels
  programs"
"  Most research related to unithood were conducted as part of a larger effort
for the determination of termhood. Consequently, novelties are rare in this
small sub-field of term extraction. In addition, existing work were mostly
empirically motivated and derived. We propose a new probabilistically-derived
measure, independent of any influences of termhood, that provides dedicated
measures to gather linguistic evidence from parsed text and statistical
evidence from Google search engine for the measurement of unithood. Our
comparative study using 1,825 test cases against an existing
empirically-derived function revealed an improvement in terms of precision,
recall and accuracy.
","Determining the Unithood of Word Sequences using a Probabilistic
  Approach"
"  Most works related to unithood were conducted as part of a larger effort for
the determination of termhood. Consequently, the number of independent research
that study the notion of unithood and produce dedicated techniques for
measuring unithood is extremely small. We propose a new approach, independent
of any influences of termhood, that provides dedicated measures to gather
linguistic evidence from parsed text and statistical evidence from Google
search engine for the measurement of unithood. Our evaluations revealed a
precision and recall of 98.68% and 91.82% respectively with an accuracy at
95.42% in measuring the unithood of 1005 test cases.
","Determining the Unithood of Word Sequences using Mutual Information and
  Independence Measure"
"  An increasing number of approaches for ontology engineering from text are
gearing towards the use of online sources such as company intranet and the
World Wide Web. Despite such rise, not much work can be found in aspects of
preprocessing and cleaning dirty texts from online sources. This paper presents
an enhancement of an Integrated Scoring for Spelling error correction,
Abbreviation expansion and Case restoration (ISSAC). ISSAC is implemented as
part of a text preprocessing phase in an ontology engineering system. New
evaluations performed on the enhanced ISSAC using 700 chat records reveal an
improved accuracy of 98% as compared to 96.5% and 71% based on the use of only
basic ISSAC and of Aspell, respectively.
",Enhanced Integrated Scoring for Cleaning Dirty Texts
"  We present a domain-independent algorithm that computes macros in a novel
way. Our algorithm computes macros ""on-the-fly"" for a given set of states and
does not require previously learned or inferred information, nor prior domain
knowledge. The algorithm is used to define new domain-independent tractable
classes of classical planning that are proved to include \emph{Blocksworld-arm}
and \emph{Towers of Hanoi}.
",On-the-fly Macros
"  In this study, we reproduce two new hybrid intelligent systems, involve three
prominent intelligent computing and approximate reasoning methods: Self
Organizing feature Map (SOM), Neruo-Fuzzy Inference System and Rough Set Theory
(RST),called SONFIS and SORST. We show how our algorithms can be construed as a
linkage of government-society interactions, where government catches various
states of behaviors: solid (absolute) or flexible. So, transition of society,
by changing of connectivity parameters (noise) from order to disorder is
inferred.
",Modeling of Social Transitions Using Intelligent Systems
"  The paper presents the investigation and implementation of the relationship
between diversity and the performance of multiple classifiers on classification
accuracy. The study is critical as to build classifiers that are strong and can
generalize better. The parameters of the neural network within the committee
were varied to induce diversity; hence structural diversity is the focus for
this study. The hidden nodes and the activation function are the parameters
that were varied. The diversity measures that were adopted from ecology such as
Shannon and Simpson were used to quantify diversity. Genetic algorithm is used
to find the optimal ensemble by using the accuracy as the cost function. The
results observed shows that there is a relationship between structural
diversity and accuracy. It is observed that the classification accuracy of an
ensemble increases as the diversity increases. There was an increase of 3%-6%
in the classification accuracy.
","Relationship between Diversity and Perfomance of Multiple Classifiers
  for Decision Support"
"  The paper presents an exponential pheromone deposition rule to modify the
basic ant system algorithm which employs constant deposition rule. A stability
analysis using differential equation is carried out to find out the values of
parameters that make the ant system dynamics stable for both kinds of
deposition rule. A roadmap of connected cities is chosen as the problem
environment where the shortest route between two given cities is required to be
discovered. Simulations performed with both forms of deposition approach using
Elitist Ant System model reveal that the exponential deposition approach
outperforms the classical one by a large extent. Exhaustive experiments are
also carried out to find out the optimum setting of different controlling
parameters for exponential deposition approach and an empirical relationship
between the major controlling parameters of the algorithm and some features of
problem environment.
","Balancing Exploration and Exploitation by an Elitist Ant System with
  Exponential Pheromone Deposition Rule"
"  This article presents a unique design for a parser using the Ant Colony
Optimization algorithm. The paper implements the intuitive thought process of
human mind through the activities of artificial ants. The scheme presented here
uses a bottom-up approach and the parsing program can directly use ambiguous or
redundant grammars. We allocate a node corresponding to each production rule
present in the given grammar. Each node is connected to all other nodes
(representing other production rules), thereby establishing a completely
connected graph susceptible to the movement of artificial ants. Each ant tries
to modify this sentential form by the production rule present in the node and
upgrades its position until the sentential form reduces to the start symbol S.
Successful ants deposit pheromone on the links that they have traversed
through. Eventually, the optimum path is discovered by the links carrying
maximum amount of pheromone concentration. The design is simple, versatile,
robust and effective and obviates the calculation of the above mentioned sets
and precedence relation tables. Further advantages of our scheme lie in i)
ascertaining whether a given string belongs to the language represented by the
grammar, and ii) finding out the shortest possible path from the given string
to the start symbol S in case multiple routes exist.
",A Novel Parser Design Algorithm Based on Artificial Ants
"  The paper presents an exponential pheromone deposition approach to improve
the performance of classical Ant System algorithm which employs uniform
deposition rule. A simplified analysis using differential equations is carried
out to study the stability of basic ant system dynamics with both exponential
and constant deposition rules. A roadmap of connected cities, where the
shortest path between two specified cities are to be found out, is taken as a
platform to compare Max-Min Ant System model (an improved and popular model of
Ant System algorithm) with exponential and constant deposition rules. Extensive
simulations are performed to find the best parameter settings for non-uniform
deposition approach and experiments with these parameter settings revealed that
the above approach outstripped the traditional one by a large extent in terms
of both solution quality and convergence time.
","Extension of Max-Min Ant System with Exponential Pheromone Deposition
  Rule"
"  We address here two major challenges presented by dynamic data mining: 1) the
stability challenge: we have implemented a rigorous incremental density-based
clustering algorithm, independent from any initial conditions and ordering of
the data-vectors stream, 2) the cognitive challenge: we have implemented a
stringent selection process of association rules between clusters at time t-1
and time t for directly generating the main conclusions about the dynamics of a
data-stream. We illustrate these points with an application to a two years and
2600 documents scientific information database.
","Document stream clustering: experimenting an incremental algorithm and
  AR-based tools for highlighting dynamic trends"
"  Data-stream clustering is an ever-expanding subdomain of knowledge
extraction. Most of the past and present research effort aims at efficient
scaling up for the huge data repositories. Our approach focuses on qualitative
improvement, mainly for ""weak signals"" detection and precise tracking of
topical evolutions in the framework of information watch - though scalability
is intrinsically guaranteed in a possibly distributed implementation. Our
GERMEN algorithm exhaustively picks up the whole set of density peaks of the
data at time t, by identifying the local perturbations induced by the current
document vector, such as changing cluster borders, or new/vanishing clusters.
Optimality yields from the uniqueness 1) of the density landscape for any value
of our zoom parameter, 2) of the cluster allocation operated by our border
propagation rule. This results in a rigorous independence from the data
presentation ranking or any initialization parameter. We present here as a
first step the only assessment of a static view resulting from one year of the
CNRS/INIST Pascal database in the field of geotechnics.
","Classification dynamique d'un flux documentaire : une \'evaluation
  statique pr\'ealable de l'algorithme GERMEN"
"  This paper gives an introduction to this issue, and presents the framework
and the main steps of the Rosa project. Four teams of researchers, agronomists,
computer scientists, psychologists and linguists were involved during five
years within this project that aimed at the development of a knowledge based
system. The purpose of the Rosa system is the modelling and the comparison of
farm spatial organizations. It relies on a formalization of agronomical
knowledge and thus induces a joint knowledge building process involving both
the agronomists and the computer scientists. The paper describes the steps of
the modelling process as well as the filming procedures set up by the
psychologists and linguists in order to make explicit and to analyze the
underlying knowledge building process.
","\'Etude longitudinale d'une proc\'edure de mod\'elisation de
  connaissances en mati\`ere de gestion du territoire agricole"
"  Collaborative tagging systems, such as Delicious, CiteULike, and others,
allow users to annotate resources, e.g., Web pages or scientific papers, with
descriptive labels called tags. The social annotations contributed by thousands
of users, can potentially be used to infer categorical knowledge, classify
documents or recommend new relevant information. Traditional text inference
methods do not make best use of social annotation, since they do not take into
account variations in individual users' perspectives and vocabulary. In a
previous work, we introduced a simple probabilistic model that takes interests
of individual annotators into account in order to find hidden topics of
annotated resources. Unfortunately, that approach had one major shortcoming:
the number of topics and interests must be specified a priori. To address this
drawback, we extend the model to a fully Bayesian framework, which offers a way
to automatically estimate these numbers. In particular, the model allows the
number of interests and topics to change as suggested by the structure of the
data. We evaluate the proposed model in detail on the synthetic and real-world
data by comparing its performance to Latent Dirichlet Allocation on the topic
extraction task. For the latter evaluation, we apply the model to infer topics
of Web resources from social annotations obtained from Delicious in order to
discover new resources similar to a specified one. Our empirical results
demonstrate that the proposed model is a promising method for exploiting social
knowledge contained in user-generated annotations.
",Modeling Social Annotation: a Bayesian Approach
"  Airport gate assignment is of great importance in airport operations. In this
paper, we study the Airport Gate Assignment Problem (AGAP), propose a new model
and implement the model with Optimization Programming language (OPL). With the
objective to minimize the number of conflicts of any two adjacent aircrafts
assigned to the same gate, we build a mathematical model with logical
constraints and the binary constraints, which can provide an efficient
evaluation criterion for the Airlines to estimate the current gate assignment.
To illustrate the feasibility of the model we construct experiments with the
data obtained from Continental Airlines, Houston Gorge Bush Intercontinental
Airport IAH, which indicate that our model is both energetic and effective.
Moreover, we interpret experimental results, which further demonstrate that our
proposed model can provide a powerful tool for airline companies to estimate
the efficiency of their current work of gate assignment.
",Airport Gate Assignment: New Model and Implementation
"  This paper investigates the use of different Artificial Intelligence methods
to predict the values of several continuous variables from a Steam Generator.
The objective was to determine how the different artificial intelligence
methods performed in making predictions on the given dataset. The artificial
intelligence methods evaluated were Neural Networks, Support Vector Machines,
and Adaptive Neuro-Fuzzy Inference Systems. The types of neural networks
investigated were Multi-Layer Perceptions, and Radial Basis Function. Bayesian
and committee techniques were applied to these neural networks. Each of the AI
methods considered was simulated in Matlab. The results of the simulations
showed that all the AI methods were capable of predicting the Steam Generator
data reasonably accurately. However, the Adaptive Neuro-Fuzzy Inference system
out performed the other methods in terms of accuracy and ease of
implementation, while still achieving a fast execution time as well as a
reasonable training time.
",Artificial Intelligence Techniques for Steam Generator Modelling
"  Theoretical analysis of machine intelligence (MI) is useful for defining a
common platform in both theoretical and applied artificial intelligence (AI).
The goal of this paper is to set canonical definitions that can assist
pragmatic research in both strong and weak AI. Described epistemological
features of machine intelligence include relationship between intelligent
behavior, intelligent and unintelligent machine characteristics, observable and
unobservable entities and classification of intelligence. The paper also
establishes algebraic definitions of efficiency and accuracy of MI tests as
their quality measure. The last part of the paper addresses the learning
process with respect to the traditional epistemology and the epistemology of MI
described here. The proposed views on MI positively correlate to the Hegelian
monistic epistemology and contribute towards amalgamating idealistic
deliberations with the AI theory, particularly in a local frame of reference.
",Elementary epistemological features of machine intelligence
"  Answer set programming (ASP) is a logic programming paradigm that can be used
to solve complex combinatorial search problems. Aggregates are an ASP construct
that plays an important role in many applications. Defining a satisfactory
semantics of aggregates turned out to be a difficult problem, and in this paper
we propose a new approach, based on an analogy between aggregates and
propositional connectives. First, we extend the definition of an answer
set/stable model to cover arbitrary propositional theories; then we define
aggregates on top of them both as primitive constructs and as abbreviations for
formulas. Our definition of an aggregate combines expressiveness and
simplicity, and it inherits many theorems about programs with nested
expressions, such as theorems about strong equivalence and splitting.
",Logic programs with propositional connectives and aggregates
"  The standard classification of emotions involves categorizing the expression
of emotions. In this paper, parameters underlying some emotions are identified
and a new classification based on these parameters is suggested.
","Identification of parameters underlying emotions and a classification of
  emotions"
"  Neural networks are powerful tools for classification and regression in
static environments. This paper describes a technique for creating an ensemble
of neural networks that adapts dynamically to changing conditions. The model
separates the input space into four regions and each network is given a weight
in each region based on its performance on samples from that region. The
ensemble adapts dynamically by constantly adjusting these weights based on the
current performance of the networks. The data set used is a collection of
financial indicators with the goal of predicting the future platinum price. An
ensemble with no weightings does not improve on the naive estimate of no weekly
change; our weighting algorithm gives an average percentage error of 63% for
twenty weeks of prediction.
","Prediction of Platinum Prices Using Dynamically Weighted Mixture of
  Experts"
"  Health Practice Guideliens are supposed to unify practices and propose
recommendations to physicians. This paper describes GemFrame, a system capable
of semi-automatically filling an XML template from free texts in the clinical
domain. The XML template includes semantic information not explicitly encoded
in the text (pairs of conditions and ac-tions/recommendations). Therefore,
there is a need to compute the exact scope of condi-tions over text sequences
expressing the re-quired actions. We present a system developped for this task.
We show that it yields good performance when applied to the analysis of French
practice guidelines. We conclude with a precise evaluation of the tool.
","Analyse et structuration automatique des guides de bonnes pratiques
  cliniques : essai d'\'evaluation"
"  The need for domain ontologies in mission critical applications such as risk
management and hazard identification is becoming more and more pressing. Most
research on ontology learning conducted in the academia remains unrealistic for
real-world applications. One of the main problems is the dependence on
non-incremental, rare knowledge and textual resources, and manually-crafted
patterns and rules. This paper reports work in progress aiming to address such
undesirable dependencies during ontology construction. Initial experiments
using a working prototype of the system revealed promising potentials in
automatically constructing high-quality domain ontologies using real-world
texts.
","Automatic Construction of Lightweight Domain Ontologies for Chemical
  Engineering Risk Management"
"  We introduce novel results for approximate inference on planar graphical
models using the loop calculus framework. The loop calculus (Chertkov and
Chernyak, 2006) allows to express the exact partition function of a graphical
model as a finite sum of terms that can be evaluated once the belief
propagation (BP) solution is known. In general, full summation over all
correction terms is intractable. We develop an algorithm for the approach
presented in (Certkov et al., 2008) which represents an efficient truncation
scheme on planar graphs and a new representation of the series in terms of
Pfaffians of matrices. We analyze the performance of the algorithm for the
partition function approximation for models with binary variables and pairwise
interactions on grids and other planar graphs. We study in detail both the loop
series and the equivalent Pfaffian series and show that the first term of the
Pfaffian series for the general, intractable planar model, can provide very
accurate approximations. The algorithm outperforms previous truncation schemes
of the loop series and is competitive with other state-of-the-art methods for
approximate inference.
","Approximate inference on planar graphs using Loop Calculus and Belief
  Propagation"
"  In this paper we present the N-norms/N-conorms in neutrosophic logic and set
as extensions of T-norms/T-conorms in fuzzy logic and set. Also, as an
extension of the Intuitionistic Fuzzy Topology we present the Neutrosophic
Topologies.
","N-norm and N-conorm in Neutrosophic Logic and Set, and the Neutrosophic
  Topologies"
"  When a considerable number of mutations have no effects on fitness values,
the fitness landscape is said neutral. In order to study the interplay between
neutrality, which exists in many real-world applications, and performances of
metaheuristics, it is useful to design landscapes which make it possible to
tune precisely neutral degree distribution. Even though many neutral landscape
models have already been designed, none of them are general enough to create
landscapes with specific neutral degree distributions. We propose three steps
to design such landscapes: first using an algorithm we construct a landscape
whose distribution roughly fits the target one, then we use a simulated
annealing heuristic to bring closer the two distributions and finally we affect
fitness values to each neutral network. Then using this new family of fitness
landscapes we are able to highlight the interplay between deceptiveness and
neutrality.
",Deceptiveness and Neutrality - the ND family of fitness landscapes
"  The pharmacovigilance databases consist of several case reports involving
drugs and adverse events (AEs). Some methods are applied consistently to
highlight all signals, i.e. all statistically significant associations between
a drug and an AE. These methods are appropriate for verification of more
complex relationships involving one or several drug(s) and AE(s) (e.g;
syndromes or interactions) but do not address the identification of them. We
propose a method for the extraction of these relationships based on Formal
Concept Analysis (FCA) associated with disproportionality measures. This method
identifies all sets of drugs and AEs which are potential signals, syndromes or
interactions. Compared to a previous experience of disproportionality analysis
without FCA, the addition of FCA was more efficient for identifying false
positives related to concomitant drugs.
",Mining for adverse drug events with formal concept analysis
"  Domain experts should provide relevant domain knowledge to an Intelligent
Tutoring System (ITS) so that it can guide a learner during problemsolving
learning activities. However, for many ill-defined domains, the domain
knowledge is hard to define explicitly. In previous works, we showed how
sequential pattern mining can be used to extract a partial problem space from
logged user interactions, and how it can support tutoring services during
problem-solving exercises. This article describes an extension of this approach
to extract a problem space that is richer and more adapted for supporting
tutoring services. We combined sequential pattern mining with (1) dimensional
pattern mining (2) time intervals, (3) the automatic clustering of valued
actions and (4) closed sequences mining. Some tutoring services have been
implemented and an experiment has been conducted in a tutoring system.
","A Knowledge Discovery Framework for Learning Task Models from User
  Interactions in Intelligent Tutoring Systems"
"  In this paper we propose the CTS (Concious Tutoring System) technology, a
biologically plausible cognitive agent based on human brain functions.This
agent is capable of learning and remembering events and any related information
such as corresponding procedures, stimuli and their emotional valences. Our
proposed episodic memory and episodic learning mechanism are closer to the
current multiple-trace theory in neuroscience, because they are inspired by it
[5] contrary to other mechanisms that are incorporated in cognitive agents.
This is because in our model emotions play a role in the encoding and
remembering of events. This allows the agent to improve its behavior by
remembering previously selected behaviors which are influenced by its emotional
mechanism. Moreover, the architecture incorporates a realistic memory
consolidation process based on a data mining algorithm.
",How Emotional Mechanism Helps Episodic Learning in a Cognitive Agent
"  Consumers of mass media must have a comprehensive, balanced and plural
selection of news to get an unbiased perspective; but achieving this goal can
be very challenging, laborious and time consuming. News stories development
over time, its (in)consistency, and different level of coverage across the
media outlets are challenges that a conscientious reader has to overcome in
order to alleviate bias.
  In this paper we present an intelligent agent framework currently
facilitating analysis of the main sources of on-line news in El Salvador. We
show how prior tools of text analysis and Web 2.0 technologies can be combined
with minimal manual intervention to help individuals on their rational decision
process, while holding media outlets accountable for their work.
",Alleviating Media Bias Through Intelligent Agent Blogging
"  We study the logic of comparative concept similarity $\CSL$ introduced by
Sheremet, Tishkovsky, Wolter and Zakharyaschev to capture a form of qualitative
similarity comparison. In this logic we can formulate assertions of the form ""
objects A are more similar to B than to C"". The semantics of this logic is
defined by structures equipped by distance functions evaluating the similarity
degree of objects. We consider here the particular case of the semantics
induced by \emph{minspaces}, the latter being distance spaces where the minimum
of a set of distances always exists. It turns out that the semantics over
arbitrary minspaces can be equivalently specified in terms of preferential
structures, typical of conditional logics. We first give a direct
axiomatisation of this logic over Minspaces. We next define a decision
procedure in the form of a tableaux calculus. Both the calculus and the
axiomatisation take advantage of the reformulation of the semantics in terms of
preferential structures.
","Comparative concept similarity over Minspaces: Axiomatisation and
  Tableaux Calculus"
"  Data mining algorithms are now able to efficiently deal with huge amount of
data. Various kinds of patterns may be discovered and may have some great
impact on the general development of knowledge. In many domains, end users may
want to have their data mined by data mining tools in order to extract patterns
that could impact their business. Nevertheless, those users are often
overwhelmed by the large quantity of patterns extracted in such a situation.
Moreover, some privacy issues, or some commercial one may lead the users not to
be able to mine the data by themselves. Thus, the users may not have the
possibility to perform many experiments integrating various constraints in
order to focus on specific patterns they would like to extract. Post processing
of patterns may be an answer to that drawback. Thus, in this paper we present a
framework that could allow end users to manage collections of patterns. We
propose to use an efficient data structure on which some algebraic operators
may be used in order to retrieve or access patterns in pattern bases.
",A Model for Managing Collections of Patterns
"  Empirical evidence suggests that hashing is an effective strategy for
dimensionality reduction and practical nonparametric estimation. In this paper
we provide exponential tail bounds for feature hashing and show that the
interaction between random subspaces is negligible with high probability. We
demonstrate the feasibility of this approach with experimental results for a
new use case -- multitask learning with hundreds of thousands of tasks.
",Feature Hashing for Large Scale Multitask Learning
"  We propose a new extended format to represent constraint networks using XML.
This format allows us to represent constraints defined either in extension or
in intension. It also allows us to reference global constraints. Any instance
of the problems CSP (Constraint Satisfaction Problem), QCSP (Quantified CSP)
and WCSP (Weighted CSP) can be represented using this format.
",XML Representation of Constraint Networks: Format XCSP 2.1
"  The present work consisted in developing a plateau game. There are the
traditional ones (monopoly, cluedo, ect.) but those which interest us leave
less place at the chance (luck) than to the strategy such that the chess game.
Kallah is an old African game, its rules are simple but the strategies to be
used are very complex to implement. Of course, they are based on a strongly
mathematical basis as in the film ""Rain-Man"" where one can see that gambling
can be payed with strategies based on mathematical theories. The Artificial
Intelligence gives the possibility ""of thinking"" to a machine and, therefore,
allows it to make decisions. In our work, we use it to give the means to the
computer choosing its best movement.
",The Semantics of Kalah Game
"  1-Nearest Neighbor with the Dynamic Time Warping (DTW) distance is one of the
most effective classifiers on time series domain. Since the global constraint
has been introduced in speech community, many global constraint models have
been proposed including Sakoe-Chiba (S-C) band, Itakura Parallelogram, and
Ratanamahatana-Keogh (R-K) band. The R-K band is a general global constraint
model that can represent any global constraints with arbitrary shape and size
effectively. However, we need a good learning algorithm to discover the most
suitable set of R-K bands, and the current R-K band learning algorithm still
suffers from an 'overfitting' phenomenon. In this paper, we propose two new
learning algorithms, i.e., band boundary extraction algorithm and iterative
learning algorithm. The band boundary extraction is calculated from the bound
of all possible warping paths in each class, and the iterative learning is
adjusted from the original R-K band learning. We also use a Silhouette index, a
well-known clustering validation technique, as a heuristic function, and the
lower bound function, LB_Keogh, to enhance the prediction speed. Twenty
datasets, from the Workshop and Challenge on Time Series Classification, held
in conjunction of the SIGKDD 2007, are used to evaluate our approach.
",Learning DTW Global Constraint for Time Series Classification
"  We propose Range and Roots which are two common patterns useful for
specifying a wide range of counting and occurrence constraints. We design
specialised propagation algorithms for these two patterns. Counting and
occurrence constraints specified using these patterns thus directly inherit a
propagation algorithm. To illustrate the capabilities of the Range and Roots
constraints, we specify a number of global constraints taken from the
literature. Preliminary experiments demonstrate that propagating counting and
occurrence constraints using these two patterns leads to a small loss in
performance when compared to specialised global constraints and is competitive
with alternative decompositions using elementary constraints.
","Range and Roots: Two Common Patterns for Specifying and Propagating
  Counting and Occurrence Constraints"
"  The management and combination of uncertain, imprecise, fuzzy and even
paradoxical or high conflicting sources of information has always been, and
still remains today, of primal importance for the development of reliable
modern information systems involving artificial reasoning. In this
introduction, we present a survey of our recent theory of plausible and
paradoxical reasoning, known as Dezert-Smarandache Theory (DSmT), developed for
dealing with imprecise, uncertain and conflicting sources of information. We
focus our presentation on the foundations of DSmT and on its most important
rules of combination, rather than on browsing specific applications of DSmT
available in literature. Several simple examples are given throughout this
presentation to show the efficiency and the generality of this new approach.
",An introduction to DSmT
"  When mathematicians present proofs they usually adapt their explanations to
their didactic goals and to the (assumed) knowledge of their addressees. Modern
automated theorem provers, in contrast, present proofs usually at a fixed level
of detail (also called granularity). Often these presentations are neither
intended nor suitable for human use. A challenge therefore is to develop user-
and goal-adaptive proof presentation techniques that obey common mathematical
practice. We present a flexible and adaptive approach to proof presentation
that exploits machine learning techniques to extract a model of the specific
granularity of proof examples and employs this model for the automated
generation of further proofs at an adapted level of granularity.
",Granularity-Adaptive Proof Presentation
"  Symmetry is an important factor in solving many constraint satisfaction
problems. One common type of symmetry is when we have symmetric values. In a
recent series of papers, we have studied methods to break value symmetries. Our
results identify computational limits on eliminating value symmetry. For
instance, we prove that pruning all symmetric values is NP-hard in general.
Nevertheless, experiments show that much value symmetry can be broken in
practice. These results may be useful to researchers in planning, scheduling
and other areas as value symmetry occurs in many different domains.
",Breaking Value Symmetry
"  An attractive mechanism to specify global constraints in rostering and other
domains is via formal languages. For instance, the Regular and Grammar
constraints specify constraints in terms of the languages accepted by an
automaton and a context-free grammar respectively. Taking advantage of the
fixed length of the constraint, we give an algorithm to transform a
context-free grammar into an automaton. We then study the use of minimization
techniques to reduce the size of such automata and speed up propagation. We
show that minimizing such automata after they have been unfolded and domains
initially reduced can give automata that are more compact than minimizing
before unfolding and reducing. Experimental results show that such
transformations can improve the size of rostering problems that we can 'model
and run'.
",Reformulating Global Grammar Constraints
"  We propose a new family of constraints which combine together lexicographical
ordering constraints for symmetry breaking with other common global
constraints. We give a general purpose propagator for this family of
constraints, and show how to improve its complexity by exploiting properties of
the included global constraints.
",Combining Symmetry Breaking and Global Constraints
"  We present an online method for estimating the cost of solving SAT problems.
Modern SAT solvers present several challenges to estimate search cost including
non-chronological backtracking, learning and restarts. Our method uses a linear
model trained on data gathered at the start of search. We show the
effectiveness of this method using random and structured problems. We
demonstrate that predictions made in early restarts can be used to improve
later predictions. We also show that we can use such cost estimations to select
a solver from a portfolio.
",Online Estimation of SAT Solving Runtime
"  In this work, we deal with the question of modeling programming exercises for
novices pointing to an e-learning scenario. Our purpose is to identify basic
requirements, raise some key questions and propose potential answers from a
conceptual perspective. Presented as a general picture, we hypothetically
situate our work in a general context where e-learning instructional material
needs to be adapted to form part of an introductory Computer Science (CS)
e-learning course at the CS1-level. Meant is a potential course which aims at
improving novices skills and knowledge on the essentials of programming by
using e-learning based approaches in connection (at least conceptually) with a
general host framework like Activemath (www.activemath.org). Our elaboration
covers contextual and, particularly, cognitive elements preparing the terrain
for eventual research stages in a derived project, as indicated. We concentrate
our main efforts on reasoning mechanisms about exercise complexity that can
eventually offer tool support for the task of exercise authoring. We base our
requirements analysis on our own perception of the exercise subsystem provided
by Activemath especially within the domain reasoner area. We enrich the
analysis by bringing to the discussion several relevant contextual elements
from the CS1 courses, its definition and implementation. Concerning cognitive
models and exercises, we build upon the principles of Bloom's Taxonomy as a
relatively standardized basis and use them as a framework for study and
analysis of complexity in basic programming exercises. Our analysis includes
requirements for the domain reasoner which are necessary for the exercise
analysis. We propose for such a purpose a three-layered conceptual model
considering exercise evaluation, programming and metaprogramming.
",On Requirements for Programming Exercises from an E-learning Perspective
"  Successful management of emotional stimuli is a pivotal issue concerning
Affective Computing (AC) and the related research. As a subfield of Artificial
Intelligence, AC is concerned not only with the design of computer systems and
the accompanying hardware that can recognize, interpret, and process human
emotions, but also with the development of systems that can trigger human
emotional response in an ordered and controlled manner. This requires the
maximum attainable precision and efficiency in the extraction of data from
emotionally annotated databases While these databases do use keywords or tags
for description of the semantic content, they do not provide either the
necessary flexibility or leverage needed to efficiently extract the pertinent
emotional content. Therefore, to this extent we propose an introduction of
ontologies as a new paradigm for description of emotionally annotated data. The
ability to select and sequence data based on their semantic attributes is vital
for any study involving metadata, semantics and ontological sorting like the
Semantic Web or the Social Semantic Desktop, and the approach described in the
paper facilitates reuse in these areas as well.
",Tagging multimedia stimuli with ontologies
"  To model combinatorial decision problems involving uncertainty and
probability, we introduce scenario based stochastic constraint programming.
Stochastic constraint programs contain both decision variables, which we can
set, and stochastic variables, which follow a discrete probability
distribution. We provide a semantics for stochastic constraint programs based
on scenario trees. Using this semantics, we can compile stochastic constraint
programs down into conventional (non-stochastic) constraint programs. This
allows us to exploit the full power of existing constraint solvers. We have
implemented this framework for decision making under uncertainty in stochastic
OPL, a language which is based on the OPL constraint modelling language
[Hentenryck et al., 1999]. To illustrate the potential of this framework, we
model a wide range of problems in areas as diverse as portfolio
diversification, agricultural planning and production/inventory management.
",Stochastic Constraint Programming: A Scenario-Based Approach
"  To model combinatorial decision problems involving uncertainty and
probability, we introduce stochastic constraint programming. Stochastic
constraint programs contain both decision variables (which we can set) and
stochastic variables (which follow a probability distribution). They combine
together the best features of traditional constraint satisfaction, stochastic
integer programming, and stochastic satisfiability. We give a semantics for
stochastic constraint programs, and propose a number of complete algorithms and
approximation procedures. Finally, we discuss a number of extensions of
stochastic constraint programming to relax various assumptions like the
independence between stochastic variables, and compare with other approaches
for decision making under uncertainty.
",Stochastic Constraint Programming
"  Often user interfaces of theorem proving systems focus on assisting
particularly trained and skilled users, i.e., proof experts. As a result, the
systems are difficult to use for non-expert users. This paper describes a paper
and pencil HCI experiment, in which (non-expert) students were asked to make
suggestions for a GUI for an interactive system for mathematical proofs. They
had to explain the usage of the GUI by applying it to construct a proof sketch
for a given theorem. The evaluation of the experiment provides insights for the
interaction design for non-expert users and the needs and wants of this user
group.
",Designing a GUI for Proofs - Evaluation of an HCI Experiment
"  The Ouroboros Model is a new conceptual proposal for an algorithmic structure
for efficient data processing in living beings as well as for artificial
agents. Its central feature is a general repetitive loop where one iteration
cycle sets the stage for the next. Sensory input activates data structures
(schemata) with similar constituents encountered before, thus expectations are
kindled. This corresponds to the highlighting of empty slots in the selected
schema, and these expectations are compared with the actually encountered
input. Depending on the outcome of this consumption analysis different next
steps like search for further data or a reset, i.e. a new attempt employing
another schema, are triggered. Monitoring of the whole process, and in
particular of the flow of activation directed by the consumption analysis,
yields valuable feedback for the optimum allocation of attention and resources
including the selective establishment of useful new memory entries.
",Flow of Activity in the Ouroboros Model
"  In a certain number of situations, human cognitive functioning is difficult
to represent with classical artificial intelligence structures. Such a
difficulty arises in the polyneuropathy diagnosis which is based on the spatial
distribution, along the nerve fibres, of lesions, together with the synthesis
of several partial diagnoses. Faced with this problem while building up an
expert system (NEUROP), we developed a heterogeneous knowledge representation
associating a finite automaton with first order logic. A number of knowledge
representation problems raised by the electromyography test features are
examined in this study and the expert system architecture allowing such a
knowledge modeling are laid out.
","Heterogeneous knowledge representation using a finite automaton and
  first order logic: a case study in electromyography"
"  In this paper a new dynamic subsumption technique for Boolean CNF formulae is
proposed. It exploits simple and sufficient conditions to detect during
conflict analysis, clauses from the original formula that can be reduced by
subsumption. During the learnt clause derivation, and at each step of the
resolution process, we simply check for backward subsumption between the
current resolvent and clauses from the original formula and encoded in the
implication graph. Our approach give rise to a strong and dynamic
simplification technique that exploits learning to eliminate literals from the
original clauses. Experimental results show that the integration of our dynamic
subsumption approach within the state-of-the-art SAT solvers Minisat and Rsat
achieves interesting improvements particularly on crafted instances.
",Learning for Dynamic subsumption
"  Today, science have a powerful tool for the description of reality - the
numbers. However, the concept of number was not immediately, lets try to trace
the evolution of the concept. The numbers emerged as the need for accurate
estimates of the amount in order to permit a comparison of some objects. So if
you see to it how many times a day a person uses the numbers and compare, it
becomes evident that the comparison is used much more frequently. However, the
comparison is not possible without two opposite basic standards. Thus, to
introduce the concept of comparison, must have two opposing standards, in turn,
the operation of comparison is necessary to introduce the concept of number.
Arguably, the scientific description of reality is impossible without the
concept of opposites.
  In this paper analyzes the concept of opposites, as the basis for the
introduction of the principle of development.
",Principle of development
"  Social Network Analysis (SNA) tries to understand and exploit the key
features of social networks in order to manage their life cycle and predict
their evolution. Increasingly popular web 2.0 sites are forming huge social
network. Classical methods from social network analysis (SNA) have been applied
to such online networks. In this paper, we propose leveraging semantic web
technologies to merge and exploit the best features of each domain. We present
how to facilitate and enhance the analysis of online social networks,
exploiting the power of semantic social network analysis.
",Semantic Social Network Analysis
"  We describe a variant of resolution rule of proof and show that it is
complete for stable semantics of logic programs. We show applications of this
result.
",Guarded resolution for answer set programming
"  A fuzzy mnesor space is a semimodule over the positive real numbers. It can
be used as theoretical framework for fuzzy sets. Hence we can prove a great
number of properties for fuzzy sets without refering to the membership
functions.
",Fuzzy Mnesors
"  We apply proof-theoretic techniques in answer Set Programming. The main
results include: 1. A characterization of continuity properties of
Gelfond-Lifschitz operator for logic program. 2. A propositional
characterization of stable models of logic programs (without referring to loop
formulas.
",An Application of Proof-Theory in Answer Set Programming
"  We show that some common and important global constraints like ALL-DIFFERENT
and GCC can be decomposed into simple arithmetic constraints on which we
achieve bound or range consistency, and in some cases even greater pruning.
These decompositions can be easily added to new solvers. They also provide
other constraints with access to the state of the propagator by sharing of
variables. Such sharing can be used to improve propagation between constraints.
We report experiments with our decomposition in a pseudo-Boolean solver.
","Decompositions of All Different, Global Cardinality and Related
  Constraints"
"  To model combinatorial decision problems involving uncertainty and
probability, we extend the stochastic constraint programming framework proposed
in [Walsh, 2002] along a number of important dimensions (e.g. to multiple
chance constraints and to a range of new objectives). We also provide a new
(but equivalent) semantics based on scenarios. Using this semantics, we can
compile stochastic constraint programs down into conventional (nonstochastic)
constraint programs. This allows us to exploit the full power of existing
constraint solvers. We have implemented this framework for decision making
under uncertainty in stochastic OPL, a language which is based on the OPL
constraint modelling language [Hentenryck et al., 1999]. To illustrate the
potential of this framework, we model a wide range of problems in areas as
diverse as finance, agriculture and production.
",Scenario-based Stochastic Constraint Programming
"  Many real life optimization problems contain both hard and soft constraints,
as well as qualitative conditional preferences. However, there is no single
formalism to specify all three kinds of information. We therefore propose a
framework, based on both CP-nets and soft constraints, that handles both hard
and soft constraints as well as conditional preferences efficiently and
uniformly. We study the complexity of testing the consistency of preference
statements, and show how soft constraints can faithfully approximate the
semantics of conditional preference statements whilst improving the
computational complexity
","Reasoning about soft constraints and conditional preferences: complexity
  results and approximation techniques"
"  We identify a new and important global (or non-binary) constraint. This
constraint ensures that the values taken by two vectors of variables, when
viewed as multisets, are ordered. This constraint is useful for a number of
different applications including breaking symmetry and fuzzy constraint
satisfaction. We propose and implement an efficient linear time algorithm for
enforcing generalised arc consistency on such a multiset ordering constraint.
Experimental results on several problem domains show considerable promise.
",Multiset Ordering Constraints
"  We relate tag clouds to other forms of visualization, including planar or
reduced dimensionality mapping, and Kohonen self-organizing maps. Using a
modified tag cloud visualization, we incorporate other information into it,
including text sequence and most pertinent words. Our notion of word pertinence
goes beyond just word frequency and instead takes a word in a mathematical
sense as located at the average of all of its pairwise relationships. We
capture semantics through context, taken as all pairwise relationships. Our
domain of application is that of filmscript analysis. The analysis of
filmscripts, always important for cinema, is experiencing a major gain in
importance in the context of television. Our objective in this work is to
visualize the semantics of filmscript, and beyond filmscript any other
partially structured, time-ordered, sequence of text segments. In particular we
develop an innovative approach to plot characterization.
",Tag Clouds for Displaying Semantics: The Case of Filmscripts
"  The paper proposes an analysis on some existent ontologies, in order to point
out ways to resolve semantic heterogeneity in information systems. Authors are
highlighting the tasks in a Knowledge Acquisiton System and identifying aspects
related to the addition of new information to an intelligent system. A solution
is proposed, as a combination of ontology reasoning services and natural
languages generation. A multi-agent system will be conceived with an extractor
agent, a reasoner agent and a competence management agent.
",Considerations on Construction Ontologies
"  We have been developing a system for recognising human activity given a
symbolic representation of video content. The input of our system is a set of
time-stamped short-term activities detected on video frames. The output of our
system is a set of recognised long-term activities, which are pre-defined
temporal combinations of short-term activities. The constraints on the
short-term activities that, if satisfied, lead to the recognition of a
long-term activity, are expressed using a dialect of the Event Calculus. We
illustrate the expressiveness of the dialect by showing the representation of
several typical complex activities. Furthermore, we present a detailed
evaluation of the system through experimentation on a benchmark dataset of
surveillance videos.
",A Logic Programming Approach to Activity Recognition
"  People have to make important decisions within a time frame. Hence, it is
imperative to employ means or strategy to aid effective decision making.
Consequently, Economic Intelligence (EI) has emerged as a field to aid
strategic and timely decision making in an organization. In the course of
attaining this goal: it is indispensable to be more optimistic towards
provision for conservation of intellectual resource invested into the process
of decision making. This intellectual resource is nothing else but the
knowledge of the actors as well as that of the various processes for effecting
decision making. Knowledge has been recognized as a strategic economic resource
for enhancing productivity and a key for innovation in any organization or
community. Thus, its adequate management with cognizance of its temporal
properties is highly indispensable. Temporal properties of knowledge refer to
the date and time (known as timestamp) such knowledge is created as well as the
duration or interval between related knowledge. This paper focuses on the needs
for a user-centered knowledge management approach as well as exploitation of
associated temporal properties. Our perspective of knowledge is with respect to
decision-problems projects in EI. Our hypothesis is that the possibility of
reasoning about temporal properties in exploitation of knowledge in EI projects
should foster timely decision making through generation of useful inferences
from available and reusable knowledge for a new project.
","Knowledge Management in Economic Intelligence with Reasoning on Temporal
  Attributes"
"  I discuss (ontologies_and_ontological_knowledge_bases /
formal_methods_and_theories) duality and its category theory extensions as a
step toward a solution to Knowledge-Based Systems Theory. In particular I focus
on the example of the design of elements of ontologies and ontological
knowledge bases of next three electronic courses: Foundations of Research
Activities, Virtual Modeling of Complex Systems and Introduction to String
Theory.
",Toward a Category Theory Design of Ontological Knowledge Bases
"  Mnesors are defined as elements of a semimodule over the min-plus integers.
This two-sorted structure is able to merge graduation properties of vectors and
idempotent properties of boolean numbers, which makes it appropriate for hybrid
systems. We apply it to the control of an inverted pendulum and design a full
logical controller, that is, without the usual algebra of real numbers.
",Mnesors for automatic control
"  We consider the following sequential decision problem. Given a set of items
of unknown utility, we need to select one of as high a utility as possible
(``the selection problem''). Measurements (possibly noisy) of item values prior
to selection are allowed, at a known cost. The goal is to optimize the overall
sequential decision process of measurements and selection.
  Value of information (VOI) is a well-known scheme for selecting measurements,
but the intractability of the problem typically leads to using myopic VOI
estimates. In the selection problem, myopic VOI frequently badly underestimates
the value of information, leading to inferior sensing plans. We relax the
strict myopic assumption into a scheme we term semi-myopic, providing a
spectrum of methods that can improve the performance of sensing plans. In
particular, we propose the efficiently computable method of ``blinkered'' VOI,
and examine theoretical bounds for special cases. Empirical evaluation of
``blinkered'' VOI in the selection problem with normally distributed item
values shows that is performs much better than pure myopic VOI.
",Semi-Myopic Sensing Plans for Value Optimization
"  There are several well-known justifications for conditioning as the
appropriate method for updating a single probability measure, given an
observation. However, there is a significant body of work arguing for sets of
probability measures, rather than single measures, as a more realistic model of
uncertainty. Conditioning still makes sense in this context--we can simply
condition each measure in the set individually, then combine the results--and,
indeed, it seems to be the preferred updating procedure in the literature. But
how justified is conditioning in this richer setting? Here we show, by
considering an axiomatic account of conditioning given by van Fraassen, that
the single-measure and sets-of-measures cases are very different. We show that
van Fraassen's axiomatization for the former case is nowhere near sufficient
for updating sets of measures. We give a considerably longer (and not as
compelling) list of axioms that together force conditioning in this setting,
and describe other update methods that are allowed once any of these axioms is
dropped.
",Updating Sets of Probabilities
"  This paper presents a novel two-stage flexible dynamic decision support based
optimal threat evaluation and defensive resource scheduling algorithm for
multi-target air-borne threats. The algorithm provides flexibility and
optimality by swapping between two objective functions, i.e. the preferential
and subtractive defense strategies as and when required. To further enhance the
solution quality, it outlines and divides the critical parameters used in
Threat Evaluation and Weapon Assignment (TEWA) into three broad categories
(Triggering, Scheduling and Ranking parameters). Proposed algorithm uses a
variant of many-to-many Stable Marriage Algorithm (SMA) to solve Threat
Evaluation (TE) and Weapon Assignment (WA) problem. In TE stage, Threat Ranking
and Threat-Asset pairing is done. Stage two is based on a new flexible dynamic
weapon scheduling algorithm, allowing multiple engagements using
shoot-look-shoot strategy, to compute near-optimal solution for a range of
scenarios. Analysis part of this paper presents the strengths and weaknesses of
the proposed algorithm over an alternative greedy algorithm as applied to
different offline scenarios.
","A Novel Two-Stage Dynamic Decision Support based Optimal Threat
  Evaluation and Defensive Resource Scheduling Algorithm for Multi Air-borne
  threats"
"  Martin and Osswald \cite{Martin07} have recently proposed many
generalizations of combination rules on quantitative beliefs in order to manage
the conflict and to consider the specificity of the responses of the experts.
Since the experts express themselves usually in natural language with
linguistic labels, Smarandache and Dezert \cite{Li07} have introduced a
mathematical framework for dealing directly also with qualitative beliefs. In
this paper we recall some element of our previous works and propose the new
combination rules, developed for the fusion of both qualitative or quantitative
beliefs.
",General combination rules for qualitative and quantitative beliefs
"  Surveillance control and reporting (SCR) system for air threats play an
important role in the defense of a country. SCR system corresponds to air and
ground situation management/processing along with information fusion,
communication, coordination, simulation and other critical defense oriented
tasks. Threat Evaluation and Weapon Assignment (TEWA) sits at the core of SCR
system. In such a system, maximal or near maximal utilization of constrained
resources is of extreme importance. Manual TEWA systems cannot provide
optimality because of different limitations e.g.surface to air missile (SAM)
can fire from a distance of 5Km, but manual TEWA systems are constrained by
human vision range and other constraints. Current TEWA systems usually work on
target-by-target basis using some type of greedy algorithm thus affecting the
optimality of the solution and failing in multi-target scenario. his paper
relates to a novel two-staged flexible dynamic decision support based optimal
threat evaluation and weapon assignment algorithm for multi-target air-borne
threats.
","A Novel Two-Staged Decision Support based Threat Evaluation and Weapon
  Assignment Algorithm, Asset-based Dynamic Weapon Scheduling using Artificial
  Intelligence Techinques"
"  Collective graphical models exploit inter-instance associative dependence to
output more accurate labelings. However existing models support very limited
kind of associativity which restricts accuracy gains. This paper makes two
major contributions. First, we propose a general collective inference framework
that biases data instances to agree on a set of {\em properties} of their
labelings. Agreement is encouraged through symmetric clique potentials. We show
that rich properties leads to bigger gains, and present a systematic inference
procedure for a large class of such properties. The procedure performs message
passing on the cluster graph, where property-aware messages are computed with
cluster specific algorithms. This provides an inference-only solution for
domain adaptation. Our experiments on bibliographic information extraction
illustrate significant test error reduction over unseen domains. Our second
major contribution consists of algorithms for computing outgoing messages from
clique clusters with symmetric clique potentials. Our algorithms are exact for
arbitrary symmetric potentials on binary labels and for max-like and
majority-like potentials on multiple labels. For majority potentials, we also
provide an efficient Lagrangian Relaxation based algorithm that compares
favorably with the exact algorithm. We present a 13/15-approximation algorithm
for the NP-hard Potts potential, with runtime sub-quadratic in the clique size.
In contrast, the best known previous guarantee for graphs with Potts potentials
is only 1/2. We empirically show that our method for Potts potentials is an
order of magnitude faster than the best alternatives, and our Lagrangian
Relaxation based algorithm for majority potentials beats the best applicable
heuristic -- ICM.
",Generalized Collective Inference with Symmetric Clique Potentials
"  This research report presents an extension of Cumulative of Choco constraint
solver, which is useful to encode over-constrained cumulative problems. This
new global constraint uses sweep and task interval violation-based algorithms.
",The Soft Cumulative Constraint
"  In this paper, we propose a first-order ontology for generalized stratified
order structure. We then classify the models of the theory using
model-theoretic techniques. An ontology mapping from this ontology to the core
theory of Process Specification Language is also discussed.
",Modelling Concurrent Behaviors in the Process Specification Language
"  The article presents a study of rather simple local search heuristics for the
single machine total weighted tardiness problem (SMTWTP), namely hillclimbing
and Variable Neighborhood Search. In particular, we revisit these approaches
for the SMTWTP as there appears to be a lack of appropriate/challenging
benchmark instances in this case. The obtained results are impressive indeed.
Only few instances remain unsolved, and even those are approximated within 1%
of the optimal/best known solutions. Our experiments support the claim that
metaheuristics for the SMTWTP are very likely to lead to good results, and
that, before refining search strategies, more work must be done with regard to
the proposition of benchmark data. Some recommendations for the construction of
such data sets are derived from our investigations.
","The Single Machine Total Weighted Tardiness Problem - Is it (for
  Metaheuristics) a Solved Problem ?"
"  The article describes the proposition and application of a local search
metaheuristic for multi-objective optimization problems. It is based on two
main principles of heuristic search, intensification through variable
neighborhoods, and diversification through perturbations and successive
iterations in favorable regions of the search space. The concept is
successfully tested on permutation flow shop scheduling problems under multiple
objectives and compared to other local search approaches. While the obtained
results are encouraging in terms of their quality, another positive attribute
of the approach is its simplicity as it does require the setting of only very
few parameters.
","Improvements for multi-objective flow shop scheduling by Pareto Iterated
  Local Search"
"  This paper discusses ""computational"" systems capable of ""computing"" functions
not computable by predefined Turing machines if the systems are not isolated
from their environment. Roughly speaking, these systems can change their finite
descriptions by interacting with their environment.
",Beyond Turing Machines
"  I propose that pattern recognition, memorization and processing are key
concepts that can be a principle set for the theoretical modeling of the mind
function. Most of the questions about the mind functioning can be answered by a
descriptive modeling and definitions from these principles. An understandable
consciousness definition can be drawn based on the assumption that a pattern
recognition system can recognize its own patterns of activity. The principles,
descriptive modeling and definitions can be a basis for theoretical and applied
research on cognitive sciences, particularly at artificial intelligence
studies.
",Pattern Recognition Theory of Mind
"  The report gives an overview about activities on the topic Semantic Web. It
has been released as technical report for the project ""KTweb -- Connecting
Knowledge Technologies Communities"" in 2003.
",Fact Sheet on Semantic Web
"  Restart strategies are an important factor in the performance of
conflict-driven Davis Putnam style SAT solvers. Selecting a good restart
strategy for a problem instance can enhance the performance of a solver.
Inspired by recent success applying machine learning techniques to predict the
runtime of SAT solvers, we present a method which uses machine learning to
boost solver performance through a smart selection of the restart strategy.
Based on easy to compute features, we train both a satisfiability classifier
and runtime models. We use these models to choose between restart strategies.
We present experimental results comparing this technique with the most commonly
used restart strategies. Our results demonstrate that machine learning is
effective in improving solver performance.
",Restart Strategy Selection using Machine Learning Techniques
"  We present two different methods for estimating the cost of solving SAT
problems. The methods focus on the online behaviour of the backtracking solver,
as well as the structure of the problem. Modern SAT solvers present several
challenges to estimate search cost including coping with nonchronological
backtracking, learning and restarts. Our first method adapt an existing
algorithm for estimating the size of a search tree to deal with these
challenges. We then suggest a second method that uses a linear model trained on
data gathered online at the start of search. We compare the effectiveness of
these two methods using random and structured problems. We also demonstrate
that predictions made in early restarts can be used to improve later
predictions. We conclude by showing that the cost of solving a set of problems
can be reduced by selecting a solver from a portfolio based on such cost
estimations.
",Online Search Cost Estimation for SAT Solvers
"  Classification is the basis of cognition. Unlike other solutions, this study
approaches it from the view of outliers. We present an expanding algorithm to
detect outliers in univariate datasets, together with the underlying
foundation. The expanding algorithm runs in a holistic way, making it a rather
robust solution. Synthetic and real data experiments show its power.
Furthermore, an application for multi-class problems leads to the introduction
of the oscillator algorithm. The corresponding result implies the potential
wide use of the expanding algorithm.
",On Classification from Outlier View
"  We consider a sequence of repeated interactions between an agent and an
environment. Uncertainty about the environment is captured by a probability
distribution over a space of hypotheses, which includes all computable
functions. Given a utility function, we can evaluate the expected utility of
any computational policy for interaction with the environment. After making
some plausible assumptions (and maybe one not-so-plausible assumption), we show
that if the utility function is unbounded, then the expected utility of any
policy is undefined.
",Convergence of Expected Utility for Universal AI
"  This study describes application of some approximate reasoning methods to
analysis of hydrocyclone performance. In this manner, using a combining of Self
Organizing Map (SOM), Neuro-Fuzzy Inference System (NFIS)-SONFIS- and Rough Set
Theory (RST)-SORST-crisp and fuzzy granules are obtained. Balancing of crisp
granules and non-crisp granules can be implemented in close-open iteration.
Using different criteria and based on granulation level balance point
(interval) or a pseudo-balance point is estimated. Validation of the proposed
methods, on the data set of the hydrocyclone is rendered.
",Knowledge Discovery of Hydrocyclone s Circuit Based on SONFIS and SORST
"  In this paper we introduce two new DSm fusion conditioning rules with
example, and as a generalization of them a class of DSm fusion conditioning
rules, and then extend them to a class of DSm conditioning rules.
",A Class of DSm Conditional Rules
"  When implementing a propagator for a constraint, one must decide about
variants: When implementing min, should one also implement max? Should one
implement linear constraints both with unit and non-unit coefficients?
Constraint variants are ubiquitous: implementing them requires considerable (if
not prohibitive) effort and decreases maintainability, but will deliver better
performance than resorting to constraint decomposition.
  This paper shows how to use views to derive perfect propagator variants. A
model for views and derived propagators is introduced. Derived propagators are
proved to be indeed perfect in that they inherit essential properties such as
correctness and domain and bounds consistency. Techniques for systematically
deriving propagators such as transformation, generalization, specialization,
and type conversion are developed. The paper introduces an implementation
architecture for views that is independent of the underlying constraint
programming system. A detailed evaluation of views implemented in Gecode shows
that derived propagators are efficient and that views often incur no overhead.
Without views, Gecode would either require 180 000 rather than 40 000 lines of
propagator code, or would lack many efficient propagator variants. Compared to
8 000 lines of code for views, the reduction in code for propagators yields a
1750% return on investment.
",View-based Propagator Derivation
"  The explorative mind-map is a dynamic framework, that emerges automatically
from the input, it gets. It is unlike a verificative modeling system where
existing (human) thoughts are placed and connected together. In this regard,
explorative mind-maps change their size continuously, being adaptive with
connectionist cells inside; mind-maps process data input incrementally and
offer lots of possibilities to interact with the user through an appropriate
communication interface. With respect to a cognitive motivated situation like a
conversation between partners, mind-maps become interesting as they are able to
process stimulating signals whenever they occur. If these signals are close to
an own understanding of the world, then the conversational partner becomes
automatically more trustful than if the signals do not or less match the own
knowledge scheme. In this (position) paper, we therefore motivate explorative
mind-maps as a cognitive engine and propose these as a decision support engine
to foster trust.
",A Cognitive Mind-map Framework to Foster Trust
"  To capture the uncertainty of information or knowledge in information
systems, various information granulations, also known as knowledge
granulations, have been proposed. Recently, several axiomatic definitions of
information granulation have been introduced. In this paper, we try to improve
these axiomatic definitions and give a universal construction of information
granulation by relating information granulations with a class of functions of
multiple variables. We show that the improved axiomatic definition has some
concrete information granulations in the literature as instances.
",An improved axiomatic definition of information granulation
"  Current research on qualitative spatial representation and reasoning mainly
focuses on one single aspect of space. In real world applications, however,
multiple spatial aspects are often involved simultaneously.
  This paper investigates problems arising in reasoning with combined
topological and directional information. We use the RCC8 algebra and the
Rectangle Algebra (RA) for expressing topological and directional information
respectively. We give examples to show that the bipath-consistency algorithm
BIPATH is incomplete for solving even basic RCC8 and RA constraints. If
topological constraints are taken from some maximal tractable subclasses of
RCC8, and directional constraints are taken from a subalgebra, termed DIR49, of
RA, then we show that BIPATH is able to separate topological constraints from
directional ones. This means, given a set of hybrid topological and directional
constraints from the above subclasses of RCC8 and RA, we can transfer the joint
satisfaction problem in polynomial time to two independent satisfaction
problems in RCC8 and RA. For general RA constraints, we give a method to
compute solutions that satisfy all topological constraints and approximately
satisfy each RA constraint to any prescribed precision.
",Reasoning with Topological and Directional Spatial Information
"  Direction relations between extended spatial objects are important
commonsense knowledge. Recently, Goyal and Egenhofer proposed a formal model,
known as Cardinal Direction Calculus (CDC), for representing direction
relations between connected plane regions. CDC is perhaps the most expressive
qualitative calculus for directional information, and has attracted increasing
interest from areas such as artificial intelligence, geographical information
science, and image retrieval. Given a network of CDC constraints, the
consistency problem is deciding if the network is realizable by connected
regions in the real plane. This paper provides a cubic algorithm for checking
consistency of basic CDC constraint networks, and proves that reasoning with
CDC is in general an NP-Complete problem. For a consistent network of basic CDC
constraints, our algorithm also returns a 'canonical' solution in cubic time.
This cubic algorithm is also adapted to cope with cardinal directions between
possibly disconnected regions, in which case currently the best algorithm is of
time complexity O(n^5).
",Reasoning about Cardinal Directions between Extended Objects
"  In this paper, we address the problem of generating preferred plans by
combining the procedural control knowledge specified by Hierarchical Task
Networks (HTNs) with rich qualitative user preferences. The outcome of our work
is a language for specifyin user preferences, tailored to HTN planning,
together with a provably optimal preference-based planner, HTNPLAN, that is
implemented as an extension of SHOP2. To compute preferred plans, we propose an
approach based on forward-chaining heuristic search. Our heuristic uses an
admissible evaluation function measuring the satisfaction of preferences over
partial plans. Our empirical evaluation demonstrates the effectiveness of our
HTNPLAN heuristics. We prove our approach sound and optimal with respect to the
plans it generates by appealing to a situation calculus semantics of our
preference language and of HTN planning. While our implementation builds on
SHOP2, the language and techniques proposed here are relevant to a broad range
of HTN planners.
",On Planning with Preferences in HTN
"  We study the notion of informedness in a client-consultant setting. Using a
software simulator, we examine the extent to which it pays off for consultants
to provide their clients with advice that is well-informed, or with advice that
is merely meant to appear to be well-informed. The latter strategy is
beneficial in that it costs less resources to keep up-to-date, but carries the
risk of a decreased reputation if the clients discover the low level of
informedness of the consultant. Our experimental results indicate that under
different circumstances, different strategies yield the optimal results (net
profit) for the consultants.
",Assessing the Impact of Informedness on a Consultant's Profit
"  We describe in this article a multiagent urban traffic simulation, as we
believe individual-based modeling is necessary to encompass the complex
influence the actions of an individual vehicle can have on the overall flow of
vehicles. We first describe how we build a graph description of the network
from purely geometric data, ESRI shapefiles. We then explain how we include
traffic related data to this graph. We go on after that with the model of the
vehicle agents: origin and destination, driving behavior, multiple lanes,
crossroads, and interactions with the other vehicles in day-to-day, ?ordinary?
traffic. We conclude with the presentation of the resulting simulation of this
model on the Rouen agglomeration.
",A multiagent urban traffic simulation Part I: dealing with the ordinary
"  2007 was the first international congress on the ?square of oppositions?. A
first attempt to structure debate using n-opposition theory was presented along
with the results of a first experiment on the web. Our proposal for this paper
is to define relations between arguments through a structure of opposition
(square of oppositions is one structure of opposition). We will be trying to
answer the following questions: How to organize debates on the web 2.0? How to
structure them in a logical way? What is the role of n-opposition theory, in
this context? We present in this paper results of three experiments
(Betapolitique 2007, ECAP 2008, Intermed 2008).
",n-Opposition theory to structure debates
"  We propose Interactive Differential Evolution (IDE) based on paired
comparisons for reducing user fatigue and evaluate its convergence speed in
comparison with Interactive Genetic Algorithms (IGA) and tournament IGA. User
interface and convergence performance are two big keys for reducing Interactive
Evolutionary Computation (IEC) user fatigue. Unlike IGA and conventional IDE,
users of the proposed IDE and tournament IGA do not need to compare whole
individuals each other but compare pairs of individuals, which largely
decreases user fatigue. In this paper, we design a pseudo-IEC user and evaluate
another factor, IEC convergence performance, using IEC simulators and show that
our proposed IDE converges significantly faster than IGA and tournament IGA,
i.e. our proposed one is superior to others from both user interface and
convergence performance points of view.
",Paired Comparisons-based Interactive Differential Evolution
"  This paper describes application of information granulation theory, on the
back analysis of Jeffrey mine southeast wall Quebec. In this manner, using a
combining of Self Organizing Map (SOM) and rough set theory (RST), crisp and
rough granules are obtained. Balancing of crisp granules and sub rough granules
is rendered in close-open iteration. Combining of hard and soft computing,
namely finite difference method (FDM) and computational intelligence and taking
in to account missing information are two main benefits of the proposed method.
As a practical example, reverse analysis on the failure of the southeast wall
Jeffrey mine is accomplished.
",Back analysis based on SOM-RST system
"  Fault diagnosis has become a very important area of research during the last
decade due to the advancement of mechanical and electrical systems in
industries. The automobile is a crucial field where fault diagnosis is given a
special attention. Due to the increasing complexity and newly added features in
vehicles, a comprehensive study has to be performed in order to achieve an
appropriate diagnosis model. A diagnosis system is capable of identifying the
faults of a system by investigating the observable effects (or symptoms). The
system categorizes the fault into a diagnosis class and identifies a probable
cause based on the supplied fault symptoms. Fault categorization and
identification are done using similarity matching techniques. The development
of diagnosis classes is done by making use of previous experience, knowledge or
information within an application area. The necessary information used may come
from several sources of knowledge, such as from system analysis. In this paper
similarity matching techniques for fault diagnosis in automotive infotainment
applications are discussed.
","Similarity Matching Techniques for Fault Diagnosis in Automotive
  Infotainment Electronics"
"  Diverse recommendation techniques have been already proposed and encapsulated
into several e-business applications, aiming to perform a more accurate
evaluation of the existing information and accordingly augment the assistance
provided to the users involved. This paper reports on the development and
integration of a recommendation module in an agent-based transportation
transactions management system. The module is built according to a novel hybrid
recommendation technique, which combines the advantages of collaborative
filtering and knowledge-based approaches. The proposed technique and supporting
module assist customers in considering in detail alternative transportation
transactions that satisfy their requests, as well as in evaluating completed
transactions. The related services are invoked through a software agent that
constructs the appropriate knowledge rules and performs a synthesis of the
recommendation policy.
","Performing Hybrid Recommendation in Intermodal Transportation-the
  FTMarket System's Recommendation Module"
"  We study decompositions of NVALUE, a global constraint that can be used to
model a wide range of problems where values need to be counted. Whilst
decomposition typically hinders propagation, we identify one decomposition that
maintains a global view as enforcing bound consistency on the decomposition
achieves bound consistency on the original global NVALUE constraint. Such
decompositions offer the prospect for advanced solving techniques like nogood
learning and impact based branching heuristics. They may also help SAT and IP
solvers take advantage of the propagation of global constraints.
",Decomposition of the NVALUE constraint
"  Symmetry is an important feature of many constraint programs. We show that
any symmetry acting on a set of symmetry breaking constraints can be used to
break symmetry. Different symmetries pick out different solutions in each
symmetry class. We use these observations in two methods for eliminating
symmetry from a problem. These methods are designed to have many of the
advantages of symmetry breaking methods that post static symmetry breaking
constraint without some of the disadvantages. In particular, the two methods
prune the search space using fast and efficient propagation of posted
constraints, whilst reducing the conflict between symmetry breaking and
branching heuristics. Experimental results show that the two methods perform
well on some standard benchmarks.
",Symmetries of Symmetry Breaking Constraints
"  Fuzzy constraints are a popular approach to handle preferences and
over-constrained problems in scenarios where one needs to be cautious, such as
in medical or space applications. We consider here fuzzy constraint problems
where some of the preferences may be missing. This models, for example,
settings where agents are distributed and have privacy issues, or where there
is an ongoing preference elicitation process. In this setting, we study how to
find a solution which is optimal irrespective of the missing preferences. In
the process of finding such a solution, we may elicit preferences from the user
if necessary. However, our goal is to ask the user as little as possible. We
define a combined solving and preference elicitation scheme with a large number
of different instantiations, each corresponding to a concrete algorithm which
we compare experimentally. We compute both the number of elicited preferences
and the ""user effort"", which may be larger, as it contains all the preference
values the user has to compute to be able to respond to the elicitation
requests. While the number of elicited preferences is important when the
concern is to communicate as little information as possible, the user effort
measures also the hidden work the user has to do to be able to communicate the
elicited preferences. Our experimental results show that some of our algorithms
are very good at finding a necessarily optimal solution while asking the user
for only a very small fraction of the missing preferences. The user effort is
also very small for the best algorithms. Finally, we test these algorithms on
hard constraint problems with possibly missing constraints, where the aim is to
find feasible solutions irrespective of the missing constraints.
","Elicitation strategies for fuzzy constraint problems with missing
  preferences: algorithms and experimental studies"
"  We propose new filtering algorithms for the SEQUENCE constraint and some
extensions of the SEQUENCE constraint based on network flows. We enforce domain
consistency on the SEQUENCE constraint in $O(n^2)$ time down a branch of the
search tree. This improves upon the best existing domain consistency algorithm
by a factor of $O(\log n)$. The flows used in these algorithms are derived from
a linear program. Some of them differ from the flows used to propagate global
constraints like GCC since the domains of the variables are encoded as costs on
the edges rather than capacities. Such flows are efficient for maintaining
bounds consistency over large domains and may be useful for other global
constraints.
",Flow-Based Propagators for the SEQUENCE and Related Global Constraints
"  We introduce the weighted CFG constraint and propose a propagation algorithm
that enforces domain consistency in $O(n^3|G|)$ time. We show that this
algorithm can be decomposed into a set of primitive arithmetic constraints
without hindering propagation.
",The Weighted CFG Constraint
"  Many models in natural and social sciences are comprised of sets of
inter-acting entities whose intensity of interaction decreases with distance.
This often leads to structures of interest in these models composed of dense
packs of entities. Fast Multipole Methods are a family of methods developed to
help with the calculation of a number of computable models such as described
above. We propose a method that builds upon FMM to detect and model the dense
structures of these systems.
",Building upon Fast Multipole Methods to Detect and Model Organizations
"  In Probabilistic Risk Management, risk is characterized by two quantities:
the magnitude (or severity) of the adverse consequences that can potentially
result from the given activity or action, and by the likelihood of occurrence
of the given adverse consequences. But a risk seldom exists in isolation: chain
of consequences must be examined, as the outcome of one risk can increase the
likelihood of other risks. Systemic theory must complement classic PRM. Indeed
these chains are composed of many different elements, all of which may have a
critical importance at many different levels. Furthermore, when urban
catastrophes are envisioned, space and time constraints are key determinants of
the workings and dynamics of these chains of catastrophes: models must include
a correct spatial topology of the studied risk. Finally, literature insists on
the importance small events can have on the risk on a greater scale: urban
risks management models belong to self-organized criticality theory. We chose
multiagent systems to incorporate this property in our model: the behavior of
an agent can transform the dynamics of important groups of them.
","A multiagent urban traffic simulation. Part II: dealing with the
  extraordinary"
"  Constrained Optimum Path (COP) problems appear in many real-life
applications, especially on communication networks. Some of these problems have
been considered and solved by specific techniques which are usually difficult
to extend. In this paper, we introduce a novel local search modeling for
solving some COPs by local search. The modeling features the compositionality,
modularity, reuse and strengthens the benefits of Constrained-Based Local
Search. We also apply the modeling to the edge-disjoint paths problem (EDP). We
show that side constraints can easily be added in the model. Computational
results show the significance of the approach.
","A Local Search Modeling for Constrained Optimum Paths Problems (Extended
  Abstract)"
"  Using constraint-based local search, we effectively model and efficiently
solve the problem of balancing the traffic demands on portions of the European
airspace while ensuring that their capacity constraints are satisfied. The
traffic demand of a portion of airspace is the hourly number of flights planned
to enter it, and its capacity is the upper bound on this number under which
air-traffic controllers can work. Currently, the only form of demand-capacity
balancing we allow is ground holding, that is the changing of the take-off
times of not yet airborne flights. Experiments with projected European flight
plans of the year 2030 show that already this first form of demand-capacity
balancing is feasible without incurring too much total delay and that it can
lead to a significantly better demand-capacity balance.
","Dynamic Demand-Capacity Balancing for Air Traffic Management Using
  Constraint-Based Local Search: First Results"
"  Stochastic local search (SLS) has been an active field of research in the
last few years, with new techniques and procedures being developed at an
astonishing rate. SLS has been traditionally associated with satisfiability
solving, that is, finding a solution for a given problem instance, as its
intrinsic nature does not address unsatisfiable problems. Unsatisfiable
instances were therefore commonly solved using backtrack search solvers. For
this reason, in the late 90s Selman, Kautz and McAllester proposed a challenge
to use local search instead to prove unsatisfiability. More recently, two SLS
solvers - Ranger and Gunsat - have been developed, which are able to prove
unsatisfiability albeit being SLS solvers. In this paper, we first compare
Ranger with Gunsat and then propose to improve Ranger performance using some of
Gunsat's techniques, namely unit propagation look-ahead and extended
resolution.
",On Improving Local Search for Unsatisfiability
"  This article introduces SatHyS (SAT HYbrid Solver), a novel hybrid approach
for propositional satisfiability. It combines local search and conflict driven
clause learning (CDCL) scheme. Each time the local search part reaches a local
minimum, the CDCL is launched. For SAT problems it behaves like a tabu list,
whereas for UNSAT ones, the CDCL part tries to focus on minimum unsatisfiable
sub-formula (MUS). Experimental results show good performances on many classes
of SAT instances from the last SAT competitions.
",Integrating Conflict Driven Clause Learning to Local Search
"  In this paper, we investigate the hybridization of constraint programming and
local search techniques within a large neighbourhood search scheme for solving
highly constrained nurse rostering problems. As identified by the research, a
crucial part of the large neighbourhood search is the selection of the fragment
(neighbourhood, i.e. the set of variables), to be relaxed and re-optimized
iteratively. The success of the large neighbourhood search depends on the
adequacy of this identified neighbourhood with regard to the problematic part
of the solution assignment and the choice of the neighbourhood size. We
investigate three strategies to choose the fragment of different sizes within
the large neighbourhood search scheme. The first two strategies are tailored
concerning the problem properties. The third strategy is more general, using
the information of the cost from the soft constraint violations and their
propagation as the indicator to choose the variables added into the fragment.
The three strategies are analyzed and compared upon a benchmark nurse rostering
problem. Promising results demonstrate the possibility of future work in the
hybrid approach.
",A Constraint-directed Local Search Approach to Nurse Rostering Problems
"  This paper presents a new method and a constraint-based objective function to
solve two problems related to the design of optical telecommunication networks,
namely the Synchronous Optical Network Ring Assignment Problem (SRAP) and the
Intra-ring Synchronous Optical Network Design Problem (IDP). These network
topology problems can be represented as a graph partitioning with capacity
constraints as shown in previous works. We present here a new objective
function and a new local search algorithm to solve these problems. Experiments
conducted in Comet allow us to compare our method to previous ones and show
that we obtain better results.
",Sonet Network Design Problems
"  We explore the use of the Cell Broadband Engine (Cell/BE for short) for
combinatorial optimization applications: we present a parallel version of a
constraint-based local search algorithm that has been implemented on a
multiprocessor BladeCenter machine with twin Cell/BE processors (total of 16
SPUs per blade). This algorithm was chosen because it fits very well the
Cell/BE architecture and requires neither shared memory nor communication
between processors, while retaining a compact memory footprint. We study the
performance on several large optimization benchmarks and show that this
achieves mostly linear time speedups, even sometimes super-linear. This is
possible because the parallel implementation might explore simultaneously
different parts of the search space and therefore converge faster towards the
best sub-space and thus towards a solution. Besides getting speedups, the
resulting times exhibit a much smaller variance, which benefits applications
where a timely reply is critical.
","Parallel local search for solving Constraint Problems on the Cell
  Broadband Engine (Preliminary Results)"
"  We explore the idea of using finite automata to implement new constraints for
local search (this is already a successful technique in constraint-based global
search). We show how it is possible to maintain incrementally the violations of
a constraint and its decision variables from an automaton that describes a
ground checker for that constraint. We establish the practicality of our
approach idea on real-life personnel rostering problems, and show that it is
competitive with the approach of [Pralong, 2007].
",Toward an automaton Constraint for Local Search
"  LSCS is a satellite workshop of the international conference on principles
and practice of Constraint Programming (CP), since 2004. It is devoted to local
search techniques in constraint satisfaction, and focuses on all aspects of
local search techniques, including: design and implementation of new
algorithms, hybrid stochastic-systematic search, reactive search optimization,
adaptive search, modeling for local-search, global constraints, flexibility and
robustness, learning methods, and specific applications.
","Proceedings 6th International Workshop on Local Search Techniques in
  Constraint Satisfaction"
"  In this paper the behavior of three combinational rules for
temporal/sequential attribute data fusion for target type estimation are
analyzed. The comparative analysis is based on: Dempster's fusion rule proposed
in Dempster-Shafer Theory; Proportional Conflict Redistribution rule no. 5
(PCR5), proposed in Dezert-Smarandache Theory and one alternative class fusion
rule, connecting the combination rules for information fusion with particular
fuzzy operators, focusing on the t-norm based Conjunctive rule as an analog of
the ordinary conjunctive rule and t-conorm based Disjunctive rule as an analog
of the ordinary disjunctive rule. The way how different t-conorms and t-norms
functions within TCN fusion rule influence over target type estimation
performance is studied and estimated.
",Tracking object's type changes with fuzzy based fusion rule
"  This paper proposes the application of particle swarm optimization (PSO) to
the problem of finite element model (FEM) selection. This problem arises when a
choice of the best model for a system has to be made from set of competing
models, each developed a priori from engineering judgment. PSO is a
population-based stochastic search algorithm inspired by the behaviour of
biological entities in nature when they are foraging for resources. Each
potentially correct model is represented as a particle that exhibits both
individualistic and group behaviour. Each particle moves within the model
search space looking for the best solution by updating the parameters values
that define it. The most important step in the particle swarm algorithm is the
method of representing models which should take into account the number,
location and variables of parameters to be updated. One example structural
system is used to show the applicability of PSO in finding an optimal FEM. An
optimal model is defined as the model that has the least number of updated
parameters and has the smallest parameter variable variation from the mean
material properties. Two different objective functions are used to compare
performance of the PSO algorithm.
",Finite element model selection using Particle Swarm Optimization
"  Motivated by Zadeh's paradigm of computing with words rather than numbers,
several formal models of computing with words have recently been proposed.
These models are based on automata and thus are not well-suited for concurrent
computing. In this paper, we incorporate the well-known model of concurrent
computing, Petri nets, together with fuzzy set theory and thereby establish a
concurrency model of computing with words--fuzzy Petri nets for computing with
words (FPNCWs). The new feature of such fuzzy Petri nets is that the labels of
transitions are some special words modeled by fuzzy sets. By employing the
methodology of fuzzy reasoning, we give a faithful extension of an FPNCW which
makes it possible for computing with more words. The language expressiveness of
the two formal models of computing with words, fuzzy automata for computing
with words and FPNCWs, is compared as well. A few small examples are provided
to illustrate the theoretical development.
",A Fuzzy Petri Nets Model for Computing With Words
"  Modelling emotion has become a challenge nowadays. Therefore, several models
have been produced in order to express human emotional activity. However, only
a few of them are currently able to express the close relationship existing
between emotion and cognition. An appraisal-coping model is presented here,
with the aim to simulate the emotional impact caused by the evaluation of a
particular situation (appraisal), along with the consequent cognitive reaction
intended to face the situation (coping). This model is applied to the
""Cascades"" problem, a small arithmetical exercise designed for ten-year-old
pupils. The goal is to create a model corresponding to a child's behaviour when
solving the problem using his own strategies.
","Emotion: Appraisal-coping model for the ""Cascades"" problem"
"  Modeling emotion has become a challenge nowadays. Therefore, several models
have been produced in order to express human emotional activity. However, only
a few of them are currently able to express the close relationship existing
between emotion and cognition. An appraisal-coping model is presented here,
with the aim to simulate the emotional impact caused by the evaluation of a
particular situation (appraisal), along with the consequent cognitive reaction
intended to face the situation (coping). This model is applied to the
?Cascades? problem, a small arithmetical exercise designed for ten-year-old
pupils. The goal is to create a model corresponding to a child's behavior when
solving the problem using his own strategies.
",Emotion : mod\`ele d'appraisal-coping pour le probl\`eme des Cascades
"  Rough set theory, a mathematical tool to deal with inexact or uncertain
knowledge in information systems, has originally described the indiscernibility
of elements by equivalence relations. Covering rough sets are a natural
extension of classical rough sets by relaxing the partitions arising from
equivalence relations to coverings. Recently, some topological concepts such as
neighborhood have been applied to covering rough sets. In this paper, we
further investigate the covering rough sets based on neighborhoods by
approximation operations. We show that the upper approximation based on
neighborhoods can be defined equivalently without using neighborhoods. To
analyze the coverings themselves, we introduce unary and composition operations
on coverings. A notion of homomorphismis provided to relate two covering
approximation spaces. We also examine the properties of approximations
preserved by the operations and homomorphisms, respectively.
","Covering rough sets based on neighborhoods: An approach without using
  neighborhoods"
"  In Pawlak's rough set theory, a set is approximated by a pair of lower and
upper approximations. To measure numerically the roughness of an approximation,
Pawlak introduced a quantitative measure of roughness by using the ratio of the
cardinalities of the lower and upper approximations. Although the roughness
measure is effective, it has the drawback of not being strictly monotonic with
respect to the standard ordering on partitions. Recently, some improvements
have been made by taking into account the granularity of partitions. In this
paper, we approach the roughness measure in an axiomatic way. After
axiomatically defining roughness measure and partition measure, we provide a
unified construction of roughness measure, called strong Pawlak roughness
measure, and then explore the properties of this measure. We show that the
improved roughness measures in the literature are special instances of our
strong Pawlak roughness measure and introduce three more strong Pawlak
roughness measures as well. The advantage of our axiomatic approach is that
some properties of a roughness measure follow immediately as soon as the
measure satisfies the relevant axiomatic definition.
",An axiomatic approach to the roughness measure of rough sets
"  Adaptation has long been considered as the Achilles' heel of case-based
reasoning since it requires some domain-specific knowledge that is difficult to
acquire. In this paper, two strategies are combined in order to reduce the
knowledge engineering cost induced by the adaptation knowledge (CA) acquisition
task: CA is learned from the case base by the means of knowledge discovery
techniques, and the CA acquisition sessions are opportunistically triggered,
i.e., at problem-solving time.
",Opportunistic Adaptation Knowledge Discovery
"  Real-time heuristic search algorithms are suitable for situated agents that
need to make their decisions in constant time. Since the original work by Korf
nearly two decades ago, numerous extensions have been suggested. One of the
most intriguing extensions is the idea of backtracking wherein the agent
decides to return to a previously visited state as opposed to moving forward
greedily. This idea has been empirically shown to have a significant impact on
various performance measures. The studies have been carried out in particular
empirical testbeds with specific real-time search algorithms that use
backtracking. Consequently, the extent to which the trends observed are
characteristic of backtracking in general is unclear. In this paper, we present
the first entirely theoretical study of backtracking in real-time heuristic
search. In particular, we present upper bounds on the solution cost exponential
and linear in a parameter regulating the amount of backtracking. The results
hold for a wide class of real-time heuristic search algorithms that includes
many existing algorithms as a small subclass.
",On Backtracking in Real-time Heuristic Search
"  This paper presents several novel generalization bounds for the problem of
learning kernels based on the analysis of the Rademacher complexity of the
corresponding hypothesis sets. Our bound for learning kernels with a convex
combination of p base kernels has only a log(p) dependency on the number of
kernels, p, which is considerably more favorable than the previous best bound
given for the same problem. We also give a novel bound for learning with a
linear combination of p base kernels with an L_2 regularization whose
dependency on p is only in p^{1/4}.
",New Generalization Bounds for Learning Kernels
"  This paper formulates a necessary and sufficient condition for a generic
graph matching problem to be equivalent to the maximum vertex and edge weight
clique problem in a derived association graph. The consequences of this results
are threefold: first, the condition is general enough to cover a broad range of
practical graph matching problems; second, a proof to establish equivalence
between graph matching and clique search reduces to showing that a given graph
matching problem satisfies the proposed condition; and third, the result sets
the scene for generic continuous solutions for a broad range of graph matching
problems. To illustrate the mathematical framework, we apply it to a number of
graph matching problems, including the problem of determining the graph edit
distance.
","A Necessary and Sufficient Condition for Graph Matching Being Equivalent
  to the Maximum Weight Clique Problem"
"  This paper extends k-means algorithms from the Euclidean domain to the domain
of graphs. To recompute the centroids, we apply subgradient methods for solving
the optimization-based formulation of the sample mean of graphs. To accelerate
the k-means algorithm for graphs without trading computational time against
solution quality, we avoid unnecessary graph distance calculations by
exploiting the triangle inequality of the underlying distance metric following
Elkan's k-means algorithm proposed in \cite{Elkan03}. In experiments we show
that the accelerated k-means algorithm are faster than the standard k-means
algorithm for graphs provided there is a cluster structure in the data.
",Elkan's k-Means for Graphs
"  Traditional staging is based on a formal approach of similarity leaning on
dramaturgical ontologies and instanciation variations. Inspired by interactive
data mining, that suggests different approaches, we give an overview of
computer science and theater researches using computers as partners of the
actor to escape the a priori specification of roles.
","Similarit\'e en intension vs en extension : \`a la crois\'ee de
  l'informatique et du th\'e\^atre"
"  We address the problem of belief change in (nonmonotonic) logic programming
under answer set semantics. Unlike previous approaches to belief change in
logic programming, our formal techniques are analogous to those of
distance-based belief revision in propositional logic. In developing our
results, we build upon the model theory of logic programs furnished by SE
models. Since SE models provide a formal, monotonic characterisation of logic
programs, we can adapt techniques from the area of belief revision to belief
change in logic programs. We introduce methods for revising and merging logic
programs, respectively. For the former, we study both subset-based revision as
well as cardinality-based revision, and we show that they satisfy the majority
of the AGM postulates for revision. For merging, we consider operators
following arbitration merging and IC merging, respectively. We also present
encodings for computing the revision as well as the merging of logic programs
within the same logic programming framework, giving rise to a direct
implementation of our approach in terms of off-the-shelf answer set solvers.
These encodings reflect in turn the fact that our change operators do not
increase the complexity of the base formalism.
",A general approach to belief change in answer set programming
"  Nearly 15 years ago, a set of qualitative spatial relations between oriented
straight line segments (dipoles) was suggested by Schlieder. This work received
substantial interest amongst the qualitative spatial reasoning community.
However, it turned out to be difficult to establish a sound constraint calculus
based on these relations. In this paper, we present the results of a new
investigation into dipole constraint calculi which uses algebraic methods to
derive sound results on the composition of relations and other properties of
dipole calculi. Our results are based on a condensed semantics of the dipole
relations.
  In contrast to the points that are normally used, dipoles are extended and
have an intrinsic direction. Both features are important properties of natural
objects. This allows for a straightforward representation of prototypical
reasoning tasks for spatial agents. As an example, we show how to generate
survey knowledge from local observations in a street network. The example
illustrates the fast constraint-based reasoning capabilities of the dipole
calculus. We integrate our results into two reasoning tools which are publicly
available.
","Oriented Straight Line Segment Algebra: Qualitative Spatial Reasoning
  about Oriented Objects"
"  In this paper we give a thorough presentation of a model proposed by Tononi
et al. for modeling \emph{integrated information}, i.e. how much information is
generated in a system transitioning from one state to the next one by the
causal interaction of its parts and \emph{above and beyond} the information
given by the sum of its parts. We also provides a more general formulation of
such a model, independent from the time chosen for the analysis and from the
uniformity of the probability distribution at the initial time instant.
Finally, we prove that integrated information is null for disconnected systems.
",On a Model for Integrated Information
"  Vector quantization(VQ) is a lossy data compression technique from signal
processing, which is restricted to feature vectors and therefore inapplicable
for combinatorial structures. This contribution presents a theoretical
foundation of graph quantization (GQ) that extends VQ to the domain of
attributed graphs. We present the necessary Lloyd-Max conditions for optimality
of a graph quantizer and consistency results for optimal GQ design based on
empirical distortion measures and stochastic optimization. These results
statistically justify existing clustering algorithms in the domain of graphs.
The proposed approach provides a template of how to link structural pattern
recognition methods other than GQ to statistical pattern recognition.
",Graph Quantization
"  Human decisional processes result from the employment of selected quantities
of relevant information, generally synthesized from environmental incoming data
and stored memories. Their main goal is the production of an appropriate and
adaptive response to a cognitive or behavioral task. Different strategies of
response production can be adopted, among which haphazard trials, formation of
mental schemes and heuristics. In this paper, we propose a model of Boolean
neural network that incorporates these strategies by recurring to global
optimization strategies during the learning session. The model characterizes as
well the passage from an unstructured/chaotic attractor neural network typical
of data-driven processes to a faster one, forward-only and representative of
schema-driven processes. Moreover, a simplified version of the Iowa Gambling
Task (IGT) is introduced in order to test the model. Our results match with
experimental data and point out some relevant knowledge coming from
psychological domain.
","Decisional Processes with Boolean Neural Network: the Emergence of
  Mental Schemes"
"  Internet and expert systems have offered new ways of sharing and distributing
knowledge, but there is a lack of researches in the area of web based expert
systems. This paper introduces a development of a web-based expert system for
the regulations of civil service in the Kingdom of Saudi Arabia named as RCSES.
It is the first time to develop such system (application of civil service
regulations) as well the development of it using web based approach. The
proposed system considers 17 regulations of the civil service system. The
different phases of developing the RCSES system are presented, as knowledge
acquiring and selection, ontology and knowledge representations using XML
format. XML Rule-based knowledge sources and the inference mechanisms were
implemented using ASP.net technique. An interactive tool for entering the
ontology and knowledge base, and the inferencing was built. It gives the
ability to use, modify, update, and extend the existing knowledge base in an
easy way. The knowledge was validated by experts in the domain of civil service
regulations, and the proposed RCSES was tested, verified, and validated by
different technical users and the developers staff. The RCSES system is
compared with other related web based expert systems, that comparison proved
the goodness, usability, and high performance of RCSES.
",Web-Based Expert System for Civil Service Regulations: RCSES
"  Many engineering optimization problems can be considered as linear
programming problems where all or some of the parameters involved are
linguistic in nature. These can only be quantified using fuzzy sets. The aim of
this paper is to solve a fuzzy linear programming problem in which the
parameters involved are fuzzy quantities with logistic membership functions. To
explore the applicability of the method a numerical example is considered to
determine the monthly production planning quotas and profit of a home textile
group.
","Application of a Fuzzy Programming Technique to Production Planning in
  the Textile Industry"
"  Mamdani Fuzzy Model is an important technique in Computational Intelligence
(CI) study. This paper presents an implementation of a supervised learning
method based on membership function training in the context of Mamdani fuzzy
models. Specifically, auto zoom function of a digital camera is modelled using
Mamdani technique. The performance of control method is verified through a
series of simulation and numerical results are provided as illustrations.
","The Application of Mamdani Fuzzy Model for Auto Zoom Function of a
  Digital Camera"
"  In this book we introduce a new procedure called \alpha-Discounting Method
for Multi-Criteria Decision Making (\alpha-D MCDM), which is as an alternative
and extension of Saaty Analytical Hierarchy Process (AHP). It works for any
number of preferences that can be transformed into a system of homogeneous
linear equations. A degree of consistency (and implicitly a degree of
inconsistency) of a decision-making problem are defined. \alpha-D MCDM is
afterwards generalized to a set of preferences that can be transformed into a
system of linear and or non-linear homogeneous and or non-homogeneous equations
and or inequalities. The general idea of \alpha-D MCDM is to assign non-null
positive parameters \alpha_1, \alpha_2, and so on \alpha_p to the coefficients
in the right-hand side of each preference that diminish or increase them in
order to transform the above linear homogeneous system of equations which has
only the null-solution, into a system having a particular non-null solution.
After finding the general solution of this system, the principles used to
assign particular values to all parameters \alpha is the second important part
of \alpha-D, yet to be deeper investigated in the future. In the current book
we propose the Fairness Principle, i.e. each coefficient should be discounted
with the same percentage (we think this is fair: not making any favoritism or
unfairness to any coefficient), but the reader can propose other principles.
For consistent decision-making problems with pairwise comparisons,
\alpha-Discounting Method together with the Fairness Principle give the same
result as AHP. But for weak inconsistent decision-making problem,
\alpha-Discounting together with the Fairness Principle give a different result
from AHP. Many consistent, weak inconsistent, and strong inconsistent examples
are given in this book.
",$\alpha$-Discounting Multi-Criteria Decision Making ($\alpha$-D MCDM)
"  This paper proposes a design for a system to generate constraint solvers that
are specialised for specific problem models. It describes the design in detail
and gives preliminary experimental results showing the feasibility and
effectiveness of the approach.
",Dominion -- A constraint solver generator
"  Machine Consciousness is the study of consciousness in a biological,
philosophical, mathematical and physical perspective and designing a model that
can fit into a programmable system architecture. Prime objective of the study
is to make the system architecture behave consciously like a biological model
does. Present work has developed a feasible definition of consciousness, that
characterizes consciousness with four parameters i.e., parasitic, symbiotic,
self referral and reproduction. Present work has also developed a biologically
inspired consciousness architecture that has following layers: quantum layer,
cellular layer, organ layer and behavioral layer and traced the characteristics
of consciousness at each layer. Finally, the work has estimated physical and
algorithmic architecture to devise a system that can behave consciously.
","Logical Evaluation of Consciousness: For Incorporating Consciousness
  into Machine Architecture"
"  To study the communication between information systems, Wang et al. [C. Wang,
C. Wu, D. Chen, Q. Hu, and C. Wu, Communicating between information systems,
Information Sciences 178 (2008) 3228-3239] proposed two concepts of type-1 and
type-2 consistent functions. Some properties of such functions and induced
relation mappings have been investigated there. In this paper, we provide an
improvement of the aforementioned work by disclosing the symmetric relationship
between type-1 and type-2 consistent functions. We present more properties of
consistent functions and induced relation mappings and improve upon several
deficient assertions in the original work. In particular, we unify and extend
type-1 and type-2 consistent functions into the so-called
neighborhood-consistent functions. This provides a convenient means for
studying the communication between information systems based on various
neighborhoods.
",Some improved results on communication between information systems
"  Recently, Wang et al. discussed the properties of fuzzy information systems
under homomorphisms in the paper [C. Wang, D. Chen, L. Zhu, Homomorphisms
between fuzzy information systems, Applied Mathematics Letters 22 (2009)
1045-1050], where homomorphisms are based upon the concepts of consistent
functions and fuzzy relation mappings. In this paper, we classify consistent
functions as predecessor-consistent and successor-consistent, and then proceed
to present more properties of consistent functions. In addition, we improve
some characterizations of fuzzy relation mappings provided by Wang et al.
",Homomorphisms between fuzzy information systems revisited
"  In this paper, research on AI based modeling technique to optimize
development of new alloys with necessitated improvements in properties and
chemical mixture over existing alloys as per functional requirements of product
is done. The current research work novels AI in lieu of predictions to
establish association between material and product customary. Advanced
computational simulation techniques like CFD, FEA interrogations are made
viable to authenticate product dynamics in context to experimental
investigations. Accordingly, the current research is focused towards binding
relationships between material design and product design domains. The input to
feed forward back propagation prediction network model constitutes of material
design features. Parameters relevant to product design strategies are furnished
as target outputs. The outcomes of ANN shows good sign of correlation between
material and product design domains. The study enriches a new path to
illustrate material factors at the time of new product development.
","Establishment of Relationships between Material Design and Product
  Design Domains by Hybrid FEM-ANN Technique"
"  Currently, criminals profile (CP) is obtained from investigators or forensic
psychologists interpretation, linking crime scene characteristics and an
offenders behavior to his or her characteristics and psychological profile.
This paper seeks an efficient and systematic discovery of nonobvious and
valuable patterns between variables from a large database of solved cases via a
probabilistic network (PN) modeling approach. The PN structure can be used to
extract behavioral patterns and to gain insight into what factors influence
these behaviors. Thus, when a new case is being investigated and the profile
variables are unknown because the offender has yet to be identified, the
observed crime scene variables are used to infer the unknown variables based on
their connections in the structure and the corresponding numerical
(probabilistic) weights. The objective is to produce a more systematic and
empirical approach to profiling, and to use the resulting PN model as a
decision tool.
",Modeling of Human Criminal Behavior using Probabilistic Networks
"  Constraint programming can definitely be seen as a model-driven paradigm. The
users write programs for modeling problems. These programs are mapped to
executable models to calculate the solutions. This paper focuses on efficient
model management (definition and transformation). From this point of view, we
propose to revisit the design of constraint-programming systems. A model-driven
architecture is introduced to map solving-independent constraint models to
solving-dependent decision models. Several important questions are examined,
such as the need for a visual highlevel modeling language, and the quality of
metamodeling techniques to implement the transformations. A main result is the
s-COMMA platform that efficiently implements the chain from modeling to solving
constraint problems
",Model-Driven Constraint Programming
"  An important challenge in constraint programming is to rewrite constraint
models into executable programs calculat- ing the solutions. This phase of
constraint processing may require translations between constraint programming
lan- guages, transformations of constraint representations, model
optimizations, and tuning of solving strategies. In this paper, we introduce a
pivot metamodel describing the common fea- tures of constraint models including
different kinds of con- straints, statements like conditionals and loops, and
other first-class elements like object classes and predicates. This metamodel
is general enough to cope with the constructions of many languages, from
object-oriented modeling languages to logic languages, but it is independent
from them. The rewriting operations manipulate metamodel instances apart from
languages. As a consequence, the rewriting operations apply whatever languages
are selected and they are able to manage model semantic information. A bridge
is created between the metamodel space and languages using parsing techniques.
Tools from the software engineering world can be useful to implement this
framework.
",Rewriting Constraint Models with Metamodels
"  Transforming constraint models is an important task in re- cent constraint
programming systems. User-understandable models are defined during the modeling
phase but rewriting or tuning them is manda- tory to get solving-efficient
models. We propose a new architecture al- lowing to define bridges between any
(modeling or solver) languages and to implement model optimizations. This
architecture follows a model- driven approach where the constraint modeling
process is seen as a set of model transformations. Among others, an interesting
feature is the def- inition of transformations as concept-oriented rules, i.e.
based on types of model elements where the types are organized into a hierarchy
called a metamodel.
","Using ATL to define advanced and flexible constraint model
  transformations"
"  The methodology of Bayesian Model Averaging (BMA) is applied for assessment
of newborn brain maturity from sleep EEG. In theory this methodology provides
the most accurate assessments of uncertainty in decisions. However, the
existing BMA techniques have been shown providing biased assessments in the
absence of some prior information enabling to explore model parameter space in
details within a reasonable time. The lack in details leads to disproportional
sampling from the posterior distribution. In case of the EEG assessment of
brain maturity, BMA results can be biased because of the absence of information
about EEG feature importance. In this paper we explore how the posterior
information about EEG features can be used in order to reduce a negative impact
of disproportional sampling on BMA performance. We use EEG data recorded from
sleeping newborns to test the efficiency of the proposed BMA technique.
","Feature Importance in Bayesian Assessment of Newborn Brain Maturity from
  EEG"
"  In this paper we describe an original computational model for solving
different types of Distributed Constraint Satisfaction Problems (DCSP). The
proposed model is called Controller-Agents for Constraints Solving (CACS). This
model is intended to be used which is an emerged field from the integration
between two paradigms of different nature: Multi-Agent Systems (MAS) and the
Constraint Satisfaction Problem paradigm (CSP) where all constraints are
treated in central manner as a black-box. This model allows grouping
constraints to form a subset that will be treated together as a local problem
inside the controller. Using this model allows also handling non-binary
constraints easily and directly so that no translating of constraints into
binary ones is needed. This paper presents the implementation outlines of a
prototype of DCSP solver, its usage methodology and overview of the CACS
application for timetabling problems.
",A new model for solution of complex distributed constrained problems
"  Model transformations operate on models conforming to precisely defined
metamodels. Consequently, it often seems relatively easy to chain them: the
output of a transformation may be given as input to a second one if metamodels
match. However, this simple rule has some obvious limitations. For instance, a
transformation may only use a subset of a metamodel. Therefore, chaining
transformations appropriately requires more information. We present here an
approach that automatically discovers more detailed information about actual
chaining constraints by statically analyzing transformations. The objective is
to provide developers who decide to chain transformations with more data on
which to base their choices. This approach has been successfully applied to the
case of a library of endogenous transformations. They all have the same source
and target metamodel but have some hidden chaining constraints. In such a case,
the simple metamodel matching rule given above does not provide any useful
information.
",Automatically Discovering Hidden Transformation Chaining Constraints
"  This article presents the results of the research carried out on the
development of a medical diagnostic system applied to the Acute Bacterial
Meningitis, using the Case Based Reasoning methodology. The research was
focused on the implementation of the adaptation stage, from the integration of
Case Based Reasoning and Rule Based Expert Systems. In this adaptation stage we
use a higher level RBC that stores and allows reutilizing change experiences,
combined with a classic rule-based inference engine. In order to take into
account the most evident clinical situation, a pre-diagnosis stage is
implemented using a rule engine that, given an evident situation, emits the
corresponding diagnosis and avoids the complete process.
","Integration of Rule Based Expert Systems and Case Based Reasoning in an
  Acute Bacterial Meningitis Clinical Decision Support System"
"  Recent advancement in web services plays an important role in business to
business and business to consumer interaction. Discovery mechanism is not only
used to find a suitable service but also provides collaboration between service
providers and consumers by using standard protocols. A static web service
discovery mechanism is not only time consuming but requires continuous human
interaction. This paper proposed an efficient dynamic web services discovery
mechanism that can locate relevant and updated web services from service
registries and repositories with timestamp based on indexing value and
categorization for faster and efficient discovery of service. The proposed
prototype focuses on quality of service issues and introduces concept of local
cache, categorization of services, indexing mechanism, CSP (Constraint
Satisfaction Problem) solver, aging and usage of translator. Performance of
proposed framework is evaluated by implementing the algorithm and correctness
of our method is shown. The results of proposed framework shows greater
performance and accuracy in dynamic discovery mechanism of web services
resolving the existing issues of flexibility, scalability, based on quality of
service, and discovers updated and most relevant services with ease of usage.
",Indexer Based Dynamic Web Services Discovery
"  Fuzzy Description Logics (DLs) are a family of logics which allow the
representation of (and the reasoning with) structured knowledge affected by
vagueness. Although most of the not very expressive crisp DLs, such as ALC,
enjoy the Finite Model Property (FMP), this is not the case once we move into
the fuzzy case. In this paper we show that if we allow arbitrary knowledge
bases, then the fuzzy DLs ALC under Lukasiewicz and Product fuzzy logics do not
verify the FMP even if we restrict to witnessed models; in other words, finite
satisfiability and witnessed satisfiability are different for arbitrary
knowledge bases. The aim of this paper is to point out the failure of FMP
because it affects several algorithms published in the literature for reasoning
under fuzzy ALC.
","On the Failure of the Finite Model Property in some Fuzzy Description
  Logics"
"  The basic aim of our study is to give a possible model for handling uncertain
information. This model is worked out in the framework of DATALOG. At first the
concept of fuzzy Datalog will be summarized, then its extensions for
intuitionistic- and interval-valued fuzzy logic is given and the concept of
bipolar fuzzy Datalog is introduced. Based on these ideas the concept of
multivalued knowledge-base will be defined as a quadruple of any background
knowledge; a deduction mechanism; a connecting algorithm, and a function set of
the program, which help us to determine the uncertainty levels of the results.
At last a possible evaluation strategy is given.
",A multivalued knowledge-base model
"  RefereeToolbox is a java package implementing combination operators for
fusing evidences. It is downloadable from:
http://refereefunction.fredericdambreville.com/releases RefereeToolbox is based
on an interpretation of the fusion rules by means of Referee Functions. This
approach implies a dissociation between the definition of the combination and
its actual implementation, which is common to all referee-based combinations.
As a result, RefereeToolbox is designed with the aim to be generic and
evolutive.
",Release ZERO.0.1 of package RefereeToolbox
"  LEXSYS, (Legume Expert System) was a project conceived at IITA (International
Institute of Tropical Agriculture) Ibadan Nigeria. It was initiated by the
COMBS (Collaborative Group on Maize-Based Systems Research in the 1990. It was
meant for a general framework for characterizing on-farm testing for technology
design for sustainable cereal-based cropping system. LEXSYS is not a true
expert system as the name would imply, but simply a user-friendly information
system. This work is an attempt to give a formal representation of the existing
system and then present areas where intelligent agent can be applied.
",LEXSYS: Architecture and Implication for Intelligent Agent systems
"  Computing value of information (VOI) is a crucial task in various aspects of
decision-making under uncertainty, such as in meta-reasoning for search; in
selecting measurements to make, prior to choosing a course of action; and in
managing the exploration vs. exploitation tradeoff. Since such applications
typically require numerous VOI computations during a single run, it is
essential that VOI be computed efficiently. We examine the issue of anytime
estimation of VOI, as frequently it suffices to get a crude estimate of the
VOI, thus saving considerable computational resources. As a case study, we
examine VOI estimation in the measurement selection problem. Empirical
evaluation of the proposed scheme in this domain shows that computational
resources can indeed be significantly reduced, at little cost in expected
rewards achieved in the overall decision problem.
",Rational Value of Information Estimation for Measurement Selection
"  Formalism based on GA is an alternative to distributed representation models
developed so far --- Smolensky's tensor product, Holographic Reduced
Representations (HRR) and Binary Spatter Code (BSC). Convolutions are replaced
by geometric products, interpretable in terms of geometry which seems to be the
most natural language for visualization of higher concepts. This paper recalls
the main ideas behind the GA model and investigates recognition test results
using both inner product and a clipped version of matrix representation. The
influence of accidental blade equality on recognition is also studied. Finally,
the efficiency of the GA model is compared to that of previously developed
models.
",Geometric Algebra Model of Distributed Representations
"  We present in this paper some examples of how to compute by hand the PCR5
fusion rule for three sources, so the reader will better understand its
mechanism. We also take into consideration the importance of sources, which is
different from the classical discounting of sources.
","Importance of Sources using the Repeated Fusion Method and the
  Proportional Conflict Redistribution Rules #5 and #6"
"  Terrorism has led to many problems in Thai societies, not only property
damage but also civilian casualties. Predicting terrorism activities in advance
can help prepare and manage risk from sabotage by these activities. This paper
proposes a framework focusing on event classification in terrorism domain using
fuzzy inference systems (FISs). Each FIS is a decision-making model combining
fuzzy logic and approximate reasoning. It is generated in five main parts: the
input interface, the fuzzification interface, knowledge base unit, decision
making unit and output defuzzification interface. Adaptive neuro-fuzzy
inference system (ANFIS) is a FIS model adapted by combining the fuzzy logic
and neural network. The ANFIS utilizes automatic identification of fuzzy logic
rules and adjustment of membership function (MF). Moreover, neural network can
directly learn from data set to construct fuzzy logic rules and MF implemented
in various applications. FIS settings are evaluated based on two comparisons.
The first evaluation is the comparison between unstructured and structured
events using the same FIS setting. The second comparison is the model settings
between FIS and ANFIS for classifying structured events. The data set consists
of news articles related to terrorism events in three southern provinces of
Thailand. The experimental results show that the classification performance of
the FIS resulting from structured events achieves satisfactory accuracy and is
better than the unstructured events. In addition, the classification of
structured events using ANFIS gives higher performance than the events using
only FIS in the prediction of terrorism events.
",Terrorism Event Classification Using Fuzzy Inference Systems
"  Most of the web user's requirements are search or navigation time and getting
correctly matched result. These constrains can be satisfied with some
additional modules attached to the existing search engines and web servers.
This paper proposes that powerful architecture for search engines with the
title of Probabilistic Semantic Web Mining named from the methods used. With
the increase of larger and larger collection of various data resources on the
World Wide Web (WWW), Web Mining has become one of the most important
requirements for the web users. Web servers will store various formats of data
including text, image, audio, video etc., but servers can not identify the
contents of the data. These search techniques can be improved by adding some
special techniques including semantic web mining and probabilistic analysis to
get more accurate results. Semantic web mining technique can provide meaningful
search of data resources by eliminating useless information with mining
process. In this technique web servers will maintain Meta information of each
and every data resources available in that particular web server. This will
help the search engine to retrieve information that is relevant to user given
input string. This paper proposing the idea of combing these two techniques
Semantic web mining and Probabilistic analysis for efficient and accurate
search results of web mining. SPF can be calculated by considering both
semantic accuracy and syntactic accuracy of data with the input string. This
will be the deciding factor for producing results.
",Probabilistic Semantic Web Mining Using Artificial Neural Analysis
"  The Nystrom method is an efficient technique to speed up large-scale learning
applications by generating low-rank approximations. Crucial to the performance
of this technique is the assumption that a matrix can be well approximated by
working exclusively with a subset of its columns. In this work we relate this
assumption to the concept of matrix coherence and connect matrix coherence to
the performance of the Nystrom method. Making use of related work in the
compressed sensing and the matrix completion literature, we derive novel
coherence-based bounds for the Nystrom method in the low-rank setting. We then
present empirical results that corroborate these theoretical bounds. Finally,
we present more general empirical results for the full-rank setting that
convincingly demonstrate the ability of matrix coherence to measure the degree
to which information can be extracted from a subset of columns.
",Matrix Coherence and the Nystrom Method
"  We define the concept of an internal symmetry. This is a symmety within a
solution of a constraint satisfaction problem. We compare this to solution
symmetry, which is a mapping between different solutions of the same problem.
We argue that we may be able to exploit both types of symmetry when finding
solutions. We illustrate the potential of exploiting internal symmetries on two
benchmark domains: Van der Waerden numbers and graceful graphs. By identifying
internal symmetries we are able to extend the state of the art in both cases.
",Symmetry within Solutions
"  We study propagation algorithms for the conjunction of two AllDifferent
constraints. Solutions of an AllDifferent constraint can be seen as perfect
matchings on the variable/value bipartite graph. Therefore, we investigate the
problem of finding simultaneous bipartite matchings. We present an extension of
the famous Hall theorem which characterizes when simultaneous bipartite
matchings exists. Unfortunately, finding such matchings is NP-hard in general.
However, we prove a surprising result that finding a simultaneous matching on a
convex bipartite graph takes just polynomial time. Based on this theoretical
result, we provide the first polynomial time bound consistency algorithm for
the conjunction of two AllDifferent constraints. We identify a pathological
problem on which this propagator is exponentially faster compared to existing
propagators. Our experiments show that this new propagator can offer
significant benefits over existing methods.
",Propagating Conjunctions of AllDifferent Constraints
"  The successful execution of a construction project is heavily impacted by
making the right decision during tendering processes. Managing tender
procedures is very complex and uncertain involving coordination of many tasks
and individuals with different priorities and objectives. Bias and inconsistent
decision are inevitable if the decision-making process is totally depends on
intuition, subjective judgement or emotion. In making transparent decision and
healthy competition tendering, there exists a need for flexible guidance tool
for decision support. Aim of this paper is to give a review on current
practices of Decision Support Systems (DSS) technology in construction
tendering processes. Current practices of general tendering processes as
applied to the most countries in different regions such as United States,
Europe, Middle East and Asia are comprehensively discussed. Applications of
Web-based tendering processes is also summarised in terms of its properties.
Besides that, a summary of Decision Support System (DSS) components is included
in the next section. Furthermore, prior researches on implementation of DSS
approaches in tendering processes are discussed in details. Current issues
arise from both of paper-based and Web-based tendering processes are outlined.
Finally, conclusion is included at the end of this paper.
",Decision Support Systems (DSS) in Construction Tendering Processes
"  The need for integration of ontologies with nonmonotonic rules has been
gaining importance in a number of areas, such as the Semantic Web. A number of
researchers addressed this problem by proposing a unified semantics for hybrid
knowledge bases composed of both an ontology (expressed in a fragment of
first-order logic) and nonmonotonic rules. These semantics have matured over
the years, but only provide solutions for the static case when knowledge does
not need to evolve. In this paper we take a first step towards addressing the
dynamics of hybrid knowledge bases. We focus on knowledge updates and,
considering the state of the art of belief update, ontology update and rule
update, we show that current solutions are only partial and difficult to
combine. Then we extend the existing work on ABox updates with rules, provide a
semantics for such evolving hybrid knowledge bases and study its basic
properties. To the best of our knowledge, this is the first time that an update
operator is proposed for hybrid knowledge bases.
",Towards Closed World Reasoning in Dynamic Open Worlds (Extended Version)
"  On the basis of an analysis of previous research, we present a generalized
approach for measuring the difference of plans with an exemplary application to
machine scheduling. Our work is motivated by the need for such measures, which
are used in dynamic scheduling and planning situations. In this context,
quantitative approaches are needed for the assessment of the robustness and
stability of schedules. Obviously, any `robustness' or `stability' of plans has
to be defined w. r. t. the particular situation and the requirements of the
human decision maker. Besides the proposition of an instability measure, we
therefore discuss possibilities of obtaining meaningful information from the
decision maker for the implementation of the introduced approach.
","On the comparison of plans: Proposition of an instability measure for
  dynamic machine scheduling"
"  We define an inference system to capture explanations based on causal
statements, using an ontology in the form of an IS-A hierarchy. We first
introduce a simple logical language which makes it possible to express that a
fact causes another fact and that a fact explains another fact. We present a
set of formal inference patterns from causal statements to explanation
statements. We introduce an elementary ontology which gives greater
expressiveness to the system while staying close to propositional reasoning. We
provide an inference system that captures the patterns discussed, firstly in a
purely propositional framework, then in a datalog (limited predicate)
framework.
",Ontology-based inference for causal explanation
"  We report (to our knowledge) the first evaluation of Constraint Satisfaction
as a computational framework for solving closest string problems. We show that
careful consideration of symbol occurrences can provide search heuristics that
provide several orders of magnitude speedup at and above the optimal distance.
We also report (to our knowledge) the first analysis and evaluation -- using
any technique -- of the computational difficulties involved in the
identification of all closest strings for a given input set. We describe
algorithms for web-scale distributed solution of closest string problems, both
purely based on AI backtrack search and also hybrid numeric-AI methods.
",The Exact Closest String Problem as a Constraint Satisfaction Problem
"  We consider the problem of jointly training structured models for extraction
from sources whose instances enjoy partial overlap. This has important
applications like user-driven ad-hoc information extraction on the web. Such
applications present new challenges in terms of the number of sources and their
arbitrary pattern of overlap not seen by earlier collective training schemes
applied on two sources. We present an agreement-based learning framework and
alternatives within it to trade-off tractability, robustness to noise, and
extent of agreement. We provide a principled scheme to discover low-noise
agreement sets in unlabeled data across the sources. Through extensive
experiments over 58 real datasets, we establish that our method of additively
rewarding agreement over maximal segments of text provides the best trade-offs,
and also scores over alternatives such as collective inference, staged
training, and multi-view learning.
",Joint Structured Models for Extraction from Overlapping Sources
"  A technique to study the dynamics of solving of a research task is suggested.
The research task was based on specially developed software Right- Wrong
Responder (RWR), with the participants having to reveal the response logic of
the program. The participants interacted with the program in the form of a
semi-binary dialogue, which implies the feedback responses of only two kinds -
""right"" or ""wrong"". The technique has been applied to a small pilot group of
volunteer participants. Some of them have successfully solved the task
(solvers) and some have not (non-solvers). In the beginning of the work, the
solvers did more wrong moves than non-solvers, and they did less wrong moves
closer to the finish of the work. A phase portrait of the work both in solvers
and non-solvers showed definite cycles that may correspond to sequences of
partially true hypotheses that may be formulated by the participants during the
solving of the task.
","An approach to visualize the course of solving of a research task in
  humans"
"  This paper constructively proves the existence of an effective procedure
generating a computable (total) function that is not contained in any given
effectively enumerable set of such functions. The proof implies the existence
of machines that process informal concepts such as computable (total) functions
beyond the limits of any given Turing machine or formal system, that is, these
machines can, in a certain sense, ""compute"" function values beyond these
limits. We call these machines creative. We argue that any ""intelligent""
machine should be capable of processing informal concepts such as computable
(total) functions, that is, it should be creative. Finally, we introduce
hypotheses on creative machines which were developed on the basis of
theoretical investigations and experiments with computer programs. The
hypotheses say that machine intelligence is the execution of a self-developing
procedure starting from any universal programming language and any input.
",Informal Concepts in Machines
"  Mountain river torrents and snow avalanches generate human and material
damages with dramatic consequences. Knowledge about natural phenomenona is
often lacking and expertise is required for decision and risk management
purposes using multi-disciplinary quantitative or qualitative approaches.
Expertise is considered as a decision process based on imperfect information
coming from more or less reliable and conflicting sources. A methodology mixing
the Analytic Hierarchy Process (AHP), a multi-criteria aid-decision method, and
information fusion using Belief Function Theory is described. Fuzzy Sets and
Possibilities theories allow to transform quantitative and qualitative criteria
into a common frame of discernment for decision in Dempster-Shafer Theory (DST
) and Dezert-Smarandache Theory (DSmT) contexts. Main issues consist in basic
belief assignments elicitation, conflict identification and management, fusion
rule choices, results validation but also in specific needs to make a
difference between importance and reliability and uncertainty in the fusion
process.
","A two-step fusion process for multi-criteria decision applied to natural
  hazards in mountains"
"  A lot of mathematical knowledge has been formalized and stored in
repositories by now: different mathematical theorems and theories have been
taken into consideration and included in mathematical repositories.
Applications more distant from pure mathematics, however --- though based on
these theories --- often need more detailed knowledge about the underlying
theories. In this paper we present an example Mizar formalization from the area
of electrical engineering focusing on stability theory which is based on
complex analysis. We discuss what kind of special knowledge is necessary here
and which amount of this knowledge is included in existing repositories.
",On Building a Knowledge Base for Stability Theory
"  It is hypothesized that creativity arises from the self-mending capacity of
an internal model of the world, or worldview. The uniquely honed worldview of a
creative individual results in a distinctive style that is recognizable within
and across domains. It is further hypothesized that creativity is domaingeneral
in the sense that there exist multiple avenues by which the distinctiveness of
one's worldview can be expressed. These hypotheses were tested using art
students and creative writing students. Art students guessed significantly
above chance both which painting was done by which of five famous artists, and
which artwork was done by which of their peers. Similarly, creative writing
students guessed significantly above chance both which passage was written by
which of five famous writers, and which passage was written by which of their
peers. These findings support the hypothesis that creative style is
recognizable. Moreover, creative writing students guessed significantly above
chance which of their peers produced particular works of art, supporting the
hypothesis that creative style is recognizable not just within but across
domains.
","Recognizability of Individual Creative Style Within and Across Domains:
  Preliminary Studies"
"  Approximate dynamic programming has been used successfully in a large variety
of domains, but it relies on a small set of provided approximation features to
calculate solutions reliably. Large and rich sets of features can cause
existing algorithms to overfit because of a limited number of samples. We
address this shortcoming using $L_1$ regularization in approximate linear
programming. Because the proposed method can automatically select the
appropriate richness of features, its performance does not degrade with an
increasing number of features. These results rely on new and stronger sampling
bounds for regularized approximate linear programs. We also propose a
computationally efficient homotopy method. The empirical evaluation of the
approach shows that the proposed method performs well on simple MDPs and
standard benchmark problems.
","Feature Selection Using Regularization in Approximate Linear Programs
  for Markov Decision Processes"
"  We discuss how to use a Genetic Regulatory Network as an evolutionary
representation to solve a typical GP reinforcement problem, the pole balancing.
The network is a modified version of an Artificial Regulatory Network proposed
a few years ago, and the task could be solved only by finding a proper way of
connecting inputs and outputs to the network. We show that the representation
is able to generalize well over the problem domain, and discuss the performance
of different models of this kind.
",Evolving Genes to Balance a Pole
"  Programs to solve so-called constraint problems are complex pieces of
software which require many design decisions to be made more or less
arbitrarily by the implementer. These decisions affect the performance of the
finished solver significantly. Once a design decision has been made, it cannot
easily be reversed, although a different decision may be more appropriate for a
particular problem.
  We investigate using machine learning to make these decisions automatically
depending on the problem to solve with the alldifferent constraint as an
example. Our system is capable of making non-trivial, multi-level decisions
that improve over always making a default choice.
","Using machine learning to make constraint solver implementation
  decisions"
"  In this paper the author presents a kind of Soft Computing Technique, mainly
an application of fuzzy set theory of Prof. Zadeh [16], on a problem of Medical
Experts Systems. The choosen problem is on design of a physician's decision
model which can take crisp as well as fuzzy data as input, unlike the
traditional models. The author presents a mathematical model based on fuzzy set
theory for physician aided evaluation of a complete representation of
information emanating from the initial interview including patient past
history, present symptoms, and signs observed upon physical examination and
results of clinical and diagnostic tests.
",A Soft Computing Model for Physicians' Decision Process
"  An important problem in computational social choice theory is the complexity
of undesirable behavior among agents, such as control, manipulation, and
bribery in election systems. These kinds of voting strategies are often
tempting at the individual level but disastrous for the agents as a whole.
Creating election systems where the determination of such strategies is
difficult is thus an important goal.
  An interesting set of elections is that of scoring protocols. Previous work
in this area has demonstrated the complexity of misuse in cases involving a
fixed number of candidates, and of specific election systems on unbounded
number of candidates such as Borda. In contrast, we take the first step in
generalizing the results of computational complexity of election misuse to
cases of infinitely many scoring protocols on an unbounded number of
candidates. Interesting families of systems include $k$-approval and $k$-veto
elections, in which voters distinguish $k$ candidates from the candidate set.
  Our main result is to partition the problems of these families based on their
complexity. We do so by showing they are polynomial-time computable, NP-hard,
or polynomial-time equivalent to another problem of interest. We also
demonstrate a surprising connection between manipulation in election systems
and some graph theory problems.
",The Complexity of Manipulating $k$-Approval Elections
"  In the last two decades, a number of methods have been proposed for
forecasting based on fuzzy time series. Most of the fuzzy time series methods
are presented for forecasting of car road accidents. However, the forecasting
accuracy rates of the existing methods are not good enough. In this paper, we
compared our proposed new method of fuzzy time series forecasting with existing
methods. Our method is based on means based partitioning of the historical data
of car road accidents. The proposed method belongs to the kth order and
time-variant methods. The proposed method can get the best forecasting accuracy
rate for forecasting the car road accidents than the existing methods.
","Inaccuracy Minimization by Partioning Fuzzy Data Sets - Validation of
  Analystical Methodology"
"  In this paper, a new learning algorithm for adaptive network intrusion
detection using naive Bayesian classifier and decision tree is presented, which
performs balance detections and keeps false positives at acceptable level for
different types of network attacks, and eliminates redundant attributes as well
as contradictory examples from training data that make the detection model
complex. The proposed algorithm also addresses some difficulties of data mining
such as handling continuous attribute, dealing with missing attribute values,
and reducing noise in training data. Due to the large volumes of security audit
data as well as the complex and dynamic properties of intrusion behaviours,
several data miningbased intrusion detection techniques have been applied to
network-based traffic data and host-based data in the last decades. However,
there remain various issues needed to be examined towards current intrusion
detection systems (IDS). We tested the performance of our proposed algorithm
with existing learning algorithms by employing on the KDD99 benchmark intrusion
detection dataset. The experimental results prove that the proposed algorithm
achieved high detection rates (DR) and significant reduce false positives (FP)
for different types of network intrusions using limited computational
resources.
",Combining Naive Bayes and Decision Tree for Adaptive Intrusion Detection
"  This paper presents a combination of several automated reasoning and proof
presentation tools with the Mizar system for formalization of mathematics. The
combination forms an online service called MizAR, similar to the SystemOnTPTP
service for first-order automated reasoning. The main differences to
SystemOnTPTP are the use of the Mizar language that is oriented towards human
mathematicians (rather than the pure first-order logic used in SystemOnTPTP),
and setting the service in the context of the large Mizar Mathematical Library
of previous theorems,definitions, and proofs (rather than the isolated problems
that are solved in SystemOnTPTP). These differences poses new challenges and
new opportunities for automated reasoning and for proof presentation tools.
This paper describes the overall structure of MizAR, and presents the automated
reasoning systems and proof presentation tools that are combined to make MizAR
a useful mathematical service.
","Automated Reasoning and Presentation Support for Formalizing Mathematics
  in Mizar"
"  Structured and semi-structured data describing entities, taxonomies and
ontologies appears in many domains. There is a huge interest in integrating
structured information from multiple sources; however integrating structured
data to infer complex common structures is a difficult task because the
integration must aggregate similar structures while avoiding structural
inconsistencies that may appear when the data is combined. In this work, we
study the integration of structured social metadata: shallow personal
hierarchies specified by many individual users on the SocialWeb, and focus on
inferring a collection of integrated, consistent taxonomies. We frame this task
as an optimization problem with structural constraints. We propose a new
inference algorithm, which we refer to as Relational Affinity Propagation (RAP)
that extends affinity propagation (Frey and Dueck 2007) by introducing
structural constraints. We validate the approach on a real-world social media
dataset, collected from the photosharing website Flickr. Our empirical results
show that our proposed approach is able to construct deeper and denser
structures compared to an approach using only the standard affinity propagation
algorithm.
",Integrating Structured Metadata with Relational Affinity Propagation
"  The paper offers a mathematical formalization of the Turing test. This
formalization makes it possible to establish the conditions under which some
Turing machine will pass the Turing test and the conditions under which every
Turing machine (or every Turing machine of the special class) will fail the
Turing test.
",A Formalization of the Turing Test
"  Many social Web sites allow users to annotate the content with descriptive
metadata, such as tags, and more recently to organize content hierarchically.
These types of structured metadata provide valuable evidence for learning how a
community organizes knowledge. For instance, we can aggregate many personal
hierarchies into a common taxonomy, also known as a folksonomy, that will aid
users in visualizing and browsing social content, and also to help them in
organizing their own content. However, learning from social metadata presents
several challenges, since it is sparse, shallow, ambiguous, noisy, and
inconsistent. We describe an approach to folksonomy learning based on
relational clustering, which exploits structured metadata contained in personal
hierarchies. Our approach clusters similar hierarchies using their structure
and tag statistics, then incrementally weaves them into a deeper, bushier tree.
We study folksonomy learning using social metadata extracted from the
photo-sharing site Flickr, and demonstrate that the proposed approach addresses
the challenges. Moreover, comparing to previous work, the approach produces
larger, more accurate folksonomies, and in addition, scales better.
","Growing a Tree in the Forest: Constructing Folksonomies by Integrating
  Structured Metadata"
"  Symmetry is an important feature of many constraint programs. We show that
any problem symmetry acting on a set of symmetry breaking constraints can be
used to break symmetry. Different symmetries pick out different solutions in
each symmetry class. This simple but powerful idea can be used in a number of
different ways. We describe one application within model restarts, a search
technique designed to reduce the conflict between symmetry breaking and the
branching heuristic. In model restarts, we restart search periodically with a
random symmetry of the symmetry breaking constraints. Experimental results show
that this symmetry breaking technique is effective in practice on some standard
benchmark problems.
",Symmetries of Symmetry Breaking Constraints
"  We propose automatically learning probabilistic Hierarchical Task Networks
(pHTNs) in order to capture a user's preferences on plans, by observing only
the user's behavior. HTNs are a common choice of representation for a variety
of purposes in planning, including work on learning in planning. Our
contributions are (a) learning structure and (b) representing preferences. In
contrast, prior work employing HTNs considers learning method preconditions
(instead of structure) and representing domain physics or search control
knowledge (rather than preferences). Initially we will assume that the observed
distribution of plans is an accurate representation of user preference, and
then generalize to the situation where feasibility constraints frequently
prevent the execution of preferred plans. In order to learn a distribution on
plans we adapt an Expectation-Maximization (EM) technique from the discipline
of (probabilistic) grammar induction, taking the perspective of task reductions
as productions in a context-free grammar over primitive actions. To account for
the difference between the distributions of possible and preferred plans we
subsequently modify this core EM technique, in short, by rescaling its input.
","Learning Probabilistic Hierarchical Task Networks to Capture User
  Preferences"
"  Brain-Like Stochastic Search (BLiSS) refers to this task: given a family of
utility functions U(u,A), where u is a vector of parameters or task
descriptors, maximize or minimize U with respect to u, using networks (Option
Nets) which input A and learn to generate good options u stochastically. This
paper discusses why this is crucial to brain-like intelligence (an area funded
by NSF) and to many applications, and discusses various possibilities for
network design and training. The appendix discusses recent research, relations
to work on stochastic optimization in operations research, and relations to
engineering-based approaches to understanding neocortex.
","Brain-Like Stochastic Search: A Research Challenge and Funding
  Opportunity"
"  We introduce a framework for representing a variety of interesting problems
as inference over the execution of probabilistic model programs. We represent a
""solution"" to such a problem as a guide program which runs alongside the model
program and influences the model program's random choices, leading the model
program to sample from a different distribution than from its priors. Ideally
the guide program influences the model program to sample from the posteriors
given the evidence. We show how the KL- divergence between the true posterior
distribution and the distribution induced by the guided model program can be
efficiently estimated (up to an additive constant) by sampling multiple
executions of the guided model program. In addition, we show how to use the
guide program as a proposal distribution in importance sampling to
statistically prove lower bounds on the probability of the evidence and on the
probability of a hypothesis and the evidence. We can use the quotient of these
two bounds as an estimate of the conditional probability of the hypothesis
given the evidence. We thus turn the inference problem into a heuristic search
for better guide programs.
",Variational Program Inference
"  The basic unit of meaning on the Semantic Web is the RDF statement, or
triple, which combines a distinct subject, predicate and object to make a
definite assertion about the world. A set of triples constitutes a graph, to
which they give a collective meaning. It is upon this simple foundation that
the rich, complex knowledge structures of the Semantic Web are built. Yet the
very expressiveness of RDF, by inviting comparison with real-world knowledge,
highlights a fundamental shortcoming, in that RDF is limited to statements of
absolute fact, independent of the context in which a statement is asserted.
This is in stark contrast with the thoroughly context-sensitive nature of human
thought. The model presented here provides a particularly simple means of
contextualizing an RDF triple by associating it with related statements in the
same graph. This approach, in combination with a notion of graph similarity, is
sufficient to select only those statements from an RDF graph which are
subjectively most relevant to the context of the requesting process.
",The Dilated Triple
"  In this Information system age many organizations consider information system
as their weapon to compete or gain competitive advantage or give the best
services for non profit organizations. Game Information System as combining
Information System and game is breakthrough to achieve organizations'
performance. The Game Information System will run the Information System with
game and how game can be implemented to run the Information System. Game is not
only for fun and entertainment, but will be a challenge to combine fun and
entertainment with Information System. The Challenge to run the information
system with entertainment, deliver the entertainment with information system
all at once. Game information system can be implemented in many sectors as like
the information system itself but in difference's view. A view of game which
people can joy and happy and do their transaction as a fun things.
",Game Information System
"  In order to get strategic positioning for competition in business
organization, the information system must be ahead in this information age
where the information as one of the weapons to win the competition and in the
right hand the information will become a right bullet. The information system
with the information technology support isn't enough if just only on internet
or implemented with internet technology. The growth of information technology
as tools for helping and making people easy to use must be accompanied by
wanting to make fun and happy when they make contact with the information
technology itself. Basically human like to play, since childhood human have
been playing, free and happy and when human grow up they can't play as much as
when human was in their childhood. We have to develop the information system
which is not perform information system itself but can help human to explore
their natural instinct for playing, making fun and happiness when they interact
with the information system. Virtual information system is the way to present
playing and having fun atmosphere on working area.
",Virtual information system on working area
"  Earthquake DSS is an information technology environment which can be used by
government to sharpen, make faster and better the earthquake mitigation
decision. Earthquake DSS can be delivered as E-government which is not only for
government itself but in order to guarantee each citizen's rights for
education, training and information about earthquake and how to overcome the
earthquake. Knowledge can be managed for future use and would become mining by
saving and maintain all the data and information about earthquake and
earthquake mitigation in Indonesia. Using Web technology will enhance global
access and easy to use. Datawarehouse as unNormalized database for
multidimensional analysis will speed the query process and increase reports
variation. Link with other Disaster DSS in one national disaster DSS, link with
other government information system and international will enhance the
knowledge and sharpen the reports.
",Indonesian Earthquake Decision Support System
"  Markov decision processes (MDPs) are widely used for modeling decision-making
problems in robotics, automated control, and economics. Traditional MDPs assume
that the decision maker (DM) knows all states and actions. However, this may
not be true in many situations of interest. We define a new framework, MDPs
with unawareness (MDPUs) to deal with the possibilities that a DM may not be
aware of all possible actions. We provide a complete characterization of when a
DM can learn to play near-optimally in an MDPU, and give an algorithm that
learns to play near-optimally when it is possible to do so, as efficiently as
possible. In particular, we characterize when a near-optimal solution can be
found in polynomial time.
",MDPs with Unawareness
"  Existing value function approximation methods have been successfully used in
many applications, but they often lack useful a priori error bounds. We propose
a new approximate bilinear programming formulation of value function
approximation, which employs global optimization. The formulation provides
strong a priori guarantees on both robust and expected policy loss by
minimizing specific norms of the Bellman residual. Solving a bilinear program
optimally is NP-hard, but this is unavoidable because the Bellman-residual
minimization itself is NP-hard. We describe and analyze both optimal and
approximate algorithms for solving bilinear programs. The analysis shows that
this algorithm offers a convergent generalization of approximate policy
iteration. We also briefly analyze the behavior of bilinear programming
algorithms under incomplete samples. Finally, we demonstrate that the proposed
approach can consistently minimize the Bellman residual on simple benchmark
problems.
",Global Optimization for Value Function Approximation
"  Different notions of equivalence, such as the prominent notions of strong and
uniform equivalence, have been studied in Answer-Set Programming, mainly for
the purpose of identifying programs that can serve as substitutes without
altering the semantics, for instance in program optimization. Such semantic
comparisons are usually characterized by various selections of models in the
logic of Here-and-There (HT). For uniform equivalence however, correct
characterizations in terms of HT-models can only be obtained for finite
theories, respectively programs. In this article, we show that a selection of
countermodels in HT captures uniform equivalence also for infinite theories.
This result is turned into coherent characterizations of the different notions
of equivalence by countermodels, as well as by a mixture of HT-models and
countermodels (so-called equivalence interpretations). Moreover, we generalize
the so-called notion of relativized hyperequivalence for programs to
propositional theories, and apply the same methodology in order to obtain a
semantic characterization which is amenable to infinite settings. This allows
for a lifting of the results to first-order theories under a very general
semantics given in terms of a quantified version of HT. We thus obtain a
general framework for the study of various notions of equivalence for theories
under answer-set semantics. Moreover, we prove an expedient property that
allows for a simplified treatment of extended signatures, and provide further
results for non-ground logic programs. In particular, uniform equivalence
coincides under open and ordinary answer-set semantics, and for finite
non-ground programs under these semantics, also the usual characterization of
uniform equivalence in terms of maximal and total HT-models of the grounding is
correct, even for infinite domains, when corresponding ground programs are
infinite.
","A General Framework for Equivalences in Answer-Set Programming by
  Countermodels in the Logic of Here-and-There"
"  Human disease diagnosis is a complicated process and requires high level of
expertise. Any attempt of developing a web-based expert system dealing with
human disease diagnosis has to overcome various difficulties. This paper
describes a project work aiming to develop a web-based fuzzy expert system for
diagnosing human diseases. Now a days fuzzy systems are being used successfully
in an increasing number of application areas; they use linguistic rules to
describe systems. This research project focuses on the research and development
of a web-based clinical tool designed to improve the quality of the exchange of
health information between health care professionals and patients.
Practitioners can also use this web-based tool to corroborate diagnosis. The
proposed system is experimented on various scenarios in order to evaluate it's
performance. In all the cases, proposed system exhibits satisfactory results.
",Human Disease Diagnosis Using a Fuzzy Expert System
"  In the area of computer science focusing on creating machines that can engage
on behaviors that humans consider intelligent. The ability to create
intelligent machines has intrigued humans since ancient times and today with
the advent of the computer and 50 years of research into various programming
techniques, the dream of smart machines is becoming a reality. Researchers are
creating systems which can mimic human thought, understand speech, beat the
best human chessplayer, and countless other feats never before possible.
Ability of the human to estimate the information is most brightly shown in
using of natural languages. Using words of a natural language for valuation
qualitative attributes, for example, the person pawns uncertainty in form of
vagueness in itself estimations. Vague sets, vague judgments, vague conclusions
takes place there and then, where and when the reasonable subject exists and
also is interested in something. The vague sets theory has arisen as the answer
to an illegibility of language the reasonable subject speaks. Language of a
reasonable subject is generated by vague events which are created by the reason
and which are operated by the mind. The theory of vague sets represents an
attempt to find such approximation of vague grouping which would be more
convenient, than the classical theory of sets in situations where the natural
language plays a significant role. Such theory has been offered by known
American mathematician Gau and Buehrer .In our paper we are describing how
vagueness of linguistic variables can be solved by using the vague set
theory.This paper is mainly designed for one of directions of the eventology
(the theory of the random vague events), which has arisen within the limits of
the probability theory and which pursue the unique purpose to describe
eventologically a movement of reason.
",Vagueness of Linguistic variable
"  Ontologies usually suffer from the semantic heterogeneity when simultaneously
used in information sharing, merging, integrating and querying processes.
Therefore, the similarity identification between ontologies being used becomes
a mandatory task for all these processes to handle the problem of semantic
heterogeneity. In this paper, we propose an efficient technique for similarity
measurement between two ontologies. The proposed technique identifies all
candidate pairs of similar concepts without omitting any similar pair. The
proposed technique can be used in different types of operations on ontologies
such as merging, mapping and aligning. By analyzing its results a reasonable
improvement in terms of completeness, correctness and overall quality of the
results has been found.
",An Efficient Technique for Similarity Identification between Ontologies
"  Many formal languages have been proposed to express or represent Ontologies,
including RDF, RDFS, DAML+OIL and OWL. Most of these languages are based on XML
syntax, but with various terminologies and expressiveness. Therefore, choosing
a language for building an Ontology is the main step. The main point of
choosing language to represent Ontology is based mainly on what the Ontology
will represent or be used for. That language should have a range of quality
support features such as ease of use, expressive power, compatibility, sharing
and versioning, internationalisation. This is because different kinds of
knowledge-based applications need different language features. The main
objective of these languages is to add semantics to the existing information on
the web. The aims of this paper is to provide a good knowledge of existing
language and understanding of these languages and how could be used.
",The State of the Art: Ontology Web-Based Languages: XML Based
"  Semantic Web is actually an extension of the current one in that it
represents information more meaningfully for humans and computers alike. It
enables the description of contents and services in machine-readable form, and
enables annotating, discovering, publishing, advertising and composing services
to be automated. It was developed based on Ontology, which is considered as the
backbone of the Semantic Web. In other words, the current Web is transformed
from being machine-readable to machine-understandable. In fact, Ontology is a
key technique with which to annotate semantics and provide a common,
comprehensible foundation for resources on the Semantic Web. Moreover, Ontology
can provide a common vocabulary, a grammar for publishing data, and can supply
a semantic description of data which can be used to preserve the Ontologies and
keep them ready for inference. This paper provides basic concepts of web
services and the Semantic Web, defines the structure and the main applications
of ontology, and provides many relevant terms are explained in order to provide
a basic understanding of ontologies.
",Understanding Semantic Web and Ontologies: Theory and Applications
"  Finding the structure of a graphical model has been received much attention
in many fields. Recently, it is reported that the non-Gaussianity of data
enables us to identify the structure of a directed acyclic graph without any
prior knowledge on the structure. In this paper, we propose a novel
non-Gaussianity based algorithm for more general type of models; chain graphs.
The algorithm finds an ordering of the disjoint subsets of variables by
iteratively evaluating the independence between the variable subset and the
residuals when the remaining variables are regressed on those. However, its
computational cost grows exponentially according to the number of variables.
Therefore, we further discuss an efficient approximate approach for applying
the algorithm to large sized graphs. We illustrate the algorithm with
artificial and real-world datasets.
",GroupLiNGAM: Linear non-Gaussian acyclic models for sets of variables
"  Notions of core, support and inversion of a soft set have been defined and
studied. Soft approximations are soft sets developed through core and support,
and are used for granulating the soft space. Membership structure of a soft set
has been probed in and many interesting properties presented. The mathematical
apparatus developed so far in this paper yields a detailed analysis of two
works viz. [N. Cagman, S. Enginoglu, Soft set theory and uni-int decision
making, European Jr. of Operational Research (article in press, available
online 12 May 2010)] and [N. Cagman, S. Enginoglu, Soft matrix theory and its
decision making, Computers and Mathematics with Applications 59 (2010) 3308 -
3314.]. We prove (Theorem 8.1) that uni-int method of Cagman is equivalent to a
core-support expression which is computationally far less expansive than
uni-int. This also highlights some shortcomings in Cagman's uni-int method and
thus motivates us to improve the method. We first suggest an improvement in
uni-int method and then present a new conjecture to solve the optimum choice
problem given by Cagman and Enginoglu. Our Example 8.6 presents a case where
the optimum choice is intuitively clear yet both uni-int methods (Cagman's and
our improved one) give wrong answer but the new conjecture solves the problem
correctly.
",Soft Approximations and uni-int Decision Making
"  In recent years there has been growing interest in solutions for the delivery
of clinical care for the elderly, due to the large increase in aging
population. Monitoring a patient in his home environment is necessary to ensure
continuity of care in home settings, but, to be useful, this activity must not
be too invasive for patients and a burden for caregivers. We prototyped a
system called SINDI (Secure and INDependent lIving), focused on i) collecting a
limited amount of data about the person and the environment through Wireless
Sensor Networks (WSN), and ii) inferring from these data enough information to
support caregivers in understanding patients' well being and in predicting
possible evolutions of their health. Our hierarchical logic-based model of
health combines data from different sources, sensor data, tests results,
common-sense knowledge and patient's clinical profile at the lower level, and
correlation rules between health conditions across upper levels. The logical
formalization and the reasoning process are based on Answer Set Programming.
The expressive power of this logic programming paradigm makes it possible to
reason about health evolution even when the available information is incomplete
and potentially incoherent, while declarativity simplifies rules specification
by caregivers and allows automatic encoding of knowledge. This paper describes
how these issues have been targeted in the application scenario of the SINDI
system.
","Reasoning Support for Risk Prediction and Prevention in Independent
  Living"
"  Iris recognition technology, used to identify individuals by photographing
the iris of their eye, has become popular in security applications because of
its ease of use, accuracy, and safety in controlling access to high-security
areas. Fusion of multiple algorithms for biometric verification performance
improvement has received considerable attention. The proposed method combines
the zero-crossing 1 D wavelet Euler number, and genetic algorithm based for
feature extraction. The output from these three algorithms is normalized and
their score are fused to decide whether the user is genuine or imposter. This
new strategies is discussed in this paper, in order to compute a multimodal
combined score.
",Improving Iris Recognition Accuracy By Score Based Fusion Method
"  We study decompositions of the global NVALUE constraint. Our main
contribution is theoretical: we show that there are propagators for global
constraints like NVALUE which decomposition can simulate with the same time
complexity but with a much greater space complexity. This suggests that the
benefit of a global propagator may often not be in saving time but in saving
space. Our other theoretical contribution is to show for the first time that
range consistency can be enforced on NVALUE with the same worst-case time
complexity as bound consistency. Finally, the decompositions we study are
readily encoded as linear inequalities. We are therefore able to use them in
integer linear programs.
",Decomposition of the NVALUE constraint
"  Symmetry can be used to help solve many problems. For instance, Einstein's
famous 1905 paper (""On the Electrodynamics of Moving Bodies"") uses symmetry to
help derive the laws of special relativity. In artificial intelligence,
symmetry has played an important role in both problem representation and
reasoning. I describe recent work on using symmetry to help solve constraint
satisfaction problems. Symmetries occur within individual solutions of problems
as well as between different solutions of the same problem. Symmetry can also
be applied to the constraints in a problem to give new symmetric constraints.
Reasoning about symmetry can speed up problem solving, and has led to the
discovery of new results in both graph and number theory.
",Symmetry within and between solutions
"  We propose an online form of the cake cutting problem. This models situations
where players arrive and depart during the process of dividing a resource. We
show that well known fair division procedures like cut-and-choose and the
Dubins-Spanier moving knife procedure can be adapted to apply to such online
problems. We propose some desirable properties that online cake cutting
procedures might possess like online forms of proportionality and
envy-freeness, and identify which properties are in fact possessed by the
different online cake procedures.
",Online Cake Cutting
"  The stable marriage problem has a wide variety of practical applications,
ranging from matching resident doctors to hospitals, to matching students to
schools, or more generally to any two-sided market. We consider a useful
variation of the stable marriage problem, where the men and women express their
preferences using a preference list with ties over a subset of the members of
the other sex. Matchings are permitted only with people who appear in these
preference lists. In this setting, we study the problem of finding a stable
matching that marries as many people as possible. Stability is an envy-free
notion: no man and woman who are not married to each other would both prefer
each other to their partners or to being single. This problem is NP-hard. We
tackle this problem using local search, exploiting properties of the problem to
reduce the size of the neighborhood and to make local moves efficiently.
Experimental results show that this approach is able to solve large problems,
quickly returning stable matchings of large and often optimal size.
",Local search for stable marriage problems with ties and incomplete lists
"  Frequent Episode Discovery framework is a popular framework in Temporal Data
Mining with many applications. Over the years many different notions of
frequencies of episodes have been proposed along with different algorithms for
episode discovery. In this paper we present a unified view of all such
frequency counting algorithms. We present a generic algorithm such that all
current algorithms are special cases of it. This unified view allows one to
gain insights into different frequencies and we present quantitative
relationships among different frequencies. Our unified view also helps in
obtaining correctness proofs for various algorithms as we show here. We also
point out how this unified view helps us to consider generalization of the
algorithm so that they can discover episodes with general partial orders.
","A unified view of Automata-based algorithms for Frequent Episode
  Discovery"
"  The stable marriage (SM) problem has a wide variety of practical
applications, ranging from matching resident doctors to hospitals, to matching
students to schools, or more generally to any two-sided market. In the
classical formulation, n men and n women express their preferences (via a
strict total order) over the members of the other sex. Solving a SM problem
means finding a stable marriage where stability is an envy-free notion: no man
and woman who are not married to each other would both prefer each other to
their partners or to being single. We consider both the classical stable
marriage problem and one of its useful variations (denoted SMTI) where the men
and women express their preferences in the form of an incomplete preference
list with ties over a subset of the members of the other sex. Matchings are
permitted only with people who appear in these lists, an we try to find a
stable matching that marries as many people as possible. Whilst the SM problem
is polynomial to solve, the SMTI problem is NP-hard. We propose to tackle both
problems via a local search approach, which exploits properties of the problems
to reduce the size of the neighborhood and to make local moves efficiently. We
evaluate empirically our algorithm for SM problems by measuring its runtime
behaviour and its ability to sample the lattice of all possible stable
marriages. We evaluate our algorithm for SMTI problems in terms of both its
runtime behaviour and its ability to find a maximum cardinality stable
marriage.For SM problems, the number of steps of our algorithm grows only as
O(nlog(n)), and that it samples very well the set of all stable marriages. It
is thus a fair and efficient approach to generate stable marriages.Furthermore,
our approach for SMTI problems is able to solve large problems, quickly
returning stable matchings of large and often optimal size despite the
NP-hardness of this problem.
",Local search for stable marriage problems
"  From the advent of the application of satellite imagery to land cover
mapping, one of the growing areas of research interest has been in the area of
image classification. Image classifiers are algorithms used to extract land
cover information from satellite imagery. Most of the initial research has
focussed on the development and application of algorithms to better existing
and emerging classifiers. In this paper, a paradigm shift is proposed whereby a
committee of classifiers is used to determine the final classification output.
Two of the key components of an ensemble system are that there should be
diversity among the classifiers and that there should be a mechanism through
which the results are combined. In this paper, the members of the ensemble
system include: Linear SVM, Gaussian SVM and Quadratic SVM. The final output
was determined through a simple majority vote of the individual classifiers.
From the results obtained it was observed that the final derived map generated
by an ensemble system can potentially improve on the results derived from the
individual classifiers making up the ensemble system. The ensemble system
classification accuracy was, in this case, better than the linear and quadratic
SVM result. It was however less than that of the RBF SVM. Areas for further
research could focus on improving the diversity of the ensemble system used in
this research.
",An svm multiclassifier approach to land cover mapping
"  A general method is given for revising degrees of belief and arriving at
consistent decisions about a system of logically constrained issues. In
contrast to other works about belief revision, here the constraints are assumed
to be fixed. The method has two variants, dual of each other, whose revised
degrees of belief are respectively above and below the original ones. The upper
[resp. lower] revised degrees of belief are uniquely characterized as the
lowest [resp. highest] ones that are invariant by a certain max-min [resp.
min-max] operation determined by the logical constraints. In both variants,
making balance between the revised degree of belief of a proposition and that
of its negation leads to decisions that are ensured to be consistent with the
logical constraints. These decisions are ensured to agree with the majority
criterion as applied to the original degrees of belief whenever this gives a
consistent result. They are also also ensured to satisfy a property of respect
for unanimity about any particular issue, as well as a property of monotonicity
with respect to the original degrees of belief. The application of the method
to certain special domains comes down to well established or increasingly
accepted methods, such as the single-link method of cluster analysis and the
method of paths in preferential voting.
",A general method for deciding about logically constrained issues
"  Strategic Environmental Assessment is a procedure aimed at introducing
systematic assessment of the environmental effects of plans and programs. This
procedure is based on the so-called coaxial matrices that define dependencies
between plan activities (infrastructures, plants, resource extractions,
buildings, etc.) and positive and negative environmental impacts, and
dependencies between these impacts and environmental receptors. Up to now, this
procedure is manually implemented by environmental experts for checking the
environmental effects of a given plan or program, but it is never applied
during the plan/program construction. A decision support system, based on a
clear logic semantics, would be an invaluable tool not only in assessing a
single, already defined plan, but also during the planning process in order to
produce an optimized, environmentally assessed plan and to study possible
alternative scenarios. We propose two logic-based approaches to the problem,
one based on Constraint Logic Programming and one on Probabilistic Logic
Programming that could be, in the future, conveniently merged to exploit the
advantages of both. We test the proposed approaches on a real energy plan and
we discuss their limitations and advantages.
",Logic-Based Decision Support for Strategic Environmental Assessment
"  Hybrid MKNF knowledge bases are one of the most prominent tightly integrated
combinations of open-world ontology languages with closed-world (non-monotonic)
rule paradigms. The definition of Hybrid MKNF is parametric on the description
logic (DL) underlying the ontology language, in the sense that non-monotonic
rules can extend any decidable DL language. Two related semantics have been
defined for Hybrid MKNF: one that is based on the Stable Model Semantics for
logic programs and one on the Well-Founded Semantics (WFS). Under WFS, the
definition of Hybrid MKNF relies on a bottom-up computation that has polynomial
data complexity whenever the DL language is tractable. Here we define a general
query-driven procedure for Hybrid MKNF that is sound with respect to the stable
model-based semantics, and sound and complete with respect to its WFS variant.
This procedure is able to answer a slightly restricted form of conjunctive
queries, and is based on tabled rule evaluation extended with an external
oracle that captures reasoning within the ontology. Such an (abstract) oracle
receives as input a query along with knowledge already derived, and replies
with a (possibly empty) set of atoms, defined in the rules, whose truth would
suffice to prove the initial query. With appropriate assumptions on the
complexity of the abstract oracle, the general procedure maintains the data
complexity of the WFS for Hybrid MKNF knowledge bases.
  To illustrate this approach, we provide a concrete oracle for EL+, a fragment
of the light-weight DL EL++. Such an oracle has practical use, as EL++ is the
language underlying OWL 2 EL, which is part of the W3C recommendations for the
Semantic Web, and is tractable for reasoning tasks such as subsumption. We show
that query-driven Hybrid MKNF preserves polynomial data complexity when using
the EL+ oracle and WFS.
",Query-driven Procedures for Hybrid MKNF Knowledge Bases
"  Answer set programming - the most popular problem solving paradigm based on
logic programs - has been recently extended to support uninterpreted function
symbols. All of these approaches have some limitation. In this paper we propose
a class of programs called FP2 that enjoys a different trade-off between
expressiveness and complexity. FP2 programs enjoy the following unique
combination of properties: (i) the ability of expressing predicates with
infinite extensions; (ii) full support for predicates with arbitrary arity;
(iii) decidability of FP2 membership checking; (iv) decidability of skeptical
and credulous stable model reasoning for call-safe queries. Odd cycles are
supported by composing FP2 programs with argument restricted programs.
",A decidable subclass of finitary programs
"  This paper models a decision support system to predict the occurance of
suicide attack in a given collection of cities. The system comprises two parts.
First part analyzes and identifies the factors which affect the prediction.
Admitting incomplete information and use of linguistic terms by experts, as two
characteristic features of this peculiar prediction problem we exploit the
Theory of Fuzzy Soft Sets. Hence the Part 2 of the model is an algorithm vz.
FSP which takes the assessment of factors given in Part 1 as its input and
produces a possibility profile of cities likely to receive the accident. The
algorithm is of O(2^n) complexity. It has been illustrated by an example solved
in detail. Simulation results for the algorithm have been presented which give
insight into the strengths and weaknesses of FSP. Three different decision
making measures have been simulated and compared in our discussion.
",Predicting Suicide Attacks: A Fuzzy Soft Set Approach
"  An approach to the revision of logic programs under the answer set semantics
is presented. For programs P and Q, the goal is to determine the answer sets
that correspond to the revision of P by Q, denoted P * Q. A fundamental
principle of classical (AGM) revision, and the one that guides the approach
here, is the success postulate. In AGM revision, this stipulates that A is in K
* A. By analogy with the success postulate, for programs P and Q, this means
that the answer sets of Q will in some sense be contained in those of P * Q.
The essential idea is that for P * Q, a three-valued answer set for Q,
consisting of positive and negative literals, is first determined. The positive
literals constitute a regular answer set, while the negated literals make up a
minimal set of naf literals required to produce the answer set from Q. These
literals are propagated to the program P, along with those rules of Q that are
not decided by these literals. The approach differs from work in update logic
programs in two main respects. First, we ensure that the revising logic program
has higher priority, and so we satisfy the success postulate; second, for the
preference implicit in a revision P * Q, the program Q as a whole takes
precedence over P, unlike update logic programs, since answer sets of Q are
propagated to P. We show that a core group of the AGM postulates are satisfied,
as are the postulates that have been proposed for update logic programs.
","A Program-Level Approach to Revising Logic Programs under the Answer Set
  Semantics"
"  We study the problem of coalitional manipulation in elections using the
unweighted Borda rule. We provide empirical evidence of the manipulability of
Borda elections in the form of two new greedy manipulation algorithms based on
intuitions from the bin-packing and multiprocessor scheduling domains. Although
we have not been able to show that these algorithms beat existing methods in
the worst-case, our empirical evaluation shows that they significantly
outperform the existing method and are able to find optimal manipulations in
the vast majority of the randomly generated elections that we tested. These
empirical results provide further evidence that the Borda rule provides little
defense against coalitional manipulation.
",An Empirical Study of Borda Manipulation
"  Autonomous planetary vehicles, also known as rovers, are small autonomous
vehicles equipped with a variety of sensors used to perform exploration and
experiments on a planet's surface. Rovers work in a partially unknown
environment, with narrow energy/time/movement constraints and, typically, small
computational resources that limit the complexity of on-line planning and
scheduling, thus they represent a great challenge in the field of autonomous
vehicles. Indeed, formal models for such vehicles usually involve hybrid
systems with nonlinear dynamics, which are difficult to handle by most of the
current planning algorithms and tools. Therefore, when offline planning of the
vehicle activities is required, for example for rovers that operate without a
continuous Earth supervision, such planning is often performed on simplified
models that are not completely realistic. In this paper we show how the
UPMurphi model checking based planning tool can be used to generate
resource-optimal plans to control the engine of an autonomous planetary
vehicle, working directly on its hybrid model and taking into account several
safety constraints, thus achieving very accurate results.
",Resource-Optimal Planning For An Autonomous Planetary Vehicle
"  This paper presents the solution about the threat of a VBIED (Vehicle-Born
Improvised Explosive Device) obtained with the DSmT (Dezert-Smarandache
Theory). This problem has been proposed recently to the authors by Simon
Maskell and John Lavery as a typical illustrative example to try to compare the
different approaches for dealing with uncertainty for decision-making support.
The purpose of this paper is to show in details how a solid justified solution
can be obtained from DSmT approach and its fusion rules thanks to a proper
modeling of the belief functions involved in this problem.
","Threat assessment of a possible Vehicle-Born Improvised Explosive Device
  using DSmT"
"  A key factor that can dramatically reduce the search space during constraint
solving is the criterion under which the variable to be instantiated next is
selected. For this purpose numerous heuristics have been proposed. Some of the
best of such heuristics exploit information about failures gathered throughout
search and recorded in the form of constraint weights, while others measure the
importance of variable assignments in reducing the search space. In this work
we experimentally evaluate the most recent and powerful variable ordering
heuristics, and new variants of them, over a wide range of benchmarks. Results
demonstrate that heuristics based on failures are in general more efficient.
Based on this, we then derive new revision ordering heuristics that exploit
recorded failures to efficiently order the propagation list when arc
consistency is maintained during search. Interestingly, in addition to reducing
the number of constraint checks and list operations, these heuristics are also
able to cut down the size of the explored search tree.
","Evaluating and Improving Modern Variable and Revision Ordering
  Strategies in CSPs"
"  The two standard branching schemes for CSPs are d-way and 2-way branching.
Although it has been shown that in theory the latter can be exponentially more
effective than the former, there is a lack of empirical evidence showing such
differences. To investigate this, we initially make an experimental comparison
of the two branching schemes over a wide range of benchmarks. Experimental
results verify the theoretical gap between d-way and 2-way branching as we move
from a simple variable ordering heuristic like dom to more sophisticated ones
like dom/ddeg. However, perhaps surprisingly, experiments also show that when
state-of-the-art variable ordering heuristics like dom/wdeg are used then d-way
can be clearly more efficient than 2-way branching in many cases. Motivated by
this observation, we develop two generic heuristics that can be applied at
certain points during search to decide whether 2-way branching or a restricted
version of 2-way branching, which is close to d-way branching, will be
followed. The application of these heuristics results in an adaptive branching
scheme. Experiments with instantiations of the two generic heuristics confirm
that search with adaptive branching outperforms search with a fixed branching
scheme on a wide range of problems.
",Adaptive Branching for Constraint Satisfaction Problems
"  Event-driven automation of reactive functionalities for complex event
processing is an urgent need in today's distributed service-oriented
architectures and Web-based event-driven environments. An important problem to
be addressed is how to correctly and efficiently capture and process the
event-based behavioral, reactive logic embodied in reaction rules, and
combining this with other conditional decision logic embodied, e.g., in
derivation rules. This paper elaborates a homogeneous integration approach that
combines derivation rules, reaction rules and other rule types such as
integrity constraints into the general framework of logic programming, the
industrial-strength version of declarative programming. We describe syntax and
semantics of the language, implement a distributed web-based middleware using
enterprise service technologies and illustrate its adequacy in terms of
expressiveness, efficiency and scalability through examples extracted from
industrial use cases. The developed reaction rule language provides expressive
features such as modular ID-based updates with support for external imports and
self-updates of the intensional and extensional knowledge bases, transactions
including integrity testing and roll-backs of update transition paths. It also
supports distributed complex event processing, event messaging and event
querying via efficient and scalable enterprise middleware technologies and
event/action reasoning based on an event/action algebra implemented by an
interval-based event calculus variant as a logic inference formalism.
",A Homogeneous Reaction Rule Language for Complex Event Processing
"  The four intensive problems to the software rose by the software industry
.i.e., User System Communication / Human Machine Interface, Meta Data
extraction, Information processing & management and Data representation are
discussed in this research paper. To contribute in the field we have proposed
and described an intelligent semantic oriented agent based search engine
including the concepts of intelligent graphical user interface, natural
language based information processing, data management and data reconstruction
for the final user end information representation.
","Semantic Oriented Agent based Approach towards Engineering Data
  Management, Web Information Retrieval and User System Communication Problems"
"  Web development is a challenging research area for its creativity and
complexity. The existing raised key challenge in web technology technologic
development is the presentation of data in machine read and process able format
to take advantage in knowledge based information extraction and maintenance.
Currently it is not possible to search and extract optimized results using full
text queries because there is no such mechanism exists which can fully extract
the semantic from full text queries and then look for particular knowledge
based information.
","An Agent based Approach towards Metadata Extraction, Modelling and
  Information Retrieval over the Web"
"  In order to study the communication between information systems, Gong and
Xiao [Z. Gong and Z. Xiao, Communicating between information systems based on
including degrees, International Journal of General Systems 39 (2010) 189--206]
proposed the concept of general relation mappings based on including degrees.
Some properties and the extension for fuzzy information systems of the general
relation mappings have been investigated there. In this paper, we point out by
counterexamples that several assertions (Lemma 3.1, Lemma 3.2, Theorem 4.1, and
Theorem 4.3) in the aforementioned work are not true in general.
","A note on communicating between information systems based on including
  degrees"
"  World Wide Web (WWW) is the most popular global information sharing and
communication system consisting of three standards .i.e., Uniform Resource
Identifier (URL), Hypertext Transfer Protocol (HTTP) and Hypertext Mark-up
Language (HTML). Information is provided in text, image, audio and video
formats over the web by using HTML which is considered to be unconventional in
defining and formalizing the meaning of the context...
",Role of Ontology in Semantic Web Development
"  Recent research has highlighted the practical benefits of subjective
interestingness measures, which quantify the novelty or unexpectedness of a
pattern when contrasted with any prior information of the data miner
(Silberschatz and Tuzhilin, 1995; Geng and Hamilton, 2006). A key challenge
here is the formalization of this prior information in a way that lends itself
to the definition of an interestingness subjective measure that is both
meaningful and practical.
  In this paper, we outline a general strategy of how this could be achieved,
before working out the details for a use case that is important in its own
right.
  Our general strategy is based on considering prior information as constraints
on a probabilistic model representing the uncertainty about the data. More
specifically, we represent the prior information by the maximum entropy
(MaxEnt) distribution subject to these constraints. We briefly outline various
measures that could subsequently be used to contrast patterns with this MaxEnt
model, thus quantifying their subjective interestingness.
","Maximum entropy models and subjective interestingness: an application to
  tiles in binary databases"
"  We examine the practicality for a user of using Answer Set Programming (ASP)
for representing logical formalisms. Our example is a formalism aiming at
capturing causal explanations from causal information. We show the naturalness
and relative efficiency of this translation job. We are interested in the ease
for writing an ASP program. Limitations of the earlier systems made that in
practice, the ``declarative aspect'' was more theoretical than practical. We
show how recent improvements in working ASP systems facilitate the translation.
","A formalism for causal explanations with an Answer Set Programming
  translation"
"  Knowledge Management is a global process in companies. It includes all the
processes that allow capitalization, sharing and evolution of the Knowledge
Capital of the firm, generally recognized as a critical resource of the
organization. Several approaches have been defined to capitalize knowledge but
few of them study how to learn from this knowledge. We present in this paper an
approach that helps to enhance learning from profession knowledge in an
organisation. We apply our approach on knitting industry.
",Learning from Profession Knowledge: Application on Knitting
"  Constraint solvers are complex pieces of software which require many design
decisions to be made by the implementer based on limited information. These
decisions affect the performance of the finished solver significantly. Once a
design decision has been made, it cannot easily be reversed, although a
different decision may be more appropriate for a particular problem.
  We investigate using machine learning to make these decisions automatically
depending on the problem to solve. We use the alldifferent constraint as a case
study. Our system is capable of making non-trivial, multi-level decisions that
improve over always making a default choice and can be implemented as part of a
general-purpose constraint solver.
","Machine learning for constraint solver design -- A case study for the
  alldifferent constraint"
"  Constraint problems can be trivially solved in parallel by exploring
different branches of the search tree concurrently. Previous approaches have
focused on implementing this functionality in the solver, more or less
transparently to the user. We propose a new approach, which modifies the
constraint model of the problem. An existing model is split into new models
with added constraints that partition the search space. Optionally, additional
constraints are imposed that rule out the search already done. The advantages
of our approach are that it can be implemented easily, computations can be
stopped and restarted, moved to different machines and indeed solved on
machines which are not able to communicate with each other at all.
",Distributed solving through model splitting
"  In many applications involving multi-media data, the definition of similarity
between items is integral to several key tasks, e.g., nearest-neighbor
retrieval, classification, and recommendation. Data in such regimes typically
exhibits multiple modalities, such as acoustic and visual content of video.
Integrating such heterogeneous data to form a holistic similarity space is
therefore a key challenge to be overcome in many real-world applications.
  We present a novel multiple kernel learning technique for integrating
heterogeneous data into a single, unified similarity space. Our algorithm
learns an optimal ensemble of kernel transfor- mations which conform to
measurements of human perceptual similarity, as expressed by relative
comparisons. To cope with the ubiquitous problems of subjectivity and
inconsistency in multi- media similarity, we develop graph-based techniques to
filter similarity measurements, resulting in a simplified and robust training
procedure.
",Learning Multi-modal Similarity
"  Consideration of the primal and dual problems together leads to important new
insights into the characteristics of boosting algorithms. In this work, we
propose a general framework that can be used to design new boosting algorithms.
A wide variety of machine learning problems essentially minimize a regularized
risk functional. We show that the proposed boosting framework, termed CGBoost,
can accommodate various loss functions and different regularizers in a
totally-corrective optimization fashion. We show that, by solving the primal
rather than the dual, a large body of totally-corrective boosting algorithms
can actually be efficiently solved and no sophisticated convex optimization
solvers are needed. We also demonstrate that some boosting algorithms like
AdaBoost can be interpreted in our framework--even their optimization is not
totally corrective. We empirically show that various boosting algorithms based
on the proposed framework perform similarly on the UCIrvine machine learning
datasets [1] that we have used in the experiments.
",Totally Corrective Boosting for Regularized Risk Minimization
"  Max Restricted Path Consistency (maxRPC) is a local consistency for binary
constraints that can achieve considerably stronger pruning than arc
consistency. However, existing maxRRC algorithms suffer from overheads and
redundancies as they can repeatedly perform many constraint checks without
triggering any value deletions. In this paper we propose techniques that can
boost the performance of maxRPC algorithms. These include the combined use of
two data structures to avoid many redundant constraint checks, and heuristics
for the efficient ordering and execution of certain operations. Based on these,
we propose two closely related algorithms. The first one which is a maxRPC
algorithm with optimal O(end^3) time complexity, displays good performance when
used stand-alone, but is expensive to apply during search. The second one
approximates maxRPC and has O(en^2d^4) time complexity, but a restricted
version with O(end^4) complexity can be very efficient when used during search.
Both algorithms have O(ed) space complexity. Experimental results demonstrate
that the resulting methods constantly outperform previous algorithms for
maxRPC, often by large margins, and constitute a more than viable alternative
to arc consistency on many problems.
",Improving the Performance of maxRPC
"  The technical report presents a generic exact solution approach for
minimizing the project duration of the resource-constrained project scheduling
problem with generalized precedences (Rcpsp/max). The approach uses lazy clause
generation, i.e., a hybrid of finite domain and Boolean satisfiability solving,
in order to apply nogood learning and conflict-driven search on the solution
generation. Our experiments show the benefit of lazy clause generation for
finding an optimal solutions and proving its optimality in comparison to other
state-of-the-art exact and non-exact methods. The method is highly robust: it
matched or bettered the best known results on all of the 2340 instances we
examined except 3, according to the currently available data on the PSPLib. Of
the 631 open instances in this set it closed 573 and improved the bounds of 51
of the remaining 58 instances.
","Solving the Resource Constrained Project Scheduling Problem with
  Generalized Precedences by Lazy Clause Generation"
"  The search strategy of a CP solver is determined by the variable and value
ordering heuristics it employs and by the branching scheme it follows. Although
the effects of variable and value ordering heuristics on search effort have
been widely studied, the effects of different branching schemes have received
less attention. In this paper we study this effect through an experimental
evaluation that includes standard branching schemes such as 2-way, d-way, and
dichotomic domain splitting, as well as variations of set branching where
branching is performed on sets of values. We also propose and evaluate a
generic approach to set branching where the partition of a domain into sets is
created using the scores assigned to values by a value ordering heuristic, and
a clustering algorithm from machine learning. Experimental results demonstrate
that although exponential differences between branching schemes, as predicted
in theory between 2-way and d-way branching, are not very common, still the
choice of branching scheme can make quite a difference on certain classes of
problems. Set branching methods are very competitive with 2-way branching and
outperform it on some problem classes. A statistical analysis of the results
reveals that our generic clustering-based set branching method is the best
among the methods compared.
",Experimental Evaluation of Branching Schemes for the CSP
"  In this paper, we address the problem of creating believable agents (virtual
characters) in video games. We consider only one meaning of believability,
``giving the feeling of being controlled by a player'', and outline the problem
of its evaluation. We present several models for agents in games which can
produce believable behaviours, both from industry and research. For high level
of believability, learning and especially imitation learning seems to be the
way to go. We make a quick overview of different approaches to make video
games' agents learn from players. To conclude we propose a two-step method to
develop new models for believable agents. First we must find the criteria for
believability for our application and define an evaluation method. Then the
model and the learning algorithm can be designed.
","The Challenge of Believability in Video Games: Definitions, Agents
  Models and Imitation Learning"
"  Classic evaluation methods of believable agents are time-consuming because
they involve many human to judge agents. They are well suited to validate work
on new believable behaviours models. However, during the implementation,
numerous experiments can help to improve agents' believability. We propose a
method which aim at assessing how much an agent's behaviour looks like humans'
behaviours. By representing behaviours with vectors, we can store data computed
for humans and then evaluate as many agents as needed without further need of
humans. We present a test experiment which shows that even a simple evaluation
following our method can reveal differences between quite believable agents and
humans. This method seems promising although, as shown in our experiment,
results' analysis can be difficult.
","Automatable Evaluation Method Oriented toward Behaviour Believability
  for Video Games"
"  In this short paper I briefly discuss 3D war Game based on artificial
intelligence concepts called AI WAR. Going in to the details, I present the
importance of CAICL language and how this language is used in AI WAR. Moreover
I also present a designed and implemented 3D War Cybug for AI WAR using CAICL
and discus the implemented strategy to defeat its enemies during the game life.
",AI 3D Cybug Gaming
"  Levesque introduced the notion of only-knowing to precisely capture the
beliefs of a knowledge base. He also showed how only-knowing can be used to
formalize non-monotonic behavior within a monotonic logic. Despite its appeal,
all attempts to extend only-knowing to the many agent case have undesirable
properties. A belief model by Halpern and Lakemeyer, for instance, appeals to
proof-theoretic constructs in the semantics and needs to axiomatize validity as
part of the logic. It is also not clear how to generalize their ideas to a
first-order case. In this paper, we propose a new account of multi-agent
only-knowing which, for the first time, has a natural possible-world semantics
for a quantified language with equality. We then provide, for the propositional
fragment, a sound and complete axiomatization that faithfully lifts Levesque's
proof theory to the many agent case. We also discuss comparisons to the earlier
approach by Halpern and Lakemeyer.
",Multi-Agent Only-Knowing Revisited
"  In this paper we present an optimal Bangla Keyboard Layout, which distributes
the load equally on both hands so that maximizing the ease and minimizing the
effort. Bangla alphabet has a large number of letters, for this it is difficult
to type faster using Bangla keyboard. Our proposed keyboard will maximize the
speed of operator as they can type with both hands parallel. Here we use the
association rule of data mining to distribute the Bangla characters in the
keyboard. First, we analyze the frequencies of data consisting of monograph,
digraph and trigraph, which are derived from data wire-house, and then used
association rule of data mining to distribute the Bangla characters in the
layout. Finally, we propose a Bangla Keyboard Layout. Experimental results on
several keyboard layout shows the effectiveness of the proposed approach with
better performance.
",Optimal Bangla Keyboard Layout using Association Rule of Data Mining
"  This paper presents an optimal Bangla Keyboard Layout, which distributes the
load equally on both hands so that maximizing the ease and minimizing the
effort. Bangla alphabet has a large number of letters, for this it is difficult
to type faster using Bangla keyboard. Our proposed keyboard will maximize the
speed of operator as they can type with both hands parallel. Here we use the
association rule of data mining to distribute the Bangla characters in the
keyboard. First, we analyze the frequencies of data consisting of monograph,
digraph and trigraph, which are derived from data wire-house, and then used
association rule of data mining to distribute the Bangla characters in the
layout. Experimental results on several data show the effectiveness of the
proposed approach with better performance.
",Optimal Bangla Keyboard Layout using Data Mining Technique
"  Bangla alphabet has a large number of letters, for this it is complicated to
type faster using Bangla keyboard. The proposed keyboard will maximize the
speed of operator as they can type with both hands parallel. Association rule
of data mining to distribute the Bangla characters in the keyboard is used
here. The frequencies of data consisting of monograph, digraph and trigraph are
analyzed, which are derived from data wire-house, and then used association
rule of data mining to distribute the Bangla characters in the layout.
Experimental results on several data show the effectiveness of the proposed
approach with better performance. This paper presents an optimal Bangla
Keyboard Layout, which distributes the load equally on both hands so that
maximizing the ease and minimizing the effort.
",The Most Advantageous Bangla Keyboard Layout Using Data Mining Technique
"  Support Vector Machines (SVMs) are popular tools for data mining tasks such
as classification, regression, and density estimation. However, original SVM
(C-SVM) only considers local information of data points on or over the margin.
Therefore, C-SVM loses robustness. To solve this problem, one approach is to
translate (i.e., to move without rotation or change of shape) the hyperplane
according to the distribution of the entire data. But existing work can only be
applied for 1-D case. In this paper, we propose a simple and efficient method
called General Scaled SVM (GS-SVM) to extend the existing approach to
multi-dimensional case. Our method translates the hyperplane according to the
distribution of data projected on the normal vector of the hyperplane. Compared
with C-SVM, GS-SVM has better performance on several data sets.
",General Scaled Support Vector Machines
"  The problem of measuring similarity of graphs and their nodes is important in
a range of practical problems. There is a number of proposed measures, some of
them being based on iterative calculation of similarity between two graphs and
the principle that two nodes are as similar as their neighbors are. In our
work, we propose one novel method of that sort, with a refined concept of
similarity of two nodes that involves matching of their neighbors. We prove
convergence of the proposed method and show that it has some additional
desirable properties that, to our knowledge, the existing methods lack. We
illustrate the method on two specific problems and empirically compare it to
other methods.
",Measuring Similarity of Graphs and their Nodes by Neighbor Matching
"  The paper proposes artificial intelligence technique called hill climbing to
find numerical solutions of Diophantine Equations. Such equations are important
as they have many applications in fields like public key cryptography, integer
factorization, algebraic curves, projective curves and data dependency in super
computers. Importantly, it has been proved that there is no general method to
find solutions of such equations. This paper is an attempt to find numerical
solutions of Diophantine equations using steepest ascent version of Hill
Climbing. The method, which uses tree representation to depict possible
solutions of Diophantine equations, adopts a novel methodology to generate
successors. The heuristic function used help to make the process of finding
solution as a minimization process. The work illustrates the effectiveness of
the proposed methodology using a class of Diophantine equations given by a1. x1
p1 + a2. x2 p2 + ...... + an . xn pn = N where ai and N are integers. The
experimental results validate that the procedure proposed is successful in
finding solutions of Diophantine Equations with sufficiently large powers and
large number of variables.
",Steepest Ascent Hill Climbing For A Mathematical Problem
"  This paper is mainly concerned with the question of how to decompose
multiclass classification problems into binary subproblems. We extend known
Jensen-Shannon bounds on the Bayes risk of binary problems to hierarchical
multiclass problems and use these bounds to develop a heuristic procedure for
constructing hierarchical multiclass decomposition for multinomials. We test
our method and compare it to the well known ""all-pairs"" decomposition. Our
tests are performed using a new authorship determination benchmark test of
machine learning authors. The new method consistently outperforms the all-pairs
decomposition when the number of classes is small and breaks even on larger
multiclass problems. Using both methods, the classification accuracy we
achieve, using an SVM over a feature set consisting of both high frequency
single tokens and high frequency token-pairs, appears to be exceptionally high
compared to known results in authorship determination.
","Hierarchical Multiclass Decompositions with Application to Authorship
  Determination"
"  The iDian (previously named as the Operation Agent System) is a framework
designed to enable computer users to operate software in natural language.
Distinct from current speech-recognition systems, our solution supports
format-free combinations of orders, and is open to both developers and
customers. We used a multi-layer structure to build the entire framework,
approached rule-based natural language processing, and implemented demos
narrowing down to Windows, text-editing and a few other applications. This
essay will firstly give an overview of the entire system, and then scrutinize
the functions and structure of the system, and finally discuss the prospective
de-velopment, esp. on-line interaction functions.
",Introduction to the iDian
"  In this work we present a protocol for self-synchronized duty-cycling in
wireless sensor networks with energy harvesting capabilities. The protocol is
implemented in Wiselib, a library of generic algorithms for sensor networks.
Simulations are conducted with the sensor network simulator Shawn. They are
based on the specifications of real hardware known as iSense sensor nodes. The
experimental results show that the proposed mechanism is able to adapt to
changing energy availabilities. Moreover, it is shown that the system is very
robust against packet loss.
","A Protocol for Self-Synchronized Duty-Cycling in Sensor Networks:
  Generic Implementation in Wiselib"
"  Active Learning Method (ALM) is a soft computing method used for modeling and
control based on fuzzy logic. All operators defined for fuzzy sets must serve
as either fuzzy S-norm or fuzzy T-norm. Despite being a powerful modeling
method, ALM does not possess operators which serve as S-norms and T-norms which
deprive it of a profound analytical expression/form. This paper introduces two
new operators based on morphology which satisfy the following conditions:
First, they serve as fuzzy S-norm and T-norm. Second, they satisfy Demorgans
law, so they complement each other perfectly. These operators are investigated
via three viewpoints: Mathematics, Geometry and fuzzy logic.
",New S-norm and T-norm Operators for Active Learning Method
"  Substitutability, interchangeability and related concepts in Constraint
Programming were introduced approximately twenty years ago and have given rise
to considerable subsequent research. We survey this work, classify, and relate
the different concepts, and indicate directions for future work, in particular
with respect to making connections with research into symmetry breaking. This
paper is a condensed version of a larger work in progress.
",A Partial Taxonomy of Substitutability and Interchangeability
"  Concept drift refers to a non stationary learning problem over time. The
training and the application data often mismatch in real life problems. In this
report we present a context of concept drift problem 1. We focus on the issues
relevant to adaptive training set formation. We present the framework and
terminology, and formulate a global picture of concept drift learners design.
We start with formalizing the framework for the concept drifting data in
Section 1. In Section 2 we discuss the adaptivity mechanisms of the concept
drift learners. In Section 3 we overview the principle mechanisms of concept
drift learners. In this chapter we give a general picture of the available
algorithms and categorize them based on their properties. Section 5 discusses
the related research fields and Section 5 groups and presents major concept
drift applications. This report is intended to give a bird's view of concept
drift research field, provide a context of the research and position it within
broad spectrum of research fields and applications.
",Learning under Concept Drift: an Overview
"  We introduce a new perspective on spectral dimensionality reduction which
views these methods as Gaussian Markov random fields (GRFs). Our unifying
perspective is based on the maximum entropy principle which is in turn inspired
by maximum variance unfolding. The resulting model, which we call maximum
entropy unfolding (MEU) is a nonlinear generalization of principal component
analysis. We relate the model to Laplacian eigenmaps and isomap. We show that
parameter fitting in the locally linear embedding (LLE) is approximate maximum
likelihood MEU. We introduce a variant of LLE that performs maximum likelihood
exactly: Acyclic LLE (ALLE). We show that MEU and ALLE are competitive with the
leading spectral approaches on a robot navigation visualization and a human
motion capture data set. Finally the maximum likelihood perspective allows us
to introduce a new approach to dimensionality reduction based on L1
regularization of the Gaussian random field via the graphical lasso.
","A Unifying Probabilistic Perspective for Spectral Dimensionality
  Reduction: Insights and New Models"
"  Human can be distinguished by different limb movements and unique ground
reaction force. Cumulative foot pressure image is a 2-D cumulative ground
reaction force during one gait cycle. Although it contains pressure spatial
distribution information and pressure temporal distribution information, it
suffers from several problems including different shoes and noise, when putting
it into practice as a new biometric for pedestrian identification. In this
paper, we propose a hierarchical translation-invariant representation for
cumulative foot pressure images, inspired by the success of Convolutional deep
belief network for digital classification. Key contribution in our approach is
discriminative hierarchical sparse coding scheme which helps to learn useful
discriminative high-level visual features. Based on the feature representation
of cumulative foot pressure images, we develop a pedestrian recognition system
which is invariant to three different shoes and slight local shape change.
Experiments are conducted on a proposed open dataset that contains more than
2800 cumulative foot pressure images from 118 subjects. Evaluations suggest the
effectiveness of the proposed method and the potential of cumulative foot
pressure images as a biometric.
",Translation-Invariant Representation for Cumulative Foot Pressure Images
"  An important issue in Qualitative Spatial Reasoning is the representation of
relative direction. In this paper we present simple geometric rules that enable
reasoning about relative direction between oriented points. This framework, the
Oriented Point Algebra OPRA_m, has a scalable granularity m. We develop a
simple algorithm for computing the OPRA_m composition tables and prove its
correctness. Using a composition table, algebraic closure for a set of OPRA
statements is sufficient to solve spatial navigation tasks. And it turns out
that scalable granularity is useful in these navigation tasks.
","Qualitative Reasoning about Relative Direction on Adjustable Levels of
  Granularity"
"  In the article a turn-based game played on four computers connected via
network is investigated. There are three computers with natural intelligence
and one with artificial intelligence. Game table is seen by each player's own
view point in all players' monitors. Domino pieces are three dimensional. For
distributed systems TCP/IP protocol is used. In order to get 3D image,
Microsoft XNA technology is applied. Domino 101 game is nondeterministic game
that is result of the game depends on the initial random distribution of the
pieces. Number of the distributions is equal to the multiplication of following
combinations: . Moreover, in this game that is played by four people, players
are divided into 2 pairs. Accordingly, we cannot predict how the player uses
the dominoes that is according to the dominoes of his/her partner or according
to his/her own dominoes. The fact that the natural intelligence can be a player
in any level affects the outcome. These reasons make it difficult to develop an
AI. In the article four levels of AI are developed. The AI in the first level
is equivalent to the intelligence of a child who knows the rules of the game
and recognizes the numbers. The AI in this level plays if it has any domino,
suitable to play or says pass. In most of the games which can be played on the
internet, the AI does the same. But the AI in the last level is a master
player, and it can develop itself according to its competitors' levels.
",A Distributed AI Aided 3D Domino Game
"  In this paper it is considered rule reduct generation problem, based on Rough
Set Theory. Rule Reduct Generation (RG) and Modified Rule Generation (MRG)
algorithms are well-known. Alternative to these algorithms Pruning Algorithm of
Generation A Minimal Set of Rule Reducts, or briefly Pruning Rule Generation
(PRG) algorithm is developed. PRG algorithm uses tree structured data type. PRG
algorithm is compared with RG and MRG algorithms
","Prunnig Algorithm of Generation a Minimal Set of Rule Reducts Based on
  Rough Set Theory"
"  The cardinal direction calculus (CDC) proposed by Goyal and Egenhofer is a
very expressive qualitative calculus for directional information of extended
objects. Early work has shown that consistency checking of complete networks of
basic CDC constraints is tractable while reasoning with the CDC in general is
NP-hard. This paper shows, however, if allowing some constraints unspecified,
then consistency checking of possibly incomplete networks of basic CDC
constraints is already intractable. This draws a sharp boundary between the
tractable and intractable subclasses of the CDC. The result is achieved by a
reduction from the well-known 3-SAT problem.
","Reasoning about Cardinal Directions between Extended Objects: The
  Hardness Result"
"  Imitation learning in robots, also called programing by demonstration, has
made important advances in recent years, allowing humans to teach context
dependant motor skills/tasks to robots. We propose to extend the usual contexts
investigated to also include acoustic linguistic expressions that might denote
a given motor skill, and thus we target joint learning of the motor skills and
their potential acoustic linguistic name. In addition to this, a modification
of a class of existing algorithms within the imitation learning framework is
made so that they can handle the unlabeled demonstration of several tasks/motor
primitives without having to inform the imitator of what task is being
demonstrated or what the number of tasks are, which is a necessity for language
learning, i.e; if one wants to teach naturally an open number of new motor
skills together with their acoustic names. Finally, a mechanism for detecting
whether or not linguistic input is relevant to the task is also proposed, and
our architecture also allows the robot to find the right framing for a given
identified motor primitive. With these additions it becomes possible to build
an imitator that bridges the gap between imitation learning and language
learning by being able to learn linguistic expressions using methods from the
imitation learning community. In this sense the imitator can learn a word by
guessing whether a certain speech pattern present in the context means that a
specific task is to be executed. The imitator is however not assumed to know
that speech is relevant and has to figure this out on its own by looking at the
demonstrations: indeed, the architecture allows the robot to transparently also
learn tasks which should not be triggered by an acoustic word, but for example
by the color or position of an object or a gesture made by someone in the
environment. To demonstrate this ability to find the ...
","Imitation learning of motor primitives and language bootstrapping in
  robots"
"  The aim of this study is to show the importance of two classification
techniques, viz. decision tree and clustering, in prediction of learning
disabilities (LD) of school-age children. LDs affect about 10 percent of all
children enrolled in schools. The problems of children with specific learning
disabilities have been a cause of concern to parents and teachers for some
time. Decision trees and clustering are powerful and popular tools used for
classification and prediction in Data mining. Different rules extracted from
the decision tree are used for prediction of learning disabilities. Clustering
is the assignment of a set of observations into subsets, called clusters, which
are useful in finding the different signs and symptoms (attributes) present in
the LD affected child. In this paper, J48 algorithm is used for constructing
the decision tree and K-means algorithm is used for creating the clusters. By
applying these classification techniques, LD in any child can be identified.
","Significance of Classification Techniques in Prediction of Learning
  Disabilities"
"  The task of verifying the compatibility between interacting web services has
traditionally been limited to checking the compatibility of the interaction
protocol in terms of message sequences and the type of data being exchanged.
Since web services are developed largely in an uncoordinated way, different
services often use independently developed ontologies for the same domain
instead of adhering to a single ontology as standard. In this work we
investigate the approaches that can be taken by the server to verify the
possibility to reach a state with semantically inconsistent results during the
execution of a protocol with a client, if the client ontology is published.
Often database is used to store the actual data along with the ontologies
instead of storing the actual data as a part of the ontology description. It is
important to observe that at the current state of the database the semantic
conflict state may not be reached even if the verification done by the server
indicates the possibility of reaching a conflict state. A relational algebra
based decision procedure is also developed to incorporate the current state of
the client and the server databases in the overall verification procedure.
","Detecting Ontological Conflicts in Protocols between Semantic Web
  Services"
"  The paper proposes a numerically stable recursive algorithm for the exact
computation of the linear-chain conditional random field gradient. It operates
as a forward algorithm over the log-domain expectation semiring and has the
purpose of enhancing memory efficiency when applied to long observation
sequences. Unlike the traditional algorithm based on the forward-backward
recursions, the memory complexity of our algorithm does not depend on the
sequence length. The experiments on real data show that it can be useful for
the problems which deal with long sequences.
","Gradient Computation In Linear-Chain Conditional Random Fields Using The
  Entropy Message Passing Algorithm"
"  In this paper, a new reinforcement learning approach is proposed which is
based on a powerful concept named Active Learning Method (ALM) in modeling. ALM
expresses any multi-input-single-output system as a fuzzy combination of some
single-input-singleoutput systems. The proposed method is an actor-critic
system similar to Generalized Approximate Reasoning based Intelligent Control
(GARIC) structure to adapt the ALM by delayed reinforcement signals. Our system
uses Temporal Difference (TD) learning to model the behavior of useful actions
of a control system. The goodness of an action is modeled on Reward-
Penalty-Plane. IDS planes will be updated according to this plane. It is shown
that the system can learn with a predefined fuzzy system or without it (through
random actions).
",Reinforcement Learning Based on Active Learning Method
"  An effective approach for energy conservation in wireless sensor networks is
scheduling sleep intervals for extraneous nodes while the remaining nodes stay
active to provide continuous service. For the sensor network to operate
successfully the active nodes must maintain both sensing coverage and network
connectivity, It proved before if the communication range of nodes is at least
twice the sensing range, complete coverage of a convex area implies
connectivity among the working set of nodes. In this paper we consider a
rectangular region A = a *b, such that R a R b s s {\pounds}, {\pounds}, where
s R is the sensing range of nodes. and put a constraint on minimum allowed
distance between nodes(s). according to this constraint we present a new lower
bound for communication range relative to sensing range of sensors(s 2 + 3 *R)
that complete coverage of considered area implies connectivity among the
working set of nodes; also we present a new distribution method, that satisfy
our constraint.
",A New Sufficient Condition for 1-Coverage to Imply Connectivity
"  In this paper, we propose a new approach for recommender systems based on
target tracking by Kalman filtering. We assume that users and their seen
resources are vectors in the multidimensional space of the categories of the
resources. Knowing this space, we propose an algorithm based on a Kalman filter
to track users and to predict the best prediction of their future position in
the recommendation space.
","Target tracking in the recommender space: Toward a new recommender
  system based on Kalman filtering"
"  We investigate projection methods, for evaluating a linear approximation of
the value function of a policy in a Markov Decision Process context. We
consider two popular approaches, the one-step Temporal Difference fix-point
computation (TD(0)) and the Bellman Residual (BR) minimization. We describe
examples, where each method outperforms the other. We highlight a simple
relation between the objective function they minimize, and show that while BR
enjoys a performance guarantee, TD(0) does not in general. We then propose a
unified view in terms of oblique projections of the Bellman equation, which
substantially simplifies and extends the characterization of (schoknecht,2002)
and the recent analysis of (Yu & Bertsekas, 2008). Eventually, we describe some
simulations that suggest that if the TD(0) solution is usually slightly better
than the BR solution, its inherent numerical instability makes it very bad in
some cases, and thus worse on average.
","Should one compute the Temporal Difference fix point or minimize the
  Bellman Residual? The unified oblique projection view"
"  Graph coloring, also known as vertex coloring, considers the problem of
assigning colors to the nodes of a graph such that adjacent nodes do not share
the same color. The optimization version of the problem concerns the
minimization of the number of used colors. In this paper we deal with the
problem of finding valid colorings of graphs in a distributed way, that is, by
means of an algorithm that only uses local information for deciding the color
of the nodes. Such algorithms prescind from any central control. Due to the
fact that quite a few practical applications require to find colorings in a
distributed way, the interest in distributed algorithms for graph coloring has
been growing during the last decade. As an example consider wireless ad-hoc and
sensor networks, where tasks such as the assignment of frequencies or the
assignment of TDMA slots are strongly related to graph coloring.
  The algorithm proposed in this paper is inspired by the calling behavior of
Japanese tree frogs. Male frogs use their calls to attract females.
Interestingly, groups of males that are located nearby each other desynchronize
their calls. This is because female frogs are only able to correctly localize
the male frogs when their calls are not too close in time. We experimentally
show that our algorithm is very competitive with the current state of the art,
using different sets of problem instances and comparing to one of the most
competitive algorithms from the literature.
","Distributed Graph Coloring: An Approach Based on the Calling Behavior of
  Japanese Tree Frogs"
"  This paper describes an application of Bayesian programming to the control of
an autonomous avatar in a multiplayer role-playing game (the example is based
on World of Warcraft). We model a particular task, which consists of choosing
what to do and to select which target in a situation where allies and foes are
present. We explain the model in Bayesian programming and show how we could
learn the conditional probabilities from data gathered during human-played
sessions.
",Bayesian Modeling of a Human MMORPG Player
"  We present a probabilistic logic programming framework to reinforcement
learning, by integrating reinforce-ment learning, in POMDP environments, with
normal hybrid probabilistic logic programs with probabilistic answer set
seman-tics, that is capable of representing domain-specific knowledge. We
formally prove the correctness of our approach. We show that the complexity of
finding a policy for a reinforcement learning problem in our approach is
NP-complete. In addition, we show that any reinforcement learning problem can
be encoded as a classical logic program with answer set semantics. We also show
that a reinforcement learning problem can be encoded as a SAT problem. We
present a new high level action description language that allows the factored
representation of POMDP. Moreover, we modify the original model of POMDP so
that it be able to distinguish between knowledge producing actions and actions
that change the environment.
","Reinforcement Learning in Partially Observable Markov Decision Processes
  using Hybrid Probabilistic Logic Programs"
"  Biometrics is the science and technology of measuring and analyzing
biological data of human body, extracting a feature set from the acquired data,
and comparing this set against to the template set in the database.
Experimental studies show that Unimodal biometric systems had many
disadvantages regarding performance and accuracy. Multimodal biometric systems
perform better than unimodal biometric systems and are popular even more
complex also. We examine the accuracy and performance of multimodal biometric
authentication systems using state of the art Commercial Off- The-Shelf (COTS)
products. Here we discuss fingerprint and face biometric systems, decision and
fusion techniques used in these systems. We also discuss their advantage over
unimodal biometric systems.
",Multimodal Biometric Systems - Study to Improve Accuracy and Performance
"  Uncertainty of decisions in safety-critical engineering applications can be
estimated on the basis of the Bayesian Markov Chain Monte Carlo (MCMC)
technique of averaging over decision models. The use of decision tree (DT)
models assists experts to interpret causal relations and find factors of the
uncertainty. Bayesian averaging also allows experts to estimate the uncertainty
accurately when a priori information on the favored structure of DTs is
available. Then an expert can select a single DT model, typically the Maximum a
Posteriori model, for interpretation purposes. Unfortunately, a priori
information on favored structure of DTs is not always available. For this
reason, we suggest a new prior on DTs for the Bayesian MCMC technique. We also
suggest a new procedure of selecting a single DT and describe an application
scenario. In our experiments on the Short-Term Conflict Alert data our
technique outperforms the existing Bayesian techniques in predictive accuracy
of the selected single DTs.
","A Bayesian Methodology for Estimating Uncertainty of Decisions in
  Safety-Critical Systems"
"  We examine the practicality for a user of using Answer Set Programming (ASP)
for representing logical formalisms. We choose as an example a formalism aiming
at capturing causal explanations from causal information. We provide an
implementation, showing the naturalness and relative efficiency of this
translation job. We are interested in the ease for writing an ASP program, in
accordance with the claimed ``declarative'' aspect of ASP. Limitations of the
earlier systems (poor data structure and difficulty in reusing pieces of
programs) made that in practice, the ``declarative aspect'' was more
theoretical than practical. We show how recent improvements in working ASP
systems facilitate a lot the translation, even if a few improvements could
still be useful.
",Using ASP with recent extensions for causal explanations
"  There are a huge number of problems, from various areas, being solved by
reducing them to SAT. However, for many applications, translation into SAT is
performed by specialized, problem-specific tools. In this paper we describe a
new system for uniform solving of a wide class of problems by reducing them to
SAT. The system uses a new specification language URSA that combines imperative
and declarative programming paradigms. The reduction to SAT is defined
precisely by the semantics of the specification language. The domain of the
approach is wide (e.g., many NP-complete problems can be simply specified and
then solved by the system) and there are problems easily solvable by the
proposed system, while they can be hardly solved by using other programming
languages or constraint programming systems. So, the system can be seen not
only as a tool for solving problems by reducing them to SAT, but also as a
general-purpose constraint solving system (for finite domains). In this paper,
we also describe an open-source implementation of the described approach. The
performed experiments suggest that the system is competitive to
state-of-the-art related modelling systems.
",URSA: A System for Uniform Reduction to SAT
"  SNOMED Clinical Terms (SNOMED CT) is one of the most widespread ontologies in
the life sciences, with more than 300,000 concepts and relationships, but is
distributed with no associated software tools. In this paper we present MySNOM,
a web-based SNOMED CT browser. MySNOM allows organizations to browse their own
distribution of SNOMED CT under a controlled environment, focuses on navigating
using the structure of SNOMED CT, and has diagramming capabilities.
",Are SNOMED CT Browsers Ready for Institutions? Introducing MySNOM
"  In this paper we dealt with the comparison and linking between lexical
resources with domain knowledge provided by ontologies. It is one of the issues
for the combination of the Semantic Web Ontologies and Text Mining. We
investigated the relations between the linguistics oriented and domain-specific
semantics, by associating the GO biological process concepts to the FrameNet
semantic frames. The result shows the gaps between the linguistics-oriented and
domain-specific semantics on the classification of events and the grouping of
target words. The result provides valuable information for the improvement of
domain ontologies supporting for text mining systems. And also, it will result
in benefits to language understanding technology.
","A study on the relation between linguistics-oriented and domain-specific
  semantics"
"  Virtual e-Science infrastructures supporting Web-based scientific workflows
are an example for knowledge-intensive collaborative and weakly-structured
processes where the interaction with the human scientists during process
execution plays a central role. In this paper we propose the lightweight
dynamic user-friendly interaction with humans during execution of scientific
workflows via the low-barrier approach of Semantic Wikis as an intuitive
interface for non-technical scientists. Our Process Makna Semantic Wiki system
is a novel combination of an business process management system adapted for
scientific workflows with a Corporate Semantic Web Wiki user interface
supporting knowledge intensive human interaction tasks during scientific
workflow execution.
",Process Makna - A Semantic Wiki for Scientific Workflows
"  ChemgaPedia is a multimedia, webbased eLearning service platform that
currently contains about 18.000 pages organized in 1.700 chapters covering the
complete bachelor studies in chemistry and related topics of chemistry,
pharmacy, and life sciences. The eLearning encyclopedia contains some 25.000
media objects and the eLearning platform provides services such as virtual and
remote labs for experiments. With up to 350.000 users per month the platform is
the most frequently used scientific educational service in the German spoken
Internet. In this demo we show the benefit of mapping the static eLearning
contents of ChemgaPedia to a Linked Data representation for Semantic Chemistry
which allows for generating dynamic eLearning paths tailored to the semantic
profiles of the users.
","Use of semantic technologies for the development of a dynamic
  trajectories generator in a Semantic Chemistry eLearning platform"
"  This research applies ideas from argumentation theory in the context of
semantic wikis, aiming to provide support for structured-large scale
argumentation between human agents. The implemented prototype is exemplified by
modelling the MMR vaccine controversy.
",Using Semantic Wikis for Structured Argument in Medical Domain
"  Creating a new Ontology: a Modular Approach
",Creating a new Ontology: a Modular Approach
"  Research in the Life Sciences depends on the integration of large,
distributed and heterogeneous data sources and web services. The discovery of
which of these resources are the most appropriate to solve a given task is a
complex research question, since there is a large amount of plausible
candidates and there is little, mostly unstructured, metadata to be able to
decide among them.We contribute a semi-automatic approach,based on semantic
techniques, to assist researchers in the discovery of the most appropriate web
services to full a set of given requirements.
","A semantic approach for the requirement-driven discovery of web services
  in the Life Sciences"
"  Semantic wikis, wikis enhanced with Semantic Web technologies, are
appropriate systems for community-authored knowledge models. They are
particularly suitable for scientific collaboration. This paper details the
design principles ofWikiBridge, a semantic wiki.
",Scientific Collaborations: principles of WikiBridge Design
"  We present Populous, a tool for gathering content with which to populate an
ontology. Domain experts need to add content, that is often repetitive in its
form, but without having to tackle the underlying ontological representation.
Populous presents users with a table based form in which columns are
constrained to take values from particular ontologies; the user can select a
concept from an ontology via its meaningful label to give a value for a given
entity attribute. Populated tables are mapped to patterns that can then be used
to automatically generate the ontology's content. Populous's contribution is in
the knowledge gathering stage of ontology development. It separates knowledge
gathering from the conceptualisation and also separates the user from the
standard ontology authoring environments. As a result, Populous can allow
knowledge to be gathered in a straight-forward manner that can then be used to
do mass production of ontology content.
",Populous: A tool for populating ontology templates
"  In this work, we develop an intelligent user interface that allows users to
enter biomedical queries in a natural language, and that presents the answers
(possibly with explanations if requested) in a natural language. We develop a
rule layer over biomedical ontologies and databases, and use automated
reasoners to answer queries considering relevant parts of the rule layer.
",Querying Biomedical Ontologies in Natural Language using Answer Set
"  There has been a long history of using fuzzy language equivalence to compare
the behavior of fuzzy systems, but the comparison at this level is too coarse.
Recently, a finer behavioral measure, bisimulation, has been introduced to
fuzzy finite automata. However, the results obtained are applicable only to
finite-state systems. In this paper, we consider bisimulation for general fuzzy
systems which may be infinite-state or infinite-event, by modeling them as
fuzzy transition systems. To help understand and check bisimulation, we
characterize it in three ways by enumerating whole transitions, comparing
individual transitions, and using a monotonic function. In addition, we address
composition operations, subsystems, quotients, and homomorphisms of fuzzy
transition systems and discuss their properties connected with bisimulation.
The results presented here are useful for comparing the behavior of general
fuzzy systems. In particular, this makes it possible to relate an infinite
fuzzy system to a finite one, which is easier to analyze, with the same
behavior.
",Bisimulations for fuzzy transition systems
"  Fuzzy automata have long been accepted as a generalization of
nondeterministic finite automata. A closer examination, however, shows that the
fundamental property---nondeterminism---in nondeterministic finite automata has
not been well embodied in the generalization. In this paper, we introduce
nondeterministic fuzzy automata with or without $\el$-moves and fuzzy languages
recognized by them. Furthermore, we prove that (deterministic) fuzzy automata,
nondeterministic fuzzy automata, and nondeterministic fuzzy automata with
$\el$-moves are all equivalent in the sense that they recognize the same class
of fuzzy languages.
",Nondeterministic fuzzy automata
"  The previous decade has brought a remarkable increase of the interest in
applications that deal with querying and mining of time series data. Many of
the research efforts in this context have focused on introducing new
representation methods for dimensionality reduction or novel similarity
measures for the underlying data. In the vast majority of cases, each
individual work introducing a particular method has made specific claims and,
aside from the occasional theoretical justifications, provided quantitative
experimental observations. However, for the most part, the comparative aspects
of these experiments were too narrowly focused on demonstrating the benefits of
the proposed methods over some of the previously introduced ones. In order to
provide a comprehensive validation, we conducted an extensive experimental
study re-implementing eight different time series representations and nine
similarity measures and their variants, and testing their effectiveness on
thirty-eight time series data sets from a wide variety of application domains.
In this paper, we give an overview of these different techniques and present
our comparative experimental findings regarding their effectiveness. In
addition to providing a unified validation of some of the existing
achievements, our experiments also indicate that, in some cases, certain claims
in the literature may be unduly optimistic.
","Experimental Comparison of Representation Methods and Distance Measures
  for Time Series Data"
"  In this paper, we propose a new approach for recommender systems based on
target tracking by Kalman filtering. We assume that users and their seen
resources are vectors in the multidimensional space of the categories of the
resources. Knowing this space, we propose an algorithm based on a Kalman filter
to track users and to predict the best prediction of their future position in
the recommendation space.
","A new Recommender system based on target tracking: a Kalman Filter
  approach"
"  Knowledge is attributed to human whose problem-solving behavior is subjective
and complex. In today's knowledge economy, the need to manage knowledge
produced by a community of actors cannot be overemphasized. This is due to the
fact that actors possess some level of tacit knowledge which is generally
difficult to articulate. Problem-solving requires searching and sharing of
knowledge among a group of actors in a particular context. Knowledge expressed
within the context of a problem resolution must be capitalized for future
reuse. In this paper, an approach that permits dynamic capitalization of
relevant and reliable actors' knowledge in solving decision problem following
Economic Intelligence process is proposed. Knowledge annotation method and
temporal attributes are used for handling the complexity in the communication
among actors and in contextualizing expressed knowledge. A prototype is built
to demonstrate the functionalities of a collaborative Knowledge Management
system based on this approach. It is tested with sample cases and the result
showed that dynamic capitalization leads to knowledge validation hence
increasing reliability of captured knowledge for reuse. The system can be
adapted to various domains
","Dynamic Capitalization and Visualization Strategy in Collaborative
  Knowledge Management System for EI Process"
"  The shift from industrial economy to knowledge economy in today's world has
revolutionalized strategic planning in organizations as well as their problem
solving approaches. The point of focus today is knowledge and service
production with more emphasis been laid on knowledge capital. Many
organizations are investing on tools that facilitate knowledge sharing among
their employees and they are as well promoting and encouraging collaboration
among their staff in order to build the organization's knowledge capital with
the ultimate goal of creating a lasting competitive advantage for their
organizations. One of the current leading approaches used for solving
organization's decision problem is the Economic Intelligence (EI) approach
which involves interactions among various actors called EI actors. These actors
collaborate to ensure the overall success of the decision problem solving
process. In the course of the collaboration, the actors express knowledge which
could be capitalized for future reuse. In this paper, we propose in the first
place, an annotation model for knowledge elicitation among EI actors. Because
of the need to build a knowledge capital, we also propose a dynamic knowledge
capitalisation approach for managing knowledge produced by the actors. Finally,
the need to manage the interactions and the interdependencies among
collaborating EI actors, led to our third proposition which constitute an
awareness mechanism for group work management.
","Dynamic Knowledge Capitalization through Annotation among Economic
  Intelligence Actors in a Collaborative Environment"
"  A new distance function dist(A,B) for fuzzy sets A and B is introduced. It is
based on the descriptive complexity, i.e., the number of bits (on average) that
are needed to describe an element in the symmetric difference of the two sets.
The distance gives the amount of additional information needed to describe any
one of the two sets given the other. We prove its mathematical properties and
perform pattern clustering on data based on this distance.
",Descriptive-complexity based distance for fuzzy sets
"  Product take-back legislation forces manufacturers to bear the costs of
collection and disposal of products that have reached the end of their useful
lives. In order to reduce these costs, manufacturers can consider reuse,
remanufacturing and/or recycling of components as an alternative to disposal.
The implementation of such alternatives usually requires an appropriate reverse
supply chain management. With the concepts of reverse supply chain are gaining
popularity in practice, the use of artificial intelligence approaches in these
areas is also becoming popular. As a result, the purpose of this paper is to
give an overview of the recent publications concerning the application of
artificial intelligence techniques to reverse supply chain with emphasis on
certain types of product returns.
","Artificial Intelligence in Reverse Supply Chain Management: The State of
  the Art"
"  Intersections constitute one of the most dangerous elements in road systems.
Traffic signals remain the most common way to control traffic at high-volume
intersections and offer many opportunities to apply intelligent transportation
systems to make traffic more efficient and safe. This paper describes an
automated method to estimate the temporal exposure of road users crossing the
conflict zone to lateral collision with road users originating from a different
approach. This component is part of a larger system relying on video sensors to
provide queue lengths and spatial occupancy that are used for real time traffic
control and monitoring. The method is evaluated on data collected during a real
world experiment.
","Automatic Estimation of the Exposure to Lateral Collision in Signalized
  Intersections using Video Sensors"
"  A conservative class of constraint satisfaction problems CSPs is a class for
which membership is preserved under arbitrary domain reductions. Many
well-known tractable classes of CSPs are conservative. It is well known that
lexleader constraints may significantly reduce the number of solutions by
excluding symmetric solutions of CSPs. We show that adding certain lexleader
constraints to any instance of any conservative class of CSPs still allows us
to find all solutions with a time which is polynomial between successive
solutions. The time is polynomial in the total size of the instance and the
additional lexleader constraints. It is well known that for complete symmetry
breaking one may need an exponential number of lexleader constraints. However,
in practice, the number of additional lexleader constraints is typically
polynomial number in the size of the instance. For polynomially many lexleader
constraints, we may in general not have complete symmetry breaking but
polynomially many lexleader constraints may provide practically useful symmetry
breaking -- and they sometimes exclude super-exponentially many solutions. We
prove that for any instance from a conservative class, the time between finding
successive solutions of the instance with polynomially many additional
lexleader constraints is polynomial even in the size of the instance without
lexleaderconstraints.
",Symmetry Breaking with Polynomial Delay
"  In the interpretation of experimental data, one is actually looking for
plausible explanations. We look for a measure of plausibility, with which we
can compare different possible explanations, and which can be combined when
there are different sets of data. This is contrasted to the conventional
measure for probabilities as well as to the proposed measure of possibilities.
We define what characteristics this measure of plausibility should have.
  In getting to the conception of this measure, we explore the relation of
plausibility to abductive reasoning, and to Bayesian probabilities. We also
compare with the Dempster-Schaefer theory of evidence, which also has its own
definition for plausibility. Abduction can be associated with biconditionality
in inference rules, and this provides a platform to relate to the
Collins-Michalski theory of plausibility. Finally, using a formalism for wiring
logic onto Hopfield neural networks, we ask if this is relevant in obtaining
this measure.
",Looking for plausibility
"  This article deals with Part family formation problem which is believed to be
moderately complicated to be solved in polynomial time in the vicinity of Group
Technology (GT). In the past literature researchers investigated that the part
family formation techniques are principally based on production flow analysis
(PFA) which usually considers operational requirements, sequences and time.
Part Coding Analysis (PCA) is merely considered in GT which is believed to be
the proficient method to identify the part families. PCA classifies parts by
allotting them to different families based on their resemblances in: (1) design
characteristics such as shape and size, and/or (2) manufacturing
characteristics (machining requirements). A novel approach based on simulated
annealing namely SAPFOCS is adopted in this study to develop effective part
families exploiting the PCA technique. Thereafter Taguchi's orthogonal design
method is employed to solve the critical issues on the subject of parameters
selection for the proposed metaheuristic algorithm. The adopted technique is
therefore tested on 5 different datasets of size 5 {\times} 9 to 27 {\times} 9
and the obtained results are compared with C-Linkage clustering technique. The
experimental results reported that the proposed metaheuristic algorithm is
extremely effective in terms of the quality of the solution obtained and has
outperformed C-Linkage algorithm in most instances.
","SAPFOCS: a metaheuristic based approach to part family formation
  problems in group technology"
"  Using the notion of an elementary loop, Gebser and Schaub refined the theorem
on loop formulas due to Lin and Zhao by considering loop formulas of elementary
loops only. In this article, we reformulate their definition of an elementary
loop, extend it to disjunctive programs, and study several properties of
elementary loops, including how maximal elementary loops are related to minimal
unfounded sets. The results provide useful insights into the stable model
semantics in terms of elementary loops. For a nondisjunctive program, using a
graph-theoretic characterization of an elementary loop, we show that the
problem of recognizing an elementary loop is tractable. On the other hand, we
show that the corresponding problem is {\sf coNP}-complete for a disjunctive
program. Based on the notion of an elementary loop, we present the class of
Head-Elementary-loop-Free (HEF) programs, which strictly generalizes the class
of Head-Cycle-Free (HCF) programs due to Ben-Eliyahu and Dechter. Like an HCF
program, an HEF program can be turned into an equivalent nondisjunctive program
in polynomial time by shifting head atoms into the body.
",On Elementary Loops of Logic Programs
"  In this paper we introduce a method for extending binary qualitative
direction calculi with adjustable granularity like OPRAm or the star calculus
with a granular distance concept. This method is similar to the concept of
extending points with an internal reference direction to get oriented points
which are the basic entities in the OPRAm calculus. Even if the spatial objects
are from a geometrical point of view infinitesimal small points locally
available reference measures are attached. In the case of OPRAm, a reference
direction is attached. The same principle works also with local reference
distances which are called elevations. The principle of attaching references
features to a point is called hidden feature attachment.
","Extending Binary Qualitative Direction Calculi with a Granular Distance
  Concept: Hidden Feature Attachment"
"  In video games, virtual characters' decision systems often use a simplified
representation of the world. To increase both their autonomy and believability
we want those characters to be able to learn this representation from human
players. We propose to use a model called growing neural gas to learn by
imitation the topology of the environment. The implementation of the model, the
modifications and the parameters we used are detailed. Then, the quality of the
learned representations and their evolution during the learning are studied
using different measures. Improvements for the growing neural gas to give more
information to the character's model are given in the conclusion.
","Learning a Representation of a Believable Virtual Character's
  Environment with an Imitation Algorithm"
"  Current work in planning with preferences assume that the user's preference
models are completely specified and aim to search for a single solution plan.
In many real-world planning scenarios, however, the user probably cannot
provide any information about her desired plans, or in some cases can only
express partial preferences. In such situations, the planner has to present not
only one but a set of plans to the user, with the hope that some of them are
similar to the plan she prefers. We first propose the usage of different
measures to capture quality of plan sets that are suitable for such scenarios:
domain-independent distance measures defined based on plan elements (actions,
states, causal links) if no knowledge of the user's preferences is given, and
the Integrated Convex Preference measure in case the user's partial preference
is provided. We then investigate various heuristic approaches to find set of
plans according to these measures, and present empirical results demonstrating
the promise of our approach.
",Planning with Partial Preference Models
"  Performing effective preference-based data retrieval requires detailed and
preferentially meaningful structurized information about the current user as
well as the items under consideration. A common problem is that representations
of items often only consist of mere technical attributes, which do not resemble
human perception. This is particularly true for integral items such as movies
or songs. It is often claimed that meaningful item features could be extracted
from collaborative rating data, which is becoming available through social
networking services. However, there is only anecdotal evidence supporting this
claim; but if it is true, the extracted information could very valuable for
preference-based data retrieval. In this paper, we propose a methodology to
systematically check this common claim. We performed a preliminary
investigation on a large collection of movie ratings and present initial
evidence.
",Extracting Features from Ratings: The Role of Factor Models
"  We suggest a procedure that is relevant both to electronic performance and
human psychology, so that the creative logic and the respect for human nature
appear in a good agreement. The idea is to create an electronic card containing
basic information about a person's psychological behavior in order to make it
possible to quickly decide about the suitability of one for another. This
""psychological electronics"" approach could be tested via student projects.
","The ""psychological map of the brain"", as a personal information card
  (file), - a project for the student of the 21st century"
"  Meaning negotiation (MN) is the general process with which agents reach an
agreement about the meaning of a set of terms. Artificial Intelligence scholars
have dealt with the problem of MN by means of argumentations schemes, beliefs
merging and information fusion operators, and ontology alignment but the
proposed approaches depend upon the number of participants. In this paper, we
give a general model of MN for an arbitrary number of agents, in which each
participant discusses with the others her viewpoint by exhibiting it in an
actual set of constraints on the meaning of the negotiated terms. We call this
presentation of individual viewpoints an angle. The agents do not aim at
forming a common viewpoint but, instead, at agreeing about an acceptable common
angle. We analyze separately the process of MN by two agents (\emph{bilateral}
or \emph{pairwise} MN) and by more than two agents (\emph{multiparty} MN), and
we use game theoretic models to understand how the process develops in both
cases: the models are Bargaining Game for bilateral MN and English Auction for
multiparty MN. We formalize the process of reaching such an agreement by giving
a deduction system that comprises of rules that are consistent and adequate for
representing MN.
",Meaning Negotiation as Inference
"  Although some information-theoretic measures of uncertainty or granularity
have been proposed in rough set theory, these measures are only dependent on
the underlying partition and the cardinality of the universe, independent of
the lower and upper approximations. It seems somewhat unreasonable since the
basic idea of rough set theory aims at describing vague concepts by the lower
and upper approximations. In this paper, we thus define new
information-theoretic entropy and co-entropy functions associated to the
partition and the approximations to measure the uncertainty and granularity of
an approximation space. After introducing the novel notions of entropy and
co-entropy, we then examine their properties. In particular, we discuss the
relationship of co-entropies between different universes. The theoretical
development is accompanied by illustrative numerical examples.
",Information-theoretic measures associated with rough set approximations
"  One of the main research areas in Artificial Intelligence is the coding of
agents (programs) which are able to learn by themselves in any situation. This
means that agents must be useful for purposes other than those they were
created for, as, for example, playing chess. In this way we try to get closer
to the pristine goal of Artificial Intelligence. One of the problems to decide
whether an agent is really intelligent or not is the measurement of its
intelligence, since there is currently no way to measure it in a reliable way.
The purpose of this project is to create an interpreter that allows for the
execution of several environments, including those which are generated
randomly, so that an agent (a person or a program) can interact with them. Once
the interaction between the agent and the environment is over, the interpreter
will measure the intelligence of the agent according to the actions, states and
rewards the agent has undergone inside the environment during the test. As a
result we will be able to measure agents' intelligence in any possible
environment, and to make comparisons between several agents, in order to
determine which of them is the most intelligent. In order to perform the tests,
the interpreter must be able to randomly generate environments that are really
useful to measure agents' intelligence, since not any randomly generated
environment will serve that purpose.
",An architecture for the evaluation of intelligent systems
"  The World Wide Web (WWW) allows the people to share the information (data)
from the large database repositories globally. The amount of information grows
billions of databases. We need to search the information will specialize tools
known generically search engine. There are many of search engines available
today, retrieving meaningful information is difficult. However to overcome this
problem in search engines to retrieve meaningful information intelligently,
semantic web technologies are playing a major role. In this paper we present
survey on the search engine generations and the role of search engines in
intelligent web and semantic search technologies.
",Intelligent Semantic Web Search Engines: A Brief Survey
"  The analysis of online least squares estimation is at the heart of many
stochastic sequential decision making problems. We employ tools from the
self-normalized processes to provide a simple and self-contained proof of a
tail bound of a vector-valued martingale. We use the bound to construct a new
tighter confidence sets for the least squares estimate.
  We apply the confidence sets to several online decision problems, such as the
multi-armed and the linearly parametrized bandit problems. The confidence sets
are potentially applicable to other problems such as sleeping bandits,
generalized linear bandits, and other linear control problems.
  We improve the regret bound of the Upper Confidence Bound (UCB) algorithm of
Auer et al. (2002) and show that its regret is with high-probability a problem
dependent constant. In the case of linear bandits (Dani et al., 2008), we
improve the problem dependent bound in the dimension and number of time steps.
Furthermore, as opposed to the previous result, we prove that our bound holds
for small sample sizes, and at the same time the worst case bound is improved
by a logarithmic factor and the constant is improved.
","Online Least Squares Estimation with Self-Normalized Processes: An
  Application to Bandit Problems"
"  This paper presents a new multi-objective hybrid model that makes cooperation
between the strength of research of neighborhood methods presented by the tabu
search (TS) and the important exploration capacity of evolutionary algorithm.
This model was implemented and tested in benchmark functions (ZDT1, ZDT2, and
ZDT3), using a network of computers.
","Hybrid Model for Solving Multi-Objective Problems Using Evolutionary
  Algorithm and Tabu Search"
"  An algorithm running in O(1.1995n) is presented for counting models for exact
satisfiability formulae(#XSAT). This is faster than the previously best
algorithm which runs in O(1.2190n). In order to improve the efficiency of the
algorithm, a new principle, i.e. the common literals principle, is addressed to
simplify formulae. This allows us to eliminate more common literals. In
addition, we firstly inject the resolution principles into solving #XSAT
problem, and therefore this further improves the efficiency of the algorithm.
",New Worst-Case Upper Bound for #XSAT
"  Rules in logic programming encode information about mutual interdependencies
between literals that is not captured by any of the commonly used semantics.
This information becomes essential as soon as a program needs to be modified or
further manipulated.
  We argue that, in these cases, a program should not be viewed solely as the
set of its models. Instead, it should be viewed and manipulated as the set of
sets of models of each rule inside it. With this in mind, we investigate and
highlight relations between the SE-model semantics and individual rules. We
identify a set of representatives of rule equivalence classes induced by
SE-models, and so pinpoint the exact expressivity of this semantics with
respect to a single rule. We also characterise the class of sets of
SE-interpretations representable by a single rule. Finally, we discuss the
introduction of two notions of equivalence, both stronger than strong
equivalence [1] and weaker than strong update equivalence [2], which seem more
suitable whenever the dependency information found in rules is of interest.
",Back and Forth Between Rules and SE-Models (Extended Version)
"  The global objective of this work is to provide practical optimization
methods to companies involved in inventory routing problems, taking into
account this new type of data. Also, companies are sometimes not able to deal
with changing plans every period and would like to adopt regular structures for
serving customers.
","Practical inventory routing: A problem definition and an optimization
  method"
"  Identification of critical or weak buses for a given operating condition is
an important task in the load dispatch centre. It has become more vital in view
of the threat of voltage instability leading to voltage collapse. This paper
presents a fuzzy approach for ranking critical buses in a power system under
normal and network contingencies based on Line Flow index and voltage profiles
at load buses. The Line Flow index determines the maximum load that is possible
to be connected to a bus in order to maintain stability before the system
reaches its bifurcation point. Line Flow index (LF index) along with voltage
profiles at the load buses are represented in Fuzzy Set notation. Further they
are evaluated using fuzzy rules to compute Criticality Index. Based on this
index, critical buses are ranked. The bus with highest rank is the weakest bus
as it can withstand a small amount of load before causing voltage collapse. The
proposed method is tested on Five Bus Test System.
","Fuzzy Approach to Critical Bus Ranking under Normal and Line Outage
  Contingencies"
"  The process-based semantic composition of Web Services is gaining a
considerable momentum as an approach for the effective integration of
distributed, heterogeneous, and autonomous applications. To compose Web
Services semantically, we need an ontology. There are several ways of inserting
semantics in Web Services. One of them consists of using description languages
like OWL-S. In this paper, we introduce our work which consists in the
proposition of a new model and the use of semantic matching technology for
semantic and dynamic composition of ebXML business processes.
","An Agent Based Architecture (Using Planning) for Dynamic and Semantic
  Web Services Composition in an EBXML Context"
"  The problem of business-IT alignment is of widespread economic concern.
  As one way of addressing the problem, this paper describes an online system
that functions as a kind of Wiki -- one that supports the collaborative writing
and running of business and scientific applications, as rules in open
vocabulary, executable English, using a browser.
  Since the rules are in English, they are indexed by Google and other search
engines. This is useful when looking for rules for a task that one has in mind.
  The design of the system integrates the semantics of data, with a semantics
of an inference method, and also with the meanings of English sentences. As
such, the system has functionality that may be useful for the Rules, Logic,
Proof and Trust requirements of the Semantic Web.
  The system accepts rules, and small numbers of facts, typed or copy-pasted
directly into a browser. One can then run the rules, again using a browser. For
larger amounts of data, the system uses information in the rules to
automatically generate and run SQL over networked databases. From a few highly
declarative rules, the system typically generates SQL that would be too
complicated to write reliably by hand. However, the system can explain its
results in step-by-step hypertexted English, at the business or scientific
level
  As befits a Wiki, shared use of the system is free.
","A Wiki for Business Rules in Open Vocabulary, Executable English"
"  We propose a long-term memory design for artificial general intelligence
based on Solomonoff's incremental machine learning methods. We use R5RS Scheme
and its standard library with a few omissions as the reference machine. We
introduce a Levin Search variant based on Stochastic Context Free Grammar
together with four synergistic update algorithms that use the same grammar as a
guiding probability distribution of programs. The update algorithms include
adjusting production probabilities, re-using previous solutions, learning
programming idioms and discovery of frequent subprograms. Experiments with two
training sequences demonstrate that our approach to incremental learning is
effective.
",Teraflop-scale Incremental Machine Learning
"  In Artificial Intelligence with Coalition Structure Generation (CSG) one
refers to those cooperative complex problems that require to find an optimal
partition, maximising a social welfare, of a set of entities involved in a
system into exhaustive and disjoint coalitions. The solution of the CSG problem
finds applications in many fields such as Machine Learning (covering machines,
clustering), Data Mining (decision tree, discretization), Graph Theory, Natural
Language Processing (aggregation), Semantic Web (service composition), and
Bioinformatics. The problem of finding the optimal coalition structure is
NP-complete. In this paper we present a greedy adaptive search procedure
(GRASP) with path-relinking to efficiently search the space of coalition
structures. Experiments and comparisons to other algorithms prove the validity
of the proposed method in solving this hard combinatorial problem.
",GRASP and path-relinking for Coalition Structure Generation
"  Signature used as a biometric is implemented in various systems as well as
every signature signed by each person is distinct at the same time. So, it is
very important to have a computerized signature verification system. In offline
signature verification system dynamic features are not available obviously, but
one can use a signature as an image and apply image processing techniques to
make an effective offline signature verification system. Author proposes a
intelligent network used directional feature and energy density both as inputs
to the same network and classifies the signature. Neural network is used as a
classifier for this system. The results are compared with both the very basic
energy density method and a simple directional feature method of offline
signature verification system and this proposed new network is found very
effective as compared to the above two methods, specially for less number of
training samples, which can be implemented practically.
","A Directional Feature with Energy based Offline Signature Verification
  Network"
"  Some recent works in conditional planning have proposed reachability
heuristics to improve planner scalability, but many lack a formal description
of the properties of their distance estimates. To place previous work in
context and extend work on heuristics for conditional planning, we provide a
formal basis for distance estimates between belief states. We give a definition
for the distance between belief states that relies on aggregating underlying
state distance measures. We give several techniques to aggregate state
distances and their associated properties. Many existing heuristics exhibit a
subset of the properties, but in order to provide a standardized comparison we
present several generalizations of planning graph heuristics that are used in a
single planner. We compliment our belief state distance estimate framework by
also investigating efficient planning graph data structures that incorporate
BDDs to compute the most effective heuristics.
  We developed two planners to serve as test-beds for our investigation. The
first, CAltAlt, is a conformant regression planner that uses A* search. The
second, POND, is a conditional progression planner that uses AO* search. We
show the relative effectiveness of our heuristic techniques within these
planners. We also compare the performance of these planners with several state
of the art approaches in conditional planning.
",Planning Graph Heuristics for Belief Space Search
"  Natural Immune system plays a vital role in the survival of the all living
being. It provides a mechanism to defend itself from external predates making
it consistent systems, capable of adapting itself for survival incase of
changes. The human immune system has motivated scientists and engineers for
finding powerful information processing algorithms that has solved complex
engineering tasks. This paper explores one of the various possibilities for
solving problem in a Multiagent scenario wherein multiple robots are deployed
to achieve a goal collectively. The final goal is dependent on the performance
of individual robot and its survival without having to lose its energy beyond a
predetermined threshold value by deploying an evolutionary computational
technique otherwise called the artificial immune system that imitates the
biological immune system.
","An Artificial Immune System Model for Multi-Agents Resource Sharing in
  Distributed Environments"
"  Most machine learning tools work with a single table where each row is an
instance and each column is an attribute. Each cell of the table contains an
attribute value for an instance. This representation prevents one important
form of learning, which is, classification based on groups of correlated
records, such as multiple exams of a single patient, internet customer
preferences, weather forecast or prediction of sea conditions for a given day.
To some extent, relational learning methods, such as inductive logic
programming, can capture this correlation through the use of intensional
predicates added to the background knowledge. In this work, we propose SPPAM,
an algorithm that aggregates past observations in one single record. We show
that applying SPPAM to the original correlated data, before the learning task,
can produce classifiers that are better than the ones trained using all
records.
",SPPAM - Statistical PreProcessing AlgorithM
"  An emotional version of Sapir-Whorf hypothesis suggests that differences in
language emotionalities influence differences among cultures no less than
conceptual differences. Conceptual contents of languages and cultures to
significant extent are determined by words and their semantic differences;
these could be borrowed among languages and exchanged among cultures. Emotional
differences, as suggested in the paper, are related to grammar and mostly
cannot be borrowed. Conceptual and emotional mechanisms of languages are
considered here along with their functions in the mind and cultural evolution.
A fundamental contradiction in human mind is considered: language evolution
requires reduced emotionality, but ""too low"" emotionality makes language
""irrelevant to life,"" disconnected from sensory-motor experience. Neural
mechanisms of these processes are suggested as well as their mathematical
models: the knowledge instinct, the language instinct, the dual model
connecting language and cognition, dynamic logic, neural modeling fields.
Mathematical results are related to cognitive science, linguistics, and
psychology. Experimental evidence and theoretical arguments are discussed.
Approximate equations for evolution of human minds and cultures are obtained.
Their solutions identify three types of cultures: ""conceptual""-pragmatic
cultures, in which emotionality of language is reduced and differentiation
overtakes synthesis resulting in fast evolution at the price of uncertainty of
values, self doubts, and internal crises; ""traditional-emotional"" cultures
where differentiation lags behind synthesis, resulting in cultural stability at
the price of stagnation; and ""multi-cultural"" societies combining fast cultural
evolution and stability. Unsolved problems and future theoretical and
experimental directions are discussed.
","Language, Emotions, and Cultures: Emotional Sapir-Whorf Hypothesis"
"  Knowledge compilation is an approach to tackle the computational
intractability of general reasoning problems. According to this approach,
knowledge bases are converted off-line into a target compilation language which
is tractable for on-line querying. Reduced ordered binary decision diagram
(ROBDD) is one of the most influential target languages. We generalize ROBDD by
associating some implied literals in each node and the new language is called
reduced ordered binary decision diagram with implied literals (ROBDD-L). Then
we discuss a kind of subsets of ROBDD-L called ROBDD-i with precisely i implied
literals (0 \leq i \leq \infty). In particular, ROBDD-0 is isomorphic to ROBDD;
ROBDD-\infty requires that each node should be associated by the implied
literals as many as possible. We show that ROBDD-i has uniqueness over some
specific variables order, and ROBDD-\infty is the most succinct subset in
ROBDD-L and can meet most of the querying requirements involved in the
knowledge compilation map. Finally, we propose an ROBDD-i compilation algorithm
for any i and a ROBDD-\infty compilation algorithm. Based on them, we implement
a ROBDD-L package called BDDjLu and then get some conclusions from preliminary
experimental results: ROBDD-\infty is obviously smaller than ROBDD for all
benchmarks; ROBDD-\infty is smaller than the d-DNNF the benchmarks whose
compilation results are relatively small; it seems that it is better to
transform ROBDDs-\infty into FBDDs and ROBDDs rather than straight compile the
benchmarks.
","Reduced Ordered Binary Decision Diagram with Implied Literals: A New
  knowledge Compilation Approach"
"  CHRONIOUS is an Open, Ubiquitous and Adaptive Chronic Disease Management
Platform for Chronic Obstructive Pulmonary Disease(COPD) Chronic Kidney Disease
(CKD) and Renal Insufficiency. It consists of several modules: an ontology
based literature search engine, a rule based decision support system, remote
sensors interacting with lifestyle interfaces (PDA, monitor touchscreen) and a
machine learning module. All these modules interact each other to allow the
monitoring of two types of chronic diseases and to help clinician in taking
decision for cure purpose. This paper illustrates how some machine learning
algorithms and a rule based decision support system can be used in smart
devices, to monitor chronic patient. We will analyse how a set of machine
learning algorithms can be used in smart devices to alert the clinician in case
of a patient health condition worsening trend.
","Using Soft Computer Techniques on Smart Devices for Monitoring Chronic
  Diseases: the CHRONIOUS case"
"  We show that several important resource allocation problems in wireless
networks fit within the common framework of Constraint Satisfaction Problems
(CSPs). Inspired by the requirements of these applications, where variables are
located at distinct network devices that may not be able to communicate but may
interfere, we define natural criteria that a CSP solver must possess in order
to be practical. We term these algorithms decentralized CSP solvers. The best
known CSP solvers were designed for centralized problems and do not meet these
criteria. We introduce a stochastic decentralized CSP solver and prove that it
will find a solution in almost surely finite time, should one exist, also
showing it has many practically desirable properties. We benchmark the
algorithm's performance on a well-studied class of CSPs, random k-SAT,
illustrating that the time the algorithm takes to find a satisfying assignment
is competitive with stochastic centralized solvers on problems with order a
thousand variables despite its decentralized nature. We demonstrate the
solver's practical utility for the problems that motivated its introduction by
using it to find a non-interfering channel allocation for a network formed from
data from downtown Manhattan.
",Decentralized Constraint Satisfaction
"  this paper presents an enhancement of the medial axis algorithm to be used
for finding the optimal shortest path for developed cognitive map. The
cognitive map has been developed, based on the architectural blueprint maps.
The idea for using the medial-axis is to find main path central pixels; each
center pixel represents the center distance between two side boarder pixels.
The need for these pixels in the algorithm comes from the need of building a
network of nodes for the path, where each node represents a turning in the real
world (left, right, critical left, critical right...). The algorithm also
ignores from finding the center pixels paths that are too small for intelligent
robot navigation. The Idea of this algorithm is to find the possible shortest
path between start and end points. The goal of this research is to extract a
simple, robust representation of the shape of the cognitive map together with
the optimal shortest path between start and end points. The intelligent robot
will use this algorithm in order to decrease the time that is needed for
sweeping the targeted building.
",Finding Shortest Path for Developed Cognitive Map Using Medial Axis
"  One of the first step in the realization of an automatic system of check
recognition is the extraction of the handwritten area. We propose in this paper
an hybrid method to extract these areas. This method is based on digit
recognition by Fourier descriptors and different steps of colored image
processing . It requires the bank recognition of its code which is located in
the check marking band as well as the handwritten color recognition by the
method of difference of histograms. The areas extraction is then carried out by
the use of some mathematical morphology tools.
","Extraction of handwritten areas from colored image of bank checks by an
  hybrid method"
"  Recently, several researchers have found that cost-based satisficing search
with A* often runs into problems. Although some ""work arounds"" have been
proposed to ameliorate the problem, there has not been any concerted effort to
pinpoint its origin. In this paper, we argue that the origins can be traced
back to the wide variance in action costs that is observed in most planning
domains. We show that such cost variance misleads A* search, and that this is
no trifling detail or accidental phenomenon, but a systemic weakness of the
very concept of ""cost-based evaluation functions + systematic search +
combinatorial graphs"". We show that satisficing search with sized-based
evaluation functions is largely immune to this problem.
",Cost Based Satisficing Search Considered Harmful
"  We propose AllDiffPrecedence, a new global constraint that combines together
an AllDifferent constraint with precedence constraints that strictly order
given pairs of variables. We identify a number of applications for this global
constraint including instruction scheduling and symmetry breaking. We give an
efficient propagation algorithm that enforces bounds consistency on this global
constraint. We show how to implement this propagator using a decomposition that
extends the bounds consistency enforcing decomposition proposed for the
AllDifferent constraint. Finally, we prove that enforcing domain consistency on
this global constraint is NP-hard in general.
",The AllDifferent Constraint with Precedences
"  Ontologies and rules are usually loosely coupled in knowledge representation
formalisms. In fact, ontologies use open-world reasoning while the leading
semantics for rules use non-monotonic, closed-world reasoning. One exception is
the tightly-coupled framework of Minimal Knowledge and Negation as Failure
(MKNF), which allows statements about individuals to be jointly derived via
entailment from an ontology and inferences from rules. Nonetheless, the
practical usefulness of MKNF has not always been clear, although recent work
has formalized a general resolution-based method for querying MKNF when rules
are taken to have the well-founded semantics, and the ontology is modeled by a
general oracle. That work leaves open what algorithms should be used to relate
the entailments of the ontology and the inferences of rules. In this paper we
provide such algorithms, and describe the implementation of a query-driven
system, CDF-Rules, for hybrid knowledge bases combining both (non-monotonic)
rules under the well-founded semantics and a (monotonic) ontology, represented
by a CDF Type-1 (ALQ) theory. To appear in Theory and Practice of Logic
Programming (TPLP)
","A Goal-Directed Implementation of Query Answering for Hybrid MKNF
  Knowledge Bases"
"  BoolVar/PB is an open source java library dedicated to the translation of
pseudo-Boolean constraints into CNF formulae. Input constraints can be
categorized with tags. Several encoding schemes are implemented in a way that
each input constraint can be translated using one or several encoders,
according to the related tags. The library can be easily extended by adding new
encoders and / or new output formats.
","BoolVar/PB v1.0, a java library for translating pseudo-Boolean
  constraints into CNF formulae"
"  In the present paper, we try to propose a self-similar network theory for the
basic understanding. By extending the natural languages to a kind of so called
idealy sufficient language, we can proceed a few steps to the investigation of
the language searching and the language understanding of AI.
  Image understanding, and the familiarity of the brain to the surrounding
environment are also discussed. Group effects are discussed by addressing the
essense of the power of influences, and constructing the influence network of a
society. We also give a discussion of inspirations.
",On Understanding and Machine Understanding
"  Phase transitions in many complex combinational problems have been widely
studied in the past decade. In this paper, we investigate phase transitions in
the knowledge compilation empirically, where DFA, OBDD and d-DNNF are chosen as
the target languages to compile random k-SAT instances. We perform intensive
experiments to analyze the sizes of compilation results and draw the following
conclusions: there exists an easy-hard-easy pattern in compilations; the peak
point of sizes in the pattern is only related to the ratio of the number of
clauses to that of variables when k is fixed, regardless of target languages;
most sizes of compilation results increase exponentially with the number of
variables growing, but there also exists a phase transition that separates a
polynomial-increment region from the exponential-increment region; Moreover, we
explain why the phase transition in compilations occurs by analyzing
microstructures of DFAs, and conclude that a kind of solution
interchangeability with more than 2 variables has a sharp transition near the
peak point of the easy-hard-easy pattern, and thus it has a great impact on
sizes of DFAs.
",Phase Transitions in Knowledge Compilation: an Experimental Study
"  A definition of intelligence is given in terms of performance that can be
quantitatively measured. In this study, we have presented a conceptual model of
Intelligent Agent System for Automatic Vehicle Checking Agent (VCA). To achieve
this goal, we have introduced several kinds of agents that exhibit intelligent
features. These are the Management agent, internal agent, External Agent,
Watcher agent and Report agent. Metrics and measurements are suggested for
evaluating the performance of Automatic Vehicle Checking Agent (VCA). Calibrate
data and test facilities are suggested to facilitate the development of
intelligent systems.
",Automatic Vehicle Checking Agent (VCA)
"  This paper presents the design and development of a proposed rule based
Decision Support System that will help students in selecting the best suitable
faculty/major decision while taking admission in Gomal University, Dera Ismail
Khan, Pakistan. The basic idea of our approach is to design a model for testing
and measuring the student capabilities like intelligence, understanding,
comprehension, mathematical concepts plus his/her past academic record plus
his/her intelligence level, and applying the module results to a rule-based
decision support system to determine the compatibility of those capabilities
with the available faculties/majors in Gomal University. The result is shown as
a list of suggested faculties/majors with the student capabilities and
abilities.
","A Proposed Decision Support System/Expert System for Guiding Fresh
  Students in Selecting a Faculty in Gomal University, Pakistan"
"  Heuristics are crucial tools in decreasing search effort in varied fields of
AI. In order to be effective, a heuristic must be efficient to compute, as well
as provide useful information to the search algorithm. However, some well-known
heuristics which do well in reducing backtracking are so heavy that the gain of
deploying them in a search algorithm might be outweighed by their overhead.
  We propose a rational metareasoning approach to decide when to deploy
heuristics, using CSP backtracking search as a case study. In particular, a
value of information approach is taken to adaptive deployment of solution-count
estimation heuristics for value ordering. Empirical results show that indeed
the proposed mechanism successfully balances the tradeoff between decreasing
backtracking and heuristic computational overhead, resulting in a significant
overall search time reduction.
",Rational Deployment of CSP Heuristics
"  Regularization is a well studied problem in the context of neural networks.
It is usually used to improve the generalization performance when the number of
input samples is relatively small or heavily contaminated with noise. The
regularization of a parametric model can be achieved in different manners some
of which are early stopping (Morgan and Bourlard, 1990), weight decay, output
smoothing that are used to avoid overfitting during the training of the
considered model. From a Bayesian point of view, many regularization techniques
correspond to imposing certain prior distributions on model parameters (Krogh
and Hertz, 1991). Using Bishop's approximation (Bishop, 1995) of the objective
function when a restricted type of noise is added to the input of a parametric
function, we derive the higher order terms of the Taylor expansion and analyze
the coefficients of the regularization terms induced by the noisy input. In
particular we study the effect of penalizing the Hessian of the mapping
function with respect to the input in terms of generalization performance. We
also show how we can control independently this coefficient by explicitly
penalizing the Jacobian of the mapping function on corrupted inputs.
","Adding noise to the input of a model trained with a regularized
  objective"
"  We solve constraint satisfaction problems through translation to answer set
programming (ASP). Our reformulations have the property that unit-propagation
in the ASP solver achieves well defined local consistency properties like arc,
bound and range consistency. Experiments demonstrate the computational value of
this approach.
",Translation-based Constraint Answer Set Solving
"  Recent papers address the issue of updating the instance level of knowledge
bases expressed in Description Logic following a model-based approach. One of
the outcomes of these papers is that the result of updating a knowledge base K
is generally not expressible in the Description Logic used to express K. In
this paper we introduce a formula-based approach to this problem, by revisiting
some research work on formula-based updates developed in the '80s, in
particular the WIDTIO (When In Doubt, Throw It Out) approach. We show that our
operator enjoys desirable properties, including that both insertions and
deletions according to such operator can be expressed in the DL used for the
original KB. Also, we present polynomial time algorithms for the evolution of
the instance level knowledge bases expressed in the most expressive Description
Logics of the DL-lite family.
",On the evolution of the instance level of DL-lite knowledge bases
"  We present in this paper a novel approach for training deterministic
auto-encoders. We show that by adding a well chosen penalty term to the
classical reconstruction cost function, we can achieve results that equal or
surpass those attained by other regularized auto-encoders as well as denoising
auto-encoders on a range of datasets. This penalty term corresponds to the
Frobenius norm of the Jacobian matrix of the encoder activations with respect
to the input. We show that this penalty term results in a localized space
contraction which in turn yields robust features on the activation layer.
Furthermore, we show how this penalty term is related to both regularized
auto-encoders and denoising encoders and how it can be seen as a link between
deterministic and non-deterministic auto-encoders. We find empirically that
this penalty helps to carve a representation that better captures the local
directions of variation dictated by the data, corresponding to a
lower-dimensional non-linear manifold, while being more invariant to the vast
majority of directions orthogonal to the manifold. Finally, we show that by
using the learned features to initialize a MLP, we achieve state of the art
classification error on a range of datasets, surpassing other methods of
pre-training.
",Learning invariant features through local space contraction
"  The study of arguments as abstract entities and their interaction as
introduced by Dung (Artificial Intelligence 177, 1995) has become one of the
most active research branches within Artificial Intelligence and Reasoning. A
main issue for abstract argumentation systems is the selection of acceptable
sets of arguments. Value-based argumentation, as introduced by Bench-Capon (J.
Logic Comput. 13, 2003), extends Dung's framework. It takes into account the
relative strength of arguments with respect to some ranking representing an
audience: an argument is subjectively accepted if it is accepted with respect
to some audience, it is objectively accepted if it is accepted with respect to
all audiences. Deciding whether an argument is subjectively or objectively
accepted, respectively, are computationally intractable problems. In fact, the
problems remain intractable under structural restrictions that render the main
computational problems for non-value-based argumentation systems tractable. In
this paper we identify nontrivial classes of value-based argumentation systems
for which the acceptance problems are polynomial-time tractable. The classes
are defined by means of structural restrictions in terms of the underlying
graphical structure of the value-based system. Furthermore we show that the
acceptance problems are intractable for two classes of value-based systems that
where conjectured to be tractable by Dunne (Artificial Intelligence 171, 2007).
",Algorithms and Complexity Results for Persuasive Argumentation
"  In this paper, we investigate the hybrid tractability of binary Quantified
Constraint Satisfaction Problems (QCSPs). First, a basic tractable class of
binary QCSPs is identified by using the broken-triangle property. In this
class, the variable ordering for the broken-triangle property must be same as
that in the prefix of the QCSP. Second, we break this restriction to allow that
existentially quantified variables can be shifted within or out of their
blocks, and thus identify some novel tractable classes by introducing the
broken-angle property. Finally, we identify a more generalized tractable class,
i.e., the min-of-max extendable class for QCSPs.
","Hybrid Tractable Classes of Binary Quantified Constraint Satisfaction
  Problems"
"  Most current planners assume complete domain models and focus on generating
correct plans. Unfortunately, domain modeling is a laborious and error-prone
task. While domain experts cannot guarantee completeness, often they are able
to circumscribe the incompleteness of the model by providing annotations as to
which parts of the domain model may be incomplete. In such cases, the goal
should be to generate plans that are robust with respect to any known
incompleteness of the domain. In this paper, we first introduce annotations
expressing the knowledge of the domain incompleteness, and formalize the notion
of plan robustness with respect to an incomplete domain model. We then propose
an approach to compiling the problem of finding robust plans to the conformant
probabilistic planning problem. We present experimental results with
Probabilistic-FF, a state-of-the-art planner, showing the promise of our
approach.
",Synthesizing Robust Plans under Incomplete Domain Models
"  Over the years, nonmonotonic rules have proven to be a very expressive and
useful knowledge representation paradigm. They have recently been used to
complement the expressive power of Description Logics (DLs), leading to the
study of integrative formal frameworks, generally referred to as hybrid
knowledge bases, where both DL axioms and rules can be used to represent
knowledge. The need to use these hybrid knowledge bases in dynamic domains has
called for the development of update operators, which, given the substantially
different way Description Logics and rules are usually updated, has turned out
to be an extremely difficult task.
  In [SL10], a first step towards addressing this problem was taken, and an
update operator for hybrid knowledge bases was proposed. Despite its
significance -- not only for being the first update operator for hybrid
knowledge bases in the literature, but also because it has some applications -
this operator was defined for a restricted class of problems where only the
ABox was allowed to change, which considerably diminished its applicability.
Many applications that use hybrid knowledge bases in dynamic scenarios require
both DL axioms and rules to be updated.
  In this paper, motivated by real world applications, we introduce an update
operator for a large class of hybrid knowledge bases where both the DL
component as well as the rule component are allowed to dynamically change. We
introduce splitting sequences and splitting theorem for hybrid knowledge bases,
use them to define a modular update semantics, investigate its basic
properties, and illustrate its use on a realistic example about cargo imports.
",Splitting and Updating Hybrid Knowledge Bases (Extended Version)
"  A fundamental task for propositional logic is to compute models of
propositional formulas. Programs developed for this task are called
satisfiability solvers. We show that transition systems introduced by
Nieuwenhuis, Oliveras, and Tinelli to model and analyze satisfiability solvers
can be adapted for solvers developed for two other propositional formalisms:
logic programming under the answer-set semantics, and the logic PC(ID). We show
that in each case the task of computing models can be seen as ""satisfiability
modulo answer-set programming,"" where the goal is to find a model of a theory
that also is an answer set of a certain program. The unifying perspective we
develop shows, in particular, that solvers CLASP and MINISATID are closely
related despite being developed for different formalisms, one for answer-set
programming and the latter for the logic PC(ID).
",Transition Systems for Model Generators - A Unifying Approach
"  This paper describes a graph clustering algorithm that aims to minimize the
normalized cut criterion and has a model order selection procedure. The
performance of the proposed algorithm is comparable to spectral approaches in
terms of minimizing normalized cut. However, unlike spectral approaches, the
proposed algorithm scales to graphs with millions of nodes and edges. The
algorithm consists of three components that are processed sequentially: a
greedy agglomerative hierarchical clustering procedure, model order selection,
and a local refinement.
  For a graph of n nodes and O(n) edges, the computational complexity of the
algorithm is O(n log^2 n), a major improvement over the O(n^3) complexity of
spectral methods. Experiments are performed on real and synthetic networks to
demonstrate the scalability of the proposed approach, the effectiveness of the
model order selection procedure, and the performance of the proposed algorithm
in terms of minimizing the normalized cut metric.
",GANC: Greedy Agglomerative Normalized Cut
"  Machine-part cell formation is used in cellular manufacturing in order to
process a large variety, quality, lower work in process levels, reducing
manufacturing lead-time and customer response time while retaining flexibility
for new products. This paper presents a new and novel approach for obtaining
machine cells and part families. In the cellular manufacturing the fundamental
problem is the formation of part families and machine cells. The present paper
deals with the Self Organising Map (SOM) method an unsupervised learning
algorithm in Artificial Intelligence, and has been used as a visually
decipherable clustering tool of machine-part cell formation. The objective of
the paper is to cluster the binary machine-part matrix through visually
decipherable cluster of SOM color-coding and labelling via the SOM map nodes in
such a way that the part families are processed in that machine cells. The
Umatrix, component plane, principal component projection, scatter plot and
histogram of SOM have been reported in the present work for the successful
visualization of the machine-part cell formation. Computational result with the
proposed algorithm on a set of group technology problems available in the
literature is also presented. The proposed SOM approach produced solutions with
a grouping efficacy that is at least as good as any results earlier reported in
the literature and improved the grouping efficacy for 70% of the problems and
found immensely useful to both industry practitioners and researchers.
","Machine-Part cell formation through visual decipherable clustering of
  Self Organizing Map"
"  Rubik's Cube is an easily-understood puzzle, which is originally called the
""magic cube"". It is a well-known planning problem, which has been studied for a
long time. Yet many simple properties remain unknown. This paper studies
whether modern SAT solvers are applicable to this puzzle. To our best
knowledge, we are the first to translate Rubik's Cube to a SAT problem. To
reduce the number of variables and clauses needed for the encoding, we replace
a naive approach of 6 Boolean variables to represent each color on each facelet
with a new approach of 3 or 2 Boolean variables. In order to be able to solve
quickly Rubik's Cube, we replace the direct encoding of 18 turns with the layer
encoding of 18-subtype turns based on 6-type turns. To speed up the solving
further, we encode some properties of two-phase algorithm as an additional
constraint, and restrict some move sequences by adding some constraint clauses.
Using only efficient encoding cannot solve this puzzle. For this reason, we
improve the existing SAT solvers, and develop a new SAT solver based on
PrecoSAT, though it is suited only for Rubik's Cube. The new SAT solver
replaces the lookahead solving strategy with an ALO (\emph{at-least-one})
solving strategy, and decomposes the original problem into sub-problems. Each
sub-problem is solved by PrecoSAT. The empirical results demonstrate both our
SAT translation and new solving technique are efficient. Without the efficient
SAT encoding and the new solving technique, Rubik's Cube will not be able to be
solved still by any SAT solver. Using the improved SAT solver, we can find
always a solution of length 20 in a reasonable time. Although our solver is
slower than Kociemba's algorithm using lookup tables, but does not require a
huge lookup table.
",Solving Rubik's Cube Using SAT Solvers
"  The World Wide Web no longer consists just of HTML pages. Our work sheds
light on a number of trends on the Internet that go beyond simple Web pages.
The hidden Web provides a wealth of data in semi-structured form, accessible
through Web forms and Web services. These services, as well as numerous other
applications on the Web, commonly use XML, the eXtensible Markup Language. XML
has become the lingua franca of the Internet that allows customized markups to
be defined for specific domains. On top of XML, the Semantic Web grows as a
common structured data source. In this work, we first explain each of these
developments in detail. Using real-world examples from scientific domains of
great interest today, we then demonstrate how these new developments can assist
the managing, harvesting, and organization of data on the Web. On the way, we
also illustrate the current research avenues in these domains. We believe that
this effort would help bridge multiple database tracks, thereby attracting
researchers with a view to extend database technology.
","The Hidden Web, XML and Semantic Web: A Scientific Data Management
  Perspective"
"  Developing smart house systems has been a great challenge for researchers and
engineers in this area because of the high cost of implementation and
evaluation process of these systems, while being very time consuming. Testing a
designed smart house before actually building it is considered as an obstacle
towards an efficient smart house project. This is because of the variety of
sensors, home appliances and devices available for a real smart environment. In
this paper, we present the design and implementation of a multi-purpose smart
house simulation system for designing and simulating all aspects of a smart
house environment. This simulator provides the ability to design the house plan
and different virtual sensors and appliances in a two dimensional model of the
virtual house environment. This simulator can connect to any external smart
house remote controlling system, providing evaluation capabilities to their
system much easier than before. It also supports detailed adding of new
emerging sensors and devices to help maintain its compatibility with future
simulation needs. Scenarios can also be defined for testing various possible
combinations of device states; so different criteria and variables can be
simply evaluated without the need of experimenting on a real environment.
",A Multi-Purpose Scenario-based Simulator for Smart House Environments
"  We introduce the Xapagy cognitive architecture: a software system designed to
perform narrative reasoning. The architecture has been designed from scratch to
model and mimic the activities performed by humans when witnessing, reading,
recalling, narrating and talking about stories.
",Xapagy: a cognitive architecture for narrative reasoning
"  This paper presents a general and efficient framework for probabilistic
inference and learning from arbitrary uncertain information. It exploits the
calculation properties of finite mixture models, conjugate families and
factorization. Both the joint probability density of the variables and the
likelihood function of the (objective or subjective) observation are
approximated by a special mixture model, in such a way that any desired
conditional distribution can be directly obtained without numerical
integration. We have developed an extended version of the expectation
maximization (EM) algorithm to estimate the parameters of mixture models from
uncertain training examples (indirect observations). As a consequence, any
piece of exact or uncertain information about both input and output values is
consistently handled in the inference and learning stages. This ability,
extremely useful in certain situations, is not found in most alternative
methods. The proposed framework is formally justified from standard
probabilistic principles and illustrative examples are provided in the fields
of nonparametric pattern classification, nonlinear regression and pattern
completion. Finally, experiments on a real application and comparative results
over standard databases provide empirical evidence of the utility of the method
in a wide range of applications.
","Probabilistic Inference from Arbitrary Uncertainty using Mixtures of
  Factorized Generalized Gaussians"
"  Decision-theoretic agents predict and evaluate the results of their actions
using a model, or ontology, of their environment. An agent's goal, or utility
function, may also be specified in terms of the states of, or entities within,
its ontology. If the agent may upgrade or replace its ontology, it faces a
crisis: the agent's original goal may not be well-defined with respect to its
new ontology. This crisis must be resolved before the agent can make plans
towards achieving its goals.
  We discuss in this paper which sorts of agents will undergo ontological
crises and why we may want to create such agents. We present some concrete
examples, and argue that a well-defined procedure for resolving ontological
crises is needed. We point to some possible approaches to solving this problem,
and evaluate these methods on our examples.
",Ontological Crises in Artificial Agents' Value Systems
"  A knowledge system S describing a part of real world does in general not
contain complete information. Reasoning with incomplete information is prone to
errors since any belief derived from S may be false in the present state of the
world. A false belief may suggest wrong decisions and lead to harmful actions.
So an important goal is to make false beliefs as unlikely as possible. This
work introduces the notions of ""typical atoms"" and ""typical models"", and shows
that reasoning with typical models minimizes the expected number of false
beliefs over all ways of using incomplete information. Various properties of
typical models are studied, in particular, correctness and stability of beliefs
suggested by typical models, and their connection to oblivious reasoning.
",Typical models: minimizing false beliefs
"  We present a new approach to path planning, called the ""Ariadne's clew
algorithm"". It is designed to find paths in high-dimensional continuous spaces
and applies to robots with many degrees of freedom in static, as well as
dynamic environments - ones where obstacles may move. The Ariadne's clew
algorithm comprises two sub-algorithms, called Search and Explore, applied in
an interleaved manner. Explore builds a representation of the accessible space
while Search looks for the target. Both are posed as optimization problems. We
describe a real implementation of the algorithm to plan paths for a six degrees
of freedom arm in a dynamic environment where another six degrees of freedom
arm is used as a moving obstacle. Experimental results show that a path is
found in about one second without any pre-processing.
",The Ariadne's Clew Algorithm
"  This article studies the problem of modifying the action ordering of a plan
in order to optimise the plan according to various criteria. One of these
criteria is to make a plan less constrained and the other is to minimize its
parallel execution time. Three candidate definitions are proposed for the first
of these criteria, constituting a sequence of increasing optimality guarantees.
Two of these are based on deordering plans, which means that ordering relations
may only be removed, not added, while the third one uses reordering, where
arbitrary modifications to the ordering are allowed. It is shown that only the
weakest one of the three criteria is tractable to achieve, the other two being
NP-hard and even difficult to approximate. Similarly, optimising the parallel
execution time of a plan is studied both for deordering and reordering of
plans. In the general case, both of these computations are NP-hard. However, it
is shown that optimal deorderings can be computed in polynomial time for a
class of planning languages based on the notions of producers, consumers and
threats, which includes most of the commonly used planning languages. Computing
optimal reorderings can potentially lead to even faster parallel executions,
but this problem remains NP-hard and difficult to approximate even under quite
severe restrictions.
",Computational Aspects of Reordering Plans
"  It is common to view programs as a combination of logic and control: the
logic part defines what the program must do, the control part -- how to do it.
The Logic Programming paradigm was developed with the intention of separating
the logic from the control. Recently, extensive research has been conducted on
automatic generation of control for logic programs. Only a few of these works
considered the issue of automatic generation of control for improving the
efficiency of logic programs. In this paper we present a novel algorithm for
automatic finding of lowest-cost subgoal orderings. The algorithm works using
the divide-and-conquer strategy. The given set of subgoals is partitioned into
smaller sets, based on co-occurrence of free variables. The subsets are ordered
recursively and merged, yielding a provably optimal order. We experimentally
demonstrate the utility of the algorithm by testing it in several domains, and
discuss the possibilities of its cooperation with other existing methods.
","The Divide-and-Conquer Subgoal-Ordering Algorithm for Speeding up Logic
  Inference"
"  Using an improved backtrack algorithm with sophisticated pruning techniques,
we revise previous observations correlating a high frequency of hard to solve
Hamiltonian Cycle instances with the Gn,m phase transition between
Hamiltonicity and non-Hamiltonicity. Instead all tested graphs of 100 to 1500
vertices are easily solved. When we artificially restrict the degree sequence
with a bounded maximum degree, although there is some increase in difficulty,
the frequency of hard graphs is still low. When we consider more regular graphs
based on a generalization of knight's tours, we observe frequent instances of
really hard graphs, but on these the average degree is bounded by a constant.
We design a set of graphs with a feature our algorithm is unable to detect and
so are very hard for our algorithm, but in these we can vary the average degree
from O(1) to O(n). We have so far found no class of graphs correlated with the
Gn,m phase transition which asymptotically produces a high frequency of hard
instances.
","The Gn,m Phase Transition is Not Hard for the Hamiltonian Cycle Problem"
"  This article presents a measure of semantic similarity in an IS-A taxonomy
based on the notion of shared information content. Experimental evaluation
against a benchmark set of human similarity judgments demonstrates that the
measure performs better than the traditional edge-counting approach. The
article presents algorithms that take advantage of taxonomic similarity in
resolving syntactic and semantic ambiguity, along with experimental results
demonstrating their effectiveness.
","Semantic Similarity in a Taxonomy: An Information-Based Measure and its
  Application to Problems of Ambiguity in Natural Language"
"  A class of interval-based temporal languages for uniformly representing and
reasoning about actions and plans is presented. Actions are represented by
describing what is true while the action itself is occurring, and plans are
constructed by temporally relating actions and world states. The temporal
languages are members of the family of Description Logics, which are
characterized by high expressivity combined with good computational properties.
The subsumption problem for a class of temporal Description Logics is
investigated and sound and complete decision procedures are given. The basic
language TL-F is considered first: it is the composition of a temporal logic TL
-- able to express interval temporal networks -- together with the non-temporal
logic F -- a Feature Description Logic. It is proven that subsumption in this
language is an NP-complete problem. Then it is shown how to reason with the
more expressive languages TLU-FU and TL-ALCF. The former adds disjunction both
at the temporal and non-temporal sides of the language, the latter extends the
non-temporal side with set-valued features (i.e., roles) and a propositionally
complete language.
",A Temporal Description Logic for Reasoning about Actions and Plans
"  Many of the artificial intelligence techniques developed to date rely on
heuristic search through large spaces. Unfortunately, the size of these spaces
and the corresponding computational effort reduce the applicability of
otherwise novel and effective algorithms. A number of parallel and distributed
approaches to search have considerably improved the performance of the search
process. Our goal is to develop an architecture that automatically selects
parallel search strategies for optimal performance on a variety of search
problems. In this paper we describe one such architecture realized in the
Eureka system, which combines the benefits of many different approaches to
parallel heuristic search. Through empirical and theoretical analyses we
observe that features of the problem space directly affect the choice of
optimal parallel search strategy. We then employ machine learning techniques to
select the optimal parallel search strategy for a given problem space. When a
new search task is input to the system, Eureka uses features describing the
search space and the chosen architecture to automatically select the
appropriate search strategy. Eureka has been tested on a MIMD parallel
processor, a distributed network of workstations, and a single workstation
using multithreading. Results generated from fifteen puzzle problems, robot arm
motion problems, artificial search spaces, and planning problems indicate that
Eureka outperforms any of the tested strategies used exclusively for all
problem instances and is able to greatly reduce the search time for these
applications.
",Adaptive Parallel Iterative Deepening Search
"  Order of magnitude reasoning - reasoning by rough comparisons of the sizes of
quantities - is often called 'back of the envelope calculation', with the
implication that the calculations are quick though approximate. This paper
exhibits an interesting class of constraint sets in which order of magnitude
reasoning is demonstrably fast. Specifically, we present a polynomial-time
algorithm that can solve a set of constraints of the form 'Points a and b are
much closer together than points c and d.' We prove that this algorithm can be
applied if `much closer together' is interpreted either as referring to an
infinite difference in scale or as referring to a finite difference in scale,
as long as the difference in scale is greater than the number of variables in
the constraint set. We also prove that the first-order theory over such
constraints is decidable.
",Order of Magnitude Comparisons of Distance
"  This paper introduces AntNet, a novel approach to the adaptive learning of
routing tables in communications networks. AntNet is a distributed, mobile
agents based Monte Carlo system that was inspired by recent work on the ant
colony metaphor for solving optimization problems. AntNet's agents concurrently
explore the network and exchange collected information. The communication among
the agents is indirect and asynchronous, mediated by the network itself. This
form of communication is typical of social insects and is called stigmergy. We
compare our algorithm with six state-of-the-art routing algorithms coming from
the telecommunications and machine learning fields. The algorithms' performance
is evaluated over a set of realistic testbeds. We run many experiments over
real and artificial IP datagram networks with increasing number of nodes and
under several paradigmatic spatial and temporal traffic distributions. Results
are very encouraging. AntNet showed superior performance under all the
experimental conditions with respect to its competitors. We analyze the main
characteristics of the algorithm and try to explain the reasons for its
superiority.
",AntNet: Distributed Stigmergetic Control for Communications Networks
"  Cox's well-known theorem justifying the use of probability is shown not to
hold in finite domains. The counterexample also suggests that Cox's assumptions
are insufficient to prove the result even in infinite domains. The same
counterexample is used to disprove a result of Fine on comparative conditional
probability.
",A Counter Example to Theorems of Cox and Fine
"  As planning is applied to larger and richer domains the effort involved in
constructing domain descriptions increases and becomes a significant burden on
the human application designer. If general planners are to be applied
successfully to large and complex domains it is necessary to provide the domain
designer with some assistance in building correctly encoded domains. One way of
doing this is to provide domain-independent techniques for extracting, from a
domain description, knowledge that is implicit in that description and that can
assist domain designers in debugging domain descriptions. This knowledge can
also be exploited to improve the performance of planners: several researchers
have explored the potential of state invariants in speeding up the performance
of domain-independent planners. In this paper we describe a process by which
state invariants can be extracted from the automatically inferred type
structure of a domain. These techniques are being developed for exploitation by
STAN, a Graphplan based planner that employs state analysis techniques to
enhance its performance.
",The Automatic Inference of State Invariants in TIM
"  The notion of class is ubiquitous in computer science and is central in many
formalisms for the representation of structured knowledge used both in
knowledge representation and in databases. In this paper we study the basic
issues underlying such representation formalisms and single out both their
common characteristics and their distinguishing features. Such investigation
leads us to propose a unifying framework in which we are able to capture the
fundamental aspects of several representation languages used in different
contexts. The proposed formalism is expressed in the style of description
logics, which have been introduced in knowledge representation as a means to
provide a semantically well-founded basis for the structural aspects of
knowledge representation systems. The description logic considered in this
paper is a subset of first order logic with nice computational characteristics.
It is quite expressive and features a novel combination of constructs that has
not been studied before. The distinguishing constructs are number restrictions,
which generalize existence and functional dependencies, inverse roles, which
allow one to refer to the inverse of a relationship, and possibly cyclic
assertions, which are necessary for capturing real world domains. We are able
to show that it is precisely such combination of constructs that makes our
logic powerful enough to model the essential set of features for defining class
structures that are common to frame systems, object-oriented database
languages, and semantic data models. As a consequence of the established
correspondences, several significant extensions of each of the above formalisms
become available. The high expressiveness of the logic we propose and the need
for capturing the reasoning in different contexts forces us to distinguish
between unrestricted and finite model reasoning. A notable feature of our
proposal is that reasoning in both cases is decidable. We argue that, by virtue
of the high expressive power and of the associated reasoning capabilities on
both unrestricted and finite models, our logic provides a common core for
class-based representation formalisms.
",Unifying Class-Based Representation Formalisms
"  In default reasoning, usually not all possible ways of resolving conflicts
between default rules are acceptable. Criteria expressing acceptable ways of
resolving the conflicts may be hardwired in the inference mechanism, for
example specificity in inheritance reasoning can be handled this way, or they
may be given abstractly as an ordering on the default rules. In this article we
investigate formalizations of the latter approach in Reiter's default logic.
Our goal is to analyze and compare the computational properties of three such
formalizations in terms of their computational complexity: the prioritized
default logics of Baader and Hollunder, and Brewka, and a prioritized default
logic that is based on lexicographic comparison. The analysis locates the
propositional variants of these logics on the second and third levels of the
polynomial hierarchy, and identifies the boundary between tractable and
intractable inference for restricted classes of prioritized default theories.
",Complexity of Prioritized Default Logics
"  We describe a general approach to optimization which we term `Squeaky Wheel'
Optimization (SWO). In SWO, a greedy algorithm is used to construct a solution
which is then analyzed to find the trouble spots, i.e., those elements, that,
if improved, are likely to improve the objective function score. The results of
the analysis are used to generate new priorities that determine the order in
which the greedy algorithm constructs the next solution. This
Construct/Analyze/Prioritize cycle continues until some limit is reached, or an
acceptable solution is found. SWO can be viewed as operating on two search
spaces: solutions and prioritizations. Successive solutions are only indirectly
related, via the re-prioritization that results from analyzing the prior
solution. Similarly, successive prioritizations are generated by constructing
and analyzing solutions. This `coupled search' has some interesting properties,
which we discuss. We report encouraging experimental results on two domains,
scheduling problems that arise in fiber-optic cable manufacturing, and graph
coloring problems. The fact that these domains are very different supports our
claim that SWO is a general technique for optimization.
",Squeaky Wheel Optimization
"  Intractable distributions present a common difficulty in inference within the
probabilistic knowledge representation framework and variational methods have
recently been popular in providing an approximate solution. In this article, we
describe a perturbational approach in the form of a cumulant expansion which,
to lowest order, recovers the standard Kullback-Leibler variational bound.
Higher-order terms describe corrections on the variational approach without
incurring much further computational cost. The relationship to other
perturbational approaches such as TAP is also elucidated. We demonstrate the
method on a particular class of undirected graphical models, Boltzmann
machines, for which our simulation results confirm improved accuracy and
enhanced stability during learning.
",Variational Cumulant Expansions for Intractable Distributions
"  STAN is a Graphplan-based planner, so-called because it uses a variety of
STate ANalysis techniques to enhance its performance. STAN competed in the
AIPS-98 planning competition where it compared well with the other competitors
in terms of speed, finding solutions fastest to many of the problems posed.
Although the domain analysis techniques STAN exploits are an important factor
in its overall performance, we believe that the speed at which STAN solved the
competition problems is largely due to the implementation of its plan graph.
The implementation is based on two insights: that many of the graph
construction operations can be implemented as bit-level logical operations on
bit vectors, and that the graph should not be explicitly constructed beyond the
fix point. This paper describes the implementation of STAN's plan graph and
provides experimental results which demonstrate the circumstances under which
advantages can be obtained from using this implementation.
",Efficient Implementation of the Plan Graph in STAN
"  Top-down and bottom-up theorem proving approaches each have specific
advantages and disadvantages. Bottom-up provers profit from strong redundancy
control but suffer from the lack of goal-orientation, whereas top-down provers
are goal-oriented but often have weak calculi when their proof lengths are
considered. In order to integrate both approaches, we try to achieve
cooperation between a top-down and a bottom-up prover in two different ways:
The first technique aims at supporting a bottom-up with a top-down prover. A
top-down prover generates subgoal clauses, they are then processed by a
bottom-up prover. The second technique deals with the use of bottom-up
generated lemmas in a top-down prover. We apply our concept to the areas of
model elimination and superposition. We discuss the ability of our techniques
to shorten proofs as well as to reorder the search space in an appropriate
manner. Furthermore, in order to identify subgoal clauses and lemmas which are
actually relevant for the proof task, we develop methods for a relevancy-based
filtering. Experiments with the provers SETHEO and SPASS performed in the
problem library TPTP reveal the high potential of our cooperation approaches.
",Cooperation between Top-Down and Bottom-Up Theorem Provers
"  A previously developed quantum search algorithm for solving 1-SAT problems in
a single step is generalized to apply to a range of highly constrained k-SAT
problems. We identify a bound on the number of clauses in satisfiability
problems for which the generalized algorithm can find a solution in a constant
number of steps as the number of variables increases. This performance
contrasts with the linear growth in the number of steps required by the best
classical algorithms, and the exponential number required by classical and
quantum methods that ignore the problem structure. In some cases, the algorithm
can also guarantee that insoluble problems in fact have no solutions, unlike
previously proposed quantum search algorithms.
",Solving Highly Constrained Search Problems with Quantum Computers
"  Planning under uncertainty is a central problem in the study of automated
sequential decision making, and has been addressed by researchers in many
different fields, including AI planning, decision analysis, operations
research, control theory and economics. While the assumptions and perspectives
adopted in these areas often differ in substantial ways, many planning problems
of interest to researchers in these fields can be modeled as Markov decision
processes (MDPs) and analyzed using the techniques of decision theory. This
paper presents an overview and synthesis of MDP-related methods, showing how
they provide a unifying framework for modeling many classes of planning
problems studied in AI. It also describes structural properties of MDPs that,
when exhibited by particular classes of problems, can be exploited in the
construction of optimal or approximately optimal policies or plans. Planning
problems commonly possess structure in the reward and value functions used to
describe performance criteria, in the functions used to describe state
transitions and observations, and in the relationships among features used to
describe states, actions, rewards, and observations. Specialized
representations, and algorithms employing these representations, can achieve
computational leverage by exploiting these various forms of structure. Certain
AI techniques -- in particular those based on the use of structured,
intensional representations -- can be viewed in this way. This paper surveys
several types of representations for both classical and decision-theoretic
planning problems, and planning algorithms that exploit these representations
in a number of different ways to ease the computational burden of constructing
policies or plans. It focuses primarily on abstraction, aggregation and
decomposition techniques based on AI-style representations.
","Decision-Theoretic Planning: Structural Assumptions and Computational
  Leverage"
"  We study the problem of probabilistic deduction with conditional constraints
over basic events. We show that globally complete probabilistic deduction with
conditional constraints over basic events is NP-hard. We then concentrate on
the special case of probabilistic deduction in conditional constraint trees. We
elaborate very efficient techniques for globally complete probabilistic
deduction. In detail, for conditional constraint trees with point
probabilities, we present a local approach to globally complete probabilistic
deduction, which runs in linear time in the size of the conditional constraint
trees. For conditional constraint trees with interval probabilities, we show
that globally complete probabilistic deduction can be done in a global approach
by solving nonlinear programs. We show how these nonlinear programs can be
transformed into equivalent linear programs, which are solvable in polynomial
time in the size of the conditional constraint trees.
",Probabilistic Deduction with Conditional Constraints over Basic Events
"  We describe a variational approximation method for efficient inference in
large-scale probabilistic models. Variational methods are deterministic
procedures that provide approximations to marginal and conditional
probabilities of interest. They provide alternatives to approximate inference
methods based on stochastic sampling or search. We describe a variational
approach to the problem of diagnostic inference in the `Quick Medical
Reference' (QMR) network. The QMR network is a large-scale probabilistic
graphical model built on statistical and expert knowledge. Exact probabilistic
inference is infeasible in this model for all but a small set of cases. We
evaluate our variational inference algorithm on a large set of diagnostic test
cases, comparing the algorithm to a state-of-the-art stochastic sampling
method.
",Variational Probabilistic Inference and the QMR-DT Network
"  This paper offers an approach to extensible knowledge representation and
reasoning for a family of formalisms known as Description Logics. The approach
is based on the notion of adding new concept constructors, and includes a
heuristic methodology for specifying the desired extensions, as well as a
modularized software architecture that supports implementing extensions. The
architecture detailed here falls in the normalize-compared paradigm, and
supports both intentional reasoning (subsumption) involving concepts, and
extensional reasoning involving individuals after incremental updates to the
knowledge base. The resulting approach can be used to extend the reasoner with
specialized notions that are motivated by specific problems or application
areas, such as reasoning about dates, plans, etc. In addition, it provides an
opportunity to implement constructors that are not currently yet sufficiently
well understood theoretically, but are needed in practice. Also, for
constructors that are provably hard to reason with (e.g., ones whose presence
would lead to undecidability), it allows the implementation of incomplete
reasoners where the incompleteness is tailored to be acceptable for the
application at hand.
",Extensible Knowledge Representation: the Case of Description Reasoners
"  The research on conditional planning rejects the assumptions that there is no
uncertainty or incompleteness of knowledge with respect to the state and
changes of the system the plans operate on. Without these assumptions the
sequences of operations that achieve the goals depend on the initial state and
the outcomes of nondeterministic changes in the system. This setting raises the
questions of how to represent the plans and how to perform plan search. The
answers are quite different from those in the simpler classical framework. In
this paper, we approach conditional planning from a new viewpoint that is
motivated by the use of satisfiability algorithms in classical planning.
Translating conditional planning to formulae in the propositional logic is not
feasible because of inherent computational limitations. Instead, we translate
conditional planning to quantified Boolean formulae. We discuss three
formalizations of conditional planning as quantified Boolean formulae, and
present experimental results obtained with a theorem-prover.
",Constructing Conditional Plans by a Theorem-Prover
"  Stacked generalization is a general method of using a high-level model to
combine lower-level models to achieve greater predictive accuracy. In this
paper we address two crucial issues which have been considered to be a `black
art' in classification tasks ever since the introduction of stacked
generalization in 1992 by Wolpert: the type of generalizer that is suitable to
derive the higher-level model, and the kind of attributes that should be used
as its input. We find that best results are obtained when the higher-level
model combines the confidence (and not just the predictions) of the lower-level
ones. We demonstrate the effectiveness of stacked generalization for combining
three different types of learning algorithms for classification tasks. We also
compare the performance of stacked generalization with majority vote and
published results of arcing and bagging.
",Issues in Stacked Generalization
"  We present PARIS, an approach for the automatic alignment of ontologies.
PARIS aligns not only instances, but also relations and classes. Alignments at
the instance-level cross-fertilize with alignments at the schema-level.
Thereby, our system provides a truly holistic solution to the problem of
ontology alignment. The heart of the approach is probabilistic. This allows
PARIS to run without any parameter tuning. We demonstrate the efficiency of the
algorithm and its precision through extensive experiments. In particular, we
obtain a precision of around 90% in experiments with two of the world's largest
ontologies.
",Ontology Alignment at the Instance and Schema Level
"  We prove that it is NP-hard for a coalition of two manipulators to compute
how to manipulate the Borda voting rule. This resolves one of the last open
problems in the computational complexity of manipulating common voting rules.
Because of this NP-hardness, we treat computing a manipulation as an
approximation problem where we try to minimize the number of manipulators.
Based on ideas from bin packing and multiprocessor scheduling, we propose two
new approximation methods to compute manipulations of the Borda rule.
Experiments show that these methods significantly outperform the previous best
known %existing approximation method. We are able to find optimal manipulations
in almost all the randomly generated elections tested. Our results suggest
that, whilst computing a manipulation of the Borda rule by a coalition is
NP-hard, computational complexity may provide only a weak barrier against
manipulation in practice.
",Complexity of and Algorithms for Borda Manipulation
"  We introduce a temporal model for reasoning on disjunctive metric constraints
on intervals and time points in temporal contexts. This temporal model is
composed of a labeled temporal algebra and its reasoning algorithms. The
labeled temporal algebra defines labeled disjunctive metric point-based
constraints, where each disjunct in each input disjunctive constraint is
univocally associated to a label. Reasoning algorithms manage labeled
constraints, associated label lists, and sets of mutually inconsistent
disjuncts. These algorithms guarantee consistency and obtain a minimal network.
Additionally, constraints can be organized in a hierarchy of alternative
temporal contexts. Therefore, we can reason on context-dependent disjunctive
metric constraints on intervals and points. Moreover, the model is able to
represent non-binary constraints, such that logical dependencies on disjuncts
in constraints can be handled. The computational cost of reasoning algorithms
is exponential in accordance with the underlying problem complexity, although
some improvements are proposed.
","Reasoning on Interval and Point-based Disjunctive Metric Constraints in
  Temporal Contexts"
"  Negation as failure and incomplete information in logic programs have been
studied by many researchers In order to explains HOW a negated conclusion was
reached, we introduce and proof a different way for negating facts to
overcoming misleads in logic programs. Negating facts can be achieved by asking
the user for constants that do not appear elsewhere in the knowledge base.
",Overcoming Misleads In Logic Programs by Redefining Negation
"  Despite the prevalence of the Computational Theory of Mind and the
Connectionist Model, the establishing of the key principles of the Cognitive
Science are still controversy and inconclusive. This paper proposes the concept
of Pattern Recognition as Necessary and Sufficient Principle for a general
cognitive science modeling, in a very ambitious scientific proposal. A formal
physical definition of the pattern recognition concept is also proposed to
solve many key conceptual gaps on the field.
","Proposal of Pattern Recognition as a necessary and sufficient Principle
  to Cognitive Science"
"  As was shown recently, many important AI problems require counting the number
of models of propositional formulas. The problem of counting models of such
formulas is, according to present knowledge, computationally intractable in a
worst case. Based on the Davis-Putnam procedure, we present an algorithm, CDP,
that computes the exact number of models of a propositional CNF or DNF formula
F. Let m and n be the number of clauses and variables of F, respectively, and
let p denote the probability that a literal l of F occurs in a clause C of F,
then the average running time of CDP is shown to be O(nm^d), where
d=-1/log(1-p). The practical performance of CDP has been estimated in a series
of experiments on a wide variety of CNF formulas.
",The Good Old Davis-Putnam Procedure Helps Counting Models
"  This paper presents a new approach to identifying and eliminating mislabeled
training instances for supervised learning. The goal of this approach is to
improve classification accuracies produced by learning algorithms by improving
the quality of the training data. Our approach uses a set of learning
algorithms to create classifiers that serve as noise filters for the training
data. We evaluate single algorithm, majority vote and consensus filters on five
datasets that are prone to labeling errors. Our experiments illustrate that
filtering significantly improves classification accuracy for noise levels up to
30 percent. An analytical and empirical evaluation of the precision of our
approach shows that consensus filters are conservative at throwing away good
data at the expense of retaining bad data and that majority filters are better
at detecting bad data at the expense of throwing away good data. This suggests
that for situations in which there is a paucity of data, consensus filters are
preferable, whereas majority vote filters are preferable for situations with an
abundance of data.
",Identifying Mislabeled Training Data
"  In many real-world learning tasks, it is expensive to acquire a sufficient
number of labeled examples for training. This paper investigates methods for
reducing annotation cost by `sample selection'. In this approach, during
training the learning program examines many unlabeled examples and selects for
labeling only those that are most informative at each stage. This avoids
redundantly labeling examples that contribute little new information. Our work
follows on previous research on Query By Committee, extending the
committee-based paradigm to the context of probabilistic classification. We
describe a family of empirical methods for committee-based sample selection in
probabilistic classification models, which evaluate the informativeness of an
example by measuring the degree of disagreement between several model variants.
These variants (the committee) are drawn randomly from a probability
distribution conditioned by the training set labeled so far. The method was
applied to the real-world natural language processing task of stochastic
part-of-speech tagging. We find that all variants of the method achieve a
significant reduction in annotation cost, although their computational
efficiency differs. In particular, the simplest variant, a two member committee
with no parameters to tune, gives excellent results. We also show that sample
selection yields a significant reduction in the size of the model used by the
tagger.
",Committee-Based Sample Selection for Probabilistic Classifiers
"  We investigate the problem of reasoning in the propositional fragment of
MBNF, the logic of minimal belief and negation as failure introduced by
Lifschitz, which can be considered as a unifying framework for several
nonmonotonic formalisms, including default logic, autoepistemic logic,
circumscription, epistemic queries, and logic programming. We characterize the
complexity and provide algorithms for reasoning in propositional MBNF. In
particular, we show that entailment in propositional MBNF lies at the third
level of the polynomial hierarchy, hence it is harder than reasoning in all the
above mentioned propositional formalisms for nonmonotonic reasoning. We also
prove the exact correspondence between negation as failure in MBNF and negative
introspection in Moore's autoepistemic logic.
",Reasoning about Minimal Belief and Negation as Failure
"  We show how to find a minimum weight loop cutset in a Bayesian network with
high probability. Finding such a loop cutset is the first step in the method of
conditioning for inference. Our randomized algorithm for finding a loop cutset
outputs a minimum loop cutset after O(c 6^k kn) steps with probability at least
1 - (1 - 1/(6^k))^c6^k, where c > 1 is a constant specified by the user, k is
the minimal size of a minimum weight loop cutset, and n is the number of
vertices. We also show empirically that a variant of this algorithm often finds
a loop cutset that is closer to the minimum weight loop cutset than the ones
found by the best deterministic algorithms known.
",Randomized Algorithms for the Loop Cutset Problem
"  Recently model checking representation and search techniques were shown to be
efficiently applicable to planning, in particular to non-deterministic
planning. Such planning approaches use Ordered Binary Decision Diagrams (OBDDs)
to encode a planning domain as a non-deterministic finite automaton and then
apply fast algorithms from model checking to search for a solution. OBDDs can
effectively scale and can provide universal plans for complex planning domains.
We are particularly interested in addressing the complexities arising in
non-deterministic, multi-agent domains. In this article, we present UMOP, a new
universal OBDD-based planning framework for non-deterministic, multi-agent
domains. We introduce a new planning domain description language, NADL, to
specify non-deterministic, multi-agent domains. The language contributes the
explicit definition of controllable agents and uncontrollable environment
agents. We describe the syntax and semantics of NADL and show how to build an
efficient OBDD-based representation of an NADL description. The UMOP planning
system uses NADL and different OBDD-based universal planning algorithms. It
includes the previously developed strong and strong cyclic planning algorithms.
In addition, we introduce our new optimistic planning algorithm that relaxes
optimality guarantees and generates plausible universal plans in some domains
where no strong nor strong cyclic solution exists. We present empirical results
applying UMOP to domains ranging from deterministic and single-agent with no
environment actions to non-deterministic and multi-agent with complex
environment actions. UMOP is shown to be a rich and efficient planning system.
","OBDD-based Universal Planning for Synchronized Agents in
  Non-Deterministic Domains"
"  This paper reviews the connections between Graphplan's planning-graph and the
dynamic constraint satisfaction problem and motivates the need for adapting CSP
search techniques to the Graphplan algorithm. It then describes how explanation
based learning, dependency directed backtracking, dynamic variable ordering,
forward checking, sticky values and random-restart search strategies can be
adapted to Graphplan. Empirical results are provided to demonstrate that these
augmentations improve Graphplan's performance significantly (up to 1000x
speedups) on several benchmark problems. Special attention is paid to the
explanation-based learning and dependency directed backtracking techniques as
they are empirically found to be most useful in improving the performance of
Graphplan.
","Planning Graph as a (Dynamic) CSP: Exploiting EBL, DDB and other CSP
  Search Techniques in Graphplan"
"  We investigate the space efficiency of a Propositional Knowledge
Representation (PKR) formalism. Intuitively, the space efficiency of a
formalism F in representing a certain piece of knowledge A, is the size of the
shortest formula of F that represents A. In this paper we assume that knowledge
is either a set of propositional interpretations (models) or a set of
propositional formulae (theorems). We provide a formal way of talking about the
relative ability of PKR formalisms to compactly represent a set of models or a
set of theorems. We introduce two new compactness measures, the corresponding
classes, and show that the relative space efficiency of a PKR formalism in
representing models/theorems is directly related to such classes. In
particular, we consider formalisms for nonmonotonic reasoning, such as
circumscription and default logic, as well as belief revision operators and the
stable model semantics for logic programs with negation. One interesting result
is that formalisms with the same time complexity do not necessarily belong to
the same space efficiency class.
",Space Efficiency of Propositional Knowledge Representation Formalisms
"  Partially observable Markov decision processes (POMDPs) provide an elegant
mathematical framework for modeling complex decision and planning problems in
stochastic domains in which states of the system are observable only
indirectly, via a set of imperfect or noisy observations. The modeling
advantage of POMDPs, however, comes at a price -- exact methods for solving
them are computationally very expensive and thus applicable in practice only to
very simple problems. We focus on efficient approximation (heuristic) methods
that attempt to alleviate the computational problem and trade off accuracy for
speed. We have two objectives here. First, we survey various approximation
methods, analyze their properties and relations and provide some new insights
into their differences. Second, we present a number of new approximation
methods and novel refinements of existing techniques. The theoretical results
are supported by experiments on a problem from the agent navigation domain.
","Value-Function Approximations for Partially Observable Markov Decision
  Processes"
"  Pearl and Dechter (1996) claimed that the d-separation criterion for
conditional independence in acyclic causal networks also applies to networks of
discrete variables that have feedback cycles, provided that the variables of
the system are uniquely determined by the random disturbances. I show by
example that this is not true in general. Some condition stronger than
uniqueness is needed, such as the existence of a causal dynamics guaranteed to
lead to the unique solution.
","On Deducing Conditional Independence from d-Separation in Causal Graphs
  with Feedback (Research Note)"
"  Functional relationships between objects, called `attributes', are of
considerable importance in knowledge representation languages, including
Description Logics (DLs). A study of the literature indicates that papers have
made, often implicitly, different assumptions about the nature of attributes:
whether they are always required to have a value, or whether they can be
partial functions. The work presented here is the first explicit study of this
difference for subclasses of the CLASSIC DL, involving the same-as concept
constructor. It is shown that although determining subsumption between concept
descriptions has the same complexity (though requiring different algorithms),
the story is different in the case of determining the least common subsumer
(lcs). For attributes interpreted as partial functions, the lcs exists and can
be computed relatively easily; even in this case our results correct and extend
three previous papers about the lcs of DLs. In the case where attributes must
have a value, the lcs may not exist, and even if it exists it may be of
exponential size. Interestingly, it is possible to decide in polynomial time if
the lcs exists.
",What's in an Attribute? Consequences for the Least Common Subsumer
"  We study the complexity of the combination of the Description Logics ALCQ and
ALCQI with a terminological formalism based on cardinality restrictions on
concepts. These combinations can naturally be embedded into C^2, the two
variable fragment of predicate logic with counting quantifiers, which yields
decidability in NExpTime. We show that this approach leads to an optimal
solution for ALCQI, as ALCQI with cardinality restrictions has the same
complexity as C^2 (NExpTime-complete). In contrast, we show that for ALCQ, the
problem can be solved in ExpTime. This result is obtained by a reduction of
reasoning with cardinality restrictions to reasoning with the (in general
weaker) terminological formalism of general axioms for ALCQ extended with
nominals. Using the same reduction, we show that, for the extension of ALCQI
with nominals, reasoning with general axioms is a NExpTime-complete problem.
Finally, we sharpen this result and show that pure concept satisfiability for
ALCQI with nominals is NExpTime-complete. Without nominals, this problem is
known to be PSpace-complete.
","The Complexity of Reasoning with Cardinality Restrictions and Nominals
  in Expressive Description Logics"
"  The local search algorithm WSat is one of the most successful algorithms for
solving the satisfiability (SAT) problem. It is notably effective at solving
hard Random 3-SAT instances near the so-called `satisfiability threshold', but
still shows a peak in search cost near the threshold and large variations in
cost over different instances. We make a number of significant contributions to
the analysis of WSat on high-cost random instances, using the
recently-introduced concept of the backbone of a SAT instance. The backbone is
the set of literals which are entailed by an instance. We find that the number
of solutions predicts the cost well for small-backbone instances but is much
less relevant for the large-backbone instances which appear near the threshold
and dominate in the overconstrained region. We show a very strong correlation
between search cost and the Hamming distance to the nearest solution early in
WSat's search. This pattern leads us to introduce a measure of the backbone
fragility of an instance, which indicates how persistent the backbone is as
clauses are removed. We propose that high-cost random instances for local
search are those with very large backbones which are also backbone-fragile. We
suggest that the decay in cost beyond the satisfiability threshold is due to
increasing backbone robustness (the opposite of backbone fragility). Our
hypothesis makes three correct predictions. First, that the backbone robustness
of an instance is negatively correlated with the local search cost when other
factors are controlled for. Second, that backbone-minimal instances (which are
3-SAT instances altered so as to be more backbone-fragile) are unusually hard
for WSat. Third, that the clauses most often unsatisfied during search are
those whose deletion has the most effect on the backbone. In understanding the
pathologies of local search methods, we hope to contribute to the development
of new and better techniques.
",Backbone Fragility and the Local Search Cost Peak
"  This paper describes a novel method by which a spoken dialogue system can
learn to choose an optimal dialogue strategy from its experience interacting
with human users. The method is based on a combination of reinforcement
learning and performance modeling of spoken dialogue systems. The reinforcement
learning component applies Q-learning (Watkins, 1989), while the performance
modeling component applies the PARADISE evaluation framework (Walker et al.,
1997) to learn the performance function (reward) used in reinforcement
learning. We illustrate the method with a spoken dialogue system named ELVIS
(EmaiL Voice Interactive System), that supports access to email over the phone.
We conduct a set of experiments for training an optimal dialogue strategy on a
corpus of 219 dialogues in which human users interact with ELVIS over the
phone. We then test that strategy on a corpus of 18 dialogues. We show that
ELVIS can learn to optimize its strategy selection for agent initiative, for
reading messages, and for summarizing email folders.
","An Application of Reinforcement Learning to Dialogue Strategy Selection
  in a Spoken Dialogue System for Email"
"  We show that for several variations of partially observable Markov decision
processes, polynomial-time algorithms for finding control policies are unlikely
to or simply don't have guarantees of finding policies within a constant factor
or a constant summand of optimal. Here ""unlikely"" means ""unless some complexity
classes collapse,"" where the collapses considered are P=NP, P=PSPACE, or P=EXP.
Until or unless these collapses are shown to hold, any control-policy designer
must choose between such performance guarantees and efficient computation.
","Nonapproximability Results for Partially Observable Markov Decision
  Processes"
"  The paper addresses the problem of computing goal orderings, which is one of
the longstanding issues in AI planning. It makes two new contributions. First,
it formally defines and discusses two different goal orderings, which are
called the reasonable and the forced ordering. Both orderings are defined for
simple STRIPS operators as well as for more complex ADL operators supporting
negation and conditional effects. The complexity of these orderings is
investigated and their practical relevance is discussed. Secondly, two
different methods to compute reasonable goal orderings are developed. One of
them is based on planning graphs, while the other investigates the set of
actions directly. Finally, it is shown how the ordering relations, which have
been derived for a given set of goals G, can be used to compute a so-called
goal agenda that divides G into an ordered set of subgoals. Any planner can
then, in principle, use the goal agenda to plan for increasing sets of
subgoals. This can lead to an exponential complexity reduction, as the solution
to a complex planning problem is found by solving easier subproblems. Since
only a polynomial overhead is caused by the goal agenda computation, a
potential exists to dramatically speed up planning algorithms as we demonstrate
in the empirical evaluation, where we use this method in the IPP planner.
","On Reasonable and Forced Goal Orderings and their Use in an
  Agenda-Driven Planning Algorithm"
"  The goal of this research is to develop agents that are adaptive and
predictable and timely. At first blush, these three requirements seem
contradictory. For example, adaptation risks introducing undesirable side
effects, thereby making agents' behavior less predictable. Furthermore,
although formal verification can assist in ensuring behavioral predictability,
it is known to be time-consuming. Our solution to the challenge of satisfying
all three requirements is the following. Agents have finite-state automaton
plans, which are adapted online via evolutionary learning (perturbation)
operators. To ensure that critical behavioral constraints are always satisfied,
agents' plans are first formally verified. They are then reverified after every
adaptation. If reverification concludes that constraints are violated, the
plans are repaired. The main objective of this paper is to improve the
efficiency of reverification after learning, so that agents have a sufficiently
rapid response time. We present two solutions: positive results that certain
learning operators are a priori guaranteed to preserve useful classes of
behavioral assurance constraints (which implies that no reverification is
needed for these operators), and efficient incremental reverification
algorithms for those learning operators that have negative a priori results.
",Asimovian Adaptive Agents
"  A major problem in machine learning is that of inductive bias: how to choose
a learner's hypothesis space so that it is large enough to contain a solution
to the problem being learnt, yet small enough to ensure reliable generalization
from reasonably-sized training sets. Typically such bias is supplied by hand
through the skill and insights of experts. In this paper a model for
automatically learning bias is investigated. The central assumption of the
model is that the learner is embedded within an environment of related learning
tasks. Within such an environment the learner can sample from multiple tasks,
and hence it can search for a hypothesis space that contains good solutions to
many of the problems in the environment. Under certain restrictions on the set
of all hypothesis spaces available to the learner, we show that a hypothesis
space that performs well on a sufficiently large number of training tasks will
also perform well when learning novel tasks in the same environment. Explicit
bounds are also derived demonstrating that learning multiple tasks within an
environment of related tasks can potentially give much better generalization
than learning a single task.
",A Model of Inductive Bias Learning
"  The chief aim of this paper is to propose mean-field approximations for a
broad class of Belief networks, of which sigmoid and noisy-or networks can be
seen as special cases. The approximations are based on a powerful mean-field
theory suggested by Plefka. We show that Saul, Jaakkola and Jordan' s approach
is the first order approximation in Plefka's approach, via a variational
derivation. The application of Plefka's theory to belief networks is not
computationally tractable. To tackle this problem we propose new approximations
based on Taylor series. Small scale experiments show that the proposed schemes
are attractive.
",Mean Field Methods for a Special Class of Belief Networks
"  The recent approaches of extending the GRAPHPLAN algorithm to handle more
expressive planning formalisms raise the question of what the formal meaning of
""expressive power"" is. We formalize the intuition that expressive power is a
measure of how concisely planning domains and plans can be expressed in a
particular formalism by introducing the notion of ""compilation schemes"" between
planning formalisms. Using this notion, we analyze the expressiveness of a
large family of propositional planning formalisms, ranging from basic STRIPS to
a formalism with conditional effects, partial state specifications, and
propositional formulae in the preconditions. One of the results is that
conditional effects cannot be compiled away if plan size should grow only
linearly but can be compiled away if we allow for polynomial growth of the
resulting plans. This result confirms that the recently proposed extensions to
the GRAPHPLAN algorithm concerning conditional effects are optimal with respect
to the ""compilability"" framework. Another result is that general propositional
formulae cannot be compiled into conditional effects if the plan size should be
preserved linearly. This implies that allowing general propositional formulae
in preconditions and effect conditions adds another level of difficulty in
generating a plan.
","On the Compilability and Expressive Power of Propositional Planning
  Formalisms"
"  In order to generate plans for agents with multiple actuators, agent teams,
or distributed controllers, we must be able to represent and plan using
concurrent actions with interacting effects. This has historically been
considered a challenging task requiring a temporal planner with the ability to
reason explicitly about time. We show that with simple modifications, the
STRIPS action representation language can be used to represent interacting
actions. Moreover, algorithms for partial-order planning require only small
modifications in order to be applied in such multiagent domains. We demonstrate
this fact by developing a sound and complete partial-order planner for planning
with concurrent interacting actions, POMP, that extends existing partial-order
planners in a straightforward way. These results open the way to the use of
partial-order planners for the centralized control of cooperative multiagent
systems.
",Partial-Order Planning with Concurrent Interacting Actions
"  Domain-independent planning is a hard combinatorial problem. Taking into
account plan quality makes the task even more difficult. This article
introduces Planning by Rewriting (PbR), a new paradigm for efficient
high-quality domain-independent planning. PbR exploits declarative
plan-rewriting rules and efficient local search techniques to transform an
easy-to-generate, but possibly suboptimal, initial plan into a high-quality
plan. In addition to addressing the issues of planning efficiency and plan
quality, this framework offers a new anytime planning algorithm. We have
implemented this planner and applied it to several existing domains. The
experimental results show that the PbR approach provides significant savings in
planning effort while generating high-quality plans.
",Planning by Rewriting
"  Partially observable Markov decision processes (POMDPs) have recently become
popular among many AI researchers because they serve as a natural model for
planning under uncertainty. Value iteration is a well-known algorithm for
finding optimal policies for POMDPs. It typically takes a large number of
iterations to converge. This paper proposes a method for accelerating the
convergence of value iteration. The method has been evaluated on an array of
benchmark problems and was found to be very effective: It enabled value
iteration to converge after only a few iterations on all the test problems.
","Speeding Up the Convergence of Value Iteration in Partially Observable
  Markov Decision Processes"
"  We tackle the problem of planning in nondeterministic domains, by presenting
a new approach to conformant planning. Conformant planning is the problem of
finding a sequence of actions that is guaranteed to achieve the goal despite
the nondeterminism of the domain. Our approach is based on the representation
of the planning domain as a finite state automaton. We use Symbolic Model
Checking techniques, in particular Binary Decision Diagrams, to compactly
represent and efficiently search the automaton. In this paper we make the
following contributions. First, we present a general planning algorithm for
conformant planning, which applies to fully nondeterministic domains, with
uncertainty in the initial condition and in action effects. The algorithm is
based on a breadth-first, backward search, and returns conformant plans of
minimal length, if a solution to the planning problem exists, otherwise it
terminates concluding that the problem admits no conformant solution. Second,
we provide a symbolic representation of the search space based on Binary
Decision Diagrams (BDDs), which is the basis for search techniques derived from
symbolic model checking. The symbolic representation makes it possible to
analyze potentially large sets of states and transitions in a single
computation step, thus providing for an efficient implementation. Third, we
present CMBP (Conformant Model Based Planner), an efficient implementation of
the data structures and algorithm described above, directly based on BDD
manipulations, which allows for a compact representation of the search layers
and an efficient implementation of the search steps. Finally, we present an
experimental comparison of our approach with the state-of-the-art conformant
planners CGP, QBFPLAN and GPT. Our analysis includes all the planning problems
from the distribution packages of these systems, plus other problems defined to
stress a number of specific factors. Our approach appears to be the most
effective: CMBP is strictly more expressive than QBFPLAN and CGP and, in all
the problems where a comparison is possible, CMBP outperforms its competitors,
sometimes by orders of magnitude.
",Conformant Planning via Symbolic Model Checking
"  Stochastic sampling algorithms, while an attractive alternative to exact
algorithms in very large Bayesian network models, have been observed to perform
poorly in evidential reasoning with extremely unlikely evidence. To address
this problem, we propose an adaptive importance sampling algorithm, AIS-BN,
that shows promising convergence rates even under extreme conditions and seems
to outperform the existing sampling algorithms consistently. Three sources of
this performance improvement are (1) two heuristics for initialization of the
importance function that are based on the theoretical properties of importance
sampling in finite-dimensional integrals and the structural advantages of
Bayesian networks, (2) a smooth learning method for the importance function,
and (3) a dynamic weighting function for combining samples from different
stages of the algorithm. We tested the performance of the AIS-BN algorithm
along with two state of the art general purpose sampling algorithms, likelihood
weighting (Fung and Chang, 1989; Shachter and Peot, 1989) and self-importance
sampling (Shachter and Peot, 1989). We used in our tests three large real
Bayesian network models available to the scientific community: the CPCS network
(Pradhan et al., 1994), the PathFinder network (Heckerman, Horvitz, and
Nathwani, 1990), and the ANDES network (Conati, Gertner, VanLehn, and Druzdzel,
1997), with evidence as unlikely as 10^-41. While the AIS-BN algorithm always
performed better than the other two algorithms, in the majority of the test
cases it achieved orders of magnitude improvement in precision of the results.
Improvement in speed given a desired precision is even more dramatic, although
we are unable to report numerical results here, as the other algorithms almost
never achieved the precision reached even by the first few iterations of the
AIS-BN algorithm.
","AIS-BN: An Adaptive Importance Sampling Algorithm for Evidential
  Reasoning in Large Bayesian Networks"
"  In recent years, many improvements to backtracking algorithms for solving
constraint satisfaction problems have been proposed. The techniques for
improving backtracking algorithms can be conveniently classified as look-ahead
schemes and look-back schemes. Unfortunately, look-ahead and look-back schemes
are not entirely orthogonal as it has been observed empirically that the
enhancement of look-ahead techniques is sometimes counterproductive to the
effects of look-back techniques. In this paper, we focus on the relationship
between the two most important look-ahead techniques---using a variable
ordering heuristic and maintaining a level of local consistency during the
backtracking search---and the look-back technique of conflict-directed
backjumping (CBJ). We show that there exists a ""perfect"" dynamic variable
ordering such that CBJ becomes redundant. We also show theoretically that as
the level of local consistency that is maintained in the backtracking search is
increased, the less that backjumping will be an improvement. Our theoretical
results partially explain why a backtracking algorithm doing more in the
look-ahead phase cannot benefit more from the backjumping look-back scheme.
Finally, we show empirically that adding CBJ to a backtracking algorithm that
maintains generalized arc consistency (GAC), an algorithm that we refer to as
GAC-CBJ, can still provide orders of magnitude speedups. Our empirical results
contrast with Bessiere and Regin's conclusion (1996) that CBJ is useless to an
algorithm that maintains arc consistency.
",Conflict-Directed Backjumping Revisited
"  This paper presents an implemented system for recognizing the occurrence of
events described by simple spatial-motion verbs in short image sequences. The
semantics of these verbs is specified with event-logic expressions that
describe changes in the state of force-dynamic relations between the
participants of the event. An efficient finite representation is introduced for
the infinite sets of intervals that occur when describing liquid and
semi-liquid events. Additionally, an efficient procedure using this
representation is presented for inferring occurrences of compound events,
described with event-logic expressions, from occurrences of primitive events.
Using force dynamics and event logic to specify the lexical semantics of events
allows the system to be more robust than prior systems based on motion profile.
","Grounding the Lexical Semantics of Verbs in Visual Perception using
  Force Dynamics and Event Logic"
"  An ensemble consists of a set of individually trained classifiers (such as
neural networks or decision trees) whose predictions are combined when
classifying novel instances. Previous research has shown that an ensemble is
often more accurate than any of the single classifiers in the ensemble. Bagging
(Breiman, 1996c) and Boosting (Freund and Shapire, 1996; Shapire, 1990) are two
relatively new but popular methods for producing ensembles. In this paper we
evaluate these methods on 23 data sets using both neural networks and decision
trees as our classification algorithm. Our results clearly indicate a number of
conclusions. First, while Bagging is almost always more accurate than a single
classifier, it is sometimes much less accurate than Boosting. On the other
hand, Boosting can create ensembles that are less accurate than a single
classifier -- especially when using neural networks. Analysis indicates that
the performance of the Boosting methods is dependent on the characteristics of
the data set being examined. In fact, further results show that Boosting
ensembles may overfit noisy data sets, thus decreasing its performance.
Finally, consistent with previous studies, our work suggests that most of the
gain in an ensemble's performance comes in the first few classifiers combined;
however, relatively large gains can be seen up to 25 classifiers when Boosting
decision trees.
",Popular Ensemble Methods: An Empirical Study
"  This paper presents an evolutionary algorithm with a new goal-sequence
domination scheme for better decision support in multi-objective optimization.
The approach allows the inclusion of advanced hard/soft priority and constraint
information on each objective component, and is capable of incorporating
multiple specifications with overlapping or non-overlapping objective functions
via logical 'OR' and 'AND' connectives to drive the search towards multiple
regions of trade-off. In addition, we propose a dynamic sharing scheme that is
simple and adaptively estimated according to the on-line population
distribution without needing any a priori parameter setting. Each feature in
the proposed algorithm is examined to show its respective contribution, and the
performance of the algorithm is compared with other evolutionary optimization
methods. It is shown that the proposed algorithm has performed well in the
diversity of evolutionary search and uniform distribution of non-dominated
individuals along the final trade-offs, without significant computational
effort. The algorithm is also applied to the design optimization of a practical
servo control system for hard disk drives with a single voice-coil-motor
actuator. Results of the evolutionary designed servo control system show a
superior closed-loop performance compared to classical PID or RPT approaches.
","An Evolutionary Algorithm with Advanced Goal and Priority Specification
  for Multi-objective Optimization"
"  This paper presents GRT, a domain-independent heuristic planning system for
STRIPS worlds. GRT solves problems in two phases. In the pre-processing phase,
it estimates the distance between each fact and the goals of the problem, in a
backward direction. Then, in the search phase, these estimates are used in
order to further estimate the distance between each intermediate state and the
goals, guiding so the search process in a forward direction and on a best-first
basis. The paper presents the benefits from the adoption of opposite directions
between the preprocessing and the search phases, discusses some difficulties
that arise in the pre-processing phase and introduces techniques to cope with
them. Moreover, it presents several methods of improving the efficiency of the
heuristic, by enriching the representation and by reducing the size of the
problem. Finally, a method of overcoming local optimal states, based on domain
axioms, is proposed. According to it, difficult problems are decomposed into
easier sub-problems that have to be solved sequentially. The performance
results from various domains, including those of the recent planning
competitions, show that GRT is among the fastest planners.
","The GRT Planning System: Backward Heuristic Construction in Forward
  State-Space Planning"
"  In the recent literature of Artificial Intelligence, an intensive research
effort has been spent, for various algebras of qualitative relations used in
the representation of temporal and spatial knowledge, on the problem of
classifying the computational complexity of reasoning problems for subsets of
algebras. The main purpose of these researches is to describe a restricted set
of maximal tractable subalgebras, ideally in an exhaustive fashion with respect
to the hosting algebras. In this paper we introduce a novel algebra for
reasoning about Spatial Congruence, show that the satisfiability problem in the
spatial algebra MC-4 is NP-complete, and present a complete classification of
tractability in the algebra, based on the individuation of three maximal
tractable subclasses, one containing the basic relations. The three algebras
are formed by 14, 10 and 9 relations out of 16 which form the full algebra.
",The Complexity of Reasoning about Spatial Congruence
"  Gradient-based approaches to direct policy search in reinforcement learning
have received much recent attention as a means to solve problems of partial
observability and to avoid some of the problems associated with policy
degradation in value-function methods. In this paper we introduce GPOMDP, a
simulation-based algorithm for generating a {\em biased} estimate of the
gradient of the {\em average reward} in Partially Observable Markov Decision
Processes (POMDPs) controlled by parameterized stochastic policies. A similar
algorithm was proposed by Kimura, Yamamura, and Kobayashi (1995). The
algorithm's chief advantages are that it requires storage of only twice the
number of policy parameters, uses one free parameter $\beta\in [0,1)$ (which
has a natural interpretation in terms of bias-variance trade-off), and requires
no knowledge of the underlying state. We prove convergence of GPOMDP, and show
how the correct choice of the parameter $\beta$ is related to the {\em mixing
time} of the controlled POMDP. We briefly describe extensions of GPOMDP to
controlled Markov chains, continuous state, observation and control spaces,
multiple-agents, higher-order derivatives, and a version for training
stochastic policies with internal states. In a companion paper (Baxter,
Bartlett, & Weaver, 2001) we show how the gradient estimates generated by
GPOMDP can be used in both a traditional stochastic gradient algorithm and a
conjugate-gradient procedure to find local optima of the average reward
",Infinite-Horizon Policy-Gradient Estimation
"  Description Logics (DLs) are suitable, well-known, logics for managing
structured knowledge. They allow reasoning about individuals and well defined
concepts, i.e., set of individuals with common properties. The experience in
using DLs in applications has shown that in many cases we would like to extend
their capabilities. In particular, their use in the context of Multimedia
Information Retrieval (MIR) leads to the convincement that such DLs should
allow the treatment of the inherent imprecision in multimedia object content
representation and retrieval. In this paper we will present a fuzzy extension
of ALC, combining Zadeh's fuzzy logic with a classical DL. In particular,
concepts becomes fuzzy and, thus, reasoning about imprecise concepts is
supported. We will define its syntax, its semantics, describe its properties
and present a constraint propagation calculus for reasoning in it.
",Reasoning within Fuzzy Description Logics
"  Top-down induction of decision trees has been observed to suffer from the
inadequate functioning of the pruning phase. In particular, it is known that
the size of the resulting tree grows linearly with the sample size, even though
the accuracy of the tree does not improve. Reduced Error Pruning is an
algorithm that has been used as a representative technique in attempts to
explain the problems of decision tree learning. In this paper we present
analyses of Reduced Error Pruning in three different settings. First we study
the basic algorithmic properties of the method, properties that hold
independent of the input decision tree and pruning examples. Then we examine a
situation that intuitively should lead to the subtree under consideration to be
replaced by a leaf node, one in which the class label and attribute values of
the pruning examples are independent of each other. This analysis is conducted
under two different assumptions. The general analysis shows that the pruning
probability of a node fitting pure noise is bounded by a function that
decreases exponentially as the size of the tree grows. In a specific analysis
we assume that the examples are distributed uniformly to the tree. This
assumption lets us approximate the number of subtrees that are pruned because
they do not receive any pruning examples. This paper clarifies the different
variants of the Reduced Error Pruning algorithm, brings new insight to its
algorithmic properties, analyses the algorithm with less imposed assumptions
than before, and includes the previously overlooked empty subtrees to the
analysis.
",An Analysis of Reduced Error Pruning
"  This paper investigates the problems arising in the construction of a program
to play the game of contract bridge. These problems include both the difficulty
of solving the game's perfect information variant, and techniques needed to
address the fact that bridge is not, in fact, a perfect information game. GIB,
the program being described, involves five separate technical advances:
partition search, the practical application of Monte Carlo techniques to
realistic problems, a focus on achievable sets to solve problems inherent in
the Monte Carlo approach, an extension of alpha-beta pruning from total orders
to arbitrary distributive lattices, and the use of squeaky wheel optimization
to find approximately optimal solutions to cardplay problems. GIB is currently
believed to be of approximately expert caliber, and is currently the strongest
computer bridge program in the world.
",GIB: Imperfect Information in a Computationally Challenging Game
"  Enforcing local consistencies is one of the main features of constraint
reasoning. Which level of local consistency should be used when searching for
solutions in a constraint network is a basic question. Arc consistency and
partial forms of arc consistency have been widely studied, and have been known
for sometime through the forward checking or the MAC search algorithms. Until
recently, stronger forms of local consistency remained limited to those that
change the structure of the constraint graph, and thus, could not be used in
practice, especially on large networks. This paper focuses on the local
consistencies that are stronger than arc consistency, without changing the
structure of the network, i.e., only removing inconsistent values from the
domains. In the last five years, several such local consistencies have been
proposed by us or by others. We make an overview of all of them, and highlight
some relations between them. We compare them both theoretically and
experimentally, considering their pruning efficiency and the time required to
enforce them.
",Domain Filtering Consistencies
"  In this paper, we present a method for recognising an agent's behaviour in
dynamic, noisy, uncertain domains, and across multiple levels of abstraction.
We term this problem on-line plan recognition under uncertainty and view it
generally as probabilistic inference on the stochastic process representing the
execution of the agent's plan. Our contributions in this paper are twofold. In
terms of probabilistic inference, we introduce the Abstract Hidden Markov Model
(AHMM), a novel type of stochastic processes, provide its dynamic Bayesian
network (DBN) structure and analyse the properties of this network. We then
describe an application of the Rao-Blackwellised Particle Filter to the AHMM
which allows us to construct an efficient, hybrid inference method for this
model. In terms of plan recognition, we propose a novel plan recognition
framework based on the AHMM as the plan execution model. The Rao-Blackwellised
hybrid inference for AHMM can take advantage of the independence properties
inherent in a model of plan execution, leading to an algorithm for online
probabilistic plan recognition that scales well with the number of levels in
the plan hierarchy. This illustrates that while stochastic models for plan
execution can be complex, they exhibit special structures which, if exploited,
can lead to efficient plan recognition algorithms. We demonstrate the
usefulness of the AHMM framework via a behaviour recognition system in a
complex spatial environment using distributed video surveillance data.
",Policy Recognition in the Abstract Hidden Markov Model
"  We describe and evaluate the algorithmic techniques that are used in the FF
planning system. Like the HSP system, FF relies on forward state space search,
using a heuristic that estimates goal distances by ignoring delete lists.
Unlike HSP's heuristic, our method does not assume facts to be independent. We
introduce a novel search strategy that combines hill-climbing with systematic
search, and we show how other powerful heuristic information can be extracted
and used to prune the search space. FF was the most successful automatic
planner at the recent AIPS-2000 planning competition. We review the results of
the competition, give data for other benchmark domains, and investigate the
reasons for the runtime performance of FF compared to HSP.
",The FF Planning System: Fast Plan Generation Through Heuristic Search
"  The First Trading Agent Competition (TAC) was held from June 22nd to July
8th, 2000. TAC was designed to create a benchmark problem in the complex domain
of e-marketplaces and to motivate researchers to apply unique approaches to a
common task. This article describes ATTac-2000, the first-place finisher in
TAC. ATTac-2000 uses a principled bidding strategy that includes several
elements of adaptivity. In addition to the success at the competition, isolated
empirical results are presented indicating the robustness and effectiveness of
ATTac-2000's adaptive strategy.
",ATTac-2000: An Adaptive Autonomous Bidding Agent
"  The theoretical properties of qualitative spatial reasoning in the RCC8
framework have been analyzed extensively. However, no empirical investigation
has been made yet. Our experiments show that the adaption of the algorithms
used for qualitative temporal reasoning can solve large RCC8 instances, even if
they are in the phase transition region -- provided that one uses the maximal
tractable subsets of RCC8 that have been identified by us. In particular, we
demonstrate that the orthogonal combination of heuristic methods is successful
in solving almost all apparently hard instances in the phase transition region
up to a certain size in reasonable time.
",Efficient Methods for Qualitative Spatial Reasoning
"  This paper presents our work on development of OWL-driven systems for formal
representation and reasoning about terminological knowledge and facts in
petrology. The long-term aim of our project is to provide solid foundations for
a large-scale integration of various kinds of knowledge, including basic terms,
rock classification algorithms, findings and reports. We describe three steps
we have taken towards that goal here. First, we develop a semi-automated
procedure for transforming a database of igneous rock samples to texts in a
controlled natural language (CNL), and then a collection of OWL ontologies.
Second, we create an OWL ontology of important petrology terms currently
described in natural language thesauri. We describe a prototype of a tool for
collecting definitions from domain experts. Third, we present an approach to
formalization of current industrial standards for classification of rock
samples, which requires linear equations in OWL 2. In conclusion, we discuss a
range of opportunities arising from the use of semantic technologies in
petrology and outline the future work in this area.
",Towards OWL-based Knowledge Representation in Petrology
"  This paper discusses a system that accelerates reinforcement learning by
using transfer from related tasks. Without such transfer, even if two tasks are
very similar at some abstract level, an extensive re-learning effort is
required. The system achieves much of its power by transferring parts of
previously learned solutions rather than a single complete solution. The system
exploits strong features in the multi-dimensional function produced by
reinforcement learning in solving a particular task. These features are stable
and easy to recognize early in the learning process. They generate a
partitioning of the state space and thus the function. The partition is
represented as a graph. This is used to index and compose functions stored in a
case base to form a close approximation to the solution of the new task.
Experiments demonstrate that function composition often produces more than an
order of magnitude increase in learning rate compared to a basic reinforcement
learning algorithm.
","Accelerating Reinforcement Learning by Composing Solutions of
  Automatically Identified Subtasks"
"  We propose a logical/mathematical framework for statistical parameter
learning of parameterized logic programs, i.e. definite clause programs
containing probabilistic facts with a parameterized distribution. It extends
the traditional least Herbrand model semantics in logic programming to
distribution semantics, possible world semantics with a probability
distribution which is unconditionally applicable to arbitrary logic programs
including ones for HMMs, PCFGs and Bayesian networks. We also propose a new EM
algorithm, the graphical EM algorithm, that runs for a class of parameterized
logic programs representing sequential decision processes where each decision
is exclusive and independent. It runs on a new data structure called support
graphs describing the logical relationship between observations and their
explanations, and learns parameters by computing inside and outside probability
generalized for logic programs. The complexity analysis shows that when
combined with OLDT search for all explanations for observations, the graphical
EM algorithm, despite its generality, has the same time complexity as existing
EM algorithms, i.e. the Baum-Welch algorithm for HMMs, the Inside-Outside
algorithm for PCFGs, and the one for singly connected Bayesian networks that
have been developed independently in each research field. Learning experiments
with PCFGs using two corpora of moderate size indicate that the graphical EM
algorithm can significantly outperform the Inside-Outside algorithm.
",Parameter Learning of Logic Programs for Symbolic-Statistical Modeling
"  I consider the problem of learning an optimal path graphical model from data
and show the problem to be NP-hard for the maximum likelihood and minimum
description length approaches and a Bayesian approach. This hardness result
holds despite the fact that the problem is a restriction of the polynomially
solvable problem of finding the optimal tree graphical model.
",Finding a Path is Harder than Finding a Tree
"  Simple conceptual graphs are considered as the kernel of most knowledge
representation formalisms built upon Sowa's model. Reasoning in this model can
be expressed by a graph homomorphism called projection, whose semantics is
usually given in terms of positive, conjunctive, existential FOL. We present
here a family of extensions of this model, based on rules and constraints,
keeping graph homomorphism as the basic operation. We focus on the formal
definitions of the different models obtained, including their operational
semantics and relationships with FOL, and we analyze the decidability and
complexity of the associated problems (consistency and deduction). As soon as
rules are involved in reasonings, these problems are not decidable, but we
exhibit a condition under which they fall in the polynomial hierarchy. These
results extend and complete the ones already published by the authors. Moreover
we systematically study the complexity of some particular cases obtained by
restricting the form of constraints and/or rules.
","Extensions of Simple Conceptual Graphs: the Complexity of Rules and
  Constraints"
"  Fusions are a simple way of combining logics. For normal modal logics,
fusions have been investigated in detail. In particular, it is known that,
under certain conditions, decidability transfers from the component logics to
their fusion. Though description logics are closely related to modal logics,
they are not necessarily normal. In addition, ABox reasoning in description
logics is not covered by the results from modal logics. In this paper, we
extend the decidability transfer results from normal modal logics to a large
class of description logics. To cover different description logics in a uniform
way, we introduce abstract description systems, which can be seen as a common
generalization of description and modal logics, and show the transfer results
in this general setting.
",Fusions of Description Logics and Abstract Description Systems
"  Inductive logic programming, or relational learning, is a powerful paradigm
for machine learning or data mining. However, in order for ILP to become
practically useful, the efficiency of ILP systems must improve substantially.
To this end, the notion of a query pack is introduced: it structures sets of
similar queries. Furthermore, a mechanism is described for executing such query
packs. A complexity analysis shows that considerable efficiency improvements
can be achieved through the use of this query pack execution mechanism. This
claim is supported by empirical results obtained by incorporating support for
query pack execution in two existing learning systems.
","Improving the Efficiency of Inductive Logic Programming Through the Use
  of Query Packs"
"  Recent trends in planning research have led to empirical comparison becoming
commonplace. The field has started to settle into a methodology for such
comparisons, which for obvious practical reasons requires running a subset of
planners on a subset of problems. In this paper, we characterize the
methodology and examine eight implicit assumptions about the problems, planners
and metrics used in many of these comparisons. The problem assumptions are:
PR1) the performance of a general purpose planner should not be
penalized/biased if executed on a sampling of problems and domains, PR2) minor
syntactic differences in representation do not affect performance, and PR3)
problems should be solvable by STRIPS capable planners unless they require ADL.
The planner assumptions are: PL1) the latest version of a planner is the best
one to use, PL2) default parameter settings approximate good performance, and
PL3) time cut-offs do not unduly bias outcome. The metrics assumptions are: M1)
performance degrades similarly for each planner when run on degraded runtime
environments (e.g., machine platform) and M2) the number of plan steps
distinguishes performance. We find that most of these assumptions are not
supported empirically; in particular, that planners are affected differently by
these assumptions. We conclude with a call to the community to devote research
resources to improving the state of the practice and especially to enhancing
the available benchmark problems.
",A Critical Assessment of Benchmark Comparison in Planning
"  An approach to the construction of classifiers from imbalanced datasets is
described. A dataset is imbalanced if the classification categories are not
approximately equally represented. Often real-world data sets are predominately
composed of ""normal"" examples with only a small percentage of ""abnormal"" or
""interesting"" examples. It is also the case that the cost of misclassifying an
abnormal (interesting) example as a normal example is often much higher than
the cost of the reverse error. Under-sampling of the majority (normal) class
has been proposed as a good means of increasing the sensitivity of a classifier
to the minority class. This paper shows that a combination of our method of
over-sampling the minority (abnormal) class and under-sampling the majority
(normal) class can achieve better classifier performance (in ROC space) than
only under-sampling the majority class. This paper also shows that a
combination of our method of over-sampling the minority class and
under-sampling the majority class can achieve better classifier performance (in
ROC space) than varying the loss ratios in Ripper or class priors in Naive
Bayes. Our method of over-sampling the minority class involves creating
synthetic minority class examples. Experiments are performed using C4.5, Ripper
and a Naive Bayes classifier. The method is evaluated using the area under the
Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
",SMOTE: Synthetic Minority Over-sampling Technique
"  Common wisdom has it that small distinctions in the probabilities
(parameters) quantifying a belief network do not matter much for the results of
probabilistic queries. Yet, one can develop realistic scenarios under which
small variations in network parameters can lead to significant changes in
computed queries. A pending theoretical question is then to analytically
characterize parameter changes that do or do not matter. In this paper, we
study the sensitivity of probabilistic queries to changes in network parameters
and prove some tight bounds on the impact that such parameters can have on
queries. Our analytic results pinpoint some interesting situations under which
parameter changes do or do not matter. These results are important for
knowledge engineers as they help them identify influential network parameters.
They also help explain some of the previous experimental results and
observations with regards to network robustness against parameter changes.
",When do Numbers Really Matter?
"  Recent years are seeing an increasing need for on-line monitoring of teams of
cooperating agents, e.g., for visualization, or performance tracking. However,
in monitoring deployed teams, we often cannot rely on the agents to always
communicate their state to the monitoring system. This paper presents a
non-intrusive approach to monitoring by 'overhearing', where the monitored
team's state is inferred (via plan-recognition) from team-members' routine
communications, exchanged as part of their coordinated task execution, and
observed (overheard) by the monitoring system. Key challenges in this approach
include the demanding run-time requirements of monitoring, the scarceness of
observations (increasing monitoring uncertainty), and the need to scale-up
monitoring to address potentially large teams. To address these, we present a
set of complementary novel techniques, exploiting knowledge of the social
structures and procedures in the monitored team: (i) an efficient probabilistic
plan-recognition algorithm, well-suited for processing communications as
observations; (ii) an approach to exploiting knowledge of the team's social
behavior to predict future observations during execution (reducing monitoring
uncertainty); and (iii) monitoring algorithms that trade expressivity for
scalability, representing only certain useful monitoring hypotheses, but
allowing for any number of agents and their different activities to be
represented in a single coherent entity. We present an empirical evaluation of
these techniques, in combination and apart, in monitoring a deployed team of
agents, running on machines physically distributed across the country, and
engaged in complex, dynamic task execution. We also compare the performance of
these techniques to human expert and novice monitors, and show that the
techniques presented are capable of monitoring at human-expert levels, despite
the difficulty of the task.
",Monitoring Teams by Overhearing: A Multi-Agent Plan-Recognition Approach
"  Spoken dialogue systems promise efficient and natural access to a large
variety of information sources and services from any phone. However, current
spoken dialogue systems are deficient in their strategies for preventing,
identifying and repairing problems that arise in the conversation. This paper
reports results on automatically training a Problematic Dialogue Predictor to
predict problematic human-computer dialogues using a corpus of 4692 dialogues
collected with the 'How May I Help You' (SM) spoken dialogue system. The
Problematic Dialogue Predictor can be immediately applied to the system's
decision of whether to transfer the call to a human customer care agent, or be
used as a cue to the system's dialogue manager to modify its behavior to repair
problems, and even perhaps, to prevent them. We show that a Problematic
Dialogue Predictor using automatically-obtainable features from the first two
exchanges in the dialogue can predict problematic dialogues 13.2% more
accurately than the baseline.
","Automatically Training a Problematic Dialogue Predictor for a Spoken
  Dialogue System"
"  Recent advances in the study of voting classification algorithms have brought
empirical and theoretical results clearly showing the discrimination power of
ensemble classifiers. It has been previously argued that the search of this
classification power in the design of the algorithms has marginalized the need
to obtain interpretable classifiers. Therefore, the question of whether one
might have to dispense with interpretability in order to keep classification
strength is being raised in a growing number of machine learning or data mining
papers. The purpose of this paper is to study both theoretically and
empirically the problem. First, we provide numerous results giving insight into
the hardness of the simplicity-accuracy tradeoff for voting classifiers. Then
we provide an efficient ""top-down and prune"" induction heuristic, WIDC, mainly
derived from recent results on the weak learning and boosting frameworks. It is
to our knowledge the first attempt to build a voting classifier as a base
formula using the weak learning framework (the one which was previously highly
successful for decision tree induction), and not the strong learning framework
(as usual for such classifiers with boosting-like approaches). While it uses a
well-known induction scheme previously successful in other classes of concept
representations, thus making it easy to implement and compare, WIDC also relies
on recent or new results we give about particular cases of boosting known as
partition boosting and ranking loss boosting. Experimental results on
thirty-one domains, most of which readily available, tend to display the
ability of WIDC to produce small, accurate, and interpretable decision
committees.
","Inducing Interpretable Voting Classifiers without Trading Accuracy for
  Simplicity: Theoretical Results, Approximation Algorithms"
"  We propose a perspective on knowledge compilation which calls for analyzing
different compilation approaches according to two key dimensions: the
succinctness of the target compilation language, and the class of queries and
transformations that the language supports in polytime. We then provide a
knowledge compilation map, which analyzes a large number of existing target
compilation languages according to their succinctness and their polytime
transformations and queries. We argue that such analysis is necessary for
placing new compilation approaches within the context of existing ones. We also
go beyond classical, flat target compilation languages based on CNF and DNF,
and consider a richer, nested class based on directed acyclic graphs (such as
OBDDs), which we show to include a relatively large number of target
compilation languages.
",A Knowledge Compilation Map
"  The problem of organizing information for multidocument summarization so that
the generated summary is coherent has received relatively little attention.
While sentence ordering for single document summarization can be determined
from the ordering of sentences in the input article, this is not the case for
multidocument summarization where summary sentences may be drawn from different
input articles. In this paper, we propose a methodology for studying the
properties of ordering information in the news genre and describe experiments
done on a corpus of multiple acceptable orderings we developed for the task.
Based on these experiments, we implemented a strategy for ordering information
that combines constraints from chronological order of events and topical
relatedness. Evaluation of our augmented algorithm shows a significant
improvement of the ordering over two baseline strategies.
","Inferring Strategies for Sentence Ordering in Multidocument News
  Summarization"
"  We consider the problem of designing the the utility functions of the
utility-maximizing agents in a multi-agent system so that they work
synergistically to maximize a global utility. The particular problem domain we
explore is the control of network routing by placing agents on all the routers
in the network. Conventional approaches to this task have the agents all use
the Ideal Shortest Path routing Algorithm (ISPA). We demonstrate that in many
cases, due to the side-effects of one agent's actions on another agent's
performance, having agents use ISPA's is suboptimal as far as global aggregate
cost is concerned, even when they are only used to route infinitesimally small
amounts of traffic. The utility functions of the individual agents are not
""aligned"" with the global utility, intuitively speaking. As a particular
example of this we present an instance of Braess' paradox in which adding new
links to a network whose agents all use the ISPA results in a decrease in
overall throughput. We also demonstrate that load-balancing, in which the
agents' decisions are collectively made to optimize the global cost incurred by
all traffic currently being routed, is suboptimal as far as global cost
averaged across time is concerned. This is also due to 'side-effects', in this
case of current routing decision on future traffic. The mathematics of
Collective Intelligence (COIN) is concerned precisely with the issue of
avoiding such deleterious side-effects in multi-agent systems, both over time
and space. We present key concepts from that mathematics and use them to derive
an algorithm whose ideal version should have better performance than that of
having all agents use the ISPA, even in the infinitesimal limit. We present
experiments verifying this, and also showing that a machine-learning-based
version of this COIN algorithm in which costs are only imprecisely estimated
via empirical means (a version potentially applicable in the real world) also
outperforms the ISPA, despite having access to less information than does the
ISPA. In particular, this COIN algorithm almost always avoids Braess' paradox.
","Collective Intelligence, Data Routing and Braess' Paradox"
"  This paper addresses the problem of planning under uncertainty in large
Markov Decision Processes (MDPs). Factored MDPs represent a complex state space
using state variables and the transition model using a dynamic Bayesian
network. This representation often allows an exponential reduction in the
representation size of structured MDPs, but the complexity of exact solution
algorithms for such MDPs can grow exponentially in the representation size. In
this paper, we present two approximate solution algorithms that exploit
structure in factored MDPs. Both use an approximate value function represented
as a linear combination of basis functions, where each basis function involves
only a small subset of the domain variables. A key contribution of this paper
is that it shows how the basic operations of both algorithms can be performed
efficiently in closed form, by exploiting both additive and context-specific
structure in a factored MDP. A central element of our algorithms is a novel
linear program decomposition technique, analogous to variable elimination in
Bayesian networks, which reduces an exponentially large LP to a provably
equivalent, polynomial-sized one. One algorithm uses approximate linear
programming, and the second approximate dynamic programming. Our dynamic
programming algorithm is novel in that it uses an approximation based on
max-norm, a technique that more directly minimizes the terms that appear in
error bounds for approximate MDP algorithms. We provide experimental results on
problems with over 10^40 states, demonstrating a promising indication of the
scalability of our approach, and compare our algorithm to an existing
state-of-the-art approach, showing, in some problems, exponential gains in
computation time.
",Efficient Solution Algorithms for Factored MDPs
"  The human intelligence lies in the algorithm, the nature of algorithm lies in
the classification, and the classification is equal to outlier detection. A lot
of algorithms have been proposed to detect outliers, meanwhile a lot of
definitions. Unsatisfying point is that definitions seem vague, which makes the
solution an ad hoc one. We analyzed the nature of outliers, and give two clear
definitions. We then develop an efficient RDD algorithm, which converts outlier
problem to pattern and degree problem. Furthermore, a collapse mechanism was
introduced by IIR algorithm, which can be united seamlessly with the RDD
algorithm and serve for the final decision. Both algorithms are originated from
the study on general AI. The combined edition is named as Pe algorithm, which
is the basis of the intelligent decision. Here we introduce longest k-turn
subsequence problem and corresponding solution as an example to interpret the
function of Pe algorithm in detecting curve-type outliers. We also give a
comparison between IIR algorithm and Pe algorithm, where we can get a better
understanding at both algorithms. A short discussion about intelligence is
added to demonstrate the function of the Pe algorithm. Related experimental
results indicate its robustness.
",Intelligent decision: towards interpreting the Pe Algorithm
"  We present a novel Natural Evolution Strategy (NES) variant, the Rank-One NES
(R1-NES), which uses a low rank approximation of the search distribution
covariance matrix. The algorithm allows computation of the natural gradient
with cost linear in the dimensionality of the parameter space, and excels in
solving high-dimensional non-separable problems, including the best result to
date on the Rosenbrock function (512 dimensions).
",A Linear Time Natural Evolution Strategy for Non-Separable Functions
"  Galles and Pearl claimed that ""for recursive models, the causal model
framework does not add any restrictions to counterfactuals, beyond those
imposed by Lewis's [possible-worlds] framework."" This claim is examined
carefully, with the goal of clarifying the exact relationship between causal
models and Lewis's framework. Recursive models are shown to correspond
precisely to a subclass of (possible-world) counterfactual structures. On the
other hand, a slight generalization of recursive models, models where all
equations have unique solutions, is shown to be incomparable in expressive
power to counterfactual structures, despite the fact that the Galles and Pearl
arguments should apply to them as well. The problem with the Galles and Pearl
argument is identified: an axiom that they viewed as irrelevant, because it
involved disjunction (which was not in their language), is not irrelevant at
all.
",From Causal Models To Counterfactual Structures
"  We look more carefully at the modeling of causality using structural
equations. It is clear that the structural equations can have a major impact on
the conclusions we draw about causality. In particular, the choice of variables
and their values can also have a significant impact on causality. These choices
are, to some extent, subjective. We consider what counts as an appropriate
choice. More generally, we consider what makes a model an appropriate model,
especially if we want to take defaults into account, as was argued is necessary
in recent work.
",Actual causation and the art of modeling
"  Two distinct algorithms are presented to extract (schemata of) resolution
proofs from closed tableaux for propositional schemata. The first one handles
the most efficient version of the tableau calculus but generates very complex
derivations (denoted by rather elaborate rewrite systems). The second one has
the advantage that much simpler systems can be obtained, however the considered
proof procedure is less efficient.
",Generating Schemata of Resolution Proofs
"  In the current study we examine an application of the machine learning
methods to model the retention constants in the thin layer chromatography
(TLC). This problem can be described with hundreds or even thousands of
descriptors relevant to various molecular properties, most of them redundant
and not relevant for the retention constant prediction. Hence we employed
feature selection to significantly reduce the number of attributes.
Additionally we have tested application of the bagging procedure to the feature
selection. The random forest regression models were built using selected
variables. The resulting models have better correlation with the experimental
data than the reference models obtained with linear regression. The
cross-validation confirms robustness of the models.
","Random forest models of the retention constants in the thin layer
  chromatography"
"  Nowadays ontologies present a growing interest in Data Fusion applications.
As a matter of fact, the ontologies are seen as a semantic tool for describing
and reasoning about sensor data, objects, relations and general domain
theories. In addition, uncertainty is perhaps one of the most important
characteristics of the data and information handled by Data Fusion. However,
the fundamental nature of ontologies implies that ontologies describe only
asserted and veracious facts of the world. Different probabilistic, fuzzy and
evidential approaches already exist to fill this gap; this paper recaps the
most popular tools. However none of the tools meets exactly our purposes.
Therefore, we constructed a Dempster-Shafer ontology that can be imported into
any specific domain ontology and that enables us to instantiate it in an
uncertain manner. We also developed a Java application that enables reasoning
about these uncertain ontological instances.
","Uncertainty in Ontologies: Dempster-Shafer Theory for Data Fusion
  Applications"
"  Individuals have an intuitive perception of what makes a good coincidence.
Though the sensitivity to coincidences has often been presented as resulting
from an erroneous assessment of probability, it appears to be a genuine
competence, based on non-trivial computations. The model presented here
suggests that coincidences occur when subjects perceive complexity drops.
Co-occurring events are, together, simpler than if considered separately. This
model leads to a possible redefinition of subjective probability.
",Coincidences and the encounter problem: A formal account
"  The study of opinions, their formation and change, is one of the defining
topics addressed by social psychology, but in recent years other disciplines,
like computer science and complexity, have tried to deal with this issue.
Despite the flourishing of different models and theories in both fields,
several key questions still remain unanswered. The understanding of how
opinions change and the way they are affected by social influence are
challenging issues requiring a thorough analysis of opinion per se but also of
the way in which they travel between agents' minds and are modulated by these
exchanges. To account for the two-faceted nature of opinions, which are mental
entities undergoing complex social processes, we outline a preliminary model in
which a cognitive theory of opinions is put forward and it is paired with a
formal description of them and of their spreading among minds. Furthermore,
investigating social influence also implies the necessity to account for the
way in which people change their minds, as a consequence of interacting with
other people, and the need to explain the higher or lower persistence of such
changes.
","Rooting opinions in the minds: a cognitive model and a formal account of
  opinions and their dynamics"
"  The study of opinions, their formation and change, is one of the defining
topics addressed by social psychology, but in recent years other disciplines,
as computer science and complexity, have addressed this challenge. Despite the
flourishing of different models and theories in both fields, several key
questions still remain unanswered. The aim of this paper is to challenge the
current theories on opinion by putting forward a cognitively grounded model
where opinions are described as specific mental representations whose main
properties are put forward. A comparison with reputation will be also
presented.
",Understanding opinions. A cognitive and formal account
"  For large, real-world inductive learning problems, the number of training
examples often must be limited due to the costs associated with procuring,
preparing, and storing the training examples and/or the computational costs
associated with learning from them. In such circumstances, one question of
practical importance is: if only n training examples can be selected, in what
proportion should the classes be represented? In this article we help to answer
this question by analyzing, for a fixed training-set size, the relationship
between the class distribution of the training data and the performance of
classification trees induced from these data. We study twenty-six data sets
and, for each, determine the best class distribution for learning. The
naturally occurring class distribution is shown to generally perform well when
classifier performance is evaluated using undifferentiated error rate (0/1
loss). However, when the area under the ROC curve is used to evaluate
classifier performance, a balanced distribution is shown to perform well. Since
neither of these choices for class distribution always generates the
best-performing classifier, we introduce a budget-sensitive progressive
sampling algorithm for selecting training examples based on the class
associated with each example. An empirical analysis of this algorithm shows
that the class distribution of the resulting training set yields classifiers
with good (nearly-optimal) classification performance.
","Learning When Training Data are Costly: The Effect of Class Distribution
  on Tree Induction"
"  In recent years research in the planning community has moved increasingly
toward s application of planners to realistic problems involving both time and
many typ es of resources. For example, interest in planning demonstrated by the
space res earch community has inspired work in observation scheduling,
planetary rover ex ploration and spacecraft control domains. Other temporal and
resource-intensive domains including logistics planning, plant control and
manufacturing have also helped to focus the community on the modelling and
reasoning issues that must be confronted to make planning technology meet the
challenges of application. The International Planning Competitions have acted
as an important motivating fo rce behind the progress that has been made in
planning since 1998. The third com petition (held in 2002) set the planning
community the challenge of handling tim e and numeric resources. This
necessitated the development of a modelling langua ge capable of expressing
temporal and numeric properties of planning domains. In this paper we describe
the language, PDDL2.1, that was used in the competition. We describe the syntax
of the language, its formal semantics and the validation of concurrent plans.
We observe that PDDL2.1 has considerable modelling power --- exceeding the
capabilities of current planning technology --- and presents a number of
important challenges to the research community.
",PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains
"  Despite the significant progress in multiagent teamwork, existing research
does not address the optimality of its prescriptions nor the complexity of the
teamwork problem. Without a characterization of the optimality-complexity
tradeoffs, it is impossible to determine whether the assumptions and
approximations made by a particular theory gain enough efficiency to justify
the losses in overall performance. To provide a tool for use by multiagent
researchers in evaluating this tradeoff, we present a unified framework, the
COMmunicative Multiagent Team Decision Problem (COM-MTDP). The COM-MTDP model
combines and extends existing multiagent theories, such as decentralized
partially observable Markov decision processes and economic team theory. In
addition to their generality of representation, COM-MTDPs also support the
analysis of both the optimality of team performance and the computational
complexity of the agents' decision problem. In analyzing complexity, we present
a breakdown of the computational complexity of constructing optimal teams under
various classes of problem domains, along the dimensions of observability and
communication cost. In analyzing optimality, we exploit the COM-MTDP's ability
to encode existing teamwork theories and models to encode two instantiations of
joint intentions theory taken from the literature. Furthermore, the COM-MTDP
model provides a basis for the development of novel team coordination
algorithms. We derive a domain-independent criterion for optimal communication
and provide a comparative analysis of the two joint intentions instantiations
with respect to this optimal policy. We have implemented a reusable,
domain-independent software package based on COM-MTDPs to analyze teamwork
coordination strategies, and we demonstrate its use by encoding and evaluating
the two joint intentions strategies within an example domain.
","The Communicative Multiagent Team Decision Problem: Analyzing Teamwork
  Theories and Models"
"  Adjustable autonomy refers to entities dynamically varying their own
autonomy, transferring decision-making control to other entities (typically
agents transferring control to human users) in key situations. Determining
whether and when such transfers-of-control should occur is arguably the
fundamental research problem in adjustable autonomy. Previous work has
investigated various approaches to addressing this problem but has often
focused on individual agent-human interactions. Unfortunately, domains
requiring collaboration between teams of agents and humans reveal two key
shortcomings of these previous approaches. First, these approaches use rigid
one-shot transfers of control that can result in unacceptable coordination
failures in multiagent settings. Second, they ignore costs (e.g., in terms of
time delays or effects on actions) to an agent's team due to such
transfers-of-control. To remedy these problems, this article presents a novel
approach to adjustable autonomy, based on the notion of a transfer-of-control
strategy. A transfer-of-control strategy consists of a conditional sequence of
two types of actions: (i) actions to transfer decision-making control (e.g.,
from an agent to a user or vice versa) and (ii) actions to change an agent's
pre-specified coordination constraints with team members, aimed at minimizing
miscoordination costs. The goal is for high-quality individual decisions to be
made with minimal disruption to the coordination of the team. We present a
mathematical model of transfer-of-control strategies. The model guides and
informs the operationalization of the strategies using Markov Decision
Processes, which select an optimal strategy, given an uncertain environment and
costs to the individuals and teams. The approach has been carefully evaluated,
including via its use in a real-world, deployed multi-agent system that assists
a research group in its daily activities.
",Towards Adjustable Autonomy for the Real World
"  In this paper, we analyze the decision version of the NK landscape model from
the perspective of threshold phenomena and phase transitions under two random
distributions, the uniform probability model and the fixed ratio model. For the
uniform probability model, we prove that the phase transition is easy in the
sense that there is a polynomial algorithm that can solve a random instance of
the problem with the probability asymptotic to 1 as the problem size tends to
infinity. For the fixed ratio model, we establish several upper bounds for the
solubility threshold, and prove that random instances with parameters above
these upper bounds can be solved polynomially. This, together with our
empirical study for random instances generated below and in the phase
transition region, suggests that the phase transition of the fixed ratio model
is also easy.
",An Analysis of Phase Transition in NK Landscapes
"  This paper presents an approach to expert-guided subgroup discovery. The main
step of the subgroup discovery process, the induction of subgroup descriptions,
is performed by a heuristic beam search algorithm, using a novel parametrized
definition of rule quality which is analyzed in detail. The other important
steps of the proposed subgroup discovery process are the detection of
statistically significant properties of selected subgroups and subgroup
visualization: statistically significant properties are used to enrich the
descriptions of induced subgroups, while the visualization shows subgroup
properties in the form of distributions of the numbers of examples in the
subgroups. The approach is illustrated by the results obtained for a medical
problem of early detection of patient risk groups.
",Expert-Guided Subgroup Discovery: Methodology and Application
"  Independence -- the study of what is relevant to a given problem of reasoning
-- has received an increasing attention from the AI community. In this paper,
we consider two basic forms of independence, namely, a syntactic one and a
semantic one. We show features and drawbacks of them. In particular, while the
syntactic form of independence is computationally easy to check, there are
cases in which things that intuitively are not relevant are not recognized as
such. We also consider the problem of forgetting, i.e., distilling from a
knowledge base only the part that is relevant to the set of queries constructed
from a subset of the alphabet. While such process is computationally hard, it
allows for a simplification of subsequent reasoning, and can thus be viewed as
a form of compilation: once the relevant part of a knowledge base has been
extracted, all reasoning tasks to be performed can be simplified.
","Propositional Independence - Formula-Variable Independence and
  Forgetting"
"  We present a probabilistic generative model for timing deviations in
expressive music performance. The structure of the proposed model is equivalent
to a switching state space model. The switch variables correspond to discrete
note locations as in a musical score. The continuous hidden variables denote
the tempo. We formulate two well known music recognition problems, namely tempo
tracking and automatic transcription (rhythm quantization) as filtering and
maximum a posteriori (MAP) state estimation tasks. Exact computation of
posterior features such as the MAP state is intractable in this model class, so
we introduce Monte Carlo methods for integration and optimization. We compare
Markov Chain Monte Carlo (MCMC) methods (such as Gibbs sampling, simulated
annealing and iterative improvement) and sequential Monte Carlo methods
(particle filters). Our simulation results suggest better results with
sequential methods. The methods can be applied in both online and batch
scenarios such as tempo tracking and transcription and are thus potentially
useful in a number of music applications such as adaptive automatic
accompaniment, score typesetting and music information retrieval.
",Monte Carlo Methods for Tempo Tracking and Rhythm Quantization
"  Bayesian belief networks have grown to prominence because they provide
compact representations for many problems for which probabilistic inference is
appropriate, and there are algorithms to exploit this compactness. The next
step is to allow compact representations of the conditional probabilities of a
variable given its parents. In this paper we present such a representation that
exploits contextual independence in terms of parent contexts; which variables
act as parents may depend on the value of other variables. The internal
representation is in terms of contextual factors (confactors) that is simply a
pair of a context and a table. The algorithm, contextual variable elimination,
is based on the standard variable elimination algorithm that eliminates the
non-query variables in turn, but when eliminating a variable, the tables that
need to be multiplied can depend on the context. This algorithm reduces to
standard variable elimination when there is no contextual independence
structure to exploit. We show how this can be much more efficient than variable
elimination when there is structure to exploit. We explain why this new method
can exploit more structure than previous methods for structured belief network
inference and an analogous algorithm that uses trees.
",Exploiting Contextual Independence In Probabilistic Inference
"  In this article we present an algorithm to compute bounds on the marginals of
a graphical model. For several small clusters of nodes upper and lower bounds
on the marginal values are computed independently of the rest of the network.
The range of allowed probability distributions over the surrounding nodes is
restricted using earlier computed bounds. As we will show, this can be
considered as a set of constraints in a linear programming problem of which the
objective function is the marginal probability of the center nodes. In this way
knowledge about the maginals of neighbouring clusters is passed to other
clusters thereby tightening the bounds on their marginals. We show that sharp
bounds can be obtained for undirected and directed graphs that are used for
practical applications, but for which exact computations are infeasible.
",Bound Propagation
"  Policies of Markov Decision Processes (MDPs) determine the next action to
execute from the current state and, possibly, the history (the past states).
When the number of states is large, succinct representations are often used to
compactly represent both the MDPs and the policies in a reduced amount of
space. In this paper, some problems related to the size of succinctly
represented policies are analyzed. Namely, it is shown that some MDPs have
policies that can only be represented in space super-polynomial in the size of
the MDP, unless the polynomial hierarchy collapses. This fact motivates the
study of the problem of deciding whether a given MDP has a policy of a given
size and reward. Since some algorithms for MDPs work by finding a succinct
representation of the value function, the problem of deciding the existence of
a succinct representation of a value function of a given size and reward is
also considered.
",On Polynomial Sized MDP Succinct Policies
"  We describe a system for specifying the effects of actions. Unlike those
commonly used in AI planning, our system uses an action description language
that allows one to specify the effects of actions using domain rules, which are
state constraints that can entail new action effects from old ones.
Declaratively, an action domain in our language corresponds to a nonmonotonic
causal theory in the situation calculus. Procedurally, such an action domain is
compiled into a set of logical theories, one for each action in the domain,
from which fully instantiated successor state-like axioms and STRIPS-like
systems are then generated. We expect the system to be a useful tool for
knowledge engineers writing action specifications for classical AI planning
systems, GOLOG systems, and other systems where formal specifications of
actions are needed.
","Compiling Causal Theories to Successor State Axioms and STRIPS-Like
  Systems"
"  VHPOP is a partial order causal link (POCL) planner loosely based on UCPOP.
It draws from the experience gained in the early to mid 1990's on flaw
selection strategies for POCL planning, and combines this with more recent
developments in the field of domain independent planning such as distance based
heuristics and reachability analysis. We present an adaptation of the additive
heuristic for plan space planning, and modify it to account for possible reuse
of existing actions in a plan. We also propose a large set of novel flaw
selection strategies, and show how these can help us solve more problems than
previously possible by POCL planners. VHPOP also supports planning with
durative actions by incorporating standard techniques for temporal constraint
reasoning. We demonstrate that the same heuristic techniques used to boost the
performance of classical POCL planning can be effective in domains with
durative actions as well. The result is a versatile heuristic POCL planner
competitive with established CSP-based and heuristic state space planners.
",VHPOP: Versatile Heuristic Partial Order Planner
"  The SHOP2 planning system received one of the awards for distinguished
performance in the 2002 International Planning Competition. This paper
describes the features of SHOP2 which enabled it to excel in the competition,
especially those aspects of SHOP2 that deal with temporal and metric planning
domains.
",SHOP2: An HTN Planning System
"  Hierarchical task decomposition is a method used in many agent systems to
organize agent knowledge. This work shows how the combination of a hierarchy
and persistent assertions of knowledge can lead to difficulty in maintaining
logical consistency in asserted knowledge. We explore the problematic
consequences of persistent assumptions in the reasoning process and introduce
novel potential solutions. Having implemented one of the possible solutions,
Dynamic Hierarchical Justification, its effectiveness is demonstrated with an
empirical analysis.
","An Architectural Approach to Ensuring Consistency in Hierarchical
  Execution"
"  The proliferation of online information sources has led to an increased use
of wrappers for extracting data from Web sources. While most of the previous
research has focused on quick and efficient generation of wrappers, the
development of tools for wrapper maintenance has received less attention. This
is an important research problem because Web sources often change in ways that
prevent the wrappers from extracting data correctly. We present an efficient
algorithm that learns structural information about data from positive examples
alone. We describe how this information can be used for two wrapper maintenance
applications: wrapper verification and reinduction. The wrapper verification
system detects when a wrapper is not extracting correct data, usually because
the Web source has changed its format. The reinduction algorithm automatically
recovers from changes in the Web source by identifying data on Web pages so
that a new wrapper may be generated for this source. To validate our approach,
we monitored 27 wrappers over a period of a year. The verification algorithm
correctly discovered 35 of the 37 wrapper changes, and made 16 mistakes,
resulting in precision of 0.73 and recall of 0.95. We validated the reinduction
algorithm on ten Web sources. We were able to successfully reinduce the
wrappers, obtaining precision and recall values of 0.90 and 0.80 on the data
extraction task.
",Wrapper Maintenance: A Machine Learning Approach
"  The cognitive research on reputation has shown several interesting properties
that can improve both the quality of services and the security in distributed
electronic environments. In this paper, the impact of reputation on
decision-making under scarcity of information will be shown. First, a cognitive
theory of reputation will be presented, then a selection of simulation
experimental results from different studies will be discussed. Such results
concern the benefits of reputation when agents need to find out good sellers in
a virtual market-place under uncertainty and informational cheating.
",Exploiting Reputation in Distributed Virtual Environments
"  In this paper we examine the application of the random forest classifier for
the all relevant feature selection problem. To this end we first examine two
recently proposed all relevant feature selection algorithms, both being a
random forest wrappers, on a series of synthetic data sets with varying size.
We show that reasonable accuracy of predictions can be achieved and that
heuristic algorithms that were designed to handle the all relevant problem,
have performance that is close to that of the reference ideal algorithm. Then,
we apply one of the algorithms to four families of semi-synthetic data sets to
assess how the properties of particular data set influence results of feature
selection. Finally we test the procedure using a well-known gene expression
data set. The relevance of nearly all previously established important genes
was confirmed, moreover the relevance of several new ones is discovered.
",The All Relevant Feature Selection using Random Forest
"  Unary operator domains -- i.e., domains in which operators have a single
effect -- arise naturally in many control problems. In its most general form,
the problem of STRIPS planning in unary operator domains is known to be as hard
as the general STRIPS planning problem -- both are PSPACE-complete. However,
unary operator domains induce a natural structure, called the domain's causal
graph. This graph relates between the preconditions and effect of each domain
operator. Causal graphs were exploited by Williams and Nayak in order to
analyze plan generation for one of the controllers in NASA's Deep-Space One
spacecraft. There, they utilized the fact that when this graph is acyclic, a
serialization ordering over any subgoal can be obtained quickly. In this paper
we conduct a comprehensive study of the relationship between the structure of a
domain's causal graph and the complexity of planning in this domain. On the
positive side, we show that a non-trivial polynomial time plan generation
algorithm exists for domains whose causal graph induces a polytree with a
constant bound on its node indegree. On the negative side, we show that even
plan existence is hard when the graph is a directed-path singly connected DAG.
More generally, we show that the number of paths in the causal graph is closely
related to the complexity of planning in the associated domain. Finally we
relate our results to the question of complexity of planning with serializable
subgoals.
",Structure and Complexity in Planning with Unary Operators
"  Recently, planning based on answer set programming has been proposed as an
approach towards realizing declarative planning systems. In this paper, we
present the language Kc, which extends the declarative planning language K by
action costs. Kc provides the notion of admissible and optimal plans, which are
plans whose overall action costs are within a given limit resp. minimum over
all plans (i.e., cheapest plans). As we demonstrate, this novel language allows
for expressing some nontrivial planning tasks in a declarative way.
Furthermore, it can be utilized for representing planning problems under other
optimality criteria, such as computing ``shortest'' plans (with the least
number of steps), and refinement combinations of cheapest and fastest plans. We
study complexity aspects of the language Kc and provide a transformation to
logic programs, such that planning problems are solved via answer set
programming. Furthermore, we report experimental results on selected problems.
Our experience is encouraging that answer set planning may be a valuable
approach to expressive planning systems in which intricate planning problems
can be naturally specified and solved.
",Answer Set Planning Under Action Costs
"  In common-interest stochastic games all players receive an identical payoff.
Players participating in such games must learn to coordinate with each other in
order to receive the highest-possible value. A number of reinforcement learning
algorithms have been proposed for this problem, and some have been shown to
converge to good solutions in the limit. In this paper we show that using very
simple model-based algorithms, much better (i.e., polynomial) convergence rates
can be attained. Moreover, our model-based algorithms are guaranteed to
converge to the optimal value, unlike many of the existing algorithms.
",Learning to Coordinate Efficiently: A Model-based Approach
"  SAPA is a domain-independent heuristic forward chaining planner that can
handle durative actions, metric resource constraints, and deadline goals. It is
designed to be capable of handling the multi-objective nature of metric
temporal planning. Our technical contributions include (i) planning-graph based
methods for deriving heuristics that are sensitive to both cost and makespan
(ii) techniques for adjusting the heuristic estimates to take action
interactions and metric resource limitations into account and (iii) a linear
time greedy post-processing technique to improve execution flexibility of the
solution plans. An implementation of SAPA using many of the techniques
presented in this paper was one of the best domain independent planners for
domains with metric and temporal constraints in the third International
Planning Competition, held at AIPS-02. We describe the technical details of
extracting the heuristics and present an empirical evaluation of the current
implementation of SAPA.
",SAPA: A Multi-objective Metric Temporal Planner
"  The recent emergence of heavily-optimized modal decision procedures has
highlighted the key role of empirical testing in this domain. Unfortunately,
the introduction of extensive empirical tests for modal logics is recent, and
so far none of the proposed test generators is very satisfactory. To cope with
this fact, we present a new random generation method that provides benefits
over previous methods for generating empirical tests. It fixes and much
generalizes one of the best-known methods, the random CNF_[]m test, allowing
for generating a much wider variety of problems, covering in principle the
whole input space. Our new method produces much more suitable test sets for the
current generation of modal decision procedures. We analyze the features of the
new method by means of an extensive collection of empirical tests.
","A New General Method to Generate Random Modal Formulae for Testing
  Decision Procedures"
"  Despite their near dominance, heuristic state search planners still lag
behind disjunctive planners in the generation of parallel plans in classical
planning. The reason is that directly searching for parallel solutions in state
space planners would require the planners to branch on all possible subsets of
parallel actions, thus increasing the branching factor exponentially. We
present a variant of our heuristic state search planner AltAlt, called AltAltp
which generates parallel plans by using greedy online parallelization of
partial plans. The greedy approach is significantly informed by the use of
novel distance heuristics that AltAltp derives from a graphplan-style planning
graph for the problem. While this approach is not guaranteed to provide optimal
parallel plans, empirical results show that AltAltp is capable of generating
good quality parallel plans at a fraction of the cost incurred by the
disjunctive planners.
",AltAltp: Online Parallelization of Plans with Heuristic State Search
"  We address the problem of propositional logic-based abduction, i.e., the
problem of searching for a best explanation for a given propositional
observation according to a given propositional knowledge base. We give a
general algorithm, based on the notion of projection; then we study
restrictions over the representations of the knowledge base and of the query,
and find new polynomial classes of abduction problems.
",New Polynomial Classes for Logic-Based Abduction
"  We present some techniques for planning in domains specified with the recent
standard language PDDL2.1, supporting 'durative actions' and numerical
quantities. These techniques are implemented in LPG, a domain-independent
planner that took part in the 3rd International Planning Competition (IPC). LPG
is an incremental, any time system producing multi-criteria quality plans. The
core of the system is based on a stochastic local search method and on a
graph-based representation called 'Temporal Action Graphs' (TA-graphs). This
paper focuses on temporal planning, introducing TA-graphs and proposing some
techniques to guide the search in LPG using this representation. The
experimental results of the 3rd IPC, as well as further results presented in
this paper, show that our techniques can be very effective. Often LPG
outperforms all other fully-automated planners of the 3rd IPC in terms of speed
to derive a solution, or quality of the solutions that can be produced.
","Planning Through Stochastic Local Search and Temporal Action Graphs in
  LPG"
"  TALplanner is a forward-chaining planner that relies on domain knowledge in
the shape of temporal logic formulas in order to prune irrelevant parts of the
search space. TALplanner recently participated in the third International
Planning Competition, which had a clear emphasis on increasing the complexity
of the problem domains being used as benchmark tests and the expressivity
required to represent these domains in a planning system. Like many other
planners, TALplanner had support for some but not all aspects of this increase
in expressivity, and a number of changes to the planner were required. After a
short introduction to TALplanner, this article describes some of the changes
that were made before and during the competition. We also describe the process
of introducing suitable domain knowledge for several of the competition
domains.
",TALplanner in IPC-2002: Extensions and Control Rules
"  The automatic generation of decision trees based on off-line reasoning on
models of a domain is a reasonable compromise between the advantages of using a
model-based approach in technical domains and the constraints imposed by
embedded applications. In this paper we extend the approach to deal with
temporal information. We introduce a notion of temporal decision tree, which is
designed to make use of relevant information as long as it is acquired, and we
present an algorithm for compiling such trees from a model-based reasoning
system.
","Temporal Decision Trees: Model-based Diagnosis of Dynamic Systems
  On-Board"
"  The performance of anytime algorithms can be improved by simultaneously
solving several instances of algorithm-problem pairs. These pairs may include
different instances of a problem (such as starting from a different initial
state), different algorithms (if several alternatives exist), or several runs
of the same algorithm (for non-deterministic algorithms). In this paper we
present a methodology for designing an optimal scheduling policy based on the
statistical characteristics of the algorithms involved. We formally analyze the
case where the processes share resources (a single-processor model), and
provide an algorithm for optimal scheduling. We analyze, theoretically and
empirically, the behavior of our scheduling algorithm for various distribution
types. Finally, we present empirical results of applying our scheduling
algorithm to the Latin Square problem.
","Optimal Schedules for Parallelizing Anytime Algorithms: The Case of
  Shared Resources"
"  Auctions are becoming an increasingly popular method for transacting
business, especially over the Internet. This article presents a general
approach to building autonomous bidding agents to bid in multiple simultaneous
auctions for interacting goods. A core component of our approach learns a model
of the empirical price dynamics based on past data and uses the model to
analytically calculate, to the greatest extent possible, optimal bids. We
introduce a new and general boosting-based algorithm for conditional density
estimation problems of this kind, i.e., supervised learning problems in which
the goal is to estimate the entire conditional distribution of the real-valued
label. This approach is fully implemented as ATTac-2001, a top-scoring agent in
the second Trading Agent Competition (TAC-01). We present experiments
demonstrating the effectiveness of our boosting-based price predictor relative
to several reasonable alternatives.
","Decision-Theoretic Bidding Based on Learned Density Models in
  Simultaneous, Interacting Auctions"
"  Planning with numeric state variables has been a challenge for many years,
and was a part of the 3rd International Planning Competition (IPC-3). Currently
one of the most popular and successful algorithmic techniques in STRIPS
planning is to guide search by a heuristic function, where the heuristic is
based on relaxing the planning task by ignoring the delete lists of the
available actions. We present a natural extension of ``ignoring delete lists''
to numeric state variables, preserving the relevant theoretical properties of
the STRIPS relaxation under the condition that the numeric task at hand is
``monotonic''. We then identify a subset of the numeric IPC-3 competition
language, ``linear tasks'', where monotonicity can be achieved by
pre-processing. Based on that, we extend the algorithms used in the heuristic
planning system FF to linear tasks. The resulting system Metric-FF is,
according to the IPC-3 results which we discuss, one of the two currently most
efficient numeric planners.
","The Metric-FF Planning System: Translating ""Ignoring Delete Lists"" to
  Numeric State Variables"
"  Nanson's and Baldwin's voting rules select a winner by successively
eliminating candidates with low Borda scores. We show that these rules have a
number of desirable computational properties. In particular, with unweighted
votes, it is NP-hard to manipulate either rule with one manipulator, whilst
with weighted votes, it is NP-hard to manipulate either rule with a small
number of candidates and a coalition of manipulators. As only a couple of other
voting rules are known to be NP-hard to manipulate with a single manipulator,
Nanson's and Baldwin's rules appear to be particularly resistant to
manipulation from a theoretical perspective. We also propose a number of
approximation methods for manipulating these two rules. Experiments demonstrate
that both rules are often difficult to manipulate in practice. These results
suggest that elimination style voting rules deserve further study.
",Manipulation of Nanson's and Baldwin's Rules
"  Search is a major technique for planning. It amounts to exploring a state
space of planning domains typically modeled as a directed graph. However,
prohibitively large sizes of the search space make search expensive. Developing
better heuristic functions has been the main technique for improving search
efficiency. Nevertheless, recent studies have shown that improving heuristics
alone has certain fundamental limits on improving search efficiency. Recently,
a new direction of research called partial order based reduction (POR) has been
proposed as an alternative to improving heuristics. POR has shown promise in
speeding up searches.
  POR has been extensively studied in model checking research and is a key
enabling technique for scalability of model checking systems. Although the POR
theory has been extensively studied in model checking, it has never been
developed systematically for planning before. In addition, the conditions for
POR in the model checking theory are abstract and not directly applicable in
planning. Previous works on POR algorithms for planning did not establish the
connection between these algorithms and existing theory in model checking.
  In this paper, we develop a theory for POR in planning. The new theory we
develop connects the stubborn set theory in model checking and POR methods in
planning. We show that previous POR algorithms in planning can be explained by
the new theory. Based on the new theory, we propose a new, stronger POR
algorithm. Experimental results on various planning domains show further search
cost reduction using the new algorithm.
",Theory and Algorithms for Partial Order Based Reduction in Planning
"  Set and multiset variables in constraint programming have typically been
represented using subset bounds. However, this is a weak representation that
neglects potentially useful information about a set such as its cardinality.
For set variables, the length-lex (LL) representation successfully provides
information about the length (cardinality) and position in the lexicographic
ordering. For multiset variables, where elements can be repeated, we consider
richer representations that take into account additional information. We study
eight different representations in which we maintain bounds according to one of
the eight different orderings: length-(co)lex (LL/LC), variety-(co)lex (VL/VC),
length-variety-(co)lex (LVL/LVC), and variety-length-(co)lex (VLL/VLC)
orderings. These representations integrate together information about the
cardinality, variety (number of distinct elements in the multiset), and
position in some total ordering. Theoretical and empirical comparisons of
expressiveness and compactness of the eight representations suggest that
length-variety-(co)lex (LVL/LVC) and variety-length-(co)lex (VLL/VLC) usually
give tighter bounds after constraint propagation. We implement the eight
representations and evaluate them against the subset bounds representation with
cardinality and variety reasoning. Results demonstrate that they offer
significantly better pruning and runtime.
","A Comparison of Lex Bounds for Multiset Variables in Constraint
  Programming"
"  This paper reports the outcome of the third in the series of biennial
international planning competitions, held in association with the International
Conference on AI Planning and Scheduling (AIPS) in 2002. In addition to
describing the domains, the planners and the objectives of the competition, the
paper includes analysis of the results. The results are analysed from several
perspectives, in order to address the questions of comparative performance
between planners, comparative difficulty of domains, the degree of agreement
between planners about the relative difficulty of individual problem instances
and the question of how well planners scale relative to one another over
increasingly difficult problems. The paper addresses these questions through
statistical analysis of the raw results of the competition, in order to
determine which results can be considered to be adequately supported by the
data. The paper concludes with a discussion of some challenges for the future
of the competition series.
",The 3rd International Planning Competition: Results and Analysis
"  As computational agents are developed for increasingly complicated e-commerce
applications, the complexity of the decisions they face demands advances in
artificial intelligence techniques. For example, an agent representing a seller
in an auction should try to maximize the seller's profit by reasoning about a
variety of possibly uncertain pieces of information, such as the maximum prices
various buyers might be willing to pay, the possible prices being offered by
competing sellers, the rules by which the auction operates, the dynamic arrival
and matching of offers to buy and sell, and so on. A naive application of
multiagent reasoning techniques would require the seller's agent to explicitly
model all of the other agents through an extended time horizon, rendering the
problem intractable for many realistically-sized problems. We have instead
devised a new strategy that an agent can use to determine its bid price based
on a more tractable Markov chain model of the auction process. We have
experimentally identified the conditions under which our new strategy works
well, as well as how well it works in comparison to the optimal performance the
agent could have achieved had it known the future. Our results show that our
new strategy in general performs well, outperforming other tractable heuristic
strategies in a majority of experiments, and is particularly effective in a
'seller?s market', where many buy offers are available.
","Use of Markov Chains to Design an Agent Bidding Strategy for Continuous
  Double Auctions"
"  This paper presents a new classifier combination technique based on the
Dempster-Shafer theory of evidence. The Dempster-Shafer theory of evidence is a
powerful method for combining measures of evidence from different classifiers.
However, since each of the available methods that estimates the evidence of
classifiers has its own limitations, we propose here a new implementation which
adapts to training data so that the overall mean square error is minimized. The
proposed technique is shown to outperform most available classifier combination
methods when tested on three different classification problems.
","A New Technique for Combining Multiple Classifiers using The
  Dempster-Shafer Theory of Evidence"
"  Although many algorithms have been designed to construct Bayesian network
structures using different approaches and principles, they all employ only two
methods: those based on independence criteria, and those based on a scoring
function and a search procedure (although some methods combine the two). Within
the score+search paradigm, the dominant approach uses local search methods in
the space of directed acyclic graphs (DAGs), where the usual choices for
defining the elementary modifications (local changes) that can be applied are
arc addition, arc deletion, and arc reversal. In this paper, we propose a new
local search method that uses a different search space, and which takes account
of the concept of equivalence between network structures: restricted acyclic
partially directed graphs (RPDAGs). In this way, the number of different
configurations of the search space is reduced, thus improving efficiency.
Moreover, although the final result must necessarily be a local optimum given
the nature of the search method, the topology of the new search space, which
avoids making early decisions about the directions of the arcs, may help to
find better local optima than those obtained by searching in the DAG space.
Detailed results of the evaluation of the proposed search method on several
test problems, including the well-known Alarm Monitoring System, are also
presented.
","Searching for Bayesian Network Structures in the Space of Restricted
  Acyclic Partially Directed Graphs"
"  The size and complexity of software and hardware systems have significantly
increased in the past years. As a result, it is harder to guarantee their
correct behavior. One of the most successful methods for automated verification
of finite-state systems is model checking. Most of the current model-checking
systems use binary decision diagrams (BDDs) for the representation of the
tested model and in the verification process of its properties. Generally, BDDs
allow a canonical compact representation of a boolean function (given an order
of its variables). The more compact the BDD is, the better performance one gets
from the verifier. However, finding an optimal order for a BDD is an
NP-complete problem. Therefore, several heuristic methods based on expert
knowledge have been developed for variable ordering. We propose an alternative
approach in which the variable ordering algorithm gains 'ordering experience'
from training models and uses the learned knowledge for finding good orders.
Our methodology is based on offline learning of pair precedence classifiers
from training models, that is, learning which variable pair permutation is more
likely to lead to a good order. For each training model, a number of training
sequences are evaluated. Every training model variable pair permutation is then
tagged based on its performance on the evaluated orders. The tagged
permutations are then passed through a feature extractor and are given as
examples to a classifier creation algorithm. Given a model for which an order
is requested, the ordering algorithm consults each precedence classifier and
constructs a pair precedence table which is used to create the order. Our
algorithm was integrated with SMV, which is one of the most widely used
verification systems. Preliminary empirical evaluation of our methodology,
using real benchmark models, shows performance that is better than random
ordering and is competitive with existing algorithms that use expert knowledge.
We believe that in sub-domains of models (alu, caches, etc.) our system will
prove even more valuable. This is because it features the ability to learn
sub-domain knowledge, something that no other ordering algorithm does.
",Learning to Order BDD Variables in Verification
"  Supply chain formation is the process of determining the structure and terms
of exchange relationships to enable a multilevel, multiagent production
activity. We present a simple model of supply chains, highlighting two
characteristic features: hierarchical subtask decomposition, and resource
contention. To decentralize the formation process, we introduce a market price
system over the resources produced along the chain. In a competitive
equilibrium for this system, agents choose locally optimal allocations with
respect to prices, and outcomes are optimal overall. To determine prices, we
define a market protocol based on distributed, progressive auctions, and
myopic, non-strategic agent bidding policies. In the presence of resource
contention, this protocol produces better solutions than the greedy protocols
common in the artificial intelligence and multiagent systems literature. The
protocol often converges to high-value supply chains, and when competitive
equilibria exist, typically to approximate competitive equilibria. However,
complementarities in agent production technologies can cause the protocol to
wastefully allocate inputs to agents that do not produce their outputs. A
subsequent decommitment phase recovers a significant fraction of the lost
surplus.
","Decentralized Supply Chain Formation: A Market Protocol and Competitive
  Equilibrium Analysis"
"  Information about user preferences plays a key role in automated decision
making. In many domains it is desirable to assess such preferences in a
qualitative rather than quantitative way. In this paper, we propose a
qualitative graphical representation of preferences that reflects conditional
dependence and independence of preference statements under a ceteris paribus
(all else being equal) interpretation. Such a representation is often compact
and arguably quite natural in many circumstances. We provide a formal semantics
for this model, and describe how the structure of the network can be exploited
in several inference tasks, such as determining whether one outcome dominates
(is preferred to) another, ordering a set outcomes according to the preference
relation, and constructing the best outcome subject to available evidence.
","CP-nets: A Tool for Representing and Reasoning withConditional Ceteris
  Paribus Preference Statements"
"  MAP is the problem of finding a most probable instantiation of a set of
variables given evidence. MAP has always been perceived to be significantly
harder than the related problems of computing the probability of a variable
instantiation Pr, or the problem of computing the most probable explanation
(MPE). This paper investigates the complexity of MAP in Bayesian networks.
Specifically, we show that MAP is complete for NP^PP and provide further
negative complexity results for algorithms based on variable elimination. We
also show that MAP remains hard even when MPE and Pr become easy. For example,
we show that MAP is NP-complete when the networks are restricted to polytrees,
and even then can not be effectively approximated. Given the difficulty of
computing MAP exactly, and the difficulty of approximating MAP while providing
useful guarantees on the resulting approximation, we investigate best effort
approximations. We introduce a generic MAP approximation framework. We provide
two instantiations of the framework; one for networks which are amenable to
exact inference Pr, and one for networks for which even exact inference is too
hard. This allows MAP approximation on networks that are too complex to even
exactly solve the easier problems, Pr and MPE. Experimental results indicate
that using these approximation algorithms provides much better solutions than
standard techniques, and provide accurate MAP estimates in many cases.
",Complexity Results and Approximation Strategies for MAP Explanations
"  The Model Checking Integrated Planning System (MIPS) is a temporal least
commitment heuristic search planner based on a flexible object-oriented
workbench architecture. Its design clearly separates explicit and symbolic
directed exploration algorithms from the set of on-line and off-line computed
estimates and associated data structures. MIPS has shown distinguished
performance in the last two international planning competitions. In the last
event the description language was extended from pure propositional planning to
include numerical state variables, action durations, and plan quality objective
functions. Plans were no longer sequences of actions but time-stamped
schedules. As a participant of the fully automated track of the competition,
MIPS has proven to be a general system; in each track and every benchmark
domain it efficiently computed plans of remarkable quality. This article
introduces and analyzes the most important algorithmic novelties that were
necessary to tackle the new layers of expressiveness in the benchmark problems
and to achieve a high level of performance. The extensions include critical
path analysis of sequentially generated plans to generate corresponding optimal
parallel plans. The linear time algorithm to compute the parallel plan bypasses
known NP hardness results for partial ordering by scheduling plans with respect
to the set of actions and the imposed precedence relations. The efficiency of
this algorithm also allows us to improve the exploration guidance: for each
encountered planning state the corresponding approximate sequential plan is
scheduled. One major strength of MIPS is its static analysis phase that grounds
and simplifies parameterized predicates, functions and operators, that infers
knowledge to minimize the state description length, and that detects domain
object symmetries. The latter aspect is analyzed in detail. MIPS has been
developed to serve as a complete and optimal state space planner, with
admissible estimates, exploration engines and branching cuts. In the
competition version, however, certain performance compromises had to be made,
including floating point arithmetic, weighted heuristic search exploration
according to an inadmissible estimate and parameterized optimization.
","Taming Numbers and Durations in the Model Checking Integrated Planning
  System"
"  We propose a formalism for representation of finite languages, referred to as
the class of IDL-expressions, which combines concepts that were only considered
in isolation in existing formalisms. The suggested applications are in natural
language processing, more specifically in surface natural language generation
and in machine translation, where a sentence is obtained by first generating a
large set of candidate sentences, represented in a compact way, and then by
filtering such a set through a parser. We study several formal properties of
IDL-expressions and compare this new formalism with more standard ones. We also
present a novel parsing algorithm for IDL-expressions and prove a non-trivial
upper bound on its time complexity.
","IDL-Expressions: A Formalism for Representing and Parsing Finite
  Languages in Natural Language Processing"
"  Hierarchical latent class (HLC) models are tree-structured Bayesian networks
where leaf nodes are observed while internal nodes are latent. There are no
theoretically well justified model selection criteria for HLC models in
particular and Bayesian networks with latent nodes in general. Nonetheless,
empirical studies suggest that the BIC score is a reasonable criterion to use
in practice for learning HLC models. Empirical studies also suggest that
sometimes model selection can be improved if standard model dimension is
replaced with effective model dimension in the penalty term of the BIC score.
Effective dimensions are difficult to compute. In this paper, we prove a
theorem that relates the effective dimension of an HLC model to the effective
dimensions of a number of latent class models. The theorem makes it
computationally feasible to compute the effective dimensions of large HLC
models. The theorem can also be used to compute the effective dimensions of
general tree models.
",Effective Dimensions of Hierarchical Latent Class Models
"  We introduce an abductive method for a coherent integration of independent
data-sources. The idea is to compute a list of data-facts that should be
inserted to the amalgamated database or retracted from it in order to restore
its consistency. This method is implemented by an abductive solver, called
Asystem, that applies SLDNFA-resolution on a meta-theory that relates
different, possibly contradicting, input databases. We also give a pure
model-theoretic analysis of the possible ways to `recover' consistent data from
an inconsistent database in terms of those models of the database that exhibit
as minimal inconsistent information as reasonably possible. This allows us to
characterize the `recovered databases' in terms of the `preferred' (i.e., most
consistent) models of the theory. The outcome is an abductive-based application
that is sound and complete with respect to a corresponding model-based,
preferential semantics, and -- to the best of our knowledge -- is more
expressive (thus more general) than any other implementation of coherent
integration of databases.
",Coherent Integration of Databases by Abductive Logic Programming
"  We present a visually-grounded language understanding model based on a study
of how people verbally describe objects in scenes. The emphasis of the model is
on the combination of individual word meanings to produce meanings for complex
referring expressions. The model has been implemented, and it is able to
understand a broad range of spatial referring expressions. We describe our
implementation of word level visually-grounded semantics and their embedding in
a compositional parsing framework. The implemented system selects the correct
referents in response to natural language expressions for a large percentage of
test cases. In an analysis of the system's successes and failures we reveal how
visual context influences the semantics of utterances and propose future
extensions to the model that take such context into account.
",Grounded Semantic Composition for Visual Scenes
"  The 2002 Trading Agent Competition (TAC) presented a challenging market game
in the domain of travel shopping. One of the pivotal issues in this domain is
uncertainty about hotel prices, which have a significant influence on the
relative cost of alternative trip schedules. Thus, virtually all participants
employ some method for predicting hotel prices. We survey approaches employed
in the tournament, finding that agents apply an interesting diversity of
techniques, taking into account differing sources of evidence bearing on
prices. Based on data provided by entrants on their agents' actual predictions
in the TAC-02 finals and semifinals, we analyze the relative efficacy of these
approaches. The results show that taking into account game-specific information
about flight prices is a major distinguishing factor. Machine learning methods
effectively induce the relationship between flight and hotel prices from game
data, and a purely analytical approach based on competitive equilibrium
analysis achieves equal accuracy with no historical data. Employing a new
measure of prediction quality, we relate absolute accuracy to bottom-line
performance in the game.
",Price Prediction in a Trading Agent Competition
"  The predominant knowledge-based approach to automated model construction,
compositional modelling, employs a set of models of particular functional
components. Its inference mechanism takes a scenario describing the constituent
interacting components of a system and translates it into a useful mathematical
model. This paper presents a novel compositional modelling approach aimed at
building model repositories. It furthers the field in two respects. Firstly, it
expands the application domain of compositional modelling to systems that can
not be easily described in terms of interacting functional components, such as
ecological systems. Secondly, it enables the incorporation of user preferences
into the model selection process. These features are achieved by casting the
compositional modelling problem as an activity-based dynamic preference
constraint satisfaction problem, where the dynamic constraints describe the
restrictions imposed over the composition of partial models and the preferences
correspond to those of the user of the automated modeller. In addition, the
preference levels are represented through the use of symbolic values that
differ in orders of magnitude.
","Compositional Model Repositories via Dynamic Constraint Satisfaction
  with Order-of-Magnitude Preferences"
"  Two major goals in machine learning are the discovery and improvement of
solutions to complex problems. In this paper, we argue that complexification,
i.e. the incremental elaboration of solutions through adding new structure,
achieves both these goals. We demonstrate the power of complexification through
the NeuroEvolution of Augmenting Topologies (NEAT) method, which evolves
increasingly complex neural network architectures. NEAT is applied to an
open-ended coevolutionary robot duel domain where robot controllers compete
head to head. Because the robot duel domain supports a wide range of
strategies, and because coevolution benefits from an escalating arms race, it
serves as a suitable testbed for studying complexification. When compared to
the evolution of networks with fixed structure, complexifying evolution
discovers significantly more sophisticated strategies. The results suggest that
in order to discover and improve complex solutions, evolution, and search in
general, should be allowed to complexify as well as optimize.
",Competitive Coevolution through Evolutionary Complexification
"  When writing a constraint program, we have to choose which variables should
be the decision variables, and how to represent the constraints on these
variables. In many cases, there is considerable choice for the decision
variables. Consider, for example, permutation problems in which we have as many
values as variables, and each variable takes an unique value. In such problems,
we can choose between a primal and a dual viewpoint. In the dual viewpoint,
each dual variable represents one of the primal values, whilst each dual value
represents one of the primal variables. Alternatively, by means of channelling
constraints to link the primal and dual variables, we can have a combined model
with both sets of variables. In this paper, we perform an extensive theoretical
and empirical study of such primal, dual and combined models for two classes of
problems: permutation problems and injection problems. Our results show that it
often be advantageous to use multiple viewpoints, and to have constraints which
channel between them to maintain consistency. They also illustrate a general
methodology for comparing different constraint models.
",Dual Modelling of Permutation and Injection Problems
"  This is the first of three planned papers describing ZAP, a satisfiability
engine that substantially generalizes existing tools while retaining the
performance characteristics of modern high-performance solvers. The fundamental
idea underlying ZAP is that many problems passed to such engines contain rich
internal structure that is obscured by the Boolean representation used; our
goal is to define a representation in which this structure is apparent and can
easily be exploited to improve computational performance. This paper is a
survey of the work underlying ZAP, and discusses previous attempts to improve
the performance of the Davis-Putnam-Logemann-Loveland algorithm by exploiting
the structure of the problem being solved. We examine existing ideas including
extensions of the Boolean language to allow cardinality constraints,
pseudo-Boolean representations, symmetry, and a limited form of quantification.
While this paper is intended as a survey, our research results are contained in
the two subsequent articles, with the theoretical structure of ZAP described in
the second paper in this series, and ZAP's implementation described in the
third.
","Generalizing Boolean Satisfiability I: Background and Survey of Existing
  Work"
"  We address the problem of finding the shortest path between two points in an
unknown real physical environment, where a traveling agent must move around in
the environment to explore unknown territory. We introduce the Physical-A*
algorithm (PHA*) for solving this problem. PHA* expands all the mandatory nodes
that A* would expand and returns the shortest path between the two points.
However, due to the physical nature of the problem, the complexity of the
algorithm is measured by the traveling effort of the moving agent and not by
the number of generated nodes, as in standard A*. PHA* is presented as a
two-level algorithm, such that its high level, A*, chooses the next node to be
expanded and its low level directs the agent to that node in order to explore
it. We present a number of variations for both the high-level and low-level
procedures and evaluate their performance theoretically and experimentally. We
show that the travel cost of our best variation is fairly close to the optimal
travel cost, assuming that the mandatory nodes of A* are known in advance. We
then generalize our algorithm to the multi-agent case, where a number of
cooperative agents are designed to solve the problem. Specifically, we provide
an experimental implementation for such a system. It should be noted that the
problem addressed here is not a navigation problem, but rather a problem of
finding the shortest path between two points for future usage.
","PHA*: Finding the Shortest Path with A* in An Unknown Physical
  Environment"
"  Value iteration is a popular algorithm for finding near optimal policies for
POMDPs. It is inefficient due to the need to account for the entire belief
space, which necessitates the solution of large numbers of linear programs. In
this paper, we study value iteration restricted to belief subsets. We show
that, together with properly chosen belief subsets, restricted value iteration
yields near-optimal policies and we give a condition for determining whether a
given belief subset would bring about savings in space and time. We also apply
restricted value iteration to two interesting classes of POMDPs, namely
informative POMDPs and near-discernible POMDPs.
",Restricted Value Iteration: Theory and Algorithms
"  Many researchers in artificial intelligence are beginning to explore the use
of soft constraints to express a set of (possibly conflicting) problem
requirements. A soft constraint is a function defined on a collection of
variables which associates some measure of desirability with each possible
combination of values for those variables. However, the crucial question of the
computational complexity of finding the optimal solution to a collection of
soft constraints has so far received very little attention. In this paper we
identify a class of soft binary constraints for which the problem of finding
the optimal solution is tractable. In other words, we show that for any given
set of such constraints, there exists a polynomial time algorithm to determine
the assignment having the best overall combined measure of desirability. This
tractable class includes many commonly-occurring soft constraints, such as 'as
near as possible' or 'as soon as possible after', as well as crisp constraints
such as 'greater than'. Finally, we show that this tractable class is maximal,
in the sense that adding any other form of soft binary constraint which is not
in the class gives rise to a class of problems which is NP-hard.
",A Maximal Tractable Class of Soft Constraints
"  Efficient implementations of DPLL with the addition of clause learning are
the fastest complete Boolean satisfiability solvers and can handle many
significant real-world problems, such as verification, planning and design.
Despite its importance, little is known of the ultimate strengths and
limitations of the technique. This paper presents the first precise
characterization of clause learning as a proof system (CL), and begins the task
of understanding its power by relating it to the well-studied resolution proof
system. In particular, we show that with a new learning scheme, CL can provide
exponentially shorter proofs than many proper refinements of general resolution
(RES) satisfying a natural property. These include regular and Davis-Putnam
resolution, which are already known to be much stronger than ordinary DPLL. We
also show that a slight variant of CL with unlimited restarts is as powerful as
RES itself. Translating these analytical results to practice, however, presents
a challenge because of the nondeterministic nature of clause learning
algorithms. We propose a novel way of exploiting the underlying problem
structure, in the form of a high level problem description such as a graph or
PDDL specification, to guide clause learning algorithms toward faster
solutions. We show that this leads to exponential speed-ups on grid and
randomized pebbling problems, as well as substantial improvements on certain
ordering formulas.
",Towards Understanding and Harnessing the Potential of Clause Learning
"  Argumentation is based on the exchange and valuation of interacting
arguments, followed by the selection of the most acceptable of them (for
example, in order to take a decision, to make a choice). Starting from the
framework proposed by Dung in 1995, our purpose is to introduce 'graduality' in
the selection of the best arguments, i.e., to be able to partition the set of
the arguments in more than the two usual subsets of 'selected' and
'non-selected' arguments in order to represent different levels of selection.
Our basic idea is that an argument is all the more acceptable if it can be
preferred to its attackers. First, we discuss general principles underlying a
'gradual' valuation of arguments based on their interactions. Following these
principles, we define several valuation models for an abstract argumentation
system. Then, we introduce 'graduality' in the concept of acceptability of
arguments. We propose new acceptability classes and a refinement of existing
classes taking advantage of an available 'gradual' valuation.
",Graduality in Argumentation
"  Inductive learning is based on inferring a general rule from a finite data
set and using it to label new data. In transduction one attempts to solve the
problem of using a labeled training set to label a set of unlabeled points,
which are given to the learner prior to learning. Although transduction seems
at the outset to be an easier task than induction, there have not been many
provably useful algorithms for transduction. Moreover, the precise relation
between induction and transduction has not yet been determined. The main
theoretical developments related to transduction were presented by Vapnik more
than twenty years ago. One of Vapnik's basic results is a rather tight error
bound for transductive classification based on an exact computation of the
hypergeometric tail. While tight, this bound is given implicitly via a
computational routine. Our first contribution is a somewhat looser but explicit
characterization of a slightly extended PAC-Bayesian version of Vapnik's
transductive bound. This characterization is obtained using concentration
inequalities for the tail of sums of random variables obtained by sampling
without replacement. We then derive error bounds for compression schemes such
as (transductive) support vector machines and for transduction algorithms based
on clustering. The main observation used for deriving these new error bounds
and algorithms is that the unlabeled test points, which in the transductive
setting are known in advance, can be used in order to construct useful data
dependent prior distributions over the hypothesis space.
","Explicit Learning Curves for Transduction and Application to Clustering
  and Compression Algorithms"
"  Decentralized control of cooperative systems captures the operation of a
group of decision makers that share a single global objective. The difficulty
in solving optimally such problems arises when the agents lack full
observability of the global state of the system when they operate. The general
problem has been shown to be NEXP-complete. In this paper, we identify classes
of decentralized control problems whose complexity ranges between NEXP and P.
In particular, we study problems characterized by independent transitions,
independent observations, and goal-oriented objective functions. Two algorithms
are shown to solve optimally useful classes of goal-oriented decentralized
processes in polynomial time. This paper also studies information sharing among
the decision-makers, which can improve their performance. We distinguish
between three ways in which agents can exchange information: indirect
communication, direct communication and sharing state features that are not
controlled by the agents. Our analysis shows that for every class of problems
we consider, introducing direct or indirect communication does not change the
worst-case complexity. The results provide a better understanding of the
complexity of decentralized control problems that arise in practice and
facilitate the development of planning algorithms for these problems.
","Decentralized Control of Cooperative Systems: Categorization and
  Complexity Analysis"
"  In this paper, we confront the problem of applying reinforcement learning to
agents that perceive the environment through many sensors and that can perform
parallel actions using many actuators as is the case in complex autonomous
robots. We argue that reinforcement learning can only be successfully applied
to this case if strong assumptions are made on the characteristics of the
environment in which the learning is performed, so that the relevant sensor
readings and motor commands can be readily identified. The introduction of such
assumptions leads to strongly-biased learning systems that can eventually lose
the generality of traditional reinforcement-learning algorithms. In this line,
we observe that, in realistic situations, the reward received by the robot
depends only on a reduced subset of all the executed actions and that only a
reduced subset of the sensor inputs (possibly different in each situation and
for each action) are relevant to predict the reward. We formalize this property
in the so called 'categorizability assumption' and we present an algorithm that
takes advantage of the categorizability of the environment, allowing a decrease
in the learning time with respect to existing reinforcement-learning
algorithms. Results of the application of the algorithm to a couple of
simulated realistic-robotic problems (landmark-based navigation and the
six-legged robot gait generation) are reported to validate our approach and to
compare it to existing flat and generalization-based reinforcement-learning
approaches.
","Reinforcement Learning for Agents with Many Sensors and Actuators Acting
  in Categorizable Environments"
"  We explore a method for computing admissible heuristic evaluation functions
for search problems. It utilizes pattern databases, which are precomputed
tables of the exact cost of solving various subproblems of an existing problem.
Unlike standard pattern database heuristics, however, we partition our problems
into disjoint subproblems, so that the costs of solving the different
subproblems can be added together without overestimating the cost of solving
the original problem. Previously, we showed how to statically partition the
sliding-tile puzzles into disjoint groups of tiles to compute an admissible
heuristic, using the same partition for each state and problem instance. Here
we extend the method and show that it applies to other domains as well. We also
present another method for additive heuristics which we call dynamically
partitioned pattern databases. Here we partition the problem into disjoint
subproblems for each state of the search dynamically. We discuss the pros and
cons of each of these methods and apply both methods to three different problem
domains: the sliding-tile puzzles, the 4-peg Towers of Hanoi problem, and
finding an optimal vertex cover of a graph. We find that in some problem
domains, static partitioning is most effective, while in others dynamic
partitioning is a better choice. In each of these problem domains, either
statically partitioned or dynamically partitioned pattern database heuristics
are the best known heuristics for the problem.
",Additive Pattern Database Heuristics
"  This paper is concerned with algorithms for prediction of discrete sequences
over a finite alphabet, using variable order Markov models. The class of such
algorithms is large and in principle includes any lossless compression
algorithm. We focus on six prominent prediction algorithms, including Context
Tree Weighting (CTW), Prediction by Partial Match (PPM) and Probabilistic
Suffix Trees (PSTs). We discuss the properties of these algorithms and compare
their performance using real life sequences from three domains: proteins,
English text and music pieces. The comparison is made with respect to
prediction quality as measured by the average log-loss. We also compare
classification algorithms based on these predictors with respect to a number of
large protein classification tasks. Our results indicate that a ""decomposed""
CTW (a variant of the CTW algorithm) and PPM outperform all other algorithms in
sequence prediction tasks. Somewhat surprisingly, a different algorithm, which
is a modification of the Lempel-Ziv compression algorithm, significantly
outperforms all algorithms on the protein classification problems.
",On Prediction Using Variable Order Markov Models
"  Many known planning tasks have inherent constraints concerning the best order
in which to achieve the goals. A number of research efforts have been made to
detect such constraints and to use them for guiding search, in the hope of
speeding up the planning process. We go beyond the previous approaches by
considering ordering constraints not only over the (top-level) goals, but also
over the sub-goals that will necessarily arise during planning. Landmarks are
facts that must be true at some point in every valid solution plan. We extend
Koehler and Hoffmann's definition of reasonable orders between top level goals
to the more general case of landmarks. We show how landmarks can be found, how
their reasonable orders can be approximated, and how this information can be
used to decompose a given planning task into several smaller sub-tasks. Our
methodology is completely domain- and planner-independent. The implementation
demonstrates that the approach can yield significant runtime performance
improvements when used as a control loop around state-of-the-art sub-optimal
planning systems, as exemplified by FF and LPG.
",Ordered Landmarks in Planning
"  Standard value function approaches to finding policies for Partially
Observable Markov Decision Processes (POMDPs) are generally considered to be
intractable for large models. The intractability of these algorithms is to a
large extent a consequence of computing an exact, optimal policy over the
entire belief space. However, in real-world POMDP problems, computing the
optimal policy for the full belief space is often unnecessary for good control
even for problems with complicated policy classes. The beliefs experienced by
the controller often lie near a structured, low-dimensional subspace embedded
in the high-dimensional belief space. Finding a good approximation to the
optimal value function for only this subspace can be much easier than computing
the full value function. We introduce a new method for solving large-scale
POMDPs by reducing the dimensionality of the belief space. We use Exponential
family Principal Components Analysis (Collins, Dasgupta and Schapire, 2002) to
represent sparse, high-dimensional belief spaces using small sets of learned
features of the belief state. We then plan only in terms of the low-dimensional
belief features. By planning in this low-dimensional space, we can find
policies for POMDP models that are orders of magnitude larger than models that
can be handled by conventional techniques. We demonstrate the use of this
algorithm on a synthetic problem and on mobile robot navigation tasks.
",Finding Approximate POMDP solutions Through Belief Compression
"  We propose a model for errors in sung queries, a variant of the hidden Markov
model (HMM). This is a solution to the problem of identifying the degree of
similarity between a (typically error-laden) sung query and a potential target
in a database of musical works, an important problem in the field of music
information retrieval. Similarity metrics are a critical component of
query-by-humming (QBH) applications which search audio and multimedia databases
for strong matches to oral queries. Our model comprehensively expresses the
types of error or variation between target and query: cumulative and
non-cumulative local errors, transposition, tempo and tempo changes,
insertions, deletions and modulation. The model is not only expressive, but
automatically trainable, or able to learn and generalize from query examples.
We present results of simulations, designed to assess the discriminatory
potential of the model, and tests with real sung queries, to demonstrate
relevance to real-world applications.
",A Comprehensive Trainable Error Model for Sung Music Queries
"  In recent years, there has been much interest in phase transitions of
combinatorial problems. Phase transitions have been successfully used to
analyze combinatorial optimization problems, characterize their typical-case
features and locate the hardest problem instances. In this paper, we study
phase transitions of the asymmetric Traveling Salesman Problem (ATSP), an
NP-hard combinatorial optimization problem that has many real-world
applications. Using random instances of up to 1,500 cities in which intercity
distances are uniformly distributed, we empirically show that many properties
of the problem, including the optimal tour cost and backbone size, experience
sharp transitions as the precision of intercity distances increases across a
critical value. Our experimental results on the costs of the ATSP tours and
assignment problem agree with the theoretical result that the asymptotic cost
of assignment problem is pi ^2 /6 the number of cities goes to infinity. In
addition, we show that the average computational cost of the well-known
branch-and-bound subtour elimination algorithm for the problem also exhibits a
thrashing behavior, transitioning from easy to difficult as the distance
precision increases. These results answer positively an open question regarding
the existence of phase transitions in the ATSP, and provide guidance on how
difficult ATSP problem instances should be generated.
","Phase Transitions and Backbones of the Asymmetric Traveling Salesman
  Problem"
"  A time series consists of a series of values or events obtained over repeated
measurements in time. Analysis of time series represents and important tool in
many application areas, such as stock market analysis, process and quality
control, observation of natural phenomena, medical treatments, etc. A vital
component in many types of time-series analysis is the choice of an appropriate
distance/similarity measure. Numerous measures have been proposed to date, with
the most successful ones based on dynamic programming. Being of quadratic time
complexity, however, global constraints are often employed to limit the search
space in the matrix during the dynamic programming procedure, in order to speed
up computation. Furthermore, it has been reported that such constrained
measures can also achieve better accuracy. In this paper, we investigate two
representative time-series distance/similarity measures based on dynamic
programming, Dynamic Time Warping (DTW) and Longest Common Subsequence (LCS),
and the effects of global constraints on them. Through extensive experiments on
a large number of time-series data sets, we demonstrate how global constrains
can significantly reduce the computation time of DTW and LCS. We also show
that, if the constraint parameter is tight enough (less than 10-15% of
time-series length), the constrained measure becomes significantly different
from its unconstrained counterpart, in the sense of producing qualitatively
different 1-nearest neighbor graphs. This observation explains the potential
for accuracy gains when using constrained measures, highlighting the need for
careful tuning of constraint parameters in order to achieve a good trade-off
between speed and accuracy.
","The Influence of Global Constraints on Similarity Measures for
  Time-Series Databases"
"  We present in this paper our law that there is always a connection present
between two entities, with a selfconnection being present at least in each
node. An entity is an object, physical or imaginary, that is connected by a
path (or connection) and which is important for achieving the desired result of
the scenario. In machine learning, we state that for any scenario, a subject
entity is always, directly or indirectly, connected and affected by single or
multiple independent / dependent entities, and their impact on the subject
entity is dependent on various factors falling into the categories such as the
existenc
",Law of Connectivity in Machine Learning
"  The importance of algorithm portfolio techniques for SAT has long been noted,
and a number of very successful systems have been devised, including the most
successful one --- SATzilla. However, all these systems are quite complex (to
understand, reimplement, or modify). In this paper we propose a new algorithm
portfolio for SAT that is extremely simple, but in the same time so efficient
that it outperforms SATzilla. For a new SAT instance to be solved, our
portfolio finds its k-nearest neighbors from the training set and invokes a
solver that performs the best at those instances. The main distinguishing
feature of our algorithm portfolio is the locality of the selection procedure
--- the selection of a SAT solver is based only on few instances similar to the
input one.
",Simple Algorithm Portfolio for SAT
"  The superiority and inferiority ranking (SIR) method is a generation of the
well-known PROMETHEE method, which can be more efficient to deal with
multi-criterion decision making (MCDM) problem. Intuitionistic fuzzy sets
(IFSs), as an important extension of fuzzy sets (IFs), include both membership
functions and non-membership functions and can be used to, more precisely
describe uncertain information. In real world, decision situations are usually
under uncertain environment and involve multiple individuals who have their own
points of view on handing of decision problems. In order to solve uncertainty
group MCDM problem, we propose a novel intuitionistic fuzzy SIR method in this
paper. This approach uses intuitionistic fuzzy aggregation operators and SIR
ranking methods to handle uncertain information; integrate individual opinions
into group opinions; make decisions on multiple-criterion; and finally
structure a specific decision map. The proposed approach is illustrated in a
simulation of group decision making problem related to supply chain management.
","A Novel Multicriteria Group Decision Making Approach With Intuitionistic
  Fuzzy SIR Method"
"  This volume contains the papers presented at the first edition of the
Doctoral Consortium of the 5th International Symposium on Rules (RuleML
2011@IJCAI) held on July 19th, 2011 in Barcelona, as well as the poster session
papers of the RuleML 2011@IJCAI main conference.
","Proceedings of the Doctoral Consortium and Poster Session of the 5th
  International Symposium on Rules (RuleML 2011@IJCAI)"
"  Informledge System (ILS) is a knowledge network with autonomous nodes and
intelligent links that integrate and structure the pieces of knowledge. In this
paper, we put forward the strategies for knowledge embedding and retrieval in
an ILS. ILS is a powerful knowledge network system dealing with logical storage
and connectivity of information units to form knowledge using autonomous nodes
and multi-lateral links. In ILS, the autonomous nodes known as Knowledge
Network Nodes (KNN)s play vital roles which are not only used in storage,
parsing and in forming the multi-lateral linkages between knowledge points but
also in helping the realization of intelligent retrieval of linked information
units in the form of knowledge. Knowledge built in to the ILS forms the shape
of sphere. The intelligence incorporated into the links of a KNN helps in
retrieving various knowledge threads from a specific set of KNNs. A developed
entity of information realized through KNN forms in to the shape of a knowledge
cone
",Knowledge Embedding and Retrieval Strategies in an Informledge System
"  The proposal of Elisa Marengo's thesis is to extend commitment protocols to
explicitly account for temporal regulations. This extension will satisfy two
needs: (1) it will allow representing, in a flexible and modular way, temporal
regulations with a normative force, posed on the interaction, so as to
represent conventions, laws and suchlike; (2) it will allow committing to
complex conditions, which describe not only what will be achieved but to some
extent also how. These two aspects will be deeply investigated in the proposal
of a unified framework, which is part of the ongoing work and will be included
in the thesis.
",Extend Commitment Protocols with Temporal Regulations: Why and How
"  Rule-Based Systems have been in use for decades to solve a variety of
problems but not in the sensor informatics domain. Rules aid the aggregation of
low-level sensor readings to form a more complete picture of the real world and
help to address 10 identified challenges for sensor network middleware. This
paper presents the reader with an overview of a system architecture and a pilot
application to demonstrate the usefulness of a system integrating rules with
sensor middleware.
",Rule-Based Semantic Sensing
"  Multi-Context Systems are an expressive formalism to model (possibly)
non-monotonic information exchange between heterogeneous knowledge bases. Such
information exchange, however, often comes with unforseen side-effects leading
to violation of constraints, making the system inconsistent, and thus unusable.
Although there are many approaches to assess and repair a single inconsistent
knowledge base, the heterogeneous nature of Multi-Context Systems poses
problems which have not yet been addressed in a satisfying way: How to identify
and explain a inconsistency that spreads over multiple knowledge bases with
different logical formalisms (e.g., logic programs and ontologies)? What are
the causes of inconsistency if inference/information exchange is non-monotonic
(e.g., absent information as cause)? How to deal with inconsistency if access
to knowledge bases is restricted (e.g., companies exchange information, but do
not allow arbitrary modifications to their knowledge bases)? Many traditional
approaches solely aim for a consistent system, but automatic removal of
inconsistency is not always desireable. Therefore a human operator has to be
supported in finding the erroneous parts contributing to the inconsistency. In
my thesis those issues will be adressed mainly from a foundational perspective,
while our research project also provides algorithms and prototype
implementations.
",Advancing Multi-Context Systems by Inconsistency Management
"  We present a description of the PhD thesis which aims to propose a rule-based
query answering method for relational data. In this approach we use an
additional knowledge which is represented as a set of rules and describes the
source data at concept (ontological) level. Queries are posed in the terms of
abstract level. We present two methods. The first one uses hybrid reasoning and
the second one exploits only forward chaining. These two methods are
demonstrated by the prototypical implementation of the system coupled with the
Jess engine. Tests are performed on the knowledge base of the selected economic
crimes: fraudulent disbursement and money laundering.
","Rule-based query answering method for a knowledge base of economic
  crimes"
"  IT Service Management deals with managing a broad range of items related to
complex system environments. As there is both, a close connection to business
interests and IT infrastructure, the application of semantic expressions which
are seamlessly integrated within applications for managing ITSM environments,
can help to improve transparency and profitability. This paper focuses on the
challenges regarding the integration of semantics and ontologies within ITSM
environments. It will describe the paradigm of relationships and inheritance
within complex service trees and will present an approach of ontologically
expressing them. Furthermore, the application of SBVR-based rules as executable
SQL triggers will be discussed. Finally, the broad range of topics for further
research, derived from the findings, will be presented.
","Semantic-ontological combination of Business Rules and Business
  Processes in IT Service Management"
"  The study proposes a framework of ONTOlogy-based Group Decision Support
System (ONTOGDSS) for decision process which exhibits the complex structure of
decision-problem and decision-group. It is capable of reducing the complexity
of problem structure and group relations. The system allows decision makers to
participate in group decision-making through the web environment, via the
ontology relation. It facilitates the management of decision process as a
whole, from criteria generation, alternative evaluation, and opinion
interaction to decision aggregation. The embedded ontology structure in
ONTOGDSS provides the important formal description features to facilitate
decision analysis and verification. It examines the software architecture, the
selection methods, the decision path, etc. Finally, the ontology application of
this system is illustrated with specific real case to demonstrate its
potentials towards decision-making development.
",An Ontology-driven Framework for Supporting Complex Decision Process
"  Fault diagnosis and failure prognosis are essential techniques in improving
the safety of many manufacturing systems. Therefore, on-line fault detection
and isolation is one of the most important tasks in safety-critical and
intelligent control systems. Computational intelligence techniques are being
investigated as extension of the traditional fault diagnosis methods. This
paper discusses the Temporal Neuro-Fuzzy Systems (TNFS) fault diagnosis within
an application study of a manufacturing system. The key issues of finding a
suitable structure for detecting and isolating ten realistic actuator faults
are described. Within this framework, data-processing interactive software of
simulation baptized NEFDIAG (NEuro Fuzzy DIAGnosis) version 1.0 is developed.
  This software devoted primarily to creation, training and test of a
classification Neuro-Fuzzy system of industrial process failures. NEFDIAG can
be represented like a special type of fuzzy perceptron, with three layers used
to classify patterns and failures. The system selected is the workshop of
SCIMAT clinker, cement factory in Algeria.
",A Temporal Neuro-Fuzzy Monitoring System to Manufacturing Systems
"  Open-text (or open-domain) semantic parsers are designed to interpret any
statement in natural language by inferring a corresponding meaning
representation (MR). Unfortunately, large scale systems cannot be easily
machine-learned due to lack of directly supervised data. We propose here a
method that learns to assign MRs to a wide range of text (using a dictionary of
more than 70,000 words, which are mapped to more than 40,000 entities) thanks
to a training scheme that combines learning from WordNet and ConceptNet with
learning from raw text. The model learns structured embeddings of words,
entities and MRs via a multi-task training process operating on these diverse
sources of data that integrates all the learnt knowledge into a single system.
This work ends up combining methods for knowledge acquisition, semantic
parsing, and word-sense disambiguation. Experiments on various tasks indicate
that our approach is indeed successful and can form a basis for future more
sophisticated systems.
","Towards Open-Text Semantic Parsing via Multi-Task Learning of Structured
  Embeddings"
"  Commute Time Distance (CTD) is a random walk based metric on graphs. CTD has
found widespread applications in many domains including personalized search,
collaborative filtering and making search engines robust against manipulation.
Our interest is inspired by the use of CTD as a metric for anomaly detection.
It has been shown that CTD can be used to simultaneously identify both global
and local anomalies. Here we propose an accurate and efficient approximation
for computing the CTD in an incremental fashion in order to facilitate
real-time applications. An online anomaly detection algorithm is designed where
the CTD of each new arriving data point to any point in the current graph can
be estimated in constant time ensuring a real-time response. Moreover, the
proposed approach can also be applied in many other applications that utilize
commute time distance.
",Online Anomaly Detection Systems Using Incremental Commute Time
"  The promise of lifted probabilistic inference is to carry out probabilistic
inference in a relational probabilistic model without needing to reason about
each individual separately (grounding out the representation) by treating the
undistinguished individuals as a block. Current exact methods still need to
ground out in some cases, typically because the representation of the
intermediate results is not closed under the lifted operations. We set out to
answer the question as to whether there is some fundamental reason why lifted
algorithms would need to ground out undifferentiated individuals. We have two
main results: (1) We completely characterize the cases where grounding is
polynomial in a population size, and show how we can do lifted inference in
time polynomial in the logarithm of the population size for these cases. (2)
For the case of no-argument and single-argument parametrized random variables
where the grounding is not polynomial in a population size, we present lifted
inference which is polynomial in the population size whereas grounding is
exponential. Neither of these cases requires reasoning separately about the
individuals that are not explicitly mentioned.
",Towards Completely Lifted Search-based Probabilistic Inference
"  Using a recently proposed model for combinatorial landscapes, Local Optima
Networks (LON), we conduct a thorough analysis of two types of instances of the
Quadratic Assignment Problem (QAP). This network model is a reduction of the
landscape in which the nodes correspond to the local optima, and the edges
account for the notion of adjacency between their basins of attraction. The
model was inspired by the notion of 'inherent network' of potential energy
surfaces proposed in physical-chemistry. The local optima networks extracted
from the so called uniform and real-like QAP instances, show features clearly
distinguishing these two types of instances. Apart from a clear confirmation
that the search difficulty increases with the problem dimension, the analysis
provides new confirming evidence explaining why the real-like instances are
easier to solve exactly using heuristic search, while the uniform instances are
easier to solve approximately. Although the local optima network model is still
under development, we argue that it provides a novel view of combinatorial
landscapes, opening up the possibilities for new analytical tools and
understanding of problem difficulty in combinatorial optimization.
",Local Optima Networks of the Quadratic Assignment Problem
"  In previous work we have introduced a network-based model that abstracts many
details of the underlying landscape and compresses the landscape information
into a weighted, oriented graph which we call the local optima network. The
vertices of this graph are the local optima of the given fitness landscape,
while the arcs are transition probabilities between local optima basins. Here
we extend this formalism to neutral fitness landscapes, which are common in
difficult combinatorial search spaces. By using two known neutral variants of
the NK family (i.e. NKp and NKq) in which the amount of neutrality can be tuned
by a parameter, we show that our new definitions of the optima networks and the
associated basins are consistent with the previous definitions for the
non-neutral case. Moreover, our empirical study and statistical analysis show
that the features of neutral landscapes interpolate smoothly between landscapes
with maximum neutrality and non-neutral ones. We found some unknown structural
differences between the two studied families of neutral landscapes. But
overall, the network features studied confirmed that neutrality, in landscapes
with percolating neutral networks, may enhance heuristic search. Our current
methodology requires the exhaustive enumeration of the underlying search space.
Therefore, sampling techniques should be developed before this analysis can
have practical implications. We argue, however, that the proposed model offers
a new perspective into the problem difficulty of combinatorial optimization
problems and may inspire the design of more effective search heuristics.
",Local Optima Networks of NK Landscapes with Neutrality
"  In this paper, we study the exploration / exploitation trade-off in cellular
genetic algorithms. We define a new selection scheme, the centric selection,
which is tunable and allows controlling the selective pressure with a single
parameter. The equilibrium model is used to study the influence of the centric
selection on the selective pressure and a new model which takes into account
problem dependent statistics and selective pressure in order to deal with the
exploration / exploitation trade-off is proposed: the punctuated equilibria
model. Performances on the quadratic assignment problem and NK-Landscapes put
in evidence an optimal exploration / exploitation trade-off on both of the
classes of problems. The punctuated equilibria model is used to explain these
results.
",Centric selection: a way to tune the exploration/exploitation trade-off
"  Negative Slope Coefficient is an indicator of problem hardness that has been
introduced in 2004 and that has returned promising results on a large set of
problems. It is based on the concept of fitness cloud and works by partitioning
the cloud into a number of bins representing as many different regions of the
fitness landscape. The measure is calculated by joining the bins centroids by
segments and summing all their negative slopes. In this paper, for the first
time, we point out a potential problem of the Negative Slope Coefficient: we
study its value for different instances of the well known NK-landscapes and we
show how this indicator is dramatically influenced by the minimum number of
points contained into a bin. Successively, we formally justify this behavior of
the Negative Slope Coefficient and we discuss pros and cons of this measure.
","NK landscapes difficulty and Negative Slope Coefficient: How Sampling
  Influences the Results"
"  Effective debugging of ontologies is an important prerequisite for their
broad application, especially in areas that rely on everyday users to create
and maintain knowledge bases, such as the Semantic Web. In such systems
ontologies capture formalized vocabularies of terms shared by its users.
However in many cases users have different local views of the domain, i.e. of
the context in which a given term is used. Inappropriate usage of terms
together with natural complications when formulating and understanding logical
descriptions may result in faulty ontologies. Recent ontology debugging
approaches use diagnosis methods to identify causes of the faults. In most
debugging scenarios these methods return many alternative diagnoses, thus
placing the burden of fault localization on the user. This paper demonstrates
how the target diagnosis can be identified by performing a sequence of
observations, that is, by querying an oracle about entailments of the target
ontology. To identify the best query we propose two query selection strategies:
a simple ""split-in-half"" strategy and an entropy-based strategy. The latter
allows knowledge about typical user errors to be exploited to minimize the
number of queries. Our evaluation showed that the entropy-based method
significantly reduces the number of required queries compared to the
""split-in-half"" approach. We experimented with different probability
distributions of user errors and different qualities of the a-priori
probabilities. Our measurements demonstrated the superiority of entropy-based
query selection even in cases where all fault probabilities are equal, i.e.
where no information about typical user errors is available.
","Interactive ontology debugging: two query strategies for efficient fault
  localization"
"  The web of data consists of data published on the web in such a way that they
can be interpreted and connected together. It is thus critical to establish
links between these data, both for the web of data and for the semantic web
that it contributes to feed. We consider here the various techniques developed
for that purpose and analyze their commonalities and differences. We propose a
general framework and show how the diverse techniques fit in the framework.
From this framework we consider the relation between data interlinking and
ontology matching. Although, they can be considered similar at a certain level
(they both relate formal entities), they serve different purposes, but would
find a mutual benefit at collaborating. We thus present a scheme under which it
is possible for data linking tools to take advantage of ontology alignments.
",MeLinDa: an interlinking framework for the web of data
"  Symmetries occur naturally in CSP or SAT problems and are not very difficult
to discover, but using them to prune the search space tends to be very
challenging. Indeed, this usually requires finding specific elements in a group
of symmetries that can be huge, and the problem of their very existence is
NP-hard. We formulate such an existence problem as a constraint problem on one
variable (the symmetry to be used) ranging over a group, and try to find
restrictions that may be solved in polynomial time. By considering a simple
form of constraints (restricted by a cardinality k) and the class of groups
that have the structure of Fp-vector spaces, we propose a partial algorithm
based on linear algebra. This polynomial algorithm always applies when k=p=2,
but may fail otherwise as we prove the problem to be NP-hard for all other
values of k and p. Experiments show that this approach though restricted should
allow for an efficient use of at least some groups of symmetries. We conclude
with a few directions to be explored to efficiently solve this problem on the
general case.
",Solving Linear Constraints in Elementary Abelian p-Groups of Symmetries
"  Given a causal model of some domain and a particular story that has taken
place in this domain, the problem of actual causation is deciding which of the
possible causes for some effect actually caused it. One of the most influential
approaches to this problem has been developed by Halpern and Pearl in the
context of structural models. In this paper, I argue that this is actually not
the best setting for studying this problem. As an alternative, I offer the
probabilistic logic programming language of CP-logic. Unlike structural models,
CP-logic incorporates the deviant/default distinction that is generally
considered an important aspect of actual causation, and it has an explicitly
dynamic semantics, which helps to formalize the stories that serve as input to
an actual causation problem.
",Actual Causation in CP-logic
"  This paper investigates under which conditions instantiation-based proof
procedures can be combined in a nested way, in order to mechanically construct
new instantiation procedures for richer theories. Interesting applications in
the field of verification are emphasized, particularly for handling extensions
of the theory of arrays.
",Instantiation Schemes for Nested Theories
"  Automating the design of heuristic search methods is an active research field
within computer science, artificial intelligence and operational research. In
order to make these methods more generally applicable, it is important to
eliminate or reduce the role of the human expert in the process of designing an
effective methodology to solve a given computational search problem.
Researchers developing such methodologies are often constrained on the number
of problem domains on which to test their adaptive, self-configuring
algorithms; which can be explained by the inherent difficulty of implementing
their corresponding domain specific software components.
  This paper presents HyFlex, a software framework for the development of
cross-domain search methodologies. The framework features a common software
interface for dealing with different combinatorial optimisation problems, and
provides the algorithm components that are problem specific. In this way, the
algorithm designer does not require a detailed knowledge the problem domains,
and thus can concentrate his/her efforts in designing adaptive general-purpose
heuristic search algorithms. Four hard combinatorial problems are fully
implemented (maximum satisfiability, one dimensional bin packing, permutation
flow shop and personnel scheduling), each containing a varied set of instance
data (including real-world industrial applications) and an extensive set of
problem specific heuristics and search operators. The framework forms the basis
for the first International Cross-domain Heuristic Search Challenge (CHeSC),
and it is currently in use by the international research community. In summary,
HyFlex represents a valuable new benchmark of heuristic search generality, with
which adaptive cross-domain algorithms are being easily developed, and reliably
compared.
",HyFlex: A Benchmark Framework for Cross-domain Heuristic Search
"  In order to address complex systems, apply pattern recongnition on their
evolution could play an key role to understand their dynamics. Global patterns
are required to detect emergent concepts and trends, some of them with
qualitative nature. Formal Concept Analysis (FCA) is a theory whose goal is to
discover and to extract Knowledge from qualitative data. It provides tools for
reasoning with implication basis (and association rules). Implications and
association rules are usefull to reasoning on previously selected attributes,
providing a formal foundation for logical reasoning. In this paper we analyse
how to apply FCA reasoning to increase confidence in sports betting, by means
of detecting temporal regularities from data. It is applied to build a
Knowledge-Based system for confidence reasoning.
",Selecting Attributes for Sport Forecasting using Formal Concept Analysis
"  Perfectly rational decision-makers maximize expected utility, but crucially
ignore the resource costs incurred when determining optimal actions. Here we
employ an axiomatic framework for bounded rational decision-making based on a
thermodynamic interpretation of resource costs as information costs. This leads
to a variational ""free utility"" principle akin to thermodynamical free energy
that trades off utility and information costs. We show that bounded optimal
control solutions can be derived from this variational principle, which leads
in general to stochastic policies. Furthermore, we show that risk-sensitive and
robust (minimax) control schemes fall out naturally from this framework if the
environment is considered as a bounded rational and perfectly rational
opponent, respectively. When resource costs are ignored, the maximum expected
utility principle is recovered.
","Information, Utility & Bounded Rationality"
"  ROC curves and cost curves are two popular ways of visualising classifier
performance, finding appropriate thresholds according to the operating
condition, and deriving useful aggregated measures such as the area under the
ROC curve (AUC) or the area under the optimal cost curve. In this note we
present some new findings and connections between ROC space and cost space, by
using the expected loss over a range of operating conditions. In particular, we
show that ROC curves can be transferred to cost space by means of a very
natural way of understanding how thresholds should be chosen, by selecting the
threshold such that the proportion of positive predictions equals the operating
condition (either in the form of cost proportion or skew). We call these new
curves {ROC Cost Curves}, and we demonstrate that the expected loss as measured
by the area under these curves is linearly related to AUC. This opens up a
series of new possibilities and clarifies the notion of cost curve and its
relation to ROC analysis. In addition, we show that for a classifier that
assigns the scores in an evenly-spaced way, these curves are equal to the Brier
Curves. As a result, this establishes the first clear connection between AUC
and the Brier score.
",Technical Note: Towards ROC Curves in Cost Space
"  OWL 2 has been standardized by the World Wide Web Consortium (W3C) as a
family of ontology languages for the Semantic Web. The most expressive of these
languages is OWL 2 Full, but to date no reasoner has been implemented for this
language. Consistency and entailment checking are known to be undecidable for
OWL 2 Full. We have translated a large fragment of the OWL 2 Full semantics
into first-order logic, and used automated theorem proving systems to do
reasoning based on this theory. The results are promising, and indicate that
this approach can be applied in practice for effective OWL reasoning, beyond
the capabilities of current Semantic Web reasoners.
  This is an extended version of a paper with the same title that has been
published at CADE 2011, LNAI 6803, pp. 446-460. The extended version provides
appendices with additional resources that were used in the reported evaluation.
","Reasoning in the OWL 2 Full Ontology Language using First-Order
  Automated Theorem Proving"
"  This paper introduces 'just enough' principles and 'systems engineering'
approach to the practice of ontology development to provide a minimal yet
complete, lightweight, agile and integrated development process, supportive of
stakeholder management and implementation independence.
",'Just Enough' Ontology Engineering
"  In this paper, we investigate the following question: how could you write
such computer programs that can work like conscious beings? The motivation
behind this question is that we want to create such applications that can see
the future. The aim of this paper is to provide an overall conceptual framework
for this new approach to machine consciousness. So we introduce a new
programming paradigm called Consciousness Oriented Programming (COP).
",Conscious Machines and Consciousness Oriented Programming
"  There has been a noticeable shift in the relative composition of the industry
in the developed countries in recent years; manufacturing is decreasing while
the service sector is becoming more important. However, currently most
simulation models for investigating service systems are still built in the same
way as manufacturing simulation models, using a process-oriented world view,
i.e. they model the flow of passive entities through a system. These kinds of
models allow studying aspects of operational management but are not well suited
for studying the dynamics that appear in service systems due to human
behaviour. For these kinds of studies we require tools that allow modelling the
system and entities using an object-oriented world view, where intelligent
objects serve as abstract ""actors"" that are goal directed and can behave
proactively. In our work we combine process-oriented discrete event simulation
modelling and object-oriented agent based simulation modelling to investigate
the impact of people management practices on retail productivity. In this
paper, we reveal in a series of experiments what impact considering proactivity
can have on the output accuracy of simulation models of human centric systems.
The model and data we use for this investigation are based on a case study in a
UK department store. We show that considering proactivity positively influences
the validity of these kinds of models and therefore allows analysts to make
better recommendations regarding strategies to apply people management
practises.
","A First Approach on Modelling Staff Proactiveness in Retail Simulation
  Models"
"  A fact apparently not observed earlier in the literature of nonmonotonic
reasoning is that Reiter, in his default logic paper, did not directly
formalize informal defaults. Instead, he translated a default into a certain
natural language proposition and provided a formalization of the latter. A few
years later, Moore noted that propositions like the one used by Reiter are
fundamentally different than defaults and exhibit a certain autoepistemic
nature. Thus, Reiter had developed his default logic as a formalization of
autoepistemic propositions rather than of defaults.
  The first goal of this paper is to show that some problems of Reiter's
default logic as a formal way to reason about informal defaults are directly
attributable to the autoepistemic nature of default logic and to the mismatch
between informal defaults and the Reiter's formal defaults, the latter being a
formal expression of the autoepistemic propositions Reiter used as a
representation of informal defaults.
  The second goal of our paper is to compare the work of Reiter and Moore.
While each of them attempted to formalize autoepistemic propositions, the modes
of reasoning in their respective logics were different. We revisit Moore's and
Reiter's intuitions and present them from the perspective of autotheoremhood,
where theories can include propositions referring to the theory's own theorems.
We then discuss the formalization of this perspective in the logics of Moore
and Reiter, respectively, using the unifying semantic framework for default and
autoepistemic logics that we developed earlier. We argue that Reiter's default
logic is a better formalization of Moore's intuitions about autoepistemic
propositions than Moore's own autoepistemic logic.
","Reiter's Default Logic Is a Logic of Autoepistemic Reasoning And a Good
  One, Too"
"  In 1991, Michael Gelfond introduced the language of epistemic specifications.
The goal was to develop tools for modeling problems that require some form of
meta-reasoning, that is, reasoning over multiple possible worlds. Despite their
relevance to knowledge representation, epistemic specifications have received
relatively little attention so far. In this paper, we revisit the formalism of
epistemic specification. We offer a new definition of the formalism, propose
several semantics (one of which, under syntactic restrictions we assume, turns
out to be equivalent to the original semantics by Gelfond), derive some
complexity results and, finally, show the effectiveness of the formalism for
modeling problems requiring meta-reasoning considered recently by Faber and
Woltran. All these results show that epistemic specifications deserve much more
attention that has been afforded to them so far.
",Revisiting Epistemic Specifications
"  We discuss the evolution of aspects of nonmonotonic reasoning towards the
computational paradigm of answer-set programming (ASP). We give a general
overview of the roots of ASP and follow up with the personal perspective on
research developments that helped verbalize the main principles of ASP and
differentiated it from the classical logic programming.
","Origins of Answer-Set Programming - Some Background And Two Personal
  Accounts"
"  UCT, a state-of-the art algorithm for Monte Carlo tree sampling (MCTS), is
based on UCB, a sampling policy for the Multi-armed Bandit Problem (MAB) that
minimizes the accumulated regret. However, MCTS differs from MAB in that only
the final choice, rather than all arm pulls, brings a reward, that is, the
simple regret, as opposite to the cumulative regret, must be minimized. This
ongoing work aims at applying meta-reasoning techniques to MCTS, which is
non-trivial. We begin by introducing policies for multi-armed bandits with
lower simple regret than UCB, and an algorithm for MCTS which combines
cumulative and simple regret minimization and outperforms UCT. We also develop
a sampling scheme loosely based on a myopic version of perfect value of
information. Finite-time and asymptotic analysis of the policies is provided,
and the algorithms are compared empirically.
",Doing Better Than UCT: Rational Monte Carlo Sampling in Trees
"  Self-Organizing Maps are commonly used for unsupervised learning purposes.
This paper is dedicated to the certain modification of SOM called SOMN
(Self-Organizing Mixture Networks) used as a mechanism for representing
grayscale digital images. Any grayscale digital image regarded as a
distribution function can be approximated by the corresponding Gaussian
mixture. In this paper, the use of SOMN is proposed in order to obtain such
approximations for input grayscale images in unsupervised manner.
","Self-Organizing Mixture Networks for Representation of Grayscale Digital
  Images"
"  Two different conceptions of emergence are reconciled as two instances of the
phenomenon of detection. In the process of comparing these two conceptions, we
find that the notions of complexity and detection allow us to form a unified
definition of emergence that clearly delineates the role of the observer.
",Detection and emergence
"  The aim of this paper is to announce the release of a novel system for
abstract argumentation which is based on decomposition and dynamic programming.
We provide first experimental evaluations to show the feasibility of this
approach.
",dynPARTIX - A Dynamic Programming Reasoner for Abstract Argumentation
"  Dung's famous abstract argumentation frameworks represent the core formalism
for many problems and applications in the field of argumentation which
significantly evolved within the last decade. Recent work in the field has thus
focused on implementations for these frameworks, whereby one of the main
approaches is to use Answer-Set Programming (ASP). While some of the
argumentation semantics can be nicely expressed within the ASP language, others
required rather cumbersome encoding techniques. Recent advances in ASP systems,
in particular, the metasp optimization frontend for the ASP-package
gringo/claspD provides direct commands to filter answer sets satisfying certain
subset-minimality (or -maximality) constraints. This allows for much simpler
encodings compared to the ones in standard ASP language. In this paper, we
experimentally compare the original encodings (for the argumentation semantics
based on preferred, semi-stable, and respectively, stage extensions) with new
metasp encodings. Moreover, we provide novel encodings for the recently
introduced resolution-based grounded semantics. Our experimental results
indicate that the metasp approach works well in those cases where the
complexity of the encoded problem is adequately mirrored within the metasp
approach.
","Making Use of Advances in Answer-Set Programming for Abstract
  Argumentation Systems"
"  In a knowledge discovery process, interpretation and evaluation of the mined
results are indispensable in practice. In the case of data clustering, however,
it is often difficult to see in what aspect each cluster has been formed. This
paper proposes a method for automatic and objective characterization or
""verbalization"" of the clusters obtained by mixture models, in which we collect
conjunctions of propositions (attribute-value pairs) that help us interpret or
evaluate the clusters. The proposed method provides us with a new, in-depth and
consistent tool for cluster interpretation/evaluation, and works for various
types of datasets including continuous attributes and missing values.
Experimental results with a couple of standard datasets exhibit the utility of
the proposed method, and the importance of the feedbacks from the
interpretation/evaluation step.
","Verbal Characterization of Probabilistic Clusters using Minimal
  Discriminative Propositions"
"  A brain-computer interface (BCI) may be used to control a prosthetic or
orthotic hand using neural activity from the brain. The core of this
sensorimotor BCI lies in the interpretation of the neural information extracted
from electroencephalogram (EEG). It is desired to improve on the interpretation
of EEG to allow people with neuromuscular disorders to perform daily
activities. This paper investigates the possibility of discriminating between
the EEG associated with wrist and finger movements. The EEG was recorded from
test subjects as they executed and imagined five essential hand movements using
both hands. Independent component analysis (ICA) and time-frequency techniques
were used to extract spectral features based on event-related
(de)synchronisation (ERD/ERS), while the Bhattacharyya distance (BD) was used
for feature reduction. Mahalanobis distance (MD) clustering and artificial
neural networks (ANN) were used as classifiers and obtained average accuracies
of 65 % and 71 % respectively. This shows that EEG discrimination between wrist
and finger movements is possible. The research introduces a new combination of
motor tasks to BCI research.
","Single-trial EEG Discrimination between Wrist and Finger Movement
  Imagery and Execution in a Sensorimotor BCI"
"  We present a constraint-based approach to interactive product configuration.
Our configurator tool FdConfig is based on feature models for the
representation of the product domain. Such models can be directly mapped into
constraint satisfaction problems and dealt with by appropriate constraint
solvers. During the interactive configuration process the user generates new
constraints as a result of his configuration decisions and even may retract
constraints posted earlier. We discuss the configuration process, explain the
underlying techniques and show optimizations.
",FdConfig: A Constraint-Based Interactive Product Configurator
"  Answer-Set Programming (ASP) is an established declarative programming
paradigm. However, classical ASP lacks subprogram calls as in procedural
programming, and access to external computations (like remote procedure calls)
in general. The feature is desired for increasing modularity and---assuming
proper access in place---(meta-)reasoning over subprogram results. While
HEX-programs extend classical ASP with external source access, they do not
support calls of (sub-)programs upfront. We present nested HEX-programs, which
extend HEX-programs to serve the desired feature, in a user-friendly manner.
Notably, the answer sets of called sub-programs can be individually accessed.
This is particularly useful for applications that need to reason over answer
sets like belief set merging, user-defined aggregate functions, or preferences
of answer sets.
",Nested HEX-Programs
"  Statistical relational learning techniques have been successfully applied in
a wide range of relational domains. In most of these applications, the human
designers capitalized on their background knowledge by following a
trial-and-error trajectory, where relational features are manually defined by a
human engineer, parameters are learned for those features on the training data,
the resulting model is validated, and the cycle repeats as the engineer adjusts
the set of features. This paper seeks to streamline application development in
large relational domains by introducing a light-weight approach that
efficiently evaluates relational features on pieces of the relational graph
that are streamed to it one at a time. We evaluate our approach on two social
media tasks and demonstrate that it leads to more accurate models that are
learned faster.
",Structure Selection from Streaming Relational Data
"  In order to give appropriate semantics to qualitative conditionals of the
form ""if A then normally B"", ordinal conditional functions (OCFs) ranking the
possible worlds according to their degree of plausibility can be used. An OCF
accepting all conditionals of a knowledge base R can be characterized as the
solution of a constraint satisfaction problem. We present a high-level,
declarative approach using constraint logic programming techniques for solving
this constraint satisfaction problem. In particular, the approach developed
here supports the generation of all minimal solutions; these minimal solutions
are of special interest as they provide a basis for model-based inference from
R.
","A Constraint Logic Programming Approach for Computing Ordinal
  Conditional Functions"
"  Publishing private data on external servers incurs the problem of how to
avoid unwanted disclosure of confidential data. We study a problem of
confidentiality in extended disjunctive logic programs and show how it can be
solved by extended abduction. In particular, we analyze how credulous
non-monotonic reasoning affects confidentiality.
","Confidentiality-Preserving Data Publishing for Credulous Users by
  Extended Abduction"
"  In this paper, we present domain-specific languages (DSLs) that we devised
for their use in the implementation of a finite domain constraint programming
system, available as library(clpfd) in SWI-Prolog and YAP-Prolog. These DSLs
are used in propagator selection and constraint reification. In these areas,
they lead to concise specifications that are easy to read and reason about. At
compilation time, these specifications are translated to Prolog code, reducing
interpretative run-time overheads. The devised languages can be used in the
implementation of other finite domain constraint solvers as well and may
contribute to their correctness, conciseness and efficiency.
","Domain-specific Languages in a Finite Domain Constraint Programming
  System"
"  In this work a stand-alone preprocessor for SAT is presented that is able to
perform most of the known preprocessing techniques. Preprocessing a formula in
SAT is important for performance since redundancy can be removed. The
preprocessor is part of the SAT solver riss and is called Coprocessor. Not only
riss, but also MiniSat 2.2 benefit from it, because the SatELite preprocessor
of MiniSat does not implement recent techniques. By using more advanced
techniques, Coprocessor is able to reduce the redundancy in a formula further
and improves the overall solving performance.
",Coprocessor - a Standalone SAT Preprocessor
"  We present an application focused on the design of resilient long-reach
passive optical networks. We specifically consider dual-parented networks
whereby each customer must be connected to two metro sites via local exchange
sites. An important property of such a placement is resilience to single metro
node failure. The objective of the application is to determine the optimal
position of a set of metro nodes such that the total optical fibre length is
minimized. We prove that this problem is NP-Complete. We present two
alternative combinatorial optimisation approaches to finding an optimal metro
node placement using: a mixed integer linear programming (MIP) formulation of
the problem; and, a hybrid approach that uses clustering as a preprocessing
step. We consider a detailed case-study based on a network for Ireland. The
hybrid approach scales well and finds solutions that are close to optimal, with
a runtime that is two orders-of-magnitude better than the MIP model.
","A Combinatorial Optimisation Approach to Designing Dual-Parented
  Long-Reach Passive Optical Networks"
"  Artificial general intelligence (AGI) refers to research aimed at tackling
the full problem of artificial intelligence, that is, create truly intelligent
agents. This sets it apart from most AI research which aims at solving
relatively narrow domains, such as character recognition, motion planning, or
increasing player satisfaction in games. But how do we know when an agent is
truly intelligent? A common point of reference in the AGI community is Legg and
Hutter's formal definition of universal intelligence, which has the appeal of
simplicity and generality but is unfortunately incomputable. Games of various
kinds are commonly used as benchmarks for ""narrow"" AI research, as they are
considered to have many important properties. We argue that many of these
properties carry over to the testing of general intelligence as well. We then
sketch how such testing could practically be carried out. The central part of
this sketch is an extension of universal intelligence to deal with finite time,
and the use of sampling of the space of games expressed in a suitably biased
game description language.
",Measuring Intelligence through Games
"  We propose a structured approach to the problem of retrieval of images by
content and present a description logic that has been devised for the semantic
indexing and retrieval of images containing complex objects. As other
approaches do, we start from low-level features extracted with image analysis
to detect and characterize regions in an image. However, in contrast with
feature-based approaches, we provide a syntax to describe segmented regions as
basic objects and complex objects as compositions of basic ones. Then we
introduce a companion extensional semantics for defining reasoning services,
such as retrieval, classification, and subsumption. These services can be used
for both exact and approximate matching, using similarity measures. Using our
logical approach as a formal specification, we implemented a complete
client-server image retrieval system, which allows a user to pose both queries
by sketch and queries by example. A set of experiments has been carried out on
a testbed of images to assess the retrieval capabilities of the system in
comparison with expert users ranking. Results are presented adopting a
well-established measure of quality borrowed from textual information
retrieval.
",Structured Knowledge Representation for Image Retrieval
"  Wind energy plays an increasing role in the supply of energy world-wide. The
energy output of a wind farm is highly dependent on the weather condition
present at the wind farm. If the output can be predicted more accurately,
energy suppliers can coordinate the collaborative production of different
energy sources more efficiently to avoid costly overproductions.
  With this paper, we take a computer science perspective on energy prediction
based on weather data and analyze the important parameters as well as their
correlation on the energy output. To deal with the interaction of the different
parameters we use symbolic regression based on the genetic programming tool
DataModeler.
  Our studies are carried out on publicly available weather and energy data for
a wind farm in Australia. We reveal the correlation of the different variables
for the energy output. The model obtained for energy prediction gives a very
reliable prediction of the energy output for newly given weather data.
","Predicting the Energy Output of Wind Farms Based on Weather Data:
  Important Variables and their Correlation"
"  We consider the problem of reconstructing vehicle trajectories from sparse
sequences of GPS points, for which the sampling interval is between 10 seconds
and 2 minutes. We introduce a new class of algorithms, called altogether path
inference filter (PIF), that maps GPS data in real time, for a variety of
trade-offs and scenarios, and with a high throughput. Numerous prior approaches
in map-matching can be shown to be special cases of the path inference filter
presented in this article. We present an efficient procedure for automatically
training the filter on new data, with or without ground truth observations. The
framework is evaluated on a large San Francisco taxi dataset and is shown to
improve upon the current state of the art. This filter also provides insights
about driving patterns of drivers. The path inference filter has been deployed
at an industrial scale inside the Mobile Millennium traffic information system,
and is used to map fleets of data in San Francisco, Sacramento, Stockholm and
Porto.
","The path inference filter: model-based low-latency map matching of probe
  vehicle data"
"  Software agents can be used to automate many of the tedious, time-consuming
information processing tasks that humans currently have to complete manually.
However, to do so, agent plans must be capable of representing the myriad of
actions and control flows required to perform those tasks. In addition, since
these tasks can require integrating multiple sources of remote information ?
typically, a slow, I/O-bound process ? it is desirable to make execution as
efficient as possible. To address both of these needs, we present a flexible
software agent plan language and a highly parallel execution system that enable
the efficient execution of expressive agent plans. The plan language allows
complex tasks to be more easily expressed by providing a variety of operators
for flexibly processing the data as well as supporting subplans (for
modularity) and recursion (for indeterminate looping). The executor is based on
a streaming dataflow model of execution to maximize the amount of operator and
data parallelism possible at runtime. We have implemented both the language and
executor in a system called THESEUS. Our results from testing THESEUS show that
streaming dataflow execution can yield significant speedups over both
traditional serial (von Neumann) as well as non-streaming dataflow-style
execution that existing software and robot agent execution systems currently
support. In addition, we show how plans written in the language we present can
represent certain types of subtasks that cannot be accomplished using the
languages supported by network query engines. Finally, we demonstrate that the
increased expressivity of our plan language does not hamper performance;
specifically, we show how data can be integrated from multiple remote sources
just as efficiently using our architecture as is possible with a
state-of-the-art streaming-dataflow network query engine.
","An Expressive Language and Efficient Execution System for Software
  Agents"
"  This work focuses on improving state-of-the-art in stochastic local search
(SLS) for solving Boolean satisfiability (SAT) instances arising from
real-world industrial SAT application domains. The recently introduced SLS
method CRSat has been shown to noticeably improve on previously suggested SLS
techniques in solving such real-world instances by combining
justification-based local search with limited Boolean constraint propagation on
the non-clausal formula representation form of Boolean circuits. In this work,
we study possibilities of further improving the performance of CRSat by
exploiting circuit-level structural knowledge for developing new search
heuristics for CRSat. To this end, we introduce and experimentally evaluate a
variety of search heuristics, many of which are motivated by circuit-level
heuristics originally developed in completely different contexts, e.g., for
electronic design automation applications. To the best of our knowledge, most
of the heuristics are novel in the context of SLS for SAT and, more generally,
SLS for constraint satisfaction problems.
","Structure-Based Local Search Heuristics for Circuit-Level Boolean
  Satisfiability"
"  This paper studies the problem of learning diagnostic policies from training
examples. A diagnostic policy is a complete description of the decision-making
actions of a diagnostician (i.e., tests followed by a diagnostic decision) for
all possible combinations of test results. An optimal diagnostic policy is one
that minimizes the expected total cost, which is the sum of measurement costs
and misdiagnosis costs. In most diagnostic settings, there is a tradeoff
between these two kinds of costs. This paper formalizes diagnostic decision
making as a Markov Decision Process (MDP). The paper introduces a new family of
systematic search algorithms based on the AO* algorithm to solve this MDP. To
make AO* efficient, the paper describes an admissible heuristic that enables
AO* to prune large parts of the search space. The paper also introduces several
greedy algorithms including some improvements over previously-published
methods. The paper then addresses the question of learning diagnostic policies
from examples. When the probabilities of diseases and test results are computed
from training data, there is a great danger of overfitting. To reduce
overfitting, regularizers are integrated into the search algorithms. Finally,
the paper compares the proposed methods on five benchmark diagnostic data sets.
The studies show that in most cases the systematic search methods produce
better diagnostic policies than the greedy methods. In addition, the studies
show that for training sets of realistic size, the systematic search algorithms
are practical on todays desktop computers.
","Integrating Learning from Examples into the Search for Diagnostic
  Policies"
"  Variable elimination is a general technique for constraint processing. It is
often discarded because of its high space complexity. However, it can be
extremely useful when combined with other techniques. In this paper we study
the applicability of variable elimination to the challenging problem of finding
still-lifes. We illustrate several alternatives: variable elimination as a
stand-alone algorithm, interleaved with search, and as a source of good quality
lower bounds. We show that these techniques are the best known option both
theoretically and empirically. In our experiments we have been able to solve
the n=20 instance, which is far beyond reach with alternative approaches.
","On the Practical use of Variable Elimination in Constraint Optimization
  Problems: 'Still-life' as a Case Study"
"  This is the second of three planned papers describing ZAP, a satisfiability
engine that substantially generalizes existing tools while retaining the
performance characteristics of modern high performance solvers. The fundamental
idea underlying ZAP is that many problems passed to such engines contain rich
internal structure that is obscured by the Boolean representation used; our
goal is to define a representation in which this structure is apparent and can
easily be exploited to improve computational performance. This paper presents
the theoretical basis for the ideas underlying ZAP, arguing that existing ideas
in this area exploit a single, recurring structure in that multiple database
axioms can be obtained by operating on a single axiom using a subgroup of the
group of permutations on the literals in the problem. We argue that the group
structure precisely captures the general structure at which earlier approaches
hinted, and give numerous examples of its use. We go on to extend the
Davis-Putnam-Logemann-Loveland inference procedure to this broader setting, and
show that earlier computational improvements are either subsumed or left intact
by the new method. The third paper in this series discusses ZAPs implementation
and presents experimental performance results.
",Generalizing Boolean Satisfiability II: Theory
"  Stochastic processes that involve the creation of objects and relations over
time are widespread, but relatively poorly studied. For example, accurate fault
diagnosis in factory assembly processes requires inferring the probabilities of
erroneous assembly operations, but doing this efficiently and accurately is
difficult. Modeled as dynamic Bayesian networks, these processes have discrete
variables with very large domains and extremely high dimensionality. In this
paper, we introduce relational dynamic Bayesian networks (RDBNs), which are an
extension of dynamic Bayesian networks (DBNs) to first-order logic. RDBNs are a
generalization of dynamic probabilistic relational models (DPRMs), which we had
proposed in our previous work to model dynamic uncertain domains. We first
extend the Rao-Blackwellised particle filtering described in our earlier work
to RDBNs. Next, we lift the assumptions associated with Rao-Blackwellization in
RDBNs and propose two new forms of particle filtering. The first one uses
abstraction hierarchies over the predicates to smooth the particle filters
estimates. The second employs kernel density estimation with a kernel function
specifically designed for relational domains. Experiments show these two
methods greatly outperform standard particle filtering on the task of assembly
plan execution monitoring.
",Relational Dynamic Bayesian Networks
"  We present a uniform non-monotonic solution to the problems of reasoning
about action on the basis of an argumentation-theoretic approach. Our theory is
provably correct relative to a sensible minimisation policy introduced on top
of a temporal propositional logic. Sophisticated problem domains can be
formalised in our framework. As much attention of researchers in the field has
been paid to the traditional and basic problems in reasoning about actions such
as the frame, the qualification and the ramification problems, approaches to
these problems within our formalisation lie at heart of the expositions
presented in this paper.
",Reasoning about Action: An Argumentation - Theoretic Approach
"  In this paper we present a new approach to modeling finite set domain
constraint problems using Reduced Ordered Binary Decision Diagrams (ROBDDs). We
show that it is possible to construct an efficient set domain propagator which
compactly represents many set domains and set constraints using ROBDDs. We
demonstrate that the ROBDD-based approach provides unprecedented flexibility in
modeling constraint satisfaction problems, leading to performance improvements.
We also show that the ROBDD-based modeling approach can be extended to the
modeling of integer and multiset constraint problems in a straightforward
manner. Since domain propagation is not always practical, we also show how to
incorporate less strict consistency notions into the ROBDD framework, such as
set bounds, cardinality bounds and lexicographic bounds consistency. Finally,
we present experimental results that demonstrate the ROBDD-based solver
performs better than various more conventional constraint solvers on several
standard set constraint problems.
",Solving Set Constraint Satisfaction Problems using ROBDDs
"  We present a novel approach to the automatic acquisition of taxonomies or
concept hierarchies from a text corpus. The approach is based on Formal Concept
Analysis (FCA), a method mainly used for the analysis of data, i.e. for
investigating and processing explicitly given information. We follow Harris
distributional hypothesis and model the context of a certain term as a vector
representing syntactic dependencies which are automatically acquired from the
text corpus with a linguistic parser. On the basis of this context information,
FCA produces a lattice that we convert into a special kind of partial order
constituting a concept hierarchy. The approach is evaluated by comparing the
resulting concept hierarchies with hand-crafted taxonomies for two domains:
tourism and finance. We also directly compare our approach with hierarchical
agglomerative clustering as well as with Bi-Section-KMeans as an instance of a
divisive clustering algorithm. Furthermore, we investigate the impact of using
different measures weighting the contribution of each attribute as well as of
applying a particular smoothing technique to cope with data sparseness.
","Learning Concept Hierarchies from Text Corpora using Formal Concept
  Analysis"
"  This is the third of three papers describing ZAP, a satisfiability engine
that substantially generalizes existing tools while retaining the performance
characteristics of modern high-performance solvers. The fundamental idea
underlying ZAP is that many problems passed to such engines contain rich
internal structure that is obscured by the Boolean representation used; our
goal has been to define a representation in which this structure is apparent
and can be exploited to improve computational performance. The first paper
surveyed existing work that (knowingly or not) exploited problem structure to
improve the performance of satisfiability engines, and the second paper showed
that this structure could be understood in terms of groups of permutations
acting on individual clauses in any particular Boolean theory. We conclude the
series by discussing the techniques needed to implement our ideas, and by
reporting on their performance on a variety of problem instances.
",Generalizing Boolean Satisfiability III: Implementation
"  When dealing with incomplete data in statistical learning, or incomplete
observations in probabilistic inference, one needs to distinguish the fact that
a certain event is observed from the fact that the observed event has happened.
Since the modeling and computational complexities entailed by maintaining this
proper distinction are often prohibitive, one asks for conditions under which
it can be safely ignored. Such conditions are given by the missing at random
(mar) and coarsened at random (car) assumptions. In this paper we provide an
in-depth analysis of several questions relating to mar/car assumptions. Main
purpose of our study is to provide criteria by which one may evaluate whether a
car assumption is reasonable for a particular data collecting or observational
process. This question is complicated by the fact that several distinct
versions of mar/car assumptions exist. We therefore first provide an overview
over these different versions, in which we highlight the distinction between
distributional and coarsening variable induced versions. We show that
distributional versions are less restrictive and sufficient for most
applications. We then address from two different perspectives the question of
when the mar/car assumption is warranted. First we provide a static analysis
that characterizes the admissibility of the car assumption in terms of the
support structure of the joint probability distribution of complete data and
incomplete observations. Here we obtain an equivalence characterization that
improves and extends a recent result by Grunwald and Halpern. We then turn to a
procedural analysis that characterizes the admissibility of the car assumption
in terms of procedural models for the actual data (or observation) generating
process. The main result of this analysis is that the stronger coarsened
completely at random (ccar) condition is arguably the most reasonable
assumption, as it alone corresponds to data coarsening procedures that satisfy
a natural robustness property.
",Ignorability in Statistical and Probabilistic Inference
"  Partially observable Markov decision processes (POMDPs) form an attractive
and principled framework for agent planning under uncertainty. Point-based
approximate techniques for POMDPs compute a policy based on a finite set of
points collected in advance from the agents belief space. We present a
randomized point-based value iteration algorithm called Perseus. The algorithm
performs approximate value backup stages, ensuring that in each backup stage
the value of each point in the belief set is improved; the key observation is
that a single backup may improve the value of many belief points. Contrary to
other point-based methods, Perseus backs up only a (randomly selected) subset
of points in the belief set, sufficient for improving the value of each belief
point in the set. We show how the same idea can be extended to dealing with
continuous action spaces. Experimental results show the potential of Perseus in
large scale POMDP problems.
",Perseus: Randomized Point-based Value Iteration for POMDPs
"  Logical hidden Markov models (LOHMMs) upgrade traditional hidden Markov
models to deal with sequences of structured symbols in the form of logical
atoms, rather than flat characters.
  This note formally introduces LOHMMs and presents solutions to the three
central inference problems for LOHMMs: evaluation, most likely hidden state
sequence and parameter estimation. The resulting representation and algorithms
are experimentally evaluated on problems from the domain of bioinformatics.
",Logical Hidden Markov Models
"  We describe the version of the GPT planner used in the probabilistic track of
the 4th International Planning Competition (IPC-4). This version, called mGPT,
solves Markov Decision Processes specified in the PPDDL language by extracting
and using different classes of lower bounds along with various heuristic-search
algorithms. The lower bounds are extracted from deterministic relaxations where
the alternative probabilistic effects of an action are mapped into different,
independent, deterministic actions. The heuristic-search algorithms use these
lower bounds for focusing the updates and delivering a consistent value
function over all states reachable from the initial state and the greedy
policy.
",mGPT: A Probabilistic Planner Based on Heuristic Search
"  Despite recent progress in AI planning, many benchmarks remain challenging
for current planners. In many domains, the performance of a planner can greatly
be improved by discovering and exploiting information about the domain
structure that is not explicitly encoded in the initial PDDL formulation. In
this paper we present and compare two automated methods that learn relevant
information from previous experience in a domain and use it to solve new
problem instances. Our methods share a common four-step strategy. First, a
domain is analyzed and structural information is extracted, then
macro-operators are generated based on the previously discovered structure. A
filtering and ranking procedure selects the most useful macro-operators.
Finally, the selected macros are used to speed up future searches. We have
successfully used such an approach in the fourth international planning
competition IPC-4. Our system, Macro-FF, extends Hoffmanns state-of-the-art
planner FF 2.3 with support for two kinds of macro-operators, and with
engineering enhancements. We demonstrate the effectiveness of our ideas on
benchmarks from international planning competitions. Our results indicate a
large reduction in search effort in those complex domains where structural
information can be inferred.
","Macro-FF: Improving AI Planning with Automatically Learned
  Macro-Operators"
"  The Optiplan planning system is the first integer programming-based planner
that successfully participated in the international planning competition. This
engineering note describes the architecture of Optiplan and provides the
integer programming formulation that enabled it to perform reasonably well in
the competition. We also touch upon some recent developments that make integer
programming encodings significantly more competitive.
",Optiplan: Unifying IP-based and Graph-based Planning
"  We study an approach to policy selection for large relational Markov Decision
Processes (MDPs). We consider a variant of approximate policy iteration (API)
that replaces the usual value-function learning step with a learning step in
policy space. This is advantageous in domains where good policies are easier to
represent and learn than the corresponding value functions, which is often the
case for the relational MDPs we are interested in. In order to apply API to
such problems, we introduce a relational policy language and corresponding
learner. In addition, we introduce a new bootstrapping routine for goal-based
planning domains, based on random walks. Such bootstrapping is necessary for
many large relational MDPs, where reward is extremely sparse, as API is
ineffective in such domains when initialized with an uninformed policy. Our
experiments show that the resulting system is able to find good policies for a
number of classical planning domains and their stochastic variants by solving
them as extremely large relational MDPs. The experiments also point to some
limitations of our approach, suggesting future work.
","Approximate Policy Iteration with a Policy Language Bias: Solving
  Relational Markov Decision Processes"
"  Tabu search is one of the most effective heuristics for locating high-quality
solutions to a diverse array of NP-hard combinatorial optimization problems.
Despite the widespread success of tabu search, researchers have a poor
understanding of many key theoretical aspects of this algorithm, including
models of the high-level run-time dynamics and identification of those search
space features that influence problem difficulty. We consider these questions
in the context of the job-shop scheduling problem (JSP), a domain where tabu
search algorithms have been shown to be remarkably effective. Previously, we
demonstrated that the mean distance between random local optima and the nearest
optimal solution is highly correlated with problem difficulty for a well-known
tabu search algorithm for the JSP introduced by Taillard. In this paper, we
discuss various shortcomings of this measure and develop a new model of problem
difficulty that corrects these deficiencies. We show that Taillards algorithm
can be modeled with high fidelity as a simple variant of a straightforward
random walk. The random walk model accounts for nearly all of the variability
in the cost required to locate both optimal and sub-optimal solutions to random
JSPs, and provides an explanation for differences in the difficulty of random
versus structured JSPs. Finally, we discuss and empirically substantiate two
novel predictions regarding tabu search algorithm behavior. First, the method
for constructing the initial solution is highly unlikely to impact the
performance of tabu search. Second, tabu tenure should be selected to be as
small as possible while simultaneously avoiding search stagnation; values
larger than necessary lead to significant degradations in performance.
","Linking Search Space Structure, Run-Time Dynamics, and Problem
  Difficulty: A Step Toward Demystifying Tabu Search"
"  Code optimization and high level synthesis can be posed as constraint
satisfaction and optimization problems, such as graph coloring used in register
allocation. Graph coloring is also used to model more traditional CSPs relevant
to AI, such as planning, time-tabling and scheduling. Provably optimal
solutions may be desirable for commercial and defense applications.
Additionally, for applications such as register allocation and code
optimization, naturally-occurring instances of graph coloring are often small
and can be solved optimally. A recent wave of improvements in algorithms for
Boolean satisfiability (SAT) and 0-1 Integer Linear Programming (ILP) suggests
generic problem-reduction methods, rather than problem-specific heuristics,
because (1) heuristics may be upset by new constraints, (2) heuristics tend to
ignore structure, and (3) many relevant problems are provably inapproximable.
  Problem reductions often lead to highly symmetric SAT instances, and
symmetries are known to slow down SAT solvers. In this work, we compare several
avenues for symmetry breaking, in particular when certain kinds of symmetry are
present in all generated instances. Our focus on reducing CSPs to SAT allows us
to leverage recent dramatic improvement in SAT solvers and automatically
benefit from future progress. We can use a variety of black-box SAT solvers
without modifying their source code because our symmetry-breaking techniques
are static, i.e., we detect symmetries and add symmetry breaking predicates
(SBPs) during pre-processing.
  An important result of our work is that among the types of
instance-independent SBPs we studied and their combinations, the simplest and
least complete constructions are the most effective. Our experiments also
clearly indicate that instance-independent symmetries should mostly be
processed together with instance-specific symmetries rather than at the
specification level, contrary to what has been suggested in the literature.
",Breaking Instance-Independent Symmetries In Exact Graph Coloring
"  A decision process in which rewards depend on history rather than merely on
the current state is called a decision process with non-Markovian rewards
(NMRDP). In decision-theoretic planning, where many desirable behaviours are
more naturally expressed as properties of execution sequences rather than as
properties of states, NMRDPs form a more natural model than the commonly
adopted fully Markovian decision process (MDP) model. While the more tractable
solution methods developed for MDPs do not directly apply in the presence of
non-Markovian rewards, a number of solution methods for NMRDPs have been
proposed in the literature. These all exploit a compact specification of the
non-Markovian reward function in temporal logic, to automatically translate the
NMRDP into an equivalent MDP which is solved using efficient MDP solution
methods. This paper presents NMRDPP (Non-Markovian Reward Decision Process
Planner), a software platform for the development and experimentation of
methods for decision-theoretic planning with non-Markovian rewards. The current
version of NMRDPP implements, under a single interface, a family of methods
based on existing as well as new approaches which we describe in detail. These
include dynamic programming, heuristic search, and structured methods. Using
NMRDPP, we compare the methods and identify certain problem features that
affect their performance. NMRDPPs treatment of non-Markovian rewards is
inspired by the treatment of domain-specific search control knowledge in the
TLPlan planner, which it incorporates as a special case. In the First
International Probabilistic Planning Competition, NMRDPP was able to compete
and perform well in both the domain-independent and hand-coded tracks, using
search control knowledge in the latter.
",Decision-Theoretic Planning with non-Markovian Rewards
"  Boolean optimization finds a wide range of application domains, that
motivated a number of different organizations of Boolean optimizers since the
mid 90s. Some of the most successful approaches are based on iterative calls to
an NP oracle, using either linear search, binary search or the identification
of unsatisfiable sub-formulas. The increasing use of Boolean optimizers in
practical settings raises the question of confidence in computed results. For
example, the issue of confidence is paramount in safety critical settings. One
way of increasing the confidence of the results computed by Boolean optimizers
is to develop techniques for validating the results. Recent work studied the
validation of Boolean optimizers based on branch-and-bound search. This paper
complements existing work, and develops methods for validating Boolean
optimizers that are based on iterative calls to an NP oracle. This entails
implementing solutions for validating both satisfiable and unsatisfiable
answers from the NP oracle. The work described in this paper can be applied to
a wide range of Boolean optimizers, that find application in Pseudo-Boolean
Optimization and in Maximum Satisfiability. Preliminary experimental results
indicate that the impact of the proposed method in overall performance is
negligible.
",On Validating Boolean Optimizers
"  The article presents a study on the biobjective inventory routing problem.
Contrary to most previous research, the problem is treated as a true
multi-objective optimization problem, with the goal of identifying
Pareto-optimal solutions. Due to the hardness of the problem at hand, a
reference point based optimization approach is presented and implemented into
an optimization and decision support system, which allows for the computation
of a true subset of the optimal outcomes. Experimental investigation involving
local search metaheuristics are conducted on benchmark data, and numerical
results are reported and analyzed.
","On the use of reference points for the biobjective Inventory Routing
  Problem"
"  Variable neighborhood search (VNS) is a metaheuristic for solving
optimization problems based on a simple principle: systematic changes of
neighborhoods within the search, both in the descent to local minima and in the
escape from the valleys which contain them. Designing these neighborhoods and
applying them in a meaningful fashion is not an easy task. Moreover, an
appropriate order in which they are applied must be determined. In this paper
we attempt to investigate this issue. Assume that we are given an optimization
problem that is intended to be solved by applying the VNS scheme, how many and
which types of neighborhoods should be investigated and what could be
appropriate selection criteria to apply these neighborhoods. More specifically,
does it pay to ""look ahead"" (see, e.g., in the context of VNS and GRASP) when
attempting to switch from one neighborhood to another?
",Neigborhood Selection in Variable Neighborhood Search
"  In this paper we demonstrate that two common problems in Machine
Learning---imbalanced and overlapping data distributions---do not have
independent effects on the performance of SVM classifiers. This result is
notable since it shows that a model of either of these factors must account for
the presence of the other. Our study of the relationship between these problems
has lead to the discovery of a previously unreported form of ""covert""
overfitting which is resilient to commonly used empirical regularization
techniques. We demonstrate the existance of this covert phenomenon through
several methods based around the parametric regularization of trained SVMs. Our
findings in this area suggest a possible approach to quantifying overlap in
real world data sets.
","A Characterization of the Combined Effects of Overlap and Imbalance on
  the SVM Classifier"
"  In the theory of belief functions, many measures of uncertainty have been
introduced. However, it is not always easy to understand what these measures
really try to represent. In this paper, we re-interpret some measures of
uncertainty in the theory of belief functions. We present some interests and
drawbacks of the existing measures. On these observations, we introduce a
measure of contradiction. Therefore, we present some degrees of non-specificity
and Bayesianity of a mass. We propose a degree of specificity based on the
distance between a mass and its most specific associated mass. We also show how
to use the degree of specificity to measure the specificity of a fusion rule.
Illustrations on simple examples are given.
","Contradiction measures and specificity degrees of basic belief
  assignments"
"  We discuss an attentional model for simultaneous object tracking and
recognition that is driven by gaze data. Motivated by theories of perception,
the model consists of two interacting pathways: identity and control, intended
to mirror the what and where pathways in neuroscience models. The identity
pathway models object appearance and performs classification using deep
(factored)-Restricted Boltzmann Machines. At each point in time the
observations consist of foveated images, with decaying resolution toward the
periphery of the gaze. The control pathway models the location, orientation,
scale and speed of the attended object. The posterior distribution of these
states is estimated with particle filtering. Deeper in the control pathway, we
encounter an attentional mechanism that learns to select gazes so as to
minimize tracking uncertainty. Unlike in our previous work, we introduce gaze
selection strategies which operate in the presence of partial information and
on a continuous action space. We show that a straightforward extension of the
existing approach to the partial information setting results in poor
performance, and we propose an alternative method based on modeling the reward
surface as a Gaussian Process. This approach gives good performance in the
presence of partial information and allows us to expand the action space from a
small, discrete set of fixation points to a continuous domain.
",Learning where to Attend with Deep Architectures for Image Tracking
"  Several rules for social choice are examined from a unifying point of view
that looks at them as procedures for revising a system of degrees of belief in
accordance with certain specified logical constraints. Belief is here a social
attribute, its degrees being measured by the fraction of people who share a
given opinion. Different known rules and some new ones are obtained depending
on which particular constraints are assumed. These constraints allow to model
different notions of choiceness. In particular, we give a new method to deal
with approval-disapproval-preferential voting.
",Social choice rules driven by propositional logic
"  We investigate training and using Gaussian kernel SVMs by approximating the
kernel with an explicit finite- dimensional polynomial feature representation
based on the Taylor expansion of the exponential. Although not as efficient as
the recently-proposed random Fourier features [Rahimi and Recht, 2007] in terms
of the number of features, we show how this polynomial representation can
provide a better approximation in terms of the computational cost involved.
This makes our ""Taylor features"" especially attractive for use on very large
data sets, in conjunction with online or stochastic training.
",Explicit Approximations of the Gaussian Kernel
"  Today, available methods that assess AI systems are focused on using
empirical techniques to measure the performance of algorithms in some specific
tasks (e.g., playing chess, solving mazes or land a helicopter). However, these
methods are not appropriate if we want to evaluate the general intelligence of
AI and, even less, if we compare it with human intelligence. The ANYNT project
has designed a new method of evaluation that tries to assess AI systems using
well known computational notions and problems which are as general as possible.
This new method serves to assess general intelligence (which allows us to learn
how to solve any new kind of problem we face) and not only to evaluate
performance on a set of specific tasks. This method not only focuses on
measuring the intelligence of algorithms, but also to assess any intelligent
system (human beings, animals, AI, aliens?,...), and letting us to place their
results on the same scale and, therefore, to be able to compare them. This new
approach will allow us (in the future) to evaluate and compare any kind of
intelligent system known or even to build/find, be it artificial or biological.
This master thesis aims at ensuring that this new method provides consistent
results when evaluating AI algorithms, this is done through the design and
implementation of prototypes of universal intelligence tests and their
application to different intelligent systems (AI algorithms and humans beings).
From the study we analyze whether the results obtained by two different
intelligent systems are properly located on the same scale and we propose
changes and refinements to these prototypes in order to, in the future, being
able to achieve a truly universal intelligence test.
","Analysis of first prototype universal intelligence tests: evaluating and
  comparing AI algorithms and humans"
"  We provide an overview of the organization and results of the deterministic
part of the 4th International Planning Competition, i.e., of the part concerned
with evaluating systems doing deterministic planning. IPC-4 attracted even more
competing systems than its already large predecessors, and the competition
event was revised in several important respects. After giving an introduction
to the IPC, we briefly explain the main differences between the deterministic
part of IPC-4 and its predecessors. We then introduce formally the language
used, called PDDL2.2 that extends PDDL2.1 by derived predicates and timed
initial literals. We list the competing systems and overview the results of the
competition. The entire set of data is far too large to be presented in full.
We provide a detailed summary; the complete data is available in an online
appendix. We explain how we awarded the competition prizes.
",The Deterministic Part of IPC-4: An Overview
"  PDDL2.1 was designed to push the envelope of what planning algorithms can do,
and it has succeeded. It adds two important features: durative actions,which
take time (and may have continuous effects); and objective functions for
measuring the quality of plans. The concept of durative actions is flawed; and
the treatment of their semantics reveals too strong an attachment to the way
many contemporary planners work. Future PDDL innovators should focus on
producing a clean semantics for additions to the language, and let planner
implementers worry about coupling their algorithms to problems expressed in the
latest version of the language.
",PDDL2.1 - The Art of the Possible? Commentary on Fox and Long
"  The addition of durative actions to PDDL2.1 sparked some controversy. Fox and
Long argued that actions should be considered as instantaneous, but can start
and stop processes. Ultimately, a limited notion of durative actions was
incorporated into the language. I argue that this notion is still impoverished,
and that the underlying philosophical position of regarding durative actions as
being a shorthand for a start action, process, and stop action ignores the
realities of modelling and execution for complex systems.
",The Case for Durative Actions: A Commentary on PDDL2.1
"  We present a partial-order, conformant, probabilistic planner, Probapop which
competed in the blind track of the Probabilistic Planning Competition in IPC-4.
We explain how we adapt distance based heuristics for use with probabilistic
domains. Probapop also incorporates heuristics based on probability of success.
We explain the successes and difficulties encountered during the design and
implementation of Probapop.
",Engineering a Conformant Probabilistic Planner
"  Between 1998 and 2004, the planning community has seen vast progress in terms
of the sizes of benchmark examples that domain-independent planners can tackle
successfully. The key technique behind this progress is the use of heuristic
functions based on relaxing the planning task at hand, where the relaxation is
to assume that all delete lists are empty. The unprecedented success of such
methods, in many commonly used benchmark examples, calls for an understanding
of what classes of domains these methods are well suited for. In the
investigation at hand, we derive a formal background to such an understanding.
We perform a case study covering a range of 30 commonly used STRIPS and ADL
benchmark domains, including all examples used in the first four international
planning competitions. We *prove* connections between domain structure and
local search topology -- heuristic cost surface properties -- under an
idealized version of the heuristic functions used in modern planners. The
idealized heuristic function is called h^+, and differs from the practically
used functions in that it returns the length of an *optimal* relaxed plan,
which is NP-hard to compute. We identify several key characteristics of the
topology under h^+, concerning the existence/non-existence of unrecognized dead
ends, as well as the existence/non-existence of constant upper bounds on the
difficulty of escaping local minima and benches. These distinctions divide the
(set of all) planning domains into a taxonomy of classes of varying h^+
topology. As it turns out, many of the 30 investigated domains lie in classes
with a relatively easy topology. Most particularly, 12 of the domains lie in
classes where FFs search algorithm, provided with h^+, is a polynomial solving
mechanism. We also present results relating h^+ to its approximation as
implemented in FF. The behavior regarding dead ends is provably the same. We
summarize the results of an empirical investigation showing that, in many
domains, the topological qualities of h^+ are largely inherited by the
approximation. The overall investigation gives a rare example of a successful
analysis of the connections between typical-case problem structure, and search
performance. The theoretical investigation also gives hints on how the
topological phenomena might be automatically recognizable by domain analysis
techniques. We outline some preliminary steps we made into that direction.
","Where 'Ignoring Delete Lists' Works: Local Search Topology in Planning
  Benchmarks"
"  A non-binary Constraint Satisfaction Problem (CSP) can be solved directly
using extended versions of binary techniques. Alternatively, the non-binary
problem can be translated into an equivalent binary one. In this case, it is
generally accepted that the translated problem can be solved by applying
well-established techniques for binary CSPs. In this paper we evaluate the
applicability of the latter approach. We demonstrate that the use of standard
techniques for binary CSPs in the encodings of non-binary problems is
problematic and results in models that are very rarely competitive with the
non-binary representation. To overcome this, we propose specialized arc
consistency and search algorithms for binary encodings, and we evaluate them
theoretically and empirically. We consider three binary representations; the
hidden variable encoding, the dual encoding, and the double encoding.
Theoretical and empirical results show that, for certain classes of non-binary
constraints, binary encodings are a competitive option, and in many cases, a
better one than the non-binary representation.
","Binary Encodings of Non-binary Constraint Satisfaction Problems:
  Algorithms and Experimental Results"
"  In a peer-to-peer inference system, each peer can reason locally but can also
solicit some of its acquaintances, which are peers sharing part of its
vocabulary. In this paper, we consider peer-to-peer inference systems in which
the local theory of each peer is a set of propositional clauses defined upon a
local vocabulary. An important characteristic of peer-to-peer inference systems
is that the global theory (the union of all peer theories) is not known (as
opposed to partition-based reasoning systems). The main contribution of this
paper is to provide the first consequence finding algorithm in a peer-to-peer
setting: DeCA. It is anytime and computes consequences gradually from the
solicited peer to peers that are more and more distant. We exhibit a sufficient
condition on the acquaintance graph of the peer-to-peer inference system for
guaranteeing the completeness of this algorithm. Another important contribution
is to apply this general distributed reasoning setting to the setting of the
Semantic Web through the Somewhere semantic peer-to-peer data management
system. The last contribution of this paper is to provide an experimental
analysis of the scalability of the peer-to-peer infrastructure that we propose,
on large networks of 1000 peers.
","Distributed Reasoning in a Peer-to-Peer Setting: Application to the
  Semantic Web"
"  In this paper, we introduce DLS-MC, a new stochastic local search algorithm
for the maximum clique problem. DLS-MC alternates between phases of iterative
improvement, during which suitable vertices are added to the current clique,
and plateau search, during which vertices of the current clique are swapped
with vertices not contained in the current clique. The selection of vertices is
solely based on vertex penalties that are dynamically adjusted during the
search, and a perturbation mechanism is used to overcome search stagnation. The
behaviour of DLS-MC is controlled by a single parameter, penalty delay, which
controls the frequency at which vertex penalties are reduced. We show
empirically that DLS-MC achieves substantial performance improvements over
state-of-the-art algorithms for the maximum clique problem over a large range
of the commonly used DIMACS benchmark instances.
",Dynamic Local Search for the Maximum Clique Problem
"  Open distributed multi-agent systems are gaining interest in the academic
community and in industry. In such open settings, agents are often coordinated
using standardized agent conversation protocols. The representation of such
protocols (for analysis, validation, monitoring, etc) is an important aspect of
multi-agent applications. Recently, Petri nets have been shown to be an
interesting approach to such representation, and radically different approaches
using Petri nets have been proposed. However, their relative strengths and
weaknesses have not been examined. Moreover, their scalability and suitability
for different tasks have not been addressed. This paper addresses both these
challenges. First, we analyze existing Petri net representations in terms of
their scalability and appropriateness for overhearing, an important task in
monitoring open multi-agent systems. Then, building on the insights gained, we
introduce a novel representation using Colored Petri nets that explicitly
represent legal joint conversation states and messages. This representation
approach offers significant improvements in scalability and is particularly
suitable for overhearing. Furthermore, we show that this new representation
offers a comprehensive coverage of all conversation features of FIPA
conversation standards. We also present a procedure for transforming AUML
conversation protocol diagrams (a standard human-readable representation), to
our Colored Petri net representation.
",Representing Conversations for Scalable Overhearing
"  The hm admissible heuristics for (sequential and temporal) regression
planning are defined by a parameterized relaxation of the optimal cost function
in the regression search space, where the parameter m offers a trade-off
between the accuracy and computational cost of theheuristic. Existing methods
for computing the hm heuristic require time exponential in m, limiting them to
small values (m andlt= 2). The hm heuristic can also be viewed as the optimal
cost function in a relaxation of the search space: this paper presents relaxed
search, a method for computing this function partially by searching in the
relaxed space. The relaxed search method, because it computes hm only
partially, is computationally cheaper and therefore usable for higher values of
m. The (complete) hm heuristic is combined with partial hm heuristics, for m =
3,..., computed by relaxed search, resulting in a more accurate heuristic.
  This use of the relaxed search method to improve on the hm heuristic is
evaluated by comparing two optimal temporal planners: TP4, which does not use
it, and HSP*a, which uses it but is otherwise identical to TP4. The comparison
is made on the domains used in the 2004 International Planning Competition, in
which both planners participated. Relaxed search is found to be cost effective
in some of these domains, but not all. Analysis reveals a characterization of
the domains in which relaxed search can be expected to be cost effective, in
terms of two measures on the original and relaxed search spaces. In the domains
where relaxed search is cost effective, expanding small states is
computationally cheaper than expanding large states and small states tend to
have small successor states.
","Improving Heuristics Through Relaxed Search - An Analysis of TP4 and
  HSP*a in the 2004 Planning Competition"
"  Recently, a variety of constraint programming and Boolean satisfiability
approaches to scheduling problems have been introduced. They have in common the
use of relatively simple propagation mechanisms and an adaptive way to focus on
the most constrained part of the problem. In some cases, these methods compare
favorably to more classical constraint programming methods relying on
propagation algorithms for global unary or cumulative resource constraints and
dedicated search heuristics. In particular, we described an approach that
combines restarting, with a generic adaptive heuristic and solution guided
branching on a simple model based on a decomposition of disjunctive
constraints. In this paper, we introduce an adaptation of this technique for an
important subclass of job shop scheduling problems (JSPs), where the objective
function involves minimization of earliness/tardiness costs. We further show
that our technique can be improved by adding domain specific information for
one variant of the JSP (involving time lag constraints). In particular we
introduce a dedicated greedy heuristic, and an improved model for the case
where the maximal time lag is 0 (also referred to as no-wait JSPs).
",Models and Strategies for Variants of the Job Shop Scheduling Problem
"  The Universal Intelligence Measure is a recently proposed formal definition
of intelligence. It is mathematically specified, extremely general, and
captures the essence of many informal definitions of intelligence. It is based
on Hutter's Universal Artificial Intelligence theory, an extension of Ray
Solomonoff's pioneering work on universal induction. Since the Universal
Intelligence Measure is only asymptotically computable, building a practical
intelligence test from it is not straightforward. This paper studies the
practical issues involved in developing a real-world UIM-based performance
metric. Based on our investigation, we develop a prototype implementation which
we use to evaluate a number of different artificial agents.
",An Approximation of the Universal Intelligence Measure
"  Multiple sequence alignment (MSA) is a ubiquitous problem in computational
biology. Although it is NP-hard to find an optimal solution for an arbitrary
number of sequences, due to the importance of this problem researchers are
trying to push the limits of exact algorithms further. Since MSA can be cast as
a classical path finding problem, it is attracting a growing number of AI
researchers interested in heuristic search algorithms as a challenge with
actual practical relevance. In this paper, we first review two previous,
complementary lines of research. Based on Hirschbergs algorithm, Dynamic
Programming needs O(kN^(k-1)) space to store both the search frontier and the
nodes needed to reconstruct the solution path, for k sequences of length N.
Best first search, on the other hand, has the advantage of bounding the search
space that has to be explored using a heuristic. However, it is necessary to
maintain all explored nodes up to the final solution in order to prevent the
search from re-expanding them at higher cost. Earlier approaches to reduce the
Closed list are either incompatible with pruning methods for the Open list, or
must retain at least the boundary of the Closed list. In this article, we
present an algorithm that attempts at combining the respective advantages; like
A* it uses a heuristic for pruning the search space, but reduces both the
maximum Open and Closed size to O(kN^(k-1)), as in Dynamic Programming. The
underlying idea is to conduct a series of searches with successively increasing
upper bounds, but using the DP ordering as the key for the Open priority queue.
With a suitable choice of thresholds, in practice, a running time below four
times that of A* can be expected. In our experiments we show that our algorithm
outperforms one of the currently most successful algorithms for optimal
multiple sequence alignments, Partial Expansion A*, both in time and memory.
Moreover, we apply a refined heuristic based on optimal alignments not only of
pairs of sequences, but of larger subsets. This idea is not new; however, to
make it practically relevant we show that it is equally important to bound the
heuristic computation appropriately, or the overhead can obliterate any
possible gain. Furthermore, we discuss a number of improvements in time and
space efficiency with regard to practical implementations. Our algorithm, used
in conjunction with higher-dimensional heuristics, is able to calculate for the
first time the optimal alignment for almost all of the problems in Reference 1
of the benchmark database BAliBASE.
",An Improved Search Algorithm for Optimal Multiple-Sequence Alignment
"  This article develops Probabilistic Hybrid Action Models (PHAMs), a realistic
causal model for predicting the behavior generated by modern percept-driven
robot plans. PHAMs represent aspects of robot behavior that cannot be
represented by most action models used in AI planning: the temporal structure
of continuous control processes, their non-deterministic effects, several modes
of their interferences, and the achievement of triggering conditions in
closed-loop robot plans.
  The main contributions of this article are: (1) PHAMs, a model of concurrent
percept-driven behavior, its formalization, and proofs that the model generates
probably, qualitatively accurate predictions; and (2) a resource-efficient
inference method for PHAMs based on sampling projections from probabilistic
action models and state descriptions. We show how PHAMs can be applied to
planning the course of action of an autonomous robot office courier based on
analytical and experimental results.
","Probabilistic Hybrid Action Models for Predicting Concurrent
  Percept-driven Robot Behavior"
"  We present a novel framework for integrating prior knowledge into
discriminative classifiers. Our framework allows discriminative classifiers
such as Support Vector Machines (SVMs) to utilize prior knowledge specified in
the generative setting. The dual objective of fitting the data and respecting
prior knowledge is formulated as a bilevel program, which is solved
(approximately) via iterative application of second-order cone programming. To
test our approach, we consider the problem of using WordNet (a semantic
database of English language) to improve low-sample classification accuracy of
newsgroup categorization. WordNet is viewed as an approximate, but readily
available source of background knowledge, and our framework is capable of
utilizing it in a flexible way.
",Generative Prior Knowledge for Discriminative Classification
"  Fast Downward is a classical planning system based on heuristic search. It
can deal with general deterministic planning problems encoded in the
propositional fragment of PDDL2.2, including advanced features like ADL
conditions and effects and derived predicates (axioms). Like other well-known
planners such as HSP and FF, Fast Downward is a progression planner, searching
the space of world states of a planning task in the forward direction. However,
unlike other PDDL planning systems, Fast Downward does not use the
propositional PDDL representation of a planning task directly. Instead, the
input is first translated into an alternative representation called
multi-valued planning tasks, which makes many of the implicit constraints of a
propositional planning task explicit. Exploiting this alternative
representation, Fast Downward uses hierarchical decompositions of planning
tasks for computing its heuristic function, called the causal graph heuristic,
which is very different from traditional HSP-like heuristics based on ignoring
negative interactions of operators.
  In this article, we give a full account of Fast Downwards approach to solving
multi-valued planning tasks. We extend our earlier discussion of the causal
graph heuristic to tasks involving axioms and conditional effects and present
some novel techniques for search control that are used within Fast Downwards
best-first search algorithm: preferred operators transfer the idea of helpful
actions from local search to global best-first search, deferred evaluation of
heuristic functions mitigates the negative effect of large branching factors on
search performance, and multi-heuristic best-first search combines several
heuristic evaluation functions within a single search algorithm in an
orthogonal way. We also describe efficient data structures for fast state
expansion (successor generators and axiom evaluators) and present a new
non-heuristic search algorithm called focused iterative-broadening search,
which utilizes the information encoded in causal graphs in a novel way.
  Fast Downward has proven remarkably successful: It won the ""classical (i.e.,
propositional, non-optimising) track of the 4th International Planning
Competition at ICAPS 2004, following in the footsteps of planners such as FF
and LPG. Our experiments show that it also performs very well on the benchmarks
of the earlier planning competitions and provide some insights about the
usefulness of the new search enhancements.
",The Fast Downward Planning System
"  Distributed Constraint Satisfaction (DCSP) has long been considered an
important problem in multi-agent systems research. This is because many
real-world problems can be represented as constraint satisfaction and these
problems often present themselves in a distributed form. In this article, we
present a new complete, distributed algorithm called Asynchronous Partial
Overlay (APO) for solving DCSPs that is based on a cooperative mediation
process. The primary ideas behind this algorithm are that agents, when acting
as a mediator, centralize small, relevant portions of the DCSP, that these
centralized subproblems overlap, and that agents increase the size of their
subproblems along critical paths within the DCSP as the problem solving
unfolds. We present empirical evidence that shows that APO outperforms other
known, complete DCSP techniques.
","Asynchronous Partial Overlay: A New Algorithm for Solving Distributed
  Constraint Satisfaction Problems"
"  As partial justification of their framework for iterated belief revision
Darwiche and Pearl convincingly argued against Boutiliers natural revision and
provided a prototypical revision operator that fits into their scheme. We show
that the Darwiche-Pearl arguments lead naturally to the acceptance of a smaller
class of operators which we refer to as admissible. Admissible revision ensures
that the penultimate input is not ignored completely, thereby eliminating
natural revision, but includes the Darwiche-Pearl operator, Nayaks
lexicographic revision operator, and a newly introduced operator called
restrained revision. We demonstrate that restrained revision is the most
conservative of admissible revision operators, effecting as few changes as
possible, while lexicographic revision is the least conservative, and point out
that restrained revision can also be viewed as a composite operator, consisting
of natural revision preceded by an application of a ""backwards revision""
operator previously studied by Papini. Finally, we propose the establishment of
a principled approach for choosing an appropriate revision operator in
different contexts and discuss future work.
",Admissible and Restrained Revision
"  In recent years, CP-nets have emerged as a useful tool for supporting
preference elicitation, reasoning, and representation. CP-nets capture and
support reasoning with qualitative conditional preference statements,
statements that are relatively natural for users to express. In this paper, we
extend the CP-nets formalism to handle another class of very natural
qualitative statements one often uses in expressing preferences in daily life -
statements of relative importance of attributes. The resulting formalism,
TCP-nets, maintains the spirit of CP-nets, in that it remains focused on using
only simple and natural preference statements, uses the ceteris paribus
semantics, and utilizes a graphical representation of this information to
reason about its consistency and to perform, possibly constrained, optimization
using it. The extra expressiveness it provides allows us to better model
tradeoffs users would like to make, more faithfully representing their
preferences.
",On Graphical Modeling of Preference and Importance
"  Linear Temporal Logic (LTL) is widely used for defining conditions on the
execution paths of dynamic systems. In the case of dynamic systems that allow
for nondeterministic evolutions, one has to specify, along with an LTL formula
f, which are the paths that are required to satisfy the formula. Two extreme
cases are the universal interpretation A.f, which requires that the formula be
satisfied for all execution paths, and the existential interpretation E.f,
which requires that the formula be satisfied for some execution path.
  When LTL is applied to the definition of goals in planning problems on
nondeterministic domains, these two extreme cases are too restrictive. It is
often impossible to develop plans that achieve the goal in all the
nondeterministic evolutions of a system, and it is too weak to require that the
goal is satisfied by some execution.
  In this paper we explore alternative interpretations of an LTL formula that
are between these extreme cases. We define a new language that permits an
arbitrary combination of the A and E quantifiers, thus allowing, for instance,
to require that each finite execution can be extended to an execution
satisfying an LTL formula (AE.f), or that there is some finite execution whose
extensions all satisfy an LTL formula (EA.f). We show that only eight of these
combinations of path quantifiers are relevant, corresponding to an alternation
of the quantifiers of length one (A and E), two (AE and EA), three (AEA and
EAE), and infinity ((AE)* and (EA)*). We also present a planning algorithm for
the new language that is based on an automata-theoretic approach, and study its
complexity.
","The Planning Spectrum - One, Two, Three, Infinity"
"  A delta-model is a satisfying assignment of a Boolean formula for which any
small alteration, such as a single bit flip, can be repaired by flips to some
small number of other bits, yielding a new satisfying assignment. These
satisfying assignments represent robust solutions to optimization problems
(e.g., scheduling) where it is possible to recover from unforeseen events
(e.g., a resource becoming unavailable). The concept of delta-models was
introduced by Ginsberg, Parkes and Roy (AAAI 1998), where it was proved that
finding delta-models for general Boolean formulas is NP-complete. In this
paper, we extend that result by studying the complexity of finding delta-models
for classes of Boolean formulas which are known to have polynomial time
satisfiability solvers. In particular, we examine 2-SAT, Horn-SAT, Affine-SAT,
dual-Horn-SAT, 0-valid and 1-valid SAT. We see a wide variation in the
complexity of finding delta-models, e.g., while 2-SAT and Affine-SAT have
polynomial time tests for delta-models, testing whether a Horn-SAT formula has
one is NP-complete.
",Fault Tolerant Boolean Satisfiability
"  Multimodal conversational interfaces provide a natural means for users to
communicate with computer systems through multiple modalities such as speech
and gesture. To build effective multimodal interfaces, automated interpretation
of user multimodal inputs is important. Inspired by the previous investigation
on cognitive status in multimodal human machine interaction, we have developed
a greedy algorithm for interpreting user referring expressions (i.e.,
multimodal reference resolution). This algorithm incorporates the cognitive
principles of Conversational Implicature and Givenness Hierarchy and applies
constraints from various sources (e.g., temporal, semantic, and contextual) to
resolve references. Our empirical results have shown the advantage of this
algorithm in efficiently resolving a variety of user references. Because of its
simplicity and generality, this approach has the potential to improve the
robustness of multimodal input interpretation.
",Cognitive Principles in Robust Multimodal Interpretation
"  This paper presents a new framework for anytime heuristic search where the
task is to achieve as many goals as possible within the allocated resources. We
show the inadequacy of traditional distance-estimation heuristics for tasks of
this type and present alternative heuristics that are more appropriate for
multiple-goal search. In particular, we introduce the marginal-utility
heuristic, which estimates the cost and the benefit of exploring a subtree
below a search node. We developed two methods for online learning of the
marginal-utility heuristic. One is based on local similarity of the partial
marginal utility of sibling nodes, and the other generalizes marginal-utility
over the state feature space. We apply our adaptive and non-adaptive
multiple-goal search algorithms to several problems, including focused
crawling, and show their superiority over existing methods.
",Multiple-Goal Heuristic Search
"  We present a heuristic search algorithm for solving first-order Markov
Decision Processes (FOMDPs). Our approach combines first-order state
abstraction that avoids evaluating states individually, and heuristic search
that avoids evaluating all states. Firstly, in contrast to existing systems,
which start with propositionalizing the FOMDP and then perform state
abstraction on its propositionalized version we apply state abstraction
directly on the FOMDP avoiding propositionalization. This kind of abstraction
is referred to as first-order state abstraction. Secondly, guided by an
admissible heuristic, the search is restricted to those states that are
reachable from the initial state. We demonstrate the usefulness of the above
techniques for solving FOMDPs with a system, referred to as FluCaP (formerly,
FCPlanner), that entered the probabilistic track of the 2004 International
Planning Competition (IPC2004) and demonstrated an advantage over other
planners on the problems represented in first-order terms.
",FluCaP: A Heuristic Search Planner for First-Order MDPs
"  Suppose we want to build a system that answers a natural language question by
representing its semantics as a logical form and computing the answer given a
structured database of facts. The core part of such a system is the semantic
parser that maps questions to logical forms. Semantic parsers are typically
trained from examples of questions annotated with their target logical forms,
but this type of annotation is expensive.
  Our goal is to learn a semantic parser from question-answer pairs instead,
where the logical form is modeled as a latent variable. Motivated by this
challenging learning problem, we develop a new semantic formalism,
dependency-based compositional semantics (DCS), which has favorable linguistic,
statistical, and computational properties. We define a log-linear distribution
over DCS logical forms and estimate the parameters using a simple procedure
that alternates between beam search and numerical optimization. On two standard
semantic parsing benchmarks, our system outperforms all existing
state-of-the-art systems, despite using no annotated logical forms.
",Learning Dependency-Based Compositional Semantics
"  It was recently proved that a sound and complete qualitative simulator does
not exist, that is, as long as the input-output vocabulary of the
state-of-the-art QSIM algorithm is used, there will always be input models
which cause any simulator with a coverage guarantee to make spurious
predictions in its output. In this paper, we examine whether a meaningfully
expressive restriction of this vocabulary is possible so that one can build a
simulator with both the soundness and completeness properties. We prove several
negative results: All sound qualitative simulators, employing subsets of the
QSIM representation which retain the operating region transition feature, and
support at least the addition and constancy constraints, are shown to be
inherently incomplete. Even when the simulations are restricted to run in a
single operating region, a constraint vocabulary containing just the addition,
constancy, derivative, and multiplication relations makes the construction of
sound and complete qualitative simulators impossible.
",Causes of Ineradicable Spurious Predictions in Qualitative Simulation
"  We study properties of programs with monotone and convex constraints. We
extend to these formalisms concepts and results from normal logic programming.
They include the notions of strong and uniform equivalence with their
characterizations, tight programs and Fages Lemma, program completion and loop
formulas. Our results provide an abstract account of properties of some recent
extensions of logic programming with aggregates, especially the formalism of
lparse programs. They imply a method to compute stable models of lparse
programs by means of off-the-shelf solvers of pseudo-boolean constraints, which
is often much faster than the smodels system.
","Properties and Applications of Programs with Monotone and Convex
  Constraints"
"  We characterize the search landscape of random instances of the job shop
scheduling problem (JSP). Specifically, we investigate how the expected values
of (1) backbone size, (2) distance between near-optimal schedules, and (3)
makespan of random schedules vary as a function of the job to machine ratio
(N/M). For the limiting cases N/M approaches 0 and N/M approaches infinity we
provide analytical results, while for intermediate values of N/M we perform
experiments. We prove that as N/M approaches 0, backbone size approaches 100%,
while as N/M approaches infinity the backbone vanishes. In the process we show
that as N/M approaches 0 (resp. N/M approaches infinity), simple priority rules
almost surely generate an optimal schedule, providing theoretical evidence of
an ""easy-hard-easy"" pattern of typical-case instance difficulty in job shop
scheduling. We also draw connections between our theoretical results and the
""big valley"" picture of JSP landscapes.
","How the Landscape of Random Job Shop Scheduling Instances Depends on the
  Ratio of Jobs to Machines"
"  We consider interactive tools that help users search for their most preferred
item in a large collection of options. In particular, we examine
example-critiquing, a technique for enabling users to incrementally construct
preference models by critiquing example options that are presented to them. We
present novel techniques for improving the example-critiquing technology by
adding suggestions to its displayed options. Such suggestions are calculated
based on an analysis of users current preference model and their potential
hidden preferences. We evaluate the performance of our model-based suggestion
techniques with both synthetic and real users. Results show that such
suggestions are highly attractive to users and can stimulate them to express
more preferences to improve the chance of identifying their most preferred item
by up to 78%.
",Preference-based Search using Example-Critiquing with Suggestions
"  The Partially Observable Markov Decision Process has long been recognized as
a rich framework for real-world planning and control problems, especially in
robotics. However exact solutions in this framework are typically
computationally intractable for all but the smallest problems. A well-known
technique for speeding up POMDP solving involves performing value backups at
specific belief points, rather than over the entire belief simplex. The
efficiency of this approach, however, depends greatly on the selection of
points. This paper presents a set of novel techniques for selecting informative
belief points which work well in practice. The point selection procedure is
combined with point-based value backups to form an effective anytime POMDP
algorithm called Point-Based Value Iteration (PBVI). The first aim of this
paper is to introduce this algorithm and present a theoretical analysis
justifying the choice of belief selection technique. The second aim of this
paper is to provide a thorough empirical comparison between PBVI and other
state-of-the-art POMDP methods, in particular the Perseus algorithm, in an
effort to highlight their similarities and differences. Evaluation is performed
using both standard POMDP domains and realistic robotic tasks.
",Anytime Point-Based Approximations for Large POMDPs
"  Efficient representations and solutions for large decision problems with
continuous and discrete variables are among the most important challenges faced
by the designers of automated decision support systems. In this paper, we
describe a novel hybrid factored Markov decision process (MDP) model that
allows for a compact representation of these problems, and a new hybrid
approximate linear programming (HALP) framework that permits their efficient
solutions. The central idea of HALP is to approximate the optimal value
function by a linear combination of basis functions and optimize its weights by
linear programming. We analyze both theoretical and computational aspects of
this approach, and demonstrate its scale-up potential on several hybrid
optimization problems.
",Solving Factored MDPs with Hybrid State and Action Variables
"  This paper introduces and analyzes a battery of inference models for the
problem of semantic role labeling: one based on constraint satisfaction, and
several strategies that model the inference as a meta-learning problem using
discriminative classifiers. These classifiers are developed with a rich set of
novel features that encode proposition and sentence-level information. To our
knowledge, this is the first work that: (a) performs a thorough analysis of
learning-based inference models for semantic role labeling, and (b) compares
several inference strategies in this context. We evaluate the proposed
inference strategies in the framework of the CoNLL-2005 shared task using only
automatically-generated syntactic information. The extensive experimental
evaluation and analysis indicates that all the proposed inference strategies
are successful -they all outperform the current best results reported in the
CoNLL-2005 evaluation exercise- but each of the proposed approaches has its
advantages and disadvantages. Several important traits of a state-of-the-art
SRL combination strategy emerge from this analysis: (i) individual models
should be combined at the granularity of candidate arguments rather than at the
granularity of complete solutions; (ii) the best combination strategy uses an
inference model based in learning; and (iii) the learning-based inference
benefits from max-margin classifiers and global feedback.
",Combination Strategies for Semantic Role Labeling
"  In contrast to the existing approaches to bisimulation for fuzzy systems, we
introduce a behavioral distance to measure the behavioral similarity of states
in a nondeterministic fuzzy-transition system. This behavioral distance is
defined as the greatest fixed point of a suitable monotonic function and
provides a quantitative analogue of bisimilarity. The behavioral distance has
the important property that two states are at zero distance if and only if they
are bisimilar. Moreover, for any given threshold, we find that states with
behavioral distances bounded by the threshold are equivalent. In addition, we
show that two system combinators---parallel composition and product---are
non-expansive with respect to our behavioral distance, which makes
compositional verification possible.
",A Behavioral Distance for Fuzzy-Transition Systems
"  In a field of research about general reasoning mechanisms, it is essential to
have appropriate benchmarks. Ideally, the benchmarks should reflect possible
applications of the developed technology. In AI Planning, researchers more and
more tend to draw their testing examples from the benchmark collections used in
the International Planning Competition (IPC). In the organization of (the
deterministic part of) the fourth IPC, IPC-4, the authors therefore invested
significant effort to create a useful set of benchmarks. They come from five
different (potential) real-world applications of planning: airport ground
traffic control, oil derivative transportation in pipeline networks,
model-checking safety properties, power supply restoration, and UMTS call
setup. Adapting and preparing such an application for use as a benchmark in the
IPC involves, at the time, inevitable (often drastic) simplifications, as well
as careful choice between, and engineering of, domain encodings. For the first
time in the IPC, we used compilations to formulate complex domain features in
simple languages such as STRIPS, rather than just dropping the more interesting
problem constraints in the simpler language subsets. The article explains and
discusses the five application domains and their adaptation to form the PDDL
test suites used in IPC-4. We summarize known theoretical results on structural
properties of the domains, regarding their computational complexity and
provable properties of their topology under the h+ function (an idealized
version of the relaxed plan heuristic). We present new (empirical) results
illuminating properties such as the quality of the most wide-spread heuristic
functions (planning graph, serial planning graph, and relaxed plan), the growth
of propositional representations over instance size, and the number of actions
available to achieve each fact; we discuss these data in conjunction with the
best results achieved by the different kinds of planners participating in
IPC-4.
","Engineering Benchmarks for Planning: the Domains Used in the
  Deterministic Part of IPC-4"
"  In this paper we present pddl+, a planning domain description language for
modelling mixed discrete-continuous planning domains. We describe the syntax
and modelling style of pddl+, showing that the language makes convenient the
modelling of complex time-dependent effects. We provide a formal semantics for
pddl+ by mapping planning instances into constructs of hybrid automata. Using
the syntax of HAs as our semantic model we construct a semantic mapping to
labelled transition systems to complete the formal interpretation of pddl+
planning instances. An advantage of building a mapping from pddl+ to HA theory
is that it forms a bridge between the Planning and Real Time Systems research
communities. One consequence is that we can expect to make use of some of the
theoretical properties of HAs. For example, for a restricted class of HAs the
Reachability problem (which is equivalent to Plan Existence) is decidable.
pddl+ provides an alternative to the continuous durative action model of
pddl2.1, adding a more flexible and robust model of time-dependent behaviour.
",Modelling Mixed Discrete-Continuous Domains for Planning
"  In this paper, we show that there is a close relation between consistency in
a constraint network and set intersection. A proof schema is provided as a
generic way to obtain consistency properties from properties on set
intersection. This approach not only simplifies the understanding of and
unifies many existing consistency results, but also directs the study of
consistency to that of set intersection properties in many situations, as
demonstrated by the results on the convexity and tightness of constraints in
this paper. Specifically, we identify a new class of tree convex constraints
where local consistency ensures global consistency. This generalizes row convex
constraints. Various consistency results are also obtained on constraint
networks where only some, in contrast to all in the existing work,constraints
are tight.
",Set Intersection and Consistency in Constraint Networks
"  In this paper, we study the possibility of designing non-trivial random CSP
models by exploiting the intrinsic connection between structures and
typical-case hardness. We show that constraint consistency, a notion that has
been developed to improve the efficiency of CSP algorithms, is in fact the key
to the design of random CSP models that have interesting phase transition
behavior and guaranteed exponential resolution complexity without putting much
restriction on the parameter of constraint tightness or the domain size of the
problem. We propose a very flexible framework for constructing problem
instances withinteresting behavior and develop a variety of concrete methods to
construct specific random CSP models that enforce different levels of
constraint consistency. A series of experimental studies with interesting
observations are carried out to illustrate the effectiveness of introducing
structural elements in random instances, to verify the robustness of our
proposal, and to investigate features of some specific models based on our
framework that are highly related to the behavior of backtracking search
algorithms.
",Consistency and Random Constraint Satisfaction Models
"  In this paper, we present two alternative approaches to defining answer sets
for logic programs with arbitrary types of abstract constraint atoms (c-atoms).
These approaches generalize the fixpoint-based and the level mapping based
answer set semantics of normal logic programs to the case of logic programs
with arbitrary types of c-atoms. The results are four different answer set
definitions which are equivalent when applied to normal logic programs. The
standard fixpoint-based semantics of logic programs is generalized in two
directions, called answer set by reduct and answer set by complement. These
definitions, which differ from each other in the treatment of
negation-as-failure (naf) atoms, make use of an immediate consequence operator
to perform answer set checking, whose definition relies on the notion of
conditional satisfaction of c-atoms w.r.t. a pair of interpretations. The other
two definitions, called strongly and weakly well-supported models, are
generalizations of the notion of well-supported models of normal logic programs
to the case of programs with c-atoms. As for the case of fixpoint-based
semantics, the difference between these two definitions is rooted in the
treatment of naf atoms. We prove that answer sets by reduct (resp. by
complement) are equivalent to weakly (resp. strongly) well-supported models of
a program, thus generalizing the theorem on the correspondence between stable
models and well-supported models of a normal logic program to the class of
programs with c-atoms. We show that the newly defined semantics coincide with
previously introduced semantics for logic programs with monotone c-atoms, and
they extend the original answer set semantics of normal logic programs. We also
study some properties of answer sets of programs with c-atoms, and relate our
definitions to several semantics for logic programs with aggregates presented
in the literature.
",Answer Sets for Logic Programs with Arbitrary Abstract Constraint Atoms
"  Many combinatorial optimization problems such as the bin packing and multiple
knapsack problems involve assigning a set of discrete objects to multiple
containers. These problems can be used to model task and resource allocation
problems in multi-agent systems and distributed systms, and can also be found
as subproblems of scheduling problems. We propose bin completion, a
branch-and-bound strategy for one-dimensional, multicontainer packing problems.
Bin completion combines a bin-oriented search space with a powerful dominance
criterion that enables us to prune much of the space. The performance of the
basic bin completion framework can be enhanced by using a number of extensions,
including nogood-based pruning techniques that allow further exploitation of
the dominance criterion. Bin completion is applied to four problems: multiple
knapsack, bin covering, min-cost covering, and bin packing. We show that our
bin completion algorithms yield new, state-of-the-art results for the multiple
knapsack, bin covering, and min-cost covering problems, outperforming previous
algorithms by several orders of magnitude with respect to runtime on some
classes of hard, random problem instances. For the bin packing problem, we
demonstrate significant improvements compared to most previous results, but
show that bin completion is not competitive with current state-of-the-art
cutting-stock based approaches.
","Bin Completion Algorithms for Multicontainer Packing, Knapsack, and
  Covering Problems"
"  In real-life temporal scenarios, uncertainty and preferences are often
essential and coexisting aspects. We present a formalism where quantitative
temporal constraints with both preferences and uncertainty can be defined. We
show how three classical notions of controllability (that is, strong, weak, and
dynamic), which have been developed for uncertain temporal problems, can be
generalized to handle preferences as well. After defining this general
framework, we focus on problems where preferences follow the fuzzy approach,
and with properties that assure tractability. For such problems, we propose
algorithms to check the presence of the controllability properties. In
particular, we show that in such a setting dealing simultaneously with
preferences and uncertainty does not increase the complexity of controllability
testing. We also develop a dynamic execution algorithm, of polynomial
complexity, that produces temporal plans under uncertainty that are optimal
with respect to fuzzy preferences.
","Uncertainty in Soft Temporal Constraint Problems:A General Framework and
  Controllability Algorithms for the Fuzzy Case"
"  In the recent years several research efforts have focused on the concept of
time granularity and its applications. A first stream of research investigated
the mathematical models behind the notion of granularity and the algorithms to
manage temporal data based on those models. A second stream of research
investigated symbolic formalisms providing a set of algebraic operators to
define granularities in a compact and compositional way. However, only very
limited manipulation algorithms have been proposed to operate directly on the
algebraic representation making it unsuitable to use the symbolic formalisms in
applications that need manipulation of granularities.
  This paper aims at filling the gap between the results from these two streams
of research, by providing an efficient conversion from the algebraic
representation to the equivalent low-level representation based on the
mathematical models. In addition, the conversion returns a minimal
representation in terms of period length. Our results have a major practical
impact: users can more easily define arbitrary granularities in terms of
algebraic operators, and then access granularity reasoning and other services
operating efficiently on the equivalent, minimal low-level representation. As
an example, we illustrate the application to temporal constraint reasoning with
multiple granularities.
  From a technical point of view, we propose an hybrid algorithm that
interleaves the conversion of calendar subexpressions into periodical sets with
the minimization of the period length. The algorithm returns set-based
granularity representations having minimal period length, which is the most
relevant parameter for the performance of the considered reasoning services.
Extensive experimental work supports the techniques used in the algorithm, and
shows the efficiency and effectiveness of the algorithm.
","Supporting Temporal Reasoning by Mapping Calendar Expressions to Minimal
  Periodic Sets"
"  We consider the problem of computing a lightest derivation of a global
structure using a set of weighted rules. A large variety of inference problems
in AI can be formulated in this framework. We generalize A* search and
heuristics derived from abstractions to a broad class of lightest derivation
problems. We also describe a new algorithm that searches for lightest
derivations using a hierarchy of abstractions. Our generalization of A* gives a
new algorithm for searching AND/OR graphs in a bottom-up fashion. We discuss
how the algorithms described here provide a general architecture for addressing
the pipeline problem --- the problem of passing information back and forth
between various stages of processing in a perceptual system. We consider
examples in computer vision and natural language processing. We apply the
hierarchical search algorithm to the problem of estimating the boundaries of
convex objects in grayscale images and compare it to other search methods. A
second set of experiments demonstrate the use of a new compositional model for
finding salient curves in images.
",The Generalized A* Architecture
"  In this paper, we construct and investigate a hierarchy of spatio-temporal
formalisms that result from various combinations of propositional spatial and
temporal logics such as the propositional temporal logic PTL, the spatial
logics RCC-8, BRCC-8, S4u and their fragments. The obtained results give a
clear picture of the trade-off between expressiveness and computational
realisability within the hierarchy. We demonstrate how different combining
principles as well as spatial and temporal primitives can produce NP-, PSPACE-,
EXPSPACE-, 2EXPSPACE-complete, and even undecidable spatio-temporal logics out
of components that are at most NP- or PSPACE-complete.
",Combining Spatial and Temporal Logics: Expressiveness vs. Complexity
"  The treatment of exogenous events in planning is practically important in
many real-world domains where the preconditions of certain plan actions are
affected by such events. In this paper we focus on planning in temporal domains
with exogenous events that happen at known times, imposing the constraint that
certain actions in the plan must be executed during some predefined time
windows. When actions have durations, handling such temporal constraints adds
an extra difficulty to planning. We propose an approach to planning in these
domains which integrates constraint-based temporal reasoning into a graph-based
planning framework using local search. Our techniques are implemented in a
planner that took part in the 4th International Planning Competition (IPC-4). A
statistical analysis of the results of IPC-4 demonstrates the effectiveness of
our approach in terms of both CPU-time and plan quality. Additional experiments
show the good performance of the temporal reasoning techniques integrated into
our planner.
","An Approach to Temporal Planning and Scheduling in Domains with
  Predictable Exogenous Events"
"  In this commentary I argue that although PDDL is a very useful standard for
the planning competition, its design does not properly consider the issue of
domain modeling. Hence, I would not advocate its use in specifying planning
domains outside of the context of the planning competition. Rather, the field
needs to explore different approaches and grapple more directly with the
problem of effectively modeling and utilizing all of the diverse pieces of
knowledge we typically have about planning domains.
",The Power of Modeling - a Response to PDDL2.1
"  PDDL was originally conceived and constructed as a lingua franca for the
International Planning Competition. PDDL2.1 embodies a set of extensions
intended to support the expression of something closer to real planning
problems. This objective has only been partially achieved, due in large part to
a deliberate focus on not moving too far from classical planning models and
solution methods.
",Imperfect Match: PDDL 2.1 and Real Applications
"  I comment on the PDDL 2.1 language and its use in the planning competition,
focusing on the choices made for accommodating time and concurrency. I also
discuss some methodological issues that have to do with the move toward more
expressive planning languages and the balance needed in planning research
between semantics and computation.
",PDDL 2.1: Representation vs. Computation
"  Most classical scheduling formulations assume a fixed and known duration for
each activity. In this paper, we weaken this assumption, requiring instead that
each duration can be represented by an independent random variable with a known
mean and variance. The best solutions are ones which have a high probability of
achieving a good makespan. We first create a theoretical framework, formally
showing how Monte Carlo simulation can be combined with deterministic
scheduling algorithms to solve this problem. We propose an associated
deterministic scheduling problem whose solution is proved, under certain
conditions, to be a lower bound for the probabilistic problem. We then propose
and investigate a number of techniques for solving such problems based on
combinations of Monte Carlo simulation, solutions to the associated
deterministic problem, and either constraint programming or tabu search. Our
empirical results demonstrate that a combination of the use of the associated
deterministic problem and Monte Carlo simulation results in algorithms that
scale best both in terms of problem size and uncertainty. Further experiments
point to the correlation between the quality of the deterministic solution and
the quality of the probabilistic solution as a major factor responsible for
this success.
","Proactive Algorithms for Job Shop Scheduling with Probabilistic
  Durations"
"  This paper is concerned with a class of algorithms that perform exhaustive
search on propositional knowledge bases. We show that each of these algorithms
defines and generates a propositional language. Specifically, we show that the
trace of a search can be interpreted as a combinational circuit, and a search
algorithm then defines a propositional language consisting of circuits that are
generated across all possible executions of the algorithm. In particular, we
show that several versions of exhaustive DPLL search correspond to such
well-known languages as FBDD, OBDD, and a precisely-defined subset of d-DNNF.
By thus mapping search algorithms to propositional languages, we provide a
uniform and practical framework in which successful search techniques can be
harnessed for compilation of knowledge into various languages of interest, and
a new methodology whereby the power and limitations of search algorithms can be
understood by looking up the tractability and succinctness of the corresponding
propositional languages.
",The Language of Search
"  The best performing algorithms for a particular oversubscribed scheduling
application, Air Force Satellite Control Network (AFSCN) scheduling, appear to
have little in common. Yet, through careful experimentation and modeling of
performance in real problem instances, we can relate characteristics of the
best algorithms to characteristics of the application. In particular, we find
that plateaus dominate the search spaces (thus favoring algorithms that make
larger changes to solutions) and that some randomization in exploration is
critical to good performance (due to the lack of gradient information on the
plateaus). Based on our explanations of algorithm performance, we develop a new
algorithm that combines characteristics of the best performers; the new
algorithms performance is better than the previous best. We show how hypothesis
driven experimentation and search modeling can both explain algorithm
performance and motivate the design of a new algorithm.
","Understanding Algorithm Performance on an Oversubscribed Scheduling
  Application"
"  This paper describes Marvin, a planner that competed in the Fourth
International Planning Competition (IPC 4). Marvin uses
action-sequence-memoisation techniques to generate macro-actions, which are
then used during search for a solution plan. We provide an overview of its
architecture and search behaviour, detailing the algorithms used. We also
empirically demonstrate the effectiveness of its features in various planning
domains; in particular, the effects on performance due to the use of
macro-actions, the novel features of its search behaviour, and the native
support of ADL and Derived Predicates.
",Marvin: A Heuristic Search Planner with Online Macro-Action Learning
"  We describe how to convert the heuristic search algorithm A* into an anytime
algorithm that finds a sequence of improved solutions and eventually converges
to an optimal solution. The approach we adopt uses weighted heuristic search to
find an approximate solution quickly, and then continues the weighted search to
find improved solutions as well as to improve a bound on the suboptimality of
the current solution. When the time available to solve a search problem is
limited or uncertain, this creates an anytime heuristic search algorithm that
allows a flexible tradeoff between search time and solution quality. We analyze
the properties of the resulting Anytime A* algorithm, and consider its
performance in three domains; sliding-tile puzzles, STRIPS planning, and
multiple sequence alignment. To illustrate the generality of this approach, we
also describe how to transform the memory-efficient search algorithm Recursive
Best-First Search (RBFS) into an anytime algorithm.
",Anytime Heuristic Search
"  In this paper we apply computer-aided theorem discovery technique to discover
theorems about strongly equivalent logic programs under the answer set
semantics. Our discovered theorems capture new classes of strongly equivalent
logic programs that can lead to new program simplification rules that preserve
strong equivalence. Specifically, with the help of computers, we discovered
exact conditions that capture the strong equivalence between a rule and the
empty set, between two rules, between two rules and one of the two rules,
between two rules and another rule, and between three rules and two of the
three rules.
",Discovering Classes of Strongly Equivalent Logic Programs
"  The QXORSAT problem is the quantified version of the satisfiability problem
XORSAT in which the connective exclusive-or is used instead of the usual or. We
study the phase transition associated with random QXORSAT instances. We give a
description of this phase transition in the case of one alternation of
quantifiers, thus performing an advanced practical and theoretical study on the
phase transition of a quantified roblem.
",Phase Transition for Random Quantified XOR-Formulas
"  The paper presents a new sampling methodology for Bayesian networks that
samples only a subset of variables and applies exact inference to the rest.
Cutset sampling is a network structure-exploiting application of the
Rao-Blackwellisation principle to sampling in Bayesian networks. It improves
convergence by exploiting memory-based inference algorithms. It can also be
viewed as an anytime approximation of the exact cutset-conditioning algorithm
developed by Pearl. Cutset sampling can be implemented efficiently when the
sampled variables constitute a loop-cutset of the Bayesian network and, more
generally, when the induced width of the networks graph conditioned on the
observed sampled variables is bounded by a constant w. We demonstrate
empirically the benefit of this scheme on a range of benchmarks.
",Cutset Sampling for Bayesian Networks
"  Numerous formalisms and dedicated algorithms have been designed in the last
decades to model and solve decision making problems. Some formalisms, such as
constraint networks, can express ""simple"" decision problems, while others are
designed to take into account uncertainties, unfeasible decisions, and
utilities. Even in a single formalism, several variants are often proposed to
model different types of uncertainty (probability, possibility...) or utility
(additive or not). In this article, we introduce an algebraic graphical model
that encompasses a large number of such formalisms: (1) we first adapt previous
structures from Friedman, Chu and Halpern for representing uncertainty,
utility, and expected utility in order to deal with generic forms of sequential
decision making; (2) on these structures, we then introduce composite graphical
models that express information via variables linked by ""local"" functions,
thanks to conditional independence; (3) on these graphical models, we finally
define a simple class of queries which can represent various scenarios in terms
of observabilities and controllabilities. A natural decision-tree semantics for
such queries is completed by an equivalent operational semantics, which induces
generic algorithms. The proposed framework, called the
Plausibility-Feasibility-Utility (PFU) framework, not only provides a better
understanding of the links between existing formalisms, but it also covers yet
unpublished frameworks (such as possibilistic influence diagrams) and unifies
formalisms such as quantified boolean formulas and influence diagrams. Our
backtrack and variable elimination generic algorithms are a first step towards
unified algorithms.
","An Algebraic Graphical Model for Decision with Uncertainties,
  Feasibilities, and Utilities"
"  Matchmaking arises when supply and demand meet in an electronic marketplace,
or when agents search for a web service to perform some task, or even when
recruiting agencies match curricula and job profiles. In such open
environments, the objective of a matchmaking process is to discover best
available offers to a given request. We address the problem of matchmaking from
a knowledge representation perspective, with a formalization based on
Description Logics. We devise Concept Abduction and Concept Contraction as
non-monotonic inferences in Description Logics suitable for modeling
matchmaking in a logical framework, and prove some related complexity results.
We also present reasonable algorithms for semantic matchmaking based on the
devised inferences, and prove that they obey to some commonsense properties.
Finally, we report on the implementation of the proposed matchmaking framework,
which has been used both as a mediator in e-marketplaces and for semantic web
services discovery.
","Semantic Matchmaking as Non-Monotonic Reasoning: A Description Logic
  Approach"
"  Solution-Guided Multi-Point Constructive Search (SGMPCS) is a novel
constructive search technique that performs a series of resource-limited tree
searches where each search begins either from an empty solution (as in
randomized restart) or from a solution that has been encountered during the
search. A small number of these ""elite solutions is maintained during the
search. We introduce the technique and perform three sets of experiments on the
job shop scheduling problem. First, a systematic, fully crossed study of SGMPCS
is carried out to evaluate the performance impact of various parameter
settings. Second, we inquire into the diversity of the elite solution set,
showing, contrary to expectations, that a less diverse set leads to stronger
performance. Finally, we compare the best parameter setting of SGMPCS from the
first two experiments to chronological backtracking, limited discrepancy
search, randomized restart, and a sophisticated tabu search algorithm on a set
of well-known benchmark problems. Results demonstrate that SGMPCS is
significantly better than the other constructive techniques tested, though lags
behind the tabu search.
",Solution-Guided Multi-Point Constructive Search for Job Shop Scheduling
"  This essay explores the limits of Turing machines concerning the modeling of
minds and suggests alternatives to go beyond those limits.
",Are Minds Computable?
"  This paper compares various optimization methods for fuzzy inference system
optimization. The optimization methods compared are genetic algorithm, particle
swarm optimization and simulated annealing. When these techniques were
implemented it was observed that the performance of each technique within the
fuzzy inference system classification was context dependent.
",Fuzzy Inference Systems Optimization
"  We introduce matrix and its block to the Dung's theory of argumentation
framework. It is showed that each argumentation framework has a matrix
representation, and the indirect attack relation and indirect defence relation
can be characterized by computing the matrix. This provide a powerful
mathematics way to determine the ""controversial arguments"" in an argumentation
framework. Also, we introduce several kinds of blocks based on the matrix, and
various prudent semantics of argumentation frameworks can all be determined by
computing and comparing the matrices and their blocks which we have defined. In
contrast with traditional method of directed graph, the matrix method has an
excellent advantage: computability(even can be realized on computer easily).
So, there is an intensive perspective to import the theory of matrix to the
research of argumentation frameworks and its related areas.
",Handling controversial arguments by matrix
"  Real-time search methods are suited for tasks in which the agent is
interacting with an initially unknown environment in real time. In such
simultaneous planning and learning problems, the agent has to select its
actions in a limited amount of time, while sensing only a local part of the
environment centered at the agents current location. Real-time heuristic search
agents select actions using a limited lookahead search and evaluating the
frontier states with a heuristic function. Over repeated experiences, they
refine heuristic values of states to avoid infinite loops and to converge to
better solutions. The wide spread of such settings in autonomous software and
hardware agents has led to an explosion of real-time search algorithms over the
last two decades. Not only is a potential user confronted with a hodgepodge of
algorithms, but he also faces the choice of control parameters they use. In
this paper we address both problems. The first contribution is an introduction
of a simple three-parameter framework (named LRTS) which extracts the core
ideas behind many existing algorithms. We then prove that LRTA*, epsilon-LRTA*,
SLA*, and gamma-Trap algorithms are special cases of our framework. Thus, they
are unified and extended with additional features. Second, we prove
completeness and convergence of any algorithm covered by the LRTS framework.
Third, we prove several upper-bounds relating the control parameters and
solution quality. Finally, we analyze the influence of the three control
parameters empirically in the realistic scalable domains of real-time
navigation on initially unknown maps from a commercial role-playing game as
well as routing in ad hoc sensor networks.
",Learning in Real-Time Search: A Unifying Framework
"  This paper introduces the SEQ BIN meta-constraint with a polytime algorithm
achieving general- ized arc-consistency according to some properties. SEQ BIN
can be used for encoding counting con- straints such as CHANGE, SMOOTH or
INCREAS- ING NVALUE. For some of these constraints and some of their variants
GAC can be enforced with a time and space complexity linear in the sum of
domain sizes, which improves or equals the best known results of the
literature.
","A Generalized Arc-Consistency Algorithm for a Class of Counting
  Constraints: Revised Edition that Incorporates One Correction"
"  The Taaable projet goal is to create a case-based reasoning system for
retrieval and adaptation of cooking recipes. Within this framework, we are
discussing the temporal aspects of recipes and the means of representing those
in order to adapt their text.
","Quels formalismes temporels pour repr\'esenter des connaissances
  extraites de textes de recettes de cuisine ?"
"  Designing component-based constraint solvers is a complex problem. Some
components are required, some are optional and there are interdependencies
between the components. Because of this, previous approaches to solver design
and modification have been ad-hoc and limited. We present a system that
transforms a description of the components and the characteristics of the
target constraint solver into a constraint problem. Solving this problem yields
the description of a valid solver. Our approach represents a significant step
towards the automated design and synthesis of constraint solvers that are
specialised for individual constraint problem classes or instances.
",Modelling Constraint Solver Architecture Design as a Constraint Problem
"  Classification of targets by radar has proved to be notoriously difficult
with the best systems still yet to attain sufficiently high levels of
performance and reliability. In the current contribution we explore a new
design of radar based target recognition, where angular diversity is used in a
cognitive manner to attain better performance. Performance is bench- marked
against conventional classification schemes. The proposed scheme can easily be
extended to cognitive target recognition based on multiple diversity
strategies.
",A cognitive diversity framework for radar target classification
"  It is widely recognized today that the management of imprecision and
vagueness will yield more intelligent and realistic knowledge-based
applications. Description Logics (DLs) are a family of knowledge representation
languages that have gained considerable attention the last decade, mainly due
to their decidability and the existence of empirically high performance of
reasoning algorithms. In this paper, we extend the well known fuzzy ALC DL to
the fuzzy SHIN DL, which extends the fuzzy ALC DL with transitive role axioms
(S), inverse roles (I), role hierarchies (H) and number restrictions (N). We
illustrate why transitive role axioms are difficult to handle in the presence
of fuzzy interpretations and how to handle them properly. Then we extend these
results by adding role hierarchies and finally number restrictions. The main
contributions of the paper are the decidability proof of the fuzzy DL languages
fuzzy-SI and fuzzy-SHIN, as well as decision procedures for the knowledge base
satisfiability problem of the fuzzy-SI and fuzzy-SHIN.
",Reasoning with Very Expressive Fuzzy Description Logics
"  Exact Max-SAT solvers, compared with SAT solvers, apply little inference at
each node of the proof tree. Commonly used SAT inference rules like unit
propagation produce a simplified formula that preserves satisfiability but,
unfortunately, solving the Max-SAT problem for the simplified formula is not
equivalent to solving it for the original formula. In this paper, we define a
number of original inference rules that, besides being applied efficiently,
transform Max-SAT instances into equivalent Max-SAT instances which are easier
to solve. The soundness of the rules, that can be seen as refinements of unit
resolution adapted to Max-SAT, are proved in a novel and simple way via an
integer programming transformation. With the aim of finding out how powerful
the inference rules are in practice, we have developed a new Max-SAT solver,
called MaxSatz, which incorporates those rules, and performed an experimental
investigation. The results provide empirical evidence that MaxSatz is very
competitive, at least, on random Max-2SAT, random Max-3SAT, Max-Cut, and Graph
3-coloring instances, as well as on the benchmarks from the Max-SAT Evaluation
2006.
",New Inference Rules for Max-SAT
"  Reputation mechanisms offer an effective alternative to verification
authorities for building trust in electronic markets with moral hazard. Future
clients guide their business decisions by considering the feedback from past
transactions; if truthfully exposed, cheating behavior is sanctioned and thus
becomes irrational.
  It therefore becomes important to ensure that rational clients have the right
incentives to report honestly. As an alternative to side-payment schemes that
explicitly reward truthful reports, we show that honesty can emerge as a
rational behavior when clients have a repeated presence in the market. To this
end we describe a mechanism that supports an equilibrium where truthful
feedback is obtained. Then we characterize the set of pareto-optimal equilibria
of the mechanism, and derive an upper bound on the percentage of false reports
that can be recorded by the mechanism. An important role in the existence of
this bound is played by the fact that rational clients can establish a
reputation for reporting honestly.
",Obtaining Reliable Feedback for Sanctioning Reputation Mechanisms
"  We present a new algorithm for probabilistic planning with no observability.
Our algorithm, called Probabilistic-FF, extends the heuristic forward-search
machinery of Conformant-FF to problems with probabilistic uncertainty about
both the initial state and action effects. Specifically, Probabilistic-FF
combines Conformant-FFs techniques with a powerful machinery for weighted model
counting in (weighted) CNFs, serving to elegantly define both the search space
and the heuristic function. Our evaluation of Probabilistic-FF shows its fine
scalability in a range of probabilistic domains, constituting a several orders
of magnitude improvement over previous results in this area. We use a
problematic case to point out the main open issue to be addressed by further
research.
","Probabilistic Planning via Heuristic Forward Search and Weighted Model
  Counting"
"  Conjunctive queries play an important role as an expressive query language
for Description Logics (DLs). Although modern DLs usually provide for
transitive roles, conjunctive query answering over DL knowledge bases is only
poorly understood if transitive roles are admitted in the query. In this paper,
we consider unions of conjunctive queries over knowledge bases formulated in
the prominent DL SHIQ and allow transitive roles in both the query and the
knowledge base. We show decidability of query answering in this setting and
establish two tight complexity bounds: regarding combined complexity, we prove
that there is a deterministic algorithm for query answering that needs time
single exponential in the size of the KB and double exponential in the size of
the query, which is optimal. Regarding data complexity, we prove containment in
co-NP.
",Conjunctive Query Answering for the Description Logic SHIQ
"  Experience in the physical sciences suggests that the only realistic means of
understanding complex systems is through the use of mathematical models.
Typically, this has come to mean the identification of quantitative models
expressed as differential equations. Quantitative modelling works best when the
structure of the model (i.e., the form of the equations) is known; and the
primary concern is one of estimating the values of the parameters in the model.
For complex biological systems, the model-structure is rarely known and the
modeler has to deal with both model-identification and parameter-estimation. In
this paper we are concerned with providing automated assistance to the first of
these problems. Specifically, we examine the identification by machine of the
structural relationships between experimentally observed variables. These
relationship will be expressed in the form of qualitative abstractions of a
quantitative model. Such qualitative models may not only provide clues to the
precise quantitative model, but also assist in understanding the essence of
that model. Our position in this paper is that background knowledge
incorporating system modelling principles can be used to constrain effectively
the set of good qualitative models. Utilising the model-identification
framework provided by Inductive Logic Programming (ILP) we present empirical
support for this position using a series of increasingly complex artificial
datasets. The results are obtained with qualitative and quantitative data
subject to varying amounts of noise and different degrees of sparsity. The
results also point to the presence of a set of qualitative states, which we
term kernel subsets, that may be necessary for a qualitative model-learner to
learn correct models. We demonstrate scalability of the method to biological
system modelling by identification of the glycolysis metabolic pathway from
data.
",Qualitative System Identification from Imperfect Data
"  Multi-robot path planning is difficult due to the combinatorial explosion of
the search space with every new robot added. Complete search of the combined
state-space soon becomes intractable. In this paper we present a novel form of
abstraction that allows us to plan much more efficiently. The key to this
abstraction is the partitioning of the map into subgraphs of known structure
with entry and exit restrictions which we can represent compactly. Planning
then becomes a search in the much smaller space of subgraph configurations.
Once an abstract plan is found, it can be quickly resolved into a correct (but
possibly sub-optimal) concrete plan without the need for further search. We
prove that this technique is sound and complete and demonstrate its practical
effectiveness on a real map.
  A contending solution, prioritised planning, is also evaluated and shown to
have similar performance albeit at the cost of completeness. The two approaches
are not necessarily conflicting; we demonstrate how they can be combined into a
single algorithm which outperforms either approach alone.
",Exploiting Subgraph Structure in Multi-Robot Path Planning
"  Ontologies and automated reasoning are the building blocks of the Semantic
Web initiative. Derivation rules can be included in an ontology to define
derived concepts, based on base concepts. For example, rules allow to define
the extension of a class or property, based on a complex relation between the
extensions of the same or other classes and properties. On the other hand, the
inclusion of negative information both in the form of negation-as-failure and
explicit negative information is also needed to enable various forms of
reasoning. In this paper, we extend RDF graphs with weak and strong negation,
as well as derivation rules. The ERDF stable model semantics of the extended
framework (Extended RDF) is defined, extending RDF(S) semantics. A distinctive
feature of our theory, which is based on Partial Logic, is that both truth and
falsity extensions of properties and classes are considered, allowing for truth
value gaps. Our framework supports both closed-world and open-world reasoning
through the explicit representation of the particular closed-world assumptions
and the ERDF ontological categories of total properties and total classes.
",Extended RDF as a Semantic Foundation of Rule Markup Languages
"  We present three new complexity results for classes of planning problems with
simple causal graphs. First, we describe a polynomial-time algorithm that uses
macros to generate plans for the class 3S of planning problems with binary
state variables and acyclic causal graphs. This implies that plan generation
may be tractable even when a planning problem has an exponentially long minimal
solution. We also prove that the problem of plan existence for planning
problems with multi-valued variables and chain causal graphs is NP-hard.
Finally, we show that plan existence for planning problems with binary state
variables and polytree causal graphs is NP-complete.
",The Complexity of Planning Problems With Simple Causal Graphs
"  We represent planning as a set of loosely coupled network flow problems,
where each network corresponds to one of the state variables in the planning
domain. The network nodes correspond to the state variable values and the
network arcs correspond to the value transitions. The planning problem is to
find a path (a sequence of actions) in each network such that, when merged,
they constitute a feasible plan. In this paper we present a number of integer
programming formulations that model these loosely coupled networks with varying
degrees of flexibility. Since merging may introduce exponentially many ordering
constraints we implement a so-called branch-and-cut algorithm, in which these
constraints are dynamically generated and added to the formulation when needed.
Our results are very promising, they improve upon previous planning as integer
programming approaches and lay the foundation for integer programming
approaches for cost optimal planning.
","Loosely Coupled Formulations for Automated Planning: An Integer
  Programming Perspective"
"  In a facility with front room and back room operations, it is useful to
switch workers between the rooms in order to cope with changing customer
demand. Assuming stochastic customer arrival and service times, we seek a
policy for switching workers such that the expected customer waiting time is
minimized while the expected back room staffing is sufficient to perform all
work. Three novel constraint programming models and several shaving procedures
for these models are presented. Experimental results show that a model based on
closed-form expressions together with a combination of shaving procedures is
the most efficient. This model is able to find and prove optimal solutions for
many problem instances within a reasonable run-time. Previously, the only
available approach was a heuristic algorithm. Furthermore, a hybrid method
combining the heuristic and the best constraint programming method is shown to
perform as well as the heuristic in terms of solution quality over time, while
achieving the same performance in terms of proving optimality as the pure
constraint programming model. This is the first work of which we are aware that
solves such queueing-based problems with constraint programming.
",A Constraint Programming Approach for Solving a Queueing Control Problem
"  Decision-theoretic planning is a popular approach to sequential decision
making problems, because it treats uncertainty in sensing and acting in a
principled way. In single-agent frameworks like MDPs and POMDPs, planning can
be carried out by resorting to Q-value functions: an optimal Q-value function
Q* is computed in a recursive manner by dynamic programming, and then an
optimal policy is extracted from Q*. In this paper we study whether similar
Q-value functions can be defined for decentralized POMDP models (Dec-POMDPs),
and how policies can be extracted from such value functions. We define two
forms of the optimal Q-value function for Dec-POMDPs: one that gives a
normative description as the Q-value function of an optimal pure joint policy
and another one that is sequentially rational and thus gives a recipe for
computation. This computation, however, is infeasible for all but the smallest
problems. Therefore, we analyze various approximate Q-value functions that
allow for efficient computation. We describe how they relate, and we prove that
they all provide an upper bound to the optimal Q-value function Q*. Finally,
unifying some previous approaches for solving Dec-POMDPs, we describe a family
of algorithms for extracting policies from such Q-value functions, and perform
an experimental evaluation on existing test problems, including a new
firefighting benchmark problem.
",Optimal and Approximate Q-value Functions for Decentralized POMDPs
"  Multi-agent planning in stochastic environments can be framed formally as a
decentralized Markov decision problem. Many real-life distributed problems that
arise in manufacturing, multi-robot coordination and information gathering
scenarios can be formalized using this framework. However, finding the optimal
solution in the general case is hard, limiting the applicability of recently
developed algorithms. This paper provides a practical approach for solving
decentralized control problems when communication among the decision makers is
possible, but costly. We develop the notion of communication-based mechanism
that allows us to decompose a decentralized MDP into multiple single-agent
problems. In this framework, referred to as decentralized semi-Markov decision
process with direct communication (Dec-SMDP-Com), agents operate separately
between communications. We show that finding an optimal mechanism is equivalent
to solving optimally a Dec-SMDP-Com. We also provide a heuristic search
algorithm that converges on the optimal decomposition. Restricting the
decomposition to some specific types of local behaviors reduces significantly
the complexity of planning. In particular, we present a polynomial-time
algorithm for the case in which individual agents perform goal-oriented
behaviors between communications. The paper concludes with an additional
tractable algorithm that enables the introduction of human knowledge, thereby
reducing the overall problem to finding the best time to communicate. Empirical
results show that these approaches provide good approximate solutions.
",Communication-Based Decomposition Mechanisms for Decentralized MDPs
"  Informally, a set of abstractions of a state space S is additive if the
distance between any two states in S is always greater than or equal to the sum
of the corresponding distances in the abstract spaces. The first known additive
abstractions, called disjoint pattern databases, were experimentally
demonstrated to produce state of the art performance on certain state spaces.
However, previous applications were restricted to state spaces with special
properties, which precludes disjoint pattern databases from being defined for
several commonly used testbeds, such as Rubiks Cube, TopSpin and the Pancake
puzzle. In this paper we give a general definition of additive abstractions
that can be applied to any state space and prove that heuristics based on
additive abstractions are consistent as well as admissible. We use this new
definition to create additive abstractions for these testbeds and show
experimentally that well chosen additive abstractions can reduce search time
substantially for the (18,4)-TopSpin puzzle and by three orders of magnitude
over state of the art methods for the 17-Pancake puzzle. We also derive a way
of testing if the heuristic value returned by additive abstractions is provably
too low and show that the use of this test can reduce search time for the
15-puzzle and TopSpin by roughly a factor of two.
",A General Theory of Additive State Space Abstractions
"  Markov decision processes capture sequential decision making under
uncertainty, where an agent must choose actions so as to optimize long term
reward. The paper studies efficient reasoning mechanisms for Relational Markov
Decision Processes (RMDP) where world states have an internal relational
structure that can be naturally described in terms of objects and relations
among them. Two contributions are presented. First, the paper develops First
Order Decision Diagrams (FODD), a new compact representation for functions over
relational structures, together with a set of operators to combine FODDs, and
novel reduction techniques to keep the representation small. Second, the paper
shows how FODDs can be used to develop solutions for RMDPs, where reasoning is
performed at the abstract level and the resulting optimal policy is independent
of domain size (number of objects) or instantiation. In particular, a variant
of the value iteration algorithm is developed by using special operations over
FODDs, and the algorithm is shown to converge to the optimal policy.
",First Order Decision Diagrams for Relational MDPs
"  Resolution is the rule of inference at the basis of most procedures for
automated reasoning. In these procedures, the input formula is first translated
into an equisatisfiable formula in conjunctive normal form (CNF) and then
represented as a set of clauses. Deduction starts by inferring new clauses by
resolution, and goes on until the empty clause is generated or satisfiability
of the set of clauses is proven, e.g., because no new clauses can be generated.
  In this paper, we restrict our attention to the problem of evaluating
Quantified Boolean Formulas (QBFs). In this setting, the above outlined
deduction process is known to be sound and complete if given a formula in CNF
and if a form of resolution, called Q-resolution, is used. We introduce
Q-resolution on terms, to be used for formulas in disjunctive normal form. We
show that the computation performed by most of the available procedures for
QBFs --based on the Davis-Logemann-Loveland procedure (DLL) for propositional
satisfiability-- corresponds to a tree in which Q-resolution on terms and
clauses alternate. This poses the theoretical bases for the introduction of
learning, corresponding to recording Q-resolution formulas associated with the
nodes of the tree. We discuss the problems related to the introduction of
learning in DLL based procedures, and present solutions extending
state-of-the-art proposals coming from the literature on propositional
satisfiability. Finally, we show that our DLL based solver extended with
learning, performs significantly better on benchmarks used in the 2003 QBF
solvers comparative evaluation.
","Clause/Term Resolution and Learning in the Evaluation of Quantified
  Boolean Formulas"
"  The theoretical transition from the graphs of production systems to the
bipartite graphs of the MIVAR nets is shown. Examples of the implementation of
the MIVAR nets in the formalisms of matrixes and graphs are given. The linear
computational complexity of algorithms for automated building of objects and
rules of the MIVAR nets is theoretically proved. On the basis of the MIVAR nets
the UDAV software complex is developed, handling more than 1.17 million objects
and more than 3.5 million rules on ordinary computers. The results of
experiments that confirm a linear computational complexity of the MIVAR method
of information processing are given.
  Keywords: MIVAR, MIVAR net, logical inference, computational complexity,
artificial intelligence, intelligent systems, expert systems, General Problem
Solver.
","MIVAR: Transition from Productions to Bipartite Graphs MIVAR Nets and
  Practical Realization of Automated Constructor of Algorithms Handling More
  than Three Million Production Rules"
"  Description logic programs (dl-programs) under the answer set semantics
formulated by Eiter {\em et al.} have been considered as a prominent formalism
for integrating rules and ontology knowledge bases. A question of interest has
been whether dl-programs can be captured in a general formalism of nonmonotonic
logic. In this paper, we study the possibility of embedding dl-programs into
default logic. We show that dl-programs under the strong and weak answer set
semantics can be embedded in default logic by combining two translations, one
of which eliminates the constraint operator from nonmonotonic dl-atoms and the
other translates a dl-program into a default theory. For dl-programs without
nonmonotonic dl-atoms but with the negation-as-failure operator, our embedding
is polynomial, faithful, and modular. In addition, our default logic encoding
can be extended in a simple way to capture recently proposed weakly
well-supported answer set semantics, for arbitrary dl-programs. These results
reinforce the argument that default logic can serve as a fruitful foundation
for query-based approaches to integrating ontology and rules. With its simple
syntax and intuitive semantics, plus available computational results, default
logic can be considered an attractive approach to integration of ontology and
rules.
",Embedding Description Logic Programs into Default Logic
"  Electronic government (e-government) has been one of the most active areas of
ontology development during the past six years. In e-government, ontologies are
being used to describe and specify e-government services (e-services) because
they enable easy composition, matching, mapping and merging of various
e-government services. More importantly, they also facilitate the semantic
integration and interoperability of e-government services. However, it is still
unclear in the current literature how an existing ontology building methodology
can be applied to develop semantic ontology models in a government service
domain. In this paper the Uschold and King ontology building methodology is
applied to develop semantic ontology models in a government service domain.
Firstly, the Uschold and King methodology is presented, discussed and applied
to build a government domain ontology. Secondly, the domain ontology is
evaluated for semantic consistency using its semi-formal representation in
Description Logic. Thirdly, an alignment of the domain ontology with the
Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) upper
level ontology is drawn to allow its wider visibility and facilitate its
integration with existing metadata standard. Finally, the domain ontology is
formally written in Web Ontology Language (OWL) to enable its automatic
processing by computers. The study aims to provide direction for the
application of existing ontology building methodologies in the Semantic Web
development processes of e-government domain specific ontology models; which
would enable their repeatability in other e-government projects and strengthen
the adoption of semantic technologies in e-government.
","Semantic-Driven e-Government: Application of Uschold and King Ontology
  Building Methodology for Semantic Ontology Models Development"
"  It has been widely observed that there is no single ""dominant"" SAT solver;
instead, different solvers perform best on different instances. Rather than
following the traditional approach of choosing the best solver for a given
class of instances, we advocate making this decision online on a per-instance
basis. Building on previous work, we describe SATzilla, an automated approach
for constructing per-instance algorithm portfolios for SAT that use so-called
empirical hardness models to choose among their constituent solvers. This
approach takes as input a distribution of problem instances and a set of
component solvers, and constructs a portfolio optimizing a given objective
function (such as mean runtime, percent of instances solved, or score in a
competition). The excellent performance of SATzilla was independently verified
in the 2007 SAT Competition, where our SATzilla07 solvers won three gold, one
silver and one bronze medal. In this article, we go well beyond SATzilla07 by
making the portfolio construction scalable and completely automated, and
improving it by integrating local search solvers as candidate solvers, by
predicting performance score instead of runtime, and by using hierarchical
hardness models that take into account different types of SAT instances. We
demonstrate the effectiveness of these new techniques in extensive experimental
results on data sets including instances from the most recent SAT competition.
",SATzilla: Portfolio-based Algorithm Selection for SAT
"  This paper shows that maintaining logical consistency of an iris recognition
system is a matter of finding a suitable partitioning of the input space in
enrollable and unenrollable pairs by negotiating the user comfort and the
safety of the biometric system. In other words, consistent enrollment is
mandatory in order to preserve system consistency. A fuzzy 3-valued
disambiguated model of iris recognition is proposed and analyzed in terms of
completeness, consistency, user comfort and biometric safety. It is also shown
here that the fuzzy 3-valued model of iris recognition is hosted by an 8-valued
Boolean algebra of modulo 8 integers that represents the computational
formalization in which a biometric system (a software agent) can achieve the
artificial understanding of iris recognition in a logically consistent manner.
",8-Valent Fuzzy Logic for Iris Recognition and Biometry
"  In voting contexts, some new candidates may show up in the course of the
process. In this case, we may want to determine which of the initial candidates
are possible winners, given that a fixed number $k$ of new candidates will be
added. We give a computational study of this problem, focusing on scoring
rules, and we provide a formal comparison with related problems such as control
via adding candidates or cloning.
","New Candidates Welcome! Possible Winners with respect to the Addition of
  New Candidates"
"  Orseau and Ring, as well as Dewey, have recently described problems,
including self-delusion, with the behavior of agents using various definitions
of utility functions. An agent's utility function is defined in terms of the
agent's history of interactions with its environment. This paper argues, via
two examples, that the behavior problems can be avoided by formulating the
utility function in two steps: 1) inferring a model of the environment from
interactions, and 2) computing utility as a function of the environment model.
Basing a utility function on a model that the agent must learn implies that the
utility function must initially be expressed in terms of specifications to be
matched to structures in the learned model. These specifications constitute
prior assumptions about the environment so this approach will not work with
arbitrary environments. But the approach should work for agents designed by
humans to act in the physical world. The paper also addresses the issue of
self-modifying agents and shows that if provided with the possibility to modify
their utility functions agents will not choose to do so, under some usual
assumptions.
",Model-based Utility Functions
"  We show that estimating the complexity (mean and distribution) of the
instances of a fixed size Constraint Satisfaction Problem (CSP) can be very
hard. We deal with the main two aspects of the problem: defining a measure of
complexity and generating random unbiased instances. For the first problem, we
rely on a general framework and a measure of complexity we presented at
CISSE08. For the generation problem, we restrict our analysis to the Sudoku
example and we provide a solution that also explains why it is so difficult.
",Unbiased Statistics of a CSP - A Controlled-Bias Generator
"  Trying to be effective (no matter who exactly and in what field) a person
face the problem which inevitably destroys all our attempts to easily get to a
desired goal. The problem is the existence of some insuperable barriers for our
mind, anotherwords barriers for principles of thinking. They are our clue and
main reason for research. Here we investigate these barriers and their features
exposing the nature of mental process. We start from special structures which
reflect the ways to define relations between objects. Then we came to realizing
about what is the material our mind uses to build thoughts, to make
conclusions, to understand, to form reasoning, etc. This can be called a mental
dynamics. After this the nature of mental barriers on the required level of
abstraction as well as the ways to pass through them became clear. We begin to
understand why thinking flows in such a way, with such specifics and with such
limitations we can observe in reality. This can help us to be more optimal. At
the final step we start to understand, what ma-thematical models can be applied
to such a picture. We start to express our thoughts in a language of
mathematics, developing an apparatus for our Spatial Theory of Mind, suitable
to represent processes and infrastructure of thinking. We use abstract algebra
and stay invariant in relation to the nature of objects.
",A Model of Spatial Thinking for Computational Intelligence
"  In this paper, we investigate the problem of mining numerical data in the
framework of Formal Concept Analysis. The usual way is to use a scaling
procedure --transforming numerical attributes into binary ones-- leading either
to a loss of information or of efficiency, in particular w.r.t. the volume of
extracted patterns. By contrast, we propose to directly work on numerical data
in a more precise and efficient way, and we prove it. For that, the notions of
closed patterns, generators and equivalent classes are revisited in the
numerical context. Moreover, two original algorithms are proposed and used in
an evaluation involving real-world data, showing the predominance of the
present approach.
",Revisiting Numerical Pattern Mining with Formal Concept Analysis
"  We identify principles characterizing Solomonoff Induction by demands on an
agent's external behaviour. Key concepts are rationality, computability,
indifference and time consistency. Furthermore, we discuss extensions to the
full AI case to derive AIXI.
",Principles of Solomonoff Induction and AIXI
"  The use of patterns in predictive models is a topic that has received a lot
of attention in recent years. Pattern mining can help to obtain models for
structured domains, such as graphs and sequences, and has been proposed as a
means to obtain more accurate and more interpretable models. Despite the large
amount of publications devoted to this topic, we believe however that an
overview of what has been accomplished in this area is missing. This paper
presents our perspective on this evolving area. We identify the principles of
pattern mining that are important when mining patterns for models and provide
an overview of pattern-based classification methods. We categorize these
methods along the following dimensions: (1) whether they post-process a
pre-computed set of patterns or iteratively execute pattern mining algorithms;
(2) whether they select patterns model-independently or whether the pattern
selection is guided by a model. We summarize the results that have been
obtained for each of these methods.
",Pattern-Based Classification: A Unifying Perspective
"  Nowadays, e-government has emerged as a government policy to improve the
quality and efficiency of public administrations. By exploiting the potential
of new information and communication technologies, government agencies are
providing a wide spectrum of online services. These services are composed of
several web services that comply with well defined processes. One of the big
challenges is the need to optimize the composition of the elementary web
services. In this paper, we present a solution for optimizing the computation
effort in web service composition. Our method is based on Graph Theory. We
model the semantic relationship between the involved web services through a
directed graph. Then, we compute all shortest paths using for the first time,
an extended version of the Floyd-Warshall algorithm.
",Graph based E-Government web service composition
"  With the fast growth of the Internet, more and more information is available
on the Web. The Semantic Web has many features which cannot be handled by using
the traditional search engines. It extracts metadata for each discovered Web
documents in RDF or OWL formats, and computes relations between documents. We
proposed a hybrid indexing and ranking technique for the Semantic Web which
finds relevant documents and computes the similarity among a set of documents.
First, it returns with the most related document from the repository of
Semantic Web Documents (SWDs) by using a modified version of the ObjectRank
technique. Then, it creates a sub-graph for the most related SWDs. Finally, It
returns the hubs and authorities of these document by using the HITS algorithm.
Our technique increases the quality of the results and decreases the execution
time of processing the user's query.
",An Enhanced Indexing And Ranking Technique On The Semantic Web
"  This paper presents an algorithm for learning a highly redundant inverse
model in continuous and non-preset environments. Our Socially Guided Intrinsic
Motivation by Demonstrations (SGIM-D) algorithm combines the advantages of both
social learning and intrinsic motivation, to specialise in a wide range of
skills, while lessening its dependence on the teacher. SGIM-D is evaluated on a
fishing skill learning experiment.
","Constraining the Size Growth of the Task Space with Socially Guided
  Intrinsic Motivation using Demonstrations"
"  We consider an extension of the setting of label ranking, in which the
learner is allowed to make predictions in the form of partial instead of total
orders. Predictions of that kind are interpreted as a partial abstention: If
the learner is not sufficiently certain regarding the relative order of two
alternatives, it may abstain from this decision and instead declare these
alternatives as being incomparable. We propose a new method for learning to
predict partial orders that improves on an existing approach, both
theoretically and empirically. Our method is based on the idea of thresholding
the probabilities of pairwise preferences between labels as induced by a
predicted (parameterized) probability distribution on the set of all rankings.
","Label Ranking with Abstention: Predicting Partial Orders by Thresholding
  Probability Distributions (Extended Abstract)"
"  Covering model provides a general framework for granular computing in that
overlapping among granules are almost indispensable. For any given covering,
both intersection and union of covering blocks containing an element are
exploited as granules to form granular worlds at different abstraction levels,
respectively, and transformations among these different granular worlds are
also discussed. As an application of the presented multi-granular perspective
on covering, relational interpretation and axiomization of four types of
covering based rough upper approximation operators are investigated, which can
be dually applied to lower ones.
",Multi-granular Perspectives on Covering
"  Slow Feature Analysis (SFA) extracts features representing the underlying
causes of changes within a temporally coherent high-dimensional raw sensory
input signal. Our novel incremental version of SFA (IncSFA) combines
incremental Principal Components Analysis and Minor Components Analysis. Unlike
standard batch-based SFA, IncSFA adapts along with non-stationary environments,
is amenable to episodic training, is not corrupted by outliers, and is
covariance-free. These properties make IncSFA a generally useful unsupervised
preprocessor for autonomous learning agents and robots. In IncSFA, the CCIPCA
and MCA updates take the form of Hebbian and anti-Hebbian updating, extending
the biological plausibility of SFA. In both single node and deep network
versions, IncSFA learns to encode its input streams (such as high-dimensional
video) by informative slow features representing meaningful abstract
environmental properties. It can handle cases where batch SFA fails.
","Incremental Slow Feature Analysis: Adaptive and Episodic Learning from
  High-Dimensional Input Streams"
"  Many performance metrics have been introduced for the evaluation of
classification performance, with different origins and niches of application:
accuracy, macro-accuracy, area under the ROC curve, the ROC convex hull, the
absolute error, and the Brier score (with its decomposition into refinement and
calibration). One way of understanding the relation among some of these metrics
is the use of variable operating conditions (either in the form of
misclassification costs or class proportions). Thus, a metric may correspond to
some expected loss over a range of operating conditions. One dimension for the
analysis has been precisely the distribution we take for this range of
operating conditions, leading to some important connections in the area of
proper scoring rules. However, we show that there is another dimension which
has not received attention in the analysis of performance metrics. This new
dimension is given by the decision rule, which is typically implemented as a
threshold choice method when using scoring models. In this paper, we explore
many old and new threshold choice methods: fixed, score-uniform, score-driven,
rate-driven and optimal, among others. By calculating the loss of these methods
for a uniform range of operating conditions we get the 0-1 loss, the absolute
error, the Brier score (mean squared error), the AUC and the refinement loss
respectively. This provides a comprehensive view of performance metrics as well
as a systematic approach to loss minimisation, namely: take a model, apply
several threshold choice methods consistent with the information which is (and
will be) available about the operating condition, and compare their expected
losses. In order to assist in this procedure we also derive several connections
between the aforementioned performance metrics, and we highlight the role of
calibration in choosing the threshold choice method.
",Threshold Choice Methods: the Missing Link
"  Probabilistic Logic Programming (PLP), exemplified by Sato and Kameya's
PRISM, Poole's ICL, Raedt et al's ProbLog and Vennekens et al's LPAD, is aimed
at combining statistical and logical knowledge representation and inference. A
key characteristic of PLP frameworks is that they are conservative extensions
to non-probabilistic logic programs which have been widely used for knowledge
representation. PLP frameworks extend traditional logic programming semantics
to a distribution semantics, where the semantics of a probabilistic logic
program is given in terms of a distribution over possible models of the
program. However, the inference techniques used in these works rely on
enumerating sets of explanations for a query answer. Consequently, these
languages permit very limited use of random variables with continuous
distributions. In this paper, we present a symbolic inference procedure that
uses constraints and represents sets of explanations without enumeration. This
permits us to reason over PLPs with Gaussian or Gamma-distributed random
variables (in addition to discrete-valued random variables) and linear equality
constraints over reals. We develop the inference procedure in the context of
PRISM; however the procedure's core ideas can be easily applied to other PLP
languages as well. An interesting aspect of our inference procedure is that
PRISM's query evaluation process becomes a special case in the absence of any
continuous random variables in the program. The symbolic inference procedure
enables us to reason over complex probabilistic models such as Kalman filters
and a large subclass of Hybrid Bayesian networks that were hitherto not
possible in PLP frameworks. (To appear in Theory and Practice of Logic
Programming).
","Inference in Probabilistic Logic Programs with Continuous Random
  Variables"
"  We consider the task of performing probabilistic inference with probabilistic
logical models. Many algorithms for approximate inference with such models are
based on sampling. From a logic programming perspective, sampling boils down to
repeatedly calling the same queries on a knowledge base composed of a static
part and a dynamic part. The larger the static part, the more redundancy there
is in these repeated calls. This is problematic since inefficient sampling
yields poor approximations.
  We show how to apply logic program specialization to make sampling-based
inference more efficient. We develop an algorithm that specializes the
definitions of the query predicates with respect to the static part of the
knowledge base. In experiments on real-world data we obtain speedups of up to
an order of magnitude, and these speedups grow with the data-size.
","Improving the Efficiency of Approximate Inference for Probabilistic
  Logical Models by means of Program Specialization"
"  In this paper, the continuity and strong continuity in domain-free
information algebras and labeled information algebras are introduced
respectively. A more general concept of continuous function which is defined
between two domain-free continuous information algebras is presented. It is
shown that, with the operations combination and focusing, the set of all
continuous functions between two domain-free s-continuous information algebras
forms a new s-continuous information algebra. By studying the relationship
between domain-free information algebras and labeled information algebras, it
is demonstrated that they do correspond to each other on s-compactness.
",Continuity in Information Algebras
"  We study propagation of the RegularGcc global constraint. This ensures that
each row of a matrix of decision variables satisfies a Regular constraint, and
each column satisfies a Gcc constraint. On the negative side, we prove that
propagation is NP-hard even under some strong restrictions (e.g. just 3 values,
just 4 states in the automaton, or just 5 columns to the matrix). On the
positive side, we identify two cases where propagation is fixed parameter
tractable. In addition, we show how to improve propagation over a simple
decomposition into separate Regular and Gcc constraints by identifying some
necessary but insufficient conditions for a solution. We enforce these
conditions with some additional weighted row automata. Experimental results
demonstrate the potential of these methods on some standard benchmark problems.
",The RegularGcc Matrix Constraint
"  Fuzzy rule based models have a capability to approximate any continuous
function to any degree of accuracy on a compact domain. The majority of FLC
design process relies on heuristic knowledge of experience operators. In order
to make the design process automatic we present a genetic approach to learn
fuzzy rules as well as membership function parameters. Moreover, several
statistical information criteria such as the Akaike information criterion
(AIC), the Bhansali-Downham information criterion (BDIC), and the
Schwarz-Rissanen information criterion (SRIC) are used to construct optimal
fuzzy models by reducing fuzzy rules. A genetic scheme is used to design
Takagi-Sugeno-Kang (TSK) model for identification of the antecedent rule
parameters and the identification of the consequent parameters. Computer
simulations are presented confirming the performance of the constructed fuzzy
logic controller.
","Optimal Fuzzy Model Construction with Statistical Information using
  Genetic Algorithm"
"  We mathematically model Ignacio Matte Blanco's principles of symmetric and
asymmetric being through use of an ultrametric topology. We use for this the
highly regarded 1975 book of this Chilean psychiatrist and pyschoanalyst (born
1908, died 1995). Such an ultrametric model corresponds to hierarchical
clustering in the empirical data, e.g. text. We show how an ultrametric
topology can be used as a mathematical model for the structure of the logic
that reflects or expresses Matte Blanco's symmetric being, and hence of the
reasoning and thought processes involved in conscious reasoning or in reasoning
that is lacking, perhaps entirely, in consciousness or awareness of itself. In
a companion paper we study how symmetric (in the sense of Matte Blanco's)
reasoning can be demarcated in a context of symmetric and asymmetric reasoning
provided by narrative text.
","Ultrametric Model of Mind, I: Review"
"  This paper is the continuation of our research work about linguistic
truth-valued concept lattice. In order to provide a mathematical tool for
mining tacit knowledge, we establish a concrete model of 6-ary linguistic
truth-valued concept lattice and introduce a mining algorithm through the
structure consistency. Specifically, we utilize the attributes to depict
knowledge, propose the 6-ary linguistic truth-valued attribute extended context
and congener context to characterize tacit knowledge, and research the
necessary and sufficient conditions of forming tacit knowledge. We respectively
give the algorithms of generating the linguistic truth-valued congener context
and constructing the linguistic truth-valued concept lattice.
","Tacit knowledge mining algorithm based on linguistic truth-valued
  concept lattice"
"  Large-scale, parallel clusters composed of commodity processors are
increasingly available, enabling the use of vast processing capabilities and
distributed RAM to solve hard search problems. We investigate Hash-Distributed
A* (HDA*), a simple approach to parallel best-first search that asynchronously
distributes and schedules work among processors based on a hash function of the
search state. We use this approach to parallelize the A* algorithm in an
optimal sequential version of the Fast Downward planner, as well as a 24-puzzle
solver. The scaling behavior of HDA* is evaluated experimentally on a shared
memory, multicore machine with 8 cores, a cluster of commodity machines using
up to 64 cores, and large-scale high-performance clusters, using up to 2400
processors. We show that this approach scales well, allowing the effective
utilization of large amounts of distributed memory to optimally solve problems
which require terabytes of RAM. We also compare HDA* to Transposition-table
Driven Scheduling (TDS), a hash-based parallelization of IDA*, and show that,
in planning, HDA* significantly outperforms TDS. A simple hybrid which combines
HDA* and TDS to exploit strengths of both algorithms is proposed and evaluated.
","Evaluation of a Simple, Scalable, Parallel Best-First Search Strategy"
"  We review some existing methods for the computation of first order moments on
junction trees using Shafer-Shenoy algorithm. First, we consider the problem of
first order moments computation as vertices problem in junction trees. In this
way, the problem is solved using the memory space of an order of the junction
tree edge-set cardinality. After that, we consider two algorithms,
Lauritzen-Nilsson algorithm, and Mau\'a et al. algorithm, which computes the
first order moments as the normalization problem in junction tree, using the
memory space of an order of the junction tree leaf-set cardinality.
",The computation of first order moments on junction trees
"  We present a technique for the animation of a 3D kinematic tongue model, one
component of the talking head of an acoustic-visual (AV) speech synthesizer.
The skeletal animation approach is adapted to make use of a deformable rig
controlled by tongue motion capture data obtained with electromagnetic
articulography (EMA), while the tongue surface is extracted from volumetric
magnetic resonance imaging (MRI) data. Initial results are shown and future
work outlined.
","Progress in animation of an EMA-controlled tongue model for
  acoustic-visual speech synthesis"
"  This paper draws on diverse areas of computer science to develop a unified
view of computation:
  (1) Optimization in operations research, where a numerical objective function
is maximized under constraints, is generalized from the numerical total order
to a non-numerical partial order that can be interpreted in terms of
information. (2) Relations are generalized so that there are relations of which
the constituent tuples have numerical indexes, whereas in other relations these
indexes are variables. The distinction is essential in our definition of
constraint satisfaction problems. (3) Constraint satisfaction problems are
formulated in terms of semantics of conjunctions of atomic formulas of
predicate logic. (4) Approximation structures, which are available for several
important domains, are applied to solutions of constraint satisfaction
problems.
  As application we treat constraint satisfaction problems over reals. These
cover a large part of numerical analysis, most significantly nonlinear
equations and inequalities. The chaotic algorithm analyzed in the paper
combines the efficiency of floating-point computation with the correctness
guarantees of arising from our logico-mathematical model of
constraint-satisfaction problems.
",Constraint Propagation as Information Maximization
"  We built a multiagent simulation of urban traffic to model both ordinary
traffic and emergency or crisis mode traffic. This simulation first builds a
modeled road network based on detailed geographical information. On this
network, the simulation creates two populations of agents: the Transporters and
the Mobiles. Transporters embody the roads themselves; they are utilitarian and
meant to handle the low level realism of the simulation. Mobile agents embody
the vehicles that circulate on the network. They have one or several
destinations they try to reach using initially their beliefs of the structure
of the network (length of the edges, speed limits, number of lanes etc.).
Nonetheless, when confronted to a dynamic, emergent prone environment (other
vehicles, unexpectedly closed ways or lanes, traffic jams etc.), the rather
reactive agent will activate more cognitive modules to adapt its beliefs,
desires and intentions. It may change its destination(s), change the tactics
used to reach the destination (favoring less used roads, following other
agents, using general headings), etc. We describe our current validation of our
model and the next planned improvements, both in validation and in
functionalities.
",A multiagent urban traffic simulation
"  After more than sixty years, Shannon's research [1-3] continues to raise
fundamental questions, such as the one formulated by Luce [4,5], which is still
unanswered: ""Why is information theory not very applicable to psychological
problems, despite apparent similarities of concepts?"" On this topic, Pinker
[6], one of the foremost defenders of the computational theory of mind [6], has
argued that thought is simply a type of computation, and that the gap between
human cognition and computational models may be illusory. In this context, in
his latest book, titled Thinking Fast and Slow [8], Kahneman [7,8] provides
further theoretical interpretation by differentiating the two assumed systems
of the cognitive functioning of the human mind. He calls them intuition (system
1) determined to be an associative (automatic, fast and perceptual) machine,
and reasoning (system 2) required to be voluntary and to operate logical-
deductively. In this paper, we propose an ansatz inspired by Ausubel's learning
theory for investigating, from the constructivist perspective [9-12],
information processing in the working memory of cognizers. Specifically, a
thought experiment is performed utilizing the mind of a dual-natured creature
known as Maxwell's demon: a tiny ""man-machine"" solely equipped with the
characteristics of system 1, which prevents it from reasoning. The calculation
presented here shows that [...]. This result indicates that when the system 2
is shut down, both an intelligent being, as well as a binary machine, incur the
same energy cost per unit of information processed, which mathematically proves
the computational attribute of the system 1, as Kahneman [7,8] theorized. This
finding links information theory to human psychological features and opens a
new path toward the conception of a multi-bit reasoning machine.
",The thermodynamic cost of fast thought
"  The holistic approach to sustainable urban planning implies using different
models in an integrated way that is capable of simulating the urban system. As
the interconnection of such models is not a trivial task, one of the key
elements that may be applied is the description of the urban geometric
properties in an ""interoperable"" way. Focusing on air quality as one of the
most pronounced urban problems, the geometric aspects of a city may be
described by objects such as those defined in CityGML, so that an appropriate
air quality model can be applied for estimating the quality of the urban air on
the basis of atmospheric flow and chemistry equations.
  In this paper we first present theoretical background and motivations for the
interconnection of 3D city models and other models related to sustainable
development and urban planning. Then we present a practical experiment based on
the interconnection of CityGML with an air quality model. Our approach is based
on the creation of an ontology of air quality models and on the extension of an
ontology of urban planning process (OUPP) that acts as an ontology mediator.
",Ontologies for the Integration of Air Quality Models and 3D City Models
"  In this paper, we will argue that if we want to understand the function of
the brain (or the control in the case of robots), we must understand how the
brain is embedded into the physical system, and how the organism interacts with
the real world. While embodiment has often been used in its trivial meaning,
i.e. 'intelligence requires a body', the concept has deeper and more important
implications, concerned with the relation between physical and information
(neural, control) processes. A number of case studies are presented to
illustrate the concept. These involve animals and robots and are concentrated
around locomotion, grasping, and visual perception. A theoretical scheme that
can be used to embed the diverse case studies will be presented. Finally, we
will establish a link between the low-level sensory-motor processes and
cognition. We will present an embodied view on categorization, and propose the
concepts of 'body schema' and 'forward models' as a natural extension of the
embodied approach toward first representations.
","The implications of embodiment for behavior and cognition: animal and
  robotic case studies"
"  This paper analyses the influence of including agents of different degrees of
intelligence in a multiagent system. The goal is to better understand how we
can develop intelligence tests that can evaluate social intelligence. We
analyse several reinforcement algorithms in several contexts of cooperation and
competition. Our experimental setting is inspired by the recently developed
Darwin-Wallace distribution.
","On the influence of intelligence in (social) intelligence testing
  environments"
"  Many methods have been developed to secure the network infrastructure and
communication over the Internet. Intrusion detection is a relatively new
addition to such techniques. Intrusion detection systems (IDS) are used to find
out if someone has intrusion into or is trying to get it the network. One big
problem is amount of Intrusion which is increasing day by day. We need to know
about network attack information using IDS, then analysing the effect. Due to
the nature of IDSs which are solely signature based, every new intrusion cannot
be detected; so it is important to introduce artificial intelligence (AI)
methods / techniques in IDS. Introduction of AI necessitates the importance of
normalization in intrusions. This work is focused on classification of AI based
IDS techniques which will help better design intrusion detection systems in the
future. We have also proposed a support vector machine for IDS to detect Smurf
attack with much reliable accuracy.
",Classification of artificial intelligence ids for smurf attack
"  Today, University Timetabling problems are occurred annually and they are
often hard and time consuming to solve. This paper describes Hyper Heuristics
(HH) method based on Great Deluge (GD) and its variants for solving large,
highly constrained timetabling problems from different domains. Generally, in
hyper heuristic framework, there are two main stages: heuristic selection and
move acceptance. This paper emphasizes on the latter stage to develop Hyper
Heuristic (HH) framework. The main contribution of this paper is that Great
Deluge (GD) and its variants: Flex Deluge(FD), Non-linear(NLGD), Extended Great
Deluge(EGD) are used as move acceptance method in HH by combining Reinforcement
learning (RL).These HH methods are tested on exam benchmark timetabling problem
and best results and comparison analysis are reported.
","Hyper heuristic based on great deluge and its variants for exam
  timetabling problem"
"  Data Mining techniques plays a vital role like extraction of required
knowledge, finding unsuspected information to make strategic decision in a
novel way which in term understandable by domain experts. A generalized frame
work is proposed by considering non - domain experts during mining process for
better understanding, making better decision and better finding new patters in
case of selecting suitable data mining techniques based on the user profile by
means of intelligent agents. KEYWORDS: Data Mining Techniques, Intelligent
Agents, User Profile, Multidimensional Visualization, Knowledge Discovery.
","A framework: Cluster detection and multidimensional visualization of
  automated data mining using intelligent agents"
"  The First-Order Variable Elimination (FOVE) algorithm allows exact inference
to be applied directly to probabilistic relational models, and has proven to be
vastly superior to the application of standard inference methods on a grounded
propositional model. Still, FOVE operators can be applied under restricted
conditions, often forcing one to resort to propositional inference. This paper
aims to extend the applicability of FOVE by providing two new model conversion
operators: the first and the primary is joint formula conversion and the second
is just-different counting conversion. These new operations allow efficient
inference methods to be applied directly on relational models, where no
existing efficient method could be applied hitherto. In addition, aided by
these capabilities, we show how to adapt FOVE to provide exact solutions to
Maximum Expected Utility (MEU) queries over relational models for decision
under uncertainty. Experimental evaluations show our algorithms to provide
significant speedup over the alternatives.
",Extended Lifted Inference with Joint Formulas
"  Bayes-optimal behavior, while well-defined, is often difficult to achieve.
Recent advances in the use of Monte-Carlo tree search (MCTS) have shown that it
is possible to act near-optimally in Markov Decision Processes (MDPs) with very
large or infinite state spaces. Bayes-optimal behavior in an unknown MDP is
equivalent to optimal behavior in the known belief-space MDP, although the size
of this belief-space MDP grows exponentially with the amount of history
retained, and is potentially infinite. We show how an agent can use one
particular MCTS algorithm, Forward Search Sparse Sampling (FSSS), in an
efficient way to act nearly Bayes-optimally for all but a polynomial number of
steps, assuming that FSSS can be used to act efficiently in any possible
underlying MDP.
","Learning is planning: near Bayes-optimal reinforcement learning via
  Monte-Carlo tree search"
"  Hierarchical problem abstraction, when applicable, may offer exponential
reductions in computational complexity. Previous work on coarse-to-fine dynamic
programming (CFDP) has demonstrated this possibility using state abstraction to
speed up the Viterbi algorithm. In this paper, we show how to apply temporal
abstraction to the Viterbi problem. Our algorithm uses bounds derived from
analysis of coarse timescales to prune large parts of the state trellis at
finer timescales. We demonstrate improvements of several orders of magnitude
over the standard Viterbi algorithm, as well as significant speedups over CFDP,
for problems whose state variables evolve at widely differing rates.
",A temporally abstracted Viterbi algorithm
"  We propose a method called EDML for learning MAP parameters in binary
Bayesian networks under incomplete data. The method assumes Beta priors and can
be used to learn maximum likelihood parameters when the priors are
uninformative. EDML exhibits interesting behaviors, especially when compared to
EM. We introduce EDML, explain its origin, and study some of its properties
both analytically and empirically.
",EDML: A Method for Learning Parameters in Bayesian Networks
"  We present a novel approach to constraint-based causal discovery, that takes
the form of straightforward logical inference, applied to a list of simple,
logical statements about causal relations that are derived directly from
observed (in)dependencies. It is both sound and complete, in the sense that all
invariant features of the corresponding partial ancestral graph (PAG) are
identified, even in the presence of latent variables and selection bias. The
approach shows that every identifiable causal relation corresponds to one of
just two fundamental forms. More importantly, as the basic building blocks of
the method do not rely on the detailed (graphical) structure of the
corresponding PAG, it opens up a range of new opportunities, including more
robust inference, detailed accountability, and application to large models.
",A Logical Characterization of Constraint-Based Causal Discovery
"  The problem of learning the structure of Bayesian networks from complete
discrete data with a limit on parent set size is considered. Learning is cast
explicitly as an optimisation problem where the goal is to find a BN structure
which maximises log marginal likelihood (BDe score). Integer programming,
specifically the SCIP framework, is used to solve this optimisation problem.
Acyclicity constraints are added to the integer program (IP) during solving in
the form of cutting planes. Finding good cutting planes is the key to the
success of the approach -the search for such cutting planes is effected using a
sub-IP. Results show that this is a particularly fast method for exact BN
learning.
",Bayesian network learning with cutting planes
"  When the information about uncertainty cannot be quantified in a simple,
probabilistic way, the topic of possibilistic decision theory is often a
natural one to consider. The development of possibilistic decision theory has
lead to a series of possibilistic criteria, e.g pessimistic possibilistic
qualitative utility, possibilistic likely dominance, binary possibilistic
utility and possibilistic Choquet integrals. This paper focuses on sequential
decision making in possibilistic decision trees. It proposes a complexity study
of the problem of finding an optimal strategy depending on the monotonicity
property of the optimization criteria which allows the application of dynamic
programming that offers a polytime reduction of the decision problem. It also
shows that possibilistic Choquet integrals do not satisfy this property, and
that in this case the optimization problem is NP - hard.
",On the Complexity of Decision Making in Possibilistic Decision Trees
"  Probabilistic logic programs are logic programs in which some of the facts
are annotated with probabilities. Several classical probabilistic inference
tasks (such as MAP and computing marginals) have not yet received a lot of
attention for this formalism. The contribution of this paper is that we develop
efficient inference algorithms for these tasks. This is based on a conversion
of the probabilistic logic program and the query and evidence to a weighted CNF
formula. This allows us to reduce the inference tasks to well-studied tasks
such as weighted model counting. To solve such tasks, we employ
state-of-the-art methods. We consider multiple methods for the conversion of
the programs as well as for inference on the weighted CNF. The resulting
approach is evaluated experimentally and shown to improve upon the
state-of-the-art in probabilistic logic programming.
",Inference in Probabilistic Logic Programs using Weighted CNF's
"  The ideas about decision making under ignorance in economics are combined
with the ideas about uncertainty representation in computer science. The
combination sheds new light on the question of how artificial agents can act in
a dynamically consistent manner. The notion of sequential consistency is
formalized by adapting the law of iterated expectation for plausibility
measures. The necessary and sufficient condition for a certainty equivalence
operator for Nehring-Puppe's preference to be sequentially consistent is given.
This result sheds light on the models of decision making under uncertainty.
",Dynamic consistency and decision making under vacuous belief
"  Inference in graphical models consists of repeatedly multiplying and summing
out potentials. It is generally intractable because the derived potentials
obtained in this way can be exponentially large. Approximate inference
techniques such as belief propagation and variational methods combat this by
simplifying the derived potentials, typically by dropping variables from them.
We propose an alternate method for simplifying potentials: quantizing their
values. Quantization causes different states of a potential to have the same
value, and therefore introduces context-specific independencies that can be
exploited to represent the potential more compactly. We use algebraic decision
diagrams (ADDs) to do this efficiently. We apply quantization and ADD reduction
to variable elimination and junction tree propagation, yielding a family of
bounded approximate inference schemes. Our experimental tests show that our new
schemes significantly outperform state-of-the-art approaches on many benchmark
instances.
",Approximation by Quantization
"  Many representation schemes combining first-order logic and probability have
been proposed in recent years. Progress in unifying logical and probabilistic
inference has been slower. Existing methods are mainly variants of lifted
variable elimination and belief propagation, neither of which take logical
structure into account. We propose the first method that has the full power of
both graphical model inference and first-order theorem proving (in finite
domains with Herbrand interpretations). We first define probabilistic theorem
proving, their generalization, as the problem of computing the probability of a
logical formula given the probabilities or weights of a set of formulas. We
then show how this can be reduced to the problem of lifted weighted model
counting, and develop an efficient algorithm for the latter. We prove the
correctness of this algorithm, investigate its properties, and show how it
generalizes previous approaches. Experiments show that it greatly outperforms
lifted variable elimination when logical structure is present. Finally, we
propose an algorithm for approximate probabilistic theorem proving, and show
that it can greatly outperform lifted belief propagation.
",Probabilistic Theorem Proving
"  This paper presents an approach for learning to translate simple narratives,
i.e., texts (sequences of sentences) describing dynamic systems, into coherent
sequences of events without the need for labeled training data. Our approach
incorporates domain knowledge in the form of preconditions and effects of
events, and we show that it outperforms state-of-the-art supervised learning
systems on the task of reconstructing RoboCup soccer games from their
commentaries.
",Reasoning about RoboCup Soccer Narratives
"  We consider how to use the Bellman residual of the dynamic programming
operator to compute suboptimality bounds for solutions to stochastic shortest
path problems. Such bounds have been previously established only in the special
case that ""all policies are proper,"" in which case the dynamic programming
operator is known to be a contraction, and have been shown to be easily
computable only in the more limited special case of discounting. Under the
condition that transition costs are positive, we show that suboptimality bounds
can be easily computed even when not all policies are proper. In the general
case when there are no restrictions on transition costs, the analysis is more
complex. But we present preliminary results that show such bounds are possible.
",Suboptimality Bounds for Stochastic Shortest Path Problems
"  We study the problem of agent-based negotiation in combinatorial domains. It
is difficult to reach optimal agreements in bilateral or multi-lateral
negotiations when the agents' preferences for the possible alternatives are not
common knowledge. Self-interested agents often end up negotiating inefficient
agreements in such situations. In this paper, we present a protocol for
negotiation in combinatorial domains which can lead rational agents to reach
optimal agreements under incomplete information setting. Our proposed protocol
enables the negotiating agents to identify efficient solutions using
distributed search that visits only a small subspace of the whole outcome
space. Moreover, the proposed protocol is sufficiently general that it is
applicable to most preference representation models in combinatorial domains.
We also present results of experiments that demonstrate the feasibility and
computational efficiency of our approach.
","An Efficient Protocol for Negotiation over Combinatorial Domains with
  Incomplete Information"
"  We present theoretical results in terms of lower and upper bounds on the
query complexity of noisy search with comparative feedback. In this search
model, the noise in the feedback depends on the distance between query points
and the search target. Consequently, the error probability in the feedback is
not fixed but varies for the queries posed by the search algorithm. Our results
show that a target out of n items can be found in O(log n) queries. We also
show the surprising result that for k possible answers per query, the speedup
is not log k (as for k-ary search) but only log log k in some cases.
",Noisy Search with Comparative Feedback
"  Situation calculus has been applied widely in artificial intelligence to
model and reason about actions and changes in dynamic systems. Since actions
carried out by agents will cause constant changes of the agents' beliefs, how
to manage these changes is a very important issue. Shapiro et al. [22] is one
of the studies that considered this issue. However, in this framework, the
problem of noisy sensing, which often presents in real-world applications, is
not considered. As a consequence, noisy sensing actions in this framework will
lead to an agent facing inconsistent situation and subsequently the agent
cannot proceed further. In this paper, we investigate how noisy sensing actions
can be handled in iterated belief change within the situation calculus
formalism. We extend the framework proposed in [22] with the capability of
managing noisy sensings. We demonstrate that an agent can still detect the
actual situation when the ratio of noisy sensing actions vs. accurate sensing
actions is limited. We prove that our framework subsumes the iterated belief
change strategy in [22] when all sensing actions are accurate. Furthermore, we
prove that our framework can adequately handle belief introspection, mistaken
beliefs, belief revision and belief update even with noisy sensing, as done in
[22] with accurate sensing actions only.
",Belief change with noisy sensing in the situation calculus
"  Previous work has shown that the problem of learning the optimal structure of
a Bayesian network can be formulated as a shortest path finding problem in a
graph and solved using A* search. In this paper, we improve the scalability of
this approach by developing a memory-efficient heuristic search algorithm for
learning the structure of a Bayesian network. Instead of using A*, we propose a
frontier breadth-first branch and bound search that leverages the layered
structure of the search graph of this problem so that no more than two layers
of the graph, plus solution reconstruction information, need to be stored in
memory at a time. To further improve scalability, the algorithm stores most of
the graph in external memory, such as hard disk, when it does not fit in RAM.
Experimental results show that the resulting algorithm solves significantly
larger problems than the current state of the art.
","Improving the Scalability of Optimal Bayesian Network Learning with
  External-Memory Frontier Breadth-First Branch and Bound Search"
"  In this paper, we develop a qualitative theory of influence diagrams that can
be used to model and solve sequential decision making tasks when only
qualitative (or imprecise) information is available. Our approach is based on
an order-of-magnitude approximation of both probabilities and utilities and
allows for specifying partially ordered preferences via sets of utility values.
We also propose a dedicated variable elimination algorithm that can be applied
for solving order-of-magnitude influence diagrams.
",Order-of-Magnitude Influence Diagrams
"  To deal with the prohibitive complexity of calculating policies in
Decentralized MDPs, researchers have proposed models that exploit structured
agent interactions. Settings where most agent actions are independent except
for few actions that affect the transitions and/or rewards of other agents can
be modeled using Event-Driven Interactions with Complex Rewards (EDI-CR).
Finding the optimal joint policy can be formulated as an optimization problem.
However, existing formulations are too verbose and/or lack optimality
guarantees. We propose a compact Mixed Integer Linear Program formulation of
EDI-CR instances. The key insight is that most action sequences of a group of
agents have the same effect on a given agent. This allows us to treat these
sequences similarly and use fewer variables. Experiments show that our
formulation is more compact and leads to faster solution times and better
solutions than existing formulations.
","Compact Mathematical Programs For DEC-MDPs With Structured Agent
  Interactions"
"  Markov decision processes (MDPs) are widely used in modeling decision making
problems in stochastic environments. However, precise specification of the
reward functions in MDPs is often very difficult. Recent approaches have
focused on computing an optimal policy based on the minimax regret criterion
for obtaining a robust policy under uncertainty in the reward function. One of
the core tasks in computing the minimax regret policy is to obtain the set of
all policies that can be optimal for some candidate reward function. In this
paper, we propose an efficient algorithm that exploits the geometric properties
of the reward function associated with the policies. We also present an
approximate version of the method for further speed up. We experimentally
demonstrate that our algorithm improves the performance by orders of magnitude.
",A Geometric Traversal Algorithm for Reward-Uncertain MDPs
"  Hidden Markov models (HMMs) and conditional random fields (CRFs) are two
popular techniques for modeling sequential data. Inference algorithms designed
over CRFs and HMMs allow estimation of the state sequence given the
observations. In several applications, estimation of the state sequence is not
the end goal; instead the goal is to compute some function of it. In such
scenarios, estimating the state sequence by conventional inference techniques,
followed by computing the functional mapping from the estimate is not
necessarily optimal. A more formal approach is to directly infer the final
outcome from the observations. In particular, we consider the specific
instantiation of the problem where the goal is to find the state trajectories
without exact transition points and derive a novel polynomial time inference
algorithm that outperforms vanilla inference techniques. We show that this
particular problem arises commonly in many disparate applications and present
experiments on three of them: (1) Toy robot tracking; (2) Single stroke
character recognition; (3) Handwritten word recognition.
",Compressed Inference for Probabilistic Sequential Models
"  Many real-world decision-theoretic planning problems can be naturally modeled
with discrete and continuous state Markov decision processes (DC-MDPs). While
previous work has addressed automated decision-theoretic planning for DCMDPs,
optimal solutions have only been defined so far for limited settings, e.g.,
DC-MDPs having hyper-rectangular piecewise linear value functions. In this
work, we extend symbolic dynamic programming (SDP) techniques to provide
optimal solutions for a vastly expanded class of DCMDPs. To address the
inherent combinatorial aspects of SDP, we introduce the XADD - a continuous
variable extension of the algebraic decision diagram (ADD) - that maintains
compact representations of the exact value function. Empirically, we
demonstrate an implementation of SDP with XADDs on various DC-MDPs, showing the
first optimal automated solutions to DCMDPs with linear and nonlinear piecewise
partitioned value functions and showing the advantages of constraint-based
pruning for XADDs.
",Symbolic Dynamic Programming for Discrete and Continuous State MDPs
"  Identifying and controlling bias is a key problem in empirical sciences.
Causal diagram theory provides graphical criteria for deciding whether and how
causal effects can be identified from observed (nonexperimental) data by
covariate adjustment. Here we prove equivalences between existing as well as
new criteria for adjustment and we provide a new simplified but still
equivalent notion of d-separation. These lead to efficient algorithms for two
important tasks in causal diagram analysis: (1) listing minimal covariate
adjustments (with polynomial delay); and (2) identifying the subdiagram
involved in biasing paths (in linear time). Our results improve upon existing
exponential-time solutions for these problems, enabling users to assess the
effects of covariate adjustment on diagrams with tens to hundreds of variables
interactively in real time.
",Adjustment Criteria in Causal Diagrams: An Algorithmic Perspective
"  We present a distributed anytime algorithm for performing MAP inference in
graphical models. The problem is formulated as a linear programming relaxation
over the edges of a graph. The resulting program has a constraint structure
that allows application of the Dantzig-Wolfe decomposition principle.
Subprograms are defined over individual edges and can be computed in a
distributed manner. This accommodates solutions to graphs whose state space
does not fit in memory. The decomposition master program is guaranteed to
compute the optimal solution in a finite number of iterations, while the
solution converges monotonically with each iteration. Formulating the MAP
inference problem as a linear program allows additional (global) constraints to
be defined; something not possible with message passing algorithms.
Experimental results show that our algorithm's solution quality outperforms
most current algorithms and it scales well to large problems.
",Distributed Anytime MAP Inference
"  Approximate Bayesian inference is NP-hard. Dagum and Luby defined the Local
Variance Bound (LVB) to measure the approximation hardness of Bayesian
inference on Bayesian networks, assuming the networks model strictly positive
joint probability distributions, i.e. zero probabilities are not permitted.
This paper introduces the k-test to measure the approximation hardness of
inference on Bayesian networks with deterministic causalities in the
probability distribution, i.e. when zero conditional probabilities are
permitted. Approximation by stochastic sampling is a widely-used inference
method that is known to suffer from inefficiencies due to sample rejection. The
k-test predicts when rejection rates of stochastic sampling a Bayesian network
will be low, modest, high, or when sampling is intractable.
","Measuring the Hardness of Stochastic Sampling on Bayesian Networks with
  Deterministic Causalities: the k-Test"
"  This paper investigates a new method for improving the learning algorithm of
Mixture of Experts (ME) model using a hybrid of Modified Cuckoo Search (MCS)
and Conjugate Gradient (CG) as a second order optimization technique. The CG
technique is combined with Back-Propagation (BP) algorithm to yield a much more
efficient learning algorithm for ME structure. In addition, the experts and
gating networks in enhanced model are replaced by CG based Multi-Layer
Perceptrons (MLPs) to provide faster and more accurate learning. The CG is
considerably depends on initial weights of connections of Artificial Neural
Network (ANN), so, a metaheuristic algorithm, the so-called Modified Cuckoo
Search is applied in order to select the optimal weights. The performance of
proposed method is compared with Gradient Decent Based ME (GDME) and Conjugate
Gradient Based ME (CGME) in classification and regression problems. The
experimental results show that hybrid MSC and CG based ME (MCS-CGME) has faster
convergence and better performance in utilized benchmark data sets.
","Extended Mixture of MLP Experts by Hybrid of Conjugate Gradient Method
  and Modified Cuckoo Search"
"  Spectrum sensing is a fundamental problem in cognitive radio. We propose a
function of covariance matrix based detection algorithm for spectrum sensing in
cognitive radio network. Monotonically increasing property of function of
matrix involving trace operation is utilized as the cornerstone for this
algorithm. The advantage of proposed algorithm is it works under extremely low
signal-to-noise ratio, like lower than -30 dB with limited sample data.
Theoretical analysis of threshold setting for the algorithm is discussed. A
performance comparison between the proposed algorithm and other
state-of-the-art methods is provided, by the simulation on captured digital
television (DTV) signal.
","Generalized FMD Detection for Spectrum Sensing Under Low Signal-to-Noise
  Ratio"
"  The purpose of statistical disclosure control (SDC) of microdata, a.k.a. data
anonymization or privacy-preserving data mining, is to publish data sets
containing the answers of individual respondents in such a way that the
respondents corresponding to the released records cannot be re-identified and
the released data are analytically useful. SDC methods are either based on
masking the original data, generating synthetic versions of them or creating
hybrid versions by combining original and synthetic data. The choice of SDC
methods for categorical data, especially nominal data, is much smaller than the
choice of methods for numerical data. We mitigate this problem by introducing a
numerical mapping for hierarchical nominal data which allows computing means,
variances and covariances on them.
","Marginality: a numerical mapping for enhanced treatment of nominal and
  hierarchical attributes"
"  The first decade of this century has seen the nascency of the first
mathematical theory of general artificial intelligence. This theory of
Universal Artificial Intelligence (UAI) has made significant contributions to
many theoretical, philosophical, and practical AI questions. In a series of
papers culminating in book (Hutter, 2005), an exciting sound and complete
mathematical model for a super intelligent agent (AIXI) has been developed and
rigorously analyzed. While nowadays most AI researchers avoid discussing
intelligence, the award-winning PhD thesis (Legg, 2008) provided the
philosophical embedding and investigated the UAI-based universal measure of
rational intelligence, which is formal, objective and non-anthropocentric.
Recently, effective approximations of AIXI have been derived and experimentally
investigated in JAIR paper (Veness et al. 2011). This practical breakthrough
has resulted in some impressive applications, finally muting earlier critique
that UAI is only a theory. For the first time, without providing any domain
knowledge, the same agent is able to self-adapt to a diverse range of
interactive environments. For instance, AIXI is able to learn from scratch to
play TicTacToe, Pacman, Kuhn Poker, and other games by trial and error, without
even providing the rules of the games.
  These achievements give new hope that the grand goal of Artificial General
Intelligence is not elusive.
  This article provides an informal overview of UAI in context. It attempts to
gently introduce a very theoretical, formal, and mathematical subject, and
discusses philosophical and technical ingredients, traits of intelligence, some
social questions, and the past and future of UAI.
",One Decade of Universal Artificial Intelligence
"  Relational representations in reinforcement learning allow for the use of
structural information like the presence of objects and relationships between
them in the description of value functions. Through this paper, we show that
such representations allow for the inclusion of background knowledge that
qualitatively describes a state and can be used to design agents that
demonstrate learning behavior in domains with large state and actions spaces
such as computer games.
",Relational Reinforcement Learning in Infinite Mario
"  In a world where communication and information sharing are at the heart of
our business, the terminology needs are most pressing. It has become imperative
to identify the terms used and defined in a consensual and coherent way while
preserving linguistic diversity. To streamline and strengthen the process of
acquisition, representation and exploitation of scenarii of train accidents, it
is necessary to harmonize and standardize the terminology used by players in
the security field. The research aims to significantly improve analytical
activities and operations of the various safety studies, by tracking the error
in system, hardware, software and human. This paper presents the contribution
of ontology to modeling scenarii for rail accidents through a knowledge model
based on a generic ontology and domain ontology. After a detailed presentation
of the state of the art material, this article presents the first results of
the developed model.
","Development of an Ontology to Assist the Modeling of Accident Scenarii
  ""Application on Railroad Transport """
"  The ability to model search in a constraint solver can be an essential asset
for solving combinatorial problems. However, existing infrastructure for
defining search heuristics is often inadequate. Either modeling capabilities
are extremely limited or users are faced with a general-purpose programming
language whose features are not tailored towards writing search heuristics. As
a result, major improvements in performance may remain unexplored.
  This article introduces search combinators, a lightweight and
solver-independent method that bridges the gap between a conceptually simple
modeling language for search (high-level, functional and naturally
compositional) and an efficient implementation (low-level, imperative and
highly non-modular). By allowing the user to define application-tailored search
strategies from a small set of primitives, search combinators effectively
provide a rich domain-specific language (DSL) for modeling search to the user.
Remarkably, this DSL comes at a low implementation cost to the developer of a
constraint solver.
  The article discusses two modular implementation approaches and shows, by
empirical evaluation, that search combinators can be implemented without
overhead compared to a native, direct implementation in a constraint solver.
",Search Combinators
"  In Multi-Source Feedback or 360 Degree Feedback, data on the performance of
an individual are collected systematically from a number of stakeholders and
are used for improving performance. The 360-Degree Feedback approach provides a
consistent management philosophy meeting the criterion outlined previously. The
360-degree feedback appraisal process describes a human resource methodology
that is frequently used for both employee appraisal and employee development.
Used in employee performance appraisals, the 360-degree feedback methodology is
differentiated from traditional, top-down appraisal methods in which the
supervisor responsible for the appraisal provides the majority of the data.
Instead it seeks to use information gained from other sources to provide a
fuller picture of employees' performances. Similarly, when this technique used
in employee development it augments employees' perceptions of training needs
with those of the people with whom they interact. The 360-degree feedback based
appraisal is a comprehensive method where in the feedback about the employee
comes from all the sources that come into contact with the employee on his/her
job. The respondents for an employee can be her/his peers, managers,
subordinates team members, customers, suppliers and vendors. Hence anyone who
comes into contact with the employee, the 360 degree appraisal has four
components that include self-appraisal, superior's appraisal, subordinate's
appraisal student's appraisal and peer's appraisal .The proposed system is an
attempt to implement the 360 degree feedback based appraisal system in
academics especially engineering colleges.
","Multi source feedback based performance appraisal system using Fuzzy
  logic decision support system"
"  We propose a simple method for combining together voting rules that performs
a run-off between the different winners of each voting rule. We prove that this
combinator has several good properties. For instance, even if just one of the
base voting rules has a desirable property like Condorcet consistency, the
combination inherits this property. In addition, we prove that combining voting
rules together in this way can make finding a manipulation more computationally
difficult. Finally, we study the impact of this combinator on approximation
methods that find close to optimal manipulations.
",Combining Voting Rules Together
"  Languages for open-universe probabilistic models (OUPMs) can represent
situations with an unknown number of objects and iden- tity uncertainty. While
such cases arise in a wide range of important real-world appli- cations,
existing general purpose inference methods for OUPMs are far less efficient
than those available for more restricted lan- guages and model classes. This
paper goes some way to remedying this deficit by in- troducing, and proving
correct, a generaliza- tion of Gibbs sampling to partial worlds with possibly
varying model structure. Our ap- proach draws on and extends previous generic
OUPM inference methods, as well as aux- iliary variable samplers for
nonparametric mixture models. It has been implemented for BLOG, a well-known
OUPM language. Combined with compile-time optimizations, the resulting
algorithm yields very substan- tial speedups over existing methods on sev- eral
test cases, and substantially improves the practicality of OUPM languages
generally.
",Gibbs Sampling in Open-Universe Stochastic Languages
"  Qualitative possibilistic networks, also known as min-based possibilistic
networks, are important tools for handling uncertain information in the
possibility theory frame- work. Despite their importance, only the junction
tree adaptation has been proposed for exact reasoning with such networks. This
paper explores alternative algorithms using compilation techniques. We first
propose possibilistic adaptations of standard compilation-based probabilistic
methods. Then, we develop a new, purely possibilistic, method based on the
transformation of the initial network into a possibilistic base. A comparative
study shows that this latter performs better than the possibilistic adap-
tations of probabilistic methods. This result is also confirmed by experimental
results.
","Compiling Possibilistic Networks: Alternative Approaches to
  Possibilistic Inference"
"  Possibilistic answer set programming (PASP) extends answer set programming
(ASP) by attaching to each rule a degree of certainty. While such an extension
is important from an application point of view, existing semantics are not
well-motivated, and do not always yield intuitive results. To develop a more
suitable semantics, we first introduce a characterization of answer sets of
classical ASP programs in terms of possibilistic logic where an ASP program
specifies a set of constraints on possibility distributions. This
characterization is then naturally generalized to define answer sets of PASP
programs. We furthermore provide a syntactic counterpart, leading to a
possibilistic generalization of the well-known Gelfond-Lifschitz reduct, and we
show how our framework can readily be implemented using standard ASP solvers.
",Possibilistic Answer Set Programming Revisited
"  Performing sensitivity analysis for influence diagrams using the decision
circuit framework is particularly convenient, since the partial derivatives
with respect to every parameter are readily available [Bhattacharjya and
Shachter, 2007; 2008]. In this paper we present three non-linear sensitivity
analysis methods that utilize this partial derivative information and therefore
do not require re-evaluating the decision situation multiple times.
Specifically, we show how to efficiently compare strategies in decision
situations, perform sensitivity to risk aversion and compute the value of
perfect hedging [Seyller, 2008].
",Three new sensitivity analysis methods for influence diagrams
"  Many machine learning applications require the ability to learn from and
reason about noisy multi-relational data. To address this, several effective
representations have been developed that provide both a language for expressing
the structural regularities of a domain, and principled support for
probabilistic inference. In addition to these two aspects, however, many
applications also involve a third aspect-the need to reason about
similarities-which has not been directly supported in existing frameworks. This
paper introduces probabilistic similarity logic (PSL), a general-purpose
framework for joint reasoning about similarity in relational domains that
incorporates probabilistic reasoning about similarities and relational
structure in a principled way. PSL can integrate any existing domain-specific
similarity measures and also supports reasoning about similarities between sets
of entities. We provide efficient inference and learning techniques for PSL and
demonstrate its effectiveness both in common relational tasks and in settings
that require reasoning about similarity.
",Probabilistic Similarity Logic
"  The Next Generation Air Transportation System will introduce new, advanced
sensor technologies into the cockpit. With the introduction of such systems,
the responsibilities of the pilot are expected to dramatically increase. In the
ALARMS (ALerting And Reasoning Management System) project for NASA, we focus on
a key challenge of this environment, the quick and efficient handling of
aircraft sensor alerts. It is infeasible to alert the pilot on the state of all
subsystems at all times. Furthermore, there is uncertainty as to the true
hazard state despite the evidence of the alerts, and there is uncertainty as to
the effect and duration of actions taken to address these alerts. This paper
reports on the first steps in the construction of an application designed to
handle Next Generation alerts. In ALARMS, we have identified 60 different
aircraft subsystems and 20 different underlying hazards. In this paper, we show
how a Bayesian network can be used to derive the state of the underlying
hazards, based on the sensor input. Then, we propose a framework whereby an
automated system can plan to address these hazards in cooperation with the
pilot, using a Time-Dependent Markov Process (TMDP). Different hazards and
pilot states will call for different alerting automation plans. We demonstrate
this emerging application of Bayesian networks and TMDPs to cockpit automation,
for a use case where a small number of hazards are present, and analyze the
resulting alerting automation policies.
","ALARMS: Alerting and Reasoning Management System for Next Generation
  Aircraft Hazards"
"  Relational Continuous Models (RCMs) represent joint probability densities
over attributes of objects, when the attributes have continuous domains. With
relational representations, they can model joint probability distributions over
large numbers of variables compactly in a natural way. This paper presents a
new exact lifted inference algorithm for RCMs, thus it scales up to large
models of real world applications. The algorithm applies to Relational Pairwise
Models which are (relational) products of potentials of arity 2. Our algorithm
is unique in two ways. First, it substantially improves the efficiency of
lifted inference with variables of continuous domains. When a relational model
has Gaussian potentials, it takes only linear-time compared to cubic time of
previous methods. Second, it is the first exact inference algorithm which
handles RCMs in a lifted way. The algorithm is illustrated over an example from
econometrics. Experimental results show that our algorithm outperforms both a
groundlevel inference algorithm and an algorithm built with previously-known
lifted methods.
",Lifted Inference for Relational Continuous Models
"  We propose a new point-based method for approximate planning in Dec-POMDP
which outperforms the state-of-the-art approaches in terms of solution quality.
It uses a heuristic estimation of the prior probability of beliefs to choose a
bounded number of policy trees: this choice is formulated as a combinatorial
optimisation problem minimising the error induced by pruning.
",Distribution over Beliefs for Memory Bounded Dec-POMDP Planning
"  Partially-Observable Markov Decision Processes (POMDPs) are typically solved
by finding an approximate global solution to a corresponding belief-MDP. In
this paper, we offer a new planning algorithm for POMDPs with continuous state,
action and observation spaces. Since such domains have an inherent notion of
locality, we can find an approximate solution using local optimization methods.
We parameterize the belief distribution as a Gaussian mixture, and use the
Extended Kalman Filter (EKF) to approximate the belief update. Since the EKF is
a first-order filter, we can marginalize over the observations analytically. By
using feedback control and state estimation during policy execution, we recover
a behavior that is effectively conditioned on incoming observations despite the
unconditioned planning. Local optimization provides no guarantees of global
optimality, but it allows us to tackle domains that are at least an order of
magnitude larger than the current state-of-the-art. We demonstrate the
scalability of our algorithm by considering a simulated hand-eye coordination
domain with 16 continuous state dimensions and 6 continuous action dimensions.
","A Scalable Method for Solving High-Dimensional Continuous POMDPs Using
  Local Approximation"
"  Computing the probability of a formula given the probabilities or weights
associated with other formulas is a natural extension of logical inference to
the probabilistic setting. Surprisingly, this problem has received little
attention in the literature to date, particularly considering that it includes
many standard inference problems as special cases. In this paper, we propose
two algorithms for this problem: formula decomposition and conditioning, which
is an exact method, and formula importance sampling, which is an approximate
method. The latter is, to our knowledge, the first application of model
counting to approximate probabilistic inference. Unlike conventional
variable-based algorithms, our algorithms work in the dual realm of logical
formulas. Theoretically, we show that our algorithms can greatly improve
efficiency by exploiting the structural information in the formulas.
Empirically, we show that they are indeed quite powerful, often achieving
substantial performance gains over state-of-the-art schemes.
",Formula-Based Probabilistic Inference
"  Decentralized POMDPs provide an expressive framework for multi-agent
sequential decision making. While fnite-horizon DECPOMDPs have enjoyed
signifcant success, progress remains slow for the infnite-horizon case mainly
due to the inherent complexity of optimizing stochastic controllers
representing agent policies. We present a promising new class of algorithms for
the infnite-horizon case, which recasts the optimization problem as inference
in a mixture of DBNs. An attractive feature of this approach is the
straightforward adoption of existing inference techniques in DBNs for solving
DEC-POMDPs and supporting richer representations such as factored or continuous
states and actions. We also derive the Expectation Maximization (EM) algorithm
to optimize the joint policy represented as DBNs. Experiments on benchmark
domains show that EM compares favorably against the state-of-the-art solvers.
",Anytime Planning for Decentralized POMDPs using Expectation Maximization
"  We describe a framework and an algorithm for solving hybrid influence
diagrams with discrete, continuous, and deterministic chance variables, and
discrete and continuous decision variables. A continuous chance variable in an
influence diagram is said to be deterministic if its conditional distributions
have zero variances. The solution algorithm is an extension of Shenoy's fusion
algorithm for discrete influence diagrams. We describe an extended
Shenoy-Shafer architecture for propagation of discrete, continuous, and utility
potentials in hybrid influence diagrams that include deterministic chance
variables. The algorithm and framework are illustrated by solving two small
examples.
",Solving Hybrid Influence Diagrams with Deterministic Variables
"  The paper introduces k-bounded MAP inference, a parameterization of MAP
inference in Markov logic networks. k-Bounded MAP states are MAP states with at
most k active ground atoms of hidden (non-evidence) predicates. We present a
novel delayed column generation algorithm and provide empirical evidence that
the algorithm efficiently computes k-bounded MAP states for meaningful
real-world graph matching problems. The underlying idea is that, instead of
solving one large optimization problem, it is often more efficient to tackle
several small ones.
","A Delayed Column Generation Strategy for Exact k-Bounded MAP Inference
  in Markov Logic Networks"
"  Rollating walkers are popular mobility aids used by older adults to improve
balance control. There is a need to automatically recognize the activities
performed by walker users to better understand activity patterns, mobility
issues and the context in which falls are more likely to happen. We design and
compare several techniques to recognize walker related activities. A
comprehensive evaluation with control subjects and walker users from a
retirement community is presented.
","Comparative Analysis of Probabilistic Models for Activity Recognition
  with an Instrumented Walker"
"  Belief merging is an important but difficult problem in Artificial
Intelligence, especially when sources of information are pervaded with
uncertainty. Many merging operators have been proposed to deal with this
problem in possibilistic logic, a weighted logic which is powerful for handling
inconsistency and deal- ing with uncertainty. They often result in a
possibilistic knowledge base which is a set of weighted formulas. Although
possibilistic logic is inconsistency tolerant, it suers from the well-known
""drowning effect"". Therefore, we may still want to obtain a consistent possi-
bilistic knowledge base as the result of merg- ing. In such a case, we argue
that it is not always necessary to keep weighted informa- tion after merging.
In this paper, we define a merging operator that maps a set of pos- sibilistic
knowledge bases and a formula rep- resenting the integrity constraints to a
clas- sical knowledge base by using lexicographic ordering. We show that it
satisfies nine pos- tulates that generalize basic postulates for propositional
merging given in [11]. These postulates capture the principle of minimal change
in some sense. We then provide an algorithm for generating the resulting knowl-
edge base of our merging operator. Finally, we discuss the compatibility of our
merging operator with propositional merging and es- tablish the advantage of
our merging opera- tor over existing semantic merging operators in the
propositional case.
","Merging Knowledge Bases in Possibilistic Logic by Lexicographic
  Aggregation"
"  The standard coherence criterion for lower previsions is expressed using an
infinite number of linear constraints. For lower previsions that are
essentially defined on some finite set of gambles on a finite possibility
space, we present a reformulation of this criterion that only uses a finite
number of constraints. Any such lower prevision is coherent if it lies within
the convex polytope defined by these constraints. The vertices of this polytope
are the extreme coherent lower previsions for the given set of gambles. Our
reformulation makes it possible to compute them. We show how this is done and
illustrate the procedure and its results.
","Characterizing the Set of Coherent Lower Previsions with a Finite Number
  of Constraints or Vertices"
"  Decision circuits perform efficient evaluation of influence diagrams,
building on the ad- vances in arithmetic circuits for belief net- work
inference [Darwiche, 2003; Bhattachar- jya and Shachter, 2007]. We show how
even more compact decision circuits can be con- structed for dynamic
programming in influ- ence diagrams with separable value functions and
conditionally independent subproblems. Once a decision circuit has been
constructed based on the diagram's ""global"" graphical structure, it can be
compiled to exploit ""lo- cal"" structure for efficient evaluation and sen-
sitivity analysis.
",Dynamic programming in in uence diagrams with decision circuits
"  In this paper, we present the Difference- Based Causality Learner (DBCL), an
algorithm for learning a class of discrete-time dynamic models that represents
all causation across time by means of difference equations driving change in a
system. We motivate this representation with real-world mechanical systems and
prove DBCL's correctness for learning structure from time series data, an
endeavour that is complicated by the existence of latent derivatives that have
to be detected. We also prove that, under common assumptions for causal
discovery, DBCL will identify the presence or absence of feedback loops, making
the model more useful for predicting the effects of manipulating variables when
the system is in equilibrium. We argue analytically and show empirically the
advantages of DBCL over vector autoregression (VAR) and Granger causality
models as well as modified forms of Bayesian and constraintbased structure
discovery algorithms. Finally, we show that our algorithm can discover causal
directions of alpha rhythms in human brains from EEG data.
",Learning Why Things Change: The Difference-Based Causality Learner
"  We present decentralized rollout sampling policy iteration (DecRSPI) - a new
algorithm for multi-agent decision problems formalized as DEC-POMDPs. DecRSPI
is designed to improve scalability and tackle problems that lack an explicit
model. The algorithm uses Monte- Carlo methods to generate a sample of
reachable belief states. Then it computes a joint policy for each belief state
based on the rollout estimations. A new policy representation allows us to
represent solutions compactly. The key benefits of the algorithm are its linear
time complexity over the number of agents, its bounded memory usage and good
solution quality. It can solve larger problems that are intractable for
existing planning algorithms. Experimental results confirm the effectiveness
and scalability of the approach.
",Rollout Sampling Policy Iteration for Decentralized POMDPs
"  A branch-and-bound approach to solving influ- ence diagrams has been
previously proposed in the literature, but appears to have never been
implemented and evaluated - apparently due to the difficulties of computing
effective bounds for the branch-and-bound search. In this paper, we describe
how to efficiently compute effective bounds, and we develop a practical
implementa- tion of depth-first branch-and-bound search for influence diagram
evaluation that outperforms existing methods for solving influence diagrams
with multiple stages.
",Solving Multistage Influence Diagrams using Branch-and-Bound Search
"  Despite the intractability of generic optimal partially observable Markov
decision process planning, there exist important problems that have highly
structured models. Previous researchers have used this insight to construct
more efficient algorithms for factored domains, and for domains with
topological structure in the flat state dynamics model. In our work, motivated
by findings from the education community relevant to automated tutoring, we
consider problems that exhibit a form of topological structure in the factored
dynamics model. Our Reachable Anytime Planner for Imprecisely-sensed Domains
(RAPID) leverages this structure to efficiently compute a good initial envelope
of reachable states under the optimal MDP policy in time linear in the number
of state variables. RAPID performs partially-observable planning over the
limited envelope of states, and slowly expands the state space considered as
time allows. RAPID performs well on a large tutoring-inspired problem
simulation with 122 state variables, corresponding to a flat state space of
over 10^30 states.
",RAPID: A Reachable Anytime Planner for Imprecisely-sensed Domains
"  UCT has recently emerged as an exciting new adversarial reasoning technique
based on cleverly balancing exploration and exploitation in a Monte-Carlo
sampling setting. It has been particularly successful in the game of Go but the
reasons for its success are not well understood and attempts to replicate its
success in other domains such as Chess have failed. We provide an in-depth
analysis of the potential of UCT in domain-independent settings, in cases where
heuristic values are available, and the effect of enhancing random playouts to
more informed playouts between two weak minimax players. To provide further
insights, we develop synthetic game tree instances and discuss interesting
properties of UCT, both empirically and analytically.
",Understanding Sampling Style Adversarial Search Methods
"  Probabilistic Logic Programming (PLP), exemplified by Sato and Kameya's
PRISM, Poole's ICL, De Raedt et al's ProbLog and Vennekens et al's LPAD,
combines statistical and logical knowledge representation and inference.
Inference in these languages is based on enumerative construction of proofs
over logic programs. Consequently, these languages permit very limited use of
random variables with continuous distributions. In this paper, we extend PRISM
with Gaussian random variables and linear equality constraints, and consider
the problem of parameter learning in the extended language. Many statistical
models such as finite mixture models and Kalman filter can be encoded in
extended PRISM. Our EM-based learning algorithm uses a symbolic inference
procedure that represents sets of derivations without enumeration. This permits
us to learn the distribution parameters of extended PRISM programs with
discrete as well as Gaussian variables. The learning algorithm naturally
generalizes the ones used for PRISM and Hybrid Bayesian Networks.
",Parameter Learning in PRISM Programs with Continuous Random Variables
"  Decision making whenever and wherever it is happened is key to organizations
success. In order to make correct decision, individuals, teams and
organizations need both knowledge management (to manage content) and
collaboration (to manage group processes) to make that more effective and
efficient. In this paper, we explain the knowledge management and collaboration
convergence. Then, we propose a formal description of mixed and multimodal
decision making (MDM) process where decision may be made by three possible
modes: individual, collective or hybrid. Finally, we explicit the MDM process
based on UML-G profile.
",Modeling of Mixed Decision Making Process
"  We consider infinite-horizon $\gamma$-discounted Markov Decision Processes,
for which it is known that there exists a stationary optimal policy. We
consider the algorithm Value Iteration and the sequence of policies
$\pi_1,...,\pi_k$ it implicitely generates until some iteration $k$. We provide
performance bounds for non-stationary policies involving the last $m$ generated
policies that reduce the state-of-the-art bound for the last stationary policy
$\pi_k$ by a factor $\frac{1-\gamma}{1-\gamma^m}$. In particular, the use of
non-stationary policies allows to reduce the usual asymptotic performance
bounds of Value Iteration with errors bounded by $\epsilon$ at each iteration
from $\frac{\gamma}{(1-\gamma)^2}\epsilon$ to
$\frac{\gamma}{1-\gamma}\epsilon$, which is significant in the usual situation
when $\gamma$ is close to 1. Given Bellman operators that can only be computed
with some error $\epsilon$, a surprising consequence of this result is that the
problem of ""computing an approximately optimal non-stationary policy"" is much
simpler than that of ""computing an approximately optimal stationary policy"",
and even slightly simpler than that of ""approximately computing the value of
some fixed policy"", since this last problem only has a guarantee of
$\frac{1}{1-\gamma}\epsilon$.
","On the Use of Non-Stationary Policies for Infinite-Horizon Discounted
  Markov Decision Processes"
"  Informledge System (ILS) is a knowledge network with autonomous nodes and
intelligent links that integrate and structure the pieces of knowledge. In this
paper, we aim to put forward the link dynamics involved in intelligent
processing of information in ILS. There has been advancement in knowledge
management field which involve managing information in databases from a single
domain. ILS works with information from multiple domains stored in distributed
way in the autonomous nodes termed as Knowledge Network Node (KNN). Along with
the concept under consideration, KNNs store the processed information linking
concepts and processors leading to the appropriate processing of information.
","Creating Intelligent Linking for Information Threading in Knowledge
  Networks"
"  Expert systems use human knowledge often stored as rules within the computer
to solve problems that generally would entail human intelligence. Today, with
information systems turning out to be more pervasive and with the myriad
advances in information technologies, automating computer fault diagnosis is
becoming so fundamental that soon every enterprise has to endorse it. This
paper proposes an expert system called Expert PC Troubleshooter for diagnosing
computer problems. The system is composed of a user interface, a rule-base, an
inference engine, and an expert interface. Additionally, the system features a
fuzzy-logic module to troubleshoot POST beep errors, and an intelligent agent
that assists in the knowledge acquisition process. The proposed system is meant
to automate the maintenance, repair, and operations (MRO) process, and free-up
human technicians from manually performing routine, laborious, and
timeconsuming maintenance tasks. As future work, the proposed system is to be
parallelized so as to boost its performance and speed-up its various
operations.
",Expert PC Troubleshooter With Fuzzy-Logic And Self-Learning Support
"  Some aspects of the result of applying unit resolution on a CNF formula can
be formalized as functions with domain a set of partial truth assignments. We
are interested in two ways for computing such functions, depending on whether
the result is the production of the empty clause or the assignment of a
variable with a given truth value. We show that these two models can compute
the same functions with formulae of polynomially related sizes, and we explain
how this result is related to the CNF encoding of Boolean constraints.
",Unit contradiction versus unit propagation
"  The development of expert system for treatment of Diabetes disease by using
natural methods is new information technology derived from Artificial
Intelligent research using ESTA (Expert System Text Animation) System. The
proposed expert system contains knowledge about various methods of natural
treatment methods (Massage, Herbal/Proper Nutrition, Acupuncture, Gems) for
Diabetes diseases of Human Beings. The system is developed in the ESTA (Expert
System shell for Text Animation) which is Visual Prolog 7.3 Application. The
knowledge for the said system will be acquired from domain experts, texts and
other related sources.
","Development of knowledge Base Expert System for Natural treatment of
  Diabetes disease"
"  In this report, we will be interested at Dynamic Bayesian Network (DBNs) as a
model that tries to incorporate temporal dimension with uncertainty. We start
with basics of DBN where we especially focus in Inference and Learning concepts
and algorithms. Then we will present different levels and methods of creating
DBNs as well as approaches of incorporating temporal dimension in static
Bayesian network.
",Characterization of Dynamic Bayesian Network
"  Through history, the human being tried to relay its daily tasks to other
creatures, which was the main reason behind the rise of civilizations. It
started with deploying animals to automate tasks in the field of
agriculture(bulls), transportation (e.g. horses and donkeys), and even
communication (pigeons). Millenniums after, come the Golden age with
""Al-jazari"" and other Muslim inventors, which were the pioneers of automation,
this has given birth to industrial revolution in Europe, centuries after. At
the end of the nineteenth century, a new era was to begin, the computational
era, the most advanced technological and scientific development that is driving
the mankind and the reason behind all the evolutions of science; such as
medicine, communication, education, and physics. At this edge of technology
engineers and scientists are trying to model a machine that behaves the same as
they do, which pushed us to think about designing and implementing ""Things
that-Thinks"", then artificial intelligence was. In this work we will cover each
of the major discoveries and studies in the field of machine cognition, which
are the ""Elementary Perceiver and Memorizer""(EPAM) and ""The General Problem
Solver""(GPS). The First one focus mainly on implementing the human-verbal
learning behavior, while the second one tries to model an architecture that is
able to solve problems generally (e.g. theorem proving, chess playing, and
arithmetic). We will cover the major goals and the main ideas of each model, as
well as comparing their strengths and weaknesses, and finally giving their
fields of applications. And Finally, we will suggest a real life implementation
of a cognitive machine.
",Machine Cognition Models: EPAM and GPS
"  We present a system for recognising human activity given a symbolic
representation of video content. The input of our system is a set of
time-stamped short-term activities (STA) detected on video frames. The output
is a set of recognised long-term activities (LTA), which are pre-defined
temporal combinations of STA. The constraints on the STA that, if satisfied,
lead to the recognition of a LTA, have been expressed using a dialect of the
Event Calculus. In order to handle the uncertainty that naturally occurs in
human activity recognition, we adapted this dialect to a state-of-the-art
probabilistic logic programming framework. We present a detailed evaluation and
comparison of the crisp and probabilistic approaches through experimentation on
a benchmark dataset of human surveillance videos.
",A Probabilistic Logic Programming Event Calculus
"  The article discusses some applications of fuzzy logic ideas to formalizing
of the Case-Based Reasoning (CBR) process and to measuring the effectiveness of
CBR systems
",Applications of fuzzy logic to Case-Based Reasoning
"  One of the big challenges in the development of probabilistic relational (or
probabilistic logical) modeling and learning frameworks is the design of
inference techniques that operate on the level of the abstract model
representation language, rather than on the level of ground, propositional
instances of the model. Numerous approaches for such ""lifted inference""
techniques have been proposed. While it has been demonstrated that these
techniques will lead to significantly more efficient inference on some specific
models, there are only very recent and still quite restricted results that show
the feasibility of lifted inference on certain syntactically defined classes of
models. Lower complexity bounds that imply some limitations for the feasibility
of lifted inference on more expressive model classes were established early on
in (Jaeger 2000). However, it is not immediate that these results also apply to
the type of modeling languages that currently receive the most attention, i.e.,
weighted, quantifier-free formulas. In this paper we extend these earlier
results, and show that under the assumption that NETIME =/= ETIME, there is no
polynomial lifted inference algorithm for knowledge bases of weighted,
quantifier- and function-free formulas. Further strengthening earlier results,
this is also shown to hold for approximate inference, and for knowledge bases
not containing the equality predicate.
",Lower Complexity Bounds for Lifted Inference
"  Statistical evidence of the influence of neighborhood topology on the
performance of particle swarm optimization (PSO) algorithms has been shown in
many works. However, little has been done about the implications could have the
percolation threshold in determining the topology of this neighborhood. This
work addresses this problem for individuals that, like robots, are able to
sense in a limited neighborhood around them. Based on the concept of
percolation threshold, and more precisely, the disk percolation model in 2D, we
show that better results are obtained for low values of radius, when
individuals occasionally ask others their best visited positions, with the
consequent decrease of computational complexity. On the other hand, since
percolation threshold is a universal measure, it could have a great interest to
compare the performance of different hybrid PSO algorithms.
",On how percolation threshold affects PSO performance
"  The solution of the biobjective IRP is rather challenging, even for
metaheuristics. We are still lacking a profound understanding of appropriate
solution representations and effective neighborhood structures. Clearly, both
the delivery volumes and the routing aspects of the alternatives need to be
reflected in an encoding, and must be modified when searching by means of local
search. Our work contributes to the better understanding of such solution
representations. On the basis of an experimental investigation, the advantages
and drawbacks of two encodings are studied and compared.
","Solution Representations and Local Search for the bi-objective Inventory
  Routing Problem"
"  Today, one's disposes of large datasets composed of thousands of geographic
objects. However, for many processes, which require the appraisal of an expert
or much computational time, only a small part of these objects can be taken
into account. In this context, robust sampling methods become necessary. In
this paper, we propose a sampling method based on clustering techniques. Our
method consists in dividing the objects in clusters, then in selecting in each
cluster, the most representative objects. A case-study in the context of a
process dedicated to knowledge revision for geographic data generalisation is
presented. This case-study shows that our method allows to select relevant
samples of objects.
",Automatic Sampling of Geographic objects
"  Both humans and artificial systems frequently use trial and error methods to
problem solving. In order to be effective, this type of strategy implies having
high quality control knowledge to guide the quest for the optimal solution.
Unfortunately, this control knowledge is rarely perfect. Moreover, in
artificial systems-as in humans-self-evaluation of one's own knowledge is often
difficult. Yet, this self-evaluation can be very useful to manage knowledge and
to determine when to revise it. The objective of our work is to propose an
automated approach to evaluate the quality of control knowledge in artificial
systems based on a specific trial and error strategy, namely the informed tree
search strategy. Our revision approach consists in analysing the system's
execution logs, and in using the belief theory to evaluate the global quality
of the knowledge. We present a real-world industrial application in the form of
an experiment using this approach in the domain of cartographic generalisation.
Thus far, the results of using our approach have been encouraging.
","Using Belief Theory to Diagnose Control Knowledge Quality. Application
  to cartographic generalisation"
"  In this paper we develop a fuzzy model for the description of the process of
Analogical Reasoning by representing its main steps as fuzzy subsets of a set
of linguistic labels characterizing the individuals' performance in each step
and we use the Shannon- Wiener diversity index as a measure of the individuals'
abilities in analogical problem solving. This model is compared with a
stochastic model presented in author's earlier papers by introducing a finite
Markov chain on the steps of the process of Analogical Reasoning. A classroom
experiment is also presented to illustrate the use of our results in practice.
",A Fuzzy Model for Analogical Problem Solving
"  Without Linked Data, transport data is limited to applications exclusively
around transport. In this paper, we present a workflow for publishing and
linking transport data on the Web. So we will be able to develop transport
applications and to add other features which will be created from other
datasets. This will be possible because transport data will be linked to these
datasets. We apply this workflow to two datasets: NEPTUNE, a French standard
describing a transport line, and Passim, a directory containing relevant
information on transport services, in every French city.
",Publishing and linking transport data on the Web
"  Attribute reduction is viewed as an important preprocessing step for pattern
recognition and data mining. Most of researches are focused on attribute
reduction by using rough sets. Recently, Tsang et al. discussed attribute
reduction with covering rough sets in the paper [E. C.C. Tsang, D. Chen, Daniel
S. Yeung, Approximations and reducts with covering generalized rough sets,
Computers and Mathematics with Applications 56 (2008) 279-289], where an
approach based on discernibility matrix was presented to compute all attribute
reducts. In this paper, we provide an improved approach by constructing simpler
discernibility matrix with covering rough sets, and then proceed to improve
some characterizations of attribute reduction provided by Tsang et al. It is
proved that the improved discernible matrix is equivalent to the old one, but
the computational complexity of discernible matrix is greatly reduced.
",An improved approach to attribute reduction with covering rough sets
"  This is the Proceedings of the Twenty-Seventh Conference on Uncertainty in
Artificial Intelligence, which was held in Barcelona, Spain, July 14 - 17 2011.
","Proceedings of the Twenty-Seventh Conference on Uncertainty in
  Artificial Intelligence (2011)"
"  This is the Proceedings of the Twenty-Sixth Conference on Uncertainty in
Artificial Intelligence, which was held on Catalina Island, CA, July 8 - 11
2010.
","Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial
  Intelligence (2010)"
"  Most Relevant Explanation (MRE) is a method for finding multivariate
explanations for given evidence in Bayesian networks [12]. This paper studies
the theoretical properties of MRE and develops an algorithm for finding
multiple top MRE solutions. Our study shows that MRE relies on an implicit soft
relevance measure in automatically identifying the most relevant target
variables and pruning less relevant variables from an explanation. The soft
measure also enables MRE to capture the intuitive phenomenon of explaining away
encoded in Bayesian networks. Furthermore, our study shows that the solution
space of MRE has a special lattice structure which yields interesting dominance
relations among the solutions. A K-MRE algorithm based on these dominance
relations is developed for generating a set of top solutions that are more
representative. Our empirical results show that MRE methods are promising
approaches for explanation in Bayesian networks.
","Most Relevant Explanation: Properties, Algorithms, and Evaluations"
"  This paper develops an inconsistency measure on conditional probabilistic
knowledge bases. The measure is based on fundamental principles for
inconsistency measures and thus provides a solid theoretical framework for the
treatment of inconsistencies in probabilistic expert systems. We illustrate its
usefulness and immediate application on several examples and present some
formal results. Building on this measure we use the Shapley value-a well-known
solution for coalition games-to define a sophisticated indicator that is not
only able to measure inconsistencies but to reveal the causes of
inconsistencies in the knowledge base. Altogether these tools guide the
knowledge engineer in his aim to restore consistency and therefore enable him
to build a consistent and usable knowledge base that can be employed in
probabilistic expert systems.
",Measuring Inconsistency in Probabilistic Knowledge Bases
"  There has been a great deal of recent interest in methods for performing
lifted inference; however, most of this work assumes that the first-order model
is given as input to the system. Here, we describe lifted inference algorithms
that determine symmetries and automatically lift the probabilistic model to
speedup inference. In particular, we describe approximate lifted inference
techniques that allow the user to trade off inference accuracy for
computational efficiency by using a handful of tunable parameters, while
keeping the error bounded. Our algorithms are closely related to the
graph-theoretic concept of bisimulation. We report experiments on both
synthetic and real data to show that in the presence of symmetries, run-times
for inference can be improved significantly, with approximate lifted inference
providing orders of magnitude speedup over ground inference.
",Bisimulation-based Approximate Lifted Inference
"  The specification of aMarkov decision process (MDP) can be difficult. Reward
function specification is especially problematic; in practice, it is often
cognitively complex and time-consuming for users to precisely specify rewards.
This work casts the problem of specifying rewards as one of preference
elicitation and aims to minimize the degree of precision with which a reward
function must be specified while still allowing optimal or near-optimal
policies to be produced. We first discuss how robust policies can be computed
for MDPs given only partial reward information using the minimax regret
criterion. We then demonstrate how regret can be reduced by efficiently
eliciting reward information using bound queries, using regret-reduction as a
means for choosing suitable queries. Empirical results demonstrate that
regret-based reward elicitation offers an effective way to produce near-optimal
policies without resorting to the precise specification of the entire reward
function.
",Regret-based Reward Elicitation for Markov Decision Processes
"  Logical inference algorithms for conditional independence (CI) statements
have important applications from testing consistency during knowledge
elicitation to constraintbased structure learning of graphical models. We prove
that the implication problem for CI statements is decidable, given that the
size of the domains of the random variables is known and fixed. We will present
an approximate logical inference algorithm which combines a falsification and a
novel validation algorithm. The validation algorithm represents each set of CI
statements as a sparse 0-1 matrix A and validates instances of the implication
problem by solving specific linear programs with constraint matrix A. We will
show experimentally that the algorithm is both effective and efficient in
validating and falsifying instances of the probabilistic CI implication
problem.
","Logical Inference Algorithms and Matrix Representations for
  Probabilistic Conditional Independence"
"  Computational analysis of time-course data with an underlying causal
structure is needed in a variety of domains, including neural spike trains,
stock price movements, and gene expression levels. However, it can be
challenging to determine from just the numerical time course data alone what is
coordinating the visible processes, to separate the underlying prima facie
causes into genuine and spurious causes and to do so with a feasible
computational complexity. For this purpose, we have been developing a novel
algorithm based on a framework that combines notions of causality in philosophy
with algorithmic approaches built on model checking and statistical techniques
for multiple hypotheses testing. The causal relationships are described in
terms of temporal logic formulae, reframing the inference problem in terms of
model checking. The logic used, PCTL, allows description of both the time
between cause and effect and the probability of this relationship being
observed. We show that equipped with these causal formulae with their
associated probabilities we may compute the average impact a cause makes to its
effect and then discover statistically significant causes through the concepts
of multiple hypothesis testing (treating each causal relationship as a
hypothesis), and false discovery control. By exploring a well-chosen family of
potentially all significant hypotheses with reasonably minimal description
length, it is possible to tame the algorithm's computational complexity while
exploring the nearly complete search-space of all prima facie causes. We have
tested these ideas in a number of domains and illustrate them here with two
examples.
",The Temporal Logic of Causal Structures
"  First-order probabilistic models combine representational power of
first-order logic with graphical models. There is an ongoing effort to design
lifted inference algorithms for first-order probabilistic models. We analyze
lifted inference from the perspective of constraint processing and, through
this viewpoint, we analyze and compare existing approaches and expose their
advantages and limitations. Our theoretical results show that the wrong choice
of constraint processing method can lead to exponential increase in
computational complexity. Our empirical tests confirm the importance of
constraint processing in lifted inference. This is the first theoretical and
empirical study of constraint processing in lifted inference.
",Constraint Processing in Lifted Probabilistic Inference
"  A major benefit of graphical models is that most knowledge is captured in the
model structure. Many models, however, produce inference problems with a lot of
symmetries not reflected in the graphical structure and hence not exploitable
by efficient inference techniques such as belief propagation (BP). In this
paper, we present a new and simple BP algorithm, called counting BP, that
exploits such additional symmetries. Starting from a given factor graph,
counting BP first constructs a compressed factor graph of clusternodes and
clusterfactors, corresponding to sets of nodes and factors that are
indistinguishable given the evidence. Then it runs a modified BP algorithm on
the compressed graph that is equivalent to running BP on the original factor
graph. Our experiments show that counting BP is applicable to a variety of
important AI tasks such as (dynamic) relational models and boolean model
counting, and that significant efficiency gains are obtainable, often by orders
of magnitude.
",Counting Belief Propagation
"  A Bayesian belief network models a joint distribution with an directed
acyclic graph representing dependencies among variables and network parameters
characterizing conditional distributions. The parameters are viewed as random
variables to quantify uncertainty about their values. Belief nets are used to
compute responses to queries; i.e., conditional probabilities of interest. A
query is a function of the parameters, hence a random variable. Van Allen et
al. (2001, 2008) showed how to quantify uncertainty about a query via a delta
method approximation of its variance. We develop more accurate approximations
for both query mean and variance. The key idea is to extend the query mean
approximation to a ""doubled network"" involving two independent replicates. Our
method assumes complete data and can be applied to discrete, continuous, and
hybrid networks (provided discrete variables have only discrete parents). We
analyze several improvements, and provide empirical studies to demonstrate
their effectiveness.
","Improved Mean and Variance Approximations for Belief Net Responses via
  Network Doubling"
"  Generating optimal plans in highly dynamic environments is challenging. Plans
are predicated on an assumed initial state, but this state can change
unexpectedly during plan generation, potentially invalidating the planning
effort. In this paper we make three contributions: (1) We propose a novel
algorithm for generating optimal plans in settings where frequent, unexpected
events interfere with planning. It is able to quickly distinguish relevant from
irrelevant state changes, and to update the existing planning search tree if
necessary. (2) We argue for a new criterion for evaluating plan adaptation
techniques: the relative running time compared to the ""size"" of changes. This
is significant since during recovery more changes may occur that need to be
recovered from subsequently, and in order for this process of repeated recovery
to terminate, recovery time has to converge. (3) We show empirically that our
approach can converge and find optimal plans in environments that would
ordinarily defy planning due to their high dynamics.
",Generating Optimal Plans in Highly-Dynamic Domains
"  We introduce a challenging real-world planning problem where actions must be
taken at each location in a spatial area at each point in time. We use forestry
planning as the motivating application. In Large Scale Spatial-Temporal (LSST)
planning problems, the state and action spaces are defined as the
cross-products of many local state and action spaces spread over a large
spatial area such as a city or forest. These problems possess state
uncertainty, have complex utility functions involving spatial constraints and
we generally must rely on simulations rather than an explicit transition model.
We define LSST problems as reinforcement learning problems and present a
solution using policy gradients. We compare two different policy formulations:
an explicit policy that identifies each location in space and the action to
take there; and an abstract policy that defines the proportion of actions to
take across all locations in space. We show that the abstract policy is more
robust and achieves higher rewards with far fewer parameters than the
elementary policy. This abstract policy is also a better fit to the properties
that practitioners in LSST problem domains require for such methods to be
widely useful.
","Seeing the Forest Despite the Trees: Large Scale Spatial-Temporal
  Decision Making"
"  This paper presents complexity analysis and variational methods for inference
in probabilistic description logics featuring Boolean operators,
quantification, qualified number restrictions, nominals, inverse roles and role
hierarchies. Inference is shown to be PEXP-complete, and variational methods
are designed so as to exploit logical inference whenever possible.
","Complexity Analysis and Variational Inference for Interpretation-based
  Probabilistic Description Logic"
"  Continuous-time Bayesian networks is a natural structured representation
language for multicomponent stochastic processes that evolve continuously over
time. Despite the compact representation, inference in such models is
intractable even in relatively simple structured networks. Here we introduce a
mean field variational approximation in which we use a product of inhomogeneous
Markov processes to approximate a distribution over trajectories. This
variational approach leads to a globally consistent distribution, which can be
efficiently queried. Additionally, it provides a lower bound on the probability
of observations, thus making it attractive for learning tasks. We provide the
theoretical foundations for the approximation, an efficient implementation that
exploits the wide range of highly optimized ordinary differential equations
(ODE) solvers, experimentally explore characterizations of processes for which
this approximation is suitable, and show applications to a large-scale
realworld inference problem.
","Mean Field Variational Approximation for Continuous-Time Bayesian
  Networks"
"  We study a subclass of POMDPs, called Deterministic POMDPs, that is
characterized by deterministic actions and observations. These models do not
provide the same generality of POMDPs yet they capture a number of interesting
and challenging problems, and permit more efficient algorithms. Indeed, some of
the recent work in planning is built around such assumptions mainly by the
quest of amenable models more expressive than the classical deterministic
models. We provide results about the fundamental properties of Deterministic
POMDPs, their relation with AND/OR search problems and algorithms, and their
computational complexity.
",Deterministic POMDPs Revisited
"  We present a new method to propagate lower bounds on conditional probability
distributions in conventional Bayesian networks. Our method guarantees to
provide outer approximations of the exact lower bounds. A key advantage is that
we can use any available algorithms and tools for Bayesian networks in order to
represent and infer lower bounds. This new method yields results that are
provable exact for trees with binary variables, and results which are
competitive to existing approximations in credal networks for all other network
structures. Our method is not limited to a specific kind of network structure.
Basically, it is also not restricted to a specific kind of inference, but we
restrict our analysis to prognostic inference in this article. The
computational complexity is superior to that of other existing approaches.
","Lower Bound Bayesian Networks - An Efficient Inference of Lower Bounds
  on Probability Distributions in Bayesian Networks"
"  Soft sets, as a mathematical tool for dealing with uncertainty, have recently
gained considerable attention, including some successful applications in
information processing, decision, demand analysis, and forecasting. To
construct new soft sets from given soft sets, some operations on soft sets have
been proposed. Unfortunately, such operations cannot keep all classical
set-theoretic laws true for soft sets. In this paper, we redefine the
intersection, complement, and difference of soft sets and investigate the
algebraic properties of these operations along with a known union operation. We
find that the new operation system on soft sets inherits all basic properties
of operations on classical sets, which justifies our definitions.
",Operations on soft sets revisited
"  Modified policy iteration (MPI) is a dynamic programming (DP) algorithm that
contains the two celebrated policy and value iteration methods. Despite its
generality, MPI has not been thoroughly studied, especially its approximation
form which is used when the state and/or action spaces are large or infinite.
In this paper, we propose three implementations of approximate MPI (AMPI) that
are extensions of well-known approximate DP algorithms: fitted-value iteration,
fitted-Q iteration, and classification-based policy iteration. We provide error
propagation analyses that unify those for approximate policy and value
iteration. On the last classification-based implementation, we develop a
finite-sample analysis that shows that MPI's main parameter allows to control
the balance between the estimation error of the classifier and the overall
value function approximation.
",Approximate Modified Policy Iteration
"  Even today in Twenty First Century Handwritten communication has its own
stand and most of the times, in daily life it is globally using as means of
communication and recording the information like to be shared with others.
Challenges in handwritten characters recognition wholly lie in the variation
and distortion of handwritten characters, since different people may use
different style of handwriting, and direction to draw the same shape of the
characters of their known script. This paper demonstrates the nature of
handwritten characters, conversion of handwritten data into electronic data,
and the neural network approach to make machine capable of recognizing hand
written characters.
",Machine Recognition of Hand Written Characters using Neural Networks
"  A simplified description of Fuzzy TOPSIS (Technique for Order Preference by
Similarity to Ideal Situation) is presented. We have adapted the TOPSIS
description from existing Fuzzy theory literature and distilled the bare
minimum concepts required for understanding and applying TOPSIS. An example has
been worked out to illustrate the application of TOPSIS for a multi-criteria
group decision making scenario.
",A Simplified Description of Fuzzy TOPSIS
"  In order to involve user knowledge in determining equality of sets, which may
not be equal in the mathematical sense, three types of approximate (rough)
equalities were introduced by Novotny and Pawlak ([8, 9, 10]). These notions
were generalized by Tripathy, Mitra and Ojha ([13]), who introduced the
concepts of approximate (rough) equivalences of sets. Rough equivalences
capture equality of sets at a higher level than rough equalities. More
properties of these concepts were established in [14]. Combining the conditions
for the two types of approximate equalities, two more approximate equalities
were introduced by Tripathy [12] and a comparative analysis of their relative
efficiency was provided. In [15], the four types of approximate equalities were
extended by considering rough fuzzy sets instead of only rough sets. In fact
the concepts of leveled approximate equalities were introduced and properties
were studied. In this paper we proceed further by introducing and studying the
approximate equalities based on rough intuitionistic fuzzy sets instead of
rough fuzzy sets. That is we introduce the concepts of approximate
(rough)equalities of intuitionistic fuzzy sets and study their properties. We
provide some real life examples to show the applications of rough equalities of
fuzzy sets and rough equalities of intuitionistic fuzzy sets.
","Approximate Equalities on Rough Intuitionistic Fuzzy Sets and an
  Analysis of Approximate Equalities"
"  The causal structure of cognition can be simulated but not implemented
computationally, just as the causal structure of a comet can be simulated but
not implemented computationally. The only thing that allows us even to imagine
otherwise is that cognition, unlike a comet, is invisible (to all but the
cognizer).
",The Causal Topography of Cognition
"  Within the framework proposed in this paper, we address the issue of
extending the certain networks to a fuzzy certain networks in order to cope
with a vagueness and limitations of existing models for decision under
imprecise and uncertain knowledge. This paper proposes a framework that
combines two disciplines to exploit their own advantages in uncertain and
imprecise knowledge representation problems. The framework proposed is a
possibilistic logic based one in which Bayesian nodes and their properties are
represented by local necessity-valued knowledge base. Data in properties are
interpreted as set of valuated formulas. In our contribution possibilistic
Bayesian networks have a qualitative part and a quantitative part, represented
by local knowledge bases. The general idea is to study how a fusion of these
two formalisms would permit representing compact way to solve efficiently
problems for knowledge representation. We show how to apply possibility and
necessity measures to the problem of knowledge representation with large scale
data. On the other hand fuzzification of crisp certainty degrees to fuzzy
variables improves the quality of the network and tends to bring smoothness and
robustness in the network performance. The general aim is to provide a new
approach for decision under uncertainty that combines three methodologies:
Bayesian networks certainty distribution and fuzzy logic.
","Fuzzy Knowledge Representation Based on Possibilistic and Necessary
  Bayesian Networks"
"  The main objective of this paper is to develop a new semantic Network
structure, based on the fuzzy sets theory, used in Artificial Intelligent
system in order to provide effective on-line assistance to users of new
technological systems. This Semantic Networks is used to describe the knowledge
of an ""ideal"" expert while fuzzy sets are used both to describe the approximate
and uncertain knowledge of novice users who intervene to match fuzzy labels of
a query with categories from an ""ideal"" expert. The technical system we
consider is a word processor software, with Objects such as ""Word"" and Goals
such as ""Cut"" or ""Copy"". We suggest to consider the set of the system's Goals
as a set of linguistic variables to which corresponds a set of possible
linguistic values based on the fuzzy set. We consider, therefore, a set of
interpretation's levels for these possible values to which corresponds a set of
membership functions. We also propose a method to measure the similarity degree
between different fuzzy linguistic variables for the partition of the semantic
network in class of similar objects to make easy the diagnosis of the user's
fuzzy queries.
","Use of Fuzzy Sets in Semantic Nets for Providing On-Line Assistance to
  User of Technological Systems"
"  Feature weighting is a technique used to approximate the optimal degree of
influence of individual features. This paper presents a feature weighting
method for Document Image Retrieval System (DIRS) based on keyword spotting. In
this method, we weight the feature using coefficient of multiple correlations.
Coefficient of multiple correlations can be used to describe the synthesized
effects and correlation of each feature. The aim of this paper is to show that
feature weighting increases the performance of DIRS. After applying the feature
weighting method to DIRS the average precision is 93.23% and average recall
become 98.66% respectively
","Feature Weighting for Improving Document Image Retrieval System
  Performance"
"  In this paper, we are trying to examine trade offs between fuzzy logic and
certain Bayesian networks and we propose to combine their respective advantages
into fuzzy certain Bayesian networks (FCBN), a certain Bayesian networks of
fuzzy random variables. This paper deals with different definitions and
classifications of uncertainty, sources of uncertainty, and theories and
methodologies presented to deal with uncertainty. Fuzzification of crisp
certainty degrees to fuzzy variables improves the quality of the network and
tends to bring smoothness and robustness in the network performance. The aim is
to provide a new approach for decision under uncertainty that combines three
methodologies: Bayesian networks certainty distribution and fuzzy logic. Within
the framework proposed in this paper, we address the issue of extending the
certain networks to a fuzzy certain networks in order to cope with a vagueness
and limitations of existing models for decision under imprecise and uncertain
knowledge.
",Certain Bayesian Network based on Fuzzy knowledge Bases
"  Holding commercial negotiations and selecting the best supplier in supply
chain management systems are among weaknesses of producers in production
process. Therefore, applying intelligent systems may have an effective role in
increased speed and improved quality in the selections .This paper introduces a
system which tries to trade using multi-agents systems and holding negotiations
between any agents. In this system, an intelligent agent is considered for each
segment of chains which it tries to send order and receive the response with
attendance in negotiation medium and communication with other agents .This
paper introduces how to communicate between agents, characteristics of
multi-agent and standard registration medium of each agent in the environment.
JADE (Java Application Development Environment) was used for implementation and
simulation of agents cooperation.
","An Intelligent Approach for Negotiating between chains in Supply Chain
  Management Systems"
"  The similarity between trajectory patterns in clustering has played an
important role in discovering movement behaviour of different groups of mobile
objects. Several approaches have been proposed to measure the similarity
between sequences in trajectory data. Most of these measures are based on
Euclidean space or on spatial network and some of them have been concerned with
temporal aspect or ordering types. However, they are not appropriate to
characteristics of spatiotemporal mobility patterns in wireless networks. In
this paper, we propose a new similarity measure for mobility patterns in
cellular space of wireless network. The framework for constructing our measure
is composed of two phases as follows. First, we present formal definitions to
capture mathematically two spatial and temporal similarity measures for
mobility patterns. And then, we define the total similarity measure by means of
a weighted combination of these similarities. The truth of the partial and
total similarity measures are proved in mathematics. Furthermore, instead of
the time interval or ordering, our work makes use of the timestamp at which two
mobility patterns share the same cell. A case study is also described to give a
comparison of the combination measure with other ones.
","A weighted combination similarity measure for mobility patterns in
  wireless networks"
"  Feature reduction is an important concept which is used for reducing
dimensions to decrease the computation complexity and time of classification.
Since now many approaches have been proposed for solving this problem, but
almost all of them just presented a fix output for each input dataset that some
of them aren't satisfied cases for classification. In this we proposed an
approach as processing input dataset to increase accuracy rate of each feature
extraction methods. First of all, a new concept called dispelling classes
gradually (DCG) is proposed to increase separability of classes based on their
labels. Next, this method is used to process input dataset of the feature
reduction approaches to decrease the misclassification error rate of their
outputs more than when output is achieved without any processing. In addition
our method has a good quality to collate with noise based on adapting dataset
with feature reduction approaches. In the result part, two conditions (With
process and without that) are compared to support our idea by using some of UCI
datasets.
","Dispelling Classes Gradually to Improve Quality of Feature Reduction
  Approaches"
"  Software aging is a phenomenon that refers to progressive performance
degradation or transient failures or even crashes in long running software
systems such as web servers. It mainly occurs due to the deterioration of
operating system resource, fragmentation and numerical error accumulation. A
primitive method to fight against software aging is software rejuvenation.
Software rejuvenation is a proactive fault management technique aimed at
cleaning up the system internal state to prevent the occurrence of more severe
crash failures in the future. It involves occasionally stopping the running
software, cleaning its internal state and restarting it. An optimized schedule
for performing the software rejuvenation has to be derived in advance because a
long running application could not be put down now and then as it may lead to
waste of cost. This paper proposes a method to derive an accurate and optimized
schedule for rejuvenation of a web server (Apache) by using Radial Basis
Function (RBF) based Feed Forward Neural Network, a variant of Artificial
Neural Networks (ANN). Aging indicators are obtained through experimental setup
involving Apache web server and clients, which acts as input to the neural
network model. This method is better than existing ones because usage of RBF
leads to better accuracy and speed in convergence.
",Software Aging Analysis of Web Server Using Neural Networks
"  A software agent may be a member of a Multi-Agent System (MAS) which is
collectively performing a range of complex and intelligent tasks. In the
hospital, scheduling decisions are finding difficult to schedule because of the
dynamic changes and distribution. In order to face this problem with dynamic
changes in the hospital, a new method, Distributed Optimized Patient Scheduling
with Grouping (DOPSG) has been proposed. The goal of this method is that there
is no necessity for knowing patient agents information globally. With minimal
information this method works effectively. Scheduling problem can be solved for
multiple departments in the hospital. Patient agents have been scheduled to the
resource agent based on the patient priority to reduce the waiting time of
patient agent and to reduce idle time of resources.
",A Distributed Optimized Patient Scheduling using Partial Information
"  The approach described here allows using membership function to represent
imprecise and uncertain knowledge by learning in Fuzzy Semantic Networks. This
representation has a great practical interest due to the possibility to realize
on the one hand, the construction of this membership function from a simple
value expressing the degree of interpretation of an Object or a Goal as
compared to an other and on the other hand, the adjustment of the membership
function during the apprenticeship. We show, how to use these membership
functions to represent the interpretation of an Object (respectively of a Goal)
user as compared to an system Object (respectively to a Goal). We also show the
possibility to make decision for each representation of an user Object compared
to a system Object. This decision is taken by determining decision coefficient
calculates according to the nucleus of the membership function of the user
Object.
","Softening Fuzzy Knowledge Representation Tool with the Learning of New
  Words in Natural Language"
"  This paper presents a method of optimization, based on both Bayesian Analysis
technical and Gallois Lattice, of a Fuzzy Semantic Networks. The technical
System we use learn by interpreting an unknown word using the links created
between this new word and known words. The main link is provided by the context
of the query. When novice's query is confused with an unknown verb (goal)
applied to a known noun denoting either an object in the ideal user's Network
or an object in the user's Network, the system infer that this new verb
corresponds to one of the known goal. With the learning of new words in natural
language as the interpretation, which was produced in agreement with the user,
the system improves its representation scheme at each experiment with a new
user and, in addition, takes advantage of previous discussions with users. The
semantic Net of user objects thus obtained by these kinds of learning is not
always optimal because some relationships between couple of user objects can be
generalized and others suppressed according to values of forces that
characterize them. Indeed, to simplify the obtained Net, we propose to proceed
to an inductive Bayesian analysis, on the Net obtained from Gallois lattice.
The objective of this analysis can be seen as an operation of filtering of the
obtained descriptive graph.
","Fuzzy Knowledge Representation, Learning and Optimization with Bayesian
  Analysis in Fuzzy Semantic Networks"
"  The approach described here allows to use the fuzzy Object Based
Representation of imprecise and uncertain knowledge. This representation has a
great practical interest due to the possibility to realize reasoning on
classification with a fuzzy semantic network based system. For instance, the
distinction between necessary, possible and user classes allows to take into
account exceptions that may appear on fuzzy knowledge-base and facilitates
integration of user's Objects in the base. This approach describes the
theoretical aspects of the architecture of the whole experimental A.I. system
we built in order to provide effective on-line assistance to users of new
technological systems: the understanding of ""how it works"" and ""how to complete
tasks"" from queries in quite natural languages. In our model, procedural
semantic networks are used to describe the knowledge of an ""ideal"" expert while
fuzzy sets are used both to describe the approximative and uncertain knowledge
of novice users in fuzzy semantic networks which intervene to match fuzzy
labels of a query with categories from our ""ideal"" expert.
","Uncertain and Approximative Knowledge Representation to Reasoning on
  Classification with a Fuzzy Networks Based System"
"  Answer Set Programming (ASP) is a well-established paradigm of declarative
programming in close relationship with other declarative formalisms such as SAT
Modulo Theories, Constraint Handling Rules, FO(.), PDDL and many others. Since
its first informal editions, ASP systems have been compared in the now
well-established ASP Competition. The Third (Open) ASP Competition, as the
sequel to the ASP Competitions Series held at the University of Potsdam in
Germany (2006-2007) and at the University of Leuven in Belgium in 2009, took
place at the University of Calabria (Italy) in the first half of 2011.
Participants competed on a pre-selected collection of benchmark problems, taken
from a variety of domains as well as real world applications. The Competition
ran on two tracks: the Model and Solve (M&S) Track, based on an open problem
encoding, and open language, and open to any kind of system based on a
declarative specification paradigm; and the System Track, run on the basis of
fixed, public problem encodings, written in a standard ASP language. This paper
discusses the format of the Competition and the rationale behind it, then
reports the results for both tracks. Comparison with the second ASP competition
and state-of-the-art solutions for some of the benchmark domains is eventually
discussed.
  To appear in Theory and Practice of Logic Programming (TPLP).
",The third open Answer Set Programming competition
"  The paper introduces AND/OR importance sampling for probabilistic graphical
models. In contrast to importance sampling, AND/OR importance sampling caches
samples in the AND/OR space and then extracts a new sample mean from the stored
samples. We prove that AND/OR importance sampling may have lower variance than
importance sampling; thereby providing a theoretical justification for
preferring it over importance sampling. Our empirical evaluation demonstrates
that AND/OR importance sampling is far more accurate than importance sampling
in many cases.
",AND/OR Importance Sampling
"  In this paper, we consider planning in stochastic shortest path (SSP)
problems, a subclass of Markov Decision Problems (MDP). We focus on medium-size
problems whose state space can be fully enumerated. This problem has numerous
important applications, such as navigation and planning under uncertainty. We
propose a new approach for constructing a multi-level hierarchy of
progressively simpler abstractions of the original problem. Once computed, the
hierarchy can be used to speed up planning by first finding a policy for the
most abstract level and then recursively refining it into a solution to the
original problem. This approach is fully automated and delivers a speed-up of
two orders of magnitude over a state-of-the-art MDP solver on sample problems
while returning near-optimal solutions. We also prove theoretical bounds on the
loss of solution optimality resulting from the use of abstractions.
","Speeding Up Planning in Markov Decision Processes via Automatically
  Constructed Abstractions"
"  The problem of learning discrete Bayesian networks from data is encoded as a
weighted MAX-SAT problem and the MaxWalkSat local search algorithm is used to
address it. For each dataset, the per-variable summands of the (BDeu) marginal
likelihood for different choices of parents ('family scores') are computed
prior to applying MaxWalkSat. Each permissible choice of parents for each
variable is encoded as a distinct propositional atom and the associated family
score encoded as a 'soft' weighted single-literal clause. Two approaches to
enforcing acyclicity are considered: either by encoding the ancestor relation
or by attaching a total order to each graph and encoding that. The latter
approach gives better results. Learning experiments have been conducted on 21
synthetic datasets sampled from 7 BNs. The largest dataset has 10,000
datapoints and 60 variables producing (for the 'ancestor' encoding) a weighted
CNF input file with 19,932 atoms and 269,367 clauses. For most datasets,
MaxWalkSat quickly finds BNs with higher BDeu score than the 'true' BN. The
effect of adding prior information is assessed. It is further shown that
Bayesian model averaging can be effected by collecting BNs generated during the
search.
",Bayesian network learning by compiling to weighted MAX-SAT
"  This paper describes a new algorithm to solve the decision making problem in
Influence Diagrams based on algorithms for credal networks. Decision nodes are
associated to imprecise probability distributions and a reformulation is
introduced that finds the global maximum strategy with respect to the expected
utility. We work with Limited Memory Influence Diagrams, which generalize most
Influence Diagram proposals and handle simultaneous decisions. Besides the
global optimum method, we explore an anytime approximate solution with a
guaranteed maximum error and show that imprecise probabilities are handled in a
straightforward way. Complexity issues and experiments with random diagrams and
an effects-based military planning problem are discussed.
",Strategy Selection in Influence Diagrams using Imprecise Probabilities
"  A graphical multiagent model (GMM) represents a joint distribution over the
behavior of a set of agents. One source of knowledge about agents' behavior may
come from gametheoretic analysis, as captured by several graphical game
representations developed in recent years. GMMs generalize this approach to
express arbitrary distributions, based on game descriptions or other sources of
knowledge bearing on beliefs about agent behavior. To illustrate the
flexibility of GMMs, we exhibit game-derived models that allow probabilistic
deviation from equilibrium, as well as models based on heuristic action choice.
We investigate three different methods of integrating these models into a
single model representing the combined knowledge sources. To evaluate the
predictive performance of the combined model, we treat as actual outcome the
behavior produced by a reinforcement learning process. We find that combining
the two knowledge sources, using any of the methods, provides better
predictions than either source alone. Among the combination methods, mixing
data outperforms the opinion pool and direct update methods investigated in
this empirical trial.
",Knowledge Combination in Graphical Multiagent Model
"  We conjecture that the worst case number of experiments necessary and
sufficient to discover a causal graph uniquely given its observational Markov
equivalence class can be specified as a function of the largest clique in the
Markov equivalence class. We provide an algorithm that computes intervention
sets that we believe are optimal for the above task. The algorithm builds on
insights gained from the worst case analysis in Eberhardt et al. (2005) for
sequences of experiments when all possible directed acyclic graphs over N
variables are considered. A simulation suggests that our conjecture is correct.
We also show that a generalization of our conjecture to other classes of
possible graph hypotheses cannot be given easily, and in what sense the
algorithm is then no longer optimal.
",Almost Optimal Intervention Sets for Causal Discovery
"  Bounded policy iteration is an approach to solving infinite-horizon POMDPs
that represents policies as stochastic finite-state controllers and iteratively
improves a controller by adjusting the parameters of each node using linear
programming. In the original algorithm, the size of the linear programs, and
thus the complexity of policy improvement, depends on the number of parameters
of each node, which grows with the size of the controller. But in practice, the
number of parameters of a node with non-zero values is often very small, and
does not grow with the size of the controller. Based on this observation, we
develop a version of bounded policy iteration that leverages the sparse
structure of a stochastic finite-state controller. In each iteration, it
improves a policy by the same amount as the original algorithm, but with much
better scalability.
",Sparse Stochastic Finite-State Controllers for POMDPs
"  Approximate inference in dynamic systems is the problem of estimating the
state of the system given a sequence of actions and partial observations. High
precision estimation is fundamental in many applications like diagnosis,
natural language processing, tracking, planning, and robotics. In this paper we
present an algorithm that samples possible deterministic executions of a
probabilistic sequence. The algorithm takes advantage of a compact
representation (using first order logic) for actions and world states to
improve the precision of its estimation. Theoretical and empirical results show
that the algorithm's expected error is smaller than propositional sampling and
Sequential Monte Carlo (SMC) sampling techniques.
",Sampling First Order Logical Particles
"  While known algorithms for sensitivity analysis and parameter tuning in
probabilistic networks have a running time that is exponential in the size of
the network, the exact computational complexity of these problems has not been
established as yet. In this paper we study several variants of the tuning
problem and show that these problems are NPPP-complete in general. We further
show that the problems remain NP-complete or PP-complete, for a number of
restricted variants. These complexity results provide insight in whether or not
recent achievements in sensitivity analysis and tuning can be extended to more
general, practicable methods.
","The Computational Complexity of Sensitivity Analysis and Parameter
  Tuning"
"  Approximate linear programming (ALP) is an efficient approach to solving
large factored Markov decision processes (MDPs). The main idea of the method is
to approximate the optimal value function by a set of basis functions and
optimize their weights by linear programming (LP). This paper proposes a new
ALP approximation. Comparing to the standard ALP formulation, we decompose the
constraint space into a set of low-dimensional spaces. This structure allows
for solving the new LP efficiently. In particular, the constraints of the LP
can be satisfied in a compact form without an exponential dependence on the
treewidth of ALP constraints. We study both practical and theoretical aspects
of the proposed approach. Moreover, we demonstrate its scale-up potential on an
MDP with more than 2^100 states.
",Partitioned Linear Programming Approximations for MDPs
"  Graphical models are usually learned without regard to the cost of doing
inference with them. As a result, even if a good model is learned, it may
perform poorly at prediction, because it requires approximate inference. We
propose an alternative: learning models with a score function that directly
penalizes the cost of inference. Specifically, we learn arithmetic circuits
with a penalty on the number of edges in the circuit (in which the cost of
inference is linear). Our algorithm is equivalent to learning a Bayesian
network with context-specific independence by greedily splitting conditional
distributions, at each step scoring the candidates by compiling the resulting
network into an arithmetic circuit, and using its size as the penalty. We show
how this can be done efficiently, without compiling a circuit from scratch for
each candidate. Experiments on several real-world domains show that our
algorithm is able to learn tractable models with very large treewidth, and
yields more accurate predictions than a standard context-specific Bayesian
network learner, in far less time.
",Learning Arithmetic Circuits
"  An efficient policy search algorithm should estimate the local gradient of
the objective function, with respect to the policy parameters, from as few
trials as possible. Whereas most policy search methods estimate this gradient
by observing the rewards obtained during policy trials, we show, both
theoretically and empirically, that taking into account the sensor data as well
gives better gradient estimates and hence faster learning. The reason is that
rewards obtained during policy execution vary from trial to trial due to noise
in the environment; sensor data, which correlates with the noise, can be used
to partially correct for this variation, resulting in an estimatorwith lower
variance.
",Improving Gradient Estimation by Incorporating Sensor Data
"  Bayesian networks can be used to extract explanations about the observed
state of a subset of variables. In this paper, we explicate the desiderata of
an explanation and confront them with the concept of explanation proposed by
existing methods. The necessity of taking into account causal approaches when a
causal graph is available is discussed. We then introduce causal explanation
trees, based on the construction of explanation trees using the measure of
causal information ow (Ay and Polani, 2006). This approach is compared to
several other methods on known networks.
",Explanation Trees for Causal Bayesian Networks
"  Model-based Bayesian reinforcement learning has generated significant
interest in the AI community as it provides an elegant solution to the optimal
exploration-exploitation tradeoff in classical reinforcement learning.
Unfortunately, the applicability of this type of approach has been limited to
small domains due to the high complexity of reasoning about the joint posterior
over model parameters. In this paper, we consider the use of factored
representations combined with online planning techniques, to improve
scalability of these methods. The main contribution of this paper is a Bayesian
framework for learning the structure and parameters of a dynamical system,
while also simultaneously planning a (near-)optimal sequence of actions.
",Model-Based Bayesian Reinforcement Learning in Large Structured Domains
"  In this work we present Cutting Plane Inference (CPI), a Maximum A Posteriori
(MAP) inference method for Statistical Relational Learning. Framed in terms of
Markov Logic and inspired by the Cutting Plane Method, it can be seen as a meta
algorithm that instantiates small parts of a large and complex Markov Network
and then solves these using a conventional MAP method. We evaluate CPI on two
tasks, Semantic Role Labelling and Joint Entity Resolution, while plugging in
two different MAP inference methods: the current method of choice for MAP
inference in Markov Logic, MaxWalkSAT, and Integer Linear Programming. We
observe that when used with CPI both methods are significantly faster than when
used alone. In addition, CPI improves the accuracy of MaxWalkSAT and maintains
the exactness of Integer Linear Programming.
",Improving the Accuracy and Efficiency of MAP Inference for Markov Logic
"  Deciding what to sense is a crucial task, made harder by dependencies and by
a nonadditive utility function. We develop approximation algorithms for
selecting an optimal set of measurements, under a dependency structure modeled
by a tree-shaped Bayesian network (BN). Our approach is a generalization of
composing anytime algorithm represented by conditional performance profiles.
This is done by relaxing the input monotonicity assumption, and extending the
local compilation technique to more general classes of performance profiles
(PPs). We apply the extended scheme to selecting a subset of measurements for
choosing a maximum expectation variable in a binary valued BN, and for
minimizing the worst variance in a Gaussian BN.
","Observation Subset Selection as Local Compilation of Performance
  Profiles"
"  This paper develops a measure for bounding the performance of AND/OR search
algorithms for solving a variety of queries over graphical models. We show how
drawing a connection to the recent notion of hypertree decompositions allows to
exploit determinism in the problem specification and produce tighter bounds. We
demonstrate on a variety of practical problem instances that we are often able
to improve upon existing bounds by several orders of magnitude.
",Bounding Search Space Size via (Hyper)tree Decompositions
"  We present and evaluate new techniques for designing algorithm portfolios. In
our view, the problem has both a scheduling aspect and a machine learning
aspect. Prior work has largely addressed one of the two aspects in isolation.
Building on recent work on the scheduling aspect of the problem, we present a
technique that addresses both aspects simultaneously and has attractive
theoretical guarantees. Experimentally, we show that this technique can be used
to improve the performance of state-of-the-art algorithms for Boolean
satisfiability, zero-one integer programming, and A.I. planning.
",New Techniques for Algorithm Portfolio Design
"  Numerous temporal inference tasks such as fault monitoring and anomaly
detection exhibit a persistence property: for example, if something breaks, it
stays broken until an intervention. When modeled as a Dynamic Bayesian Network,
persistence adds dependencies between adjacent time slices, often making exact
inference over time intractable using standard inference algorithms. However,
we show that persistence implies a regular structure that can be exploited for
efficient inference. We present three successively more general classes of
models: persistent causal chains (PCCs), persistent causal trees (PCTs) and
persistent polytrees (PPTs), and the corresponding exact inference algorithms
that exploit persistence. We show that analytic asymptotic bounds for our
algorithms compare favorably to junction tree inference; and we demonstrate
empirically that we can perform exact smoothing on the order of 100 times
faster than the approximate Boyen-Koller method on randomly generated instances
of persistent tree models. We also show how to handle non-persistent variables
and how persistence can be exploited effectively for approximate filtering.
",Efficient inference in persistent Dynamic Bayesian Networks
"  Planning can often be simpli ed by decomposing the task into smaller tasks
arranged hierarchically. Charlin et al. [4] recently showed that the hierarchy
discovery problem can be framed as a non-convex optimization problem. However,
the inherent computational di culty of solving such an optimization problem
makes it hard to scale to realworld problems. In another line of research,
Toussaint et al. [18] developed a method to solve planning problems by
maximumlikelihood estimation. In this paper, we show how the hierarchy
discovery problem in partially observable domains can be tackled using a
similar maximum likelihood approach. Our technique rst transforms the problem
into a dynamic Bayesian network through which a hierarchical structure can
naturally be discovered while optimizing the policy. Experimental results
demonstrate that this approach scales better than previous techniques based on
non-convex optimization.
",Hierarchical POMDP Controller Optimization by Likelihood Maximization
"  We address the problem of identifying dynamic sequential plans in the
framework of causal Bayesian networks, and show that the problem is reduced to
identifying causal effects, for which there are complete identi cation
algorithms available in the literature.
",Identifying Dynamic Sequential Plans
"  In this paper we introduce Refractor Importance Sampling (RIS), an
improvement to reduce error variance in Bayesian network importance sampling
propagation under evidential reasoning. We prove the existence of a collection
of importance functions that are close to the optimal importance function under
evidential reasoning. Based on this theoretic result we derive the RIS
algorithm. RIS approaches the optimal importance function by applying localized
arc changes to minimize the divergence between the evidence-adjusted importance
function and the optimal importance function. The validity and performance of
RIS is empirically tested with a large setof synthetic Bayesian networks and
two real-world networks.
",Refractor Importance Sampling
"  The paper introduces a generalization for known probabilistic models such as
log-linear and graphical models, called here multiplicative models. These
models, that express probabilities via product of parameters are shown to
capture multiple forms of contextual independence between variables, including
decision graphs and noisy-OR functions. An inference algorithm for
multiplicative models is provided and its correctness is proved. The complexity
analysis of the inference algorithm uses a more refined parameter than the
tree-width of the underlying graph, and shows the computational cost does not
exceed that of the variable elimination algorithm in graphical models. The
paper ends with examples where using the new models and algorithm is
computationally beneficial.
",Inference for Multiplicative Models
"  Online learning aims to perform nearly as well as the best hypothesis in
hindsight. For some hypothesis classes, though, even finding the best
hypothesis offline is challenging. In such offline cases, local search
techniques are often employed and only local optimality guaranteed. For online
decision-making with such hypothesis classes, we introduce local regret, a
generalization of regret that aims to perform nearly as well as only nearby
hypotheses. We then present a general algorithm to minimize local regret with
arbitrary locality graphs. We also show how the graph structure can be
exploited to drastically speed learning. These algorithms are then demonstrated
on a diverse set of online problems: online disjunct learning, online Max-SAT,
and online decision tree learning.
",On Local Regret
"  The rules of d-separation provide a framework for deriving conditional
independence facts from model structure. However, this theory only applies to
simple directed graphical models. We introduce relational d-separation, a
theory for deriving conditional independence in relational models. We provide a
sound, complete, and computationally efficient method for relational
d-separation, and we present empirical results that demonstrate effectiveness.
",Identifying Independence in Relational Models
"  Decision circuits have been developed to perform efficient evaluation of
influence diagrams [Bhattacharjya and Shachter, 2007], building on the advances
in arithmetic circuits for belief network inference [Darwiche,2003]. In the
process of model building and analysis, we perform sensitivity analysis to
understand how the optimal solution changes in response to changes in the
model. When sequential decision problems under uncertainty are represented as
decision circuits, we can exploit the efficient solution process embodied in
the decision circuit and the wealth of derivative information available to
compute the value of information for the uncertainties in the problem and the
effects of changes to model parameters on the value and the optimal strategy.
",Sensitivity analysis in decision circuits
"  This is the Proceedings of the Twenty-Fifth Conference on Uncertainty in
Artificial Intelligence, which was held in Montreal, QC, Canada, June 18 - 21
2009.
","Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial
  Intelligence (2009)"
"  Computing the probability of evidence even with known error bounds is
NP-hard. In this paper we address this hard problem by settling on an easier
problem. We propose an approximation which provides high confidence lower
bounds on probability of evidence but does not have any guarantees in terms of
relative or absolute error. Our proposed approximation is a randomized
importance sampling scheme that uses the Markov inequality. However, a
straight-forward application of the Markov inequality may lead to poor lower
bounds. We therefore propose several heuristic measures to improve its
performance in practice. Empirical evaluation of our scheme with state-of-
the-art lower bounding schemes reveals the promise of our approach.
","Studies in Lower Bounding Probabilities of Evidence using the Markov
  Inequality"
"  Choquet expected utility (CEU) is one of the most sophisticated decision
criteria used in decision theory under uncertainty. It provides a
generalisation of expected utility enhancing both descriptive and prescriptive
possibilities. In this paper, we investigate the use of CEU for path-planning
under uncertainty with a special focus on robust solutions. We first recall the
main features of the CEU model and introduce some examples showing its
descriptive potential. Then we focus on the search for Choquet-optimal paths in
multivalued implicit graphs where costs depend on different scenarios. After
discussing complexity issues, we propose two different heuristic search
algorithms to solve the problem. Finally, numerical experiments are reported,
showing the practical efficiency of the proposed algorithms.
",Search for Choquet-optimal paths under uncertainty
"  The ways in which an agent's actions affect the world can often be modeled
compactly using a set of relational probabilistic planning rules. This paper
addresses the problem of learning such rule sets for multiple related tasks. We
take a hierarchical Bayesian approach, in which the system learns a prior
distribution over rule sets. We present a class of prior distributions
parameterized by a rule set prototype that is stochastically modified to
produce a task-specific rule set. We also describe a coordinate ascent
algorithm that iteratively optimizes the task-specific rule sets and the prior
distribution. Experiments using this algorithm show that transferring
information from related tasks significantly reduces the amount of training
data required to predict action effects in blocks-world domains.
",Learning Probabilistic Relational Dynamics for Multiple Tasks
"  We formulate in this paper the mini-bucket algorithm for approximate
inference in terms of exact inference on an approximate model produced by
splitting nodes in a Bayesian network. The new formulation leads to a number of
theoretical and practical implications. First, we show that branchand- bound
search algorithms that use minibucket bounds may operate in a drastically
reduced search space. Second, we show that the proposed formulation inspires
new minibucket heuristics and allows us to analyze existing heuristics from a
new perspective. Finally, we show that this new formulation allows mini-bucket
approximations to benefit from recent advances in exact inference, allowing one
to significantly increase the reach of these approximations.
","Node Splitting: A Scheme for Generating Upper Bounds in Bayesian
  Networks"
"  We describe the semantic foundations for elicitation of generalized
additively independent (GAI) utilities using the minimax regret criterion, and
propose several new query types and strategies for this purpose. Computational
feasibility is obtained by exploiting the local GAI structure in the model. Our
results provide a practical approach for implementing preference-based
constrained configuration optimization as well as effective search in
multiattribute product databases.
",Minimax regret based elicitation of generalized additive utilities
"  Although a number of related algorithms have been developed to evaluate
influence diagrams, exploiting the conditional independence in the diagram, the
exact solution has remained intractable for many important problems. In this
paper we introduce decision circuits as a means to exploit the local structure
usually found in decision problems and to improve the performance of influence
diagram analysis. This work builds on the probabilistic inference algorithms
using arithmetic circuits to represent Bayesian belief networks [Darwiche,
2003]. Once compiled, these arithmetic circuits efficiently evaluate
probabilistic queries on the belief network, and methods have been developed to
exploit both the global and local structure of the network. We show that
decision circuits can be constructed in a similar fashion and promise similar
benefits.
",Evaluating influence diagrams with decision circuits
"  We present a memory-bounded optimization approach for solving
infinite-horizon decentralized POMDPs. Policies for each agent are represented
by stochastic finite state controllers. We formulate the problem of optimizing
these policies as a nonlinear program, leveraging powerful existing nonlinear
optimization techniques for solving the problem. While existing solvers only
guarantee locally optimal solutions, we show that our formulation produces
higher quality controllers than the state-of-the-art approach. We also
incorporate a shared source of randomness in the form of a correlation device
to further increase solution quality with only a limited increase in space and
time. Our experimental results show that nonlinear optimization can be used to
provide high quality, concise solutions to decentralized decision problems
under uncertainty.
",Optimizing Memory-Bounded Controllers for Decentralized POMDPs
"  Most real-world dynamic systems are composed of different components that
often evolve at very different rates. In traditional temporal graphical models,
such as dynamic Bayesian networks, time is modeled at a fixed granularity,
generally selected based on the rate at which the fastest component evolves.
Inference must then be performed at this fastest granularity, potentially at
significant computational cost. Continuous Time Bayesian Networks (CTBNs) avoid
time-slicing in the representation by modeling the system as evolving
continuously over time. The expectation-propagation (EP) inference algorithm of
Nodelman et al. (2005) can then vary the inference granularity over time, but
the granularity is uniform across all parts of the system, and must be selected
in advance. In this paper, we provide a new EP algorithm that utilizes a
general cluster graph architecture where clusters contain distributions that
can overlap in both space (set of variables) and time. This architecture allows
different parts of the system to be modeled at very different time
granularities, according to their current rate of evolution. We also provide an
information-theoretic criterion for dynamically re-partitioning the clusters
during inference to tune the level of approximation to the current rate of
evolution. This avoids the need to hand-select the appropriate granularity, and
allows the granularity to adapt as information is transmitted across the
network. We present experiments demonstrating that this approach can result in
significant computational savings.
",Reasoning at the Right Time Granularity
"  Compiling graphical models has recently been under intense investigation,
especially for probabilistic modeling and processing. We present here a novel
data structure for compiling weighted graphical models (in particular,
probabilistic models), called AND/OR Multi-Valued Decision Diagram (AOMDD).
This is a generalization of our previous work on constraint networks, to
weighted models. The AOMDD is based on the frameworks of AND/OR search spaces
for graphical models, and Ordered Binary Decision Diagrams (OBDD). The AOMDD is
a canonical representation of a graphical model, and its size and compilation
time are bounded exponentially by the treewidth of the graph, rather than
pathwidth as is known for OBDDs. We discuss a Variable Elimination schedule for
compilation, and present the general APPLY algorithm that combines two weighted
AOMDDs, and also present a search based method for compilation method. The
preliminary experimental evaluation is quite encouraging, showing the potential
of the AOMDD data structure.
","AND/OR Multi-Valued Decision Diagrams (AOMDDs) for Weighted Graphical
  Models"
"  The paper evaluates the power of best-first search over AND/OR search spaces
for solving the Most Probable Explanation (MPE) task in Bayesian networks. The
main virtue of the AND/OR representation of the search space is its sensitivity
to the structure of the problem, which can translate into significant time
savings. In recent years depth-first AND/OR Branch-and- Bound algorithms were
shown to be very effective when exploring such search spaces, especially when
using caching. Since best-first strategies are known to be superior to
depth-first when memory is utilized, exploring the best-first control strategy
is called for. The main contribution of this paper is in showing that a recent
extension of AND/OR search algorithms from depth-first Branch-and-Bound to
best-first is indeed very effective for computing the MPE in Bayesian networks.
We demonstrate empirically the superiority of the best-first search approach on
various probabilistic networks.
",Best-First AND/OR Search for Most Probable Explanations
"  Searching the complete space of possible Bayesian networks is intractable for
problems of interesting size, so Bayesian network structure learning
algorithms, such as the commonly used Sparse Candidate algorithm, employ
heuristics. However, these heuristics also restrict the types of relationships
that can be learned exclusively from data. They are unable to learn
relationships that exhibit ""correlation-immunity"", such as parity. To learn
Bayesian networks in the presence of correlation-immune relationships, we
extend the Sparse Candidate algorithm with a technique called ""skewing"". This
technique uses the observation that relationships that are correlation-immune
under a specific input distribution may not be correlation-immune under
another, sufficiently different distribution. We show that by extending Sparse
Candidate with this technique we are able to discover relationships between
random variables that are approximately correlation-immune, with a
significantly lower computational cost than the alternative of considering
multiple parents of a node at a time.
",Learning Bayesian Network Structure from Correlation-Immune Data
"  Survey propagation (SP) is an exciting new technique that has been remarkably
successful at solving very large hard combinatorial problems, such as
determining the satisfiability of Boolean formulas. In a promising attempt at
understanding the success of SP, it was recently shown that SP can be viewed as
a form of belief propagation, computing marginal probabilities over certain
objects called covers of a formula. This explanation was, however, shortly
dismissed by experiments suggesting that non-trivial covers simply do not exist
for large formulas. In this paper, we show that these experiments were
misleading: not only do covers exist for large hard random formulas, SP is
surprisingly accurate at computing marginals over these covers despite the
existence of many cycles in the formulas. This re-opens a potentially simpler
line of reasoning for understanding SP, in contrast to some alternative lines
of explanation that have been proposed assuming covers do not exist.
",Survey Propagation Revisited
"  Relational Markov Random Fields are a general and flexible framework for
reasoning about the joint distribution over attributes of a large number of
interacting entities. The main computational difficulty in learning such models
is inference. Even when dealing with complete data, where one can summarize a
large domain by sufficient statistics, learning requires one to compute the
expectation of the sufficient statistics given different parameter choices. The
typical solution to this problem is to resort to approximate inference
procedures, such as loopy belief propagation. Although these procedures are
quite efficient, they still require computation that is on the order of the
number of interactions (or features) in the model. When learning a large
relational model over a complex domain, even such approximations require
unrealistic running time. In this paper we show that for a particular class of
relational MRFs, which have inherent symmetry, we can perform the inference
needed for learning procedures using a template-level belief propagation. This
procedure's running time is proportional to the size of the relational model
rather than the size of the domain. Moreover, we show that this computational
procedure is equivalent to sychronous loopy belief propagation. This enables a
dramatic speedup in inference and learning time. We use this procedure to learn
relational MRFs for capturing the joint distribution of large protein-protein
interaction networks.
",Template Based Inference in Symmetric Relational Markov Random Fields
"  Preferences play an important role in our everyday lives. CP-networks, or
CP-nets in short, are graphical models for representing conditional qualitative
preferences under ceteris paribus (""all else being equal"") assumptions. Despite
their intuitive nature and rich representation, dominance testing with CP-nets
is computationally complex, even when the CP-nets are restricted to
binary-valued preferences. Tractable algorithms exist for binary CP-nets, but
these algorithms are incomplete for multi-valued CPnets. In this paper, we
identify a class of multivalued CP-nets, which we call more-or-less CPnets,
that have the same computational complexity as binary CP-nets. More-or-less
CP-nets exploit the monotonicity of the attribute values and use intervals to
aggregate values that induce similar preferences. We then present a search
control rule for dominance testing that effectively prunes the search space
while preserving completeness.
",More-or-Less CP-Networks
"  Relational Markov Decision Processes are a useful abstraction for complex
reinforcement learning problems and stochastic planning problems. Recent work
developed representation schemes and algorithms for planning in such problems
using the value iteration algorithm. However, exact versions of more complex
algorithms, including policy iteration, have not been developed or analyzed.
The paper investigates this potential and makes several contributions. First we
observe two anomalies for relational representations showing that the value of
some policies is not well defined or cannot be calculated for restricted
representation schemes used in the literature. On the other hand, we develop a
variant of policy iteration that can get around these anomalies. The algorithm
includes an aspect of policy improvement in the process of policy evaluation
and thus differs from the original algorithm. We show that despite this
difference the algorithm converges to the optimal policy.
",Policy Iteration for Relational MDPs
"  Combining first-order logic and probability has long been a goal of AI.
Markov logic (Richardson & Domingos, 2006) accomplishes this by attaching
weights to first-order formulas and viewing them as templates for features of
Markov networks. Unfortunately, it does not have the full power of first-order
logic, because it is only defined for finite domains. This paper extends Markov
logic to infinite domains, by casting it in the framework of Gibbs measures
(Georgii, 1988). We show that a Markov logic network (MLN) admits a Gibbs
measure as long as each ground atom has a finite number of neighbors. Many
interesting cases fall in this category. We also show that an MLN admits a
unique measure if the weights of its non-unit clauses are small enough. We then
examine the structure of the set of consistent measures in the non-unique case.
Many important phenomena, including systems with phase transitions, are
represented by MLNs with non-unique measures. We relate the problem of
satisfiability in first-order logic to the properties of MLN measures, and
discuss how Markov logic relates to previous infinite models.
",Markov Logic in Infinite Domains
"  Counterfactual statements, e.g., ""my headache would be gone had I taken an
aspirin"" are central to scientific discourse, and are formally interpreted as
statements derived from ""alternative worlds"". However, since they invoke
hypothetical states of affairs, often incompatible with what is actually known
or observed, testing counterfactuals is fraught with conceptual and practical
difficulties. In this paper, we provide a complete characterization of
""testable counterfactuals,"" namely, counterfactual statements whose
probabilities can be inferred from physical experiments. We provide complete
procedures for discerning whether a given counterfactual is testable and, if
so, expressing its probability in terms of experimental data.
",What Counterfactuals Can Be Tested
"  Memory-Bounded Dynamic Programming (MBDP) has proved extremely effective in
solving decentralized POMDPs with large horizons. We generalize the algorithm
and improve its scalability by reducing the complexity with respect to the
number of observations from exponential to polynomial. We derive error bounds
on solution quality with respect to this new approximation and analyze the
convergence behavior. To evaluate the effectiveness of the improvements, we
introduce a new, larger benchmark problem. Experimental results show that
despite the high complexity of decentralized POMDPs, scalable solution
techniques such as MBDP perform surprisingly well.
",Improved Memory-Bounded Dynamic Programming for Decentralized POMDPs
"  Assistive systems for persons with cognitive disabilities (e.g. dementia) are
difficult to build due to the wide range of different approaches people can
take to accomplishing the same task, and the significant uncertainties that
arise from both the unpredictability of client's behaviours and from noise in
sensor readings. Partially observable Markov decision process (POMDP) models
have been used successfully as the reasoning engine behind such assistive
systems for small multi-step tasks such as hand washing. POMDP models are a
powerful, yet flexible framework for modelling assistance that can deal with
uncertainty and utility. Unfortunately, POMDPs usually require a very labour
intensive, manual procedure for their definition and construction. Our previous
work has described a knowledge driven method for automatically generating POMDP
activity recognition and context sensitive prompting systems for complex tasks.
We call the resulting POMDP a SNAP (SyNdetic Assistance Process). The
spreadsheet-like result of the analysis does not correspond to the POMDP model
directly and the translation to a formal POMDP representation is required. To
date, this translation had to be performed manually by a trained POMDP expert.
In this paper, we formalise and automate this translation process using a
probabilistic relational model (PRM) encoded in a relational database. We
demonstrate the method by eliciting three assistance tasks from non-experts. We
validate the resulting POMDP models using case-based simulations to show that
they are reasonable for the domains. We also show a complete case study of a
designer specifying one database, including an evaluation in a real-life
experiment with a human actor.
","Relational Approach to Knowledge Engineering for POMDP-based Assistance
  Systems as a Translation of a Psychological Model"
"  There are several contexts of non-monotonic reasoning where a priority
between rules is established whose purpose is preventing conflicts.
  One formalism that has been widely employed for non-monotonic reasoning is
the sceptical one known as Defeasible Logic. In Defeasible Logic the tool used
for conflict resolution is a preference relation between rules, that
establishes the priority among them.
  In this paper we investigate how to modify such a preference relation in a
defeasible logic theory in order to change the conclusions of the theory
itself. We argue that the approach we adopt is applicable to legal reasoning
where users, in general, cannot change facts or rules, but can propose their
preferences about the relative strength of the rules.
  We provide a comprehensive study of the possible combinatorial cases and we
identify and analyse the cases where the revision process is successful.
  After this analysis, we identify three revision/update operators and study
them against the AGM postulates for belief revision operators, to discover that
only a part of these postulates are satisfied by the three operators.
",Revision of Defeasible Logic Preferences
"  We apply decision theoretic techniques to construct non-player characters
that are able to assist a human player in collaborative games. The method is
based on solving Markov decision processes, which can be difficult when the
game state is described by many variables. To scale to more complex games, the
method allows decomposition of a game task into subtasks, each of which can be
modelled by a Markov decision process. Intention recognition is used to infer
the subtask that the human is currently performing, allowing the helper to
assist the human in performing the correct task. Experiments show that the
method can be effective, giving near-human level performance in helping a human
in a collaborative game.
",CAPIR: Collaborative Action Planning with Intention Recognition
"  We consider the problem of using a heuristic policy to improve the value
approximation by the Upper Confidence Bound applied in Trees (UCT) algorithm in
non-adversarial settings such as planning with large-state space Markov
Decision Processes. Current improvements to UCT focus on either changing the
action selection formula at the internal nodes or the rollout policy at the
leaf nodes of the search tree. In this work, we propose to add an auxiliary arm
to each of the internal nodes, and always use the heuristic policy to roll out
simulations at the auxiliary arms. The method aims to get fast convergence to
optimal values at states where the heuristic policy is optimal, while retaining
similar approximation as the original UCT in other states. We show that
bootstrapping with the proposed method in the new algorithm, UCT-Aux, performs
better compared to the original UCT algorithm and its variants in two benchmark
experiment settings. We also examine conditions under which UCT-Aux works well.
",Bootstrapping Monte Carlo Tree Search with an Imperfect Heuristic
"  We consider in this paper the formulation of approximate inference in
Bayesian networks as a problem of exact inference on an approximate network
that results from deleting edges (to reduce treewidth). We have shown in
earlier work that deleting edges calls for introducing auxiliary network
parameters to compensate for lost dependencies, and proposed intuitive
conditions for determining these parameters. We have also shown that our method
corresponds to IBP when enough edges are deleted to yield a polytree, and
corresponds to some generalizations of IBP when fewer edges are deleted. In
this paper, we propose a different criteria for determining auxiliary
parameters based on optimizing the KL-divergence between the original and
approximate networks. We discuss the relationship between the two methods for
selecting parameters, shedding new light on IBP and its generalizations. We
also discuss the application of our new method to approximating inference
problems which are exponential in constrained treewidth, including MAP and
nonmyopic value of information.
","A Variational Approach for Approximating Bayesian Networks by Edge
  Deletion"
"  In Bayesian networks, a Most Probable Explanation (MPE) is a complete
variable instantiation with a highest probability given the current evidence.
In this paper, we discuss the problem of finding robustness conditions of the
MPE under single parameter changes. Specifically, we ask the question: How much
change in a single network parameter can we afford to apply while keeping the
MPE unchanged? We will describe a procedure, which is the first of its kind,
that computes this answer for each parameter in the Bayesian network variable
in time O(n exp(w)), where n is the number of network variables and w is its
treewidth.
",On the Robustness of Most Probable Explanations
"  The paper analyzes theoretically and empirically the performance of
likelihood weighting (LW) on a subset of nodes in Bayesian networks. The
proposed scheme requires fewer samples to converge due to reduction in sampling
variance. The method exploits the structure of the network to bound the
complexity of exact inference used to compute sampling distributions, similar
to Gibbs cutset sampling. Yet, the extension of the previosly proposed cutset
sampling principles to likelihood weighting is non-trivial due to differences
in the sampling processes of Gibbs sampler and LW. We demonstrate empirically
that likelihood weighting on a cutset (LWLC) is effective time-wise and has a
lower rejection rate than LW when applied to networks with many deterministic
probabilities. Finally, we show that the performance of likelihood weighting on
a cutset can be improved further by caching computed sampling distributions
and, consequently, learning 'zeros' of the target distribution.
",Cutset Sampling with Likelihood Weighting
"  Linear-time computational techniques have been developed for combining
evidence which is available on a number of contending hypotheses. They offer a
means of making the computation-intensive calculations involved more efficient
in certain circumstances. Unfortunately, they restrict the orthogonal sum of
evidential functions to the dichotomous structure applies only to elements and
their complements. In this paper, we present a novel evidence structure in
terms of a triplet and a set of algorithms for evidential reasoning. The merit
of this structure is that it divides a set of evidence into three subsets,
distinguishing trivial evidential elements from important ones focusing some
particular elements. It avoids the deficits of the dichotomous structure in
representing the preference of evidence and estimating the basic probability
assignment of evidence. We have established a formalism for this structure and
the general formulae for combining pieces of evidence in the form of the
triplet, which have been theoretically justified.
",An Efficient Triplet-based Algorithm for Evidential Reasoning
"  Separable Bayesian Networks, or the Influence Model, are dynamic Bayesian
Networks in which the conditional probability distribution can be separated
into a function of only the marginal distribution of a node's neighbors,
instead of the joint distributions. In terms of modeling, separable networks
has rendered possible siginificant reduction in complexity, as the state space
is only linear in the number of variables on the network, in contrast to a
typical state space which is exponential. In this work, We describe the
connection between an arbitrary Conditional Probability Table (CPT) and
separable systems using linear algebra. We give an alternate proof on the
equivalence of sufficiency and separability. We present a computational method
for testing whether a given CPT is separable.
",Linear Algebra Approach to Separable Bayesian Networks
"  This paper is concerned with graphical criteria that can be used to solve the
problem of identifying casual effects from nonexperimental data in a causal
Bayesian network structure, i.e., a directed acyclic graph that represents
causal relationships. We first review Pearl's work on this topic [Pearl, 1995],
in which several useful graphical criteria are presented. Then we present a
complete algorithm [Huang and Valtorta, 2006b] for the identifiability problem.
By exploiting the completeness of this algorithm, we prove that the three basic
do-calculus rules that Pearl presents are complete, in the sense that, if a
causal effect is identifiable, there exists a sequence of applications of the
rules of the do-calculus that transforms the causal effect formula into a
formula that only includes observational quantities.
",Pearl's Calculus of Intervention Is Complete
"  This paper studies a new and more general axiomatization than one presented
previously for preference on likelihood gambles. Likelihood gambles describe
actions in a situation where a decision maker knows multiple probabilistic
models and a random sample generated from one of those models but does not know
prior probability of models. This new axiom system is inspired by Jensen's
axiomatization of probabilistic gambles. Our approach provides a new
perspective to the role of data in decision making under ambiguity. It avoids
one of the most controversial issue of Bayesian methodology namely the
assumption of prior probability.
",A new axiomatization for likelihood gambles
"  Continuous-time Bayesian networks (CTBNs) are graphical representations of
multi-component continuous-time Markov processes as directed graphs. The edges
in the network represent direct influences among components. The joint rate
matrix of the multi-component process is specified by means of conditional rate
matrices for each component separately. This paper addresses the situation
where some of the components evolve on a time scale that is much shorter
compared to the time scale of the other components. In this paper, we prove
that in the limit where the separation of scales is infinite, the Markov
process converges (in distribution, or weakly) to a reduced, or effective
Markov process that only involves the slow components. We also demonstrate that
for reasonable separation of scale (an order of magnitude) the reduced process
is a good approximation of the marginal process over the slow components. We
provide a simple procedure for building a reduced CTBN for this effective
process, with conditional rate matrices that can be directly calculated from
the original CTBN, and discuss the implications for approximate reasoning in
large systems.
","Dimension Reduction in Singularly Perturbed Continuous-Time Bayesian
  Networks"
"  A popular approach to solving large probabilistic systems relies on
aggregating states based on a measure of similarity. Many approaches in the
literature are heuristic. A number of recent methods rely instead on metrics
based on the notion of bisimulation, or behavioral equivalence between states
(Givan et al, 2001, 2003; Ferns et al, 2004). An integral component of such
metrics is the Kantorovich metric between probability distributions. However,
while this metric enables many satisfying theoretical properties, it is costly
to compute in practice. In this paper, we use techniques from network
optimization and statistical sampling to overcome this problem. We obtain in
this manner a variety of distance functions for MDP state aggregation, which
differ in the tradeoff between time and space complexity, as well as the
quality of the aggregation. We provide an empirical evaluation of these
trade-offs.
",Methods for computing state similarity in Markov Decision Processes
"  Inference for probabilistic graphical models is still very much a practical
challenge in large domains. The commonly used and effective belief propagation
(BP) algorithm and its generalizations often do not converge when applied to
hard, real-life inference tasks. While it is widely recognized that the
scheduling of messages in these algorithms may have significant consequences,
this issue remains largely unexplored. In this work, we address the question of
how to schedule messages for asynchronous propagation so that a fixed point is
reached faster and more often. We first show that any reasonable asynchronous
BP converges to a unique fixed point under conditions similar to those that
guarantee convergence of synchronous BP. In addition, we show that the
convergence rate of a simple round-robin schedule is at least as good as that
of synchronous propagation. We then propose residual belief propagation (RBP),
a novel, easy-to-implement, asynchronous propagation algorithm that schedules
messages in an informed way, that pushes down a bound on the distance from the
fixed point. Finally, we demonstrate the superiority of RBP over
state-of-the-art methods for a variety of challenging synthetic and real-life
problems: RBP converges significantly more often than other methods; and it
significantly reduces running time until convergence, even when other methods
converge.
","Residual Belief Propagation: Informed Scheduling for Asynchronous
  Message Passing"
"  Directed possibly cyclic graphs have been proposed by Didelez (2000) and
Nodelmann et al. (2002) in order to represent the dynamic dependencies among
stochastic processes. These dependencies are based on a generalization of
Granger-causality to continuous time, first developed by Schweder (1970) for
Markov processes, who called them local dependencies. They deserve special
attention as they are asymmetric unlike stochastic (in)dependence. In this
paper we focus on their graphical representation and develop a suitable, i.e.
asymmetric notion of separation, called delta-separation. The properties of
this graph separation as well as of local independence are investigated in
detail within a framework of asymmetric (semi)graphoids allowing a deeper
insight into what information can be read off these graphs.
",Asymmetric separation for local independence graphs
"  There exist several architectures to solve influence diagrams using local
computations, such as the Shenoy-Shafer, the HUGIN, or the Lazy Propagation
architectures. They all extend usual variable elimination algorithms thanks to
the use of so-called 'potentials'. In this paper, we introduce a new
architecture, called the Multi-operator Cluster DAG architecture, which can
produce decompositions with an improved constrained induced-width, and
therefore induce potentially exponential gains. Its principle is to benefit
from the composite nature of influence diagrams, instead of using uniform
potentials, in order to better analyze the problem structure.
",From influence diagrams to multi-operator cluster DAGs
"  Tasks such as record linkage and multi-target tracking, which involve
reconstructing the set of objects that underlie some observed data, are
particularly challenging for probabilistic inference. Recent work has achieved
efficient and accurate inference on such problems using Markov chain Monte
Carlo (MCMC) techniques with customized proposal distributions. Currently,
implementing such a system requires coding MCMC state representations and
acceptance probability calculations that are specific to a particular
application. An alternative approach, which we pursue in this paper, is to use
a general-purpose probabilistic modeling language (such as BLOG) and a generic
Metropolis-Hastings MCMC algorithm that supports user-supplied proposal
distributions. Our algorithm gains flexibility by using MCMC states that are
only partial descriptions of possible worlds; we provide conditions under which
MCMC over partial worlds yields correct answers to queries. We also show how to
use a context-specific Bayes net to identify the factors in the acceptance
probability that need to be computed for a given proposed move. Experimental
results on a citation matching task show that our general-purpose MCMC engine
compares favorably with an application-specific system.
",General-Purpose MCMC Inference over Relational Structures
"  In recent years Bayesian networks (BNs) with a mixture of continuous and
discrete variables have received an increasing level of attention. We present
an architecture for exact belief update in Conditional Linear Gaussian BNs (CLG
BNs). The architecture is an extension of lazy propagation using operations of
Lauritzen & Jensen [6] and Cowell [2]. By decomposing clique and separator
potentials into sets of factors, the proposed architecture takes advantage of
independence and irrelevance properties induced by the structure of the graph
and the evidence. The resulting benefits are illustrated by examples. Results
of a preliminary empirical performance evaluation indicate a significant
potential of the proposed architecture.
",Belief Update in CLG Bayesian Networks With Lazy Propagation
"  We set up a model for reasoning about metric spaces with belief theoretic
measures. The uncertainty in these spaces stems from both probability and
metric. To represent both aspect of uncertainty, we choose an expected distance
function as a measure of uncertainty. A formal logical system is constructed
for the reasoning about expected distance. Soundness and completeness are shown
for this logic. For reasoning on product metric space with uncertainty, a new
metric is defined and shown to have good properties.
",Reasoning about Uncertainty in Metric Spaces
"  The National Airspace System (NAS) is a large and complex system with
thousands of interrelated components: administration, control centers,
airports, airlines, aircraft, passengers, etc. The complexity of the NAS
creates many difficulties in management and control. One of the most pressing
problems is flight delay. Delay creates high cost to airlines, complaints from
passengers, and difficulties for airport operations. As demand on the system
increases, the delay problem becomes more and more prominent. For this reason,
it is essential for the Federal Aviation Administration to understand the
causes of delay and to find ways to reduce delay. Major contributing factors to
delay are congestion at the origin airport, weather, increasing demand, and air
traffic management (ATM) decisions such as the Ground Delay Programs (GDP).
Delay is an inherently stochastic phenomenon. Even if all known causal factors
could be accounted for, macro-level national airspace system (NAS) delays could
not be predicted with certainty from micro-level aircraft information. This
paper presents a stochastic model that uses Bayesian Networks (BNs) to model
the relationships among different components of aircraft delay and the causal
factors that affect delays. A case study on delays of departure flights from
Chicago O'Hare international airport (ORD) to Hartsfield-Jackson Atlanta
International Airport (ATL) reveals how local and system level environmental
and human-caused factors combine to affect components of delay, and how these
components contribute to the final arrival delay at the destination airport.
",Propagation of Delays in the National Airspace System
"  We introduce a new dynamic model with the capability of recognizing both
activities that an individual is performing as well as where that ndividual is
located. Our model is novel in that it utilizes a dynamic graphical model to
jointly estimate both activity and spatial context over time based on the
simultaneous use of asynchronous observations consisting of GPS measurements,
and measurements from a small mountable sensor board. Joint inference is quite
desirable as it has the ability to improve accuracy of the model. A key goal,
however, in designing our overall system is to be able to perform accurate
inference decisions while minimizing the amount of hardware an individual must
wear. This minimization leads to greater comfort and flexibility, decreased
power requirements and therefore increased battery life, and reduced cost. We
show results indicating that our joint measurement model outperforms
measurements from either the sensor board or GPS alone, using two types of
probabilistic inference procedures, namely particle filtering and pruned exact
inference.
",Recognizing Activities and Spatial Context Using Wearable Sensors
"  We study the problem of learning the best Bayesian network structure with
respect to a decomposable score such as BDe, BIC or AIC. This problem is known
to be NP-hard, which means that solving it becomes quickly infeasible as the
number of variables increases. Nevertheless, in this paper we show that it is
possible to learn the best Bayesian network structure with over 30 variables,
which covers many practically interesting cases. Our algorithm is less
complicated and more efficient than the techniques presented earlier. It can be
easily parallelized, and offers a possibility for efficient exploration of the
best networks consistent with different variable orderings. In the experimental
part of the paper we compare the performance of the algorithm to the previous
state-of-the-art algorithm. Free source-code and an online-demo can be found at
http://b-course.hiit.fi/bene.
","A simple approach for finding the globally optimal Bayesian network
  structure"
"  Recent work on approximate linear programming (ALP) techniques for
first-order Markov Decision Processes (FOMDPs) represents the value function
linearly w.r.t. a set of first-order basis functions and uses linear
programming techniques to determine suitable weights. This approach offers the
advantage that it does not require simplification of the first-order value
function, and allows one to solve FOMDPs independent of a specific domain
instantiation. In this paper, we address several questions to enhance the
applicability of this work: (1) Can we extend the first-order ALP framework to
approximate policy iteration to address performance deficiencies of previous
approaches? (2) Can we automatically generate basis functions and evaluate
their impact on value function quality? (3) How can we decompose intractable
problems with universally quantified rewards into tractable subproblems? We
propose answers to these questions along with a number of novel optimizations
and provide a comparative empirical evaluation on logistics problems from the
ICAPS 2004 Probabilistic Planning Competition.
",Practical Linear Value-approximation Techniques for First-order MDPs
"  In this paper we promote introducing software verification and control flow
graph similarity measurement in automated evaluation of students' programs. We
present a new grading framework that merges results obtained by combination of
these two approaches with results obtained by automated testing, leading to
improved quality and precision of automated grading. These two approaches are
also useful in providing a comprehensible feedback that can help students to
improve the quality of their programs We also present our corresponding tools
that are publicly available and open source. The tools are based on LLVM
low-level intermediate code representation, so they could be applied to a
number of programming languages. Experimental evaluation of the proposed
grading framework is performed on a corpus of university students' programs
written in programming language C. Results of the experiments show that
automatically generated grades are highly correlated with manually determined
grades suggesting that the presented tools can find real-world applications in
studying and grading.
","Software Verification and Graph Similarity for Automated Evaluation of
  Students' Assignments"
"  The use of Artificial Intelligence is finding prominence not only in core
computer areas, but also in cross disciplinary areas including medical
diagnosis. In this paper, we present a rule based Expert System used in
diagnosis of Cerebral Palsy. The expert system takes user input and depending
on the symptoms of the patient, diagnoses if the patient is suffering from
Cerebral Palsy. The Expert System also classifies the Cerebral Palsy as mild,
moderate or severe based on the presented symptoms.
",Rule Based Expert System for Cerebral Palsy Diagnosis
"  This paper focuses on the restart strategy of CMA-ES on multi-modal
functions. A first alternative strategy proceeds by decreasing the initial
step-size of the mutation while doubling the population size at each restart. A
second strategy adaptively allocates the computational budget among the restart
settings in the BIPOP scheme. Both restart strategies are validated on the BBOB
benchmark; their generality is also demonstrated on an independent real-world
problem suite related to spacecraft trajectory optimization.
",Alternative Restart Strategies for CMA-ES
"  Covering is an important type of data structure while covering-based rough
sets provide an efficient and systematic theory to deal with covering data. In
this paper, we use boolean matrices to represent and axiomatize three types of
covering approximation operators. First, we define two types of characteristic
matrices of a covering which are essentially square boolean ones, and their
properties are studied. Through the characteristic matrices, three important
types of covering approximation operators are concisely equivalently
represented. Second, matrix representations of covering approximation operators
are used in boolean matrix decomposition. We provide a sufficient and necessary
condition for a square boolean matrix to decompose into the boolean product of
another one and its transpose. And we develop an algorithm for this boolean
matrix decomposition. Finally, based on the above results, these three types of
covering approximation operators are axiomatized using boolean matrices. In a
word, this work borrows extensively from boolean matrices and present a new
view to study covering-based rough sets.
","Characteristic matrix of covering and its application to boolean matrix
  decomposition and axiomatization"
"  Principal Component Analysis (PCA) finds a linear mapping and maximizes the
variance of the data which makes PCA sensitive to outliers and may cause wrong
eigendirection. In this paper, we propose techniques to solve this problem; we
use the data-centering method and reestimate the covariance matrix using robust
statistic techniques such as median, robust scaling which is a booster to
data-centering and Huber M-estimator which measures the presentation of
outliers and reweight them with small values. The results on several real world
data sets show that our proposed method handles outliers and gains better
results than the original PCA and provides the same accuracy with lower
computation cost than the Kernel PCA using the polynomial kernel in
classification tasks.
",Robust Principal Component Analysis Using Statistical Estimators
"  A new generalized multilinear regression model, termed the Higher-Order
Partial Least Squares (HOPLS), is introduced with the aim to predict a tensor
(multiway array) $\tensor{Y}$ from a tensor $\tensor{X}$ through projecting the
data onto the latent space and performing regression on the corresponding
latent variables. HOPLS differs substantially from other regression models in
that it explains the data by a sum of orthogonal Tucker tensors, while the
number of orthogonal loadings serves as a parameter to control model complexity
and prevent overfitting. The low dimensional latent space is optimized
sequentially via a deflation operation, yielding the best joint subspace
approximation for both $\tensor{X}$ and $\tensor{Y}$. Instead of decomposing
$\tensor{X}$ and $\tensor{Y}$ individually, higher order singular value
decomposition on a newly defined generalized cross-covariance tensor is
employed to optimize the orthogonal loadings. A systematic comparison on both
synthetic data and real-world decoding of 3D movement trajectories from
electrocorticogram (ECoG) signals demonstrate the advantages of HOPLS over the
existing methods in terms of better predictive ability, suitability to handle
small sample sizes, and robustness to noise.
","Higher-Order Partial Least Squares (HOPLS): A Generalized Multi-Linear
  Regression Method"
"  While POMDPs provide a general platform for non-deterministic conditional
planning under a variety of quality metrics they have limited scalability. On
the other hand, non-deterministic conditional planners scale very well, but
many lack the ability to optimize plan quality metrics. We present a novel
generalization of planning graph based heuristics that helps conditional
planners both scale and generate high quality plans when using actions with
nonuniform costs. We make empirical comparisons with two state of the art
planners to show the benefit of our techniques.
",Cost Sensitive Reachability Heuristics for Handling State Uncertainty
"  With the aid of the concept of stable independence we can construct, in an
efficient way, a compact representation of a semi-graphoid independence
relation. We show that this representation provides a new necessary condition
for the existence of a directed perfect map for the relation. The test for this
condition is based to a large extent on the transitivity property of a special
form of d-separation. The complexity of the test is linear in the size of the
representation. The test, moreover, brings the additional benefit that it can
be used to guide the early stages of network construction.
",Stable Independence in Perfect Maps
"  Many real world sequences such as protein secondary structures or shell logs
exhibit a rich internal structures. Traditional probabilistic models of
sequences, however, consider sequences of flat symbols only. Logical hidden
Markov models have been proposed as one solution. They deal with logical
sequences, i.e., sequences over an alphabet of logical atoms. This comes at the
expense of a more complex model selection problem. Indeed, different
abstraction levels have to be explored. In this paper, we propose a novel
method for selecting logical hidden Markov models from data called SAGEM. SAGEM
combines generalized expectation maximization, which optimizes parameters, with
structure search for model selection using inductive logic programming
refinement operators. We provide convergence and experimental results that show
SAGEM's effectiveness.
",'Say EM' for Selecting Probabilistic Models for Logical Sequences
"  Intelligent systems in an open world must reason about many interacting
entities related to each other in diverse ways and having uncertain features
and relationships. Traditional probabilistic languages lack the expressive
power to handle relational domains. Classical first-order logic is sufficiently
expressive, but lacks a coherent plausible reasoning capability. Recent years
have seen the emergence of a variety of approaches to integrating first-order
logic, probability, and machine learning. This paper presents Multi-entity
Bayesian networks (MEBN), a formal system that integrates First Order Logic
(FOL) with Bayesian probability theory. MEBN extends ordinary Bayesian networks
to allow representation of graphical models with repeated sub-structures, and
can express a probability distribution over models of any consistent, finitely
axiomatizable first-order theory. We present the logic using an example
inspired by the Paramount Series StarTrek.
",Of Starships and Klingons: Bayesian Logic for the 23rd Century
"  In this paper we present a differential semantics of Lazy AR Propagation
(LARP) in discrete Bayesian networks. We describe how both single and multi
dimensional partial derivatives of the evidence may easily be calculated from a
junction tree in LARP equilibrium. We show that the simplicity of the
calculations stems from the nature of LARP. Based on the differential semantics
we describe how variable propagation in the LARP architecture may give access
to additional partial derivatives. The cautious LARP (cLARP) scheme is derived
to produce a flexible cLARP equilibrium that offers additional opportunities
for calculating single and multidimensional partial derivatives of the evidence
and subsets of the evidence from a single propagation. The results of an
empirical evaluation illustrates how the access to a largely increased number
of partial derivatives comes at a low computational cost.
",A Differential Semantics of Lazy AR Propagation
"  This paper deals with the following problem: modify a Bayesian network to
satisfy a given set of probability constraints by only change its conditional
probability tables, and the probability distribution of the resulting network
should be as close as possible to that of the original network. We propose to
solve this problem by extending IPFP (iterative proportional fitting procedure)
to probability distributions represented by Bayesian networks. The resulting
algorithm E-IPFP is further developed to D-IPFP, which reduces the
computational cost by decomposing a global EIPFP into a set of smaller local
E-IPFP problems. Limited analysis is provided, including the convergence proofs
of the two algorithms. Computer experiments were conducted to validate the
algorithms. The results are consistent with the theoretical analysis.
",Modifying Bayesian Networks by Probability Constraints
"  Studying the effects of one-way variation of any number of parameters on any
number of output probabilities quickly becomes infeasible in practice,
especially if various evidence profiles are to be taken into consideration. To
provide for identifying the parameters that have a potentially large effect
prior to actually performing the analysis, we need properties of sensitivity
functions that are independent of the network under study, of the available
evidence, or of both. In this paper, we study properties that depend upon just
the probability of the entered evidence. We demonstrate that these properties
provide for establishing an upper bound on the sensitivity value for a
parameter; they further provide for establishing the region in which the vertex
of the sensitivity function resides, thereby serving to identify parameters
with a low sensitivity value that may still have a large impact on the
probability of interest for relatively small parameter variations.
",Exploiting Evidence-dependent Sensitivity Bounds
"  We present multi-agent A* (MAA*), the first complete and optimal heuristic
search algorithm for solving decentralized partially-observable Markov decision
problems (DEC-POMDPs) with finite horizon. The algorithm is suitable for
computing optimal plans for a cooperative group of agents that operate in a
stochastic environment such as multirobot coordination, network traffic
control, `or distributed resource allocation. Solving such problems efiectively
is a major challenge in the area of planning under uncertainty. Our solution is
based on a synthesis of classical heuristic search and decentralized control
theory. Experimental results show that MAA* has significant advantages. We
introduce an anytime variant of MAA* and conclude with a discussion of
promising extensions such as an approach to solving infinite horizon problems.
",MAA*: A Heuristic Search Algorithm for Solving Decentralized POMDPs
"  Inferring from inconsistency and making decisions are two problems which have
always been treated separately by researchers in Artificial Intelligence.
Consequently, different models have been proposed for each category. Different
argumentation systems [2, 7, 10, 11] have been developed for handling
inconsistency in knowledge bases. Recently, other argumentation systems [3, 4,
8] have been defined for making decisions under uncertainty. The aim of this
paper is to present a general argumentation framework in which both inferring
from inconsistency and decision making are captured. The proposed framework can
be used for decision under uncertainty, multiple criteria decision, rule-based
decision and finally case-based decision. Moreover, works on classical decision
suppose that the information about environment is coherent, and this no longer
required by this general framework.
","A unified setting for inference and decision: An argumentation-based
  approach"
"  When a hybrid Bayesian network has conditionally deterministic variables with
continuous parents, the joint density function for the continuous variables
does not exist. Conditional linear Gaussian distributions can handle such cases
when the continuous variables have a multi-variate normal distribution and the
discrete variables do not have continuous parents. In this paper, operations
required for performing inference with conditionally deterministic variables in
hybrid Bayesian networks are developed. These methods allow inference in
networks with deterministic variables where continuous variables may be
non-Gaussian, and their density functions can be approximated by mixtures of
truncated exponentials. There are no constraints on the placement of continuous
and discrete nodes in the network.
",Hybrid Bayesian Networks with Linear Deterministic Variables
"  We consider the problem of deleting edges from a Bayesian network for the
purpose of simplifying models in probabilistic inference. In particular, we
propose a new method for deleting network edges, which is based on the evidence
at hand. We provide some interesting bounds on the KL-divergence between
original and approximate networks, which highlight the impact of given evidence
on the quality of approximation and shed some light on good and bad candidates
for edge deletion. We finally demonstrate empirically the promise of the
proposed edge deletion technique as a basis for approximate inference.
",On Bayesian Network Approximation by Edge Deletion
"  We define the notion of compiling a Bayesian network with evidence and
provide a specific approach for evidence-based compilation, which makes use of
logical processing. The approach is practical and advantageous in a number of
application areas-including maximum likelihood estimation, sensitivity
analysis, and MAP computations-and we provide specific empirical results in the
domain of genetic linkage analysis. We also show that the approach is
applicable for networks that do not contain determinism, and show that it
empirically subsumes the performance of the quickscore algorithm when applied
to noisy-or networks.
",Exploiting Evidence in Probabilistic Inference
"  A model of the world built from sensor data may be incorrect even if the
sensors are functioning correctly. Possible causes include the use of
inappropriate sensors (e.g. a laser looking through glass walls), sensor
inaccuracies accumulate (e.g. localization errors), the a priori models are
wrong, or the internal representation does not match the world (e.g. a static
occupancy grid used with dynamically moving objects). We are interested in the
case where the constructed model of the world is flawed, but there is no access
to the ground truth that would allow the system to see the discrepancy, such as
a robot entering an unknown environment. This paper considers the problem of
determining when something is wrong using only the sensor data used to
construct the world model. It proposes 11 interpretation inconsistency
indicators based on the Dempster-Shafer conflict metric, Con, and evaluates
these indicators according to three criteria: ability to distinguish true
inconsistency from sensor noise (classification), estimate the magnitude of
discrepancies (estimation), and determine the source(s) (if any) of sensing
problems in the environment (isolation). The evaluation is conducted using data
from a mobile robot with sonar and laser range sensors navigating indoor
environments under controlled conditions. The evaluation shows that the Gambino
indicator performed best in terms of estimation (at best 0.77 correlation),
isolation, and classification of the sensing situation as degraded (7% false
negative rate) or normal (0% false positive rate).
","Use of Dempster-Shafer Conflict Metric to Detect Interpretation
  Inconsistency"
"  The Bayesian Logic (BLOG) language was recently developed for defining
first-order probability models over worlds with unknown numbers of objects. It
handles important problems in AI, including data association and population
estimation. This paper extends BLOG by adopting generative processes over
function spaces - known as nonparametrics in the Bayesian literature. We
introduce syntax for reasoning about arbitrary collections of objects, and
their properties, in an intuitive manner. By exploiting exchangeability,
distributions over unknown objects and their attributes are cast as Dirichlet
processes, which resolve difficulties in model selection and inference caused
by varying numbers of objects. We demonstrate these concepts with application
to citation matching.
",Nonparametric Bayesian Logic
"  We propose an efficient algorithm for estimation of possibility based
qualitative expected utility. It is useful for decision making mechanisms where
each possible decision is assigned a multi-attribute possibility distribution.
The computational complexity of ordinary methods calculating the expected
utility based on discretization is growing exponentially with the number of
attributes, and may become infeasible with a high number of these attributes.
We present series of theorems and lemmas proving the correctness of our
algorithm that exibits a linear computational complexity. Our algorithm has
been applied in the context of selecting the most prospective partners in
multi-party multi-attribute negotiation, and can also be used in making
decisions about potential offers during the negotiation as other similar
problems.
","Efficient algorithm for estimation of qualitative expected utility in
  possibilistic case-based reasoning"
"  The local Markov condition for a DAG to be an independence map of a
probability distribution is well known. For DAGs with latent variables,
represented as bi-directed edges in the graph, the local Markov property may
invoke exponential number of conditional independencies. This paper shows that
the number of conditional independence relations required may be reduced if the
probability distributions satisfy the composition axiom. In certain types of
graphs, only linear number of conditional independencies are required. The
result has applications in testing linear structural equation models with
correlated errors.
",Local Markov Property for Models Satisfying Composition Axiom
"  We present a framework to discover and characterize different classes of
everyday activities from event-streams. We begin by representing activities as
bags of event n-grams. This allows us to analyze the global structural
information of activities, using their local event statistics. We demonstrate
how maximal cliques in an undirected edge-weighted graph of activities, can be
used for activity-class discovery in an unsupervised manner. We show how
modeling an activity as a variable length Markov process, can be used to
discover recurrent event-motifs to characterize the discovered
activity-classes. We present results over extensive data-sets, collected from
multiple active environments, to show the competence and generalizability of
our proposed framework.
",Unsupervised Activity Discovery and Characterization From Event-Streams
"  This paper describes a general framework called Hybrid Dynamic Mixed Networks
(HDMNs) which are Hybrid Dynamic Bayesian Networks that allow representation of
discrete deterministic information in the form of constraints. We propose
approximate inference algorithms that integrate and adjust well known
algorithmic principles such as Generalized Belief Propagation,
Rao-Blackwellised Particle Filtering and Constraint Propagation to address the
complexity of modeling and reasoning in HDMNs. We use this framework to model a
person's travel activity over time and to predict destination and routes given
the current location. We present a preliminary empirical evaluation
demonstrating the effectiveness of our modeling framework and algorithms using
several variants of the activity model.
",Modeling Transportation Routines using Hybrid Dynamic Mixed Networks
"  In this paper, we consider Hybrid Mixed Networks (HMN) which are Hybrid
Bayesian Networks that allow discrete deterministic information to be modeled
explicitly in the form of constraints. We present two approximate inference
algorithms for HMNs that integrate and adjust well known algorithmic principles
such as Generalized Belief Propagation, Rao-Blackwellised Importance Sampling
and Constraint Propagation to address the complexity of modeling and reasoning
in HMNs. We demonstrate the performance of our approximate inference algorithms
on randomly generated HMNs.
","Approximate Inference Algorithms for Hybrid Bayesian Networks with
  Discrete Constraints"
"  We present metrics for measuring state similarity in Markov decision
processes (MDPs) with infinitely many states, including MDPs with continuous
state spaces. Such metrics provide a stable quantitative analogue of the notion
of bisimulation for MDPs, and are suitable for use in MDP approximation. We
show that the optimal value function associated with a discounted infinite
horizon planning task varies continuously with respect to our metric distances.
",Metrics for Markov Decision Processes with Infinite State Spaces
"  Decision-theoretic planning with risk-sensitive planning objectives is
important for building autonomous agents or decision-support systems for
real-world applications. However, this line of research has been largely
ignored in the artificial intelligence and operations research communities
since planning with risk-sensitive planning objectives is more complicated than
planning with risk-neutral planning objectives. To remedy this situation, we
derive conditions that guarantee that the optimal expected utilities of the
total plan-execution reward exist and are finite for fully observable Markov
decision process models with non-linear utility functions. In case of Markov
decision process models with both positive and negative rewards, most of our
results hold for stationary policies only, but we conjecture that they can be
generalized to non stationary policies.
","Existence and Finiteness Conditions for Risk-Sensitive Planning: Results
  and Conjectures"
"  A fundamental issue in real-world systems, such as sensor networks, is the
selection of observations which most effectively reduce uncertainty. More
specifically, we address the long standing problem of nonmyopically selecting
the most informative subset of variables in a graphical model. We present the
first efficient randomized algorithm providing a constant factor
(1-1/e-epsilon) approximation guarantee for any epsilon > 0 with high
confidence. The algorithm leverages the theory of submodular functions, in
combination with a polynomial bound on sample complexity. We furthermore prove
that no polynomial time algorithm can provide a constant factor approximation
better than (1 - 1/e) unless P = NP. Finally, we provide extensive evidence of
the effectiveness of our method on two complex real-world datasets.
",Near-optimal Nonmyopic Value of Information in Graphical Models
"  In this paper, we propose a revision-based approach for conflict resolution
by generalizing the Disjunctive Maxi-Adjustment (DMA) approach (Benferhat et
al. 2004). Revision operators can be classified into two different families:
the model-based ones and the formula-based ones. So the revision-based approach
has two different versions according to which family of revision operators is
chosen. Two particular revision operators are considered, one is the Dalal's
revision operator, which is a model-based revision operator, and the other is
the cardinality-maximal based revision operator, which is a formulabased
revision operator. When the Dalal's revision operator is chosen, the
revision-based approach is independent of the syntactic form in each stratum
and it captures some notion of minimal change. When the cardinalitymaximal
based revision operator is chosen, the revision-based approach is equivalent to
the DMA approach. We also show that both approaches are computationally easier
than the DMA approach.
",A Revision-Based Approach to Resolving Conflicting Information
"  Systems such as sensor networks and teams of autonomous robots consist of
multiple autonomous entities that interact with each other in a distributed,
asynchronous manner. These entities need to keep track of the state of the
system as it evolves. Asynchronous systems lead to special challenges for
monitoring, as nodes must update their beliefs independently of each other and
no central coordination is possible. Furthermore, the state of the system
continues to change as beliefs are being updated. Previous approaches to
developing distributed asynchronous probabilistic reasoning systems have used
static models. We present an approach using dynamic models, that take into
account the way the system changes state over time. Our approach, which is
based on belief propagation, is fully distributed and asynchronous, and allows
the world to keep on changing as messages are being sent around. Experimental
results show that our approach compares favorably to the factored frontier
algorithm.
",Asynchronous Dynamic Bayesian Networks
"  Continuous time Bayesian networks (CTBNs) describe structured stochastic
processes with finitely many states that evolve over continuous time. A CTBN is
a directed (possibly cyclic) dependency graph over a set of variables, each of
which represents a finite state continuous time Markov process whose transition
model is a function of its parents. As shown previously, exact inference in
CTBNs is intractable. We address the problem of approximate inference, allowing
for general queries conditioned on evidence over continuous time intervals and
at discrete time points. We show how CTBNs can be parameterized within the
exponential family, and use that insight to develop a message passing scheme in
cluster graphs and allows us to apply expectation propagation to CTBNs. The
clusters in our cluster graph do not contain distributions over the cluster
variables at individual time points, but distributions over trajectories of the
variables throughout a duration. Thus, unlike discrete time temporal models
such as dynamic Bayesian networks, we can adapt the time granularity at which
we reason for different variables and in different conditions.
",Expectation Propagation for Continuous Time Bayesian Networks
"  Continuous time Bayesian networks (CTBNs) describe structured stochastic
processes with finitely many states that evolve over continuous time. A CTBN is
a directed (possibly cyclic) dependency graph over a set of variables, each of
which represents a finite state continuous time Markov process whose transition
model is a function of its parents. We address the problem of learning the
parameters and structure of a CTBN from partially observed data. We show how to
apply expectation maximization (EM) and structural expectation maximization
(SEM) to CTBNs. The availability of the EM algorithm allows us to extend the
representation of CTBNs to allow a much richer class of transition durations
distributions, known as phase distributions. This class is a highly expressive
semi-parametric representation, which can approximate any duration distribution
arbitrarily closely. This extension to the CTBN framework addresses one of the
main limitations of both CTBNs and DBNs - the restriction to exponentially /
geometrically distributed duration. We present experimental results on a real
data set of people's life spans, showing that our algorithm learns reasonable
models - structure and parameters - from partially observed data, and, with the
use of phase distributions, achieves better performance than DBNs.
","Expectation Maximization and Complex Duration Distributions for
  Continuous Time Bayesian Networks"
"  We derive novel sufficient conditions for convergence of Loopy Belief
Propagation (also known as the Sum-Product algorithm) to a unique fixed point.
Our results improve upon previously known conditions. For binary variables with
(anti-)ferromagnetic interactions, our conditions seem to be sharp.
",Sufficient conditions for convergence of Loopy Belief Propagation
"  In this paper we compare search and inference in graphical models through the
new framework of AND/OR search. Specifically, we compare Variable Elimination
(VE) and memoryintensive AND/OR Search (AO) and place algorithms such as
graph-based backjumping and no-good and good learning, as well as Recursive
Conditioning [7] and Value Elimination [2] within the AND/OR search framework.
",The Relationship Between AND/OR Search and Variable Elimination
"  This paper addresses a fundamental issue central to approximation methods for
solving large Markov decision processes (MDPs): how to automatically learn the
underlying representation for value function approximation? A novel
theoretically rigorous framework is proposed that automatically generates
geometrically customized orthonormal sets of basis functions, which can be used
with any approximate MDP solver like least squares policy iteration (LSPI). The
key innovation is a coordinate-free representation of value functions, using
the theory of smooth functions on a Riemannian manifold. Hodge theory yields a
constructive method for generating basis functions for approximating value
functions based on the eigenfunctions of the self-adjoint (Laplace-Beltrami)
operator on manifolds. In effect, this approach performs a global Fourier
analysis on the state space graph to approximate value functions, where the
basis functions reflect the largescale topology of the underlying state space.
A new class of algorithms called Representation Policy Iteration (RPI) are
presented that automatically learn both basis functions and approximately
optimal policies. Illustrative experiments compare the performance of RPI with
that of LSPI using two handcoded basis functions (RBF and polynomial state
encodings).
",Representation Policy Iteration
"  Existing complexity bounds for point-based POMDP value iteration algorithms
focus either on the curse of dimensionality or the curse of history. We derive
a new bound that relies on both and uses the concept of discounted
reachability; our conclusions may help guide future algorithm design. We also
discuss recent improvements to our (point-based) heuristic search value
iteration algorithm. Our new implementation calculates tighter initial bounds,
avoids solving linear programs, and makes more effective use of sparsity.
",Point-Based POMDP Algorithms: Improved Analysis and Implementation
"  We introduce a new approximate solution technique for first-order Markov
decision processes (FOMDPs). Representing the value function linearly w.r.t. a
set of first-order basis functions, we compute suitable weights by casting the
corresponding optimization as a first-order linear program and show how
off-the-shelf theorem prover and LP software can be effectively used. This
technique allows one to solve FOMDPs independent of a specific domain
instantiation; furthermore, it allows one to determine bounds on approximation
error that apply equally to all domain instantiations. We apply this solution
technique to the task of elevator scheduling with a rich feature space and
multi-criteria additive reward, and demonstrate that it outperforms a number of
intuitive, heuristicallyguided policies.
",Approximate Linear Programming for First-order MDPs
"  Models of dynamical systems based on predictive state representations (PSRs)
are defined strictly in terms of observable quantities, in contrast with
traditional models (such as Hidden Markov Models) that use latent variables or
statespace representations. In addition, PSRs have an effectively infinite
memory, allowing them to model some systems that finite memory-based models
cannot. Thus far, PSR models have primarily been developed for domains with
discrete observations. Here, we develop the Predictive Linear-Gaussian (PLG)
model, a class of PSR models for domains with continuous observations. We show
that PLG models subsume Linear Dynamical System models (also called Kalman
filter models or state-space models) while using fewer parameters. We also
introduce an algorithm to estimate PLG parameters from data, and contrast it
with standard Expectation Maximization (EM) algorithms used to estimate Kalman
filter parameters. We show that our algorithm is a consistent estimation
procedure and present preliminary empirical results suggesting that our
algorithm outperforms EM, particularly as the model dimension increases.
",Predictive Linear-Gaussian Models of Stochastic Dynamical Systems
"  We consider the problem of diagnosing faults in a system represented by a
Bayesian network, where diagnosis corresponds to recovering the most likely
state of unobserved nodes given the outcomes of tests (observed nodes). Finding
an optimal subset of tests in this setting is intractable in general. We show
that it is difficult even to compute the next most-informative test using
greedy test selection, as it involves several entropy terms whose exact
computation is intractable. We propose an approximate approach that utilizes
the loopy belief propagation infrastructure to simultaneously compute
approximations of marginal and conditional entropies on multiple subsets of
nodes. We apply our method to fault diagnosis in computer networks, and show
the algorithm to be very effective on realistic Internet-like topologies. We
also provide theoretical justification for the greedy test selection approach,
along with some performance guarantees.
",Efficient Test Selection in Active Diagnosis via Entropy Approximation
"  One of the main problems of importance sampling in Bayesian networks is
representation of the importance function, which should ideally be as close as
possible to the posterior joint distribution. Typically, we represent an
importance function as a factorization, i.e., product of conditional
probability tables (CPTs). Given diagnostic evidence, we do not have explicit
forms for the CPTs in the networks. We first derive the exact form for the CPTs
of the optimal importance function. Since the calculation is hard, we usually
only use their approximations. We review several popular strategies and point
out their limitations. Based on an analysis of the influence of evidence, we
propose a method for approximating the exact form of importance function by
explicitly modeling the most important additional dependence relations
introduced by evidence. Our experimental results show that the new
approximation strategy offers an immediate improvement in the quality of the
importance function.
","Importance Sampling in Bayesian Networks: An Influence-Based
  Approximation Strategy for Importance Functions"
"  GBP and EP are two successful algorithms for approximate probabilistic
inference, which are based on different approximation strategies. An open
problem in both algorithms has been how to choose an appropriate approximation
structure. We introduce 'structured region graphs', a formalism which marries
these two strategies, reveals a deep connection between them, and suggests how
to choose good approximation structures. In this formalism, each region has an
internal structure which defines an exponential family, whose sufficient
statistics must be matched by the parent region. Reduction operators on these
structures allow conversion between EP and GBP free energies. Thus it is
revealed that all EP approximations on discrete variables are special cases of
GBP, and conversely that some wellknown GBP approximations, such as overlapping
squares, are special cases of EP. Furthermore, region graphs derived from EP
have a number of good structural properties, including maxent-normality and
overall counting number of one. The result is a convenient framework for
producing high-quality approximations with a user-adjustable level of
complexity
",Structured Region Graphs: Morphing EP into GBP
"  In recent years, there has been an increased need for the use of active
systems - systems required to act automatically based on events, or changes in
the environment. Such systems span many areas, from active databases to
applications that drive the core business processes of today's enterprises.
However, in many cases, the events to which the system must respond are not
generated by monitoring tools, but must be inferred from other events based on
complex temporal predicates. In addition, in many applications, such inference
is inherently uncertain. In this paper, we introduce a formal framework for
knowledge representation and reasoning enabling such event inference. Based on
probability theory, we define the representation of the associated uncertainty.
In addition, we formally define the probability space, and show how the
relevant probabilities can be calculated by dynamically constructing a Bayesian
network. To the best of our knowledge, this is the first work that enables
taking such uncertainty into account in the context of active systems.
herefore, our contribution is twofold: We formally define the representation
and semantics of event composition for probabilistic settings, and show how to
apply these extensions to the quantification of the occurrence probability of
events. These results enable any active system to handle such uncertainty.
",A Model for Reasoning with Uncertain Rules in Event Composition Systems
"  The feature of our method different from other fuzzy grey relation method for
supermixed multiple attribute group decision-making is that all of the
subjective and objective weights are obtained by interval grey number and that
the group decisionmaking is performed based on the relative approach degree of
grey TOPSIS, the relative approach degree of grey incidence and the relative
membership degree of grey incidence using 4-dimensional Euclidean distance. The
weighted Borda method is used to obtain final rank by using the results of four
methods. An example shows the applicability of the proposed approach.
","Super-Mixed Multiple Attribute Group Decision Making Method Based on
  Hybrid Fuzzy Grey Relation Approach Degree"
"  The multiple attribute mixed type decision making is performed by four
methods, that is, the relative approach degree of grey TOPSIS method, the
relative approach degree of grey incidence, the relative membership degree of
grey incidence and the grey relation relative approach degree method using the
maximum entropy estimation, respectively. In these decision making methods, the
grey incidence degree in four-dimensional Euclidean space is used. The final
arrangement result is obtained by weighted Borda method. An example illustrates
the applicability of the proposed approach.
","Generalized Hybrid Grey Relation Method for Multiple Attribute Mixed
  Type Decision Making"
"  We revisit the SeqBin constraint. This meta-constraint subsumes a number of
important global constraints like Change, Smooth and IncreasingNValue. We show
that the previously proposed filtering algorithm for SeqBin has two drawbacks
even under strong restrictions: it does not detect bounds disentailment and it
is not idempotent. We identify the cause for these problems, and propose a new
propagator that overcomes both issues. Our algorithm is based on a connection
to the problem of finding a path of a given cost in a restricted $n$-partite
graph. Our propagator enforces domain consistency in O(nd^2) and, for special
cases of SeqBin that include Change, Smooth and IncreasingNValue, in O(nd)
time.
",The SeqBin Constraint Revisited
"  This article introduces the benefits of using computer as a tool for foreign
language teaching and learning. It describes the effect of using Natural
Language Processing (NLP) tools for learning Arabic. The technique explored in
this particular case is the employment of pedagogically indexed corpora. This
text-based method provides the teacher the advantage of building activities
based on texts adapted to a particular pedagogical situation. This paper also
presents ARAC: a Platform dedicated to language educators allowing them to
create activities within their own pedagogical area of interest.
",Arabic CALL system based on pedagogically indexed text
"  This article describes different models based on Bayesian networks RB
modeling expertise in the diagnosis of brain tumors. Indeed, they are well
adapted to the representation of the uncertainty in the process of diagnosis of
these tumors. In our work, we first tested several structures derived from the
Bayesian network reasoning performed by doctors on the one hand and structures
generated automatically on the other. This step aims to find the best structure
that increases diagnostic accuracy. The machine learning algorithms relate
MWST-EM algorithms, SEM and SEM + T. To estimate the parameters of the Bayesian
network from a database incomplete, we have proposed an extension of the EM
algorithm by adding a priori knowledge in the form of the thresholds calculated
by the first phase of the algorithm RBE . The very encouraging results obtained
are discussed at the end of the paper
","Etude de Mod\`eles \`a base de r\'eseaux Bay\'esiens pour l'aide au
  diagnostic de tumeurs c\'er\'ebrales"
"  This paper proposes a grey interval relation TOPSIS for the decision making
in which all of the attribute weights and attribute values are given by the
interval grey numbers. The feature of our method different from other grey
relation decision-making is that all of the subjective and objective weights
are obtained by interval grey number and that decisionmaking is performed based
on the relative approach degree of grey TOPSIS, the relative approach degree of
grey incidence and the relative membership degree of grey incidence using
2-dimensional Euclidean distance. The weighted Borda method is used for
combining the results of three methods. An example shows the applicability of
the proposed approach.
","Novel Grey Interval Weight Determining and Hybrid Grey Interval Relation
  Method in Multiple Attribute Decision-Making"
"  Symbolic event recognition systems have been successfully applied to a
variety of application domains, extracting useful information in the form of
events, allowing experts or other systems to monitor and respond when
significant events are recognised. In a typical event recognition application,
however, these systems often have to deal with a significant amount of
uncertainty. In this paper, we address the issue of uncertainty in logic-based
event recognition by extending the Event Calculus with probabilistic reasoning.
Markov Logic Networks are a natural candidate for our logic-based formalism.
However, the temporal semantics of the Event Calculus introduce a number of
challenges for the proposed model. We show how and under what assumptions we
can overcome these problems. Additionally, we study how probabilistic modelling
changes the behaviour of the formalism, affecting its key property, the inertia
of fluents. Furthermore, we demonstrate the advantages of the probabilistic
Event Calculus through examples and experiments in the domain of activity
recognition, using a publicly available dataset for video surveillance.
",Probabilistic Event Calculus for Event Recognition
"  Understanding the structure and dynamics of biological networks is one of the
important challenges in system biology. In addition, increasing amount of
experimental data in biological networks necessitate the use of efficient
methods to analyze these huge amounts of data. Such methods require to
recognize common patterns to analyze data. As biological networks can be
modeled by graphs, the problem of common patterns recognition is equivalent
with frequent sub graph mining in a set of graphs. In this paper, at first the
challenges of frequent subgrpahs mining in biological networks are introduced
and the existing approaches are classified for each challenge. then the
algorithms are analyzed on the basis of the type of the approach they apply for
each of the challenges.
","Classification of Approaches and Challenges of Frequent Subgraphs Mining
  in Biological Networks"
"  This paper proposes a grey interval relation TOPSIS method for the decision
making in which all of the attribute weights and attribute values are given by
the interval grey numbers. In this paper, all of the subjective and objective
weights are obtained by interval grey number and decision-making is based on
four methods such as the relative approach degree of grey TOPSIS, the relative
approach degree of grey incidence and the relative approach degree method using
the maximum entropy estimation using 2-dimensional Euclidean distance. A
multiple attribute decision-making example for evaluation of artistic talent of
Kayagum (stringed Korean harp) players is given to show practicability of the
proposed approach.
","Hybrid Grey Interval Relation Decision-Making in Artistic Talent
  Evaluation of Player"
"  The behavior composition problem involves automatically building a controller
that is able to realize a desired, but unavailable, target system (e.g., a
house surveillance) by suitably coordinating a set of available components
(e.g., video cameras, blinds, lamps, a vacuum cleaner, phones, etc.) Previous
work has almost exclusively aimed at bringing about the desired component in
its totality, which is highly unsatisfactory for unsolvable problems. In this
work, we develop an approach for approximate behavior composition without
departing from the classical setting, thus making the problem applicable to a
much wider range of cases. Based on the notion of simulation, we characterize
what a maximal controller and the ""closest"" implementable target module
(optimal approximation) are, and show how these can be computed using ATL model
checking technology for a special case. We show the uniqueness of optimal
approximations, and prove their soundness and completeness with respect to
their imported controllers.
",Qualitative Approximate Behavior Composition
"  We propose a variant of Alternating-time Temporal Logic (ATL) grounded in the
agents' operational know-how, as defined by their libraries of abstract plans.
Inspired by ATLES, a variant itself of ATL, it is possible in our logic to
explicitly refer to ""rational"" strategies for agents developed under the
Belief-Desire-Intention agent programming paradigm. This allows us to express
and verify properties of BDI systems using ATL-type logical frameworks.
",Reasoning about Agent Programs using ATL-like Logics
"  We consider the problem of computing optimal generalised policies for
relational Markov decision processes. We describe an approach combining some of
the benefits of purely inductive techniques with those of symbolic dynamic
programming methods. The latter reason about the optimal value function using
first-order decision theoretic regression and formula rewriting, while the
former, when provided with a suitable hypotheses language, are capable of
generalising value functions or policies for small instances. Our idea is to
use reasoning and in particular classical first-order regression to
automatically generate a hypotheses language dedicated to the domain at hand,
which is then used as input by an inductive solver. This approach avoids the
more complex reasoning of symbolic dynamic programming while focusing the
inductive solver's attention on concepts that are specifically relevant to the
optimal value function for the domain considered.
",Exploiting First-Order Regression in Inductive Policy Selection
"  This paper proposes a decision theory for a symbolic generalization of
probability theory (SP). Darwiche and Ginsberg [2,3] proposed SP to relax the
requirement of using numbers for uncertainty while preserving desirable
patterns of Bayesian reasoning. SP represents uncertainty by symbolic supports
that are ordered partially rather than completely as in the case of standard
probability. We show that a preference relation on acts that satisfies a number
of intuitive postulates is represented by a utility function whose domain is a
set of pairs of supports. We argue that a subjective interpretation is as
useful and appropriate for SP as it is for numerical probability. It is useful
because the subjective interpretation provides a basis for uncertainty
elicitation. It is appropriate because we can provide a decision theory that
explains how preference on acts is based on support comparison.
",Decision Making for Symbolic Probability
"  We present metrics for measuring the similarity of states in a finite Markov
decision process (MDP). The formulation of our metrics is based on the notion
of bisimulation for MDPs, with an aim towards solving discounted infinite
horizon reinforcement learning tasks. Such metrics can be used to aggregate
states, as well as to better structure other value function approximators
(e.g., memory-based or nearest-neighbor approximators). We provide bounds that
relate our metric distances to the optimal values of states in the given MDP.
",Metrics for Finite Markov Decision Processes
"  We describe an approach for exploiting structure in Markov Decision Processes
with continuous state variables. At each step of the dynamic programming, the
state space is dynamically partitioned into regions where the value function is
the same throughout the region. We first describe the algorithm for piecewise
constant representations. We then extend it to piecewise linear
representations, using techniques from POMDPs to represent and reason about
linear surfaces efficiently. We show that for complex, structured problems, our
approach exploits the natural structure so that optimal solutions can be
computed efficiently.
",Dynamic Programming for Structured Continuous Markov Decision Problems
"  We present a major improvement to the incremental pruning algorithm for
solving partially observable Markov decision processes. Our technique targets
the cross-sum step of the dynamic programming (DP) update, a key source of
complexity in POMDP algorithms. Instead of reasoning about the whole belief
space when pruning the cross-sums, our algorithm divides the belief space into
smaller regions and performs independent pruning in each region. We evaluate
the benefits of the new technique both analytically and experimentally, and
show that it produces very significant performance gains. The results
contribute to the scalability of POMDP algorithms to domains that cannot be
handled by the best existing techniques.
",Region-Based Incremental Pruning for POMDPs
"  The aim of this work is to provide a unified framework for ordinal
representations of uncertainty lying at the crosswords between possibility and
probability theories. Such confidence relations between events are commonly
found in monotonic reasoning, inconsistency management, or qualitative decision
theory. They start either from probability theory, making it more qualitative,
or from possibility theory, making it more expressive. We show these two trends
converge to a class of genuine probability theories. We provide
characterization results for these useful tools that preserve the qualitative
nature of possibility rankings, while enjoying the power of expressivity of
additive representations.
",A Unified framework for order-of-magnitude confidence relations
"  The paper introduces mixed networks, a new framework for expressing and
reasoning with probabilistic and deterministic information. The framework
combines belief networks with constraint networks, defining the semantics and
graphical representation. We also introduce the AND/OR search space for
graphical models, and develop a new linear space search algorithm. This
provides the basis for understanding the benefits of processing the constraint
information separately, resulting in the pruning of the search space. When the
constraint part is tractable or has a small number of solutions, using the
mixed representation can be exponentially more effective than using pure belief
networks which odel constraints as conditional probability tables.
","Mixtures of Deterministic-Probabilistic Networks and their AND/OR Search
  Space"
"  The representation of independence relations generally builds upon the
well-known semigraphoid axioms of independence. Recently, a representation has
been proposed that captures a set of dominant statements of an independence
relation from which any other statement can be generated by means of the
axioms; the cardinality of this set is taken to indicate the complexity of the
relation. Building upon the idea of dominance, we introduce the concept of
stability to provide for a more compact representation of independence. We give
an associated algorithm for establishing such a representation.We show that,
with our concept of stability, many independence relations are found to be of
lower complexity than with existing representations.
",Stable Independance and Complexity of Representation
"  This paper investigates a representation language with flexibility inspired
by probabilistic logic and compactness inspired by relational Bayesian
networks. The goal is to handle propositional and first-order constructs
together with precise, imprecise, indeterminate and qualitative probabilistic
assessments. The paper shows how this can be achieved through the theory of
credal networks. New exact and approximate inference algorithms based on
multilinear programming and iterated/loopy propagation of interval
probabilities are presented; their superior performance, compared to existing
ones, is shown empirically.
","Propositional and Relational Bayesian Networks Associated with Imprecise
  and Qualitative Probabilistic Assesments"
"  Defeasible argumentation frameworks have evolved to become a sound setting to
formalize commonsense, qualitative reasoning from incomplete and potentially
inconsistent knowledge. Defeasible Logic Programming (DeLP) is a defeasible
argumentation formalism based on an extension of logic programming. Although
DeLP has been successfully integrated in a number of different real-world
applications, DeLP cannot deal with explicit uncertainty, nor with vague
knowledge, as defeasibility is directly encoded in the object language. This
paper introduces P-DeLP, a new logic programming language that extends original
DeLP capabilities for qualitative reasoning by incorporating the treatment of
possibilistic uncertainty and fuzzy knowledge. Such features will be formalized
on the basis of PGL, a possibilistic logic based on Godel fuzzy logic.
","A Logic Programming Framework for Possibilistic Argumentation with Vague
  Knowledge"
"  Previous work on sensitivity analysis in Bayesian networks has focused on
single parameters, where the goal is to understand the sensitivity of queries
to single parameter changes, and to identify single parameter changes that
would enforce a certain query constraint. In this paper, we expand the work to
multiple parameters which may be in the CPT of a single variable, or the CPTs
of multiple variables. Not only do we identify the solution space of multiple
parameter changes that would be needed to enforce a query constraint, but we
also show how to find the optimal solution, that is, the one which disturbs the
current probability distribution the least (with respect to a specific measure
of disturbance). We characterize the computational complexity of our new
techniques and discuss their applications to developing and debugging Bayesian
networks, and to the problem of reasoning about the value (reliability) of new
information.
","Sensitivity Analysis in Bayesian Networks: From Single to Multiple
  Parameters"
"  We consider the challenge of preference elicitation in systems that help
users discover the most desirable item(s) within a given database. Past work on
preference elicitation focused on structured models that provide a factored
representation of users' preferences. Such models require less information to
construct and support efficient reasoning algorithms. This paper makes two
substantial contributions to this area: (1) Strong representation theorems for
factored value functions. (2) A methodology that utilizes our representation
results to address the problem of optimal item selection.
",Compact Value-Function Representations for Qualitative Preferences
"  Humans currently use arguments for explaining choices which are already made,
or for evaluating potential choices. Each potential choice has usually pros and
cons of various strengths. In spite of the usefulness of arguments in a
decision making process, there have been few formal proposals handling this
idea if we except works by Fox and Parsons and by Bonet and Geffner. In this
paper we propose a possibilistic logic framework where arguments are built from
an uncertain knowledge base and a set of prioritized goals. The proposed
approach can compute two kinds of decisions by distinguishing between
pessimistic and optimistic attitudes. When the available, maybe uncertain,
knowledge is consistent, as well as the set of prioritized goals (which have to
be fulfilled as far as possible), the method for evaluating decisions on the
basis of arguments agrees with the possibility theory-based approach to
decision-making under uncertainty. Taking advantage of its relation with formal
approaches to defeasible argumentation, the proposed framework can be
generalized in case of partially inconsistent knowledge, or goal bases.
",Using arguments for making decisions: A possibilistic logic approach
"  We introduce a probabilistic formalism subsuming Markov random fields of
bounded tree width and probabilistic context free grammars. Our models are
based on a representation of Boolean formulas that we call case-factor diagrams
(CFDs). CFDs are similar to binary decision diagrams (BDDs) but are concise for
circuits of bounded tree width (unlike BDDs) and can concisely represent the
set of parse trees over a given string undera given context free grammar (also
unlike BDDs). A probabilistic model consists of aCFD defining a feasible set of
Boolean assignments and a weight (or cost) for each individual Boolean
variable. We give an insideoutside algorithm for simultaneously computing the
marginal of each Boolean variable, and a Viterbi algorithm for finding the
mininum cost variable assignment. Both algorithms run in time proportional to
the size of the CFD.
",Case-Factor Diagrams for Structured Probabilistic Modeling
"  Based on a recent development in the area of error control coding, we
introduce the notion of convolutional factor graphs (CFGs) as a new class of
probabilistic graphical models. In this context, the conventional factor graphs
are referred to as multiplicative factor graphs (MFGs). This paper shows that
CFGs are natural models for probability functions when summation of independent
latent random variables is involved. In particular, CFGs capture a large class
of linear models, where the linearity is in the sense that the observed
variables are obtained as a linear ransformation of the latent variables taking
arbitrary distributions. We use Gaussian models and independent factor models
as examples to emonstrate the use of CFGs. The requirement of a linear
transformation between latent variables (with certain independence restriction)
and the bserved variables, to an extent, limits the modelling flexibility of
CFGs. This structural restriction however provides a powerful analytic tool to
the framework of CFGs; that is, upon taking the Fourier transform of the
function represented by the CFG, the resulting function is represented by a FG
with identical structure. This Fourier transform duality allows inference
problems on a CFG to be solved on the corresponding dual MFG.
",Convolutional Factor Graphs as Probabilistic Models
"  As real-world Bayesian networks continue to grow larger and more complex, it
is important to investigate the possibilities for improving the performance of
existing algorithms of probabilistic inference. Motivated by examples, we
investigate the dependency of the performance of Lazy propagation on the
message computation algorithm. We show how Symbolic Probabilistic Inference
(SPI) and Arc-Reversal (AR) can be used for computation of clique to clique
messages in the addition to the traditional use of Variable Elimination (VE).
In addition, the paper resents the results of an empirical evaluation of the
performance of Lazy propagation using VE, SPI, and AR as the message
computation algorithm. The results of the empirical evaluation show that for
most networks, the performance of inference did not depend on the choice of
message computation algorithm, but for some randomly generated networks the
choice had an impact on both space and time performance. In the cases where the
choice had an impact, AR produced the best results.
",An Empirical Evaluation of Possible Variations of Lazy Propagation
"  Although many real-world stochastic planning problems are more naturally
formulated by hybrid models with both discrete and continuous variables,
current state-of-the-art methods cannot adequately address these problems. We
present the first framework that can exploit problem structure for modeling and
solving hybrid problems efficiently. We formulate these problems as hybrid
Markov decision processes (MDPs with continuous and discrete state and action
variables), which we assume can be represented in a factored way using a hybrid
dynamic Bayesian network (hybrid DBN). This formulation also allows us to apply
our methods to collaborative multiagent settings. We present a new linear
program approximation method that exploits the structure of the hybrid MDP and
lets us compute approximate value functions more efficiently. In particular, we
describe a new factored discretization of continuous variables that avoids the
exponential blow-up of traditional approaches. We provide theoretical bounds on
the quality of such an approximation and on its scale-up potential. We support
our theoretical arguments with experiments on a set of control problems with up
to 28-dimensional continuous state space and 22-dimensional action space.
",Solving Factored MDPs with Continuous and Discrete Variables
"  Maximum a Posteriori assignment (MAP) is the problem of finding the most
probable instantiation of a set of variables given the partial evidence on the
other variables in a Bayesian network. MAP has been shown to be a NP-hard
problem [22], even for constrained networks, such as polytrees [18]. Hence,
previous approaches often fail to yield any results for MAP problems in large
complex Bayesian networks. To address this problem, we propose AnnealedMAP
algorithm, a simulated annealing-based MAP algorithm. The AnnealedMAP algorithm
simulates a non-homogeneous Markov chain whose invariant function is a
probability density that concentrates itself on the modes of the target
density. We tested this algorithm on several real Bayesian networks. The
results show that, while maintaining good quality of the MAP solutions, the
AnnealedMAP algorithm is also able to solve many problems that are beyond the
reach of previous approaches.
",Annealed MAP
"  For many real-life Bayesian networks, common knowledge dictates that the
output established for the main variable of interest increases with higher
values for the observable variables. We define two concepts of monotonicity to
capture this type of knowledge. We say that a network is isotone in
distribution if the probability distribution computed for the output variable
given specific observations is stochastically dominated by any such
distribution given higher-ordered observations; a network is isotone in mode if
a probability distribution given higher observations has a higher mode. We show
that establishing whether a network exhibits any of these properties of
monotonicity is coNPPP-complete in general, and remains coNP-complete for
polytrees. We present an approximate algorithm for deciding whether a network
is monotone in distribution and illustrate its application to a real-life
network in oncology.
",Monotonicity in Bayesian Networks
"  We present a novel POMDP planning algorithm called heuristic search value
iteration (HSVI).HSVI is an anytime algorithm that returns a policy and a
provable bound on its regret with respect to the optimal policy. HSVI gets its
power by combining two well-known techniques: attention-focusing search
heuristics and piecewise linear convex representations of the value function.
HSVI's soundness and convergence have been proven. On some benchmark problems
from the literature, HSVI displays speedups of greater than 100 with respect to
other state-of-the-art POMDP value iteration algorithms. We also apply HSVI to
a new rover exploration problem 10 times larger than most POMDP problems in the
literature.
",Heuristic Search Value Iteration for POMDPs
"  We characterize probabilities in Bayesian networks in terms of algebraic
expressions called quasi-probabilities. These are arrived at by casting
Bayesian networks as noisy AND-OR-NOT networks, and viewing the subnetworks
that lead to a node as arguments for or against a node. Quasi-probabilities are
in a sense the ""natural"" algebra of Bayesian networks: we can easily compute
the marginal quasi-probability of any node recursively, in a compact form; and
we can obtain the joint quasi-probability of any set of nodes by multiplying
their marginals (using an idempotent product operator). Quasi-probabilities are
easily manipulated to improve the efficiency of probabilistic inference. They
also turn out to be representable as square-wave pulse trains, and joint and
marginal distributions can be computed by multiplication and complementation of
pulse trains.
",A New Characterization of Probabilities in Bayesian Networks
"  The sensitivities revealed by a sensitivity analysis of a probabilistic
network typically depend on the entered evidence. For a real-life network
therefore, the analysis is performed a number of times, with different
evidence. Although efficient algorithms for sensitivity analysis exist, a
complete analysis is often infeasible because of the large range of possible
combinations of observations. In this paper we present a method for studying
sensitivities that are invariant to the evidence entered. Our method builds
upon the idea of establishing bounds between which a parameter can be varied
without ever inducing a change in the most likely value of a variable of
interest.
",Evidence-invariant Sensitivity Bounds
"  We consider the problem of estimating the distribution underlying an observed
sample of data. Instead of maximum likelihood, which maximizes the probability
of the ob served values, we propose a different estimate, the high-profile
distribution, which maximizes the probability of the observed profile the
number of symbols appearing any given number of times. We determine the
high-profile distribution of several data samples, establish some of its
general properties, and show that when the number of distinct symbols observed
is small compared to the data size, the high-profile and maximum-likelihood
distributions are roughly the same, but when the number of symbols is large,
the distributions differ, and high-profile better explains the data.
",On Modeling Profiles instead of Values
"  A diagnostic policy specifies what test to perform next, based on the results
of previous tests, and when to stop and make a diagnosis. Cost-sensitive
diagnostic policies perform tradeoffs between (a) the cost of tests and (b) the
cost of misdiagnoses. An optimal diagnostic policy minimizes the expected total
cost. We formalize this diagnosis process as a Markov Decision Process (MDP).
We investigate two types of algorithms for solving this MDP: systematic search
based on AO* algorithm and greedy search (particularly the Value of Information
method). We investigate the issue of learning the MDP probabilities from
examples, but only as they are relevant to the search for good policies. We do
not learn nor assume a Bayesian network for the diagnosis process. Regularizers
are developed to control overfitting and speed up the search. This research is
the first that integrates overfitting prevention into systematic search. The
paper has two contributions: it discusses the factors that make systematic
search feasible for diagnosis, and it shows experimentally, on benchmark data
sets, that systematic search methods produce better diagnostic policies than
greedy methods.
",Learning Diagnostic Policies from Examples by Systematic Search
"  Mixtures of truncated exponentials (MTE) potentials are an alternative to
discretization for representing continuous chance variables in influence
diagrams. Also, MTE potentials can be used to approximate utility functions.
This paper introduces MTE influence diagrams, which can represent decision
problems without restrictions on the relationships between continuous and
discrete chance variables, without limitations on the distributions of
continuous chance variables, and without limitations on the nature of the
utility functions. In MTE influence diagrams, all probability distributions and
the joint utility function (or its multiplicative factors) are represented by
MTE potentials and decision nodes are assumed to have discrete state spaces.
MTE influence diagrams are solved by variable elimination using a fusion
algorithm.
",Hybrid Influence Diagrams Using Mixtures of Truncated Exponentials
"  Straightedge and compass construction problems are one of the oldest and most
challenging problems in elementary mathematics. The central challenge, for a
human or for a computer program, in solving construction problems is a huge
search space. In this paper we analyze one family of triangle construction
problems, aiming at detecting a small core of the underlying geometry
knowledge. The analysis leads to a small set of needed definitions, lemmas and
primitive construction steps, and consequently, to a simple algorithm for
automated solving of problems from this family. The same approach can be
applied to other families of construction problems.
",Towards Understanding Triangle Construction Problems
"  In this article we introduce the Arcade Learning Environment (ALE): both a
challenge problem and a platform and methodology for evaluating the development
of general, domain-independent AI technology. ALE provides an interface to
hundreds of Atari 2600 game environments, each one different, interesting, and
designed to be a challenge for human players. ALE presents significant research
challenges for reinforcement learning, model learning, model-based planning,
imitation learning, transfer learning, and intrinsic motivation. Most
importantly, it provides a rigorous testbed for evaluating and comparing
approaches to these problems. We illustrate the promise of ALE by developing
and benchmarking domain-independent agents designed using well-established AI
techniques for both reinforcement learning and planning. In doing so, we also
propose an evaluation methodology made possible by ALE, reporting empirical
results on over 55 different games. All of the software, including the
benchmark agents, is publicly available.
","The Arcade Learning Environment: An Evaluation Platform for General
  Agents"
"  Most merging operators are defined by semantics methods which have very high
computational complexity. In order to have operators with a lower computational
complexity, some merging operators defined in a syntactical way have be
proposed. In this work we define some syntactical merging operators and
exploring its rationality properties. To do that we constrain the belief bases
to be sets of formulas very close to logic programs and the underlying logic is
defined through forward chaining rule (Modus Ponens). We propose two types of
operators: arbitration operators when the inputs are only two bases and fusion
with integrity constraints operators. We introduce a set of postulates inspired
of postulates LS, proposed by Liberatore and Shaerf and then we analyzed the
first class of operators through these postulates. We also introduce a set of
postulates inspired of postulates KP, proposed by Konieczny and Pino P\'erez
and then we analyzed the second class of operators through these postulates.
","Exploring the rationality of some syntactic merging operators (extended
  version)"
"  The Direct Torque Control (DTC) is well known as an effective control
technique for high performance drives in a wide variety of industrial
applications and conventional DTC technique uses two constant reference value:
torque and stator flux. In this paper, fuzzy logic based stator flux
optimization technique for DTC drives that has been proposed. The proposed
fuzzy logic based stator flux optimizer self-regulates the stator flux
reference using induction motor load situation without need of any motor
parameters. Simulation studies have been carried out with Matlab/Simulink to
compare the proposed system behaviors at vary load conditions. Simulation
results show that the performance of the proposed DTC technique has been
improved and especially at low-load conditions torque ripple are greatly
reduced with respect to the conventional DTC.
",Stator flux optimization on direct torque control with fuzzy logic
"  Sequential decision problems are often approximately solvable by simulating
possible future action sequences. {\em Metalevel} decision procedures have been
developed for selecting {\em which} action sequences to simulate, based on
estimating the expected improvement in decision quality that would result from
any particular simulation; an example is the recent work on using bandit
algorithms to control Monte Carlo tree search in the game of Go. In this paper
we develop a theoretical basis for metalevel decisions in the statistical
framework of Bayesian {\em selection problems}, arguing (as others have done)
that this is more appropriate than the bandit framework. We derive a number of
basic results applicable to Monte Carlo selection problems, including the first
finite sampling bounds for optimal policies in certain cases; we also provide a
simple counterexample to the intuitive conjecture that an optimal policy will
necessarily reach a decision in all cases. We then derive heuristic
approximations in both Bayesian and distribution-free settings and demonstrate
their superiority to bandit-based heuristics in one-shot decision problems and
in Go.
",Selecting Computations: Theory and Applications
"  The rules of Sudoku are often specified using twenty seven
\texttt{all\_different} constraints, referred to as the {\em big} \mrules.
Using graphical proofs and exploratory logic programming, the following main
and new result is obtained: many subsets of six of these big \mrules are
redundant (i.e., they are entailed by the remaining twenty one \mrules), and
six is maximal (i.e., removing more than six \mrules is not possible while
maintaining equivalence). The corresponding result for binary inequality
constraints, referred to as the {\em small} \mrules, is stated as a conjecture.
",Redundant Sudoku Rules
"  A recently identified problem is that of finding an optimal investment plan
for a transportation network, given that a disaster such as an earthquake may
destroy links in the network. The aim is to strengthen key links to preserve
the expected network connectivity. A network based on the Istanbul highway
system has thirty links and therefore a billion scenarios, but it has been
estimated that sampling a million scenarios gives reasonable accuracy. In this
paper we use symmetry reasoning to reduce the number of scenarios to a much
smaller number, making sampling unnecessary. This result can be used to
facilitate metaheuristic and exact approaches to the problem.
",Earthquake Scenario Reduction by Symmetry Reasoning
"  There is increasing awareness in the planning community that depending on
complete models impedes the applicability of planning technology in many real
world domains where the burden of specifying complete domain models is too
high. In this paper, we consider a novel solution for this challenge that
combines generative planning on incomplete domain models with a library of plan
cases that are known to be correct. While this was arguably the original
motivation for case-based planning, most existing case-based planners assume
(and depend on) from-scratch planners that work on complete domain models. In
contrast, our approach views the plan generated with respect to the incomplete
model as a ""skeletal plan"" and augments it with directed mining of plan
fragments from library cases. We will present the details of our approach and
present an empirical evaluation of our method in comparison to a
state-of-the-art case-based planner that depends on complete domain models.
",Model-Lite Case-Based Planning
"  Gas Transmission Networks are large-scale complex systems, and corresponding
design and control problems are challenging. In this paper, we consider the
problem of control and management of these systems in crisis situations. We
present these networks by a hybrid systems framework that provides required
analysis models. Further, we discuss decision-making using computational
discrete and hybrid optimization methods. In particular, several reinforcement
learning methods are employed to explore decision space and achieve the best
policy in a specific crisis situation. Simulations are presented to illustrate
the efficiency of the method.
",Hybrid systems modeling for gas transmission network
"  Fuzzy rule based classification systems are one of the most popular fuzzy
modeling systems used in pattern classification problems. This paper
investigates the effect of applying nine different T-norms in fuzzy rule based
classification systems. In the recent researches, fuzzy versions of confidence
and support merits from the field of data mining have been widely used for both
rules selecting and weighting in the construction of fuzzy rule based
classification systems. For calculating these merits the product has been
usually used as a T-norm. In this paper different T-norms have been used for
calculating the confidence and support measures. Therefore, the calculations in
rule selection and rule weighting steps (in the process of constructing the
fuzzy rule based classification systems) are modified by employing these
T-norms. Consequently, these changes in calculation results in altering the
overall accuracy of rule based classification systems. Experimental results
obtained on some well-known data sets show that the best performance is
produced by employing the Aczel-Alsina operator in terms of the classification
accuracy, the second best operator is Dubois-Prade and the third best operator
is Dombi. In experiments, we have used 12 data sets with numerical attributes
from the University of California, Irvine machine learning repository (UCI).
",Comparison of different T-norm operators in classification problems
"  This paper presents a novel fuzzy logic based Adaptive Super-twisting Sliding
Mode Controller for the control of dynamic uncertain systems. The proposed
controller combines the advantages of Second order Sliding Mode Control, Fuzzy
Logic Control and Adaptive Control. The reaching conditions, stability and
robustness of the system with the proposed controller are guaranteed. In
addition, the proposed controller is well suited for simple design and
implementation. The effectiveness of the proposed controller over the first
order Sliding Mode Fuzzy Logic controller is illustrated by Matlab based
simulations performed on a DC-DC Buck converter. Based on this comparison, the
proposed controller is shown to obtain the desired transient response without
causing chattering and error under steady-state conditions. The proposed
controller is able to give robust performance in terms of rejection to input
voltage variations and load variations.
","A Novel Fuzzy Logic Based Adaptive Supertwisting Sliding Mode Control
  Algorithm for Dynamic Uncertain Systems"
"  This paper deals with the implementation of Least Mean Square (LMS) algorithm
in Decision Feedback Equalizer (DFE) for removal of Inter Symbol Interference
(ISI) at the receiver. The channel disrupts the transmitted signal by spreading
it in time. Although, the LMS algorithm is robust and reliable, it is slow in
convergence. In order to increase the speed of convergence, modifications have
been made in the algorithm where the weights get updated depending on the
severity of disturbance.
",Elimination of ISI Using Improved LMS Based Decision Feedback Equalizer
"  The early classifications of the computational complexity of planning under
various restrictions in STRIPS (Bylander) and SAS+ (Baeckstroem and Nebel) have
influenced following research in planning in many ways. We go back and
reanalyse their subclasses, but this time using the more modern tool of
parameterized complexity analysis. This provides new results that together with
the old results give a more detailed picture of the complexity landscape. We
demonstrate separation results not possible with standard complexity theory,
which contributes to explaining why certain cases of planning have seemed
simpler in practice than theory has predicted. In particular, we show that
certain restrictions of practical interest are tractable in the parameterized
sense of the term, and that a simple heuristic is sufficient to make a
well-known partial-order planner exploit this fact.
",The Complexity of Planning Revisited - A Parameterized Analysis
"  Cumulative resource constraints can model scarce resources in scheduling
problems or a dimension in packing and cutting problems. In order to
efficiently solve such problems with a constraint programming solver, it is
important to have strong and fast propagators for cumulative resource
constraints. One such propagator is the recently developed
time-table-edge-finding propagator, which considers the current resource
profile during the edge-finding propagation. Recently, lazy clause generation
solvers, i.e. constraint programming solvers incorporating nogood learning,
have proved to be excellent at solving scheduling and cutting problems. For
such solvers, concise and accurate explanations of the reasons for propagation
are essential for strong nogood learning. In this paper, we develop the first
explaining version of time-table-edge-finding propagation and show preliminary
results on resource-constrained project scheduling problems from various
standard benchmark suites. On the standard benchmark suite PSPLib, we were able
to close one open instance and to improve the lower bound of about 60% of the
remaining open instances. Moreover, 6 of those instances were closed.
","Explaining Time-Table-Edge-Finding Propagation for the Cumulative
  Resource Constraint"
"  In the field of ontology matching, the most systematic evaluation of matching
systems is established by the Ontology Alignment Evaluation Initiative (OAEI),
which is an annual campaign for evaluating ontology matching systems organized
by different groups of researchers. In this paper, we report on the results of
an intermediary OAEI campaign called OAEI 2011.5. The evaluations of this
campaign are divided in five tracks. Three of these tracks are new or have been
improved compared to previous OAEI campaigns. Overall, we evaluated 18 matching
systems. We discuss lessons learned, in terms of scalability, multilingual
issues and the ability do deal with real world cases from different domains.
","Evaluating Ontology Matching Systems on Large, Multilingual and
  Real-world Test Cases"
"  Today, we can find many search engines which provide us with information
which is more operational in nature. None of the search engines provide domain
specific information. This becomes very troublesome to a novice user who wishes
to have information in a particular domain. In this paper, we have developed an
ontology which can be used by a domain specific search engine. We have
developed an ontology on human anatomy, which captures information regarding
cardiovascular system, digestive system, skeleton and nervous system. This
information can be used by people working in medical and health care domain.
",OntoAna: Domain Ontology for Human Anatomy
"  Various methods for lifted probabilistic inference have been proposed, but
our understanding of these methods and the relationships between them is still
limited, compared to their propositional counterparts. The only existing
theoretical characterization of lifting is for weighted first-order model
counting (WFOMC), which was shown to be complete domain-lifted for the class of
2-logvar models. This paper makes two contributions to lifted variable
elimination (LVE). First, we introduce a novel inference operator called group
inversion. Second, we prove that LVE augmented with this operator is complete
in the same sense as WFOMC.
",Lifted Variable Elimination: A Novel Operator and Completeness Results
"  The Generalized Traveling Salesman Problem (GTSP) is one of the NP-hard
combinatorial optimization problems. A variant of GTSP is E-GTSP where E,
meaning equality, has the constraint: exactly one node from a cluster of a
graph partition is visited. The main objective of the E-GTSP is to find a
minimum cost tour passing through exactly one node from each cluster of an
undirected graph. Agent-based approaches involving are successfully used
nowadays for solving real life complex problems. The aim of the current paper
is to illustrate some variants of agent-based algorithms including ant-based
models with specific properties for solving E-GTSP.
","A Unifying Survey of Reinforced, Sensitive and Stigmergic Agent-Based
  Approaches for E-GTSP"
"  The current paper introduces a new parallel computing technique based on ant
colony optimization for a dynamic routing problem. In the dynamic traveling
salesman problem the distances between cities as travel times are no longer
fixed. The new technique uses a parallel model for a problem variant that
allows a slight movement of nodes within their Neighborhoods. The algorithm is
tested with success on several large data sets.
",Parallel ACO with a Ring Neighborhood for Dynamic TSP
"  This is the Proceedings of the Twenty-Fourth Conference on Uncertainty in
Artificial Intelligence, which was held in Helsinki, Finland, July 9 - 12 2008.
","Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial
  Intelligence (2008)"
"  This is the Proceedings of the Twenty-Third Conference on Uncertainty in
Artificial Intelligence, which was held in Vancouver, British Columbia, July 19
- 22 2007.
","Proceedings of the Twenty-Third Conference on Uncertainty in Artificial
  Intelligence (2007)"
"  This is the Proceedings of the Twenty-First Conference on Uncertainty in
Artificial Intelligence, which was held in Edinburgh, Scotland July 26 - 29
2005.
","Proceedings of the Twenty-First Conference on Uncertainty in Artificial
  Intelligence (2005)"
"  This is the Proceedings of the Twenty-Second Conference on Uncertainty in
Artificial Intelligence, which was held in Cambridge, MA, July 13 - 16 2006.
","Proceedings of the Twenty-Second Conference on Uncertainty in Artificial
  Intelligence (2006)"
"  This is the Proceedings of the Twentieth Conference on Uncertainty in
Artificial Intelligence, which was held in Banff, Canada, July 7 - 11 2004.
","Proceedings of the Twentieth Conference on Uncertainty in Artificial
  Intelligence (2004)"
"  In this paper it is introduced a biobjective ant algorithm for constructing
low cost routing networks. The new algorithm is called the Distributed Pharaoh
System (DPS). DPS is based on AntNet algorithm. The algorithm is using Pharaoh
Ant System (PAS) with an extra-exploration phase and a 'no-entry' condition in
order to improve the solutions for the Low Cost Network Routing problem.
Additionally it is used a cost model for overlay network construction that
includes network traffic demands. The Pharaoh ants (Monomorium pharaonis)
includes negative pheromones with signals concentrated at decision points where
trails fork. The negative pheromones may complement positive pheromone or could
help ants to escape from an unnecessarily long route to food that is being
reinforced by attractive signals. Numerical experiments were made for a random
10-node network. The average node degree of the network tested was 4.0. The
results are encouraging. The algorithm converges to the shortest path while
converging on a low cost overlay routing network topology.
",Distributed Pharaoh System for Network Routing
"  The Matrix Bandwidth Minimization Problem (MBMP) seeks for a simultaneous
reordering of the rows and the columns of a square matrix such that the nonzero
entries are collected within a band of small width close to the main diagonal.
The MBMP is a NP-complete problem, with applications in many scientific
domains, linear systems, artificial intelligence, and real-life situations in
industry, logistics, information recovery. The complex problems are hard to
solve, that is why any attempt to improve their solutions is beneficent.
Genetic algorithms and ant-based systems are Soft Computing methods used in
this paper in order to solve some MBMP instances. Our approach is based on a
learning agent-based model involving a local search procedure. The algorithm is
compared with the classical Cuthill-McKee algorithm, and with a hybrid genetic
algorithm, using several instances from Matrix Market collection. Computational
experiments confirm a good performance of the proposed algorithms for the
considered set of MBMP instances. On Soft Computing basis, we also propose a
new theoretical Reinforcement Learning model for solving the MBMP problem.
",Soft Computing approaches on the Bandwidth Problem
"  Network intrusion detection systems have become a crucial issue for computer
systems security infrastructures. Different methods and algorithms are
developed and proposed in recent years to improve intrusion detection systems.
The most important issue in current systems is that they are poor at detecting
novel anomaly attacks. These kinds of attacks refer to any action that
significantly deviates from the normal behaviour which is considered intrusion.
This paper proposed a model to improve this problem based on data mining
techniques. Apriori algorithm is used to predict novel attacks and generate
real-time rules for firewall. Apriori algorithm extracts interesting
correlation relationships among large set of data items. This paper illustrates
how to use Apriori algorithm in intrusion detection systems to cerate a
automatic firewall rules generator to detect novel anomaly attack. Apriori is
the best-known algorithm to mine association rules. This is an innovative way
to find association rules on large scale.
","Automatic firewall rules generator for anomaly detection systems with
  Apriori algorithm"
"  Two-dimensional bin packing problems are highly relevant combinatorial
optimization problems. They find a large number of applications, for example,
in the context of transportation or warehousing, and for the cutting of
different materials such as glass, wood or metal. In this work we deal with the
oriented two-dimensional bin packing problem under free guillotine cutting. In
this specific problem a set of oriented rectangular items is given which must
be packed into a minimum number of bins of equal size. The first algorithm
proposed in this work is a randomized multi-start version of a constructive
one-pass heuristic from the literature. Additionally we propose the use of this
randomized one-pass heuristic within an evolutionary algorithm. The results of
the two proposed algorithms are compared to the best approaches from the
literature. In particular the evolutionary algorithm compares very favorably to
current state-of-the-art approaches. The optimal solution for 4 previously
unsolved instances could be found.
","On Solving the Oriented Two-Dimensional Bin Packing Problem under Free
  Guillotine Cutting: Exploiting the Power of Probabilistic Solution
  Construction"
"  Modern ontology debugging methods allow efficient identification and
localization of faulty axioms defined by a user while developing an ontology.
The ontology development process in this case is characterized by rather
frequent and regular calls to a reasoner resulting in an early user awareness
of modeling errors. In such a scenario an ontology usually includes only a
small number of conflict sets, i.e. sets of axioms preserving the faults. This
property allows efficient use of standard model-based diagnosis techniques
based on the application of hitting set algorithms to a number of given
conflict sets. However, in many use cases such as ontology alignment the
ontologies might include many more conflict sets than in usual ontology
development settings, thus making precomputation of conflict sets and
consequently ontology diagnosis infeasible. In this paper we suggest a
debugging approach based on a direct computation of diagnoses that omits
calculation of conflict sets. Embedded in an ontology debugger, the proposed
algorithm is able to identify diagnoses for an ontology which includes a large
number of faults and for which application of standard diagnosis methods fails.
The evaluation results show that the approach is practicable and is able to
identify a fault in adequate time.
",Direct computation of diagnoses for ontology debugging
"  The matrices and their sub-blocks are introduced into the study of
determining various extensions in the sense of Dung's theory of argumentation
frameworks. It is showed that each argumentation framework has its matrix
representations, and the core semantics defined by Dung can be characterized by
specific sub-blocks of the matrix. Furthermore, the elementary permutations of
a matrix are employed by which an efficient matrix approach for finding out all
extensions under a given semantics is obtained. Different from several
established approaches, such as the graph labelling algorithm, Constraint
Satisfaction Problem algorithm, the matrix approach not only put the mathematic
idea into the investigation for finding out various extensions, but also
completely achieve the goal to compute all the extensions needed.
",A matrix approach for computing extensions of argumentation frameworks
"  The aim of this paper is to develop a methodology that is useful for
analysing from a microeconomic perspective the incentives to entry, permanence
and exit in the market for pharmaceutical generics under fuzzy conditions. In
an empirical application of our proposed methodology, the potential towards
permanence of labs with different characteristics has been estimated. The case
we deal with is set in an open market where global players diversify into
different national markets of pharmaceutical generics. Risk issues are
significantly important in deterring decision makers from expanding in the
generic pharmaceutical business. However, not all players are affected in the
same way and/or to the same extent. Small, non-diversified generics labs are in
the worse position. We have highlighted that the expected NPV and the number of
generics in the portfolio of a pharmaceutical lab are important variables, but
that it is also important to consider the degree of diversification. Labs with
a higher potential for diversification across markets have an advantage over
smaller labs. We have described a fuzzy decision support system based on the
Mamdani model in order to determine the incentives for a laboratory to remain
in the market both when it is stable and when it is growing.
","On firm specific characteristics of pharmaceutical generics and
  incentives to permanence under fuzzy conditions"
"  Several variants of the Constraint Satisfaction Problem have been proposed
and investigated in the literature for modelling those scenarios where
solutions are associated with some given costs. Within these frameworks
computing an optimal solution is an NP-hard problem in general; yet, when
restricted over classes of instances whose constraint interactions can be
modelled via (nearly-)acyclic graphs, this problem is known to be solvable in
polynomial time. In this paper, larger classes of tractable instances are
singled out, by discussing solution approaches based on exploiting hypergraph
acyclicity and, more generally, structural decomposition methods, such as
(hyper)tree decompositions.
","Tractable Optimization Problems through Hypergraph-Based Structural
  Restrictions"
"  Efficient ontology debugging is a cornerstone for many activities in the
context of the Semantic Web, especially when automatic tools produce (parts of)
ontologies such as in the field of ontology matching. The best currently known
interactive debugging systems rely upon some meta information in terms of fault
probabilities, which can speed up the debugging procedure in the good case, but
can also have negative impact on the performance in the bad case. The problem
is that assessment of the meta information is only possible a-posteriori.
Consequently, as long as the actual fault is unknown, there is always some risk
of suboptimal interactive diagnoses discrimination. As an alternative, one
might prefer to rely on a tool which pursues a no-risk strategy. In this case,
however, possibly well-chosen meta information cannot be exploited, resulting
again in inefficient debugging actions. In this work we present a reinforcement
learning strategy that continuously adapts its behavior depending on the
performance achieved and minimizes the risk of using low-quality meta
information. Therefore, this method is suitable for application scenarios where
reliable a-priori fault estimates are difficult to obtain. Using problematic
ontologies in the field of ontology matching, we show that the proposed
risk-aware query strategy outperforms both active learning approaches and
no-risk strategies on average in terms of required amount of user interaction.
",RIO: Minimizing User Interaction in Ontology Debugging
"  In Programming by Example, a system attempts to infer a program from input
and output examples, generally by searching for a composition of certain base
functions. Performing a naive brute force search is infeasible for even mildly
involved tasks. We note that the examples themselves often present clues as to
which functions to compose, and how to rank the resulting programs. In text
processing, which is our domain of interest, clues arise from simple textual
features: for example, if parts of the input and output strings are
permutations of one another, this suggests that sorting may be useful. We
describe a system that learns the reliability of such clues, allowing for
faster search and a principled ranking over programs. Experiments on a
prototype of this system show that this learning scheme facilitates efficient
inference on a range of text processing tasks.
",Textual Features for Programming by Example
"  Knowledge representation (KR) and inference mechanism are most desirable
thing to make the system intelligent. System is known to an intelligent if its
intelligence is equivalent to the intelligence of human being for a particular
domain or general. Because of incomplete ambiguous and uncertain information
the task of making intelligent system is very difficult. The objective of this
paper is to present the hybrid KR technique for making the system effective &
Optimistic. The requirement for (effective & optimistic) is because the system
must be able to reply the answer with a confidence of some factor. This paper
also presents the comparison between various hybrid KR techniques with the
proposed one.
","Hybrid technique for effective knowledge representation & a comparative
  study"
"  Existing theoretical universal algorithmic intelligence models are not
practically realizable. More pragmatic approach to artificial general
intelligence is based on cognitive architectures, which are, however,
non-universal in sense that they can construct and use models of the
environment only from Turing-incomplete model spaces. We believe that the way
to the real AGI consists in bridging the gap between these two approaches. This
is possible if one considers cognitive functions as a ""cognitive bias"" (priors
and search heuristics) that should be incorporated into the models of universal
algorithmic intelligence without violating their universality. Earlier reported
results suiting this approach and its overall feasibility are discussed on the
example of perception, planning, knowledge representation, attention, theory of
mind, language, and some others.
",Cognitive Bias for Universal Algorithmic Intelligence
"  The paper presents a comparison of various soft computing techniques used for
filtering and enhancing speech signals. The three major techniques that fall
under soft computing are neural networks, fuzzy systems and genetic algorithms.
Other hybrid techniques such as neuro-fuzzy systems are also available. In
general, soft computing techniques have been experimentally observed to give
far superior performance as compared to non-soft computing techniques in terms
of robustness and accuracy.
",Speech Signal Filters based on Soft Computing Techniques: A Comparison
"  This paper discusses the merits and demerits of crisp logic and fuzzy logic
with respect to their applicability in intelligent response generation by a
human being and by a robot. Intelligent systems must have the capability of
taking decisions that are wise and handle situations intelligently. A direct
relationship exists between the level of perfection in handling a situation and
the level of completeness of the available knowledge or information or data
required to handle the situation. The paper concludes that the use of crisp
logic with complete knowledge leads to perfection in handling situations
whereas fuzzy logic can handle situations imperfectly only. However, in the
light of availability of incomplete knowledge fuzzy theory is more effective
but may be disadvantageous as compared to crisp logic.
","Applicability of Crisp and Fuzzy Logic in Intelligent Response
  Generation"
"  For the past few decades, man has been trying to create an intelligent
computer which can talk and respond like he can. The task of creating a system
that can talk like a human being is the primary objective of Automatic Speech
Recognition. Various Speech Recognition techniques have been developed in
theory and have been applied in practice. This paper discusses the problems
that have been encountered in developing Speech Recognition, the techniques
that have been applied to automate the task, and a representation of the core
problems of present day Speech Recognition by using Fuzzy Mathematics.
","Application of Fuzzy Mathematics to Speech-to-Text Conversion by
  Elimination of Paralinguistic Content"
"  A definition of Artificial Intelligence was proposed in [1] but this
definition was not absolutely formal at least because the word ""Human"" was
used. In this paper we will formalize the definition from [1]. The biggest
problem in this definition was that the level of intelligence of AI is compared
to the intelligence of a human being. In order to change this we will introduce
some parameters to which AI will depend. One of this parameters will be the
level of intelligence and we will define one AI to each level of intelligence.
We assume that for some level of intelligence the respective AI will be more
intelligent than a human being. Nevertheless, we cannot say which is this level
because we cannot calculate its exact value.
",Formal Definition of AI
"  The theory of rough sets is concerned with the lower and upper approximations
of objects through a binary relation on a universe. It has been applied to
machine learning, knowledge discovery and data mining. The theory of matroids
is a generalization of linear independence in vector spaces. It has been used
in combinatorial optimization and algorithm design. In order to take advantages
of both rough sets and matroids, in this paper we propose a matroidal structure
of rough sets based on a serial and transitive relation on a universe. We
define the family of all minimal neighborhoods of a relation on a universe, and
prove it satisfy the circuit axioms of matroids when the relation is serial and
transitive. In order to further study this matroidal structure, we investigate
the inverse of this construction: inducing a relation by a matroid. The
relationships between the upper approximation operators of rough sets based on
relations and the closure operators of matroids in the above two constructions
are studied. Moreover, we investigate the connections between the above two
constructions.
","Matroidal structure of rough sets based on serial and transitive
  relations"
"  In this paper, we propose a new type of matroids, namely covering matroids,
and investigate the connections with the second type of covering-based rough
sets and some existing special matroids. Firstly, as an extension of
partitions, coverings are more natural combinatorial objects and can sometimes
be more efficient to deal with problems in the real world. Through extending
partitions to coverings, we propose a new type of matroids called covering
matroids and prove them to be an extension of partition matroids. Secondly,
since some researchers have successfully applied partition matroids to
classical rough sets, we study the relationships between covering matroids and
covering-based rough sets which are an extension of classical rough sets.
Thirdly, in matroid theory, there are many special matroids, such as
transversal matroids, partition matroids, 2-circuit matroid and
partition-circuit matroids. The relationships among several special matroids
and covering matroids are studied.
",Covering matroid
"  Recently, the relationship between matroids and generalized rough sets based
on relations has been studied from the viewpoint of linear independence of
matrices. In this paper, we reveal more relationships by the predecessor and
successor neighborhoods from relations. First, through these two neighborhoods,
we propose a pair of matroids, namely predecessor relation matroid and
successor relation matroid, respectively. Basic characteristics of this pair of
matroids, such as dependent sets, circuits, the rank function and the closure
operator, are described by the predecessor and successor neighborhoods from
relations. Second, we induce a relation from a matroid through the circuits of
the matroid. We prove that the induced relation is always an equivalence
relation. With these two inductions, a relation induces a relation matroid, and
the relation matroid induces an equivalence relation, then the connection
between the original relation and the induced equivalence relation is studied.
Moreover, the relationships between the upper approximation operator in
generalized rough sets and the closure operator in matroids are investigated.
","Relation matroid and its relationship with generalized rough set based
  on relation"
"  Rough sets are efficient for data pre-process in data mining. Lower and upper
approximations are two core concepts of rough sets. This paper studies
generalized rough sets based on symmetric and transitive relations from the
operator-oriented view by matroidal approaches. We firstly construct a
matroidal structure of generalized rough sets based on symmetric and transitive
relations, and provide an approach to study the matroid induced by a symmetric
and transitive relation. Secondly, this paper establishes a close relationship
between matroids and generalized rough sets. Approximation quality and
roughness of generalized rough sets can be computed by the circuit of matroid
theory. At last, a symmetric and transitive relation can be constructed by a
matroid with some special properties.
","Matroidal structure of generalized rough sets based on symmetric and
  transitive relations"
"  At present, practical application and theoretical discussion of rough sets
are two hot problems in computer science. The core concepts of rough set theory
are upper and lower approximation operators based on equivalence relations.
Matroid, as a branch of mathematics, is a structure that generalizes linear
independence in vector spaces. Further, matroid theory borrows extensively from
the terminology of linear algebra and graph theory. We can combine rough set
theory with matroid theory through using rough sets to study some
characteristics of matroids. In this paper, we apply rough sets to matroids
through defining a family of sets which are constructed from the upper
approximation operator with respect to an equivalence relation. First, we prove
the family of sets satisfies the support set axioms of matroids, and then we
obtain a matroid. We say the matroids induced by the equivalence relation and a
type of matroid, namely support matroid, is induced. Second, through rough
sets, some characteristics of matroids such as independent sets, support sets,
bases, hyperplanes and closed sets are investigated.
",Some characteristics of matroids through rough sets
"  Neighborhood is an important concept in covering based rough sets. That under
what condition neighborhoods form a partition is a meaningful issue induced by
this concept. Many scholars have paid attention to this issue and presented
some necessary and sufficient conditions. However, there exists one common
trait among these conditions, that is they are established on the basis of all
neighborhoods have been obtained. In this paper, we provide a necessary and
sufficient condition directly based on the covering itself. First, we
investigate the influence of that there are reducible elements in the covering
on neighborhoods. Second, we propose the definition of uniform block and obtain
a sufficient condition from it. Third, we propose the definitions of repeat
degree and excluded number. By means of the two concepts, we obtain a necessary
and sufficient condition for neighborhoods to form a partition. In a word, we
have gained a deeper and more direct understanding of the essence over that
neighborhoods form a partition.
","Condition for neighborhoods in covering based rough sets to form a
  partition"
"  Rough sets are efficient for data pre-processing in data mining. As a
generalization of the linear independence in vector spaces, matroids provide
well-established platforms for greedy algorithms. In this paper, we apply rough
sets to matroids and study the contraction of the dual of the corresponding
matroid. First, for an equivalence relation on a universe, a matroidal
structure of the rough set is established through the lower approximation
operator. Second, the dual of the matroid and its properties such as
independent sets, bases and rank function are investigated. Finally, the
relationships between the contraction of the dual matroid to the complement of
a single point set and the contraction of the dual matroid to the complement of
the equivalence class of this point are studied.
",Rough sets and matroidal contraction
"  It is a meaningful issue that under what condition neighborhoods induced by a
covering are equal to the covering itself. A necessary and sufficient condition
for this issue has been provided by some scholars. In this paper, through a
counter-example, we firstly point out the necessary and sufficient condition is
false. Second, we present a necessary and sufficient condition for this issue.
Third, we concentrate on the inverse issue of computing neighborhoods by a
covering, namely giving an arbitrary covering, whether or not there exists
another covering such that the neighborhoods induced by it is just the former
covering. We present a necessary and sufficient condition for this issue as
well. In a word, through the study on the two fundamental issues induced by
neighborhoods, we have gained a deeper understanding of the relationship
between neighborhoods and the covering which induce the neighborhoods.
","Condition for neighborhoods induced by a covering to be equal to the
  covering itself"
"  Rough sets are efficient for data pre-processing in data mining. Matroids are
based on linear algebra and graph theory, and have a variety of applications in
many fields. Both rough sets and matroids are closely related to lattices. For
a serial and transitive relation on a universe, the collection of all the
regular sets of the generalized rough set is a lattice. In this paper, we use
the lattice to construct a matroid and then study relationships between the
lattice and the closed-set lattice of the matroid. First, the collection of all
the regular sets based on a serial and transitive relation is proved to be a
semimodular lattice. Then, a matroid is constructed through the height function
of the semimodular lattice. Finally, we propose an approach to obtain all the
closed sets of the matroid from the semimodular lattice. Borrowing from
matroids, results show that lattice theory provides an interesting view to
investigate rough sets.
","Closed-set lattice of regular sets based on a serial and transitive
  relation through matroids"
"  Covering is a common type of data structure and covering-based rough set
theory is an efficient tool to process this data. Lattice is an important
algebraic structure and used extensively in investigating some types of
generalized rough sets. In this paper, we propose two family of sets and study
the conditions that these two sets become some lattice structures. These two
sets are consisted by the fixed point of the lower approximations of the first
type and the sixth type of covering-based rough sets, respectively. These two
sets are called the fixed point set of neighborhoods and the fixed point set of
covering, respectively. First, for any covering, the fixed point set of
neighborhoods is a complete and distributive lattice, at the same time, it is
also a double p-algebra. Especially, when the neighborhood forms a partition of
the universe, the fixed point set of neighborhoods is both a boolean lattice
and a double Stone algebra. Second, for any covering, the fixed point set of
covering is a complete lattice.When the covering is unary, the fixed point set
of covering becomes a distributive lattice and a double p-algebra. a
distributive lattice and a double p-algebra when the covering is unary.
Especially, when the reduction of the covering forms a partition of the
universe, the fixed point set of covering is both a boolean lattice and a
double Stone algebra.
","Lattice structures of fixed points of the lower approximations of two
  types of covering-based rough sets"
"  Taaable is a case-based reasoning system that adapts cooking recipes to user
constraints. Within it, the preparation part of recipes is formalised as a
graph. This graph is a semantic representation of the sequence of instructions
composing the cooking process and is used to compute the procedure adaptation,
conjointly with the textual adaptation. It is composed of cooking actions and
ingredients, among others, represented as vertices, and semantic relations
between those, shown as arcs, and is built automatically thanks to natural
language processing. The results of the automatic annotation process is often a
disconnected graph, representing an incomplete annotation, or may contain
errors. Therefore, a validating and correcting step is required. In this paper,
we present an existing graphic tool named \kcatos, conceived for representing
and editing decision trees, and show how it has been adapted and integrated in
WikiTaaable, the semantic wiki in which the knowledge used by Taaable is
stored. This interface provides the wiki users with a way to correct the case
representation of the cooking process, improving at the same time the quality
of the knowledge about cooking procedures stored in WikiTaaable.
","Semi-automatic annotation process for procedural texts: An application
  on cooking recipes"
"  Efficient Natural Evolution Strategies (eNES) is a novel alternative to
conventional evolutionary algorithms, using the natural gradient to adapt the
mutation distribution. Unlike previous methods based on natural gradients, eNES
uses a fast algorithm to calculate the inverse of the exact Fisher information
matrix, thus increasing both robustness and performance of its evolution
gradient estimation, even in higher dimensions. Additional novel aspects of
eNES include optimal fitness baselines and importance mixing (a procedure for
updating the population with very few fitness evaluations). The algorithm
yields competitive results on both unimodal and multimodal benchmarks.
",Efficient Natural Evolution Strategies
"  This paper assumes the hypothesis that human learning is perception based,
and consequently, the learning process and perceptions should not be
represented and investigated independently or modeled in different simulation
spaces. In order to keep the analogy between the artificial and human learning,
the former is assumed here as being based on the artificial perception. Hence,
instead of choosing to apply or develop a Computational Theory of (human)
Perceptions, we choose to mirror the human perceptions in a numeric
(computational) space as artificial perceptions and to analyze the
interdependence between artificial learning and artificial perception in the
same numeric space, using one of the simplest tools of Artificial Intelligence
and Soft Computing, namely the perceptrons. As practical applications, we
choose to work around two examples: Optical Character Recognition and Iris
Recognition. In both cases a simple Turing test shows that artificial
perceptions of the difference between two characters and between two irides are
fuzzy, whereas the corresponding human perceptions are, in fact, crisp.
","Examples of Artificial Perceptions in Optical Character Recognition and
  Iris Recognition"
"  In E-learning, there is still the problem of knowing how to ensure an
individualized and continuous learner's follow-up during learning process,
indeed among the numerous tools proposed, very few systems concentrate on a
real time learner's follow-up. Our work in this field develops the design and
implementation of a Multi-Agents System Based on Dynamic Case Based Reasoning
which can initiate learning and provide an individualized follow-up of learner.
When interacting with the platform, every learner leaves his/her traces in the
machine. These traces are stored in a basis under the form of scenarios which
enrich collective past experience. The system monitors, compares and analyses
these traces to keep a constant intelligent watch and therefore detect
difficulties hindering progress and/or avoid possible dropping out. The system
can support any learning subject. The success of a case-based reasoning system
depends critically on the performance of the retrieval step used and, more
specifically, on similarity measure used to retrieve scenarios that are similar
to the course of the learner (traces in progress). We propose a complementary
similarity measure, named Inverse Longest Common Sub-Sequence (ILCSS). To help
and guide the learner, the system is equipped with combined virtual and human
tutors.
","Multi-Agents Dynamic Case Based Reasoning and The Inverse Longest Common
  Sub-Sequence And Individualized Follow-up of Learners in The CEHL"
"  Covering-based rough set theory is a useful tool to deal with inexact,
uncertain or vague knowledge in information systems. Topology, one of the most
important subjects in mathematics, provides mathematical tools and interesting
topics in studying information systems and rough sets. In this paper, we
present the topological characterizations to three types of covering
approximation operators. First, we study the properties of topology induced by
the sixth type of covering lower approximation operator. Second, some
topological characterizations to the covering lower approximation operator to
be an interior operator are established. We find that the topologies induced by
this operator and by the sixth type of covering lower approximation operator
are the same. Third, we study the conditions which make the first type of
covering upper approximation operator be a closure operator, and find that the
topology induced by the operator is the same as the topology induced by the
fifth type of covering upper approximation operator. Forth, the conditions of
the second type of covering upper approximation operator to be a closure
operator and the properties of topology induced by it are established. Finally,
these three topologies space are compared. In a word, topology provides a
useful method to study the covering-based rough sets.
","Topological characterizations to three types of covering approximation
  operators"
"  Covering-based rough set theory is a useful tool to deal with inexact,
uncertain or vague knowledge in information systems. Geometric lattice has
widely used in diverse fields, especially search algorithm design which plays
important role in covering reductions. In this paper, we construct four
geometric lattice structures of covering-based rough sets through matroids, and
compare their relationships. First, a geometric lattice structure of
covering-based rough sets is established through the transversal matroid
induced by the covering, and its characteristics including atoms, modular
elements and modular pairs are studied. We also construct a one-to-one
correspondence between this type of geometric lattices and transversal matroids
in the context of covering-based rough sets. Second, sufficient and necessary
conditions for three types of covering upper approximation operators to be
closure operators of matroids are presented. We exhibit three types of matroids
through closure axioms, and then obtain three geometric lattice structures of
covering-based rough sets. Third, these four geometric lattice structures are
compared. Some core concepts such as reducible elements in covering-based rough
sets are investigated with geometric lattices. In a word, this work points out
an interesting view, namely geometric lattice, to study covering-based rough
sets.
","Geometric lattice structure of covering-based rough sets through
  matroids"
"  The measurement error with normal distribution is universal in applications.
Generally, smaller measurement error requires better instrument and higher test
cost. In decision making based on attribute values of objects, we shall select
an attribute subset with appropriate measurement error to minimize the total
test cost. Recently, error-range-based covering rough set with uniform
distribution error was proposed to investigate this issue. However, the
measurement errors satisfy normal distribution instead of uniform distribution
which is rather simple for most applications. In this paper, we introduce
normal distribution measurement errors to covering-based rough set model, and
deal with test-cost-sensitive attribute reduction problem in this new model.
The major contributions of this paper are four-fold. First, we build a new data
model based on normal distribution measurement errors. With the new data model,
the error range is an ellipse in a two-dimension space. Second, the
covering-based rough set with normal distribution measurement errors is
constructed through the ""3-sigma"" rule. Third, the test-cost-sensitive
attribute reduction problem is redefined on this covering-based rough set.
Fourth, a heuristic algorithm is proposed to deal with this problem. The
algorithm is tested on ten UCI (University of California - Irvine) datasets.
The experimental results show that the algorithm is more effective and
efficient than the existing one. This study is a step toward realistic
applications of cost-sensitive learning.
","Test-cost-sensitive attribute reduction of data with normal distribution
  measurement errors"
"  Recently, in order to broad the application and theoretical areas of rough
sets and matroids, some authors have combined them from many different
viewpoints, such as circuits, rank function, spanning sets and so on. In this
paper, we connect the second type of covering-based rough sets and matroids
from the view of closure operators. On one hand, we establish a closure system
through the fixed point family of the second type of covering lower
approximation operator, and then construct a closure operator. For a covering
of a universe, the closure operator is a closure one of a matroid if and only
if the reduct of the covering is a partition of the universe. On the other
hand, we investigate the sufficient and necessary condition that the second
type of covering upper approximation operation is a closure one of a matroid.
","Relationship between the second type of covering-based rough set and
  matroid via closure operator"
"  The questions which we will consider here are ""What is AI?"" and ""How can we
make AI?"". Here we will present the definition of AI in terms of multi-agent
systems. This means that here you will not find a new answer to the question
""What is AI?"", but an old answer in a new form.
  This new form of the definition of AI is of interest for the theory of
multi-agent systems because it gives us better understanding of this theory.
More important is that this work will help us answer the second question. We
want to make a program which is capable of constructing a model of its
environment. Every multi-agent model is equivalent to a single-agent model but
multi-agent models are more natural and accordingly more easily discoverable.
",The Definition of AI in Terms of Multi Agent Systems
"  In this paper we offer a formal definition of Artificial Intelligence and
this directly gives us an algorithm for construction of this object. Really,
this algorithm is useless due to the combinatory explosion.
  The main innovation in our definition is that it does not include the
knowledge as a part of the intelligence. So according to our definition a newly
born baby also is an Intellect. Here we differs with Turing's definition which
suggests that an Intellect is a person with knowledge gained through the years.
",A Definition of Artificial Intelligence
"  Answer Set Programming (ASP) is a well-known problem solving approach based
on nonmonotonic logic programs and efficient solvers. To enable access to
external information, HEX-programs extend programs with external atoms, which
allow for a bidirectional communication between the logic program and external
sources of computation (e.g., description logic reasoners and Web resources).
Current solvers evaluate HEX-programs by a translation to ASP itself, in which
values of external atoms are guessed and verified after the ordinary answer set
computation. This elegant approach does not scale with the number of external
accesses in general, in particular in presence of nondeterminism (which is
instrumental for ASP). In this paper, we present a novel, native algorithm for
evaluating HEX-programs which uses learning techniques. In particular, we
extend conflict-driven ASP solving techniques, which prevent the solver from
running into the same conflict again, from ordinary to HEX-programs. We show
how to gain additional knowledge from external source evaluations and how to
use it in a conflict-driven algorithm. We first target the uninformed case,
i.e., when we have no extra information on external sources, and then extend
our approach to the case where additional meta-information is available.
Experiments show that learning from external sources can significantly decrease
both the runtime and the number of considered candidate compatible sets.
",Conflict-driven ASP Solving with External Sources
"  In order to build AI we have to create a program which copes well in an
arbitrary world. In this paper we will restrict our attention on one concrete
world, which represents the game Tick-Tack-Toe. This world is a very simple one
but it is sufficiently complicated for our task because most people cannot
manage with it. The main difficulty in this world is that the player cannot see
the entire internal state of the world so he has to build a model in order to
understand the world. The model which we will offer will consist of final
automata and first order formulas.
",AI in arbitrary world
"  Supply Chain coordination has become a critical success factor for Supply
Chain management (SCM) and effectively improving the performance of
organizations in various industries. Companies are increasingly located at the
intersection of one or more corporate networks which are designated by ""Supply
Chain"". Managing this chain is mainly based on an 'information sharing' and
redeployment activities between the various links that comprise it. Several
attempts have been made by industrialists and researchers to educate
policymakers about the gains to be made by the implementation of cooperative
relationships. The approach presented in this paper here is among the works
that aim to propose solutions related to information systems distributed Supply
Chains to enable the different actors of the chain to improve their
performance. We propose in particular solutions that focus on cooperation
between actors in the Supply Chain.
",An Agent-based framework for cooperation in Supply Chain
"  Local Optima Networks (LONs) have been recently proposed as an alternative
model of combinatorial fitness landscapes. The model compresses the information
given by the whole search space into a smaller mathematical object that is the
graph having as vertices the local optima and as edges the possible weighted
transitions between them. A new set of metrics can be derived from this model
that capture the distribution and connectivity of the local optima in the
underlying configuration space. This paper departs from the descriptive
analysis of local optima networks, and actively studies the correlation between
network features and the performance of a local search heuristic. The NK family
of landscapes and the Iterated Local Search metaheuristic are considered. With
a statistically-sound approach based on multiple linear regression, it is shown
that some LONs' features strongly influence and can even partly predict the
performance of a heuristic search algorithm. This study validates the
expressive power of LONs as a model of combinatorial fitness landscapes.
",Local optima networks and the performance of iterated local search
"  We propose an approach to lifted approximate inference for first-order
probabilistic models, such as Markov logic networks. It is based on performing
exact lifted inference in a simplified first-order model, which is found by
relaxing first-order constraints, and then compensating for the relaxation.
These simplified models can be incrementally improved by carefully recovering
constraints that have been relaxed, also at the first-order level. This leads
to a spectrum of approximations, with lifted belief propagation on one end, and
exact lifted inference on the other. We discuss how relaxation, compensation,
and recovery can be performed, all at the firstorder level, and show
empirically that our approach substantially improves on the approximations of
both propositional solvers and lifted belief propagation.
","Lifted Relax, Compensate and then Recover: From Approximate to Exact
  Lifted Probabilistic Inference"
"  The MPE (Most Probable Explanation) query plays an important role in
probabilistic inference. MPE solution algorithms for probabilistic relational
models essentially adapt existing belief assessment method, replacing summation
with maximization. But the rich structure and symmetries captured by relational
models together with the properties of the maximization operator offer an
opportunity for additional simplification with potentially significant
computational ramifications. Specifically, these models often have groups of
variables that define symmetric distributions over some population of formulas.
The maximizing choice for different elements of this group is the same. If we
can realize this ahead of time, we can significantly reduce the size of the
model by eliminating a potentially significant portion of random variables.
This paper defines the notion of uniformly assigned and partially uniformly
assigned sets of variables, shows how one can recognize these sets efficiently,
and how the model can be greatly simplified once we recognize them, with little
computational effort. We demonstrate the effectiveness of these ideas
empirically on a number of models.
",Exploiting Uniform Assignments in First-Order MPE
"  This paper provides some new guidance in the construction of region graphs
for Generalized Belief Propagation (GBP). We connect the problem of choosing
the outer regions of a LoopStructured Region Graph (SRG) to that of finding a
fundamental cycle basis of the corresponding Markov network. We also define a
new class of tree-robust Loop-SRG for which GBP on any induced (spanning) tree
of the Markov network, obtained by setting to zero the off-tree interactions,
is exact. This class of SRG is then mapped to an equivalent class of
tree-robust cycle bases on the Markov network. We show that a treerobust cycle
basis can be identified by proving that for every subset of cycles, the graph
obtained from the edges that participate in a single cycle only, is multiply
connected. Using this we identify two classes of tree-robust cycle bases:
planar cycle bases and ""star"" cycle bases. In experiments we show that
tree-robustness can be successfully exploited as a design principle to improve
the accuracy and convergence of GBP.
",Generalized Belief Propagation on Tree Robust Structured Region Graphs
"  We consider the problem of sampling from solutions defined by a set of hard
constraints on a combinatorial space. We propose a new sampling technique that,
while enforcing a uniform exploration of the search space, leverages the
reasoning power of a systematic constraint solver in a black-box scheme. We
present a series of challenging domains, such as energy barriers and highly
asymmetric spaces, that reveal the difficulties introduced by hard constraints.
We demonstrate that standard approaches such as Simulated Annealing and Gibbs
Sampling are greatly affected, while our new technique can overcome many of
these difficulties. Finally, we show that our sampling scheme naturally defines
a new approximate model counting technique, which we empirically show to be
very accurate on a range of benchmark problems.
",Uniform Solution Sampling Using a Constraint Solver As an Oracle
"  Stochastic Shortest Path (SSP) MDPs is a problem class widely studied in AI,
especially in probabilistic planning. They describe a wide range of scenarios
but make the restrictive assumption that the goal is reachable from any state,
i.e., that dead-end states do not exist. Because of this, SSPs are unable to
model various scenarios that may have catastrophic events (e.g., an airplane
possibly crashing if it flies into a storm). Even though MDP algorithms have
been used for solving problems with dead ends, a principled theory of SSP
extensions that would allow dead ends, including theoretically sound algorithms
for solving such MDPs, has been lacking. In this paper, we propose three new
MDP classes that admit dead ends under increasingly weaker assumptions. We
present Value Iteration-based as well as the more efficient heuristic search
algorithms for optimally solving each class, and explore theoretical
relationships between these classes. We also conduct a preliminary empirical
study comparing the performance of our algorithms on different MDP classes,
especially on scenarios with unavoidable dead ends.
",A Theory of Goal-Oriented MDPs with Dead Ends
"  We develop several algorithms taking advantage of two common approaches for
bounding MPE queries in graphical models: minibucket elimination and
message-passing updates for linear programming relaxations. Both methods are
quite similar, and offer useful perspectives for the other; our hybrid
approaches attempt to balance the advantages of each. We demonstrate the power
of our hybrid algorithms through extensive empirical evaluation. Most notably,
a Branch and Bound search guided by the heuristic function calculated by one of
our new algorithms has recently won first place in the PASCAL2 inference
challenge.
",Join-graph based cost-shifting schemes
"  We consider the problem of selecting a subset of alternatives given noisy
evaluations of the relative strength of different alternatives. We wish to
select a k-subset (for a given k) that provides a maximum likelihood estimate
for one of several objectives, e.g., containing the strongest alternative.
Although this problem is NP-hard, we show that when the noise level is
sufficiently high, intuitive methods provide the optimal solution. We thus
generalize classical results about singling out one alternative and identifying
the hidden ranking of alternatives by strength. Extensive experiments show that
our methods perform well in practical settings.
",A Maximum Likelihood Approach For Selecting Sets of Alternatives
"  We study the problem of complexity estimation in the context of parallelizing
an advanced Branch and Bound-type algorithm over graphical models. The
algorithm's pruning power makes load balancing, one crucial element of every
distributed system, very challenging. We propose using a statistical regression
model to identify and tackle disproportionally complex parallel subproblems,
the cause of load imbalance, ahead of time. The proposed model is evaluated and
analyzed on various levels and shown to yield robust predictions. We then
demonstrate its effectiveness for load balancing in practice.
","A Case Study in Complexity Estimation: Towards Parallel Branch-and-Bound
  over Graphical Models"
"  Influence diagrams allow for intuitive and yet precise description of complex
situations involving decision making under uncertainty. Unfortunately, most of
the problems described by influence diagrams are hard to solve. In this paper
we discuss the complexity of approximately solving influence diagrams. We do
not assume no-forgetting or regularity, which makes the class of problems we
address very broad. Remarkably, we show that when both the tree-width and the
cardinality of the variables are bounded the problem admits a fully
polynomial-time approximation scheme.
",The Complexity of Approximately Solving Influence Diagrams
"  Variational inference algorithms such as belief propagation have had
tremendous impact on our ability to learn and use graphical models, and give
many insights for developing or understanding exact and approximate inference.
However, variational approaches have not been widely adoped for decision making
in graphical models, often formulated through influence diagrams and including
both centralized and decentralized (or multi-agent) decisions. In this work, we
present a general variational framework for solving structured cooperative
decision-making problems, use it to propose several belief propagation-like
algorithms, and analyze them both theoretically and empirically.
",Belief Propagation for Structured Decision Making
"  We describe multi-objective influence diagrams, based on a set of p
objectives, where utility values are vectors in Rp, and are typically only
partially ordered. These can still be solved by a variable elimination
algorithm, leading to a set of maximal values of expected utility. If the
Pareto ordering is used this set can often be prohibitively large. We consider
approximate representations of the Pareto set based on e-coverings, allowing
much larger problems to be solved. In addition, we define a method for
incorporating user tradeoffs, which also greatly improves the efficiency.
",Multi-objective Influence Diagrams
"  Planning in partially observable Markov decision processes (POMDPs) remains a
challenging topic in the artificial intelligence community, in spite of recent
impressive progress in approximation techniques. Previous research has
indicated that online planning approaches are promising in handling large-scale
POMDP domains efficiently as they make decisions ""on demand"" instead of
proactively for the entire state space. We present a Factored Hybrid Heuristic
Online Planning (FHHOP) algorithm for large POMDPs. FHHOP gets its power by
combining a novel hybrid heuristic search strategy with a recently developed
factored state representation. On several benchmark problems, FHHOP
substantially outperformed state-of-the-art online heuristic search approaches
in terms of both scalability and quality.
","FHHOP: A Factored Hybrid Heuristic Online Planning Algorithm for Large
  POMDPs"
"  We introduce a new cluster-cumulant expansion (CCE) based on the fixed points
of iterative belief propagation (IBP). This expansion is similar in spirit to
the loop-series (LS) recently introduced in [1]. However, in contrast to the
latter, the CCE enjoys the following important qualities: 1) it is defined for
arbitrary state spaces 2) it is easily extended to fixed points of generalized
belief propagation (GBP), 3) disconnected groups of variables will not
contribute to the CCE and 4) the accuracy of the expansion empirically improves
upon that of the LS. The CCE is based on the same M\""obius transform as the
Kikuchi approximation, but unlike GBP does not require storing the beliefs of
the GBP-clusters nor does it suffer from convergence issues during belief
updating.
",A Cluster-Cumulant Expansion at the Fixed Points of Belief Propagation
"  Rough set theory is a useful tool to deal with uncertain, granular and
incomplete knowledge in information systems. And it is based on equivalence
relations or partitions. Matroid theory is a structure that generalizes linear
independence in vector spaces, and has a variety of applications in many
fields. In this paper, we propose a new type of matroids, namely,
partition-circuit matroids, which are induced by partitions. Firstly, a
partition satisfies circuit axioms in matroid theory, then it can induce a
matroid which is called a partition-circuit matroid. A partition and an
equivalence relation on the same universe are one-to-one corresponding, then
some characteristics of partition-circuit matroids are studied through rough
sets. Secondly, similar to the upper approximation number which is proposed by
Wang and Zhu, we define the lower approximation number. Some characteristics of
partition-circuit matroids and the dual matroids of them are investigated
through the lower approximation number and the upper approximation number.
",Characteristic of partition-circuit matroid through approximation number
"  In this work we investigate the systems that implements algorithms for the
planning problem in Artificial Intelligence, called planners, with especial
attention to the planners based on the plan graph. We analyze the problem of
comparing the performance of the different algorithms and we propose an
environment for the development and analysis of planners.
",Ambiente de Planejamento Ip\^e
"  For the exploration of large state spaces, symbolic search using binary
decision diagrams (BDDs) can save huge amounts of memory and computation time.
State sets are represented and modified by accessing and manipulating their
characteristic functions. BDD partitioning is used to compute the image as the
disjunction of smaller subimages.
  In this paper, we propose a novel BDD partitioning option. The partitioning
is lexicographical in the binary representation of the states contained in the
set that is represented by a BDD and uniform with respect to the number of
states represented. The motivation of controlling the state set sizes in the
partitioning is to eventually bridge the gap between explicit and symbolic
search.
  Let n be the size of the binary state vector. We propose an O(n) ranking and
unranking scheme that supports negated edges and operates on top of precomputed
satcount values. For the uniform split of a BDD, we then use unranking to
provide paths along which we partition the BDDs. In a shared BDD representation
the efforts are O(n). The algorithms are fully integrated in the CUDD library
and evaluated in strongly solving general game playing benchmarks.
",Lex-Partitioning: A New Option for BDD Search
"  In this paper we present the results of unstructured data clustering in this
case a textual data from Reuters 21578 corpus with a new biomimetic approach
using immune system. Before experimenting our immune system, we digitalized
textual data by the n-grams approach. The novelty lies on hybridization of
n-grams and immune systems for clustering. The experimental results show that
the recommended ideas are promising and prove that this method can solve the
text clustering problem.
","A Biomimetic Approach Based on Immune Systems for Classification of
  Unstructured Data"
"  With the increased use of ontologies in semantically-enabled applications,
the issue of debugging defects in ontologies has become increasingly important.
These defects can lead to wrong or incomplete results for the applications.
Debugging consists of the phases of detection and repairing. In this paper we
focus on the repairing phase of a particular kind of defects, i.e. the missing
relations in the is-a hierarchy. Previous work has dealt with the case of
taxonomies. In this work we extend the scope to deal with ALC ontologies that
can be represented using acyclic terminologies. We present algorithms and
discuss a system.
","Get my pizza right: Repairing missing is-a relations in ALC ontologies
  (extended version)"
"  The Algorithm Selection Problem is concerned with selecting the best
algorithm to solve a given problem on a case-by-case basis. It has become
especially relevant in the last decade, as researchers are increasingly
investigating how to identify the most suitable existing algorithm for solving
a problem instead of developing new algorithms. This survey presents an
overview of this work focusing on the contributions made in the area of
combinatorial search problems, where Algorithm Selection techniques have
achieved significant performance improvements. We unify and organise the vast
literature according to criteria that determine Algorithm Selection systems in
practice. The comprehensive classification of approaches identifies and
analyses the different directions from which Algorithm Selection has been
approached. This paper contrasts and compares different methods for solving the
problem as well as ways of using these solutions. It closes by identifying
directions of current and future research.
",Algorithm Selection for Combinatorial Search Problems: A Survey
"  Rough sets were proposed to deal with the vagueness and incompleteness of
knowledge in information systems. There are may optimization issues in this
field such as attribute reduction. Matroids generalized from matrices are
widely used in optimization. Therefore, it is necessary to connect matroids
with rough sets. In this paper, we take field into consideration and introduce
matrix to study rough sets through vector matroids. First, a matrix
representation of an equivalence relation is proposed, and then a matroidal
structure of rough sets over a field is presented by the matrix. Second, the
properties of the matroidal structure including circuits, bases and so on are
studied through two special matrix solution spaces, especially null space.
Third, over a binary field, we construct an equivalence relation from matrix
null space, and establish an algebra isomorphism from the collection of
equivalence relations to the collection of sets, which any member is a family
of the minimal non-empty sets that are supports of members of null space of a
binary dependence matrix. In a word, matrix provides a new viewpoint to study
rough sets.
",Matrix approach to rough sets through vector matroids over a field
"  There are few knowledge representation (KR) techniques available for
efficiently representing knowledge. However, with the increase in complexity,
better methods are needed. Some researchers came up with hybrid mechanisms by
combining two or more methods. In an effort to construct an intelligent
computer system, a primary consideration is to represent large amounts of
knowledge in a way that allows effective use and efficiently organizing
information to facilitate making the recommended inferences. There are merits
and demerits of combinations, and standardized method of KR is needed. In this
paper, various hybrid schemes of KR were explored at length and details
presented.
",Hybrid Systems for Knowledge Representation in Artificial Intelligence
"  We describe an inference task in which a set of timestamped event
observations must be clustered into an unknown number of temporal sequences
with independent and varying rates of observations. Various existing approaches
to multi-object tracking assume a fixed number of sources and/or a fixed
observation rate; we develop an approach to inferring structure in timestamped
data produced by a mixture of an unknown and varying number of similar Markov
renewal processes, plus independent clutter noise. The inference simultaneously
distinguishes signal from noise as well as clustering signal observations into
separate source streams. We illustrate the technique via a synthetic experiment
as well as an experiment to track a mixture of singing birds.
",Segregating event streams and noise with a Markov renewal process model
"  Decision tree is an effective classification approach in data mining and
machine learning. In applications, test costs and misclassification costs
should be considered while inducing decision trees. Recently, some
cost-sensitive learning algorithms based on ID3 such as CS-ID3, IDX,
\lambda-ID3 have been proposed to deal with the issue. These algorithms deal
with only symbolic data. In this paper, we develop a decision tree algorithm
inspired by C4.5 for numeric data. There are two major issues for our
algorithm. First, we develop the test cost weighted information gain ratio as
the heuristic information. According to this heuristic information, our
algorithm is to pick the attribute that provides more gain ratio and costs less
for each selection. Second, we design a post-pruning strategy through
considering the tradeoff between test costs and misclassification costs of the
generated decision tree. In this way, the total cost is reduced. Experimental
results indicate that (1) our algorithm is stable and effective; (2) the
post-pruning technique reduces the total cost significantly; (3) the
competition strategy is effective to obtain a cost-sensitive decision tree with
low cost.
",Cost-sensitive C4.5 with post-pruning and competition
"  Case Based Reasoning (CBR) is an intelligent way of thinking based on
experience and capitalization of already solved cases (source cases) to find a
solution to a new problem (target case). Retrieval phase consists on
identifying source cases that are similar to the target case. This phase may
lead to erroneous results if the existing knowledge imperfections are not taken
into account. This work presents a novel solution based on Fuzzy logic
techniques and adaptation measures which aggregate weighted similarities to
improve the retrieval results. To confirm the efficiency of our solution, we
have applied it to the industrial diagnosis domain. The obtained results are
more efficient results than those obtained by applying typical measures.
",A Logic and Adaptive Approach for Efficient Diagnosis Systems using CBR
"  This paper advocates the exploration of the full state of recorded real-time
strategy (RTS) games, by human or robotic players, to discover how to reason
about tactics and strategy. We present a dataset of StarCraft games
encompassing the most of the games' state (not only player's orders). We
explain one of the possible usages of this dataset by clustering armies on
their compositions. This reduction of armies compositions to mixtures of
Gaussian allow for strategic reasoning at the level of the components. We
evaluated this clustering method by predicting the outcomes of battles based on
armies compositions' mixtures components
",A Dataset for StarCraft AI \& an Example of Armies Clustering
"  Many cognitive systems deploy multiple, closed, individually consistent
models which can represent interpretations of the present state of the world,
moments in the past, possible futures or alternate versions of reality. While
they appear under different names, these structures can be grouped under the
general term of worlds. The Xapagy architecture is a story-oriented cognitive
system which relies exclusively on the autobiographical memory implemented as a
raw collection of events organized into world-type structures called {\em
scenes}. The system performs reasoning by shadowing current events with events
from the autobiography. The shadows are then extrapolated into headless shadows
corresponding to predictions, hidden events or inferred relations.
","Shadows and headless shadows: a worlds-based, autobiographical approach
  to reasoning"
"  This paper argues that the problem of identity is a critical challenge in
agents which are able to reason about stories. The Xapagy architecture has been
built from scratch to perform narrative reasoning and relies on a somewhat
unusual approach to represent instances and identity. We illustrate the
approach by a representation of the story of Little Red Riding Hood in the
architecture, with a focus on the problem of identity raised by the narrative.
",Modeling problems of identity in Little Red Riding Hood
"  The Xapagy architecture is a story-oriented cognitive system which relies
exclusively on the autobiographical memory implemented as a raw collection of
events. Reasoning is performed by shadowing current events with events from the
autobiography. The shadows are then extrapolated into headless shadows (HLSs).
In a story following mood, HLSs can be used to track the level of surprise of
the agent, to infer hidden actions or relations between the participants, and
to summarize ongoing events. In recall mood, the HLSs can be used to create new
stories ranging from exact recall to free-form confabulation.
","Shadows and Headless Shadows: an Autobiographical Approach to Narrative
  Reasoning"
"  Computational thinking is a new problem soling method named for its extensive
use of computer science techniques. It synthesizes critical thinking and
existing knowledge and applies them in solving complex technological problems.
The term was coined by J. Wing, but the relationship between computational and
critical thinking, the two modes of thiking in solving problems, has not been
yet learly established. This paper aims at shedding some light into this
relationship. We also present two classroom experiments performed recently at
the Graduate Technological Educational Institute of Patras in Greece. The
results of these experiments give a strong indication that the use of computers
as a tool for problem solving enchances the students' abilities in solving real
world problems involving mathematical modelling. This is also crossed by
earlier findings of other researchers for the problem solving process in
general (not only for mathematical problems).
",Problem Solving and Computational Thinking in a Learning Environment
"  Traffic regulation must be respected by all vehicles, either human- or
computer- driven. However, extreme traffic situations might exhibit practical
cases in which a vehicle should safely and reasonably relax traffic regulation,
e.g., in order not to be indefinitely blocked and to keep circulating. In this
paper, we propose a high-level representation of an automated vehicle, other
vehicles and their environment, which can assist drivers in taking such
""illegal"" but practical relaxation decisions. This high-level representation
(an ontology) includes topological knowledge and inference rules, in order to
compute the next high-level motion an automated vehicle should take, as
assistance to a driver. Results on practical cases are presented.
","An ontology-based approach to relax traffic regulation for autonomous
  vehicle assistance"
"  Soft Constraint Logic Programming is a natural and flexible declarative
programming formalism, which allows to model and solve real-life problems
involving constraints of different types.
  In this paper, after providing a slightly more general and elegant
presentation of the framework, we show how we can apply it to the e-mobility
problem of coordinating electric vehicles in order to overcome both energetic
and temporal constraints and so to reduce their running cost. In particular, we
focus on the journey optimization sub-problem, considering sequences of trips
from a user's appointment to another one. Solutions provide the best
alternatives in terms of time and energy consumption, including route sequences
and possible charging events.
","Soft Constraint Logic Programming for Electric Vehicle Travel
  Optimization"
"  We look at the problem of revising fuzzy belief bases, i.e., belief base
revision in which both formulas in the base as well as revision-input formulas
can come attached with varying truth-degrees. Working within a very general
framework for fuzzy logic which is able to capture a variety of types of
inference under uncertainty, such as truth-functional fuzzy logics and certain
types of probabilistic inference, we show how the idea of rational change from
'crisp' base revision, as embodied by the idea of partial meet revision, can be
faithfully extended to revising fuzzy belief bases. We present and axiomatise
an operation of partial meet fuzzy revision and illustrate how the operation
works in several important special instances of the framework.
",On revising fuzzy belief bases
"  WA qualitative probabilistic network models the probabilistic relationships
between its variables by means of signs. Non-monotonic influences have
associated an ambiguous sign. These ambiguous signs typically lead to
uninformative results upon inference. A non-monotonic influence can, however,
be associated with a, more informative, sign that indicates its effect in the
current state of the network. To capture this effect, we introduce the concept
of situational sign. Furthermore, if the network converts to a state in which
all variables that provoke the non-monotonicity have been observed, a
non-monotonic influence reduces to a monotonic influence. We study the
persistence and propagation of situational signs upon inference and give a
method to establish the sign of a reduced influence.
",Upgrading Ambiguous Signs in QPNs
"  Dependability modeling and evaluation is aimed at investigating that a system
performs its function correctly in time. A usual way to achieve a high
reliability, is to design redundant systems that contain several replicas of
the same subsystem or component. State space methods for dependability analysis
may suffer of the state space explosion problem in such a kind of situation.
Combinatorial models, on the other hand, require the simplified assumption of
statistical independence; however, in case of redundant systems, this does not
guarantee a reduced number of modeled elements. In order to provide a more
compact system representation, parametric system modeling has been investigated
in the literature, in such a way that a set of replicas of a given subsystem is
parameterized so that only one representative instance is explicitly included.
While modeling aspects can be suitably addressed by these approaches,
analytical tools working on parametric characterizations are often more
difficult to be defined and the standard approach is to 'unfold' the parametric
model, in order to exploit standard analysis algorithms working at the unfolded
'ground' level. Moreover, parameterized combinatorial methods still require the
statistical independence assumption. In the present paper we consider the
formalism of Parametric Fault Tree (PFT) and we show how it can be related to
Probabilistic Horn Abduction (PHA). Since PHA is a framework where both
modeling and analysis can be performed in a restricted first-order language, we
aim at showing that converting a PFT into a PHA knowledge base will allow an
approach to dependability analysis directly exploiting parametric
representation. We will show that classical qualitative and quantitative
dependability measures can be characterized within PHA. Furthermore, additional
modeling aspects (such as noisy gates and local dependencies) as well as
additional reliability measures (such as posterior probability analysis) can be
naturally addressed by this conversion. A simple example of a multi-processor
system with several replicated units is used to illustrate the approach.
",Parametric Dependability Analysis through Probabilistic Horn Abduction
"  This paper introduces new methodology to triangulate dynamic Bayesian
networks (DBNs) and dynamic graphical models (DGMs). While most methods to
triangulate such networks use some form of constrained elimination scheme based
on properties of the underlying directed graph, we find it useful to view
triangulation and elimination using properties only of the resulting undirected
graph, obtained after the moralization step. We first briefly introduce the
Graphical model toolkit (GMTK) and its notion of dynamic graphical models, one
that slightly extends the standard notion of a DBN. We next introduce the
'boundary algorithm', a method to find the best boundary between partitions in
a dynamic model. We find that using this algorithm, the notions of forward- and
backward-interface become moot - namely, the size and fill-in of the best
forward- and backward- interface are identical. Moreover, we observe that
finding a good partition boundary allows for constrained elimination orders
(and therefore graph triangulations) that are not possible using standard
slice-by-slice constrained eliminations. More interestingly, with certain
boundaries it is possible to obtain constrained elimination schemes that lie
outside the space of possible triangulations using only unconstrained
elimination. Lastly, we report triangulation results on invented graphs,
standard DBNs from the literature, novel DBNs used in speech recognition
research systems, and also random graphs. Using a number of different
triangulation quality measures (max clique size, state-space, etc.), we find
that with our boundary algorithm the triangulation quality can dramatically
improve.
",On Triangulating Dynamic Graphical Models
"  The paper studies empirically the time-space trade-off between sampling and
inference in a sl cutset sampling algorithm. The algorithm samples over a
subset of nodes in a Bayesian network and applies exact inference over the
rest. Consequently, while the size of the sampling space decreases, requiring
less samples for convergence, the time for generating each single sample
increases. The w-cutset sampling selects a sampling set such that the
induced-width of the network when the sampling set is observed is bounded by w,
thus requiring inference whose complexity is exponential in w. In this paper,
we investigate performance of w-cutset sampling over a range of w values and
measure the accuracy of w-cutset sampling as a function of w. Our experiments
demonstrate that the cutset sampling idea is quite powerful showing that an
optimal balance between inference and sampling benefits substantially from
restricting the cutset size, even at the cost of more complex inference.
",An Empirical Study of w-Cutset Sampling for Bayesian Networks
"  In a standard possibilistic logic, prioritized information are encoded by
means of weighted knowledge base. This paper proposes an extension of
possibilistic logic for dealing with partially ordered information. We Show
that all basic notions of standard possibilitic logic (sumbsumption, syntactic
and semantic inference, etc.) have natural couterparts when dealing with
partially ordered information. We also propose an algorithm which computes
possibilistic conclusions of a partial knowledge base of a partially ordered
knowlege base.
",A possibilistic handling of partially ordered information
"  Backtracking search is a powerful algorithmic paradigm that can be used to
solve many problems. It is in a certain sense the dual of variable elimination;
but on many problems, e.g., SAT, it is vastly superior to variable elimination
in practice. Motivated by this we investigate the application of backtracking
search to the problem of Bayesian inference (Bayes). We show that natural
generalizations of known techniques allow backtracking search to achieve
performance guarantees similar to standard algorithms for Bayes, and that there
exist problems on which backtracking can in fact do much better. We also
demonstrate that these ideas can be applied to implement a Bayesian inference
engine whose performance is competitive with standard algorithms. Since
backtracking search can very naturally take advantage of context specific
structure, the potential exists for performance superior to standard algorithms
on many problems.
",Value Elimination: Bayesian Inference via Backtracking Search
"  Recursive Conditioning (RC) was introduced recently as the first any-space
algorithm for inference in Bayesian networks which can trade time for space by
varying the size of its cache at the increment needed to store a floating point
number. Under full caching, RC has an asymptotic time and space complexity
which is comparable to mainstream algorithms based on variable elimination and
clustering (exponential in the network treewidth and linear in its size). We
show two main results about RC in this paper. First, we show that its actual
space requirements under full caching are much more modest than those needed by
mainstream methods and study the implications of this finding. Second, we show
that RC can effectively deal with determinism in Bayesian networks by employing
standard logical techniques, such as unit resolution, allowing a significant
reduction in its time requirements in certain cases. We illustrate our results
using a number of benchmark networks, including the very challenging ones that
arise in genetic linkage analysis.
",New Advances in Inference by Recursive Conditioning
"  Most methods of exact probability propagation in Bayesian networks do not
carry out the inference directly over the network, but over a secondary
structure known as a junction tree or a join tree (JT). The process of
obtaining a JT is usually termed {sl compilation}. As compilation is usually
viewed as a whole process; each time the network is modified, a new compilation
process has to be carried out. The possibility of reusing an already existing
JT, in order to obtain the new one regarding only the modifications in the
network has received only little attention in the literature. In this paper we
present a method for incremental compilation of a Bayesian network, following
the classical scheme in which triangulation plays the key role. In order to
perform incremental compilation we propose to recompile only those parts of the
JT which can have been affected by the networks modifications. To do so, we
exploit the technique OF maximal prime subgraph decomposition in determining
the minimal subgraph(s) that have to be recompiled, and thereby the minimal
subtree(s) of the JT that should be replaced by new subtree(s).We focus on
structural modifications : addition and deletion of links and variables.
",Incremental Compilation of Bayesian networks
"  This paper is directed towards combining Pearl's structural-model approach to
causal reasoning with high-level formalisms for reasoning about actions. More
precisely, we present a combination of Pearl's structural-model approach with
Poole's independent choice logic. We show how probabilistic theories in the
independent choice logic can be mapped to probabilistic causal models. This
mapping provides the independent choice logic with appealing concepts of
causality and explanation from the structural-model approach. We illustrate
this along Halpern and Pearl's sophisticated notions of actual cause,
explanation, and partial explanation. This mapping also adds first-order
modeling capabilities and explicit actions to the structural-model approach.
",Structure-Based Causes and Explanations in the Independent Choice Logic
"  Symbolic representations have been used successfully in off-line planning
algorithms for Markov decision processes. We show that they can also improve
the performance of on-line planners. In addition to reducing computation time,
symbolic generalization can reduce the amount of costly real-world interactions
required for convergence. We introduce Symbolic Real-Time Dynamic Programming
(or sRTDP), an extension of RTDP. After each step of on-line interaction with
an environment, sRTDP uses symbolic model-checking techniques to generalizes
its experience by updating a group of states rather than a single state. We
examine two heuristic approaches to dynamic grouping of states and show that
they accelerate the planning process significantly in terms of both CPU time
and the number of steps of interaction with the environment.
",Symbolic Generalization for On-line Planning
"  We present the language {m P}{cal C}+ for probabilistic reasoning about
actions, which is a generalization of the action language {cal C}+ that allows
to deal with probabilistic as well as nondeterministic effects of actions. We
define a formal semantics of {m P}{cal C}+ in terms of probabilistic
transitions between sets of states. Using a concept of a history and its belief
state, we then show how several important problems in reasoning about actions
can be concisely formulated in our formalism.
",Probabilistic Reasoning about Actions in Nonmonotonic Causal Theories
"  In Non - ergodic belief networks the posterior belief OF many queries given
evidence may become zero.The paper shows that WHEN belief propagation IS
applied iteratively OVER arbitrary networks(the so called, iterative OR loopy
belief propagation(IBP)) it IS identical TO an arc - consistency algorithm
relative TO zero - belief queries(namely assessing zero posterior
probabilities). This implies that zero - belief conclusions derived BY belief
propagation converge AND are sound.More importantly it suggests that the
inference power OF IBP IS AS strong AND AS weak, AS that OF arc -
consistency.This allows the synthesis OF belief networks FOR which belief
propagation IS useless ON one hand, AND focuses the investigation OF classes OF
belief network FOR which belief propagation may be zero - complete.Finally, ALL
the above conclusions apply also TO Generalized belief propagation algorithms
that extend loopy belief propagation AND allow a crisper understanding OF their
power.
",A Simple Insight into Iterative Belief Propagation's Success
"  Pearls concept OF a d - connecting path IS one OF the foundations OF the
modern theory OF graphical models : the absence OF a d - connecting path IN a
DAG indicates that conditional independence will hold IN ANY distribution
factorising according TO that graph. IN this paper we show that IN singly -
connected Gaussian DAGs it IS possible TO USE the form OF a d - connection TO
obtain qualitative information about the strength OF conditional
dependence.More precisely, the squared partial correlations BETWEEN two given
variables, conditioned ON different subsets may be partially ordered BY
examining the relationship BETWEEN the d - connecting path AND the SET OF
variables conditioned upon.
","Using the structure of d-connecting paths as a qualitative measure of
  the strength of dependence"
"  In this paper, we introduce a method for approximating the solution to
inference and optimization tasks in uncertain and deterministic reasoning. Such
tasks are in general intractable for exact algorithms because of the large
number of dependency relationships in their structure. Our method effectively
maps such a dense problem to a sparser one which is in some sense ""closest"".
Exact methods can be run on the sparser problem to derive bounds on the
original answer, which can be quite sharp. We present empirical results
demonstrating that our method works well on the tasks of belief inference and
finding the probability of the most probable explanation in belief networks,
and finding the cost of the solution that violates the smallest number of
constraints in constraint satisfaction problems. On one large CPCS network, for
example, we were able to calculate upper and lower bounds on the conditional
probability of a variable, given evidence, that were almost identical in the
average case.
","Approximate Decomposition: A Method for Bounding and Estimating
  Probabilistic and Deterministic Queries"
"  Real-world distributed systems and networks are often unreliable and subject
to random failures of its components. Such a stochastic behavior affects
adversely the complexity of optimization tasks performed routinely upon such
systems, in particular, various resource allocation tasks. In this work we
investigate and develop Monte Carlo solutions for a class of two-stage
optimization problems in stochastic networks in which the expected value of
resource allocations before and after stochastic failures needs to be
optimized. The limitation of these problems is that their exact solutions are
exponential in the number of unreliable network components: thus, exact methods
do not scale-up well to large networks often seen in practice. We first prove
that Monte Carlo optimization methods can overcome the exponential bottleneck
of exact methods. Next we support our theoretical findings on resource
allocation experiments and show a very good scale-up potential of the new
methods to large stochastic networks.
","Monte-Carlo optimizations for resource allocation problems in stochastic
  network systems"
"  This paper examines a number of solution methods for decision processes with
non-Markovian rewards (NMRDPs). They all exploit a temporal logic specification
of the reward function to automatically translate the NMRDP into an equivalent
Markov decision process (MDP) amenable to well-known MDP solution methods. They
differ however in the representation of the target MDP and the class of MDP
solution methods to which they are suited. As a result, they adopt different
temporal logics and different translations. Unfortunately, no implementation of
these methods nor experimental let alone comparative results have ever been
reported. This paper is the first step towards filling this gap. We describe an
integrated system for solving NMRDPs which implements these methods and several
variants under a common interface; we use it to compare the various approaches
and identify the problem features favoring one over the other.
","Implementation and Comparison of Solution Methods for Decision Processes
  with Non-Markovian Rewards"
"  This paper studies decision making for Walley's partially consonant belief
functions (pcb). In a pcb, the set of foci are partitioned. Within each
partition, the foci are nested. The pcb class includes probability functions
and possibility functions as extreme cases. Unlike earlier proposals for a
decision theory with belief functions, we employ an axiomatic approach. We
adopt an axiom system similar in spirit to von Neumann - Morgenstern's linear
utility theory for a preference relation on pcb lotteries. We prove a
representation theorem for this relation. Utility for a pcb lottery is a
combination of linear utility for probabilistic lottery and binary utility for
possibilistic lottery.
",Decision Making with Partially Consonant Belief Functions
"  The two most popular types of graphical model are directed models (Bayesian
networks) and undirected models (Markov random fields, or MRFs). Directed and
undirected models offer complementary properties in model construction,
expressing conditional independencies, expressing arbitrary factorizations of
joint distributions, and formulating message-passing inference algorithms. We
show that the strengths of these two representations can be combined in a
single type of graphical model called a 'factor graph'. Every Bayesian network
or MRF can be easily converted to a factor graph that expresses the same
conditional independencies, expresses the same factorization of the joint
distribution, and can be used for probabilistic inference through application
of a single, simple message-passing algorithm. In contrast to chain graphs,
where message-passing is implemented on a hypergraph, message-passing can be
directly implemented on the factor graph. We describe a modified 'Bayes-ball'
algorithm for establishing conditional independence in factor graphs, and we
show that factor graphs form a strict superset of Bayesian networks and MRFs.
In particular, we give an example of a commonly-used 'mixture of experts' model
fragment, whose independencies cannot be represented in a Bayesian network or
an MRF, but can be represented in a factor graph. We finish by giving examples
of real-world problems that are not well suited to representation in Bayesian
networks and MRFs, but are well-suited to representation in factor graphs.
","Extending Factor Graphs so as to Unify Directed and Undirected Graphical
  Models"
"  This paper is devoted to the search of robust solutions in state space graphs
when costs depend on scenarios. We first present axiomatic requirements for
preference compatibility with the intuitive idea of robustness.This leads us to
propose the Lorenz dominance rule as a basis for robustness analysis. Then,
after presenting complexity results about the determination of robust
solutions, we propose a new sophistication of A* specially designed to
determine the set of robust paths in a state space graph. The behavior of the
algorithm is illustrated on a small example. Finally, an axiomatic
justification of the refinement of robustness by an OWA criterion is provided.
","An Axiomatic Approach to Robustness in Search Problems with Multiple
  Scenarios"
"  MAP is the problem of finding a most probable instantiation of a set of
variables in a Bayesian network given some evidence. Unlike computing posterior
probabilities, or MPE (a special case of MAP), the time and space complexity of
structural solutions for MAP are not only exponential in the network treewidth,
but in a larger parameter known as the ""constrained"" treewidth. In practice,
this means that computing MAP can be orders of magnitude more expensive than
computing posterior probabilities or MPE. This paper introduces a new, simple
upper bound on the probability of a MAP solution, which admits a tradeoff
between the bound quality and the time needed to compute it. The bound is shown
to be generally much tighter than those of other methods of comparable
complexity. We use this proposed upper bound to develop a branch-and-bound
search algorithm for solving MAP exactly. Experimental results demonstrate that
the search algorithm is able to solve many problems that are far beyond the
reach of any structure-based method for MAP. For example, we show that the
proposed algorithm can compute MAP exactly and efficiently for some networks
whose constrained treewidth is more than 40.
",Solving MAP Exactly using Systematic Search
"  The aim of this research is to develop a reasoning under uncertainty strategy
in the context of the Fuzzy Inductive Reasoning (FIR) methodology. FIR emerged
from the General Systems Problem Solving developed by G. Klir. It is a data
driven methodology based on systems behavior rather than on structural
knowledge. It is a very useful tool for both the modeling and the prediction of
those systems for which no previous structural knowledge is available. FIR
reasoning is based on pattern rules synthesized from the available data. The
size of the pattern rule base can be very large making the prediction process
quite difficult. In order to reduce the size of the pattern rule base, it is
possible to automatically extract classical Sugeno fuzzy rules starting from
the set of pattern rules. The Sugeno rule base preserves pattern rules
knowledge as much as possible. In this process some information is lost but
robustness is considerably increased. In the forecasting process either the
pattern rule base or the Sugeno fuzzy rule base can be used. The first option
is desirable when the computational resources make it possible to deal with the
overall pattern rule base or when the extracted fuzzy rules are not accurate
enough due to uncertainty associated to the original data. In the second
option, the prediction process is done by means of the classical Sugeno
inference system. If the amount of uncertainty associated to the data is small,
the predictions obtained using the Sugeno fuzzy rule base will be very
accurate. In this paper a mixed pattern/fuzzy rules strategy is proposed to
deal with uncertainty in such a way that the best of both perspectives is used.
Areas in the data space with a higher level of uncertainty are identified by
means of the so-called error models. The prediction process in these areas
makes use of a mixed pattern/fuzzy rules scheme, whereas areas identified with
a lower level of uncertainty only use the Sugeno fuzzy rule base. The proposed
strategy is applied to a real biomedical system, i.e., the central nervous
system control of the cardiovascular system.
",Dealing with uncertainty in fuzzy inductive reasoning methodology
"  For a given problem, the optimal Markov policy can be considerred as a
conditional or contingent plan containing a (potentially large) number of
branches. Unfortunately, there are applications where it is desirable to
strictly limit the number of decision points and branches in a plan. For
example, it may be that plans must later undergo more detailed simulation to
verify correctness and safety, or that they must be simple enough to be
understood and analyzed by humans. As a result, it may be necessary to limit
consideration to plans with only a small number of branches. This raises the
question of how one goes about finding optimal plans containing only a limited
number of branches. In this paper, we present an any-time algorithm for optimal
k-contingency planning (OKP). It is the first optimal algorithm for limited
contingency planning that is not an explicit enumeration of possible contingent
plans. By modelling the problem as a Partially Observable Markov Decision
Process, it implements the Bellman optimality principle and prunes the solution
space. We present experimental results of applying this algorithm to some
simple test cases.
",Optimal Limited Contingency Planning
"  The paper continues the study of partitioning based inference of heuristics
for search in the context of solving the Most Probable Explanation task in
Bayesian Networks. We compare two systematic Branch and Bound search
algorithms, BBBT (for which the heuristic information is constructed during
search and allows dynamic variable/value ordering) and its predecessor BBMB
(for which the heuristic information is pre-compiled), against a number of
popular local search algorithms for the MPE problem. We show empirically that,
when viewed as approximation schemes, BBBT/BBMB are superior to all of these
best known SLS algorithms, especially when the domain sizes increase beyond 2.
This is in contrast with the performance of SLS vs. systematic search on
CSP/SAT problems, where SLS often significantly outperforms systematic
algorithms. As far as we know, BBBT/BBMB are currently the best performing
algorithms for solving the MPE task.
",Systematic vs. Non-systematic Algorithms for Solving the MPE Task
"  Precision achieved by stochastic sampling algorithms for Bayesian networks
typically deteriorates in face of extremely unlikely evidence. To address this
problem, we propose the Evidence Pre-propagation Importance Sampling algorithm
(EPIS-BN), an importance sampling algorithm that computes an approximate
importance function by the heuristic methods: loopy belief Propagation and
e-cutoff. We tested the performance of e-cutoff on three large real Bayesian
networks: ANDES, CPCS, and PATHFINDER. We observed that on each of these
networks the EPIS-BN algorithm gives us a considerable improvement over the
current state of the art algorithm, the AIS-BN algorithm. In addition, it
avoids the costly learning stage of the AIS-BN algorithm.
",An Importance Sampling Algorithm Based on Evidence Pre-propagation
"  In this paper we examine the problem of inference in Bayesian Networks with
discrete random variables that have very large or even unbounded domains. For
example, in a domain where we are trying to identify a person, we may have
variables that have as domains, the set of all names, the set of all postal
codes, or the set of all credit card numbers. We cannot just have big tables of
the conditional probabilities, but need compact representations. We provide an
inference algorithm, based on variable elimination, for belief networks
containing both large domain and normal discrete random variables. We use
intensional (i.e., in terms of procedures) and extensional (in terms of listing
the elements) representations of conditional probabilities and of the
intermediate factors.
",Efficient Inference in Large Discrete Domains
"  We present CLP(BN), a novel approach that aims at expressing Bayesian
networks through the constraint logic programming framework. Arguably, an
important limitation of traditional Bayesian networks is that they are
propositional, and thus cannot represent relations between multiple similar
objects in multiple contexts. Several researchers have thus proposed
first-order languages to describe such networks. Namely, one very successful
example of this approach are the Probabilistic Relational Models (PRMs), that
combine Bayesian networks with relational database technology. The key
difficulty that we had to address when designing CLP(cal{BN}) is that logic
based representations use ground terms to denote objects. With probabilitic
data, we need to be able to uniquely represent an object whose value we are not
sure about. We use {sl Skolem functions} as unique new symbols that uniquely
represent objects with unknown value. The semantics of CLP(cal{BN}) programs
then naturally follow from the general framework of constraint logic
programming, as applied to a specific domain where we have probabilistic data.
This paper introduces and defines CLP(cal{BN}), and it describes an
implementation and initial experiments. The paper also shows how CLP(cal{BN})
relates to Probabilistic Relational Models (PRMs), Ngo and Haddawys
Probabilistic Logic Programs, AND Kersting AND De Raedts Bayesian Logic
Programs.
",CLP(BN): Constraint Logic Programming for Probabilistic Knowledge
"  We use princiles of fuzzy logic to develop a general model representing
several processes in a system's operation characterized by a degree of
vagueness and/or uncertainy. Further, we introduce three altenative measures of
a fuzzy system's effectiveness connected to the above model. An applcation is
also developed for the Mathematical Modelling process illustrating our results.
",A Study on Fuzzy Systems
"  In their nature configuration problems are combinatorial (optimization)
problems. In order to find a configuration a solver has to instantiate a number
of components of a some type and each of these components can be used in a
relation defined for a type. Therefore, many solutions of a configuration
problem have symmetric ones which can be obtained by replacing some component
of a solution by another one of the same type. These symmetric solutions
decrease performance of optimization algorithms because of two reasons: a) they
satisfy all requirements and cannot be pruned out from the search space; and b)
existence of symmetric optimal solutions does not allow to prove the optimum in
a feasible time.
",Study: Symmetry breaking for ASP
"  Results are presented on the performance of Adaptive Neuro-Fuzzy Inference
system (ANFIS) for wind velocity forecasts in the Isthmus of Tehuantepec region
in the state of Oaxaca, Mexico. The data bank was provided by the
meteorological station located at the University of Isthmus, Tehuantepec
campus, and this data bank covers the period from 2008 to 2011. Three data
models were constructed to carry out 16, 24 and 48 hours forecasts using the
following variables: wind velocity, temperature, barometric pressure, and date.
The performance measure for the three models is the mean standard error (MSE).
In this work, performance analysis in short-term prediction is presented,
because it is essential in order to define an adequate wind speed model for
eolian parks, where a right planning provide economic benefits.
",Performance Analysis of ANFIS in short term Wind Speed Prediction
"  ConArg is a Constraint Programming-based tool that can be used to model and
solve different problems related to Abstract Argumentation Frameworks (AFs). To
implement this tool we have used JaCoP, a Java library that provides the user
with a Finite Domain Constraint Programming paradigm. ConArg is able to
randomly generate networks with small-world properties in order to find
conflict-free, admissible, complete, stable grounded, preferred, semi-stable,
stage and ideal extensions on such interaction graphs. We present the main
features of ConArg and we report the performance in time, showing also a
comparison with ASPARTIX [1], a similar tool using Answer Set Programming. The
use of techniques for constraint solving can tackle the complexity of the
problems presented in [2]. Moreover we suggest semiring-based soft constraints
as a mean to parametrically represent and solve Weighted Argumentation
Frameworks: different kinds of preference levels related to attacks, e.g., a
score representing a ""fuzziness"", a ""cost"" or a probability, can be represented
by choosing different instantiation of the semiring algebraic structure. The
basic idea is to provide a common computational and quantitative framework.
","ConArg: a Tool to Solve (Weighted) Abstract Argumentation Frameworks
  with (Soft) Constraints"
"  The Semantic Web ontology language OWL 2 DL comes with a variety of language
features that enable sophisticated and practically useful modeling. However,
the use of these features has been severely restricted in order to retain
decidability of the language. For example, OWL 2 DL does not allow a property
to be both transitive and asymmetric, which would be desirable, e.g., for
representing an ancestor relation. In this paper, we argue that the so-called
global restrictions of OWL 2 DL preclude many useful forms of modeling, by
providing a catalog of basic modeling patterns that would be available in OWL 2
DL if the global restrictions were discarded. We then report on the results of
evaluating several state-of-the-art OWL 2 DL reasoners on problems that use
combinations of features in a way that the global restrictions are violated.
The systems turn out to rely heavily on the global restrictions and are thus
largely incapable of coping with the modeling patterns. Next we show how
off-the-shelf first-order logic theorem proving technology can be used to
perform reasoning in the OWL 2 direct semantics, the semantics that underlies
OWL 2 DL, but without requiring the global restrictions. Applying a naive
proof-of-concept implementation of this approach to the test problems was
successful in all cases. Based on our observations, we make suggestions for
future lines of research on expressive description logic-style OWL reasoning.
",Modeling in OWL 2 without Restrictions
"  All standard AI planners to-date can only handle a single objective, and the
only way for them to take into account multiple objectives is by aggregation of
the objectives. Furthermore, and in deep contrast with the single objective
case, there exists no benchmark problems on which to test the algorithms for
multi-objective planning. Divide and Evolve (DAE) is an evolutionary planner
that won the (single-objective) deterministic temporal satisficing track in the
last International Planning Competition. Even though it uses intensively the
classical (and hence single-objective) planner YAHSP, it is possible to turn
DAE-YAHSP into a multi-objective evolutionary planner. A tunable benchmark
suite for multi-objective planning is first proposed, and the performances of
several variants of multi-objective DAE-YAHSP are compared on different
instances of this benchmark, hopefully paving the road to further
multi-objective competitions in AI planning.
",Multi-Objective AI Planning: Evaluating DAE-YAHSP on a Tunable Benchmark
"  We investigate the concept of symmetry and its role in problem solving. This
paper first defines precisely the elements that constitute a ""problem"" and its
""solution,"" and gives several examples to illustrate these definitions. Given
precise definitions of problems, it is relatively straightforward to construct
a search process for finding solutions. Finally this paper attempts to exploit
the concept of symmetry in improving problem solving.
",Improving problem solving by exploiting the concept of symmetry
"  We have a lot of relation to the encoding and the Theory of Information, when
considering thinking. This is a natural process and, at once, the complex thing
we investigate. This always was a challenge - to understand how our mind works,
and we are trying to find some universal models for this. A lot of ways have
been considered so far, but we are looking for Something, we seek for
approaches. And the goal is to find a consistent, noncontradictory view, which
should at once be enough flexible in any dimensions to allow to represent
various kinds of processes and environments, matters of different nature and
diverse objects. Developing of such a model is the destination of this article.
","Irrespective Priority-Based Regular Properties of High-Intensity Virtual
  Environments"
"  Neuroevolution has yet to scale up to complex reinforcement learning tasks
that require large networks. Networks with many inputs (e.g. raw video) imply a
very high dimensional search space if encoded directly. Indirect methods use a
more compact genotype representation that is transformed into networks of
potentially arbitrary size. In this paper, we present an indirect method where
networks are encoded by a set of Fourier coefficients which are transformed
into network weight matrices via an inverse Fourier-type transform. Because
there often exist network solutions whose weight matrices contain regularity
(i.e. adjacent weights are correlated), the number of coefficients required to
represent these networks in the frequency domain is much smaller than the
number of weights (in the same way that natural images can be compressed by
ignore high-frequency components). This ""compressed"" encoding is compared to
the direct approach where search is conducted in the weight space on the
high-dimensional octopus arm task. The results show that representing networks
in the frequency domain can reduce the search-space dimensionality by as much
as two orders of magnitude, both accelerating convergence and yielding more
general solutions.
",A Frequency-Domain Encoding for Neuroevolution
"  We propose AD3, a new algorithm for approximate maximum a posteriori (MAP)
inference on factor graphs based on the alternating directions method of
multipliers. Like dual decomposition algorithms, AD3 uses worker nodes to
iteratively solve local subproblems and a controller node to combine these
local solutions into a global update. The key characteristic of AD3 is that
each local subproblem has a quadratic regularizer, leading to a faster
consensus than subgradient-based dual decomposition, both theoretically and in
practice. We provide closed-form solutions for these AD3 subproblems for binary
pairwise factors and factors imposing first-order logic constraints. For
arbitrary factors (large or combinatorial), we introduce an active set method
which requires only an oracle for computing a local MAP configuration, making
AD3 applicable to a wide range of problems. Experiments on synthetic and
realworld problems show that AD3 compares favorably with the state-of-the-art.
",Alternating Directions Dual Decomposition
"  Travel sharing, i.e., the problem of finding parts of routes which can be
shared by several travellers with different points of departure and
destinations, is a complex multiagent problem that requires taking into account
individual agents' preferences to come up with mutually acceptable joint plans.
In this paper, we apply state-of-the-art planning techniques to real-world
public transportation data to evaluate the feasibility of multiagent planning
techniques in this domain. The potential application value of improving travel
sharing technology has great application value due to its ability to reduce the
environmental impact of travelling while providing benefits to travellers at
the same time. We propose a three-phase algorithm that utilises performant
single-agent planners to find individual plans in a simplified domain first,
then merges them using a best-response planner which ensures resulting
solutions are individually rational, and then maps the resulting plan onto the
full temporal planning domain to schedule actual journeys. The evaluation of
our algorithm on real-world, multi-modal public transportation data for the
United Kingdom shows linear scalability both in the scenario size and in the
number of agents, where trade-offs have to be made between total cost
improvement, the percentage of feasible timetables identified for journeys, and
the prolongation of these journeys. Our system constitutes the first
implementation of strategic multiagent planning algorithms in large-scale
domains and provides insights into the engineering process of translating
general domain-independent multiagent planning algorithms to real-world
applications.
","Applying Strategic Multiagent Planning to Real-World Travel Sharing
  Problems"
"  Robust optimization is one of the fundamental approaches to deal with
uncertainty in combinatorial optimization. This paper considers the robust
spanning tree problem with interval data, which arises in a variety of
telecommunication applications. It proposes a constraint satisfaction approach
using a combinatorial lower bound, a pruning component that removes infeasible
and suboptimal edges, as well as a search strategy exploring the most uncertain
edges first. The resulting algorithm is shown to produce very dramatic
improvements over the mathematical programming approach of Yaman et al. and to
enlarge considerably the class of problems amenable to effective solutions
","A constraint satisfaction approach to the robust spanning tree problem
  with interval data"
"  The problem of learning Markov equivalence classes of Bayesian network
structures may be solved by searching for the maximum of a scoring metric in a
space of these classes. This paper deals with the definition and analysis of
one such search space. We use a theoretically motivated neighbourhood, the
inclusion boundary, and represent equivalence classes by essential graphs. We
show that this search space is connected and that the score of the neighbours
can be evaluated incrementally. We devise a practical way of building this
neighbourhood for an essential graph that is purely graphical and does not
explicitely refer to the underlying independences. We find that its size can be
intractable, depending on the complexity of the essential graph of the
equivalence class. The emphasis is put on the potential use of this space with
greedy hill -climbing search
","On the Construction of the Inclusion Boundary Neighbourhood for Markov
  Equivalence Classes of Bayesian Network Structures"
"  Recently, it has been emphasized that the possibility theory framework allows
us to distinguish between i) what is possible because it is not ruled out by
the available knowledge, and ii) what is possible for sure. This distinction
may be useful when representing knowledge, for modelling values which are not
impossible because they are consistent with the available knowledge on the one
hand, and values guaranteed to be possible because reported from observations
on the other hand. It is also of interest when expressing preferences, to point
out values which are positively desired among those which are not rejected.
This distinction can be encoded by two types of constraints expressed in terms
of necessity measures and in terms of guaranteed possibility functions, which
induce a pair of possibility distributions at the semantic level. A consistency
condition should ensure that what is claimed to be guaranteed as possible is
indeed not impossible. The present paper investigates the representation of
this bipolar view, including the case when it is stated by means of conditional
measures, or by means of comparative context-dependent constraints. The
interest of this bipolar framework, which has been recently stressed for
expressing preferences, is also pointed out in the representation of diagnostic
knowledge.
",Bipolar Possibilistic Representations
"  We develop a qualitative theory of Markov Decision Processes (MDPs) and
Partially Observable MDPs that can be used to model sequential decision making
tasks when only qualitative information is available. Our approach is based
upon an order-of-magnitude approximation of both probabilities and utilities,
similar to epsilon-semantics. The result is a qualitative theory that has close
ties with the standard maximum-expected-utility theory and is amenable to
general planning techniques.
",Qualitative MDPs and POMDPs: An Order-Of-Magnitude Approximation
"  The ability to make decisions and to assess potential courses of action is a
corner-stone of many AI applications, and usually this requires explicit
information about the decision-maker s preferences. IN many applications,
preference elicitation IS a serious bottleneck.The USER either does NOT have
the time, the knowledge, OR the expert support required TO specify complex
multi - attribute utility functions. IN such cases, a method that IS based ON
intuitive, yet expressive, preference statements IS required. IN this paper we
suggest the USE OF TCP - nets, an enhancement OF CP - nets, AS a tool FOR
representing, AND reasoning about qualitative preference statements.We present
AND motivate this framework, define its semantics, AND show how it can be used
TO perform constrained optimization.
",Introducing Variable Importance Tradeoffs into CP-Nets
"  We outline a class of problems, typical of Mars rover operations, that are
problematic for current methods of planning under uncertainty. The existing
methods fail because they suffer from one or more of the following limitations:
1) they rely on very simple models of actions and time, 2) they assume that
uncertainty is manifested in discrete action outcomes, 3) they are only
practical for very small problems. For many real world problems, these
assumptions fail to hold. In particular, when planning the activities for a
Mars rover, none of the above assumptions is valid: 1) actions can be
concurrent and have differing durations, 2) there is uncertainty concerning
action durations and consumption of continuous resources like power, and 3)
typical daily plans involve on the order of a hundred actions. This class of
problems may be of particular interest to the UAI community because both
classical and decision-theoretic planning techniques may be useful in solving
it. We describe the rover problem, discuss previous work on planning under
uncertainty, and present a detailed, but very small, example illustrating some
of the difficulties of finding good plans.
","Planning under Continuous Time and Resource Uncertainty: A Challenge for
  AI"
"  This paper concerns the assessment of direct causal effects from a
combination of: (i) non-experimental data, and (ii) qualitative domain
knowledge. Domain knowledge is encoded in the form of a directed acyclic graph
(DAG), in which all interactions are assumed linear, and some variables are
presumed to be unobserved. We provide a generalization of the well-known method
of Instrumental Variables, which allows its application to models with few
conditional independeces.
",Generalized Instrumental Variables
"  In this paper, we derive optimality results for greedy Bayesian-network
search algorithms that perform single-edge modifications at each step and use
asymptotically consistent scoring criteria. Our results extend those of Meek
(1997) and Chickering (2002), who demonstrate that in the limit of large
datasets, if the generative distribution is perfect with respect to a DAG
defined over the observable variables, such search algorithms will identify
this optimal (i.e. generative) DAG model. We relax their assumption about the
generative distribution, and assume only that this distribution satisfies the
{em composition property} over the observable variables, which is a more
realistic assumption for real domains. Under this assumption, we guarantee that
the search algorithms identify an {em inclusion-optimal} model; that is, a
model that (1) contains the generative distribution and (2) has no sub-model
that contains this distribution. In addition, we show that the composition
property is guaranteed to hold whenever the dependence relationships in the
generative distribution can be characterized by paths between singleton
elements in some generative graphical model (e.g. a DAG, a chain graph, or a
Markov network) even when the generative model includes unobserved variables,
and even when the observed data is subject to selection bias.
",Finding Optimal Bayesian Networks
"  The paper presents an iterative version of join-tree clustering that applies
the message passing of join-tree clustering algorithm to join-graphs rather
than to join-trees, iteratively. It is inspired by the success of Pearl's
belief propagation algorithm as an iterative approximation scheme on one hand,
and by a recently introduced mini-clustering i. success as an anytime
approximation method, on the other. The proposed Iterative Join-graph
Propagation IJGP belongs to the class of generalized belief propagation
methods, recently proposed using analogy with algorithms in statistical
physics. Empirical evaluation of this approach on a number of problem classes
demonstrates that even the most time-efficient variant is almost always
superior to IBP and MC i, and is sometimes more accurate by as much as several
orders of magnitude.
",Iterative Join-Graph Propagation
"  In this paper, we continue our research on the algorithmic aspects of Halpern
and Pearl's causes and explanations in the structural-model approach. To this
end, we present new characterizations of weak causes for certain classes of
causal models, which show that under suitable restrictions deciding causes and
explanations is tractable. To our knowledge, these are the first explicit
tractability results for the structural-model approach.
","Causes and Explanations in the Structural-Model Approach: Tractable
  Cases"
"  We formulate necessary and sufficient conditions for an arbitrary discrete
probability distribution to factor according to an undirected graphical model,
or a log-linear model, or other more general exponential models. This result
generalizes the well known Hammersley-Clifford Theorem.
",Factorization of Discrete Probability Distributions
"  This paper presents a decision-theoretic approach to statistical inference
that satisfies the likelihood principle (LP) without using prior information.
Unlike the Bayesian approach, which also satisfies LP, we do not assume
knowledge of the prior distribution of the unknown parameter. With respect to
information that can be obtained from an experiment, our solution is more
efficient than Wald's minimax solution.However, with respect to information
assumed to be known before the experiment, our solution demands less input than
the Bayesian solution.
","Statistical Decisions Using Likelihood Information Without Prior
  Probabilities"
"  We present a principled and efficient planning algorithm for collaborative
multiagent dynamical systems. All computation, during both the planning and the
execution phases, is distributed among the agents; each agent only needs to
model and plan for a small part of the system. Each of these local subsystems
is small, but once they are combined they can represent an exponentially larger
problem. The subsystems are connected through a subsystem hierarchy.
Coordination and communication between the agents is not imposed, but derived
directly from the structure of this hierarchy. A globally consistent plan is
achieved by a message passing algorithm, where messages correspond to natural
local reward functions and are computed by local linear programs; another
message passing algorithm allows us to execute the resulting policy. When two
portions of the hierarchy share the same structure, our algorithm can reuse
plans and messages to speed up computation.
",Distributed Planning in Hierarchical Factored MDPs
"  We describe expectation propagation for approximate inference in dynamic
Bayesian networks as a natural extension of Pearl s exact belief
propagation.Expectation propagation IS a greedy algorithm, converges IN many
practical cases, but NOT always.We derive a DOUBLE - loop algorithm, guaranteed
TO converge TO a local minimum OF a Bethe free energy.Furthermore, we show that
stable fixed points OF (damped) expectation propagation correspond TO local
minima OF this free energy, but that the converse need NOT be the CASE .We
illustrate the algorithms BY applying them TO switching linear dynamical
systems AND discuss implications FOR approximate inference IN general Bayesian
networks.
","Expectation Propogation for approximate inference in dynamic Bayesian
  networks"
"  We extend the language of influence diagrams to cope with decision scenarios
where the order of decisions and observations is not determined. As the
ordering of decisions is dependent on the evidence, a step-strategy of such a
scenario is a sequence of dependent choices of the next action. A strategy is a
step-strategy together with selection functions for decision actions. The
structure of a step-strategy can be represented as a DAG with nodes labeled
with action variables. We introduce the concept of GS-DAG: a DAG incorporating
an optimal step-strategy for any instantiation. We give a method for
constructing GS-DAGs, and we show how to use a GS-DAG for determining an
optimal strategy. Finally we discuss how analysis of relevant past can be used
to reduce the size of the GS-DAG.
",Unconstrained Influence Diagrams
"  We introduce a new Bayesian network (BN) scoring metric called the Global
Uniform (GU) metric. This metric is based on a particular type of default
parameter prior. Such priors may be useful when a BN developer is not willing
or able to specify domain-specific parameter priors. The GU parameter prior
specifies that every prior joint probability distribution P consistent with a
BN structure S is considered to be equally likely. Distribution P is consistent
with S if P includes just the set of independence relations defined by S. We
show that the GU metric addresses some undesirable behavior of the BDeu and K2
Bayesian network scoring metrics, which also use particular forms of default
parameter priors. A closed form formula for computing GU for special classes of
BNs is derived. Efficiently computing GU for an arbitrary BN remains an open
problem.
","A Bayesian Network Scoring Metric That Is Based On Globally Uniform
  Parameter Priors"
"  This paper investigates value function approximation in the context of
zero-sum Markov games, which can be viewed as a generalization of the Markov
decision process (MDP) framework to the two-agent case. We generalize error
bounds from MDPs to Markov games and describe generalizations of reinforcement
learning algorithms to Markov games. We present a generalization of the optimal
stopping problem to a two-player simultaneous move Markov game. For this
special problem, we provide stronger bounds and can guarantee convergence for
LSTD and temporal difference learning with linear value function approximation.
We demonstrate the viability of value function approximation for Markov games
by using the Least squares policy iteration (LSPI) algorithm to learn good
policies for a soccer domain and a flow control problem.
",Value Function Approximation in Zero-Sum Markov Games
"  The Reverse Water Gas Shift system (RWGS) is a complex physical system
designed to produce oxygen from the carbon dioxide atmosphere on Mars. If sent
to Mars, it would operate without human supervision, thus requiring a reliable
automated system for monitoring and control. The RWGS presents many challenges
typical of real-world systems, including: noisy and biased sensors, nonlinear
behavior, effects that are manifested over different time granularities, and
unobservability of many important quantities. In this paper we model the RWGS
using a hybrid (discrete/continuous) Dynamic Bayesian Network (DBN), where the
state at each time slice contains 33 discrete and 184 continuous variables. We
show how the system state can be tracked using probabilistic inference over the
model. We discuss how to deal with the various challenges presented by the
RWGS, providing a suite of techniques that are likely to be useful in a wide
range of applications. In particular, we describe a general framework for
dealing with nonlinear behavior using numerical integration techniques,
extending the successful Unscented Filter. We also show how to use a
fixed-point computation to deal with effects that develop at different time
scales, specifically rapid changes occurring during slowly changing processes.
We test our model using real data collected from the RWGS, demonstrating the
feasibility of hybrid DBNs for monitoring complex real-world physical systems.
",Monitoring a Complez Physical System using a Hybrid Dynamic Bayes Net
"  We propose a formal treatment of scenarios in the context of a dialectical
argumentation formalism for qualitative reasoning about uncertain propositions.
Our formalism extends prior work in which arguments for and against uncertain
propositions were presented and compared in interaction spaces called Agoras.
We now define the notion of a scenario in this framework and use it to define a
set of qualitative uncertainty labels for propositions across a collection of
scenarios. This work is intended to lead to a formal theory of scenarios and
scenario analysis.
",Formalizing Scenario Analysis
"  This paper is about searching the combinatorial space of contingency tables
during the inner loop of a nonlinear statistical optimization. Examples of this
operation in various data analytic communities include searching for nonlinear
combinations of attributes that contribute significantly to a regression
(Statistics), searching for items to include in a decision list (machine
learning) and association rule hunting (Data Mining).
  This paper investigates a new, efficient approach to this class of problems,
called RADSEARCH (Real-valued All-Dimensions-tree Search). RADSEARCH finds the
global optimum, and this gives us the opportunity to empirically evaluate the
question: apart from algorithmic elegance what does this attention to
optimality buy us?
  We compare RADSEARCH with other recent successful search algorithms such as
CN2, PRIM, APriori, OPUS and DenseMiner. Finally, we introduce RADREG, a new
regression algorithm for learning real-valued outputs based on RADSEARCHing for
high-order interactions.
","Real-valued All-Dimensions search: Low-overhead rapid searching over
  subsets of attributes"
"  Exact monitoring in dynamic Bayesian networks is intractable, so approximate
algorithms are necessary. This paper presents a new family of approximate
monitoring algorithms that combine the best qualities of the particle filtering
and Boyen-Koller methods. Our algorithms maintain an approximate representation
the belief state in the form of sets of factored particles, that correspond to
samples of clusters of state variables. Empirical results show that our
algorithms outperform both ordinary particle filtering and the Boyen-Koller
algorithm on large systems.
",Factored Particles for Scalable Monitoring
"  In this paper we present a language for finite state continuous time Bayesian
networks (CTBNs), which describe structured stochastic processes that evolve
over continuous time. The state of the system is decomposed into a set of local
variables whose values change over time. The dynamics of the system are
described by specifying the behavior of each local variable as a function of
its parents in a directed (possibly cyclic) graph. The model specifies, at any
given point in time, the distribution over two aspects: when a local variable
changes its value and the next value it takes. These distributions are
determined by the variable s CURRENT value AND the CURRENT VALUES OF its
parents IN the graph.More formally, each variable IS modelled AS a finite state
continuous time Markov process whose transition intensities are functions OF
its parents.We present a probabilistic semantics FOR the language IN terms OF
the generative model a CTBN defines OVER sequences OF events.We list types OF
queries one might ask OF a CTBN, discuss the conceptual AND computational
difficulties associated WITH exact inference, AND provide an algorithm FOR
approximate inference which takes advantage OF the structure within the
process.
",Continuous Time Bayesian Networks
"  MAP is the problem of finding a most probable instantiation of a set of
nvariables in a Bayesian network, given some evidence. MAP appears to be a
significantly harder problem than the related problems of computing the
probability of evidence Pr, or MPE a special case of MAP. Because of the
complexity of MAP, and the lack of viable algorithms to approximate it,MAP
computations are generally avoided by practitioners. This paper investigates
the complexity of MAP. We show that MAP is complete for NP. We also provide
negative complexity results for elimination based algorithms. It turns out that
MAP remains hard even when MPE, and Pr are easy. We show that MAP is NPcomplete
when the networks are restricted to polytrees, and even then can not be
effectively approximated. Because there is no approximation algorithm with
guaranteed results, we investigate best effort approximations. We introduce a
generic MAP approximation framework. As one instantiation of it, we implement
local search coupled with belief propagation BP to approximate MAP. We show how
to extract approximate evidence retraction information from belief propagation
which allows us to perform efficient local search. This allows MAP
approximation even on networks that are too complex to even exactly solve the
easier problems of computing Pr or MPE. Experimental results indicate that
using BP and local search provides accurate MAP estimates in many cases.
",MAP Complexity Results and Approximation Methods
"  Quantification is well known to be a major obstacle in the construction of a
probabilistic network, especially when relying on human experts for this
purpose. The construction of a qualitative probabilistic network has been
proposed as an initial step in a network s quantification, since the
qualitative network can be used TO gain preliminary insight IN the projected
networks reasoning behaviour. We extend on this idea and present a new type of
network in which both signs and numbers are specified; we further present an
associated algorithm for probabilistic inference. Building upon these
semi-qualitative networks, a probabilistic network can be quantified and
studied in a stepwise manner. As a result, modelling inadequacies can be
detected and amended at an early stage in the quantification process.
",From Qualitative to Quantitative Probabilistic Networks
"  We present new algorithms for inference in credal networks --- directed
acyclic graphs associated with sets of probabilities. Credal networks are here
interpreted as encoding strong independence relations among variables. We first
present a theory of credal networks based on separately specified sets of
probabilities. We also show that inference with polytrees is NP-hard in this
setting. We then introduce new techniques that reduce the computational effort
demanded by inference, particularly in polytrees, by exploring separability of
credal sets.
","Inference with Seperately Specified Sets of Probabilities in Credal
  Networks"
"  An increasing number of applications require real-time reasoning under
uncertainty with streaming input. The temporal (dynamic) Bayes net formalism
provides a powerful representational framework for such applications. However,
existing exact inference algorithms for dynamic Bayes nets do not scale to the
size of models required for real world applications which often contain
hundreds or even thousands of variables for each time slice. In addition,
existing algorithms were not developed with real-time processing in mind. We
have developed a new computational approach to support real-time exact
inference in large temporal Bayes nets. Our approach tackles scalability by
recognizing that the complexity of the inference depends on the number of
interface nodes between time slices and by exploiting the distinction between
static and dynamic nodes in order to reduce the number of interface nodes and
to factorize their joint probability distribution. We approach the real-time
issue by organizing temporal Bayes nets into static representations, and then
using the symbolic probabilistic inference algorithm to derive analytic
expressions for the static representations. The parts of these expressions that
do not change at each time step are pre-computed. The remaining parts are
compiled into efficient procedural code so that the memory and CPU resources
required by the inference are small and fixed.
",Real-Time Inference with Large-Scale Temporal Bayes Nets
"  We address the question of convergence in the loopy belief propagation (LBP)
algorithm. Specifically, we relate convergence of LBP to the existence of a
weak limit for a sequence of Gibbs measures defined on the LBP s associated
computation tree.Using tools FROM the theory OF Gibbs measures we develop
easily testable sufficient conditions FOR convergence.The failure OF
convergence OF LBP implies the existence OF multiple phases FOR the associated
Gibbs specification.These results give new insight INTO the mechanics OF the
algorithm.
",Loopy Belief Propogation and Gibbs Measures
"  A popular approach to solving a decision process with non-Markovian rewards
(NMRDP) is to exploit a compact representation of the reward function to
automatically translate the NMRDP into an equivalent Markov decision process
(MDP) amenable to our favorite MDP solution method. The contribution of this
paper is a representation of non-Markovian reward functions and a translation
into MDP aimed at making the best possible use of state-based anytime
algorithms as the solution method. By explicitly constructing and exploring
only parts of the state space, these algorithms are able to trade computation
time for policy quality, and have proven quite effective in dealing with large
MDPs. Our representation extends future linear temporal logic (FLTL) to express
rewards. Our translation has the effect of embedding model-checking in the
solution method. It results in an MDP of the minimal size achievable without
stepping outside the anytime framework, and consequently in better policies by
the deadline.
","Anytime State-Based Solution Methods for Decision Processes with
  non-Markovian Rewards"
"  The validity OF a causal model can be tested ONLY IF the model imposes
constraints ON the probability distribution that governs the generated data. IN
the presence OF unmeasured variables, causal models may impose two types OF
constraints : conditional independencies, AS READ through the d - separation
criterion, AND functional constraints, FOR which no general criterion IS
available.This paper offers a systematic way OF identifying functional
constraints AND, thus, facilitates the task OF testing causal models AS well AS
inferring such models FROM data.
",On the Testable Implications of Causal Models with Hidden Variables
"  We propose an efficient method for Bayesian network inference in models with
functional dependence. We generalize the multiplicative factorization method
originally designed by Takikawa and D Ambrosio(1999) FOR models WITH
independence OF causal influence.Using a hidden variable, we transform a
probability potential INTO a product OF two - dimensional potentials.The
multiplicative factorization yields more efficient inference. FOR example, IN
junction tree propagation it helps TO avoid large cliques. IN ORDER TO keep
potentials small, the number OF states OF the hidden variable should be
minimized.We transform this problem INTO a combinatorial problem OF minimal
base IN a particular space.We present an example OF a computerized adaptive
test, IN which the factorization method IS significantly more efficient than
previous inference methods.
",Exploiting Functional Dependence in Bayesian Network Inference
"  This paper uses decision-theoretic principles to obtain new insights into the
assessment and updating of probabilities. First, a new foundation of
Bayesianism is given. It does not require infinite atomless uncertainties as
did Savage s classical result, AND can therefore be applied TO ANY finite
Bayesian network.It neither requires linear utility AS did de Finetti s
classical result, AND r ntherefore allows FOR the empirically AND normatively
desirable risk r naversion.Finally, BY identifying AND fixing utility IN an
elementary r nmanner, our result can readily be applied TO identify methods OF
r nprobability updating.Thus, a decision - theoretic foundation IS given r nto
the computationally efficient method OF inductive reasoning r ndeveloped BY
Rudolf Carnap.Finally, recent empirical findings ON r nprobability assessments
are discussed.It leads TO suggestions FOR r ncorrecting biases IN probability
assessments, AND FOR an alternative r nto the Dempster - Shafer belief
functions that avoids the reduction TO r ndegeneracy after multiple updatings.r
n
","Decision Principles to justify Carnap's Updating Method and to Suggest
  Corrections of Probability Judgments (Invited Talks)"
"  We select policies for large Markov Decision Processes (MDPs) with compact
first-order representations. We find policies that generalize well as the
number of objects in the domain grows, potentially without bound. Existing
dynamic-programming approaches based on flat, propositional, or first-order
representations either are impractical here or do not naturally scale as the
number of objects grows without bound. We implement and evaluate an alternative
approach that induces first-order policies using training data constructed by
solving small problem instances using PGraphplan (Blum & Langford, 1999). Our
policies are represented as ensembles of decision lists, using a taxonomic
concept language. This approach extends the work of Martin and Geffner (2000)
to stochastic domains, ensemble learning, and a wider variety of problems.
Empirically, we find ""good"" policies for several stochastic first-order MDPs
that are beyond the scope of previous approaches. We also discuss the
application of this work to the relational reinforcement-learning problem.
",Inductive Policy Selection for First-Order MDPs
"  NP-SPEC is a language for specifying problems in NP in a declarative way.
Despite the fact that the semantics of the language was given by referring to
Datalog with circumscription, which is very close to ASP, so far the only
existing implementations are by means of ECLiPSe Prolog and via Boolean
satisfiability solvers. In this paper, we present translations from NP-SPEC
into various forms of ASP and analyze them. We also argue that it might be
useful to incorporate certain language constructs of NP-SPEC into mainstream
ASP.
",Translating NP-SPEC into ASP
"  In this paper we continue the work on our extension of Answer Set Programming
by non-Herbrand functions and add to the language support for arithmetic
expressions and various inequality relations over non-Herbrand functions, as
well as consistency-restoring rules from CR-Prolog. We demonstrate the use of
this latest version of the language in the representation of important kinds of
knowledge.
","Language ASP{f} with Arithmetic Expressions and Consistency-Restoring
  Rules"
"  Within the area of computational models of argumentation, the
instantiation-based approach is gaining more and more attention, not at least
because meaningful input for Dung's abstract frameworks is provided in that
way. In a nutshell, the aim of instantiation-based argumentation is to form,
from a given knowledge base, a set of arguments and to identify the conflicts
between them. The resulting network is then evaluated by means of
extension-based semantics on an abstract level, i.e. on the resulting graph.
While several systems are nowadays available for the latter step, the
automation of the instantiation process itself has received less attention. In
this work, we provide a novel approach to construct and visualize an
argumentation framework from a given knowledge base. The system we propose
relies on Answer-Set Programming and follows a two-step approach. A first
program yields the logic-based arguments as its answer-sets; a second program
is then used to specify the relations between arguments based on the
answer-sets of the first program. As it turns out, this approach not only
allows for a flexible and extensible tool for instantiation-based
argumentation, but also provides a new method for answer-set visualization in
general.
",Utilizing ASP for Generating and Visualizing Argumentation Frameworks
"  In this paper we present an Action Language-Answer Set Programming based
approach to solving planning and scheduling problems in hybrid domains -
domains that exhibit both discrete and continuous behavior. We use action
language H to represent the domain and then translate the resulting theory into
an A-Prolog program. In this way, we reduce the problem of finding solutions to
planning and scheduling problems to computing answer sets of A-Prolog programs.
We cite a planning and scheduling example from the literature and show how to
model it in H. We show how to translate the resulting H theory into an
equivalent A-Prolog program. We compute the answer sets of the resulting
program using a hybrid solver called EZCSP which loosely integrates a
constraint solver with an answer set solver. The solver allows us reason about
constraints over reals and compute solutions to complex planning and scheduling
problems. Results have shown that our approach can be applied to any planning
and scheduling problem in hybrid domains.
",Planning and Scheduling in Hybrid Domains Using Answer Set Programming
"  The advance of Internet and Sensor technology has brought about new
challenges evoked by the emergence of continuous data streams. Beyond rapid
data processing, application areas like ambient assisted living, robotics, or
dynamic scheduling involve complex reasoning tasks. We address such scenarios
and elaborate upon approaches to knowledge-intense stream reasoning, based on
Answer Set Programming (ASP). While traditional ASP methods are devised for
singular problem solving, we develop new techniques to formulate and process
problems dealing with emerging as well as expiring data in a seamless way.
",Answer Set Programming for Stream Reasoning
"  DL-Lite is an important family of description logics. Recently, there is an
increasing interest in handling inconsistency in DL-Lite as the constraint
imposed by a TBox can be easily violated by assertions in ABox in DL-Lite. In
this paper, we present a distance-based paraconsistent semantics based on the
notion of feature in DL-Lite, which provides a novel way to rationally draw
meaningful conclusions even from an inconsistent knowledge base. Finally, we
investigate several important logical properties of this entailment relation
based on the new semantics and show its promising advantages in non-monotonic
reasoning for DL-Lite.
",A Distance-based Paraconsistent Semantics for DL-Lite
"  This paper presents a novel approach based on variable forgetting, which is a
useful tool in resolving contradictory by filtering some given variables, to
merging multiple knowledge bases. This paper first builds a relationship
between belief merging and variable forgetting by using dilation. Variable
forgetting is applied to capture belief merging operation. Finally, some new
merging operators are developed by modifying candidate variables to amend the
shortage of traditional merging operators. Different from model selection of
traditional merging operators, as an alternative approach, variable selection
in those new operators could provide intuitive information about an atom
variable among whole knowledge bases.
",A Forgetting-based Approach to Merging Knowledge Bases
"  In an open, constantly changing and collaborative environment like the
forthcoming Semantic Web, it is reasonable to expect that knowledge sources
will contain noise and inaccuracies. It is well known, as the logical
foundation of the Semantic Web, description logic is lack of the ability of
tolerating inconsistent or incomplete data. Recently, the ability of
paraconsistent approaches in Semantic Web is weaker in this paper, we present a
tableau algorithm based on sign transformation in Semantic Web which holds the
stronger ability of reasoning. We prove that the tableau algorithm is decidable
which hold the same function of classical tableau algorithm for consistent
knowledge bases.
","A Paraconsistent Tableau Algorithm Based on Sign Transformation in
  Semantic Web"
"  This volume contains the papers presented at the fifth workshop on Answer Set
Programming and Other Computing Paradigms (ASPOCP 2012) held on September 4th,
2012 in Budapest, co-located with the 28th International Conference on Logic
Programming (ICLP 2012). It thus continues a series of previous events
co-located with ICLP, aiming at facilitating the discussion about crossing the
boundaries of current ASP techniques in theory, solving, and applications, in
combination with or inspired by other computing paradigms.
","Proceedings of Answer Set Programming and Other Computing Paradigms
  (ASPOCP 2012), 5th International Workshop, September 4, 2012, Budapest,
  Hungary"
"  We present a general framework for defining priors on model structure and
sampling from the posterior using the Metropolis-Hastings algorithm. The key
idea is that structure priors are defined via a probability tree and that the
proposal mechanism for the Metropolis-Hastings algorithm operates by traversing
this tree, thereby defining a cheaply computable acceptance probability. We
have applied this approach to Bayesian net structure learning using a number of
priors and tree traversal strategies. Our results show that these must be
chosen appropriately for this approach to be successful.
",Markov Chain Monte Carlo using Tree-Based Priors on Model Structure
"  Possibility theory offers either a qualitive, or a numerical framework for
representing uncertainty, in terms of dual measures of possibility and
necessity. This leads to the existence of two kinds of possibilistic causal
graphs where the conditioning is either based on the minimum, or the product
operator. Benferhat et al. (1999) have investigated the connections between
min-based graphs and possibilistic logic bases (made of classical formulas
weighted in terms of certainty). This paper deals with a more difficult issue :
the product-based graphical representations of possibilistic bases, which
provides an easy structural reading of possibilistic bases. Moreover, this
paper also provides another reading of possibilistic bases in terms of
comparative preferences of the form ""in the context p, q is preferred to not
q"". This enables us to explicit preferences underlying a set of goals with
different levels of priority.
",Graphical readings of possibilistic logic bases
"  This paper presents a sound and completecalculus for causal relevance, based
onPearl's functional models semantics.The calculus consists of axioms and
rulesof inference for reasoning about causalrelevance relationships.We extend
the set of known axioms for causalrelevance with three new axioms, andintroduce
two new rules of inference forreasoning about specific subclasses
ofmodels.These subclasses give a more refinedcharacterization of causal models
than the one given in Halpern's axiomatizationof counterfactual
reasoning.Finally, we show how the calculus for causalrelevance can be used in
the task ofidentifying causal structure from non-observational data.
",A Calculus for Causal Relevance
"  We propose a new directed graphical representation of utility functions,
called UCP-networks, that combines aspects of two existing graphical models:
generalized additive models and CP-networks. The network decomposes a utility
function into a number of additive factors, with the directionality of the arcs
reflecting conditional dependence of preference statements - in the underlying
(qualitative) preference ordering - under a {em ceteris paribus} (all else
being equal) interpretation. This representation is arguably natural in many
settings. Furthermore, the strong CP-semantics ensures that computation of
optimization and dominance queries is very efficient. We also demonstrate the
value of this representation in decision making. Finally, we describe an
interactive elicitation procedure that takes advantage of the linear nature of
the constraints on ""`tradeoff weights"" imposed by a UCP-network. This procedure
allows the network to be refined until the regret of the decision with minimax
regret (with respect to the incompletely specified utility function) falls
below a specified threshold (e.g., the cost of further questioning.
","UCP-Networks: A Directed Graphical Representation of Conditional
  Utilities"
"  We present two sampling algorithms for probabilistic confidence inference in
Bayesian networks. These two algorithms (we call them AIS-BN-mu and
AIS-BN-sigma algorithms) guarantee that estimates of posterior probabilities
are with a given probability within a desired precision bound. Our algorithms
are based on recent advances in sampling algorithms for (1) estimating the mean
of bounded random variables and (2) adaptive importance sampling in Bayesian
networks. In addition to a simple stopping rule for sampling that they provide,
the AIS-BN-mu and AIS-BN-sigma algorithms are capable of guiding the learning
process in the AIS-BN algorithm. An empirical evaluation of the proposed
algorithms shows excellent performance, even for very unlikely evidence.
",Confidence Inference in Bayesian Networks
"  It is ""well known"" that in linear models: (1) testable constraints on the
marginal distribution of observed variables distinguish certain cases in which
an unobserved cause jointly influences several observed variables; (2) the
technique of ""instrumental variables"" sometimes permits an estimation of the
influence of one variable on another even when the association between the
variables may be confounded by unobserved common causes; (3) the association
(or conditional probability distribution of one variable given another) of two
variables connected by a path or trek can be computed directly from the
parameter values associated with each edge in the path or trek; (4) the
association of two variables produced by multiple treks can be computed from
the parameters associated with each trek; and (5) the independence of two
variables conditional on a third implies the corresponding independence of the
sums of the variables over all units conditional on the sums over all units of
each of the original conditioning variables.These properties are exploited in
search procedures. It is also known that properties (2)-(5) do not hold for all
Bayes nets with binary variables. We show that (1) holds for all Bayes nets
with binary variables and (5) holds for all singly trek-connected Bayes nets of
that kind. We further show that all five properties hold for Bayes nets with
any DAG and binary variables parameterized with noisy-or and noisy-and gates.
",Linearity Properties of Bayes Nets with Binary Variables
"  This paper explores algorithms for processing probabilistic and deterministic
information when the former is represented as a belief network and the latter
as a set of boolean clauses. The motivating tasks are 1. evaluating beliefs
networks having a large number of deterministic relationships and2. evaluating
probabilities of complex boolean querie over a belief network. We propose a
parameterized family of variable elimination algorithms that exploit both types
of information, and that allows varying levels of constraint propagation
inferences. The complexity of the scheme is controlled by the induced-width of
the graph {em augmented} by the dependencies introduced by the boolean
constraints. Preliminary empirical evaluation demonstrate the effect of
constraint propagation on probabilistic computation.
",Hybrid Processing of Beliefs and Constraints
"  In this paper we analyze two recent axiomatic approaches proposed by Dubois
et al and by Giang and Shenoy to qualitative decision making where uncertainty
is described by possibility theory. Both axiomtizations are inspired by von
Neumann and Morgenstern's system of axioms for the case of probability theory.
We show that our approach naturally unifies two axiomatic systems that
correspond respectively to pessimistic and optimistic decision criteria
proposed by Dubois et al. The simplifying unification is achieved by (i)
replacing axioms that are supposed to reflect two informational attitudes
(uncertainty aversion and uncertainty attraction) by an axiom that imposes
order on set of standard lotteries and (ii) using a binary utility scale in
which each utility level is represented by a pair of numbers.
","A Comparison of Axiomatic Approaches to Qualitative Decision Making
  Using Possibility Theory"
"  Graphical Markov models determined by acyclic digraphs (ADGs), also called
directed acyclic graphs (DAGs), are widely studied in statistics, computer
science (as Bayesian networks), operations research (as influence diagrams),
and many related fields. Because different ADGs may determine the same Markov
equivalence class, it long has been of interest to determine the efficiency
gained in model specification and search by working directly with Markov
equivalence classes of ADGs rather than with ADGs themselves. A computer
program was written to enumerate the equivalence classes of ADG models as
specified by Pearl & Verma's equivalence criterion. The program counted
equivalence classes for models up to and including 10 vertices. The ratio of
number of classes to ADGs appears to approach an asymptote of about 0.267.
Classes were analyzed according to number of edges and class size. By edges,
the distribution of number of classes approaches a Gaussian shape. By class
size, classes of size 1 are most common, with the proportions for larger sizes
initially decreasing but then following a more irregular pattern. The maximum
number of classes generated by any undirected graph was found to increase
approximately factorially. The program also includes a new variation of orderly
algorithm for generating undirected graphs.
",Enumerating Markov Equivalence Classes of Acyclic Digraph Models
"  In previous work cite{Ha98:Towards} we presented a case-based approach to
eliciting and reasoning with preferences. A key issue in this approach is the
definition of similarity between user preferences. We introduced the
probabilistic distance as a measure of similarity on user preferences, and
provided an algorithm to compute the distance between two partially specified
{em value} functions. This is for the case of decision making under {em
certainty}. In this paper we address the more challenging issue of computing
the probabilistic distance in the case of decision making under{em
uncertainty}. We provide an algorithm to compute the probabilistic distance
between two partially specified {em utility} functions. We demonstrate the use
of this algorithm with a medical data set of partially specified patient
preferences,where none of the other existing distancemeasures appear definable.
Using this data set, we also demonstrate that the case-based approach to
preference elicitation isapplicable in domains with uncertainty. Finally, we
provide a comprehensive analytical comparison of the probabilistic distance
with some existing distance measures on preferences.
","Similarity Measures on Preference Structures, Part II: Utility Functions"
"  We propose a new definition of actual causes, using structural equations to
model counterfactuals.We show that the definitions yield a plausible and
elegant account ofcausation that handles well examples which have caused
problems forother definitions and resolves major difficulties in the
traditionalaccount. In a companion paper, we show how the definition of
causality can beused to give an elegant definition of (causal) explanation.
",Causes and Explanations: A Structural-Model Approach --- Part 1: Causes
"  We are developing a general framework for using learned Bayesian models for
decision-theoretic control of search and reasoningalgorithms. We illustrate the
approach on the specific task of controlling both general and domain-specific
solvers on a hard class of structured constraint satisfaction problems. A
successful strategyfor reducing the high (and even infinite) variance in
running time typically exhibited by backtracking search algorithms is to cut
off and restart the search if a solution is not found within a certainamount of
time. Previous work on restart strategies have employed fixed cut off values.
We show how to create a dynamic cut off strategy by learning a Bayesian model
that predicts the ultimate length of a trial based on observing the early
behavior of the search algorithm. Furthermore, we describe the general
conditions under which a dynamic restart strategy can outperform the
theoretically optimal fixed strategy.
",A Bayesian Approach to Tackling Hard Computational Problems
"  Every directed acyclic graph (DAG) over a finite non-empty set of variables
(= nodes) N induces an independence model over N, which is a list of
conditional independence statements over N.The inclusion problem is how to
characterize (in graphical terms) whether all independence statements in the
model induced by a DAG K are in the model induced by a second DAG L. Meek
(1997) conjectured that this inclusion holds iff there exists a sequence of
DAGs from L to K such that only certain 'legal' arrow reversal and 'legal'
arrow adding operations are performed to get the next DAG in the sequence.In
this paper we give several characterizations of inclusion of DAG models and
verify Meek's conjecture in the case that the DAGs K and L differ in at most
one adjacency. As a warming up a rigorous proof of well-known graphical
characterizations of equivalence of DAGs, which is a highly related problem, is
given.
",On characterizing Inclusion of Bayesian Networks
"  This article deals with plausible reasoning from incomplete knowledge about
large-scale spatial properties. The availableinformation, consisting of a set
of pointwise observations,is extrapolated to neighbour points. We make use of
belief functions to represent the influence of the knowledge at a given point
to another point; the quantitative strength of this influence decreases when
the distance between both points increases. These influences arethen aggregated
using a variant of Dempster's rule of combination which takes into account the
relative dependence between observations.
",Plausible reasoning from spatial observations
"  This paper considers the problem of knowledge-based model construction in the
presence of uncertainty about the association of domain entities to random
variables. Multi-entity Bayesian networks (MEBNs) are defined as a
representation for knowledge in domains characterized by uncertainty in the
number of relevant entities, their interrelationships, and their association
with observables. An MEBN implicitly specifies a probability distribution in
terms of a hierarchically structured collection of Bayesian network fragments
that together encode a joint probability distribution over arbitrarily many
interrelated hypotheses. Although a finite query-complete model can always be
constructed, association uncertainty typically makes exact model construction
and evaluation intractable. The objective of hypothesis management is to
balance tractability against accuracy. We describe an application to the
problem of using intelligence reports to infer the organization and activities
of groups of military vehicles. Our approach is compared to related work in the
tracking and fusion literature.
",Hypothesis Management in Situation-Specific Network Construction
"  An important subclass of hybrid Bayesian networks are those that represent
Conditional Linear Gaussian (CLG) distributions --- a distribution with a
multivariate Gaussian component for each instantiation of the discrete
variables. In this paper we explore the problem of inference in CLGs. We show
that inference in CLGs can be significantly harder than inference in Bayes
Nets. In particular, we prove that even if the CLG is restricted to an
extremely simple structure of a polytree in which every continuous node has at
most one discrete ancestor, the inference task is NP-hard.To deal with the
often prohibitive computational cost of the exact inference algorithm for CLGs,
we explore several approximate inference algorithms. These algorithms try to
find a small subset of Gaussians which are a good approximation to the full
mixture distribution. We consider two Monte Carlo approaches and a novel
approach that enumerates mixture components in order of prior probability. We
compare these methods on a variety of problems and show that our novel
algorithm is very promising for large, hybrid diagnosis problems.
","Inference in Hybrid Networks: Theoretical Limits and Practical
  Algorithms"
"  Many real life domains contain a mixture of discrete and continuous variables
and can be modeled as hybrid Bayesian Networks. Animportant subclass of hybrid
BNs are conditional linear Gaussian (CLG) networks, where the conditional
distribution of the continuous variables given an assignment to the discrete
variables is a multivariate Gaussian. Lauritzen's extension to the clique tree
algorithm can be used for exact inference in CLG networks. However, many
domains also include discrete variables that depend on continuous ones, and CLG
networks do not allow such dependencies to berepresented. No exact inference
algorithm has been proposed for these enhanced CLG networks. In this paper, we
generalize Lauritzen's algorithm, providing the first ""exact"" inference
algorithm for augmented CLG networks - networks where continuous nodes are
conditional linear Gaussians but that also allow discrete children ofcontinuous
parents. Our algorithm is exact in the sense that it computes the exact
distributions over the discrete nodes, and the exact first and second moments
of the continuous ones, up to the accuracy obtained by numerical integration
used within thealgorithm. When the discrete children are modeled with softmax
CPDs (as is the case in many real world domains) the approximation of the
continuous distributions using the first two moments is particularly accurate.
Our algorithm is simple to implement and often comparable in its complexity to
Lauritzen's algorithm. We show empirically that it achieves substantially
higher accuracy than previous approximate algorithms.
",Exact Inference in Networks with Discrete Children of Continuous Parents
"  We present probabilistic logic programming under inheritance with overriding.
This approach is based on new notions of entailment for reasoning with
conditional constraints, which are obtained from the classical notion of
logical entailment by adding the principle of inheritance with overriding. This
is done by using recent approaches to probabilistic default reasoning with
conditional constraints. We analyze the semantic properties of the new
entailment relations. We also present algorithms for probabilistic logic
programming under inheritance with overriding, and program transformations for
an increased efficiency.
",Probabilistic Logic Programming under Inheritance with Overriding
"  In this paper we compare three different architectures for the evaluation of
influence diagrams: HUGIN, Shafer-Shenoy, and Lazy Evaluation architecture. The
computational complexity of the architectures are compared on the LImited
Memory Influence Diagram (LIMID): a diagram where only the requiste information
for the computation of the optimal policies are depicted. Because the requsite
information is explicitly represented in the LIMID the evaluation can take
advantage of it, and significant savings in computational can be obtained. In
this paper we show how the obtained savings is considerably increased when the
computations performed on the LIMID is according to the Lazy Evaluation scheme.
","Solving Influence Diagrams using HUGIN, Shafer-Shenoy and Lazy
  Propagation"
"  We consider the task of aggregating beliefs of severalexperts. We assume that
these beliefs are represented as probabilitydistributions. We argue that the
evaluation of any aggregationtechnique depends on the semantic context of this
task. We propose aframework, in which we assume that nature generates samples
from a`true' distribution and different experts form their beliefs based onthe
subsets of the data they have a chance to observe. Naturally, theideal
aggregate distribution would be the one learned from thecombined sample sets.
Such a formulation leads to a natural way tomeasure the accuracy of the
aggregation mechanism.We show that the well-known aggregation operator LinOP is
ideallysuited for that task. We propose a LinOP-based learning
algorithm,inspired by the techniques developed for Bayesian learning,
whichaggregates the experts' distributions represented as Bayesiannetworks. Our
preliminary experiments show that this algorithmperforms well in practice.
",Aggregating Learned Probabilistic Beliefs
"  We propose using recognition networks for approximate inference inBayesian
networks (BNs). A recognition network is a multilayerperception (MLP) trained
to predict posterior marginals given observedevidence in a particular BN. The
input to the MLP is a vector of thestates of the evidential nodes. The activity
of an output unit isinterpreted as a prediction of the posterior marginal of
thecorresponding variable. The MLP is trained using samples generated fromthe
corresponding BN.We evaluate a recognition network that was trained to do
inference ina large Bayesian network, similar in structure and complexity to
theQuick Medical Reference, Decision Theoretic (QMR-DT). Our networkis a
binary, two-layer, noisy-OR network containing over 4000 potentially observable
nodes and over 600 unobservable, hidden nodes. Inreal medical diagnosis, most
observables are unavailable, and there isa complex and unknown bias that
selects which ones are provided. Weincorporate a very basic type of selection
bias in our network: a knownpreference that available observables are positive
rather than negative.Even this simple bias has a significant effect on the
posterior. We compare the performance of our recognition network
tostate-of-the-art approximate inference algorithms on a large set oftest
cases. In order to evaluate the effect of our simplistic modelof the selection
bias, we evaluate algorithms using a variety ofincorrectly modeled observation
biases. Recognition networks performwell using both correct and incorrect
observation biases.
",Recognition Networks for Approximate Inference in BN20 Networks
"  The Factored Frontier (FF) algorithm is a simple approximate
inferencealgorithm for Dynamic Bayesian Networks (DBNs). It is very similar
tothe fully factorized version of the Boyen-Koller (BK) algorithm, butinstead
of doing an exact update at every step followed bymarginalisation (projection),
it always works with factoreddistributions. Hence it can be applied to models
for which the exactupdate step is intractable. We show that FF is equivalent to
(oneiteration of) loopy belief propagation (LBP) on the original DBN, andthat
BK is equivalent (to one iteration of) LBP on a DBN where wecluster some of the
nodes. We then show empirically that byiterating, LBP can improve on the
accuracy of both FF and BK. Wecompare these algorithms on two real-world DBNs:
the first is a modelof a water treatment plant, and the second is a coupled
HMM, used tomodel freeway traffic.
",The Factored Frontier Algorithm for Approximate Inference in DBNs
"  Most successful Bayesian network (BN) applications to datehave been built
through knowledge elicitation from experts.This is difficult and time
consuming, which has lead to recentinterest in automated methods for learning
BNs from data. We present a case study in the construction of a BN in
anintelligent tutoring application, specifically decimal misconceptions.
Wedescribe the BN construction using expert elicitation and then investigate
how certainexisting automated knowledge discovery methods might support the BN
knowledge engineering process.
","A Case Study in Knowledge Discovery and Elicitation in an Intelligent
  Tutoring Application"
"  MAP is the problem of finding a most probable instantiation of a set of
variables in a Bayesian network, given evidence. Unlike computing marginals,
posteriors, and MPE (a special case of MAP), the time and space complexity of
MAP is not only exponential in the network treewidth, but also in a larger
parameter known as the ""constrained"" treewidth. In practice, this means that
computing MAP can be orders of magnitude more expensive than
computingposteriors or MPE. Thus, practitioners generally avoid MAP
computations, resorting instead to approximating them by the most likely value
for each MAP variableseparately, or by MPE.We present a method for
approximating MAP using local search. This method has space complexity which is
exponential onlyin the treewidth, as is the complexity of each search step. We
investigate the effectiveness of different local searchmethods and several
initialization strategies and compare them to otherapproximation
schemes.Experimental results show that local search provides a much more
accurate approximation of MAP, while requiring few search steps.Practically,
this means that the complexity of local search is often exponential only in
treewidth as opposed to the constrained treewidth, making approximating MAP as
efficient as other computations.
",Approximating MAP using Local Search
"  Suppose we are given the conditional probability of one variable given some
other variables.Normally the full joint distribution over the conditioning
variablesis required to determine the probability of the conditioned
variable.Under what circumstances are the marginal distributions over the
conditioning variables sufficient to determine the probability ofthe
conditioned variable?Sufficiency in this sense is equivalent to additive
separability ofthe conditional probability distribution.Such separability
structure is natural and can be exploited forefficient inference.Separability
has a natural generalization to conditional separability.Separability provides
a precise notion of weaklyinteracting subsystems in temporal probabilistic
models.Given a system that is decomposed into separable subsystems,
exactmarginal probabilities over subsystems at future points in time can
becomputed by propagating marginal subsystem probabilities, rather thancomplete
system joint probabilities.Thus, separability can make exact prediction
tractable.However, observations can break separability,so exact monitoring of
dynamic systems remains hard.
","Sufficiency, Separability and Temporal Probabilistic Models"
"  There is increasing interest within the research community in the design and
use of recursive probability models. Although there still remains concern about
computational complexity costs and the fact that computing exact solutions can
be intractable for many nonrecursive models and impossible in the general case
for recursive problems, several research groups are actively developing
computational techniques for recursive stochastic languages. We have developed
an extension to the traditional lambda-calculus as a framework for families of
Turing complete stochastic languages. We have also developed a class of exact
inference algorithms based on the traditional reductions of the
lambda-calculus. We further propose that using the deBruijn notation (a
lambda-calculus notation with nameless dummies) supports effective caching in
such systems (caching being an essential component of efficient computation).
Finally, our extension to the lambda-calculus offers a foundation and general
theory for the construction of recursive stochastic modeling languages as well
as promise for effective caching and efficient approximation algorithms for
inference.
",Toward General Analysis of Recursive Probability Models
"  We propose a new approach to value-directed belief state approximation for
POMDPs. The value-directed model allows one to choose approximation methods for
belief state monitoring that have a small impact on decision quality. Using a
vector space analysis of the problem, we devise two new search procedures for
selecting an approximation scheme that have much better computational
properties than existing methods. Though these provide looser error bounds, we
show empirically that they have a similar impact on decision quality in
practice, and run up to two orders of magnitude more quickly.
",Vector-space Analysis of Belief-state Approximation for POMDPs
"  We consider the problem of approximate belief-state monitoring using particle
filtering for the purposes of implementing a policy for a partially-observable
Markov decision process (POMDP). While particle filtering has become a
widely-used tool in AI for monitoring dynamical systems, rather scant attention
has been paid to their use in the context of decision making. Assuming the
existence of a value function, we derive error bounds on decision quality
associated with filtering using importance sampling. We also describe an
adaptive procedure that can be used to dynamically determine the number of
samples required to meet specific error bounds. Empirical evidence is offered
supporting this technique as a profitable means of directing sampling effort
where it is needed to distinguish policies.
",Value-Directed Sampling Methods for POMDPs
"  We investigate a model for planning under uncertainty with temporallyextended
actions, where multiple actions can be taken concurrently at each decision
epoch. Our model is based on the options framework, and combines it with
factored state space models,where the set of options can be partitioned into
classes that affectdisjoint state variables. We show that the set of
decisionepochs for concurrent options defines a semi-Markov decisionprocess, if
the underlying temporally extended actions being parallelized arerestricted to
Markov options. This property allows us to use SMDPalgorithms for computing the
value function over concurrentoptions. The concurrent options model allows
overlapping execution ofoptions in order to achieve higher performance or in
order to performa complex task. We describe a simple experiment using a
navigationtask which illustrates how concurrent options results in a faster
planwhen compared to the case when only one option is taken at a time.
",Decision-Theoretic Planning with Concurrent Temporally Extended Actions
"  We consider a partially observable Markov decision problem (POMDP) that
models a class of sequencing problems. Although POMDPs are typically
intractable, our formulation admits tractable solution. Instead of maintaining
a value function over a high-dimensional set of belief states, we reduce the
state space to one of smaller dimension, in which grid-based dynamic
programming techniques are effective. We develop an error bound for the
resulting approximation, and discuss an application of the model to a problem
in targeted advertising.
",A Tractable POMDP for a Class of Sequencing Problems
"  We propose a new method of discovering causal structures, based on the
detection of local, spontaneous changes in the underlying data-generating
model. We analyze the classes of structures that are equivalent relative to a
stream of distributions produced by local changes, and devise algorithms that
output graphical representations of these equivalence classes. We present
experimental results, using simulated data, and examine the errors associated
with detection of changes and recovery of structures.
",Causal Discovery from Changes
"  A Bayesian Belief Network (BN) is a model of a joint distribution over a
setof n variables, with a DAG structure to represent the immediate
dependenciesbetween the variables, and a set of parameters (aka CPTables) to
represent thelocal conditional probabilities of a node, given each assignment
to itsparents. In many situations, these parameters are themselves random
variables - this may reflect the uncertainty of the domain expert, or may come
from atraining sample used to estimate the parameter values. The distribution
overthese ""CPtable variables"" induces a distribution over the response the
BNwill return to any ""What is Pr(H | E)?"" query. This paper investigates
thevariance of this response, showing first that it is asymptotically
normal,then providing its mean and asymptotical variance. We then present
aneffective general algorithm for computing this variance, which has the
samecomplexity as simply computing the (mean value of) the response itself -
ie,O(n 2^w), where n is the number of variables and w is the effective
treewidth. Finally, we provide empirical evidence that this algorithm,
whichincorporates assumptions and approximations, works effectively in
practice,given only small samples.
",Bayesian Error-Bars for Belief Net Inference
"  With the advance of efficient analytical methods for sensitivity analysis
ofprobabilistic networks, the interest in the sensitivities revealed by
real-life networks is rekindled. As the amount of data resulting from a
sensitivity analysis of even a moderately-sized network is alreadyoverwhelming,
methods for extracting relevant information are called for. One such methodis
to study the derivative of the sensitivity functions yielded for a network's
parameters. We further propose to build upon the concept of admissible
deviation, that is, the extent to which a parameter can deviate from the true
value without inducing a change in the most likely outcome. We illustrate these
concepts by means of a sensitivity analysis of a real-life probabilistic
network in oncology.
",Analysing Sensitivity Data from Probabilistic Networks
"  Uncertainty plays a central role in spoken dialogue systems. Some stochastic
models like Markov decision process (MDP) are used to model the dialogue
manager. But the partially observable system state and user intention hinder
the natural representation of the dialogue state. MDP-based system degrades
fast when uncertainty about a user's intention increases. We propose a novel
dialogue model based on the partially observable Markov decision process
(POMDP). We use hidden system states and user intentions as the state set,
parser results and low-level information as the observation set, domain actions
and dialogue repair actions as the action set. Here the low-level information
is extracted from different input modals, including speech, keyboard, mouse,
etc., using Bayesian networks. Because of the limitation of the exact
algorithms, we focus on heuristic approximation algorithms and their
applicability in POMDP for dialogue management. We also propose two methods for
grid point selection in grid-based approximation algorithms.
","Planning and Acting under Uncertainty: A New Model for Spoken Dialogue
  Systems"
"  Recently, there has been a burst in the number of research projects on human
computation via crowdsourcing. Multiple choice (or labeling) questions could be
referred to as a common type of problem which is solved by this approach. As an
application, crowd labeling is applied to find true labels for large machine
learning datasets. Since crowds are not necessarily experts, the labels they
provide are rather noisy and erroneous. This challenge is usually resolved by
collecting multiple labels for each sample, and then aggregating them to
estimate the true label. Although the mechanism leads to high-quality labels,
it is not actually cost-effective. As a result, efforts are currently made to
maximize the accuracy in estimating true labels, while fixing the number of
acquired labels.
  This paper surveys methods to aggregate redundant crowd labels in order to
estimate unknown true labels. It presents a unified statistical latent model
where the differences among popular methods in the field correspond to
different choices for the parameters of the model. Afterwards, algorithms to
make inference on these models will be surveyed. Moreover, adaptive methods
which iteratively collect labels based on the previously collected labels and
estimated models will be discussed. In addition, this paper compares the
distinguished methods, and provides guidelines for future work required to
address the current open issues.
",Crowd Labeling: a survey
"  In this paper we present a propositional logic programming language for
reasoning under possibilistic uncertainty and representing vague knowledge.
Formulas are represented by pairs (A, c), where A is a many-valued proposition
and c is value in the unit interval [0,1] which denotes a lower bound on the
belief on A in terms of necessity measures. Belief states are modeled by
possibility distributions on the set of all many-valued interpretations. In
this framework, (i) we define a syntax and a semantics of the general
underlying uncertainty logic; (ii) we provide a modus ponens-style calculus for
a sublanguage of Horn-rules and we prove that it is complete for determining
the maximum degree of possibilistic belief with which a fuzzy propositional
variable can be entailed from a set of formulas; and finally, (iii) we show how
the computation of a partial matching between fuzzy propositional variables, in
terms of necessity measures for fuzzy sets, can be included in our logic
programming system.
","A Complete Calculus for Possibilistic Logic Programming with Fuzzy
  Propositional Variables"
"  We show that if a strictly positive joint probability distribution for a set
of binary random variables factors according to a tree, then vertex separation
represents all and only the independence relations enclosed in the
distribution. The same result is shown to hold also for multivariate strictly
positive normal distributions. Our proof uses a new property of conditional
independence that holds for these two classes of probability distributions.
",Perfect Tree-Like Markovian Distributions
"  Possibilistic logic offers a qualitative framework for representing pieces of
information associated with levels of uncertainty of priority. The fusion of
multiple sources information is discussed in this setting. Different classes of
merging operators are considered including conjunctive, disjunctive,
reinforcement, adaptive and averaging operators. Then we propose to analyse
these classes in terms of postulates. This is done by first extending the
postulate for merging classical bases to the case where priorites are avaialbe.
",A Principled Analysis of Merging Operations in Possibilistic Logic
"  Planning for distributed agents with partial state information is considered
from a decision- theoretic perspective. We describe generalizations of both the
MDP and POMDP models that allow for decentralized control. For even a small
number of agents, the finite-horizon problems corresponding to both of our
models are complete for nondeterministic exponential time. These complexity
results illustrate a fundamental difference between centralized and
decentralized control of Markov processes. In contrast to the MDP and POMDP
problems, the problems we consider provably do not admit polynomial-time
algorithms and most likely require doubly exponential time to solve in the
worst case. We have thus provided mathematical evidence corresponding to the
intuition that decentralized planning problems cannot easily be reduced to
centralized problems and solved exactly using established techniques.
",The Complexity of Decentralized Control of Markov Decision Processes
"  Monitoring plan preconditions can allow for replanning when a precondition
fails, generally far in advance of the point in the plan where the precondition
is relevant. However, monitoring is generally costly, and some precondition
failures have a very small impact on plan quality. We formulate a model for
optimal precondition monitoring, using partially-observable Markov decisions
processes, and describe methods for solving this model efficitively, though
approximately. Specifically, we show that the single-precondition monitoring
problem is generally tractable, and the multiple-precondition monitoring
policies can be efficitively approximated using single-precondition soultions.
",Approximately Optimal Monitoring of Plan Preconditions
"  Monte Carlo sampling has become a major vehicle for approximate inference in
Bayesian networks. In this paper, we investigate a family of related simulation
approaches, known collectively as quasi-Monte Carlo methods based on
deterministic low-discrepancy sequences. We first outline several theoretical
aspects of deterministic low-discrepancy sequences, show three examples of such
sequences, and then discuss practical issues related to applying them to belief
updating in Bayesian networks. We propose an algorithm for selecting direction
numbers for Sobol sequence. Our experimental results show that low-discrepancy
sequences (especially Sobol sequence) significantly improve the performance of
simulation algorithms in Bayesian networks compared to Monte Carlo sampling.
","Computational Investigation of Low-Discrepancy Sequences in Simulation
  Algorithms for Bayesian Networks"
"  A simple advertising strategy that can be used to help increase sales of a
product is to mail out special offers to selected potential customers. Because
there is a cost associated with sending each offer, the optimal mailing
strategy depends on both the benefit obtained from a purchase and how the offer
affects the buying behavior of the customers. In this paper, we describe two
methods for partitioning the potential customers into groups, and show how to
perform a simple cost-benefit analysis to decide which, if any, of the groups
should be targeted. In particular, we consider two decision-tree learning
algorithms. The first is an ""off the shelf"" algorithm used to model the
probability that groups of customers will buy the product. The second is a new
algorithm that is similar to the first, except that for each group, it
explicitly models the probability of purchase under the two mailing scenarios:
(1) the mail is sent to members of that group and (2) the mail is not sent to
members of that group. Using data from a real-world advertising experiment, we
compare the algorithms to each other and to a naive mail-to-all strategy.
",A Decision Theoretic Approach to Targeted Advertising
"  This paper describes a Bayesian method for learning causal networks using
samples that were selected in a non-random manner from a population of
interest. Examples of data obtained by non-random sampling include convenience
samples and case-control data in which a fixed number of samples with and
without some condition is collected; such data are not uncommon. The paper
describes a method for combining data under selection with prior beliefs in
order to derive a posterior probability for a model of the causal processes
that are generating the data in the population of interest. The priors include
beliefs about the nature of the non-random sampling procedure. Although exact
application of the method would be computationally intractable for most
realistic datasets, efficient special-case and approximation methods are
discussed. Finally, the paper describes how to combine learning under selection
with previous methods for learning from observational and experimental data
that are obtained on random samples of the population of interest. The net
result is a Bayesian methodology that supports causal modeling and discovery
from a rich mixture of different types of data.
",A Bayesian Method for Causal Modeling and Discovery Under Selection
"  This paper analyzes independence concepts for sets of probability measures
associated with directed acyclic graphs. The paper shows that epistemic
independence and the standard Markov condition violate desirable separation
properties. The adoption of a contraction condition leads to d-separation but
still fails to guarantee a belief separation property. To overcome this
unsatisfactory situation, a strong Markov condition is proposed, based on
epistemic independence. The main result is that the strong Markov condition
leads to strong independence and does enforce separation properties; this
result implies that (1) separation properties of Bayesian networks do extend to
epistemic independence and sets of probability measures, and (2) strong
independence has a clear justification based on epistemic independence and the
strong Markov condition.
",Separation Properties of Sets of Probability Measures
"  Algorithms for exact and approximate inference in stochastic logic programs
(SLPs) are presented, based respectively, on variable elimination and
importance sampling. We then show how SLPs can be used to represent prior
distributions for machine learning, using (i) logic programs and (ii) Bayes net
structures as examples. Drawing on existing work in statistics, we apply the
Metropolis-Hasting algorithm to construct a Markov chain which samples from the
posterior distribution. A Prolog implementation for this is described. We also
discuss the possibility of constructing explicit representations of the
posterior.
","Stochastic Logic Programs: Sampling, Inference and Applications"
"  We present a new approach for inference in Bayesian networks, which is mainly
based on partial differentiation. According to this approach, one compiles a
Bayesian network into a multivariate polynomial and then computes the partial
derivatives of this polynomial with respect to each variable. We show that once
such derivatives are made available, one can compute in constant-time answers
to a large class of probabilistic queries, which are central to classical
inference, parameter estimation, model validation and sensitivity analysis. We
present a number of complexity results relating to the compilation of such
polynomials and to the computation of their partial derivatives. We argue that
the combined simplicity, comprehensiveness and computational complexity of the
presented framework is unique among existing frameworks for inference in
Bayesian networks.
",A Differential Approach to Inference in Bayesian Networks
"  We have recently introduced an any-space algorithm for exact inference in
Bayesian networks, called Recursive Conditioning, RC, which allows one to trade
space with time at increments of X-bytes, where X is the number of bytes needed
to cache a floating point number. In this paper, we present three key
extensions of RC. First, we modify the algorithm so it applies to more general
factorization of probability distributions, including (but not limited to)
Bayesian network factorizations. Second, we present a forgetting mechanism
which reduces the space requirements of RC considerably and then compare such
requirmenets with those of variable elimination on a number of realistic
networks, showing orders of magnitude improvements in certain cases. Third, we
present a version of RC for computing maximum a posteriori hypotheses (MAP),
which turns out to be the first MAP algorithm allowing a smooth time-space
tradeoff. A key advantage of presented MAP algorithm is that it does not have
to start from scratch each time a new query is presented, but can reuse some of
its computations across multiple queries, leading to significant savings in
ceratain cases.
",Any-Space Probabilistic Inference
"  In this paper, we use evidence-specific value abstraction for speeding
Bayesian networks inference. This is done by grouping variable values and
treating the combined values as a single entity. As we show, such abstractions
can exploit regularities in conditional probability distributions and also the
specific values of observed variables. To formally justify value abstraction,
we define the notion of safe value abstraction and devise inference algorithms
that use it to reduce the cost of inference. Our procedure is particularly
useful for learning complex networks with many hidden variables. In such cases,
repeated likelihood computations are required for EM or other parameter
optimization techniques. Since these computations are repeated with respect to
the same evidence set, our methods can provide significant speedup to the
learning procedure. We demonstrate the algorithm on genetic linkage problems
where the use of value abstraction sometimes differentiates between a feasible
and non-feasible solution.
",Likelihood Computations Using Value Abstractions
"  In this paper, we formulate a qualitative ""linear"" utility theory for
lotteries in which uncertainty is expressed qualitatively using a Spohnian
disbelief function. We argue that a rational decision maker facing an uncertain
decision problem in which the uncertainty is expressed qualitatively should
behave so as to maximize ""qualitative expected utility."" Our axiomatization of
the qualitative utility is similar to the axiomatization developed by von
Neumann and Morgenstern for probabilistic lotteries. We compare our results
with other recent results in qualitative decision making.
","A Qualitative Linear Utility Theory for Spohn's Theory of Epistemic
  Beliefs"
"  Many intelligent user interfaces employ application and user models to
determine the user's preferences, goals and likely future actions. Such models
require application analysis, adaptation and expansion. Building and
maintaining such models adds a substantial amount of time and labour to the
application development cycle. We present a system that observes the interface
of an unmodified application and records users' interactions with the
application. From a history of such observations we build a coarse state space
of observed interface states and actions between them. To refine the space, we
hypothesize sub-states based upon the histories that led users to a given
state. We evaluate the information gain of possible state splits, varying the
length of the histories considered in such splits. In this way, we
automatically produce a stochastic dynamic model of the application and of how
it is used. To evaluate our approach, we present models derived from real-world
application usage data.
",Building a Stochastic Dynamic Model of Application Use
"  We give an interpretation of the Maximum Entropy (MaxEnt) Principle in
game-theoretic terms. Based on this interpretation, we make a formal
distinction between different ways of {em applying/} Maximum Entropy
distributions. MaxEnt has frequently been criticized on the grounds that it
leads to highly representation dependent results. Our distinction allows us to
avoid this problem in many cases.
",Maximum Entropy and the Glasses You Are Looking Through
"  We document a connection between constraint reasoning and probabilistic
reasoning. We present an algorithm, called {em probabilistic arc consistency},
which is both a generalization of a well known algorithm for arc consistency
used in constraint reasoning, and a specialization of the belief updating
algorithm for singly-connected networks. Our algorithm is exact for singly-
connected constraint problems, but can work well as an approximation for
arbitrary problems. We briefly discuss some empirical results, and related
methods.
","Probabilistic Arc Consistency: A Connection between Constraint Reasoning
  and Probabilistic Reasoning"
"  Composition of low-dimensional distributions, whose foundations were laid in
the papaer published in the Proceeding of UAI'97 (Jirousek 1997), appeared to
be an alternative apparatus to describe multidimensional probabilistic models.
In contrast to Graphical Markov Models, which define multidomensinoal
distributions in a declarative way, this approach is rather procedural.
Ordering of low-dimensional distributions into a proper sequence fully defines
the resepctive computational procedure; therefore, a stury of different type of
generating sequences is one fo the central problems in this field. Thus, it
appears that an important role is played by special sequences that are called
perfect. Their main characterization theorems are presetned in this paper.
However, the main result of this paper is a solution to the problem of
margnialization for general sequences. The main theorem describes a way to
obtain a generating sequence that defines the model corresponding to the
marginal of the distribution defined by an arbitrary genearting sequence. From
this theorem the reader can see to what extent these comutations are local;
i.e., the sequence consists of marginal distributions whose computation must be
made by summing up over the values of the variable eliminated (the paper deals
with finite model).
",Marginalization in Composed Probabilistic Models
"  To investigate the robustness of the output probabilities of a Bayesian
network, a sensitivity analysis can be performed. A one-way sensitivity
analysis establishes, for each of the probability parameters of a network, a
function expressing a posterior marginal probability of interest in terms of
the parameter. Current methods for computing the coefficients in such a
function rely on a large number of network evaluations. In this paper, we
present a method that requires just a single outward propagation in a junction
tree for establishing the coefficients in the functions for all possible
parameters; in addition, an inward propagation is required for processing
evidence. Conversely, the method requires a single outward propagation for
computing the coefficients in the functions expressing all possible posterior
marginals in terms of a single parameter. We extend these results to an n-way
sensitivity analysis in which sets of parameters are studied.
",Making Sensitivity Analysis Computationally Efficient
"  Many large MDPs can be represented compactly using a dynamic Bayesian
network. Although the structure of the value function does not retain the
structure of the process, recent work has shown that value functions in
factored MDPs can often be approximated well using a decomposed value function:
a linear combination of <I>restricted</I> basis functions, each of which refers
only to a small subset of variables. An approximate value function for a
particular policy can be computed using approximate dynamic programming, but
this approach (and others) can only produce an approximation relative to a
distance metric which is weighted by the stationary distribution of the current
policy. This type of weighted projection is ill-suited to policy improvement.
We present a new approach to value determination, that uses a simple
closed-form computation to directly compute a least-squares decomposed
approximation to the value function <I>for any weights</I>. We then use this
value determination algorithm as a subroutine in a policy iteration process. We
show that, under reasonable restrictions, the policies induced by a factored
value function are compactly represented, and can be manipulated efficiently in
a policy iteration process. We also present a method for computing error bounds
for decomposed value functions using a variable-elimination algorithm for
function optimization. The complexity of all of our algorithms depends on the
factorization of system dynamics and of the approximate value function.
",Policy Iteration for Factored MDPs
"  We propose a framework for building graphical causal model that is based on
the concept of causal mechanisms. Causal models are intuitive for human users
and, more importantly, support the prediction of the effect of manipulation. We
describe an implementation of the proposed framework as an interactive model
construction module, ImaGeNIe, in SMILE (Structural Modeling, Inference, and
Learning Engine) and in GeNIe (SMILE's Windows user interface).
",Causal Mechanism-based Model Construction
"  We apply the principle of maximum entropy to select a unique joint
probability distribution from the set of all joint probability distributions
specified by a credal network. In detail, we start by showing that the unique
joint distribution of a Bayesian tree coincides with the maximum entropy model
of its conditional distributions. This result, however, does not hold anymore
for general Bayesian networks. We thus present a new kind of maximum entropy
models, which are computed sequentially. We then show that for all general
Bayesian networks, the sequential maximum entropy model coincides with the
unique joint distribution. Moreover, we apply the new principle of sequential
maximum entropy to interval Bayesian networks and more generally to credal
networks. We especially show that this application is equivalent to a number of
small local entropy maximizations.
",Credal Networks under Maximum Entropy
"  We propose a formal framework for intelligent systems which can reason about
scientific domains, in particular about the carcinogenicity of chemicals, and
we study its properties. Our framework is grounded in a philosophy of
scientific enquiry and discourse, and uses a model of dialectical
argumentation. The formalism enables representation of scientific uncertainty
and conflict in a manner suitable for qualitative reasoning about the domain.
",Risk Agoras: Dialectical Argumentation for Scientific Reasoning
"  Many applications of intelligent systems require reasoning about the mental
states of agents in the domain. We may want to reason about an agent's beliefs,
including beliefs about other agents; we may also want to reason about an
agent's preferences, and how his beliefs and preferences relate to his
behavior. We define a probabilistic epistemic logic (PEL) in which belief
statements are given a formal semantics, and provide an algorithm for asserting
and querying PEL formulas in Bayesian networks. We then show how to reason
about an agent's behavior by modeling his decision process as an influence
diagram and assuming that he behaves rationally. PEL can then be used for
reasoning from an agent's observed actions to conclusions about other aspects
of the domain, including unobserved domain variables and the agent's mental
states.
",Probabilistic Models for Agents' Beliefs and Decisions
"  This paper deals with the representation and solution of asymmetric Bayesian
decision problems. We present a formal framework, termed asymmetric influence
diagrams, that is based on the influence diagram and allows an efficient
representation of asymmetric decision problems. As opposed to existing
frameworks, the asymmetric influece diagram primarily encodes asymmetry at the
qualitative level and it can therefore be read directly from the model. We give
an algorithm for solving asymmetric influence diagrams. The algorithm initially
decomposes the asymmetric decision problem into a structure of symmetric
subproblems organized as a tree. A solution to the decision problem can then be
found by propagating from the leaves toward the root using existing evaluation
methods to solve the sub-problems.
",Representing and Solving Asymmetric Bayesian Decision Problems
"  When using Bayesian networks for modelling the behavior of man-made
machinery, it usually happens that a large part of the model is deterministic.
For such Bayesian networks deterministic part of the model can be represented
as a Boolean function, and a central part of belief updating reduces to the
task of calculating the number of satisfying configurations in a Boolean
function. In this paper we explore how advances in the calculation of Boolean
functions can be adopted for belief updating, in particular within the context
of troubleshooting. We present experimental results indicating a substantial
speed-up compared to traditional junction tree propagation.
","Using ROBDDs for Inference in Bayesian Networks with Troubleshooting as
  an Example"
"  We present a new approach to the solution of decision problems formulated as
influence diagrams. The approach converts the influence diagram into a simpler
structure, the LImited Memory Influence Diagram (LIMID), where only the
requisite information for the computation of optimal policies is depicted.
Because the requisite information is explicitly represented in the diagram, the
evaluation procedure can take advantage of it. In this paper we show how to
convert an influence diagram to a LIMID and describe the procedure for finding
an optimal strategy. Our approach can yield significant savings of memory and
computational time when compared to traditional methods.
",Evaluating Influence Diagrams using LIMIDs
"  Conversations abound with uncetainties of various kinds. Treating
conversation as inference and decision making under uncertainty, we propose a
task independent, multimodal architecture for supporting robust continuous
spoken dialog called Quartet. We introduce four interdependent levels of
analysis, and describe representations, inference procedures, and decision
strategies for managing uncertainties within and between the levels. We
highlight the approach by reviewing interactions between a user and two spoken
dialog systems developed using the Quartet architecture: Prsenter, a prototype
system for navigating Microsoft PowerPoint presentations, and the Bayesian
Receptionist, a prototype system for dealing with tasks typically handled by
front desk receptionists at the Microsoft corporate campus.
",Conversation as Action Under Uncertainty
"  We consider the problem belief-state monitoring for the purposes of
implementing a policy for a partially-observable Markov decision process
(POMDP), specifically how one might approximate the belief state. Other schemes
for belief-state approximation (e.g., based on minimixing a measures such as
KL-diveregence between the true and estimated state) are not necessarily
appropriate for POMDPs. Instead we propose a framework for analyzing
value-directed approximation schemes, where approximation quality is determined
by the expected error in utility rather than by the error in the belief state
itself. We propose heuristic methods for finding good projection schemes for
belief state estimation - exhibiting anytime characteristics - given a POMDP
value fucntion. We also describe several algorithms for constructing bounds on
the error in decision quality (expected utility) associated with acting in
accordance with a given belief state approximation.
",Value-Directed Belief State Approximation for POMDPs
"  Techniques for plan recognition under uncertainty require a stochastic model
of the plan-generation process. We introduce Probabilistic State-Dependent
Grammars (PSDGs) to represent an agent's plan-generation process. The PSDG
language model extends probabilistic context-free grammars (PCFGs) by allowing
production probabilities to depend on an explicit model of the planning agent's
internal and external state. Given a PSDG description of the plan-generation
process, we can then use inference algorithms that exploit the particular
independence properties of the PSDG language to efficiently answer
plan-recognition queries. The combination of the PSDG language model and
inference algorithms extends the range of plan-recognition domains for which
practical probabilistic inference is possible, as illustrated by applications
in traffic monitoring and air combat.
",Probabilistic State-Dependent Grammars for Plan Recognition
"  Qualitative probabilistic networks have been designed for probabilistic
reasoning in a qualitative way. Due to their coarse level of representation
detail, qualitative probabilistic networks do not provide for resolving
trade-offs and typically yield ambiguous results upon inference. We present an
algorithm for computing more insightful results for unresolved trade-offs. The
algorithm builds upon the idea of using pivots to zoom in on the trade-offs and
identifying the information that would serve to resolve them.
",Pivotal Pruning of Trade-offs in QPNs
"  This paper describes a domain-specific knowledge acquisition tool for
intelligent automated troubleshooters based on Bayesian networks. No Bayesian
network knowledge is required to use the tool, and troubleshooting information
can be specified as natural and intuitive as possible. Probabilities can be
specified in the direction that is most natural to the domain expert. Thus, the
knowledge acquisition efficiently removes the traditional knowledge acquisition
bottleneck of Bayesian networks.
",A Knowledge Acquisition Tool for Bayesian-Network Troubleshooters
"  In this paper, we present a heuristic operator which aims at simultaneously
optimizing the orientations of all the edges in an intermediate Bayesian
network structure during the search process. This is done by alternating
between the space of directed acyclic graphs (DAGs) and the space of skeletons.
The found orientations of the edges are based on a scoring function rather than
on induced conditional independences. This operator can be used as an extension
to commonly employed search strategies. It is evaluated in experiments with
artificial and real-world data.
",On the Use of Skeletons when Learning in Bayesian Networks
"  This paper deals with the problem of estimating the probability that one
event was a cause of another in a given scenario. Using structural-semantical
definitions of the probabilities of necessary or sufficient causation (or
both), we show how to optimally bound these quantities from data obtained in
experimental and observational studies, making minimal assumptions concerning
the data-generating process. In particular, we strengthen the results of Pearl
(1999) by weakening the data-generation assumptions and deriving theoretically
sharp bounds on the probabilities of causation. These results delineate
precisely how empirical data can be used both in settling questions of
attribution and in solving attribution-related problems of decision making.
",Probabilities of Causation: Bounds and Identification
"  Conditional independence and Markov properties are powerful tools allowing
expression of multidimensional probability distributions by means of
low-dimensional ones. As multidimensional possibilistic models have been
studied for several years, the demand for analogous tools in possibility theory
seems to be quite natural. This paper is intended to be a promotion of de
Cooman's measure-theoretic approcah to possibility theory, as this approach
allows us to find analogies to many important results obtained in probabilistic
framework. First, we recall semi-graphoid properties of conditional
possibilistic independence, parameterized by a continuous t-norm, and find
sufficient conditions for a class of Archimedean t-norms to have the graphoid
property. Then we introduce Markov properties and factorization of possibility
distrubtions (again parameterized by a continuous t-norm) and find the
relationships between them. These results are accompanied by a number of
conterexamples, which show that the assumptions of specific theorems are
substantial.
",Conditional Independence and Markov Properties in Possibility Theory
"  Algorithms for learning the conditional probabilities of Bayesian networks
with hidden variables typically operate within a high-dimensional search space
and yield only locally optimal solutions. One way of limiting the search space
and avoiding local optima is to impose qualitative constraints that are based
on background knowledge concerning the domain. We present a method for
integrating formal statements of qualitative constraints into two learning
algorithms, APN and EM. In our experiments with synthetic data, this method
yielded networks that satisfied the constraints almost perfectly. The accuracy
of the learned networks was consistently superior to that of corresponding
networks learned without constraints. The exploitation of qualitative
constraints therefore appears to be a promising way to increase both the
interpretability and the accuracy of learned Bayesian networks with known
structure.
","Exploiting Qualitative Knowledge in the Learning of Conditional
  Probabilities of Bayesian Networks"
"  Constraints that may be obtained by composition from simpler constraints are
present, in some way or another, in almost every constraint program. The
decomposition of such constraints is a standard technique for obtaining an
adequate propagation algorithm from a combination of propagators designed for
simpler constraints. The decomposition approach is appealing in several ways.
Firstly because creating a specific propagator for every constraint is clearly
infeasible since the number of constraints is infinite. Secondly, because
designing a propagation algorithm for complex constraints can be very
challenging. Finally, reusing existing propagators allows to reduce the size of
code to be developed and maintained. Traditionally, constraint solvers
automatically decompose constraints into simpler ones using additional
auxiliary variables and propagators, or expect the users to perform such
decomposition themselves, eventually leading to the same propagation model. In
this paper we explore views, an alternative way to create efficient propagators
for such constraints in a modular, simple and correct way, which avoids the
introduction of auxiliary variables and propagators.
",View-based propagation of decomposable constraints
"  Elicitation of probabilities is one of the most laborious tasks in building
decision-theoretic models, and one that has so far received only moderate
attention in decision-theoretic systems. We propose a set of user interface
tools for graphical probabilistic models, focusing on two aspects of
probability elicitation: (1) navigation through conditional probability tables
and (2) interactive graphical assessment of discrete probability distributions.
We propose two new graphical views that aid navigation in very large
conditional probability tables: the CPTree (Conditional Probability Tree) and
the SCPT (shrinkable Conditional Probability Table). Based on what is known
about graphical presentation of quantitative data to humans, we offer several
useful enhancements to probability wheel and bar graph, including different
chart styles and options that can be adapted to user preferences and needs. We
present the results of a simple usability study that proves the value of the
proposed tools.
","User Interface Tools for Navigation in Conditional Probability Tables
  and Elicitation of Probabilities in Bayesian Networks"
"  This is the Proceedings of the Twenty-Eighth Conference on Uncertainty in
Artificial Intelligence, which was held on Catalina Island, CA August 14-18
2012.
","Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial
  Intelligence (2012)"
"  This is the Proceedings of the Nineteenth Conference on Uncertainty in
Artificial Intelligence, which was held in Acapulco, Mexico, August 7-10 2003
","Proceedings of the Nineteenth Conference on Uncertainty in Artificial
  Intelligence (2003)"
"  This is the Proceedings of the Seventeenth Conference on Uncertainty in
Artificial Intelligence, which was held in Seattle, WA, August 2-5 2001
","Proceedings of the Seventeenth Conference on Uncertainty in Artificial
  Intelligence (2001)"
"  This is the Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence, which was held in Alberta, Canada, August 1-4 2002
","Proceedings of the Eighteenth Conference on Uncertainty in Artificial
  Intelligence (2002)"
"  Handwriting is one of the most important means of daily communication.
Although the problem of handwriting recognition has been considered for more
than 60 years there are still many open issues, especially in the task of
unconstrained handwritten sentence recognition. This paper focuses on the
automatic system that recognizes continuous English sentence through a
mouse-based gestures in real-time based on Artificial Neural Network. The
proposed Artificial Neural Network is trained using the traditional
backpropagation algorithm for self supervised neural network which provides the
system with great learning ability and thus has proven highly successful in
training for feed-forward Artificial Neural Network. The designed algorithm is
not only capable of translating discrete gesture moves, but also continuous
gestures through the mouse. In this paper we are using the efficient neural
network approach for recognizing English sentence drawn by mouse. This approach
shows an efficient way of extracting the boundary of the English Sentence and
specifies the area of the recognition English sentence where it has been drawn
in an image and then used Artificial Neural Network to recognize the English
sentence. The proposed approach English sentence recognition (ESR) system is
designed and tested successfully. Experimental results show that the higher
speed and accuracy were examined.
","English Sentence Recognition using Artificial Neural Network through
  Mouse-based Gestures"
"  This paper presents a knowledge-based detection of objects approach using the
OWL ontology language, the Semantic Web Rule Language, and 3D processing
built-ins aiming at combining geometrical analysis of 3D point clouds and
specialist's knowledge. Here, we share our experience regarding the creation of
3D semantic facility model out of unorganized 3D point clouds. Thus, a
knowledge-based detection approach of objects using the OWL ontology language
is presented. This knowledge is used to define SWRL detection rules. In
addition, the combination of 3D processing built-ins and topological Built-Ins
in SWRL rules allows a more flexible and intelligent detection, and the
annotation of objects contained in 3D point clouds. The created WiDOP prototype
takes a set of 3D point clouds as input, and produces as output a populated
ontology corresponding to an indexed scene visualized within VRML language. The
context of the study is the detection of railway objects materialized within
the Deutsche Bahn scene such as signals, technical cupboards, electric poles,
etc. Thus, the resulting enriched and populated ontology, that contains the
annotations of objects in the point clouds, is used to feed a GIS system or an
IFC file for architecture purposes.
","Knowledge Base Approach for 3D Objects Detection in Point Clouds Using
  3D Processing and Specialists Knowledge"
"  This paper presents a method to compute automatically topological relations
using SWRL rules. The calculation of these rules is based on the definition of
a Selective Nef Complexes Nef Polyhedra structure generated from standard
Polyhedron. The Selective Nef Complexes is a data model providing a set of
binary Boolean operators such as Union, Difference, Intersection and Symmetric
difference, and unary operators such as Interior, Closure and Boundary. In this
work, these operators are used to compute topological relations between objects
defined by the constraints of the 9 Intersection Model (9-IM) from Egenhofer.
With the help of these constraints, we defined a procedure to compute the
topological relations on Nef polyhedra. These topological relationships are
Disjoint, Meets, Contains, Inside, Covers, CoveredBy, Equals and Overlaps, and
defined in a top-level ontology with a specific semantic definition on relation
such as Transitive, Symmetric, Asymmetric, Functional, Reflexive, and
Irreflexive. The results of the computation of topological relationships are
stored in an OWL-DL ontology allowing after what to infer on these new
relationships between objects. In addition, logic rules based on the Semantic
Web Rule Language allows the definition of logic programs that define which
topological relationships have to be computed on which kind of objects with
specific attributes. For instance, a ""Building"" that overlaps a ""Railway"" is a
""RailStation"".
","From 9-IM Topological Operators to Qualitative Spatial Relations using
  3D Selective Nef Complexes and Logic Rules for bodies"
"  Computer Poker's unique characteristics present a well-suited challenge for
research in artificial intelligence. For that reason, and due to the Poker's
market increase in popularity in Portugal since 2008, several members of LIACC
have researched in this field. Several works were published as papers and
master theses and more recently a member of LIACC engaged on a research in this
area as a Ph.D. thesis in order to develop a more extensive and in-depth work.
This paper describes the existing research in LIACC about Computer Poker, with
special emphasis on the completed master's theses and plans for future work.
This paper means to present a summary of the lab's work to the research
community in order to encourage the exchange of ideas with other labs /
individuals. LIACC hopes this will improve research in this area so as to reach
the goal of creating an agent that surpasses the best human players.
",Computer Poker Research at LIACC
"  Medical diagnosis process vary in the degree to which they attempt to deal
with different complicating aspects of diagnosis such as relative importance of
symptoms, varied symptom pattern and the relation between diseases them selves.
Based on decision theory, in the past many mathematical models such as crisp
set, probability distribution, fuzzy set, intuitionistic fuzzy set were
developed to deal with complicating aspects of diagnosis. But, many such models
are failed to include important aspects of the expert decisions. Therefore, an
effort has been made to process inconsistencies in data being considered by
Pawlak with the introduction of rough set theory. Though rough set has major
advantages over the other methods, but it generates too many rules that create
many difficulties while taking decisions. Therefore, it is essential to
minimize the decision rules. In this paper, we use two processes such as pre
process and post process to mine suitable rules and to explore the relationship
among the attributes. In pre process we use rough set theory to mine suitable
rules, whereas in post process we use formal concept analysis from these
suitable rules to explore better knowledge and most important factors affecting
the decision making.
","A Framework for Intelligent Medical Diagnosis using Rough Set with
  Formal Concept Analysis"
"  The dependency graph is a data architecture that models all the dependencies
between the different types of assets in the game. It depicts the
dependency-based relationships between the assets of a game. For example, a
player must construct an arsenal before he can build weapons. It is vital that
the dependency graph of a game is designed logically to ensure a logical
sequence of game play. However, a mere logical dependency graph is not
sufficient in sustaining the players' enduring interests in a game, which
brings the problem of game balancing into picture. The issue of game balancing
arises when the players do not feel the chances of winning the game over their
AI opponents who are more skillful in the game play. At the current state of
research, the architecture of dependency graph is monolithic for the players.
The sequence of asset possession is always foreseeable because there is only a
single dependency graph. Game balancing is impossible when the assets of AI
players are overwhelmingly outnumbering that of human players. This paper
proposes a parallel architecture of dependency graph for the AI players and
human players. Instead of having a single dependency graph, a parallel
architecture is proposed where the dependency graph of AI player is adjustable
with that of human player using a support dependency as a game balancing
mechanism. This paper exhibits that the parallel dependency graph helps to
improve game balancing.
",Developing Parallel Dependency Graph In Improving Game Balancing
"  The main prospective aim of modern research related to Artificial
Intelligence is the creation of technical systems that implement the idea of
Strong Intelligence. According our point of view the path to the development of
such systems comes through the research in the field related to perceptions.
Here we formulate the model of the perception of external world which may be
used for the description of perceptual activity of intelligent beings. We
consider a number of issues related to the development of the set of patterns
which will be used by the intelligent system when interacting with environment.
The key idea of the presented perception model is the idea of subjective
reality. The principle of the relativity of perceived world is formulated. It
is shown that this principle is the immediate consequence of the idea of
subjective reality. In this paper we show how the methodology of subjective
reality may be used for the creation of different types of Strong AI systems.
",Subjective Reality and Strong Artificial Intelligence
"  Diagnosis and prediction in some domains, like medical and industrial
diagnosis, require a representation that combines uncertainty management and
temporal reasoning. Based on the fact that in many cases there are few state
changes in the temporal range of interest, we propose a novel representation
called Temporal Nodes Bayesian Networks (TNBN). In a TNBN each node represents
an event or state change of a variable, and an arc corresponds to a
causal-temporal relationship. The temporal intervals can differ in number and
size for each temporal node, so this allows multiple granularity. Our approach
is contrasted with a dynamic Bayesian network for a simple medical example. An
empirical evaluation is presented for a more complex problem, a subsystem of a
fossil power plant, in which this approach is used for fault diagnosis and
prediction with good results.
",A Temporal Bayesian Network for Diagnosis and Prediction
"  Possibilistic logic bases and possibilistic graphs are two different
frameworks of interest for representing knowledge. The former stratifies the
pieces of knowledge (expressed by logical formulas) according to their level of
certainty, while the latter exhibits relationships between variables. The two
types of representations are semantically equivalent when they lead to the same
possibility distribution (which rank-orders the possible interpretations). A
possibility distribution can be decomposed using a chain rule which may be
based on two different kinds of conditioning which exist in possibility theory
(one based on product in a numerical setting, one based on minimum operation in
a qualitative setting). These two types of conditioning induce two kinds of
possibilistic graphs. In both cases, a translation of these graphs into
possibilistic bases is provided. The converse translation from a possibilistic
knowledge base into a min-based graph is also described.
",Possibilistic logic bases and possibilistic graphs
"  Our hypothesis is that by equipping certain agents in a multi-agent system
controlling an intelligent building with automated decision support, two
important factors will be increased. The first is energy saving in the
building. The second is customer value---how the people in the building
experience the effects of the actions of the agents. We give evidence for the
truth of this hypothesis through experimental findings related to tools for
artificial decision making. A number of assumptions related to agent control,
through monitoring and delegation of tasks to other kinds of agents, of rooms
at a test site are relaxed. Each assumption controls at least one uncertainty
that complicates considerably the procedures for selecting actions part of each
such agent. We show that in realistic decision situations, room-controlling
agents can make bounded rational decisions even under dynamic real-time
constraints. This result can be, and has been, generalized to other domains
with even harsher time constraints.
",Artificial Decision Making Under Uncertainty in Intelligent Buildings
"  In many domains it is desirable to assess the preferences of users in a
qualitative rather than quantitative way. Such representations of qualitative
preference orderings form an importnat component of automated decision tools.
We propose a graphical representation of preferences that reflects conditional
dependence and independence of preference statements under a ceteris paribus
(all else being equal) interpretation. Such a representation is ofetn compact
and arguably natural. We describe several search algorithms for dominance
testing based on this representation; these algorithms are quite effective,
especially in specific network topologies, such as chain-and tree- structured
networks, as well as polytrees.
",Reasoning With Conditional Ceteris Paribus Preference Statem
"  This paper describes a Bayesian method for combining an arbitrary mixture of
observational and experimental data in order to learn causal Bayesian networks.
Observational data are passively observed. Experimental data, such as that
produced by randomized controlled trials, result from the experimenter
manipulating one or more variables (typically randomly) and observing the
states of other variables. The paper presents a Bayesian method for learning
the causal structure and parameters of the underlying causal process that is
generating the data, given that (1) the data contains a mixture of
observational and experimental case records, and (2) the causal process is
modeled as a causal Bayesian network. This learning method was applied using as
input various mixtures of experimental and observational data that were
generated from the ALARM causal Bayesian network. In these experiments, the
absolute and relative quantities of experimental and observational data were
varied systematically. For each of these training datasets, the learning method
was applied to predict the causal structure and to estimate the causal
parameters that exist among randomly selected pairs of nodes in ALARM that are
not confounded. The paper reports how these structure predictions and parameter
estimates compare with the true causal structures and parameters as given by
the ALARM network.
",Causal Discovery from a Mixture of Experimental and Observational Data
"  Recent work on loglinear models in probabilistic constraint logic programming
is applied to first-order probabilistic reasoning. Probabilities are defined
directly on the proofs of atomic formulae, and by marginalisation on the atomic
formulae themselves. We use Stochastic Logic Programs (SLPs) composed of
labelled and unlabelled definite clauses to define the proof probabilities. We
have a conservative extension of first-order reasoning, so that, for example,
there is a one-one mapping between logical and random variables. We show how,
in this framework, Inductive Logic Programming (ILP) can be used to induce the
features of a loglinear model from data. We also compare the presented
framework with other approaches to first-order probabilistic reasoning.
",Loglinear models for first-order probabilistic reasoning
"  We present a hybrid constraint-based/Bayesian algorithm for learning causal
networks in the presence of sparse data. The algorithm searches the space of
equivalence classes of models (essential graphs) using a heuristic based on
conventional constraint-based techniques. Each essential graph is then
converted into a directed acyclic graph and scored using a Bayesian scoring
metric. Two variants of the algorithm are developed and tested using data from
randomly generated networks of sizes from 15 to 45 nodes with data sizes
ranging from 250 to 2000 records. Both variations are compared to, and found to
consistently outperform two variations of greedy search with restarts.
","A Hybrid Anytime Algorithm for the Constructiion of Causal Models From
  Sparse Data"
"  Hybrid Probabilistic Programs (HPPs) are logic programs that allow the
programmer to explicitly encode his knowledge of the dependencies between
events being described in the program. In this paper, we classify HPPs into
three classes called HPP_1,HPP_2 and HPP_r,r>= 3. For these classes, we provide
three types of results for HPPs. First, we develop algorithms to compute the
set of all ground consequences of an HPP. Then we provide algorithms and
complexity results for the problems of entailment (""Given an HPP P and a query
Q as input, is Q a logical consequence of P?"") and consistency (""Given an HPP P
as input, is P consistent?""). Our results provide a fine characterization of
when polynomial algorithms exist for the above problems, and when these
problems become intractable.
",Hybrid Probabilistic Programs: Algorithms and Complexity
"  The problem of assessing the value of a candidate is viewed here as a
multiple combination problem. On the one hand a candidate can be evaluated
according to different criteria, and on the other hand several experts are
supposed to assess the value of candidates according to each criterion.
Criteria are not equally important, experts are not equally competent or
reliable. Moreover levels of satisfaction of criteria, or levels of confidence
are only assumed to take their values in qualitative scales which are just
linearly ordered. The problem is discussed within two frameworks, the
transferable belief model and the qualitative possibility theory. They
respectively offer a quantitative and a qualitative setting for handling the
problem, providing thus a way to compare the nature of the underlying
assumptions.
","Assessing the value of a candidate. Comparing belief function and
  possibility theories"
"  This paper investigates a purely qualitative version of Savage's theory for
decision making under uncertainty. Until now, most representation theorems for
preference over acts rely on a numerical representation of utility and
uncertainty where utility and uncertainty are commensurate. Disrupting the
tradition, we relax this assumption and introduce a purely ordinal axiom
requiring that the Decision Maker (DM) preference between two acts only depends
on the relative position of their consequences for each state. Within this
qualitative framework, we determine the only possible form of the decision rule
and investigate some instances compatible with the transitivity of the strict
preference. Finally we propose a mild relaxation of our ordinality axiom,
leaving room for a new family of qualitative decision rules compatible with
transitivity.
","Qualitative Models for Decision Under Uncertainty without the
  Commensurability Assumption"
"  In this paper, we analyze the relationship between probability and Spohn's
theory for representation of uncertain beliefs. Using the intuitive idea that
the more probable a proposition is, the more believable it is, we study
transformations from probability to Sphonian disbelief and vice-versa. The
transformations described in this paper are different from those described in
the literature. In particular, the former satisfies the principles of ordinal
congruence while the latter does not. Such transformations between probability
and Spohn's calculi can contribute to (1) a clarification of the semantics of
nonprobabilistic degree of uncertain belief, and (2) to a construction of a
decision theory for such calculi. In practice, the transformations will allow a
meaningful combination of more than one calculus in different stages of using
an expert system such as knowledge acquisition, inference, and interpretation
of results.
",On Transformations between Probability and Spohnian Disbelief Functions
"  We present a new abductive, probabilistic theory of plan recognition. This
model differs from previous plan recognition theories in being centered around
a model of plan execution: most previous methods have been based on plans as
formal objects or on rules describing the recognition process. We show that our
new model accounts for phenomena omitted from most previous plan recognition
theories: notably the cumulative effect of a sequence of observations of
partially-ordered, interleaved plans and the effect of context on plan
adoption. The model also supports inferences about the evolution of plan
execution in situations where another agent intervenes in plan execution. This
facility provides support for using plan recognition to build systems that will
intelligently assist a user.
",A New Model of Plan Recognition
"  Classical Decision Theory provides a normative framework for representing and
reasoning about complex preferences. Straightforward application of this theory
to automate decision making is difficult due to high elicitation cost. In
response to this problem, researchers have recently developed a number of
qualitative, logic-oriented approaches for representing and reasoning about
references. While effectively addressing some expressiveness issues, these
logics have not proven powerful enough for building practical automated
decision making systems. In this paper we present a hybrid approach to
preference elicitation and decision making that is grounded in classical
multi-attribute utility theory, but can make effective use of the expressive
power of qualitative approaches. Specifically, assuming a partially specified
multilinear utility function, we show how comparative statements about classes
of decision alternatives can be used to further constrain the utility function
and thus identify sup-optimal alternatives. This work demonstrates that
quantitative and qualitative approaches can be synergistically integrated to
provide effective and flexible decision support.
",A Hybrid Approach to Reasoning with Partially Elicited Preference Models
"  A conceptual foundation for approximation of belief functions is proposed and
investigated. It is based on the requirements of consistency and closeness. An
optimal approximation is studied. Unfortunately, the computation of the optimal
approximation turns out to be intractable. Hence, various heuristic methods are
proposed and experimantally evaluated both in terms of their accuracy and in
terms of the speed of computation. These methods are compared to the earlier
proposed approximations of belief functions.
",Faithful Approximations of Belief Functions
"  Markov decisions processes (MDPs) are becoming increasing popular as models
of decision theoretic planning. While traditional dynamic programming methods
perform well for problems with small state spaces, structured methods are
needed for large problems. We propose and examine a value iteration algorithm
for MDPs that uses algebraic decision diagrams(ADDs) to represent value
functions and policies. An MDP is represented using Bayesian networks and ADDs
and dynamic programming is applied directly to these ADDs. We demonstrate our
method on large MDPs (up to 63 million states) and show that significant gains
can be had when compared to tree-structured representations (with up to a
thirty-fold reduction in the number of nodes required to represent optimal
value functions).
",SPUDD: Stochastic Planning using Decision Diagrams
"  We outline a method to estimate the value of computation for a flexible
algorithm using empirical data. To determine a reasonable trade-off between
cost and value, we build an empirical model of the value obtained through
computation, and apply this model to estimate the value of computation for
quite different problems. In particular, we investigate this trade-off for the
problem of constructing policies for decision problems represented as influence
diagrams. We show how two features of our anytime algorithm provide reasonable
estimates of the value of computation in this domain.
",Estimating the Value of Computation in Flexible Information Refinement
"  The paper is a second in a series of two papers evaluating the power of a new
scheme that generates search heuristics mechanically. The heuristics are
extracted from an approximation scheme called mini-bucket elimination that was
recently introduced. The first paper introduced the idea and evaluated it
within Branch-and-Bound search. In the current paper the idea is further
extended and evaluated within Best-First search. The resulting algorithms are
compared on coding and medical diagnosis problems, using varying strength of
the mini-bucket heuristics.
  Our results demonstrate an effective search scheme that permits controlled
tradeoff between preprocessing (for heuristic generation) and search.
Best-first search is shown to outperform Branch-and-Bound, when supplied with
good heuristics, and sufficient memory space.
",Mini-Bucket Heuristics for Improved Search
"  The clique tree algorithm is the standard method for doing inference in
Bayesian networks. It works by manipulating clique potentials - distributions
over the variables in a clique. While this approach works well for many
networks, it is limited by the need to maintain an exact representation of the
clique potentials. This paper presents a new unified approach that combines
approximate inference and the clique tree algorithm, thereby circumventing this
limitation. Many known approximate inference algorithms can be viewed as
instances of this approach. The algorithm essentially does clique tree
propagation, using approximate inference to estimate the densities in each
clique. In many settings, the computation of the approximate clique potential
can be done easily using statistical importance sampling. Iterations are used
to gradually improve the quality of the estimation.
","A General Algorithm for Approximate Inference and its Application to
  Hybrid Bayes Nets"
"  Most fuzzy systems including fuzzy decision support and fuzzy control systems
provide out-puts in the form of fuzzy sets that represent the inferred
conclusions. Linguistic interpretation of such outputs often involves the use
of linguistic approximation that assigns a linguistic label to a fuzzy set
based on the predefined primary terms, linguistic modifiers and linguistic
connectives. More generally, linguistic approximation can be formalized in the
terms of the re-translation rules that correspond to the translation rules in
ex-plicitation (e.g. simple, modifier, composite, quantification and
qualification rules) in com-puting with words [Zadeh 1996]. However most
existing methods of linguistic approximation use the simple, modifier and
composite re-translation rules only. Although these methods can provide a
sufficient approximation of simple fuzzy sets the approximation of more complex
ones that are typical in many practical applications of fuzzy systems may be
less satisfactory. Therefore the question arises why not use in linguistic
ap-proximation also other re-translation rules corre-sponding to the
translation rules in explicitation to advantage. In particular linguistic
quantifica-tion may be desirable in situations where the conclusions
interpreted as quantified linguistic propositions can be more informative and
natu-ral. This paper presents some aspects of linguis-tic approximation in the
context of the re-translation rules and proposes an approach to linguistic
approximation with the use of quantifi-cation rules, i.e. quantified linguistic
approxima-tion. Two methods of the quantified linguistic approximation are
considered with the use of lin-guistic quantifiers based on the concepts of the
non-fuzzy and fuzzy cardinalities of fuzzy sets. A number of examples are
provided to illustrate the proposed approach.
",On Quantified Linguistic Approximation
"  There is available an ever-increasing variety of procedures for managing
uncertainty. These methods are discussed in the literature of artificial
intelligence, as well as in the literature of philosophy of science. Heretofore
these methods have been evaluated by intuition, discussion, and the general
philosophical method of argument and counterexample. Almost any method of
uncertainty management will have the property that in the long run it will
deliver numbers approaching the relative frequency of the kinds of events at
issue. To find a measure that will provide a meaningful evaluation of these
treatments of uncertainty, we must look, not at the long run, but at the short
or intermediate run. Our project attempts to develop such a measure in terms of
short or intermediate length performance. We represent the effects of practical
choices by the outcomes of bets offered to agents characterized by two
uncertainty management approaches: the subjective Bayesian approach and the
Classical confidence interval approach. Experimental evaluation suggests that
the confidence interval approach can outperform the subjective approach in the
relatively short run.
",Choosing Among Interpretations of Probability
"  We consider the problem of finding good finite-horizon policies for POMDPs
under the expected reward metric. The policies considered are {em free
finite-memory policies with limited memory}; a policy is a mapping from the
space of observation-memory pairs to the space of action-memeory pairs (the
policy updates the memory as it goes), and the number of possible memory states
is a parameter of the input to the policy-finding algorithms. The algorithms
considered here are preliminary implementations of three search heuristics:
local search, simulated annealing, and genetic algorithms. We compare their
outcomes to each other and to the optimal policies for each instance. We
compare run times of each policy and of a dynamic programming algorithm for
POMDPs developed by Hansen that iteratively improves a finite-state controller
--- the previous state of the art for finite memory policies. The value of the
best policy can only improve as the amount of memory increases, up to the
amount needed for an optimal finite-memory policy. Our most surprising finding
is that more memory helps in another way: given more memory than is needed for
an optimal policy, the algorithms are more likely to converge to optimal-valued
policies.
",My Brain is Full: When More Memory Helps
"  Solving symmetric Bayesian decision problems is a computationally intensive
task to perform regardless of the algorithm used. In this paper we propose a
method for improving the efficiency of algorithms for solving Bayesian decision
problems. The method is based on the principle of lazy evaluation - a principle
recently shown to improve the efficiency of inference in Bayesian networks. The
basic idea is to maintain decompositions of potentials and to postpone
computations for as long as possible. The efficiency improvements obtained with
the lazy evaluation based method is emphasized through examples. Finally, the
lazy evaluation based method is compared with the hugin and valuation-based
systems architectures for solving symmetric Bayesian decision problems.
",Lazy Evaluation of Symmetric Bayesian Decision Problems
"  This paper extends previous work with network fragments and
situation-specific network construction. We formally define the asymmetry
network, an alternative representation for a conditional probability table. We
also present an object-oriented representation for partially specified
asymmetry networks. We show that the representation is parsimonious. We define
an algebra for the elements of the representation that allows us to 'factor'
any CPT and to soundly combine the partially specified asymmetry networks.
",Representing and Combining Partially Specified CPTs
"  Decision-making problems in uncertain or stochastic domains are often
formulated as Markov decision processes (MDPs). Policy iteration (PI) is a
popular algorithm for searching over policy-space, the size of which is
exponential in the number of states. We are interested in bounds on the
complexity of PI that do not depend on the value of the discount factor. In
this paper we prove the first such non-trivial, worst-case, upper bounds on the
number of iterations required by PI to converge to the optimal policy. Our
analysis also sheds new light on the manner in which PI progresses through the
space of policies.
",On the Complexity of Policy Iteration
"  We are interested in the problem of planning for factored POMDPs. Building on
the recent results of Kearns, Mansour and Ng, we provide a planning algorithm
for factored POMDPs that exploits the accuracy-efficiency tradeoff in the
belief state simplification introduced by Boyen and Koller.
","Approximate Planning for Factored POMDPs using Belief State
  Simplification"
"  Solving partially observable Markov decision processes (POMDPs) is highly
intractable in general, at least in part because the optimal policy may be
infinitely large. In this paper, we explore the problem of finding the optimal
policy from a restricted set of policies, represented as finite state automata
of a given size. This problem is also intractable, but we show that the
complexity can be greatly reduced when the POMDP and/or policy are further
constrained. We demonstrate good empirical results with a branch-and-bound
method for finding globally optimal deterministic policies, and a
gradient-ascent method for finding locally optimal stochastic policies.
",Solving POMDPs by Searching the Space of Finite Policies
"  Influence diagrams serve as a powerful tool for modelling symmetric decision
problems. When solving an influence diagram we determine a set of strategies
for the decisions involved. A strategy for a decision variable is in principle
a function over its past. However, some of the past may be irrelevant for the
decision, and for computational reasons it is important not to deal with
redundant variables in the strategies. We show that current methods (e.g. the
""Decision Bayes-ball"" algorithm by Shachter UAI98) do not determine the
relevant past, and we present a complete algorithm.
  Actually, this paper takes a more general outset: When formulating a decision
scenario as an influence diagram, a linear temporal ordering of the decisions
variables is required. This constraint ensures that the decision scenario is
welldefined. However, the structure of a decision scenario often yields certain
decisions conditionally independent, and it is therefore unnecessary to impose
a linear temporal ordering on the decisions. In this paper we deal with partial
influence diagrams i.e. influence diagrams with only a partial temporal
ordering specified. We present a set of conditions which are necessary and
sufficient to ensure that a partial influence diagram is welldefined. These
conditions are used as a basis for the construction of an algorithm for
determining whether or not a partial influence diagram is welldefined.
",Welldefined Decision Scenarios
"  Graphical models based on conditional independence support concise encodings
of the subjective belief of a single agent. A natural question is whether the
consensus belief of a group of agents can be represented with equal parsimony.
We prove, under relatively mild assumptions, that even if everyone agrees on a
common graph topology, no method of combining beliefs can maintain that
structure. Even weaker conditions rule out local aggregation within conditional
probability tables. On a more positive note, we show that if probabilities are
combined with the logarithmic opinion pool (LogOP), then commonly held Markov
independencies are maintained. This suggests a straightforward procedure for
constructing a consensus Markov network. We describe an algorithm for computing
the LogOP with time complexity comparable to that of exact Bayesian inference.
",Graphical Representations of Consensus Belief
"  In previous work, we pointed out the limitations of standard Bayesian
networks as a modeling framework for large, complex domains. We proposed a new,
richly structured modeling language, {em Object-oriented Bayesian Netorks},
that we argued would be able to deal with such domains. However, it turns out
that OOBNs are not expressive enough to model many interesting aspects of
complex domains: the existence of specific named objects, arbitrary relations
between objects, and uncertainty over domain structure. These aspects are
crucial in real-world domains such as battlefield awareness. In this paper, we
present SPOOK, an implemented system that addresses these limitations. SPOOK
implements a more expressive language that allows it to represent the
battlespace domain naturally and compactly. We present a new inference
algorithm that utilizes the model structure in a fundamental way, and show
empirically that it achieves orders of magnitude speedup over existing
approaches.
","SPOOK: A System for Probabilistic Object-Oriented Knowledge
  Representation"
"  Bayesian Networks (BN) provide robust probabilistic methods of reasoning
under uncertainty, but despite their formal grounds are strictly based on the
notion of conditional dependence, not much attention has been paid so far to
their use in dependability analysis. The aim of this paper is to propose BN as
a suitable tool for dependability analysis, by challenging the formalism with
basic issues arising in dependability tasks. We will discuss how both modeling
and analysis issues can be naturally dealt with by BN. Moreover, we will show
how some limitations intrinsic to combinatorial dependability methods such as
Fault Trees can be overcome using BN. This will be pursued through the study of
a real-world example concerning the reliability analysis of a redundant digital
Programmable Logic Controller (PLC) with majority voting 2:3
","Bayesian Networks for Dependability Analysis: an Application to Digital
  Control Reliability"
"  Qualitative probabilistic networks have been introduced as qualitative
abstractions of Bayesian belief networks. One of the major drawbacks of these
qualitative networks is their coarse level of detail, which may lead to
unresolved trade-offs during inference. We present an enhanced formalism for
qualitative networks with a finer level of detail. An enhanced qualitative
probabilistic network differs from a regular qualitative network in that it
distinguishes between strong and weak influences. Enhanced qualitative
probabilistic networks are purely qualitative in nature, as regular qualitative
networks are, yet allow for efficiently resolving trade-offs during inference.
",Enhancing QPNs for Trade-off Resolution
"  In this article we propose a qualitative (ordinal) counterpart for the
Partially Observable Markov Decision Processes model (POMDP) in which the
uncertainty, as well as the preferences of the agent, are modeled by
possibility distributions. This qualitative counterpart of the POMDP model
relies on a possibilistic theory of decision under uncertainty, recently
developed. One advantage of such a qualitative framework is its ability to
escape from the classical obstacle of stochastic POMDPs, in which even with a
finite state space, the obtained belief state space of the POMDP is infinite.
Instead, in the possibilistic framework even if exponentially larger than the
state space, the belief state space remains finite.
","A Possibilistic Model for Qualitative Sequential Decision Problems under
  Uncertainty in Partially Observable Environments"
"  One of the most useful sensitivity analysis techniques of decision analysis
is the computation of value of information (or clairvoyance), the difference in
value obtained by changing the decisions by which some of the uncertainties are
observed. In this paper, some simple but powerful extensions to previous
algorithms are introduced which allow an efficient value of information
calculation on the rooted cluster tree (or strong junction tree) used to solve
the original decision problem.
",Efficient Value of Information Computation
"  Hidden Markov models (HMMs) and partially observable Markov decision
processes (POMDPs) form a useful tool for modeling dynamical systems. They are
particularly useful for representing environments such as road networks and
office buildings, which are typical for robot navigation and planning. The work
presented here is concerned with acquiring such models. We demonstrate how
domain-specific information and constraints can be incorporated into the
statistical estimation process, greatly improving the learned models in terms
of the model quality, the number of iterations required for convergence and
robustness to reduction in the amount of available data. We present new
initialization heuristics which can be used even when the data suffers from
cumulative rotational error, new update rules for the model parameters, as an
instance of generalized EM, and a strategy for enforcing complete geometrical
consistency in the model. Experimental results demonstrate the effectiveness of
our approach for both simulated and real robot data, in traditionally
hard-to-learn environments.
",Learning Hidden Markov Models with Geometrical Constraints
"  We present examples where the use of belief functions provided sound and
elegant solutions to real life problems. These are essentially characterized by
?missing' information. The examples deal with 1) discriminant analysis using a
learning set where classes are only partially known; 2) an information
retrieval systems handling inter-documents relationships; 3) the combination of
data from sensors competent on partially overlapping frames; 4) the
determination of the number of sources in a multi-sensor environment by
studying the inter-sensors contradiction. The purpose of the paper is to report
on such applications where the use of belief functions provides a convenient
tool to handle ?messy' data problems.
",Practical Uses of Belief Functions
"  The noisy-or and its generalization noisy-max have been utilized to reduce
the complexity of knowledge acquisition. In this paper, we present a new
representation of noisy-max that allows for efficient inference in general
Bayesian networks. Empirical studies show that our method is capable of
computing queries in well-known large medical networks, QMR-DT and CPCS, for
which no previous exact inference method has been shown to perform well.
",Multiplicative Factorization of Noisy-Max
"  Structure and parameters in a Bayesian network uniquely specify the
probability distribution of the modeled domain. The locality of both structure
and probabilistic information are the great benefits of Bayesian networks and
require the modeler to only specify local information. On the other hand this
locality of information might prevent the modeler - and even more any other
person - from obtaining a general overview of the important relationships
within the domain. The goal of the work presented in this paper is to provide
an ""alternative"" view on the knowledge encoded in a Bayesian network which
might sometimes be very helpful for providing insights into the underlying
domain. The basic idea is to calculate a mixture approximation to the
probability distribution represented by the Bayesian network. The mixture
component densities can be thought of as representing typical scenarios implied
by the Bayesian model, providing intuition about the basic relationships. As an
additional benefit, performing inference in the approximate model is very
simple and intuitive and can provide additional insights. The computational
complexity for the calculation of the mixture approximations criticaly depends
on the measure which defines the distance between the probability distribution
represented by the Bayesian network and the approximate distribution. Both the
KL-divergence and the backward KL-divergence lead to inefficient algorithms.
Incidentally, the latter is used in recent work on mixtures of mean field
solutions to which the work presented here is closely related. We show,
however, that using a mean squared error cost function leads to update
equations which can be solved using the junction tree algorithm. We conclude
that the mean squared error cost function can be used for Bayesian networks in
which inference based on the junction tree is tractable. For large networks,
however, one may have to rely on mean field approximations.
",Mixture Approximations to Bayesian Networks
"  In building Bayesian belief networks, the elicitation of all probabilities
required can be a major obstacle. We learned the extent of this often-cited
observation in the construction of the probabilistic part of a complex
influence diagram in the field of cancer treatment. Based upon our negative
experiences with existing methods, we designed a new method for probability
elicitation from domain experts. The method combines various ideas, among which
are the ideas of transcribing probabilities and of using a scale with both
numerical and verbal anchors for marking assessments. In the construction of
the probabilistic part of our influence diagram, the method proved to allow for
the elicitation of many probabilities in little time.
",How to Elicit Many Probabilities
"  The AGM theory of belief revision has become an important paradigm for
investigating rational belief changes. Unfortunately, researchers working in
this paradigm have restricted much of their attention to rather simple
representations of belief states, namely logically closed sets of propositional
sentences. In our opinion, this has resulted in a too abstract categorisation
of belief change operations: expansion, revision, or contraction. Occasionally,
in the AGM paradigm, also probabilistic belief changes have been considered,
and it is widely accepted that the probabilistic version of expansion is
conditioning. However, we argue that it may be more correct to view
conditioning and expansion as two essentially different kinds of belief change,
and that what we call constraining is a better candidate for being considered
probabilistic expansion.
","Probabilistic Belief Change: Expansion, Conditioning and Constraining"
"  It is well-known that the notion of (strong) conditional independence (CI) is
too restrictive to capture independencies that only hold in certain contexts.
This kind of contextual independency, called context-strong independence (CSI),
can be used to facilitate the acquisition, representation, and inference of
probabilistic knowledge. In this paper, we suggest the use of contextual weak
independence (CWI) in Bayesian networks. It should be emphasized that the
notion of CWI is a more general form of contextual independence than CSI.
Furthermore, if the contextual strong independence holds for all contexts, then
the notion of CSI becomes strong CI. On the other hand, if the weak contextual
independence holds for all contexts, then the notion of CWI becomes weak
independence (WI) nwhich is a more general noncontextual independency than
strong CI. More importantly, complete axiomatizations are studied for both the
class of WI and the class of CI and WI together. Finally, the interesting
property of WI being a necessary and sufficient condition for ensuring
consistency in granular probabilistic networks is shown.
",Contextual Weak Independence in Bayesian Networks
"  As Bayesian networks are applied to larger and more complex problem domains,
search for flexible modeling and more efficient inference methods is an ongoing
effort. Multiply sectioned Bayesian networks (MSBNs) extend the HUGIN inference
for Bayesian networks into a coherent framework for flexible modeling and
distributed inference.Lazy propagation extends the Shafer-Shenoy and HUGIN
inference methods with reduced space complexity. We apply the Shafer-Shenoy and
lazy propagation to inference in MSBNs. The combination of the MSBN framework
and lazy propagation provides a better framework for modeling and inference in
very large domains. It retains the modeling flexibility of MSBNs and reduces
the runtime space complexity, allowing exact inference in much larger domains
given the same computational resources.
","Inference in Multiply Sectioned Bayesian Networks with Extended
  Shafer-Shenoy and Lazy Propagation"
"  Recent interests in dynamic decision modeling have led to the development of
several representation and inference methods. These methods however, have
limited application under time critical conditions where a trade-off between
model quality and computational tractability is essential. This paper presents
an approach to time-critical dynamic decision modeling. A knowledge
representation and modeling method called the time-critical dynamic influence
diagram is proposed. The formalism has two forms. The condensed form is used
for modeling and model abstraction, while the deployed form which can be
converted from the condensed form is used for inference purposes. The proposed
approach has the ability to represent space-temporal abstraction within the
model. A knowledge-based meta-reasoning approach is proposed for the purpose of
selecting the best abstracted model that provide the optimal trade-off between
model quality and model tractability. An outline of the knowledge-based model
construction algorithm is also provided.
",Time-Critical Dynamic Decision Making
"  We present a technique for speeding up the convergence of value iteration for
partially observable Markov decisions processes (POMDPs). The underlying idea
is similar to that behind modified policy iteration for fully observable Markov
decision processes (MDPs). The technique can be easily incorporated into any
existing POMDP value iteration algorithms. Experiments have been conducted on
several test problems with one POMDP value iteration algorithm called
incremental pruning. We find that the technique can make incremental pruning
run several orders of magnitude faster.
","A Method for Speeding Up Value Iteration in Partially Observable Markov
  Decision Processes"
"  The notion of rough set captures indiscernibility of elements in a set. But,
in many real life situations, an information system establishes the relation
between different universes. This gave the extension of rough set on single
universal set to rough set on two universal sets. In this paper, we introduce
approximation of classifications and measures of uncertainty basing upon rough
set on two universal sets employing the knowledge due to binary relations.
","Approximation of Classification and Measures of Uncertainty in Rough Set
  on Two Universal Sets"
"  Possibilistic logic is a well-known graded logic of uncertainty suitable to
reason under incomplete information and partially inconsistent knowledge, which
is built upon classical first order logic. There exists for Possibilistic logic
a proof procedure based on a refutation complete resolution-style calculus.
Recently, a syntactical extension of first order Possibilistic logic (called
PLFC) dealing with fuzzy constants and fuzzily restricted quantifiers has been
proposed. Our aim is to present steps towards both the formalization of PLFC
itself and an automated deduction system for it by (i) providing a formal
semantics; (ii) defining a sound resolution-style calculus by refutation; and
(iii) describing a first-order proof procedure for PLFC clauses based on (ii)
and on a novel notion of most general substitution of two literals in a
resolution step. In contrast to standard Possibilistic logic semantics,
truth-evaluation of formulas with fuzzy constants are many-valued instead of
boolean, and consequently an extended notion of possibilistic uncertainty is
also needed.
","On the Semantics and Automated Deduction for PLFC, a Logic of
  Possibilistic Uncertainty and Fuzziness"
"  Argumentation is a promising model for reasoning with uncertain knowledge.
The key concept of acceptability enables to differentiate arguments and
counterarguments: The certainty of a proposition can then be evaluated through
the most acceptable arguments for that proposition. In this paper, we
investigate different complementary points of view: - an acceptability based on
the existence of direct counterarguments, - an acceptability based on the
existence of defenders. Pursuing previous work on preference-based
argumentation principles, we enforce both points of view by taking into account
preference orderings for comparing arguments. Our approach is illustrated in
the context of reasoning with stratified knowldge bases.
",On the Acceptability of Arguments in Preference-Based Argumentation
"  This paper addresses the problem of merging uncertain information in the
framework of possibilistic logic. It presents several syntactic combination
rules to merge possibilistic knowledge bases, provided by different sources,
into a new possibilistic knowledge base. These combination rules are first
described at the meta-level outside the language of possibilistic logic. Next,
an extension of possibilistic logic, where the combination rules are inside the
language, is proposed. A proof system in a sequent form, which is sound and
complete with respect to the possibilistic logic semantics, is given.
",Merging Uncertain Knowledge Bases in a Possibilistic Logic Framework
"  There exist two general forms of exact algorithms for updating probabilities
in Bayesian Networks. The first approach involves using a structure, usually a
clique tree, and performing local message based calculation to extract the
belief in each variable. The second general class of algorithm involves the use
of non-serial dynamic programming techniques to extract the belief in some
desired group of variables. In this paper we present a hybrid algorithm based
on the latter approach yet possessing the ability to retrieve the belief in all
single variables. The technique is advantageous in that it saves a NP-hard
computation step over using one algorithm of each type. Furthermore, this
technique re-enforces a conjecture of Jensen and Jensen [JJ94] in that it still
requires a single NP-hard step to set up the structure on which inference is
performed, as we show by confirming Li and D'Ambrosio's [LD94] conjectured
NP-hardness of OFP.
","A Hybrid Algorithm to Compute Marginal and Joint Beliefs in Bayesian
  Networks and Its Complexity"
"  Recent research in decision theoretic planning has focussed on making the
solution of Markov decision processes (MDPs) more feasible. We develop a family
of algorithms for structured reachability analysis of MDPs that are suitable
when an initial state (or set of states) is known. Using compact, structured
representations of MDPs (e.g., Bayesian networks), our methods, which vary in
the tradeoff between complexity and accuracy, produce structured descriptions
of (estimated) reachable states that can be used to eliminate variables or
variable values from the problem description, reducing the size of the MDP and
making it easier to solve. One contribution of our work is the extension of
ideas from GRAPHPLAN to deal with the distributed nature of action
representations typically embodied within Bayes nets and the problem of
correlated action effects. We also demonstrate that our algorithm can be made
more complete by using k-ary constraints instead of binary constraints. Another
contribution is the illustration of how the compact representation of
reachability constraints can be exploited by several existing (exact and
approximate) abstraction algorithms for MDPs.
",Structured Reachability Analysis for Markov Decision Processes
"  The monitoring and control of any dynamic system depends crucially on the
ability to reason about its current status and its future trajectory. In the
case of a stochastic system, these tasks typically involve the use of a belief
state- a probability distribution over the state of the process at a given
point in time. Unfortunately, the state spaces of complex processes are very
large, making an explicit representation of a belief state intractable. Even in
dynamic Bayesian networks (DBNs), where the process itself can be represented
compactly, the representation of the belief state is intractable. We
investigate the idea of maintaining a compact approximation to the true belief
state, and analyze the conditions under which the errors due to the
approximations taken over the lifetime of the process do not accumulate to make
our answers completely irrelevant. We show that the error in a belief state
contracts exponentially as the process evolves. Thus, even with multiple
approximations, the error in our process remains bounded indefinitely. We show
how the additional structure of a DBN can be used to design our approximation
scheme, improving its performance significantly. We demonstrate the
applicability of our ideas in the context of a monitoring task, showing that
orders of magnitude faster inference can be achieved with only a small
degradation in accuracy.
",Tractable Inference for Complex Stochastic Processes
"  The situation assessment problem is considered, in terms of object,
condition, activity, and plan recognition, based on data coming from the
real-word {em via} various sensors. It is shown that uncertainty issues are
linked both to the models and to the matching algorithm. Three different types
of uncertainties are identified, and within each one, the numerical and the
symbolic cases are distinguished. The emphasis is then put on purely symbolic
uncertainties: it is shown that they can be dealt with within a purely symbolic
framework resulting from a transposition of classical numerical estimation
tools.
","Dealing with Uncertainty in Situation Assessment: towards a Symbolic
  Approach"
"  Given an undirected graph G or hypergraph X model for a given set of
variables V, we introduce two marginalization operators for obtaining the
undirected graph GA or hypergraph HA associated with a given subset A c V such
that the marginal distribution of A factorizes according to GA or HA,
respectively. Finally, we illustrate the method by its application to some
practical examples. With them we show that hypergraph models allow defining a
finer factorization or performing a more precise conditional independence
analysis than undirected graph models.
",Marginalizing in Undirected Graph and Hypergraph Models
"  We investigate the application of classification techniques to utility
elicitation. In a decision problem, two sets of parameters must generally be
elicited: the probabilities and the utilities. While the prior and conditional
probabilities in the model do not change from user to user, the utility models
do. Thus it is necessary to elicit a utility model separately for each new
user. Elicitation is long and tedious, particularly if the outcome space is
large and not decomposable. There are two common approaches to utility function
elicitation. The first is to base the determination of the users utility
function solely ON elicitation OF qualitative preferences.The second makes
assumptions about the form AND decomposability OF the utility function.Here we
take a different approach: we attempt TO identify the new USERs utility
function based on classification relative to a database of previously collected
utility functions. We do this by identifying clusters of utility functions that
minimize an appropriate distance measure. Having identified the clusters, we
develop a classification scheme that requires many fewer and simpler
assessments than full utility elicitation and is more robust than utility
elicitation based solely on preferences. We have tested our algorithm on a
small database of utility functions in a prenatal diagnosis domain and the
results are quite promising.
",Utility Elicitation as a Classification Problem
"  This paper analyzes irrelevance and independence relations in graphical
models associated with convex sets of probability distributions (called
Quasi-Bayesian networks). The basic question in Quasi-Bayesian networks is, How
can irrelevance/independence relations in Quasi-Bayesian networks be detected,
enforced and exploited? This paper addresses these questions through Walley's
definitions of irrelevance and independence. Novel algorithms and results are
presented for inferences with the so-called natural extensions using fractional
linear programming, and the properties of the so-called type-1 extensions are
clarified through a new generalization of d-separation.
",Irrelevance and Independence Relations in Quasi-Bayesian Networks
"  It is well known that one can ignore parts of a belief network when computing
answers to certain probabilistic queries. It is also well known that the
ignorable parts (if any) depend on the specific query of interest and,
therefore, may change as the query changes. Algorithms based on jointrees,
however, do not seem to take computational advantage of these facts given that
they typically construct jointrees for worst-case queries; that is, queries for
which every part of the belief network is considered relevant. To address this
limitation, we propose in this paper a method for reconfiguring jointrees
dynamically as the query changes. The reconfiguration process aims at
maintaining a jointree which corresponds to the underlying belief network after
it has been pruned given the current query. Our reconfiguration method is
marked by three characteristics: (a) it is based on a non-classical definition
of jointrees; (b) it is relatively efficient; and (c) it can reuse some of the
computations performed before a jointree is reconfigured. We present
preliminary experimental results which demonstrate significant savings over
using static jointrees when query changes are considerable.
",Dynamic Jointrees
"  The variability of structure in a finite Markov equivalence class of causally
sufficient models represented by directed acyclic graphs has been fully
characterized. Without causal sufficiency, an infinite semi-Markov equivalence
class of models has only been characterized by the fact that each model in the
equivalence class entails the same marginal statistical dependencies. In this
paper, we study the variability of structure of causal models within a
semi-Markov equivalence class and propose a systematic approach to construct
models entailing any specific marginal statistical dependencies.
",On the Semi-Markov Equivalence of Causal Models
"  This paper relates comparative belief structures and a general view of belief
management in the setting of deductively closed logical representations of
accepted beliefs. We show that the range of compatibility between the classical
deductive closure and uncertain reasoning covers precisely the nonmonotonic
'preferential' inference system of Kraus, Lehmann and Magidor and nothing else.
In terms of uncertain reasoning any possibility or necessity measure gives
birth to a structure of accepted beliefs. The classes of probability functions
and of Shafer's belief functions which yield belief sets prove to be very
special ones.
","Comparative Uncertainty, Belief Functions and Accepted Beliefs"
"  This paper presents an axiomatic framework for qualitative decision under
uncertainty in a finite setting. The corresponding utility is expressed by a
sup-min expression, called Sugeno (or fuzzy) integral. Technically speaking,
Sugeno integral is a median, which is indeed a qualitative counterpart to the
averaging operation underlying expected utility. The axiomatic justification of
Sugeno integral-based utility is expressed in terms of preference between acts
as in Savage decision theory. Pessimistic and optimistic qualitative utilities,
based on necessity and possibility measures, previously introduced by two of
the authors, can be retrieved in this setting by adding appropriate axioms.
",Qualitative Decision Theory with Sugeno Integrals
"  While decision theory provides an appealing normative framework for
representing rich preference structures, eliciting utility or value functions
typically incurs a large cost. For many applications involving interactive
systems this overhead precludes the use of formal decision-theoretic models of
preference. Instead of performing elicitation in a vacuum, it would be useful
if we could augment directly elicited preferences with some appropriate default
information. In this paper we propose a case-based approach to alleviating the
preference elicitation bottleneck. Assuming the existence of a population of
users from whom we have elicited complete or incomplete preference structures,
we propose eliciting the preferences of a new user interactively and
incrementally, using the closest existing preference structures as potential
defaults. Since a notion of closeness demands a measure of distance among
preference structures, this paper takes the first step of studying various
distance measures over fully and partially specified preference structures. We
explore the use of Euclidean distance, Spearmans footrule, and define a new
measure, the probabilistic distance. We provide computational techniques for
all three measures.
","Towards Case-Based Preference Elicitation: Similarity Measures on
  Preference Structures"
"  Most algorithms for solving POMDPs iteratively improve a value function that
implicitly represents a policy and are said to search in value function space.
This paper presents an approach to solving POMDPs that represents a policy
explicitly as a finite-state controller and iteratively improves the controller
by search in policy space. Two related algorithms illustrate this approach. The
first is a policy iteration algorithm that can outperform value iteration in
solving infinitehorizon POMDPs. It provides the foundation for a new heuristic
search algorithm that promises further speedup by focusing computational effort
on regions of the problem space that are reachable, or likely to be reached,
from a start state.
",Solving POMDPs by Searching in Policy Space
"  We investigate the use of temporally abstract actions, or macro-actions, in
the solution of Markov decision processes. Unlike current models that combine
both primitive actions and macro-actions and leave the state space unchanged,
we propose a hierarchical model (using an abstract MDP) that works with
macro-actions only, and that significantly reduces the size of the state space.
This is achieved by treating macroactions as local policies that act in certain
regions of state space, and by restricting states in the abstract MDP to those
at the boundaries of regions. The abstract MDP approximates the original and
can be solved more efficiently. We discuss several ways in which macro-actions
can be generated to ensure good solution quality. Finally, we consider ways in
which macro-actions can be reused to solve multiple, related MDPs; and we show
that this can justify the computational overhead of macro-action generation.
",Hierarchical Solution of Markov Decision Processes using Macro-actions
"  Stochastic search algorithms are among the most sucessful approaches for
solving hard combinatorial problems. A large class of stochastic search
approaches can be cast into the framework of Las Vegas Algorithms (LVAs). As
the run-time behavior of LVAs is characterized by random variables, the
detailed knowledge of run-time distributions provides important information for
the analysis of these algorithms. In this paper we propose a novel methodology
for evaluating the performance of LVAs, based on the identification of
empirical run-time distributions. We exemplify our approach by applying it to
Stochastic Local Search (SLS) algorithms for the satisfiability problem (SAT)
in propositional logic. We point out pitfalls arising from the use of improper
empirical methods and discuss the benefits of the proposed methodology for
evaluating and comparing LVAs.
",Evaluating Las Vegas Algorithms - Pitfalls and Remedies
"  We present an anytime algorithm which computes policies for decision problems
represented as multi-stage influence diagrams. Our algorithm constructs
policies incrementally, starting from a policy which makes no use of the
available information. The incremental process constructs policies which
includes more of the information available to the decision maker at each step.
While the process converges to the optimal policy, our approach is designed for
situations in which computing the optimal policy is infeasible. We provide
examples of the process on several large decision problems, showing that, for
these examples, the process constructs valuable (but sub-optimal) policies
before the optimal policy would be available by traditional methods.
",An Anytime Algorithm for Decision Making under Uncertainty
"  For many real time applications, it is important to validate the information
received from the sensors before entering higher levels of reasoning. This
paper presents an any time probabilistic algorithm for validating the
information provided by sensors. The system consists of two Bayesian network
models. The first one is a model of the dependencies between sensors and it is
used to validate each sensor. It provides a list of potentially faulty sensors.
To isolate the real faults, a second Bayesian network is used, which relates
the potential faults with the real faults. This second model is also used to
make the validation algorithm any time, by validating first the sensors that
provide more information. To select the next sensor to validate, and measure
the quality of the results at each stage, an entropy function is used. This
function captures in a single quantity both the certainty and specificity
measures of any time algorithms. Together, both models constitute a mechanism
for validating sensors in an any time fashion, providing at each step the
probability of correct/faulty for each sensor, and the total quality of the
results. The algorithm has been tested in the validation of temperature sensors
of a power plant.
",Any Time Probabilistic Reasoning for Sensor Validation
"  We take another look at the general problem of selecting a preferred
probability measure among those that comply with some given constraints. The
dominant role that entropy maximization has obtained in this context is
questioned by arguing that the minimum information principle on which it is
based could be supplanted by an at least as plausible ""likelihood of evidence""
principle. We then review a method for turning given selection functions into
representation independent variants, and discuss the tradeoffs involved in this
transformation.
","Measure Selection: Notions of Rationality and Representation
  Independence"
"  In the literature on graphical models, there has been increased attention
paid to the problems of learning hidden structure (see Heckerman [H96] for
survey) and causal mechanisms from sample data [H96, P88, S93, P95, F98]. In
most settings we should expect the former to be difficult, and the latter
potentially impossible without experimental intervention. In this work, we
examine some restricted settings in which perfectly reconstruct the hidden
structure solely on the basis of observed sample data.
","Exact Inference of Hidden Structure from Sample Data in Noisy-OR
  Networks"
"  In the last decade, several architectures have been proposed for exact
computation of marginals using local computation. In this paper, we compare
three architectures - Lauritzen-Spiegelhalter, Hugin, and Shenoy-Shafer - from
the perspective of graphical structure for message propagation, message-passing
scheme, computational efficiency, and storage efficiency.
","A Comparison of Lauritzen-Spiegelhalter, Hugin, and Shenoy-Shafer
  Architectures for Computing Marginals of Probability Distributions"
"  Qualitative probabilistic reasoning in a Bayesian network often reveals
tradeoffs: relationships that are ambiguous due to competing qualitative
influences. We present two techniques that combine qualitative and numeric
probabilistic reasoning to resolve such tradeoffs, inferring the qualitative
relationship between nodes in a Bayesian network. The first approach
incrementally marginalizes nodes that contribute to the ambiguous qualitative
relationships. The second approach evaluates approximate Bayesian networks for
bounds of probability distributions, and uses these bounds to determinate
qualitative relationships in question. This approach is also incremental in
that the algorithm refines the state spaces of random variables for tighter
bounds until the qualitative relationships are resolved. Both approaches
provide systematic methods for tradeoff resolution at potentially lower
computational cost than application of purely numeric methods.
",Incremental Tradeoff Resolution in Qualitative Probabilistic Networks
"  We exploit qualitative probabilistic relationships among variables for
computing bounds of conditional probability distributions of interest in
Bayesian networks. Using the signs of qualitative relationships, we can
implement abstraction operations that are guaranteed to bound the distributions
of interest in the desired direction. By evaluating incrementally improved
approximate networks, our algorithm obtains monotonically tightening bounds
that converge to exact distributions. For supermodular utility functions, the
tightening bounds monotonically reduce the set of admissible decision
alternatives as well.
",Using Qualitative Relationships for Bounding Probability Distributions
"  We present locally complete inference rules for probabilistic deduction from
taxonomic and probabilistic knowledge-bases over conjunctive events. Crucially,
in contrast to similar inference rules in the literature, our inference rules
are locally complete for conjunctive events and under additional taxonomic
knowledge. We discover that our inference rules are extremely complex and that
it is at first glance not clear at all where the deduced tightest bounds come
from. Moreover, analyzing the global completeness of our inference rules, we
find examples of globally very incomplete probabilistic deductions. More
generally, we even show that all systems of inference rules for taxonomic and
probabilistic knowledge-bases over conjunctive events are globally incomplete.
We conclude that probabilistic deduction by the iterative application of
inference rules on interval restrictions for conditional probabilities, even
though considered very promising in the literature so far, seems very limited
in its field of application.
","Magic Inference Rules for Probabilistic Deduction under Taxonomic
  Knowledge"
"  The efficiency of algorithms using secondary structures for probabilistic
inference in Bayesian networks can be improved by exploiting independence
relations induced by evidence and the direction of the links in the original
network. In this paper we present an algorithm that on-line exploits
independence relations induced by evidence and the direction of the links in
the original network to reduce both time and space costs. Instead of
multiplying the conditional probability distributions for the various cliques,
we determine on-line which potentials to multiply when a message is to be
produced. The performance improvement of the algorithm is emphasized through
empirical evaluations involving large real world Bayesian networks, and we
compare the method with the HUGIN and Shafer-Shenoy inference algorithms.
",Lazy Propagation in Junction Trees
"  This paper describes a process for constructing situation-specific belief
networks from a knowledge base of network fragments. A situation-specific
network is a minimal query complete network constructed from a knowledge base
in response to a query for the probability distribution on a set of target
variables given evidence and context variables. We present definitions of query
completeness and situation-specific networks. We describe conditions on the
knowledge base that guarantee query completeness. The relationship of our work
to earlier work on KBMC is also discussed.
",Constructing Situation Specific Belief Networks
"  Several authors have explained that the likelihood ratio measures the
strength of the evidence represented by observations in statistical problems.
This idea works fine when the goal is to evaluate the strength of the available
evidence for a simple hypothesis versus another simple hypothesis. However, the
applicability of this idea is limited to simple hypotheses because the
likelihood function is primarily defined on points (simple hypotheses) of the
parameter space. In this paper we define a general weight of evidence that is
applicable to both simple and composite hypotheses. It is based on the
Dempster-Shafer concept of plausibility and is shown to be a generalization of
the likelihood ratio. Functional models are of a fundamental importance for the
general weight of evidence proposed in this paper. The relevant concepts and
ideas are explained by means of a familiar urn problem and the general analysis
of a real-world medical problem is presented.
",From Likelihood to Plausibility
"  Distributed knowledge based applications in open domain rely on common sense
information which is bound to be uncertain and incomplete. To draw the useful
conclusions from ambiguous data, one must address uncertainties and conflicts
incurred in a holistic view. No integrated frameworks are viable without an
in-depth analysis of conflicts incurred by uncertainties. In this paper, we
give such an analysis and based on the result, propose an integrated framework.
Our framework extends definite argumentation theory to model uncertainty. It
supports three views over conflicting and uncertain knowledge. Thus, knowledge
engineers can draw different conclusions depending on the application context
(i.e. view). We also give an illustrative example on strategical decision
support to show the practical usefulness of our framework.
",Resolving Conflicting Arguments under Uncertainties
"  This paper presents two new approaches to decomposing and solving large
Markov decision problems (MDPs), a partial decoupling method and a complete
decoupling method. In these approaches, a large, stochastic decision problem is
divided into smaller pieces. The first approach builds a cache of policies for
each part of the problem independently, and then combines the pieces in a
separate, light-weight step. A second approach also divides the problem into
smaller pieces, but information is communicated between the different problem
pieces, allowing intelligent decisions to be made about which piece requires
the most attention. Both approaches can be used to find optimal policies or
approximately optimal policies with provable bounds. These algorithms also
provide a framework for the efficient transfer of knowledge across problems
that share similar structure.
","Flexible Decomposition Algorithms for Weakly Coupled Markov Decision
  Problems"
"  I present a parallel algorithm for exact probabilistic inference in Bayesian
networks. For polytree networks with n variables, the worst-case time
complexity is O(log n) on a CREW PRAM (concurrent-read, exclusive-write
parallel random-access machine) with n processors, for any constant number of
evidence variables. For arbitrary networks, the time complexity is O(r^{3w}*log
n) for n processors, or O(w*log n) for r^{3w}*n processors, where r is the
maximum range of any variable, and w is the induced width (the maximum clique
size), after moralizing and triangulating the network.
",Logarithmic Time Parallel Bayesian Inference
"  The process of diagnosis involves learning about the state of a system from
various observations of symptoms or findings about the system. Sophisticated
Bayesian (and other) algorithms have been developed to revise and maintain
beliefs about the system as observations are made. Nonetheless, diagnostic
models have tended to ignore some common sense reasoning exploited by human
diagnosticians; In particular, one can learn from which observations have not
been made, in the spirit of conversational implicature. There are two concepts
that we describe to extract information from the observations not made. First,
some symptoms, if present, are more likely to be reported before others.
Second, most human diagnosticians and expert systems are economical in their
data-gathering, searching first where they are more likely to find symptoms
present. Thus, there is a desirable bias toward reporting symptoms that are
present. We develop a simple model for these concepts that can significantly
improve diagnostic inference.
",Learning From What You Don't Observe
"  There is evidence that the numbers in probabilistic inference don't really
matter. This paper considers the idea that we can make a probabilistic model
simpler by making fewer distinctions. Unfortunately, the level of a Bayesian
network seems too coarse; it is unlikely that a parent will make little
difference for all values of the other parents. In this paper we consider an
approximation scheme where distinctions can be ignored in some contexts, but
not in other contexts. We elaborate on a notion of a parent context that allows
a structured context-specific decomposition of a probability distribution and
the associated probabilistic inference scheme called probabilistic partial
evaluation (Poole 1997). This paper shows a way to simplify a probabilistic
model by ignoring distinctions which have similar probabilities, a method to
exploit the simpler model, a bound on the resulting errors, and some
preliminary empirical results on simple networks.
",Context-Specific Approximation in Probabilistic Inference
"  It was recently shown that the problem of decoding messages transmitted
through a noisy channel can be formulated as a belief updating task over a
probabilistic network [McEliece]. Moreover, it was observed that iterative
application of the (linear time) Pearl's belief propagation algorithm designed
for polytrees outperformed state of the art decoding algorithms, even though
the corresponding networks may have many cycles. This paper demonstrates
empirically that an approximation algorithm approx-mpe for solving the most
probable explanation (MPE) problem, developed within the recently proposed
mini-bucket elimination framework [Dechter96], outperforms iterative belief
propagation on classes of coding networks that have bounded induced width. Our
experiments suggest that approximate MPE decoders can be good competitors to
the approximate belief updating decoders.
","Empirical Evaluation of Approximation Algorithms for Probabilistic
  Decoding"
"  This paper describes a decision theoretic formulation of learning the
graphical structure of a Bayesian Belief Network from data. This framework
subsumes the standard Bayesian approach of choosing the model with the largest
posterior probability as the solution of a decision problem with a 0-1 loss
function and allows the use of more general loss functions able to trade-off
the complexity of the selected model and the error of choosing an
oversimplified model. A new class of loss functions, called disintegrable, is
introduced, to allow the decision problem to match the decomposability of the
graphical model. With this class of loss functions, the optimal solution to the
decision problem can be found using an efficient bottom-up search strategy.
",Decision Theoretic Foundations of Graphical Model Selection
"  One of the benefits of belief networks and influence diagrams is that so much
knowledge is captured in the graphical structure. In particular, statements of
conditional irrelevance (or independence) can be verified in time linear in the
size of the graph. To resolve a particular inference query or decision problem,
only some of the possible states and probability distributions must be
specified, the ""requisite information.""
  This paper presents a new, simple, and efficient ""Bayes-ball"" algorithm which
is well-suited to both new students of belief networks and state of the art
implementations. The Bayes-ball algorithm determines irrelevant sets and
requisite information more efficiently than existing methods, and is linear in
the size of the graph for belief networks and influence diagrams.
","Bayes-Ball: The Rational Pastime (for Determining Irrelevance and
  Requisite Information in Belief Networks and Influence Diagrams)"
"  AThe paper gives a few arguments in favour of the use of chain graphs for
description of probabilistic conditional independence structures. Every
Bayesian network model can be equivalently introduced by means of a
factorization formula with respect to a chain graph which is Markov equivalent
to the Bayesian network. A graphical characterization of such graphs is given.
The class of equivalent graphs can be represented by a distinguished graph
which is called the largest chain graph. The factorization formula with respect
to the largest chain graph is a basis of a proposal of how to represent the
corresponding (discrete) probability distribution in a computer (i.e.
parametrize it). This way does not depend on the choice of a particular
Bayesian network from the class of equivalent networks and seems to be the most
efficient way from the point of view of memory demands. A separation criterion
for reading independency statements from a chain graph is formulated in a
simpler way. It resembles the well-known d-separation criterion for Bayesian
networks and can be implemented locally.
",Bayesian Networks from the Point of View of Chain Graphs
"  This paper is about reducing influence diagram (ID) evaluation into Bayesian
network (BN) inference problems. Such reduction is interesting because it
enables one to readily use one's favorite BN inference algorithm to efficiently
evaluate IDs. Two such reduction methods have been proposed previously (Cooper
1988, Shachter and Peot 1992). This paper proposes a new method. The BN
inference problems induced by the mew method are much easier to solve than
those induced by the two previous methods.
",Probabilistic Inference in Influence Diagrams
"  There is much interest in using partially observable Markov decision
processes (POMDPs) as a formal model for planning in stochastic domains. This
paper is concerned with finding optimal policies for POMDPs. We propose several
improvements to incremental pruning, presently the most efficient exact
algorithm for solving POMDPs.
","Planning with Partially Observable Markov Decision Processes: Advances
  in Exact Solution Method"
"  In the real world, insufficient information, limited computation resources,
and complex problem structures often force an autonomous agent to make a
decision in time less than that required to solve the problem at hand
completely. Flexible and approximate computations are two approaches to
decision making under limited computation resources. Flexible computation helps
an agent to flexibly allocate limited computation resources so that the overall
system utility is maximized. Approximate computation enables an agent to find
the best satisfactory solution within a deadline. In this paper, we present two
state-space reduction methods for flexible and approximate computation:
quantitative reduction to deal with inaccurate heuristic information, and
structural reduction to handle complex problem structures. These two methods
can be applied successively to continuously improve solution quality if more
computation is available. Our results show that these reduction methods are
effective and efficient, finding better solutions with less computation than
some existing well-known methods.
",Flexible and Approximate Computation through State-Space Reduction
"  Two different definitions of the Artificial Intelligence concept have been
proposed in papers [1] and [2]. The first definition is informal. It says that
any program that is cleverer than a human being, is acknowledged as Artificial
Intelligence. The second definition is formal because it avoids reference to
the concept of human being. The readers of papers [1] and [2] might be left
with the impression that both definitions are equivalent and the definition in
[2] is simply a formal version of that in [1]. This paper will compare both
definitions of Artificial Intelligence and, hopefully, will bring a better
understanding of the concept.
",Comparison between the two definitions of AI
"  Class algebra provides a natural framework for sharing of ISA hierarchies
between users that may be unaware of each other's definitions. This permits
data from relational databases, object-oriented databases, and tagged XML
documents to be unioned into one distributed ontology, sharable by all users
without the need for prior negotiation or the development of a ""standard""
ontology for each field. Moreover, class algebra produces a functional
correspondence between a class's class algebraic definition (i.e. its ""intent"")
and the set of all instances which satisfy the expression (i.e. its ""extent"").
The framework thus provides assistance in quickly locating examples and
counterexamples of various definitions. This kind of information is very
valuable when developing models of the real world, and serves as an invaluable
tool assisting in the proof of theorems concerning these class algebra
expressions. Finally, the relative frequencies of objects in the ISA hierarchy
can produce a useful Boolean algebra of probabilities. The probabilities can be
used by traditional information-theoretic classification methodologies to
obtain optimal ways of classifying objects in the database.
",Class Algebra for Ontology Reasoning
"  We give an effective procedure that produces a natural number in its output
from any natural number in its input, that is, it computes a total function.
The elementary operations of the procedure are Turing-computable. The procedure
has a second input which can contain the Goedel number of any Turing-computable
total function whose range is a subset of the set of the Goedel numbers of all
Turing-computable total functions. We prove that the second input cannot be set
to the Goedel number of any Turing-computable function that computes the output
from any natural number in its first input. In this sense, there is no Turing
program that computes the output from its first input. The procedure is used to
define creative procedures which compute functions that are not
Turing-computable. We argue that creative procedures model an aspect of
reasoning that cannot be modeled by Turing machines.
","An Effective Procedure for Computing ""Uncomputable"" Functions"
"  The paper considers the class of information systems capable of solving
heuristic problems on basis of formal theory that was termed modal and vector
theory of formal intelligent systems (FIS). The paper justifies the
construction of FIS resolution algorithm, defines the main features of these
systems and proves theorems that underlie the theory. The principle of
representation diversity of FIS construction is formulated. The paper deals
with the main principles of constructing and functioning formal intelligent
system (FIS) on basis of FIS modal and vector theory. The following phenomena
are considered: modular architecture of FIS presentation sub-system, algorithms
of data processing at every step of the stage of creating presentations.
Besides the paper suggests the structure of neural elements, i.e. zone
detectors and processors that are the basis for FIS construction.
",Principles of modal and vector theory of formal intelligence systems
"  Wide-angle sonar mapping of the environment by mobile robot is nontrivial due
to several sources of uncertainty: dropouts due to ""specular"" reflections,
obstacle location uncertainty due to the wide beam, and distance measurement
error. Earlier papers address the latter problems, but dropouts remain a
problem in many environments. We present an approach that lifts the
overoptimistic independence assumption used in earlier work, and use Bayes nets
to represent the dependencies between objects of the model. Objects of the
model consist of readings, and of regions in which ""quasi location invariance""
of the (possible) obstacles exists, with respect to the readings. Simulation
supports the method's feasibility. The model is readily extensible to allow for
prior distributions, as well as other types of sensing operations.
",Bayes Networks for Sonar Sensor Fusion
"  A modelling language is described which is suitable for the correlation of
information when the underlying functional model of the system is incomplete or
uncertain and the temporal dependencies are imprecise. An efficient and
incremental implementation is outlined which depends on cost functions
satisfying certain criteria. Possibilistic logic and probability theory (as it
is used in the applications targetted) satisfy these criteria.
",Exploiting Uncertain and Temporal Information in Correlation
"  Much recent research in decision theoretic planning has adopted Markov
decision processes (MDPs) as the model of choice, and has attempted to make
their solution more tractable by exploiting problem structure. One particular
algorithm, structured policy construction achieves this by means of a decision
theoretic analog of goal regression using action descriptions based on Bayesian
networks with tree-structured conditional probability tables. The algorithm as
presented is not able to deal with actions with correlated effects. We describe
a new decision theoretic regression operator that corrects this weakness. While
conceptually straightforward, this extension requires a somewhat more
complicated technical approach.
",Correlated Action Effects in Decision Theoretic Regression
"  Performance prediction or forecasting sporting outcomes involves a great deal
of insight into the particular area one is dealing with, and a considerable
amount of intuition about the factors that bear on such outcomes and
performances. The mathematical Theory of Evidence offers representation
formalisms which grant experts a high degree of freedom when expressing their
subjective beliefs in the context of decision-making situations like
performance prediction. Furthermore, this reasoning framework incorporates a
powerful mechanism to systematically pool the decisions made by individual
subject matter experts. The idea behind such a combination of knowledge is to
improve the competence (quality) of the overall decision-making process. This
paper reports on a performance prediction experiment carried out during the
European Football Championship in 1996. Relying on the knowledge of four
predictors, Evidence Theory was used to forecast the final scores of all 31
matches. The results of this empirical study are very encouraging.
",Corporate Evidential Decision Making in Performance Prediction Domains
"  Decomposable dependency models and their graphical counterparts, i.e.,
chordal graphs, possess a number of interesting and useful properties. On the
basis of two characterizations of decomposable models in terms of independence
relationships, we develop an exact algorithm for recovering the chordal
graphical representation of any given decomposable model. We also propose an
algorithm for learning chordal approximations of dependency models isomorphic
to general undirected graphs.
",Algorithms for Learning Decomposable Models and Chordal Graphs
"  Most exact algorithms for general partially observable Markov decision
processes (POMDPs) use a form of dynamic programming in which a
piecewise-linear and convex representation of one value function is transformed
into another. We examine variations of the ""incremental pruning"" method for
solving this problem and compare them to earlier algorithms from theoretical
and empirical perspectives. We find that incremental pruning is presently the
most efficient exact method for solving POMDPs.
","Incremental Pruning: A Simple, Fast, Exact Method for Partially
  Observable Markov Decision Processes"
"  As probabilistic systems gain popularity and are coming into wider use, the
need for a mechanism that explains the system's findings and recommendations
becomes more critical. The system will also need a mechanism for ordering
competing explanations. We examine two representative approaches to explanation
in the literature - one due to G\""ardenfors and one due to Pearl - and show
that both suffer from significant problems. We propose an approach to defining
a notion of ""better explanation"" that combines some of the features of both
together with more recent work by Pearl and others on causality.
",Defining Explanation in Probabilistic Systems
"  We present an algorithm for arc reversal in Bayesian networks with
tree-structured conditional probability tables, and consider some of its
advantages, especially for the simulation of dynamic probabilistic networks. In
particular, the method allows one to produce CPTs for nodes involved in the
reversal that exploit regularities in the conditional distributions. We argue
that this approach alleviates some of the overhead associated with arc
reversal, plays an important role in evidence integration and can be used to
restrict sampling of variables in DPNs. We also provide an algorithm that
detects the dynamic irrelevance of state variables in forward simulation. This
algorithm exploits the structured CPTs in a reversed network to determine, in a
time-independent fashion, the conditions under which a variable does or does
not need to be sampled.
",Structured Arc Reversal and Simulation of Dynamic Probabilistic Networks
"  Robust Bayesian inference is the calculation of posterior probability bounds
given perturbations in a probabilistic model. This paper focuses on
perturbations that can be expressed locally in Bayesian networks through convex
sets of distributions. Two approaches for combination of local models are
considered. The first approach takes the largest set of joint distributions
that is compatible with the local sets of distributions; we show how to reduce
this type of robust inference to a linear programming problem. The second
approach takes the convex hull of joint distributions generated from the local
sets of distributions; we demonstrate how to apply interior-point optimization
methods to generate posterior bounds and how to generate approximations that
are guaranteed to converge to correct posterior bounds. We also discuss
calculation of bounds for expected utilities and variances, and global
perturbation models.
","Robustness Analysis of Bayesian Networks with Local Convex Sets of
  Distributions"
"  This paper proposes a novel, algorithm-independent approach to optimizing
belief network inference. rather than designing optimizations on an algorithm
by algorithm basis, we argue that one should use an unoptimized algorithm to
generate a Q-DAG, a compiled graphical representation of the belief network,
and then optimize the Q-DAG and its evaluator instead. We present a set of
Q-DAG optimizations that supplant optimizations designed for traditional
inference algorithms, including zero compression, network pruning and caching.
We show that our Q-DAG optimizations require time linear in the Q-DAG size, and
significantly simplify the process of designing algorithms for optimizing
belief network inference.
","A Standard Approach for Optimizing Belief Network Inference using Query
  DAGs"
"  We present a method for solving implicit (factored) Markov decision processes
(MDPs) with very large state spaces. We introduce a property of state space
partitions which we call epsilon-homogeneity. Intuitively, an
epsilon-homogeneous partition groups together states that behave approximately
the same under all or some subset of policies. Borrowing from recent work on
model minimization in computer-aided software verification, we present an
algorithm that takes a factored representation of an MDP and an 0<=epsilon<=1
and computes a factored epsilon-homogeneous partition of the state space. This
partition defines a family of related MDPs - those MDPs with state space equal
to the blocks of the partition, and transition probabilities ""approximately""
like those of any (original MDP) state in the source block. To formally study
such families of MDPs, we introduce the new notion of a ""bounded parameter MDP""
(BMDP), which is a family of (traditional) MDPs defined by specifying upper and
lower bounds on the transition probabilities and rewards. We describe
algorithms that operate on BMDPs to find policies that are approximately
optimal with respect to the original MDP. In combination, our method for
reducing a large implicit MDP to a possibly much smaller BMDP using an
epsilon-homogeneous partition, and our methods for selecting actions in BMDPs
constitute a new approach for analyzing large implicit MDPs. Among its
advantages, this new approach provides insight into existing algorithms to
solving implicit MDPs, provides useful connections to work in automata theory
and model minimization, and suggests methods, which involve varying epsilon, to
trade time and space (specifically in terms of the size of the corresponding
state space) for solution quality.
","Model Reduction Techniques for Computing Approximately Optimal Solutions
  for Markov Decision Processes"
"  This paper describes a class of probabilistic approximation algorithms based
on bucket elimination which offer adjustable levels of accuracy and efficiency.
We analyze the approximation for several tasks: finding the most probable
explanation, belief updating and finding the maximum a posteriori hypothesis.
We identify regions of completeness and provide preliminary empirical
evaluation on randomly generated networks.
",A Scheme for Approximating Probabilistic Inference
"  We present a method for calculation of myopic value of information in
influence diagrams (Howard & Matheson, 1981) based on the strong junction tree
framework (Jensen, Jensen & Dittmer, 1994). The difference in instantiation
order in the influence diagrams is reflected in the corresponding junction
trees by the order in which the chance nodes are marginalized. This order of
marginalization can be changed by table expansion and in effect the same
junction tree with expanded tables may be used for calculating the expected
utility for scenarios with different instantiation order. We also compare our
method to the classic method of modeling different instantiation orders in the
same influence diagram.
",Myopic Value of Information in Influence Diagrams
"  Poole has shown that nonmonotonic logics do not handle the lottery paradox
correctly. In this paper we will show that Pollock's theory of defeasible
reasoning fails for the same reason: defeasible reasoning is incompatible with
the skeptical notion of derivability.
",Limitations of Skeptical Default Reasoning
"  This paper investigates the problem of finding a preference relation on a set
of acts from the knowledge of an ordering on events (subsets of states of the
world) describing the decision-maker (DM)s uncertainty and an ordering of
consequences of acts, describing the DMs preferences. However, contrary to
classical approaches to decision theory, we try to do it without resorting to
any numerical representation of utility nor uncertainty, and without even using
any qualitative scale on which both uncertainty and preference could be mapped.
It is shown that although many axioms of Savage theory can be preserved and
despite the intuitive appeal of the method for constructing a preference over
acts, the approach is inconsistent with a probabilistic representation of
uncertainty, but leads to the kind of uncertainty theory encountered in
non-monotonic reasoning (especially preferential and rational inference),
closely related to possibility theory. Moreover the method turns out to be
either very little decisive or to lead to very risky decisions, although its
basic principles look sound. This paper raises the question of the very
possibility of purely symbolic approaches to Savage-like decision-making under
uncertainty and obtains preliminary negative results.
",Decision-making Under Ordinal Preferences and Comparative Uncertainty
"  We examine the computational complexity of testing and finding small plans in
probabilistic planning domains with succinct representations. We find that many
problems of interest are complete for a variety of complexity classes: NP,
co-NP, PP, NP^PP, co-NP^PP, and PSPACE. Of these, the probabilistic classes PP
and NP^PP are likely to be of special interest in the field of uncertainty in
artificial intelligence and are deserving of additional study. These results
suggest a fruitful direction of future algorithmic development.
",The Complexity of Plan Existence and Evaluation in Probabilistic Domains
"  Stochastic algorithms are among the best for solving computationally hard
search and reasoning problems. The runtime of such procedures is characterized
by a random variable. Different algorithms give rise to different probability
distributions. One can take advantage of such differences by combining several
algorithms into a portfolio, and running them in parallel or interleaving them
on a single processor. We provide a detailed evaluation of the portfolio
approach on distributions of hard combinatorial search problems. We show under
what conditions the protfolio approach can have a dramatic computational
advantage over the best traditional methods.
",Algorithm Portfolio Design: Theory vs. Practice
"  Conditioning is the generally agreed-upon method for updating probability
distributions when one learns that an event is certainly true. But it has been
argued that we need other rules, in particular the rule of cross-entropy
minimization, to handle updates that involve uncertain information. In this
paper we re-examine such a case: van Fraassen's Judy Benjamin problem, which in
essence asks how one might update given the value of a conditional probability.
We argue that -- contrary to the suggestions in the literature -- it is
possible to use simple conditionalization in this case, and thereby obtain
answers that agree fully with intuition. This contrasts with proposals such as
cross-entropy, which are easier to apply but can give unsatisfactory answers.
Based on the lessons from this example, we speculate on some general
philosophical issues concerning probability update.
",Probability Update: Conditioning vs. Cross-Entropy
"  Valuation based systems verifying an idempotent property are studied. A
partial order is defined between the valuations giving them a lattice
structure. Then, two different strategies are introduced to represent
valuations: as infimum of the most informative valuations or as supremum of the
least informative ones. It is studied how to carry out computations with both
representations in an efficient way. The particular cases of finite sets and
convex polytopes are considered.
",Inference with Idempotent Valuations
"  We review the problem of time-critical action and discuss a reformulation
that shifts knowledge acquisition from the assessment of complex temporal
probabilistic dependencies to the direct assessment of time-dependent utilities
over key outcomes of interest. We dwell on a class of decision problems
characterized by the centrality of diagnosing and reacting in a timely manner
to pathological processes. We motivate key ideas in the context of trauma-care
triage and transportation decisions.
",Time-Critical Reasoning: Representations and Application
"  A new method is developed to represent probabilistic relations on multiple
random events. Where previously knowledge bases containing probabilistic rules
were used for this purpose, here a probability distribution over the relations
is directly represented by a Bayesian network. By using a powerful way of
specifying conditional probability distributions in these networks, the
resulting formalism is more expressive than the previous ones. Particularly, it
provides for constraints on equalities of events, and it allows to define
complex, nested combination functions.
",Relational Bayesian Networks
"  Decomposable models and Bayesian networks can be defined as sequences of
oligo-dimensional probability measures connected with operators of composition.
The preliminary results suggest that the probabilistic models allowing for
effective computational procedures are represented by sequences possessing a
special property; we shall call them perfect sequences. The paper lays down the
elementary foundation necessary for further study of iterative application of
operators of composition. We believe to develop a technique describing several
graph models in a unifying way. We are convinced that practically all
theoretical results and procedures connected with decomposable models and
Bayesian networks can be translated into the terminology introduced in this
paper. For example, complexity of computational procedures in these models is
closely dependent on possibility to change the ordering of oligo-dimensional
measures defining the model. Therefore, in this paper, lot of attention is paid
to possibility to change ordering of the operators of composition.
",Composition of Probability Measures on Finite Spaces
"  The efficiency of inference in both the Hugin and, most notably, the
Shafer-Shenoy architectures can be improved by exploiting the independence
relations induced by the incoming messages of a clique. That is, the message to
be sent from a clique can be computed via a factorization of the clique
potential in the form of a junction tree. In this paper we show that by
exploiting such nested junction trees in the computation of messages both space
and time costs of the conventional propagation methods may be reduced. The
paper presents a structured way of exploiting the nested junction trees
technique to achieve such reductions. The usefulness of the method is
emphasized through a thorough empirical evaluation involving ten large
real-world Bayesian networks and the Hugin inference algorithm.
",Nested Junction Trees
"  Bayesian networks provide a modeling language and associated inference
algorithm for stochastic domains. They have been successfully applied in a
variety of medium-scale applications. However, when faced with a large complex
domain, the task of modeling using Bayesian networks begins to resemble the
task of programming using logical circuits. In this paper, we describe an
object-oriented Bayesian network (OOBN) language, which allows complex domains
to be described in terms of inter-related objects. We use a Bayesian network
fragment to describe the probabilistic relations between the attributes of an
object. These attributes can themselves be objects, providing a natural
framework for encoding part-of hierarchies. Classes are used to provide a
reusable probabilistic model which can be applied to multiple similar objects.
Classes also support inheritance of model fragments from a class to a subclass,
allowing the common aspects of related classes to be defined only once. Our
language has clear declarative semantics: an OOBN can be interpreted as a
stochastic functional program, so that it uniquely specifies a probabilistic
model. We provide an inference algorithm for OOBNs, and show that much of the
structural information encoded by an OOBN--particularly the encapsulation of
variables within an object and the reuse of model fragments in different
contexts--can also be used to speed up the inference process.
",Object-Oriented Bayesian Networks
"  We consider probabilistic inference in general hybrid networks, which include
continuous and discrete variables in an arbitrary topology. We reexamine the
question of variable discretization in a hybrid network aiming at minimizing
the information loss induced by the discretization. We show that a nonuniform
partition across all variables as opposed to uniform partition of each variable
separately reduces the size of the data structures needed to represent a
continuous function. We also provide a simple but efficient procedure for
nonuniform partition. To represent a nonuniform discretization in the computer
memory, we introduce a new data structure, which we call a Binary Split
Partition (BSP) tree. We show that BSP trees can be an exponential factor
smaller than the data structures in the standard uniform discretization in
multiple dimensions and show how the BSP trees can be used in the standard join
tree algorithm. We show that the accuracy of the inference process can be
significantly improved by adjusting discretization with evidence. We construct
an iterative anytime algorithm that gradually improves the quality of the
discretization and the accuracy of the answer on a query. We provide empirical
evidence that the algorithm converges.
",Nonuniform Dynamic Discretization in Hybrid Networks
"  The idea of fully accepting statements when the evidence has rendered them
probable enough faces a number of difficulties. We leave the interpretation of
probability largely open, but attempt to suggest a contextual approach to full
belief. We show that the difficulties of probabilistic acceptance are not as
severe as they are sometimes painted, and that though there are oddities
associated with probabilistic acceptance they are in some instances less
awkward than the difficulties associated with other nonmonotonic formalisms. We
show that the structure at which we arrive provides a natural home for
statistical inference.
",Probabilistic Acceptance
"  In most current applications of belief networks, domain knowledge is
represented by a single belief network that applies to all problem instances in
the domain. In more complex domains, problem-specific models must be
constructed from a knowledge base encoding probabilistic relationships in the
domain. Most work in knowledge-based model construction takes the rule as the
basic unit of knowledge. We present a knowledge representation framework that
permits the knowledge base designer to specify knowledge in larger semantically
meaningful units which we call network fragments. Our framework provides for
representation of asymmetric independence and canonical intercausal
interaction. We discuss the combination of network fragments to form
problem-specific models to reason about particular problem instances. The
framework is illustrated using examples from the domain of military situation
awareness.
","Network Fragments: Representing Knowledge for Constructing Probabilistic
  Models"
"  This paper introduces a computational framework for reasoning in Bayesian
belief networks that derives significant advantages from focused inference and
relevance reasoning. This framework is based on d -separation and other simple
and computationally efficient techniques for pruning irrelevant parts of a
network. Our main contribution is a technique that we call relevance-based
decomposition. Relevance-based decomposition approaches belief updating in
large networks by focusing on their parts and decomposing them into partially
overlapping subnetworks. This makes reasoning in some intractable networks
possible and, in addition, often results in significant speedup, as the total
time taken to update all subnetworks is in practice often considerably less
than the time taken to update the network as a whole. We report results of
empirical tests that demonstrate practical significance of our approach.
","Computational Advantages of Relevance Reasoning in Bayesian Belief
  Networks"
"  A submarine's sonar team is responsible for detecting, localising and
classifying targets using information provided by the platform's sensor suite.
The information used to make these assessments is typically uncertain and/or
incomplete and is likely to require a measure of confidence in its reliability.
Moreover, improvements in sensor and communication technology are resulting in
increased amounts of on-platform and off-platform information available for
evaluation. This proliferation of imprecise information increases the risk of
overwhelming the operator. To assist the task of localisation and
classification a concept demonstration decision aid (Horizon), based on
evidential reasoning, has been developed. Horizon is an information fusion
software package for representing and fusing imprecise information about the
state of the world, expressed across suitable frames of reference. The Horizon
software is currently at prototype stage.
",A Target Classification Decision Aid
"  By discussing several examples, the theory of generalized functional models
is shown to be very natural for modeling some situations of reasoning under
uncertainty. A generalized functional model is a pair (f, P) where f is a
function describing the interactions between a parameter variable, an
observation variable and a random source, and P is a probability distribution
for the random source. Unlike traditional functional models, generalized
functional models do not require that there is only one value of the parameter
variable that is compatible with an observation and a realization of the random
source. As a consequence, the results of the analysis of a generalized
functional model are not expressed in terms of probability distributions but
rather by support and plausibility functions. The analysis of a generalized
functional model is very logical and is inspired from ideas already put forward
by R.A. Fisher in his theory of fiducial probability.
",Support and Plausibility Degrees in Generalized Functional Models
"  There is a brief description of the probabilistic causal graph model for
representing, reasoning with, and learning causal structure using Bayesian
networks. It is then argued that this model is closely related to how humans
reason with and learn causal structure. It is shown that studies in psychology
on discounting (reasoning concerning how the presence of one cause of an effect
makes another cause less probable) support the hypothesis that humans reach the
same judgments as algorithms for doing inference in Bayesian networks. Next, it
is shown how studies by Piaget indicate that humans learn causal structure by
observing the same independencies and dependencies as those used by certain
algorithms for learning the structure of a Bayesian network. Based on this
indication, a subjective definition of causality is forwarded. Finally, methods
for further testing the accuracy of these claims are discussed.
",The Cognitive Processing of Causal Knowledge
"  Bayesian knowledge bases (BKBs) are a generalization of Bayes networks and
weighted proof graphs (WAODAGs), that allow cycles in the causal graph.
Reasoning in BKBs requires finding the most probable inferences consistent with
the evidence. The cost-sharing heuristic for finding least-cost explanations in
WAODAGs was presented and shown to be effective by Charniak and Husain.
However, the cycles in BKBs would make the definition of cost-sharing cyclic as
well, if applied directly to BKBs. By treating the defining equations of
cost-sharing as a system of equations, one can properly define an admissible
cost-sharing heuristic for BKBs. Empirical evaluation shows that cost-sharing
improves performance significantly when applied to BKBs.
",Cost-Sharing in Bayesian Knowledge Bases
"  Default logic encounters some conceptual difficulties in representing common
sense reasoning tasks. We argue that we should not try to formulate modular
default rules that are presumed to work in all or most circumstances. We need
to take into account the importance of the context which is continuously
evolving during the reasoning process. Sequential thresholding is a
quantitative counterpart of default logic which makes explicit the role context
plays in the construction of a non-monotonic extension. We present a semantic
characterization of generic non-monotonic reasoning, as well as the
instantiations pertaining to default logic and sequential thresholding. This
provides a link between the two mechanisms as well as a way to integrate the
two that can be beneficial to both.
",Sequential Thresholds: Context Sensitive Default Extensions
"  A stable joint plan should guarantee the achievement of a designer's goal in
a multi-agent environment, while ensuring that deviations from the prescribed
plan would be detected. We present a computational framework where stable joint
plans can be studied, as well as several basic results about the
representation, verification and synthesis of stable joint plans.
",On Stable Multi-Agent Behavior in Face of Uncertainty
"  This paper is concerned with planning in stochastic domains by means of
partially observable Markov decision processes (POMDPs). POMDPs are difficult
to solve. This paper identifies a subclass of POMDPs called region observable
POMDPs, which are easier to solve and can be used to approximate general POMDPs
to arbitrary accuracy.
",Region-Based Approximations for Planning in Stochastic Domains
"  This paper explores the role of independence of causal influence (ICI) in
Bayesian network inference. ICI allows one to factorize a conditional
probability table into smaller pieces. We describe a method for exploiting the
factorization in clique tree propagation (CTP) - the state-of-the-art exact
inference algorithm for Bayesian networks. We also present empirical results
showing that the resulting algorithm is significantly more efficient than the
combination of CTP and previous techniques for exploiting ICI.
",Independence of Causal Influence and Clique Tree Propagation
"  Planning problems where effects of actions are non-deterministic can be
modeled as Markov decision processes. Planning problems are usually
goal-directed. This paper proposes several techniques for exploiting the
goal-directedness to accelerate value iteration, a standard algorithm for
solving Markov decision processes. Empirical studies have shown that the
techniques can bring about significant speedups.
",Fast Value Iteration for Goal-Directed Markov Decision Processes
"  We analyse the complexity of environments according to the policies that need
to be used to achieve high performance. The performance results for a
population of policies leads to a distribution that is examined in terms of
policy complexity and analysed through several diagrams and indicators. The
notion of environment response curve is also introduced, by inverting the
performance results into an ability scale. We apply all these concepts,
diagrams and indicators to a minimalistic environment class, agent-populated
elementary cellular automata, showing how the difficulty, discriminating power
and ranges (previous to normalisation) may vary for several environments.
",Complexity distribution of agent policies
"  The best currently known interactive debugging systems rely upon some
meta-information in terms of fault probabilities in order to improve their
efficiency. However, misleading meta information might result in a dramatic
decrease of the performance and its assessment is only possible a-posteriori.
Consequently, as long as the actual fault is unknown, there is always some risk
of suboptimal interactions. In this work we present a reinforcement learning
strategy that continuously adapts its behavior depending on the performance
achieved and minimizes the risk of using low-quality meta information.
Therefore, this method is suitable for application scenarios where reliable
prior fault estimates are difficult to obtain. Using diverse real-world
knowledge bases, we show that the proposed interactive query strategy is
scalable, features decent reaction time, and outperforms both entropy-based and
no-risk strategies on average w.r.t. required amount of user interaction.
",RIO: Minimizing User Interaction in Debugging of Knowledge Bases
"  The criterion commonly used in directed acyclic graphs (dags) for testing
graphical independence is the well-known d-separation criterion. It allows us
to build graphical representations of dependency models (usually probabilistic
dependency models) in the form of belief networks, which make easy
interpretation and management of independence relationships possible, without
reference to numerical parameters (conditional probabilities). In this paper,
we study the following combinatorial problem: finding the minimum d-separating
set for two nodes in a dag. This set would represent the minimum information
(in the sense of minimum number of variables) necessary to prevent these two
nodes from influencing each other. The solution to this basic problem and some
of its extensions can be useful in several ways, as we shall see later. Our
solution is based on a two-step process: first, we reduce the original problem
to the simpler one of finding a minimum separating set in an undirected graph,
and second, we develop an algorithm for solving it.
",An Algorithm for Finding Minimum d-Separating Sets in Belief Networks
"  This paper works through the optimization of a real world planning problem,
with a combination of a generative planning tool and an influence diagram
solver. The problem is taken from an existing application in the domain of oil
spill emergency response. The planning agent manages constraints that order
sets of feasible equipment employment actions. This is mapped at an
intermediate level of abstraction onto an influence diagram. In addition, the
planner can apply a surveillance operator that determines observability of the
state---the unknown trajectory of the oil. The uncertain world state and the
objective function properties are part of the influence diagram structure, but
not represented in the planning agent domain. By exploiting this structure
under the constraints generated by the planning agent, the influence diagram
solution complexity simplifies considerably, and an optimum solution to the
employment problem based on the objective function is found. Finding this
optimum is equivalent to the simultaneous evaluation of a range of plans. This
result is an example of bounded optimality, within the limitations of this
hybrid generative planner and influence diagram architecture.
","Constraining Influence Diagram Structure by Generative Planning: An
  Application to the Optimization of Oil Spill Response"
"  We extend Gaussian networks - directed acyclic graphs that encode
probabilistic relationships between variables - to its vector form. Vector
Gaussian continuous networks consist of composite nodes representing
multivariates, that take continuous values. These vector or composite nodes can
represent correlations between parents, as opposed to conventional univariate
nodes. We derive rules for inference in these networks based on two methods:
message propagation and topology transformation. These two approaches lead to
the development of algorithms, that can be implemented in either a centralized
or a decentralized manner. The domain of application of these networks are
monitoring and estimation problems. This new representation along with the
rules for inference developed here can be used to derive current Bayesian
algorithms such as the Kalman filter, and provide a rich foundation to develop
new algorithms. We illustrate this process by deriving the decentralized form
of the Kalman filter. This work unifies concepts from artificial intelligence
and modern control theory.
","Inference Using Message Propagation and Topology Transformation in
  Vector Gaussian Continuous Networks"
"  We developed the language of Modifiable Temporal Belief Networks (MTBNs) as a
structural and temporal extension of Bayesian Belief Networks (BNs) to
facilitate normative temporal and causal modeling under uncertainty. In this
paper we present definitions of the model, its components, and its fundamental
properties. We also discuss how to represent various types of temporal
knowledge, with an emphasis on hybrid temporal-explicit time modeling, dynamic
structures, avoiding causal temporal inconsistencies, and dealing with models
that involve simultaneously actions (decisions) and causal and non-causal
associations. We examine the relationships among BNs, Modifiable Belief
Networks, and MTBNs with a single temporal granularity, and suggest areas of
application suitable to each one of them.
","A Structurally and Temporally Extended Bayesian Belief Network Model:
  Definitions, Properties, and Modeling Techniques"
"  Graphical Markov models use graphs, either undirected, directed, or mixed, to
represent possible dependences among statistical variables. Applications of
undirected graphs (UDGs) include models for spatial dependence and image
analysis, while acyclic directed graphs (ADGs), which are especially convenient
for statistical analysis, arise in such fields as genetics and psychometrics
and as models for expert systems and Bayesian belief networks. Lauritzen,
Wermuth and Frydenberg (LWF) introduced a Markov property for chain graphs,
which are mixed graphs that can be used to represent simultaneously both causal
and associative dependencies and which include both UDGs and ADGs as special
cases. In this paper an alternative Markov property (AMP) for chain graphs is
introduced, which in some ways is a more direct extension of the ADG Markov
property than is the LWF property for chain graph.
",An Alternative Markov Property for Chain Graphs
"  Approximate models of world state transitions are necessary when building
plans for complex systems operating in dynamic environments. External event
probabilities can depend on state feature values as well as time spent in that
particular state. We assign temporally -dependent probability functions to
state transitions. These functions are used to locally compute state
probabilities, which are then used to select highly probable goal paths and
eliminate improbable states. This probabilistic model has been implemented in
the Cooperative Intelligent Real-time Control Architecture (CIRCA), which
combines an AI planner with a separate real-time system such that plans are
developed, scheduled, and executed with real-time guarantees. We present flight
simulation tests that demonstrate how our probabilistic model may improve CIRCA
performance.
",Plan Development using Local Probabilistic Models
"  A nonmonotonic logic of thresholded generalizations is presented. Given
propositions A and B from a language L and a positive integer k, the
thresholded generalization A=>B{k} means that the conditional probability
P(B|A) falls short of one by no more than c*d^k. A two-level probability
structure is defined. At the lower level, a model is defined to be a
probability function on L. At the upper level, there is a probability
distribution over models. A definition is given of what it means for a
collection of thresholded generalizations to entail another thresholded
generalization. This nonmonotonic entailment relation, called ""entailment in
probability"", has the feature that its conclusions are ""probabilistically
trustworthy"" meaning that, given true premises, it is improbable that an
entailed conclusion would be false. A procedure is presented for ascertaining
whether any given collection of premises entails any given conclusion. It is
shown that entailment in probability is closely related to Goldszmidt and
Pearl's System-Z^+, thereby demonstrating that the conclusions of System-Z^+
are probabilistically trustworthy.
",Entailment in Probability of Thresholded Generalizations
"  The computational complexity of reasoning within the Dempster-Shafer theory
of evidence is one of the main points of criticism this formalism has to face.
To overcome this difficulty various approximation algorithms have been
suggested that aim at reducing the number of focal elements in the belief
functions involved. Besides introducing a new algorithm using this method, this
paper describes an empirical study that examines the appropriateness of these
approximation procedures in decision making situations. It presents the
empirical findings and discusses the various tradeoffs that have to be taken
into account when actually applying one of these methods.
","Approximations for Decision Making in the Dempster-Shafer Theory of
  Evidence"
"  Possibility theory offers a framework where both Lehmann's ""preferential
inference"" and the more productive (but less cautious) ""rational closure
inference"" can be represented. However, there are situations where the second
inference does not provide expected results either because it cannot produce
them, or even provide counter-intuitive conclusions. This state of facts is not
due to the principle of selecting a unique ordering of interpretations (which
can be encoded by one possibility distribution), but rather to the absence of
constraints expressing pieces of knowledge we have implicitly in mind. It is
advocated in this paper that constraints induced by independence information
can help finding the right ordering of interpretations. In particular,
independence constraints can be systematically assumed with respect to formulas
composed of literals which do not appear in the conditional knowledge base, or
for default rules with respect to situations which are ""normal"" according to
the other default rules in the base. The notion of independence which is used
can be easily expressed in the qualitative setting of possibility theory.
Moreover, when a counter-intuitive plausible conclusion of a set of defaults,
is in its rational closure, but not in its preferential closure, it is always
possible to repair the set of defaults so as to produce the desired conclusion.
","Coping with the Limitations of Rational Inference in the Framework of
  Possibility Theory"
"  We develop a qualitative model of decision making with two aims: to describe
how people make simple decisions and to enable computer programs to do the
same. Current approaches based on Planning or Decisions Theory either ignore
uncertainty and tradeoffs, or provide languages and algorithms that are too
complex for this task. The proposed model provides a language based on rules, a
semantics based on high probabilities and lexicographical preferences, and a
transparent decision procedure where reasons for and against decisions
interact. The model is no substitude for Decision Theory, yet for decisions
that people find easy to explain it may provide an appealing alternative.
",Arguing for Decisions: A Qualitative Model of Decision Making
"  Bayesian networks provide a language for qualitatively representing the
conditional independence properties of a distribution. This allows a natural
and compact representation of the distribution, eases knowledge acquisition,
and supports effective inference algorithms. It is well-known, however, that
there are certain independencies that we cannot capture qualitatively within
the Bayesian network structure: independencies that hold only in certain
contexts, i.e., given a specific assignment of values to certain variables. In
this paper, we propose a formal notion of context-specific independence (CSI),
based on regularities in the conditional probability tables (CPTs) at a node.
We present a technique, analogous to (and based on) d-separation, for
determining when such independence holds in a given network. We then focus on a
particular qualitative representation scheme - tree-structured CPTs - for
capturing CSI. We suggest ways in which this representation can be used to
support effective inference algorithms. In particular, we present a structural
decomposition of the resulting network which can improve the performance of
clustering algorithms, and an alternative algorithm based on cutset
conditioning.
",Context-Specific Independence in Bayesian Networks
"  We develop and extend existing decision-theoretic methods for troubleshooting
a nonfunctioning device. Traditionally, diagnosis with Bayesian networks has
focused on belief updating---determining the probabilities of various faults
given current observations. In this paper, we extend this paradigm to include
taking actions. In particular, we consider three classes of actions: (1) we can
make observations regarding the behavior of a device and infer likely faults as
in traditional diagnosis, (2) we can repair a component and then observe the
behavior of the device to infer likely faults, and (3) we can change the
configuration of the device, observe its new behavior, and infer the likelihood
of faults. Analysis of latter two classes of troubleshooting actions requires
incorporating notions of persistence into the belief-network formalism used for
probabilistic inference.
","Decision-Theoretic Troubleshooting: A Framework for Repair and
  Experiment"
"  It is shown that the ability of the interval probability representation to
capture epistemological independence is severely limited. Two events are
epistemologically independent if knowledge of the first event does not alter
belief (i.e., probability bounds) about the second. However, independence in
this form can only exist in a 2-monotone probability function in degenerate
cases i.e., if the prior bounds are either point probabilities or entirely
vacuous. Additional limitations are characterized for other classes of lower
probabilities as well. It is argued that these phenomena are simply a matter of
interpretation. They appear to be limitations when one interprets probability
bounds as a measure of epistemological indeterminacy (i.e., uncertainty arising
from a lack of knowledge), but are exactly as one would expect when probability
intervals are interpreted as representations of ontological indeterminacy
(indeterminacy introduced by structural approximations). The ontological
interpretation is introduced and discussed.
",Independence with Lower and Upper Probabilities
"  Lower and upper probabilities, also known as Choquet capacities, are widely
used as a convenient representation for sets of probability distributions. This
paper presents a graphical decomposition and exact propagation algorithm for
computing marginal posteriors of 2-monotone lower probabilities (equivalently,
2-alternating upper probabilities).
",Propagation of 2-Monotone Lower Probabilities on an Undirected Graph
"  Quasi-Bayesian theory uses convex sets of probability distributions and
expected loss to represent preferences about plans. The theory focuses on
decision robustness, i.e., the extent to which plans are affected by deviations
in subjective assessments of probability. The present work presents solutions
for plan generation when robustness of probability assessments must be
included: plans contain information about the robustness of certain actions.
The surprising result is that some problems can be solved faster in the
Quasi-Bayesian framework than within usual Bayesian theory. We investigate this
on the planning to observe problem, i.e., an agent must decide whether to take
new observations or not. The fundamental question is: How, and how much, to
search for a ""best"" plan, based on the robustness of probability assessments?
Plan generation algorithms are derived in the context of material
classification with an acoustic robotic probe. A package that constructs
Quasi-Bayesian plans is available through anonymous ftp.
","Quasi-Bayesian Strategies for Efficient Plan Generation: Application to
  the Planning to Observe Problem"
"  Real-time Decision algorithms are a class of incremental resource-bounded
[Horvitz, 89] or anytime [Dean, 93] algorithms for evaluating influence
diagrams. We present a test domain for real-time decision algorithms, and the
results of experiments with several Real-time Decision Algorithms in this
domain. The results demonstrate high performance for two algorithms, a
decision-evaluation variant of Incremental Probabilisitic Inference [D'Ambrosio
93] and a variant of an algorithm suggested by Goldszmidt, [Goldszmidt, 95],
PK-reduced. We discuss the implications of these experimental results and
explore the broader applicability of these algorithms.
",Some Experiments with Real-Time Decision Algorithms
"  Probabilistic inference algorithms for finding the most probable explanation,
the maximum aposteriori hypothesis, and the maximum expected utility and for
updating belief are reformulated as an elimination--type algorithm called
bucket elimination. This emphasizes the principle common to many of the
algorithms appearing in that literature and clarifies their relationship to
nonserial dynamic programming algorithms. We also present a general way of
combining conditioning and elimination within this framework. Bounds on
complexity are given for all the algorithms as a function of the problem's
structure.
","Bucket Elimination: A Unifying Framework for Several Probabilistic
  Inference"
"  In this paper we propose a family of algorithms combining tree-clustering
with conditioning that trade space for time. Such algorithms are useful for
reasoning in probabilistic and deterministic networks as well as for
accomplishing optimization tasks. By analyzing the problem structure it will be
possible to select from a spectrum the algorithm that best meets a given
time-space specification.
",Topological Parameters for Time-Space Tradeoff
"  This paper provides a formal and practical framework for sound abstraction of
probabilistic actions. We start by precisely defining the concept of sound
abstraction within the context of finite-horizon planning (where each plan is a
finite sequence of actions). Next we show that such abstraction cannot be
performed within the traditional probabilistic action representation, which
models a world with a single probability distribution over the state space. We
then present the constraint mass assignment representation, which models the
world with a set of probability distributions and is a generalization of mass
assignment representations. Within this framework, we present sound abstraction
procedures for three types of action abstraction. We end the paper with
discussions and related work on sound and approximate abstraction. We give
pointers to papers in which we discuss other sound abstraction-related issues,
including applications, estimating loss due to abstraction, and automatically
generating abstraction hierarchies.
","Sound Abstraction of Probabilistic Actions in The Constraint Mass
  Assignment Framework"
"  This paper discusses belief revision under uncertain inputs in the framework
of possibility theory. Revision can be based on two possible definitions of the
conditioning operation, one based on min operator which requires a purely
ordinal scale only, and another based on product, for which a richer structure
is needed, and which is a particular case of Dempster's rule of conditioning.
Besides, revision under uncertain inputs can be understood in two different
ways depending on whether the input is viewed, or not, as a constraint to
enforce. Moreover, it is shown that M.A. Williams' transmutations, originally
defined in the setting of Spohn's functions, can be captured in this framework,
as well as Boutilier's natural revision.
",Belief Revision with Uncertain Inputs in the Possibilistic Setting
"  Many algorithms for processing probabilistic networks are dependent on the
topological properties of the problem's structure. Such algorithms (e.g.,
clustering, conditioning) are effective only if the problem has a sparse graph
captured by parameters such as tree width and cycle-cut set size. In this paper
we initiate a study to determine the potential of structure-based algorithms in
real-life applications. We analyze empirically the structural properties of
problems coming from the circuit diagnosis domain. Specifically, we locate
those properties that capture the effectiveness of clustering and conditioning
as well as of a family of conditioning+clustering algorithms designed to
gradually trade space for time. We perform our analysis on 11 benchmark
circuits widely used in the testing community. We also report on the effect of
ordering heuristics on tree-clustering and show that, on our benchmarks, the
well-known max-cardinality ordering is substantially inferior to an ordering
called min-degree.
","An Evaluation of Structural Parameters for Probabilistic Reasoning:
  Results on Benchmark Circuits"
"  The study of belief change has been an active area in philosophy and AI. In
recent years two special cases of belief change, belief revision and belief
update, have been studied in detail. Roughly, revision treats a surprising
observation as a sign that previous beliefs were wrong, while update treats a
surprising observation as an indication that the world has changed. In general,
we would expect that an agent making an observation may both want to revise
some earlier beliefs and assume that some change has occurred in the world. We
define a novel approach to belief change that allows us to do this, by applying
ideas from probability theory in a qualitative setting. The key idea is to use
a qualitative Markov assumption, which says that state transitions are
independent. We show that a recent approach to modeling qualitative uncertainty
using plausibility measures allows us to make such a qualitative Markov
assumption in a relatively straightforward way, and show how the Markov
assumption can be used to provide an attractive belief-change model.
",A Qualitative Markov Assumption and its Implications for Belief Change
"  Modeling worlds and actions under uncertainty is one of the central problems
in the framework of decision-theoretic planning. The representation must be
general enough to capture real-world problems but at the same time it must
provide a basis upon which theoretical results can be derived. The central
notion in the framework we propose here is that of the affine-operator, which
serves as a tool for constructing (convex) sets of probability distributions,
and which can be considered as a generalization of belief functions and
interval mass assignments. Uncertainty in the state of the worlds is modeled
with sets of probability distributions, represented by affine-trees while
actions are defined as tree-manipulators. A small set of key properties of the
affine-operator is presented, forming the basis for most existing
operator-based definitions of probabilistic action projection and action
abstraction. We derive and prove correct three projection rules, which vividly
illustrate the precision-complexity tradeoff in plan projection. Finally, we
show how the three types of action abstraction identified by Haddawy and Doan
are manifested in the present framework.
",Theoretical Foundations for Abstraction-Based Probabilistic Planning
"  Recent research has found that diagnostic performance with Bayesian belief
networks is often surprisingly insensitive to imprecision in the numerical
probabilities. For example, the authors have recently completed an extensive
study in which they applied random noise to the numerical probabilities in a
set of belief networks for medical diagnosis, subsets of the CPCS network, a
subset of the QMR (Quick Medical Reference) focused on liver and bile diseases.
The diagnostic performance in terms of the average probabilities assigned to
the actual diseases showed small sensitivity even to large amounts of noise. In
this paper, we summarize the findings of this study and discuss possible
explanations of this low sensitivity. One reason is that the criterion for
performance is average probability of the true hypotheses, rather than average
error in probability, which is insensitive to symmetric noise distributions.
But, we show that even asymmetric, logodds-normal noise has modest effects. A
second reason is that the gold-standard posterior probabilities are often near
zero or one, and are little disturbed by noise.
","Why Is Diagnosis Using Belief Networks Insensitive to Imprecision In
  Probabilities?"
"  We report on work towards flexible algorithms for solving decision problems
represented as influence diagrams. An algorithm is given to construct a tree
structure for each decision node in an influence diagram. Each tree represents
a decision function and is constructed incrementally. The improvements to the
tree converge to the optimal decision function (neglecting computational costs)
and the asymptotic behaviour is only a constant factor worse than dynamic
programming techniques, counting the number of Bayesian network queries.
Empirical results show how expected utility increases with the size of the tree
and the number of Bayesian net calculations.
",Flexible Policy Construction by Information Refinement
"  Inference algorithms for arbitrary belief networks are impractical for large,
complex belief networks. Inference algorithms for specialized classes of belief
networks have been shown to be more efficient. In this paper, we present a
search-based algorithm for approximate inference on arbitrary, noisy-OR belief
networks, generalizing earlier work on search-based inference for two-level,
noisy-OR belief networks. Initial experimental results appear promising.
","Efficient Search-Based Inference for Noisy-OR Belief Networks:
  TopEpsilon"
"  The validation of data from sensors has become an important issue in the
operation and control of modern industrial plants. One approach is to use
knowledge based techniques to detect inconsistencies in measured data. This
article presents a probabilistic model for the detection of such
inconsistencies. Based on probability propagation, this method is able to find
the existence of a possible fault among the set of sensors. That is, if an
error exists, many sensors present an apparent fault due to the propagation
from the sensor(s) with a real fault. So the fault detection mechanism can only
tell if a sensor has a potential fault, but it can not tell if the fault is
real or apparent. So the central problem is to develop a theory, and then an
algorithm, for distinguishing real and apparent faults, given that one or more
sensors can fail at the same time. This article then, presents an approach
based on two levels: (i) probabilistic reasoning, to detect a potential fault,
and (ii) constraint management, to distinguish the real fault from the apparent
ones. The proposed approach is exemplified by applying it to a power plant
model.
",A Probabilistic Model For Sensor Validation
"  We present deterministic techniques for computing upper and lower bounds on
marginal probabilities in sigmoid and noisy-OR networks. These techniques
become useful when the size of the network (or clique size) precludes exact
computations. We illustrate the tightness of the bounds by numerical
experiments.
",Computing Upper and Lower Bounds on Likelihoods in Intractable Networks
"  We present a prototype of a decision support system for management of the
fungal disease mildew in winter wheat. The prototype is based on an influence
diagram which is used to determine the optimal time and dose of mildew
treatments. This involves multiple decision opportunities over time,
stochasticity, inaccurate information and incomplete knowledge. The paper
describes the practical and theoretical problems encountered during the
construction of the influence diagram, and also the experience with the
prototype.
",MIDAS - An Influence Diagram for Management of Mildew in Winter Wheat
"  Although probabilistic inference in a general Bayesian belief network is an
NP-hard problem, computation time for inference can be reduced in most
practical cases by exploiting domain knowledge and by making approximations in
the knowledge representation. In this paper we introduce the property of
similarity of states and a new method for approximate knowledge representation
and inference which is based on this property. We define two or more states of
a node to be similar when the ratio of their probabilities, the likelihood
ratio, does not depend on the instantiations of the other nodes in the network.
We show that the similarity of states exposes redundancies in the joint
probability distribution which can be exploited to reduce the computation time
of probabilistic inference in networks with multiple similar states, and that
the computational complexity in the networks with exponentially many similar
states might be polynomial. We demonstrate our ideas on the example of a BN2O
network -- a two layer network often used in diagnostic problems -- by reducing
it to a very close network with multiple similar states. We show that the
answers to practical queries converge very fast to the answers obtained with
the original network. The maximum error is as low as 5% for models that require
only 10% of the computation time needed by the original BN2O model.
","Computational Complexity Reduction for BN2O Networks Using Similarity of
  States"
"  Uncertainty may be taken to characterize inferences, their conclusions, their
premises or all three. Under some treatments of uncertainty, the inferences
itself is never characterized by uncertainty. We explore both the significance
of uncertainty in the premises and in the conclusion of an argument that
involves uncertainty. We argue that for uncertainty to characterize the
conclusion of an inference is natural, but that there is an interplay between
uncertainty in the premises and uncertainty in the procedure of argument
itself. We show that it is possible in principle to incorporate all uncertainty
in the premises, rendering uncertainty arguments deductively valid. But we then
argue (1) that this does not reflect human argument, (2) that it is
computationally costly, and (3) that the gain in simplicity obtained by
allowing uncertainty inference can sometimes outweigh the loss of flexibility
it entails.
",Uncertain Inferences and Uncertain Conclusions
"  Like any large system development effort, the construction of a complex
belief network model requires systems engineering to manage the design and
construction process. We propose a rapid prototyping approach to network
engineering. We describe criteria for identifying network modules and the use
of ""stubs"" to represent not-yet-constructed modules. We propose an object
oriented representation for belief networks which captures the semantics of the
problem in addition to conditional independencies and probabilities. Methods
for evaluating complex belief network models are discussed. The ideas are
illustrated with examples from a large belief network construction problem in
the military intelligence domain.
",Network Engineering for Complex Belief Networks
"  In this paper we propose a framework for combining Disjunctive Logic
Programming and Poole's Probabilistic Horn Abduction. We use the concept of
hypothesis to specify the probability structure. We consider the case in which
probabilistic information is not available. Instead of using probability
intervals, we allow for the specification of the probabilities of disjunctions.
Because minimal models are used as characteristic models in disjunctive logic
programming, we apply the principle of indifference on the set of minimal
models to derive default probability values. We define the concepts of
explanation and partial explanation of a formula, and use them to determine the
default probability distribution(s) induced by a program. An algorithm for
calculating the default probability of a goal is presented.
",Probabilistic Disjunctive Logic Programming
"  A naive (or Idiot) Bayes network is a network with a single hypothesis node
and several observations that are conditionally independent given the
hypothesis. We recently surveyed a number of members of the UAI community and
discovered a general lack of understanding of the implications of the Naive
Bayes assumption on the kinds of problems that can be solved by these networks.
It has long been recognized [Minsky 61] that if observations are binary, the
decision surfaces in these networks are hyperplanes. We extend this result
(hyperplane separability) to Naive Bayes networks with m-ary observations. In
addition, we illustrate the effect of observation-observation dependencies on
decision surfaces. Finally, we discuss the implications of these results on
knowledge acquisition and research in learning.
",Geometric Implications of the Naive Bayes Assumption
"  We show that the d -separation criterion constitutes a valid test for
conditional independence relationships that are induced by feedback systems
involving discrete variables.
",Identifying Independencies in Causal Graphs with Feedback
"  We derive qualitative relationships about the informational relevance of
variables in graphical decision models based on a consideration of the topology
of the models. Specifically, we identify dominance relations for the expected
value of information on chance variables in terms of their position and
relationships in influence diagrams. The qualitative relationships can be
harnessed to generate nonnumerical procedures for ordering uncertain variables
in a decision model by their informational relevance.
",A Graph-Theoretic Analysis of Information Value
"  This paper shows how we can combine logical representations of actions and
decision theory in such a manner that seems natural for both. In particular we
assume an axiomatization of the domain in terms of situation calculus, using
what is essentially Reiter's solution to the frame problem, in terms of the
completion of the axioms defining the state change. Uncertainty is handled in
terms of the independent choice logic, which allows for independent choices and
a logic program that gives the consequences of the choices. As part of the
consequences are a specification of the utility of (final) states. The robot
adopts robot plans, similar to the GOLOG programming language. Within this
logic, we can define the expected utility of a conditional plan, based on the
axiomatization of the actions, the uncertainty and the utility. The ?planning'
problem is to find the plan with the highest expected utility. This is related
to recent structured representations for POMDPs; here we use stochastic
situation calculus rules to specify the state transition function and the
reward/value function. Finally we show that with stochastic frame axioms,
actions representations in probabilistic STRIPS are exponentially larger than
using the representation proposed here.
","A Framework for Decision-Theoretic Planning I: Combining the Situation
  Calculus, Conditional Plans, Probability and Utility"
"  We present two Monte Carlo sampling algorithms for probabilistic inference
that guarantee polynomial-time convergence for a larger class of network than
current sampling algorithms provide. These new methods are variants of the
known likelihood weighting algorithm. We use of recent advances in the theory
of optimal stopping rules for Monte Carlo simulation to obtain an inference
approximation with relative error epsilon and a small failure probability
delta. We present an empirical evaluation of the algorithms which demonstrates
their improved performance.
",Optimal Monte Carlo Estimation of Belief Network Inference
"  Directed acyclic graphs have been used fruitfully to represent causal
strucures (Pearl 1988). However, in the social sciences and elsewhere models
are often used which correspond both causally and statistically to directed
graphs with directed cycles (Spirtes 1995). Pearl (1993) discussed predicting
the effects of intervention in models of this kind, so-called linear
non-recursive structural equation models. This raises the question of whether
it is possible to make inferences about causal structure with cycles, form
sample data. In particular do there exist general, informative, feasible and
reliable precedures for inferring causal structure from conditional
independence relations among variables in a sample generated by an unknown
causal structure? In this paper I present a discovery algorithm that is correct
in the large sample limit, given commonly (but often implicitly) made plausible
assumptions, and which provides information about the existence or
non-existence of causal pathways from one variable to another. The algorithm is
polynomial on sparse graphs.
",A Discovery Algorithm for Directed Cyclic Graphs
"  Although the concept of d-separation was originally defined for directed
acyclic graphs (see Pearl 1988), there is a natural extension of he concept to
directed cyclic graphs. When exactly the same set of d-separation relations
hold in two directed graphs, no matter whether respectively cyclic or acyclic,
we say that they are Markov equivalent. In other words, when two directed
cyclic graphs are Markov equivalent, the set of distributions that satisfy a
natural extension of the Global Directed Markov condition (Lauritzen et al.
1990) is exactly the same for each graph. There is an obvious exponential (in
the number of vertices) time algorithm for deciding Markov equivalence of two
directed cyclic graphs; simply chech all of the d-separation relations in each
graph. In this paper I state a theorem that gives necessary and sufficient
conditions for the Markov equivalence of two directed cyclic graphs, where each
of the conditions can be checked in polynomial time. Hence, the theorem can be
easily adapted into a polynomial time algorithm for deciding the Markov
equivalence of two directed cyclic graphs. Although space prohibits inclusion
of correctness proofs, they are fully described in Richardson (1994b).
","A Polynomial-Time Algorithm for Deciding Markov Equivalence of Directed
  Cyclic Graphical Models"
"  SPIRIT is an expert system shell for probabilistic knowledge bases. Knowledge
acquisition is performed by processing facts and rules on discrete variables in
a rich syntax. The shell generates a probability distribution which respects
all acquired facts and rules and which maximizes entropy. The user-friendly
devices of SPIRIT to define variables, formulate rules and create the knowledge
base are revealed in detail. Inductive learning is possible. Medium sized
applications show the power of the system.
",Coherent Knowledge Processing at Maximum Entropy by SPIRIT
"  Belief updating in Bayes nets, a well known computationally hard problem, has
recently been approximated by several deterministic algorithms, and by various
randomized approximation algorithms. Deterministic algorithms usually provide
probability bounds, but have an exponential runtime. Some randomized schemes
have a polynomial runtime, but provide only probability estimates. We present
randomized algorithms that enumerate high-probability partial instantiations,
resulting in probability bounds. Some of these algorithms are also sampling
algorithms. Specifically, we introduce and evaluate a variant of backward
sampling, both as a sampling algorithm and as a randomized enumeration
algorithm. We also relax the implicit assumption used by both sampling and
accumulation algorithms, that query nodes must be instantiated in all the
samples.
",Sample-and-Accumulate Algorithms for Belief Updating in Bayes Networks
"  We propose a decision-analytical approach to comparing the flexibility of
decision situations from the perspective of a decision-maker who exhibits
constant risk-aversion over a monetary value model. Our approach is simple yet
seems to be consistent with a variety of flexibility concepts, including robust
and adaptive alternatives. We try to compensate within the model for
uncertainty that was not anticipated or not modeled. This approach not only
allows one to compare the flexibility of plans, but also guides the search for
new, more flexible alternatives.
",A Measure of Decision Flexibility
"  The main goal of this paper is to describe a data structure called binary
join trees that are useful in computing multiple marginals efficiently using
the Shenoy-Shafer architecture. We define binary join trees, describe their
utility, and sketch a procedure for constructing them.
",Binary Join Trees
"  Over the past several years Bayesian networks have been applied to a wide
variety of problems. A central problem in applying Bayesian networks is that of
finding one or more of the most probable instantiations of a network. In this
paper we develop an efficient algorithm that incrementally enumerates the
instantiations of a Bayesian network in decreasing order of probability. Such
enumeration algorithms are applicable in a variety of applications ranging from
medical expert systems to model-based diagnosis. Fundamentally, our algorithm
is simply performing a lazy enumeration of the sorted list of all
instantiations of the network. This insight leads to a very concise algorithm
statement which is both easily understood and implemented. We show that for
singly connected networks, our algorithm generates the next instantiation in
time polynomial in the size of the network. The algorithm extends to arbitrary
Bayesian networks using standard conditioning techniques. We empirically
evaluate the enumeration algorithm and demonstrate its practicality.
",Efficient Enumeration of Instantiations in Bayesian Networks
"  Chain graphs give a natural unifying point of view on Markov and Bayesian
networks and enlarge the potential of graphical models for description of
conditional independence structures. In the paper a direct graphical separation
criterion for chain graphs, called c-separation, which generalizes the
d-separation criterion for Bayesian networks is introduced (recalled). It is
equivalent to the classic moralization criterion for chain graphs and complete
in sense that for every chain graph there exists a probability distribution
satisfying exactly conditional independencies derivable from the chain graph by
the c-separation criterion. Every class of Markov equivalent chain graphs can
be uniquely described by a natural representative, called the largest chain
graph. A recovery algorithm, which on basis of the (conditional) dependency
model induced by an unknown chain graph finds the corresponding largest chain
graph, is presented.
",On Separation Criterion and Recovery Algorithm for Chain Graphs
"  When we work with information from multiple sources, the formalism each
employs to handle uncertainty may not be uniform. In order to be able to
combine these knowledge bases of different formats, we need to first establish
a common basis for characterizing and evaluating the different formalisms, and
provide a semantics for the combined mechanism. A common framework can provide
an infrastructure for building an integrated system, and is essential if we are
to understand its behavior. We present a unifying framework based on an ordered
partition of possible worlds called partition sequences, which corresponds to
our intuitive notion of biasing towards certain possible scenarios when we are
uncertain of the actual situation. We show that some of the existing
formalisms, namely, default logic, autoepistemic logic, probabilistic
conditioning and thresholding (generalized conditioning), and possibility
theory can be incorporated into this general framework.
","Possible World Partition Sequences: A Unifying Framework for Uncertain
  Reasoning"
"  Integrating diagnosis and repair is particularly crucial when gaining
sufficient information to discriminate between several candidate diagnoses
requires carrying out some repair actions. A typical case is supply restoration
in a faulty power distribution system. This problem, which is a major concern
for electricity distributors, features partial observability, and stochastic
repair actions which are more elaborate than simple replacement of components.
This paper analyses the difficulties in applying existing work on integrating
model-based diagnosis and repair and on planning in partially observable
stochastic domains to this real-world problem, and describes the pragmatic
approach we have retained so far.
","Supply Restoration in Power Distribution Systems - A Case Study in
  Integrating Model-Based Diagnosis and Repair Planning"
"  For real time evaluation of a Bayesian network when there is not sufficient
time to obtain an exact solution, a guaranteed response time, approximate
solution is required. It is shown that nontraditional methods utilizing
estimators based on an archive of trial solutions and genetic search can
provide an approximate solution that is considerably superior to the
traditional Monte Carlo simulation methods.
",Real Time Estimation of Bayesian Networks
"  Axiomatization has been widely used for testing logical implications. This
paper suggests a non-axiomatic method, the chase, to test if a new dependency
follows from a given set of probabilistic dependencies. Although the chase
computation may require exponential time in some cases, this technique is a
powerful tool for establishing nontrivial theoretical results. More
importantly, this approach provides valuable insight into the intriguing
connection between relational databases and probabilistic reasoning systems.
",Testing Implication of Probabilistic Dependencies
"  We examine a standard factory scheduling problem with stochastic processing
and setup times, minimizing the expectation of the weighted number of tardy
jobs. Because the costs of operators in the schedule are stochastic and
sequence dependent, standard dynamic programming algorithms such as A* may fail
to find the optimal schedule. The SDA* (Stochastic Dominance A*) algorithm
remedies this difficulty by relaxing the pruning condition. We present an
improved state-space search formulation for these problems and discuss the
conditions under which stochastic scheduling problems can be solved optimally
using SDA*. In empirical testing on randomly generated problems, we found that
in 70%, the expected cost of the optimal stochastic solution is lower than that
of the solution derived using a deterministic approximation, with comparable
search effort.
",Optimal Factory Scheduling using Stochastic Dominance A*
"  In learning belief networks, the single link lookahead search is widely
adopted to reduce the search space. We show that there exists a class of
probabilistic domain models which displays a special pattern of dependency. We
analyze the behavior of several learning algorithms using different scoring
metrics such as the entropy, conditional independence, minimal description
length and Bayesian metrics. We demonstrate that single link lookahead search
procedures (employed in these algorithms) cannot learn these models correctly.
Thus, when the underlying domain model actually belongs to this class, the use
of a single link search procedure will result in learning of an incorrect
model. This may lead to inference errors when the model is used. Our analysis
suggests that if the prior knowledge about a domain does not rule out the
possible existence of these models, a multi-link lookahead search or other
heuristics should be used for the learning process.
",Critical Remarks on Single Link Search in Learning Belief Networks
"  We extend the theory of d-separation to cases in which data instances are not
independent and identically distributed. We show that applying the rules of
d-separation directly to the structure of probabilistic models of relational
data inaccurately infers conditional independence. We introduce relational
d-separation, a theory for deriving conditional independence facts from
relational models. We provide a new representation, the abstract ground graph,
that enables a sound, complete, and computationally efficient method for
answering d-separation queries about relational models, and we present
empirical results that demonstrate effectiveness.
",Reasoning about Independence in Probabilistic Models of Relational Data
"  Probabilistic independence can dramatically simplify the task of eliciting,
representing, and computing with probabilities in large domains. A key
technique in achieving these benefits is the idea of graphical modeling. We
survey existing notions of independence for utility functions in a
multi-attribute space, and suggest that these can be used to achieve similar
advantages. Our new results concern conditional additive independence, which we
show always has a perfect representation as separation in an undirected graph
(a Markov network). Conditional additive independencies entail a particular
functional for the utility function that is analogous to a product
decomposition of a probability function, and confers analogous benefits. This
functional form has been utilized in the Bayesian network and influence diagram
literature, but generally without an explanation in terms of independence. The
functional form yields a decomposition of the utility function that can greatly
speed up expected utility calculations, particularly when the utility graph has
a similar topology to the probabilistic network being used.
",Graphical Models for Preference and Utility
"  Evaluation of counterfactual queries (e.g., ""If A were true, would C have
been true?"") is important to fault diagnosis, planning, determination of
liability, and policy analysis. We present a method of revaluating
counterfactuals when the underlying causal model is represented by structural
models - a nonlinear generalization of the simultaneous equations models
commonly used in econometrics and social sciences. This new method provides a
coherent means for evaluating policies involving the control of variables
which, prior to enacting the policy were influenced by other variables in the
system.
",Counterfactuals and Policy Analysis in Structural Models
"  We present a new approach to dealing with default information based on the
theory of belief functions. Our semantic structures, inspired by Adams'
epsilon-semantics, are epsilon-belief assignments, where values committed to
focal elements are either close to 0 or close to 1. We define two systems based
on these structures, and relate them to other non-monotonic systems presented
in the literature. We show that our second system correctly addresses the
well-known problems of specificity, irrelevance, blocking of inheritance,
ambiguity, and redundancy.
",Belief Functions and Default Reasoning
"  We describe an application of belief networks to the diagnosis of bottlenecks
in computer systems. The technique relies on a high-level functional model of
the interaction between application workloads, the Windows NT operating system,
and system hardware. Given a workload description, the model predicts the
values of observable system counters available from the Windows NT performance
monitoring tool. Uncertainty in workloads, predictions, and counter values are
characterized with Gaussian distributions. During diagnostic inference, we use
observed performance monitor values to find the most probable assignment to the
workload parameters. In this paper we provide some background on automated
bottleneck detection, describe the structure of the system model, and discuss
empirical procedures for model calibration and verification. Part of the
calibration process includes generating a dataset to estimate a multivariate
Gaussian error model. Initial results in diagnosing bottlenecks are presented.
",Automating Computer Bottleneck Detection with Belief Nets
"  Chain graphs combine directed and undirected graphs and their underlying
mathematics combines properties of the two. This paper gives a simplified
definition of chain graphs based on a hierarchical combination of Bayesian
(directed) and Markov (undirected) networks. Examples of a chain graph are
multivariate feed-forward networks, clustering with conditional interaction
between variables, and forms of Bayes classifiers. Chain graphs are then
extended using the notation of plates so that samples and data analysis
problems can be represented in a graphical model as well. Implications for
learning are discussed in the conclusion.
",Chain Graphs for Learning
"  We can perform inference in Bayesian belief networks by enumerating
instantiations with high probability thus approximating the marginals. In this
paper, we present a method for determining the fraction of instantiations that
has to be considered such that the absolute error in the marginals does not
exceed a predefined value. The method is based on extreme value theory.
Essentially, the proposed method uses the reversed generalized Pareto
distribution to model probabilities of instantiations below a given threshold.
Based on this distribution, an estimate of the maximal absolute error if
instantiations with probability smaller than u are disregarded can be made.
",Error Estimation in Approximate Bayesian Belief Network Inference
"  The aim of this paper is to present a method for identifying the structure of
a rule in a fuzzy model. For this purpose, an ATMS shall be used (Zurita 1994).
An algorithm obtaining the identification of the structure will be suggested
(Castro 1995). The minimal structure of the rule (with respect to the number of
variables that must appear in the rule) will be found by this algorithm.
Furthermore, the identification parameters shall be obtained simultaneously.
The proposed method shall be applied for classification to an example. The {em
Iris Plant Database} shall be learnt for all three kinds of plants.
",Generating the Structure of a Fuzzy Rule under Uncertainty
"  An approach to fault isolation that exploits vastly incomplete models is
presented. It relies on separate descriptions of each component behavior,
together with the links between them, which enables focusing of the reasoning
to the relevant part of the system. As normal observations do not need
explanation, the behavior of the components is limited to anomaly propagation.
Diagnostic solutions are disorders (fault modes or abnormal signatures) that
are consistent with the observations, as well as abductive explanations. An
ordinal representation of uncertainty based on possibility theory provides a
simple exception-tolerant description of the component behaviors. We can for
instance distinguish between effects that are more or less certainly present
(or absent) and effects that are more or less certainly present (or absent)
when a given anomaly is present. A realistic example illustrates the benefits
of this approach.
","Practical Model-Based Diagnosis with Qualitative Possibilistic
  Uncertainty"
"  The development of new methods and representations for temporal
decision-making requires a principled basis for characterizing and measuring
the flexibility of decision strategies in the face of uncertainty. Our goal in
this paper is to provide a framework - not a theory - for observing how
decision policies behave in the face of informational perturbations, to gain
clues as to how they might behave in the face of unanticipated, possibly
unarticulated uncertainties. To this end, we find it beneficial to distinguish
between two types of uncertainty: ""Small World"" and ""Large World"" uncertainty.
The first type can be resolved by posing an unambiguous question to a
""clairvoyant,"" and is anchored on some well-defined aspect of a decision frame.
The second type is more troublesome, yet it is often of greater interest when
we address the issue of flexibility; this type of uncertainty can be resolved
only by consulting a ""psychic."" We next observe that one approach to
flexibility used in the economics literature is already implicitly accounted
for in the Maximum Expected Utility (MEU) principle from decision theory.
Though simple, the observation establishes the context for a more illuminating
notion of flexibility, what we term flexibility with respect to information
revelation. We show how to perform flexibility analysis of a static (i.e.,
single period) decision problem using a simple example, and we observe that the
most flexible alternative thus identified is not necessarily the MEU
alternative. We extend our analysis for a dynamic (i.e., multi-period) model,
and we demonstrate how to calculate the value of flexibility for decision
strategies that allow downstream revision of an upstream commitment decision.
",Decision Flexibility
"  We present a simple characterization of equivalent Bayesian network
structures based on local transformations. The significance of the
characterization is twofold. First, we are able to easily prove several new
invariant properties of theoretical interest for equivalent structures. Second,
we use the characterization to derive an efficient algorithm that identifies
all of the compelled edges in a structure. Compelled edge identification is of
particular importance for learning Bayesian network structures from data
because these edges indicate causal relationships when certain assumptions
hold.
","A Transformational Characterization of Equivalent Bayesian Network
  Structures"
"  We present two algorithms for exact and approximate inference in causal
networks. The first algorithm, dynamic conditioning, is a refinement of cutset
conditioning that has linear complexity on some networks for which cutset
conditioning is exponential. The second algorithm, B-conditioning, is an
algorithm for approximate inference that allows one to trade-off the quality of
approximations with the computation time. We also present some experimental
results illustrating the properties of the proposed algorithms.
","Conditioning Methods for Exact and Approximate Inference in Causal
  Networks"
"  In this paper we study different concepts of independence for convex sets of
probabilities. There will be two basic ideas for independence. The first is
irrelevance. Two variables are independent when a change on the knowledge about
one variable does not affect the other. The second one is factorization. Two
variables are independent when the joint convex set of probabilities can be
decomposed on the product of marginal convex sets. In the case of the Theory of
Probability, these two starting points give rise to the same definition. In the
case of convex sets of probabilities, the resulting concepts will be strongly
related, but they will not be equivalent. As application of the concept of
independence, we shall consider the problem of building a global convex set
from marginal convex sets of probabilities.
",Independence Concepts for Convex Sets of Probabilities
"  The undirected technique for evaluating belief networks [Jensen, et.al.,
1990, Lauritzen and Spiegelhalter, 1988] requires clustering the nodes in the
network into a junction tree. In the traditional view, the junction tree is
constructed from the cliques of the moralized and triangulated belief network:
triangulation is taken to be the primitive concept, the goal towards which any
clustering algorithm (e.g. node elimination) is directed. In this paper, we
present an alternative conception of clustering, in which clusters and the
junction tree property play the role of primitives: given a graph (not a tree)
of clusters which obey (a modified version of) the junction tree property, we
transform this graph until we have obtained a tree. There are several
advantages to this approach: it is much clearer and easier to understand, which
is important for humans who are constructing belief networks; it admits a wider
range of heuristics which may enable more efficient or superior clustering
algorithms; and it serves as the natural basis for an incremental clustering
scheme, which we describe.
",Clustering Without (Thinking About) Triangulation
"  Bayesian networks provide a method of representing conditional independence
between random variables and computing the probability distributions associated
with these random variables. In this paper, we extend Bayesian network
structures to compute probability density functions for continuous random
variables. We make this extension by approximating prior and conditional
densities using sums of weighted Gaussian distributions and then finding the
propagation rules for updating the densities in terms of these weights. We
present a simple example that illustrates the Bayesian network for continuous
variables; this example shows the effect of the network structure and
approximation errors on the computation of densities for variables in the
network.
","Implementation of Continuous Bayesian Networks Using Sums of Weighted
  Gaussians"
"  Although the usefulness of belief networks for reasoning under uncertainty is
widely accepted, obtaining numerical probabilities that they require is still
perceived a major obstacle. Often not enough statistical data is available to
allow for reliable probability estimation. Available information may not be
directly amenable for encoding in the network. Finally, domain experts may be
reluctant to provide numerical probabilities. In this paper, we propose a
method for elicitation of probabilities from a domain expert that is
non-invasive and accommodates whatever probabilistic information the expert is
willing to state. We express all available information, whether qualitative or
quantitative in nature, in a canonical form consisting of (in) equalities
expressing constraints on the hyperspace of possible joint probability
distributions. We then use this canonical form to derive second-order
probability distributions over the desired probabilities.
","Elicitation of Probabilities for Belief Networks: Combining Qualitative
  and Quantitative Information"
"  Accepting a proposition means that our confidence in this proposition is
strictly greater than the confidence in its negation. This paper investigates
the subclass of uncertainty measures, expressing confidence, that capture the
idea of acceptance, what we call acceptance functions. Due to the monotonicity
property of confidence measures, the acceptance of a proposition entails the
acceptance of any of its logical consequences. In agreement with the idea that
a belief set (in the sense of Gardenfors) must be closed under logical
consequence, it is also required that the separate acceptance o two
propositions entail the acceptance of their conjunction. Necessity (and
possibility) measures agree with this view of acceptance while probability and
belief functions generally do not. General properties of acceptance functions
are estabilished. The motivation behind this work is the investigation of a
setting for belief revision more general than the one proposed by Alchourron,
Gardenfors and Makinson, in connection with the notion of conditioning.
",Numerical Representations of Acceptance
"  The fraud/uncollectible debt problem in the telecommunications industry
presents two technical challenges: the detection and the treatment of the
account given the detection. In this paper, we focus on the first problem of
detection using Bayesian network models, and we briefly discuss the application
of a normative expert system for the treatment at the end. We apply Bayesian
network models to the problem of fraud/uncollectible debt detection for
telecommunication services. In addition to being quite successful at predicting
rare event outcomes, it is able to handle a mixture of categorical and
continuous data. We present a performance comparison using linear and
non-linear discriminant analysis, classification and regression trees, and
Bayesian network models
","Fraud/Uncollectible Debt Detection Using a Bayesian Network Based
  Learning System: A Rare Binary Outcome with Mixed Data Structures"
"  The Constraint Satisfaction Problem (CSP) framework offers a simple and sound
basis for representing and solving simple decision problems, without
uncertainty. This paper is devoted to an extension of the CSP framework
enabling us to deal with some decisions problems under uncertainty. This
extension relies on a differentiation between the agent-controllable decision
variables and the uncontrollable parameters whose values depend on the
occurrence of uncertain events. The uncertainty on the values of the parameters
is assumed to be given under the form of a probability distribution. Two
algorithms are given, for computing respectively decisions solving the problem
with a maximal probability, and conditional decisions mapping the largest
possible amount of possible cases to actual decisions.
",A Constraint Satisfaction Approach to Decision under Uncertainty
"  We examine a new approach to modeling uncertainty based on plausibility
measures, where a plausibility measure just associates with an event its
plausibility, an element is some partially ordered set. This approach is easily
seen to generalize other approaches to modeling uncertainty, such as
probability measures, belief functions, and possibility measures. The lack of
structure in a plausibility measure makes it easy for us to add structure on an
""as needed"" basis, letting us examine what is required to ensure that a
plausibility measure has certain properties of interest. This gives us insight
into the essential features of the properties in question, while allowing us to
prove general results that apply to many approaches to reasoning about
uncertainty. Plausibility measures have already proved useful in analyzing
default reasoning. In this paper, we examine their ""algebraic properties,""
analogues to the use of + and * in probability theory. An understanding of such
properties will be essential if plausibility measures are to be used in
practice as a representation tool.
",Plausibility Measures: A User's Guide
"  This paper concerns the probabilistic evaluation of the effects of actions in
the presence of unmeasured variables. We show that the identification of causal
effect between a singleton variable X and a set of variables Y can be
accomplished systematically, in time polynomial in the number of variables in
the graph. When the causal effect is identifiable, a closed-form expression can
be obtained for the probability that the action will achieve a specified goal,
or a set of goals.
",Testing Identifiability of Causal Effects
"  We present an algorithm, called Predict, for updating beliefs in causal
networks quantified with order-of-magnitude probabilities. The algorithm takes
advantage of both the structure and the quantification of the network and
presents a polynomial asymptotic complexity. Predict exhibits a conservative
behavior in that it is always sound but not always complete. We provide
sufficient conditions for completeness and present algorithms for testing these
conditions and for computing a complete set of plausible values. We propose
Predict as an efficient method to estimate probabilistic values and illustrate
its use in conjunction with two known algorithms for probabilistic inference.
Finally, we describe an application of Predict to plan evaluation, present
experimental results, and discuss issues regarding its use with conditional
logics of belief, and in the characterization of irrelevance.
",Fast Belief Update Using Order-of-Magnitude Probabilities
"  We show how to transform any set of prioritized propositional defaults into
an equivalent set of parallel (i.e., unprioritized) defaults, in
circumscription. We give an algorithm to implement the transform. We show how
to use the transform algorithm as a generator of a whole family of inferencing
algorithms for circumscription. The method is to employ the transform algorithm
as a front end to any inferencing algorithm, e.g., one of the previously
available, that handles the parallel (empty) case of prioritization. Our
algorithms provide not just coverage of a new expressive class, but also
alternatives to previous algorithms for implementing the previously covered
class (?layered?) of prioritization. In particular, we give a new
query-answering algorithm for prioritized cirumscription which is sound and
complete for the full expressive class of unrestricted finite prioritization
partial orders, for propositional defaults (or minimized predicates). By
contrast, previous algorithms required that the prioritization partial order be
layered, i.e., structured similar to the system of rank in the military. Our
algorithm enables, for the first time, the implementation of the most useful
class of prioritization: non-layered prioritization partial orders. Default
inheritance, for example, typically requires non-layered prioritization to
represent specificity adequately. Our algorithm enables not only the
implementation of default inheritance (and specificity) within prioritized
circumscription, but also the extension and combination of default inheritance
with other kinds of prioritized default reasoning, e.g.: with stratified logic
programs with negation-as-failure. Such logic programs are previously known to
be representable equivalently as layered-priority predicate circumscriptions.
Worst-case, the transform increases the number of defaults exponentially. We
discuss how inferencing is practically implementable nevertheless in two kinds
of situations: general expressiveness but small numbers of defaults, or
expressive special cases with larger numbers of defaults. One such expressive
special case is non-?top-heaviness? of the prioritization partial order. In
addition to its direct implementation, the transform can also be exploited
analytically to generate special case algorithms, e.g., a tractable transform
for a class within default inheritance (detailed in another, forthcoming
paper). We discuss other aspects of the significance of the fundamental result.
One can view the transform as reducing n degrees of partially ordered belief
confidence to just 2 degrees of confidence: for-sure and (unprioritized)
default. Ordinary, parallel default reasoning, e.g., in parallel
circumscription or Poole's Theorist, can be viewed in these terms as reducing 2
degrees of confidence to just 1 degree of confidence: that of the non-monotonic
theory's conclusions. The expressive reduction's computational complexity
suggests that prioritization is valuable for its expressive conciseness, just
as defaults are for theirs. For Reiter's Default Logic and Poole's Theorist,
the transform implies how to extend those formalisms so as to equip them with a
concept of prioritization that is exactly equivalent to that in
circumscription. This provides an interesting alternative to Brewka's approach
to equipping them with prioritization-type precedence.
",Transforming Prioritized Defaults and Specificity into Parallel Defaults
"  This paper discusses techniques for performing efficient decision-theoretic
planning. We give an overview of the DRIPS decision-theoretic refinement
planning system, which uses abstraction to efficiently identify optimal plans.
We present techniques for automatically generating search control information,
which can significantly improve the planner's performance. We evaluate the
efficiency of DRIPS both with and without the search control rules on a complex
medical planning problem and compare its performance to that of a
branch-and-bound decision tree algorithm.
",Efficient Decision-Theoretic Planning: Techniques and Empirical Analysis
"  In this paper we deal with a new approach to probabilistic reasoning in a
logical framework. Nearly almost all logics of probability that have been
proposed in the literature are based on classical two-valued logic. After
making clear the differences between fuzzy logic and probability theory, here
we propose a {em fuzzy} logic of probability for which completeness results (in
a probabilistic sense) are provided. The main idea behind this approach is that
probability values of crisp propositions can be understood as truth-values of
some suitable fuzzy propositions associated to the crisp ones. Moreover,
suggestions and examples of how to extend the formalism to cope with
conditional probabilities and with other uncertainty formalisms are also
provided.
",Fuzzy Logic and Probability
"  This paper presents a probabilistic model for reasoning about the state of a
system as it changes over time, both due to exogenous and endogenous
influences. Our target domain is a class of medical prediction problems that
are neither so urgent as to preclude careful diagnosis nor progress so slowly
as to allow arbitrary testing and treatment options. In these domains there is
typically enough time to gather information about the patient's state and
consider alternative diagnoses and treatments, but the temporal interaction
between the timing of tests, treatments, and the course of the disease must
also be considered. Our approach is to elicit a qualitative structural model of
the patient from a human expert---the model identifies important attributes,
the way in which exogenous changes affect attribute values, and the way in
which the patient's condition changes endogenously. We then elicit
probabilistic information to capture the expert's uncertainty about the effects
of tests and treatments and the nature and timing of endogenous state changes.
This paper describes the model in the context of a problem in treating vehicle
accident trauma, and suggests a method for solving the model based on the
technique of sequential imputation. A complementary goal of this work is to
understand and synthesize a disparate collection of research efforts all using
the name ?probabilistic temporal reasoning.? This paper analyzes related work
and points out essential differences between our proposed model and other
approaches in the literature.
",Probabilistic Temporal Reasoning with Endogenous Change
"  This is a working paper summarizing results of an ongoing research project
whose aim is to uniquely characterize the uncertainty measure for the
Dempster-Shafer Theory. A set of intuitive axiomatic requirements is presented,
some of their implications are shown, and the proof is given of the minimality
of recently proposed measure AU among all measures satisfying the proposed
requirements.
","Toward a Characterization of Uncertainty Measure for the Dempster-Shafer
  Theory"
"  We present a precise definition of cause and effect in terms of a fundamental
notion called unresponsiveness. Our definition is based on Savage's (1954)
formulation of decision theory and departs from the traditional view of
causation in that our causal assertions are made relative to a set of
decisions. An important consequence of this departure is that we can reason
about cause locally, not requiring a causal explanation for every dependency.
Such local reasoning can be beneficial because it may not be necessary to
determine whether a particular dependency is causal to make a decision. Also in
this paper, we examine the graphical encoding of causal relationships. We show
that influence diagrams in canonical form are an accurate and efficient
representation of causal relationships. In addition, we establish a
correspondence between canonical form and Pearl's causal theory.
",A Definition and Graphical Representation for Causality
"  We examine Bayesian methods for learning Bayesian networks from a combination
of prior knowledge and statistical data. In particular, we unify the approaches
we presented at last year's conference for discrete and Gaussian domains. We
derive a general Bayesian scoring metric, appropriate for both domains. We then
use this metric in combination with well-known statistical facts about the
Dirichlet and normal--Wishart distributions to derive our metrics for discrete
and Gaussian domains.
","Learning Bayesian Networks: A Unification for Discrete and Gaussian
  Domains"
"  Whereas acausal Bayesian networks represent probabilistic independence,
causal Bayesian networks represent causal relationships. In this paper, we
examine Bayesian methods for learning both types of networks. Bayesian methods
for learning acausal networks are fairly well developed. These methods often
employ assumptions to facilitate the construction of priors, including the
assumptions of parameter independence, parameter modularity, and likelihood
equivalence. We show that although these assumptions also can be appropriate
for learning causal networks, we need additional assumptions in order to learn
causal networks. We introduce two sufficient assumptions, called {em mechanism
independence} and {em component independence}. We show that these new
assumptions, when combined with parameter independence, parameter modularity,
and likelihood equivalence, allow us to apply methods for learning acausal
networks to learn causal networks.
",A Bayesian Approach to Learning Causal Networks
"  We describe methods for managing the complexity of information displayed to
people responsible for making high-stakes, time-critical decisions. The
techniques provide tools for real-time control of the configuration and
quantity of information displayed to a user, and a methodology for designing
flexible human-computer interfaces for monitoring applications. After defining
a prototypical set of display decision problems, we introduce the expected
value of revealed information (EVRI) and the related measure of expected value
of displayed information (EVDI). We describe how these measures can be used to
enhance computer displays used for monitoring complex systems. We motivate the
presentation by discussing our efforts to employ decision-theoretic control of
displays for a time-critical monitoring application at the NASA Mission Control
Center in Houston.
",Display of Information for Time-Critical Decision Making
"  In earlier work, we introduced flexible inference and decision-theoretic
metareasoning to address the intractability of normative inference. Here,
rather than pursuing the task of computing beliefs and actions with decision
models composed of distinctions about uncertain events, we examine methods for
inferring beliefs about mathematical truth before an automated theorem prover
completes a proof. We employ a Bayesian analysis to update belief in truth,
given theorem-proving progress, and show how decision-theoretic methods can be
used to determine the value of continuing to deliberate versus taking immediate
action in time-critical situations.
","Reasoning, Metareasoning, and Mathematical Truth: Studies of Theorem
  Proving under Limited Resources"
"  Bayesian networks offer great potential for use in automating large scale
diagnostic reasoning tasks. Gibbs sampling is the main technique used to
perform diagnostic reasoning in large richly interconnected Bayesian networks.
Unfortunately Gibbs sampling can take an excessive time to generate a
representative sample. In this paper we describe and test a number of heuristic
strategies for improving sampling in noisy-or Bayesian networks. The strategies
include Monte Carlo Markov chain sampling techniques other than Gibbs sampling.
Emphasis is put on strategies that can be implemented in distributed systems.
",Improved Sampling for Diagnostic Reasoning in Bayesian Networks
"  Consider the situation where some evidence e has been entered to a Bayesian
network. When performing conflict analysis, sensitivity analysis, or when
answering questions like ""What if the finding on X had been y instead of x?""
you need probabilities P (e'| h), where e' is a subset of e, and h is a
configuration of a (possibly empty) set of variables. Cautious propagation is a
modification of HUGIN propagation into a Shafer-Shenoy-like architecture. It is
less efficient than HUGIN propagation; however, it provides easy access to P
(e'| h) for a great deal of relevant subsets e'.
",Cautious Propagation in Bayesian Networks
"  In this paper we extend the influence diagram (ID) representation for
decisions under uncertainty. In the standard ID, arrows into a decision node
are only informational; they do not represent constraints on what the decision
maker can do. We can represent such constraints only indirectly, using arrows
to the children of the decision and sometimes adding more variables to the
influence diagram, thus making the ID more complicated. Users of influence
diagrams often want to represent constraints by arrows into decision nodes. We
represent constraints on decisions by allowing relevance arrows into decision
nodes. We call the resulting representation information/relevance influence
diagrams (IRIDs). Information/relevance influence diagrams allow for direct
representation and specification of constrained decisions. We use a combination
of stochastic dynamic programming and Gibbs sampling to solve IRIDs. This
method is especially useful when exact methods for solving IDs fail.
",Information/Relevance Influence Diagrams
"  Stochastic simulation algorithms such as likelihood weighting often give
fast, accurate approximations to posterior probabilities in probabilistic
networks, and are the methods of choice for very large networks. Unfortunately,
the special characteristics of dynamic probabilistic networks (DPNs), which are
used to represent stochastic temporal processes, mean that standard simulation
algorithms perform very poorly. In essence, the simulation trials diverge
further and further from reality as the process is observed over time. In this
paper, we present simulation algorithms that use the evidence observed at each
time step to push the set of trials back towards reality. The first algorithm,
""evidence reversal"" (ER) restructures each time slice of the DPN so that the
evidence nodes for the slice become ancestors of the state variables. The
second algorithm, called ""survival of the fittest"" sampling (SOF),
""repopulates"" the set of trials at each time step using a stochastic
reproduction rate weighted by the likelihood of the evidence according to each
trial. We compare the performance of each algorithm with likelihood weighting
on the original network, and also investigate the benefits of combining the ER
and SOF methods. The ER/SOF combination appears to maintain bounded error
independent of the number of time steps in the simulation.
",Stochastic Simulation Algorithms for Dynamic Probabilistic Networks
"  Sequential decision tasks with incomplete information are characterized by
the exploration problem; namely the trade-off between further exploration for
learning more about the environment and immediate exploitation of the accrued
information for decision-making. Within artificial intelligence, there has been
an increasing interest in studying planning-while-learning algorithms for these
decision tasks. In this paper we focus on the exploration problem in
reinforcement learning and Q-learning in particular. The existing exploration
strategies for Q-learning are of a heuristic nature and they exhibit limited
scaleability in tasks with large (or infinite) state and action spaces.
Efficient experimentation is needed for resolving uncertainties when possible
plans are compared (i.e. exploration). The experimentation should be sufficient
for selecting with statistical significance a locally optimal plan (i.e.
exploitation). For this purpose, we develop a probabilistic hill-climbing
algorithm that uses a statistical selection procedure to decide how much
exploration is needed for selecting a plan which is, with arbitrarily high
probability, arbitrarily close to a locally optimal one. Due to its generality
the algorithm can be employed for the exploration strategy of robust
Q-learning. An experiment on a relatively complex control task shows that the
proposed exploration strategy performs better than a typical exploration
strategy.
",Probabilistic Exploration in Planning while Learning
"  An important issue in the use of expert systems is the so-called brittleness
problem. Expert systems model only a limited part of the world. While the
explicit management of uncertainty in expert systems itigates the brittleness
problem, it is still possible for a system to be used, unwittingly, in ways
that the system is not prepared to address. Such a situation may be detected by
the method of straw models, first presented by Jensen et al. [1990] and later
generalized and justified by Laskey [1991]. We describe an algorithm, which we
have implemented, that takes as input an annotated diagnostic Bayesian network
(the base model) and constructs, without assistance, a bipartite network to be
used as a straw model. We show that in some cases this straw model is better
that the independent straw model of Jensen et al., the only other straw model
for which a construction algorithm has been designed and implemented.
","On the Detection of Conflicts in Diagnostic Bayesian Networks Using
  Abstraction"
"  Dawid, Kjaerulff and Lauritzen (1994) provided a preliminary description of a
hybrid between Monte-Carlo sampling methods and exact local computations in
junction trees. Utilizing the strengths of both methods, such hybrid inference
methods has the potential of expanding the class of problems which can be
solved under bounded resources as well as solving problems which otherwise
resist exact solutions. The paper provides a detailed description of a
particular instance of such a hybrid scheme; namely, combination of exact
inference and Gibbs sampling in discrete Bayesian networks. We argue that this
combination calls for an extension of the usual message passing scheme of
ordinary junction trees.
",HUGS: Combining Exact Inference and Gibbs Sampling in Junction Trees
"  We show an alternative way of representing a Bayesian belief network by
sensitivities and probability distributions. This representation is equivalent
to the traditional representation by conditional probabilities, but makes
dependencies between nodes apparent and intuitively easy to understand. We also
propose a QR matrix representation for the sensitivities and/or conditional
probabilities which is more efficient, in both memory requirements and
computational speed, than the traditional representation for computer-based
implementations of probabilistic inference. We use sensitivities to show that
for a certain class of binary networks, the computation time for approximate
probabilistic inference with any positive upper bound on the error of the
result is independent of the size of the network. Finally, as an alternative to
traditional algorithms that use conditional probabilities, we describe an exact
algorithm for probabilistic inference that uses the QR-representation for
sensitivities and updates probability distributions of nodes in a network
according to messages from the neighbors.
","Sensitivities: An Alternative to Conditional Probabilities for Bayesian
  Belief Networks"
"  Classically, risk is characterized by a point value probability indicating
the likelihood of occurrence of an adverse effect. However, there are domains
where the attainability of objective numerical risk characterizations is
increasingly being questioned. This paper reviews the arguments in favour of
extending classical techniques of risk assessment to incorporate meaningful
qualitative and weak quantitative risk characterizations. A technique in which
linguistic uncertainty terms are defined in terms of patterns of argument is
then proposed. The technique is demonstrated using a prototype computer-based
system for predicting the carcinogenic risk due to novel chemical compounds.
",Is There a Role for Qualitative Risk Assessment?
"  Markov decision problems (MDPs) provide the foundations for a number of
problems of interest to AI researchers studying automated planning and
reinforcement learning. In this paper, we summarize results regarding the
complexity of solving MDPs and the running time of MDP solution algorithms. We
argue that, although MDPs can be solved efficiently in theory, more study is
needed to reveal practical algorithms for solving large problems quickly. To
encourage future research, we sketch some alternative methods of analysis that
rely on the structure of MDPs.
",On the Complexity of Solving Markov Decision Problems
"  This paper presents correct algorithms for answering the following two
questions; (i) Does there exist a causal explanation consistent with a set of
background knowledge which explains all of the observed independence facts in a
sample? (ii) Given that there is such a causal explanation what are the causal
relationships common to every such causal explanation?
",Causal Inference and Causal Explanation with Background Knowledge
"  A completeness result for d-separation applied to discrete Bayesian networks
is presented and it is shown that in a strong measure-theoretic sense almost
all discrete distributions for a given network structure are faithful; i.e. the
independence facts true of the distribution are all and only those entailed by
the network structure.
",Strong Completeness and Faithfulness in Bayesian Networks
"  We define a context-sensitive temporal probability logic for representing
classes of discrete-time temporal Bayesian networks. Context constraints allow
inference to be focused on only the relevant portions of the probabilistic
knowledge. We provide a declarative semantics for our language. We present a
Bayesian network construction algorithm whose generated networks give sound and
complete answers to queries. We use related concepts in logic programming to
justify our approach. We have implemented a Bayesian network construction
algorithm for a subset of the theory and demonstrate it's application to the
problem of evaluating the effectiveness of treatments for acute cardiac
conditions.
","A Theoretical Framework for Context-Sensitive Temporal Probability Model
  Construction with Application to Plan Projection"
"  In recent years there has been a spate of papers describing systems for
probabilisitic reasoning which do not use numerical probabilities. In some
cases the simple set of values used by these systems make it impossible to
predict how a probability will change or which hypothesis is most likely given
certain evidence. This paper concentrates on such situations, and suggests a
number of ways in which they may be resolved by refining the representation.
",Refining Reasoning in Qualitative Probabilistic Networks
"  Certain causal models involving unmeasured variables induce no independence
constraints among the observed variables but imply, nevertheless, inequality
contraints on the observed distribution. This paper derives a general formula
for such instrumental variables, that is, exogenous variables that directly
affect some variables but not all. With the help of this formula, it is
possible to test whether a model involving instrumental variables may account
for the data, or, conversely, whether a given variables can be deemed
instrumental.
","On the Testability of Causal Models with Latent and Instrumental
  Variables"
"  The paper concerns the probabilistic evaluation of plans in the presence of
unmeasured variables, each plan consisting of several concurrent or sequential
actions. We establish a graphical criterion for recognizing when the effects of
a given plan can be predicted from passive observations on measured variables
only. When the criterion is satisfied, a closed-form expression is provided for
the probability that the plan will achieve a specified goal.
","Probabilistic Evaluation of Sequential Plans from Causal Models with
  Hidden Variables"
"  This paper introduces the independent choice logic, and in particular the
""single agent with nature"" instance of the independent choice logic, namely
ICLdt. This is a logical framework for decision making uncertainty that extends
both logic programming and stochastic models such as influence diagrams. This
paper shows how the representation of a decision problem within the independent
choice logic can be exploited to cut down the combinatorics of dynamic
programming. One of the main problems with influence diagram evaluation
techniques is the need to optimise a decision for all values of the 'parents'
of a decision variable. In this paper we show how the rule based nature of the
ICLdt can be exploited so that we only make distinctions in the values of the
information available for a decision that will make a difference to utility.
","Exploiting the Rule Structure for Decision Making within the Independent
  Choice Logic"
"  Bayesian belief networks are bing increasingly used as a knowledge
representation for diagnostic reasoning. One simple method for conducting
diagnostic reasoning is to represent system faults and observations only. In
this paper, we investigate how having intermediate nodes-nodes other than fault
and observation nodes affects the diagnostic performance of a Bayesian belief
network. We conducted a series of experiments on a set of real belief networks
for medical diagnosis in liver and bile disease. We compared the effects on
diagnostic performance of a two-level network consisting just of disease and
finding nodes with that of a network which models intermediate
pathophysiological disease states as well. We provide some theoretical evidence
for differences observed between the abstracted two-level network and the full
network.
","Abstraction in Belief Networks: The Role of Intermediate States in
  Diagnostic Reasoning"
"  Typical approaches to plan recognition start from a representation of an
agent's possible plans, and reason evidentially from observations of the
agent's actions to assess the plausibility of the various candidates. A more
expansive view of the task (consistent with some prior work) accounts for the
context in which the plan was generated, the mental state and planning process
of the agent, and consequences of the agent's actions in the world. We present
a general Bayesian framework encompassing this view, and focus on how context
can be exploited in plan recognition. We demonstrate the approach on a problem
in traffic monitoring, where the objective is to induce the plan of the driver
from observation of vehicle movements. Starting from a model of how the driver
generates plans, we show how the highway context can appropriately influence
the recognizer's interpretation of observed driver behavior.
","Accounting for Context in Plan Recognition, with Application to Traffic
  Monitoring"
"  The main goal of this paper is to describe a new pruning method for solving
decision trees and game trees. The pruning method for decision trees suggests a
slight variant of decision trees that we call scenario trees. In scenario
trees, we do not need a conditional probability for each edge emanating from a
chance node. Instead, we require a joint probability for each path from the
root node to a leaf node. We compare the pruning method to the traditional
rollback method for decision trees and game trees. For problems that require
Bayesian revision of probabilities, a scenario tree representation with the
pruning method is more efficient than a decision tree representation with the
rollback method. For game trees, the pruning method is more efficient than the
rollback method.
",A New Pruning Method for Solving Decision Trees and Game Trees
"  The use of directed acyclic graphs (DAGs) to represent conditional
independence relations among random variables has proved fruitful in a variety
of ways. Recursive structural equation models are one kind of DAG model.
However, non-recursive structural equation models of the kinds used to model
economic processes are naturally represented by directed cyclic graphs with
independent errors, a characterization of conditional independence errors, a
characterization of conditional independence constraints is obtained, and it is
shown that the result generalizes in a natural way to systems in which the
error variables or noises are statistically dependent. For non-linear systems
with independent errors a sufficient condition for conditional independence of
variables in associated distributions is obtained.
",Directed Cyclic Graphical Representations of Feedback Models
"  We show that there is a general, informative and reliable procedure for
discovering causal relations when, for all the investigator knows, both latent
variables and selection bias may be at work. Given information about
conditional independence and dependence relations between measured variables,
even when latent variables and selection bias may be present, there are
sufficient conditions for reliably concluding that there is a causal path from
one variable to another, and sufficient conditions for reliably concluding when
no such causal path exists.
",Causal Inference in the Presence of Latent Variables and Selection Bias
"  Probabilistic model-based diagnosis computes the posterior probabilities of
failure of components from the prior probabilities of component failure and
observations of system behavior. One problem with this method is that such
priors are almost never directly available. One of the reasons is that the
prior probability estimates include an implicit notion of a time interval over
which they are specified -- for example, if the probability of failure of a
component is 0.05, is this over the period of a day or is this over a week? A
second problem facing probabilistic model-based diagnosis is the modeling of
persistence. Say we have an observation about a system at time t_1 and then
another observation at a later time t_2. To compute posterior probabilities
that take into account both the observations, we need some model of how the
state of the system changes from time t_1 to t_2. In this paper, we address
these problems using techniques from Reliability theory. We show how to compute
the failure prior of a component from an empirical measure of its reliability
-- the Mean Time Between Failure (MTBF). We also develop a scheme to model
persistence when handling multiple time tagged observations.
",Modeling Failure Priors and Persistence in Model-Based Diagnosis
"  The goal of diagnosis is to compute good repair strategies in response to
anomalous system behavior. In a decision theoretic framework, a good repair
strategy has low expected cost. In a general formulation of the problem, the
computation of the optimal (lowest expected cost) repair strategy for a system
with multiple faults is intractable. In this paper, we consider an interesting
and natural restriction on the behavior of the system being diagnosed: (a) the
system exhibits faulty behavior if and only if one or more components is
malfunctioning. (b) The failures of the system components are independent.
Given this restriction on system behavior, we develop a polynomial time
algorithm for computing the optimal repair strategy. We then go on to introduce
a system hierarchy and the notion of inspecting (testing) components before
repair. We develop a linear time algorithm for computing an optimal repair
strategy for the hierarchical system which includes both repair and inspection.
","A Polynomial Algorithm for Computing the Optimal Repair Strategy in a
  System with Independent Component Failures"
"  The goal of model-based diagnosis is to isolate causes of anomalous system
behavior and recommend inexpensive repair actions in response. In general,
precomputing optimal repair policies is intractable. To date, investigators
addressing this problem have explored approximations that either impose
restrictions on the system model (such as a single fault assumption) or compute
an immediate best action with limited lookahead. In this paper, we develop a
formulation of repair in model-based diagnosis and a repair algorithm that
computes optimal sequences of actions. This optimal approach is costly but can
be applied to precompute an optimal repair strategy for compact systems. We
show how we can exploit a hierarchical system specification to make this
approach tractable for large systems. When introducing hierarchy, we also
consider the tradeoff between simply replacing a component and decomposing it
to repair its subcomponents. The hierarchical repair algorithm is suitable for
off-line precomputation of an optimal repair strategy. A modification of the
algorithm takes advantage of an iterative deepening scheme to trade off
inference time and the quality of the computed strategy.
","Exploiting System Hierarchy to Compute Repair Plans in Probabilistic
  Model-based Diagnosis"
"  Standard algorithms for finding the shortest path in a graph require that the
cost of a path be additive in edge costs, and typically assume that costs are
deterministic. We consider the problem of uncertain edge costs, with potential
probabilistic dependencies among the costs. Although these dependencies violate
the standard dynamic-programming decomposition, we identify a weaker stochastic
consistency condition that justifies a generalized dynamic-programming approach
based on stochastic dominance. We present a revised path-planning algorithm and
prove that it produces optimal paths under time-dependent uncertain costs. We
test the algorithm by applying it to a model of stochastic bus networks, and
present empirical performance results comparing it to some alternatives.
Finally, we consider extensions of these concepts to a more general class of
problems of heuristic search under uncertainty.
",Path Planning under Time-Dependent Uncertainty
"  We develop a new semantics for defeasible inference based on extended
probability measures allowed to take infinitesimal values, on the
interpretation of defaults as generalized conditional probability constraints
and on a preferred-model implementation of entropy maximization.
","Defaults and Infinitesimals: Defeasible Inference by Nonarchimedean
  Entropy-Maximization"
"  This paper develops a simple calculus for order of magnitude reasoning. A
semantics is given with soundness and completeness results. Order of magnitude
probability functions are easily defined and turn out to be equivalent to kappa
functions, which are slight generalizations of Spohn's Natural Conditional
Functions. The calculus also gives rise to an order of magnitude decision
theory, which can be used to justify an amended version of Pearl's decision
theory for kappa functions, although the latter is weaker and less expressive.
",An Order of Magnitude Calculus
"  This paper discusses a method for implementing a probabilistic inference
system based on an extended relational data model. This model provides a
unified approach for a variety of applications such as dynamic programming,
solving sparse linear equations, and constraint propagation. In this framework,
the probability model is represented as a generalized relational database.
Subsequent probabilistic requests can be processed as standard relational
queries. Conventional database management systems can be easily adopted for
implementing such an approximate reasoning system.
",A Method for Implementing a Probabilistic Model as a Relational Database
"  Recent developments show that Multiply Sectioned Bayesian Networks (MSBNs)
can be used for diagnosis of natural systems as well as for model-based
diagnosis of artificial systems. They can be applied to single-agent oriented
reasoning systems as well as multi-agent distributed probabilistic reasoning
systems. Belief propagation between a pair of subnets plays a central role in
maintenance of global consistency in a MSBN. This paper studies the operation
UpdateBelief, presented originally with MSBNs, for inter-subnet propagation. We
analyze how the operation achieves its intended functionality, which provides
hints as for how its efficiency can be improved. We then define two new
versions of UpdateBelief that reduce the computation time for inter-subnet
propagation. One of them is optimal in the sense that the minimum amount of
computation for coordinating multi-linkage belief propagation is required. The
optimization problem is solved through the solution of a graph-theoretic
problem: the minimum weight open tour in a tree.
","Optimization of Inter-Subnet Belief Updating in Multiply Sectioned
  Bayesian Networks"
"  In this paper, we present two methods to provide explanations for reasoning
with belief functions in the valuation-based systems. One approach, inspired by
Strat's method, is based on sensitivity analysis, but its computation is
simpler thus easier to implement than Strat's. The other one is to examine the
impact of evidence on the conclusion based on the measure of the information
content in the evidence. We show the property of additivity for the pieces of
evidence that are conditional independent within the context of the
valuation-based systems. We will give an example to show how these approaches
are applied in an evidential network.
",Generating Explanations for Evidential Reasoning
"  This paper reports experiments with the causal independence inference
algorithm proposed by Zhang and Poole (1994b) on the CPSC network created by
Pradhan et al. (1994). It is found that the algorithm is able to answer 420 of
the 422 possible zero-observation queries, 94 of 100 randomly generated
five-observation queries, 87 of 100 randomly generated ten-observation queries,
and 69 of 100 randomly generated twenty-observation queries.
",Inference with Causal Independence in the CPSC Network
"  The information retrieval systems that are present nowadays are mainly based
on full text matching of keywords or topic based classification. This matching
of keywords often returns a large number of irrelevant information and this
does not meet the users query requirement. In order to solve this problem and
to enhance the search using semantic environment, a technique named ontology is
implemented for the field of poultry in this paper. Ontology is an emerging
technique in the current field of research in semantic environment. This paper
constructs ontology using the tool named Protege version 4.0 and this also
generates Resource Description Framework schema and XML scripts for using
poultry ontology in web.
","An Ontology Construction Approach for the Domain Of Poultry Science
  Using Protege"
"  Handling visual complexity is a challenging problem in visualization owing to
the subjectiveness of its definition and the difficulty in devising
generalizable quantitative metrics. In this paper we address this challenge by
measuring the visual complexity of two common forms of cluster-based
visualizations: scatter plots and parallel coordinatess. We conceptualize
visual complexity as a form of visual uncertainty, which is a measure of the
degree of difficulty for humans to interpret a visual representation correctly.
We propose an algorithm for estimating visual complexity for the aforementioned
visualizations using Allen's interval algebra. We first establish a set of
primitive 2-cluster cases in scatter plots and another set for parallel
coordinatess based on symmetric isomorphism. We confirm that both are the
minimal sets and verify the correctness of their members computationally. We
score the uncertainty of each primitive case based on its topological
properties, including the existence of overlapping regions, splitting regions
and meeting points or edges. We compare a few optional scoring schemes against
a set of subjective scores by humans, and identify the one that is the most
consistent with the subjective scores. Finally, we extend the 2-cluster measure
to k-cluster measure as a general purpose estimator of visual complexity for
these two forms of cluster-based visualization.
",Measuring Visual Complexity of Cluster-Based Visualizations
"  Modification of a conceptual clustering algorithm Cobweb for the purpose of
its application for numerical data is offered. Keywords: clustering, algorithm
Cobweb, numerical data, fuzzy membership function.
","Modification of conceptual clustering algorithm Cobweb for numerical
  data using fuzzy membership function"
"  Modelling of complex systems is mainly based on the decomposition of these
systems in autonomous elements, and the identification and definitio9n of
possible interactions between these elements. For this, the agent-based
approach is a modelling solution often proposed. Complexity can also be due to
external events or internal to systems, whose main characteristics are
uncertainty, imprecision, or whose perception is subjective (i.e. interpreted).
Insofar as fuzzy logic provides a solution for modelling uncertainty, the
concept of fuzzy agent can model both the complexity and uncertainty. This
paper focuses on introducing the concept of fuzzy agent: a classical
architecture of agent is redefined according to a fuzzy perspective. A
pedagogical illustration of fuzzy agentification of a smart watering system is
then proposed.
",A Modelling Approach Based on Fuzzy Agents
"  Bayesian learning of belief networks (BLN) is a method for automatically
constructing belief networks (BNs) from data using search and Bayesian scoring
techniques. K2 is a particular instantiation of the method that implements a
greedy search strategy. To evaluate the accuracy of K2, we randomly generated a
number of BNs and for each of those we simulated data sets. K2 was then used to
induce the generating BNs from the simulated data. We examine the performance
of the program, and the factors that influence it. We also present a simple BN
model, developed from our results, which predicts the accuracy of K2, when
given various characteristics of the data set.
","An Evaluation of an Algorithm for Inductive Learning of Bayesian Belief
  Networks Usin"
"  We have previously reported a Bayesian algorithm for determining the
coordinates of points in three-dimensional space from uncertain constraints.
This method is useful in the determination of biological molecular structure.
It is limited, however, by the requirement that the uncertainty in the
constraints be normally distributed. In this paper, we present an extension of
the original algorithm that allows constraint uncertainty to be represented as
a mixture of Gaussians, and thereby allows arbitrary constraint distributions.
We illustrate the performance of this algorithm on a problem drawn from the
domain of molecular structure determination, in which a multicomponent
constraint representation produces a much more accurate solution than the old
single component mechanism. The new mechanism uses mixture distributions to
decompose the problem into a set of independent problems with unimodal
constraint uncertainty. The results of the unimodal subproblems are
periodically recombined using Bayes' law, to avoid combinatorial explosion. The
new algorithm is particularly suited for parallel implementation.
",Probabilistic Constraint Satisfaction with Non-Gaussian Noise
"  This paper examines the ""K2"" network scoring metric of Cooper and Herskovits.
It shows counterintuitive results from applying this metric to simple networks.
One family of noninformative priors is suggested for assigning equal scores to
equivalent networks.
",A Bayesian Method Reexamined
"  Laplace's method, a family of asymptotic methods used to approximate
integrals, is presented as a potential candidate for the tool box of techniques
used for knowledge acquisition and probabilistic inference in belief networks
with continuous variables. This technique approximates posterior moments and
marginal posterior distributions with reasonable accuracy [errors are O(n^-2)
for posterior means] in many interesting cases. The method also seems promising
for computing approximations for Bayes factors for use in the context of model
selection, model uncertainty and mixtures of pdfs. The limitations, regularity
conditions and computational difficulties for the implementation of Laplace's
method are comparable to those associated with the methods of maximum
likelihood and posterior mode analysis.
","Laplace's Method Approximations for Probabilistic Inference in Belief
  Networks with Continuous Variables"
"  In previous work [BGHK92, BGHK93], we have studied the random-worlds approach
-- a particular (and quite powerful) method for generating degrees of belief
(i.e., subjective probabilities) from a knowledge base consisting of objective
(first-order, statistical, and default) information. But allowing a knowledge
base to contain only objective information is sometimes limiting. We
occasionally wish to include information about degrees of belief in the
knowledge base as well, because there are contexts in which old beliefs
represent important information that should influence new beliefs. In this
paper, we describe three quite general techniques for extending a method that
generates degrees of belief from objective information to one that can make use
of degrees of belief as well. All of our techniques are bloused on well-known
approaches, such as cross-entropy. We discuss general connections between the
techniques and in particular show that, although conceptually and technically
quite different, all of the techniques give the same answer when applied to the
random-worlds method.
",Generating New Beliefs From Old
"  Evaluation of counterfactual queries (e.g., ""If A were true, would C have
been true?"") is important to fault diagnosis, planning, and determination of
liability. In this paper we present methods for computing the probabilities of
such queries using the formulation proposed in [Balke and Pearl, 1994], where
the antecedent of the query is interpreted as an external action that forces
the proposition A to be true. When a prior probability is available on the
causal mechanisms governing the domain, counterfactual probabilities can be
evaluated precisely. However, when causal knowledge is specified as conditional
probabilities on the observables, only bounds can computed. This paper develops
techniques for evaluating these bounds, and demonstrates their use in two
applications: (1) the determination of treatment efficacy from studies in which
subjects may choose their own treatment, and (2) the determination of liability
in product-safety litigation.
","Counterfactual Probabilities: Computational Methods, Bounds and
  Applications"
"  We discuss the problem of construction of inference procedures which can
manipulate with uncertainties measured in ordinal scales and fulfill to the
property of strict monotonicity of conclusion. The class of A-valuations of
plausibility is considered where operations based only on information about
linear ordering of plausibility values are used. In this class the modus ponens
generating function fulfiling to the property of strict monotonicity of
conclusions is introduced.
","Modus Ponens Generating Function in the Class of ^-valuations of
  Plausibility"
"  We propose an integration of possibility theory into non-classical logics. We
obtain many formal results that generalize the case where possibility and
necessity functions are based on classical logic. We show how useful such an
approach is by applying it to reasoning under uncertain and inconsistent
information.
",Possibility and Necessity Functions over Non-classical Logics
"  Some instances of creative thinking require an agent to build and test
hypothetical theories. Such a reasoner needs to explore the space of not only
those situations that have occurred in the past, but also those that are
rationally conceivable. In this paper we present a formalism for exploring the
space of conceivable situation-models for those domains in which the knowledge
is primarily probabilistic in nature. The formalism seeks to construct
consistent, minimal, and desirable situation-descriptions by selecting suitable
domain-attributes and dependency relationships from the available domain
knowledge.
",Exploratory Model Building
"  I describe a planning methodology for domains with uncertainty in the form of
external events that are not completely predictable. The events are represented
by enabling conditions and probabilities of occurrence. The planner is
goal-directed and backward chaining, but the subgoals are suggested by
analyzing the probability of success of the partial plan rather than being
simply the open conditions of the operators in the plan. The partial plan is
represented as a Bayesian belief net to compute its probability of success.
Since calculating the probability of success of a plan can be very expensive I
introduce two other techniques for computing it, one that uses Monte Carlo
simulation to estimate it and one based on a Markov chain representation that
uses knowledge about the dependencies between the predicates describing the
domain.
",Planning with External Events
"  Bayesian belief network learning algorithms have three basic components: a
measure of a network structure and a database, a search heuristic that chooses
network structures to be considered, and a method of estimating the probability
tables from the database. This paper contributes to all these three topics. The
behavior of the Bayesian measure of Cooper and Herskovits and a minimum
description length (MDL) measure are compared with respect to their properties
for both limiting size and finite size databases. It is shown that the MDL
measure has more desirable properties than the Bayesian measure when a
distribution is to be learned. It is shown that selecting belief networks with
certain minimallity properties is NP-hard. This result justifies the use of
search heuristics instead of exact algorithms for choosing network structures
to be considered. In some cases, a collection of belief networks can be
represented by a single belief network which leads to a new kind of probability
table estimation called smoothing. We argue that smoothing can be efficiently
implemented by incorporating it in the search heuristic. Experimental results
suggest that for learning probabilities of belief networks smoothing is
helpful.
",Properties of Bayesian Belief Network Learning Algorithms
"  Simulation schemes for probabilistic inference in Bayesian belief networks
offer many advantages over exact algorithms; for example, these schemes have a
linear and thus predictable runtime while exact algorithms have exponential
runtime. Experiments have shown that likelihood weighting is one of the most
promising simulation schemes. In this paper, we present a new simulation scheme
that generates samples more evenly spread in the sample space than the
likelihood weighting scheme. We show both theoretically and experimentally that
the stratified scheme outperforms likelihood weighting in average runtime and
error in estimates of beliefs.
",A Stratified Simulation Scheme for Inference in Bayesian Belief Networks
"  The expected value of information (EVI) is the most powerful measure of
sensitivity to uncertainty in a decision model: it measures the potential of
information to improve the decision, and hence measures the expected value of
outcome. Standard methods for computing EVI use discrete variables and are
computationally intractable for models that contain more than a few variables.
Monte Carlo simulation provides the basis for more tractable evaluation of
large predictive models with continuous and discrete variables, but so far
computation of EVI in a Monte Carlo setting also has appeared impractical. We
introduce an approximate approach based on pre-posterior analysis for
estimating EVI in Monte Carlo models. Our method uses a linear approximation to
the value function and multiple linear regression to estimate the linear model
from the samples. The approach is efficient and practical for extremely large
models. It allows easy estimation of EVI for perfect or partial information on
individual variables or on combinations of variables. We illustrate its
implementation within Demos (a decision modeling system), and its application
to a large model for crisis transportation planning.
",Efficient Estimation of the Value of Information in Monte Carlo Models
"  A BN2O network is a two level belief net in which the parent interactions are
modeled using the noisy-or interaction model. In this paper we discuss
application of the SPI local expression language to efficient inference in
large BN2O networks. In particular, we show that there is significant
structure, which can be exploited to improve over the Quickscore result. We
further describe how symbolic techniques can provide information which can
significantly reduce the computation required for computing all cause posterior
marginals. Finally, we present a novel approximation technique with preliminary
experimental results.
",Symbolic Probabilitistic Inference in Large BN2O Networks
"  This work proposes action networks as a semantically well-founded framework
for reasoning about actions and change under uncertainty. Action networks add
two primitives to probabilistic causal networks: controllable variables and
persistent variables. Controllable variables allow the representation of
actions as directly setting the value of specific events in the domain, subject
to preconditions. Persistent variables provide a canonical model of persistence
according to which both the state of a variable and the causal mechanism
dictating its value persist over time unless intervened upon by an action (or
its consequences). Action networks also allow different methods for quantifying
the uncertainty in causal relationships, which go beyond traditional
probabilistic quantification. This paper describes both recent results and work
in progress.
","Action Networks: A Framework for Reasoning about Actions and Change
  under Uncertainty"
"  We study the connection between kappa calculus and probabilistic reasoning in
diagnosis applications. Specifically, we abstract a probabilistic belief
network for diagnosing faults into a kappa network and compare the ordering of
faults computed using both methods. We show that, at least for the example
examined, the ordering of faults coincide as long as all the causal relations
in the original probabilistic network are taken into account. We also provide a
formal analysis of some network structures where the two methods will differ.
Both kappa rankings and infinitesimal probabilities have been used extensively
to study default reasoning and belief revision. But little has been done on
utilizing their connection as outlined above. This is partly because the
relation between kappa and probability calculi assumes that probabilities are
arbitrarily close to one (or zero). The experiments in this paper investigate
this relation when this assumption is not satisfied. The reported results have
important implications on the use of kappa rankings to enhance the knowledge
engineering of uncertainty models.
",On the Relation between Kappa Calculus and Probabilistic Reasoning
"  When agents devise plans for execution in the real world, they face two
important forms of uncertainty: they can never have complete knowledge about
the state of the world, and they do not have complete control, as the effects
of their actions are uncertain. While most classical planning methods avoid
explicit uncertainty reasoning, we believe that uncertainty should be
explicitly represented and reasoned about. We develop a probabilistic
representation for states and actions, based on belief networks. We define
conditional belief nets (CBNs) to capture the probabilistic dependency of the
effects of an action upon the state of the world. We also use a CBN to
represent the intrinsic relationships among entities in the environment, which
persist from state to state. We present a simple projection algorithm to
construct the belief network of the state succeeding an action, using the
environment CBN model to infer indirect effects. We discuss how the qualitative
aspects of belief networks and CBNs make them appropriate for the various
stages of the problem solving process, from model construction to the design of
planning algorithms.
","A Structured, Probabilistic Representation of Action"
"  We investigate planning in time-critical domains represented as Markov
Decision Processes, showing that search based techniques can be a very powerful
method for finding close to optimal plans. To reduce the computational cost of
planning in these domains, we execute actions as we construct the plan, and
sacrifice optimality by searching to a fixed depth and using a heuristic
function to estimate the value of states. Although this paper concentrates on
the search algorithm, we also discuss ways of constructing heuristic functions
suitable for this approach. Our results show that by interleaving search and
execution, close to optimal policies can be found without the computational
requirements of other approaches.
",Integrating Planning and Execution in Stochastic Domains
"  Most algorithms for propagating evidence through belief networks have been
exact and exhaustive: they produce an exact (point-valued) marginal probability
for every node in the network. Often, however, an application will not need
information about every n ode in the network nor will it need exact
probabilities. We present the localized partial evaluation (LPE) propagation
algorithm, which computes interval bounds on the marginal probability of a
specified query node by examining a subset of the nodes in the entire network.
Conceptually, LPE ignores parts of the network that are ""too far away"" from the
queried node to have much impact on its value. LPE has the ""anytime"" property
of being able to produce better solutions (tighter intervals) given more time
to consider more of the network.
",Localized Partial Evaluation of Belief Networks
"  AI planning algorithms have addressed the problem of generating sequences of
operators that achieve some input goal, usually assuming that the planning
agent has perfect control over and information about the world. Relaxing these
assumptions requires an extension to the action representation that allows
reasoning both about the changes an action makes and the information it
provides. This paper presents an action representation that extends the
deterministic STRIPS model, allowing actions to have both causal and
informational effects, both of which can be context dependent and noisy. We
also demonstrate how a standard least-commitment planning algorithm can be
extended to include informational actions and contingent execution.
","A Probabilistic Model of Action for Least-Commitment Planning with
  Information Gather"
"  Several Artificial Intelligence schemes for reasoning under uncertainty
explore either explicitly or implicitly asymmetries among probabilities of
various states of their uncertain domain models. Even though the correct
working of these schemes is practically contingent upon the existence of a
small number of probable states, no formal justification has been proposed of
why this should be the case. This paper attempts to fill this apparent gap by
studying asymmetries among probabilities of various states of uncertain models.
By rewriting the joint probability distribution over a model's variables into a
product of individual variables' prior and conditional probability
distributions, and applying central limit theorem to this product, we can
demonstrate that the probabilities of individual states of the model can be
expected to be drawn from highly skewed, log-normal distributions. With
sufficient asymmetry in individual prior and conditional probability
distributions, a small fraction of states can be expected to cover a large
portion of the total probability space with the remaining states having
practically negligible probability. Theoretical discussion is supplemented by
simulation results and an illustrative real-world example.
",Some Properties of Joint Probability Distributions
"  An ordinal view of independence is studied in the framework of possibility
theory. We investigate three possible definitions of dependence, of increasing
strength. One of them is the counterpart to the multiplication law in
probability theory, and the two others are based on the notion of conditional
possibility. These two have enough expressive power to support the whole
possibility theory, and a complete axiomatization is provided for the strongest
one. Moreover we show that weak independence is well-suited to the problems of
belief change and plausible reasoning, especially to address the problem of
blocking of property inheritance in exception-tolerant taxonomic reasoning.
",An Ordinal View of Independence with Application to Plausible Reasoning
"  Penalty logic, introduced by Pinkas, associates to each formula of a
knowledge base the price to pay if this formula is violated. Penalties may be
used as a criterion for selecting preferred consistent subsets in an
inconsistent knowledge base, thus inducing a non-monotonic inference relation.
A precise formalization and the main properties of penalty logic and of its
associated non-monotonic inference relation are given in the first part. We
also show that penalty logic and Dempster-Shafer theory are related, especially
in the infinitesimal case.
",Penalty logic and its Link with Dempster-Shafer Theory
"  In this paper, we introduce evidence propagation operations on influence
diagrams and a concept of value of evidence, which measures the value of
experimentation. Evidence propagation operations are critical for the
computation of the value of evidence, general update and inference operations
in normative expert systems which are based on the influence diagram
(generalized Bayesian network) paradigm. The value of evidence allows us to
compute directly an outcome sensitivity, a value of perfect information and a
value of control which are used in decision analysis (the science of decision
making under uncertainty). More specifically, the outcome sensitivity is the
maximum difference among the values of evidence, the value of perfect
information is the expected value of the values of evidence, and the value of
control is the optimal value of the values of evidence. We also discuss an
implementation and a relative computational efficiency issues related to the
value of evidence and the value of perfect information.
",Value of Evidence on Influence Diagrams
"  Possibilistic conditional independence is investigated: we propose a
definition of this notion similar to the one used in probability theory. The
links between independence and non-interactivity are investigated, and
properties of these relations are given. The influence of the conjunction used
to define a conditional measure of possibility is also highlighted: we examine
three types of conjunctions: Lukasiewicz - like T-norms, product-like T-norms
and the minimum operator.
",Conditional Independence in Possibility Theory
"  Backward simulation is an approximate inference technique for Bayesian belief
networks. It differs from existing simulation methods in that it starts
simulation from the known evidence and works backward (i.e., contrary to the
direction of the arcs). The technique's focus on the evidence leads to improved
convergence in situations where the posterior beliefs are dominated by the
evidence rather than by the prior probabilities. Since this class of situations
is large, the technique may make practical the application of approximate
inference in Bayesian belief networks to many real-world problems.
",Backward Simulation in Bayesian Networks
"  Testing the validity of probabilistic models containing unmeasured (hidden)
variables is shown to be a hard task. We show that the task of testing whether
models are structurally incompatible with the data at hand, requires an
exponential number of independence evaluations, each of the form: ""X is
conditionally independent of Y, given Z."" In contrast, a linear number of such
evaluations is required to test a standard Bayesian network (one per vertex).
On the positive side, we show that if a network with hidden variables G has a
tree skeleton, checking whether G represents a given probability model P
requires the polynomial number of such independence evaluations. Moreover, we
provide an algorithm that efficiently constructs a tree-structured Bayesian
network (with hidden variables) that represents P if such a network exists, and
further recognizes when such a network does not exist.
","On Testing Whether an Embedded Bayesian Network Represents a Probability
  Model"
"  We introduce an approach to high-level conditional planning we call
epsilon-safe planning. This probabilistic approach commits us to planning to
meet some specified goal with a probability of success of at least 1-epsilon
for some user-supplied epsilon. We describe several algorithms for epsilon-safe
planning based on conditional planners. The two conditional planners we discuss
are Peot and Smith's nonlinear conditional planner, CNLP, and our own linear
conditional planner, PLINTH. We present a straightforward extension to
conditional planners for which computing the necessary probabilities is simple,
employing a commonly-made but perhaps overly-strong independence assumption. We
also discuss a second approach to epsilon-safe planning which relaxes this
independence assumption, involving the incremental construction of a
probability dependence model in conjunction with the construction of the plan
graph.
",Epsilon-Safe Planning
"  We present a method for dynamically generating Bayesian networks from
knowledge bases consisting of first-order probability logic sentences. We
present a subset of probability logic sufficient for representing the class of
Bayesian networks with discrete-valued nodes. We impose constraints on the form
of the sentences that guarantee that the knowledge base contains all the
probabilistic information necessary to generate a network. We define the
concept of d-separation for knowledge bases and prove that a knowledge base
with independence conditions defined by d-separation is a complete
specification of a probability distribution. We present a network generation
algorithm that, given an inference problem in the form of a query Q and a set
of evidence E, generates a network to compute P(Q|E). We prove the algorithm to
be correct.
",Generating Bayesian Networks from Probability Logic Knowledge Bases
"  This paper discusses the problem of abstracting conditional probabilistic
actions. We identify two distinct types of abstraction: intra-action
abstraction and inter-action abstraction. We define what it means for the
abstraction of an action to be correct and then derive two methods of
intra-action abstraction and two methods of inter-action abstraction which are
correct according to this criterion. We illustrate the developed techniques by
applying them to actions described with the temporal action representation used
in the DRIPS decision-theoretic planner and we describe how the planner uses
abstraction to reduce the complexity of planning.
",Abstracting Probabilistic Actions
"  Heckerman (1993) defined causal independence in terms of a set of temporal
conditional independence statements. These statements formalized certain types
of causal interaction where (1) the effect is independent of the order that
causes are introduced and (2) the impact of a single cause on the effect does
not depend on what other causes have previously been applied. In this paper, we
introduce an equivalent a temporal characterization of causal independence
based on a functional representation of the relationship between causes and the
effect. In this representation, the interaction between causes and effect can
be written as a nested decomposition of functions. Causal independence can be
exploited by representing this decomposition in the belief network, resulting
in representations that are more efficient for inference than general causal
models. We present empirical results showing the benefits of a
causal-independence representation for belief-network inference.
",A New Look at Causal Independence
"  We describe algorithms for learning Bayesian networks from a combination of
user knowledge and statistical data. The algorithms have two components: a
scoring metric and a search procedure. The scoring metric takes a network
structure, statistical data, and a user's prior knowledge, and returns a score
proportional to the posterior probability of the network structure given the
data. The search procedure generates networks for evaluation by the scoring
metric. Our contributions are threefold. First, we identify two important
properties of metrics, which we call event equivalence and parameter
modularity. These properties have been mostly ignored, but when combined,
greatly simplify the encoding of a user's prior knowledge. In particular, a
user can express her knowledge-for the most part-as a single prior Bayesian
network for the domain. Second, we describe local search and annealing
algorithms to be used in conjunction with scoring metrics. In the special case
where each node has at most one parent, we show that heuristic search can be
replaced with a polynomial algorithm to identify the networks with the highest
score. Third, we describe a methodology for evaluating Bayesian-network
learning algorithms. We apply this approach to a comparison of metrics and
search procedures.
","Learning Bayesian Networks: The Combination of Knowledge and Statistical
  Data"
"  Most traditional models of uncertainty have focused on the associational
relationship among variables as captured by conditional dependence. In order to
successfully manage intelligent systems for decision making, however, we must
be able to predict the effects of actions. In this paper, we attempt to unite
two branches of research that address such predictions: causal modeling and
decision analysis. First, we provide a definition of causal dependence in
decision-analytic terms, which we derive from consequences of causal dependence
cited in the literature. Using this definition, we show how causal dependence
can be represented within an influence diagram. In particular, we identify two
inadequacies of an ordinary influence diagram as a representation for cause. We
introduce a special class of influence diagrams, called causal influence
diagrams, which corrects one of these problems, and identify situations where
the other inadequacy can be eliminated. In addition, we describe the
relationships between Howard Canonical Form and existing graphical
representations of cause.
",A Decision-Based View of Causality
"  On the one hand, classical terminological knowledge representation excludes
the possibility of handling uncertain concept descriptions involving, e.g.,
""usually true"" concept properties, generalized quantifiers, or exceptions. On
the other hand, purely numerical approaches for handling uncertainty in general
are unable to consider terminological knowledge. This paper presents the
language ACP which is a probabilistic extension of terminological logics and
aims at closing the gap between the two areas of research. We present the
formal semantics underlying the language ALUP and introduce the probabilistic
formalism that is based on classes of probabilities and is realized by means of
probabilistic constraints. Besides inferring implicitly existent probabilistic
relationships, the constraints guarantee terminological and probabilistic
consistency. Altogether, the new language ALUP applies to domains where both
term descriptions and uncertainty have to be handled.
",Probabilistic Description Logics
"  Qualitative and infinitesimal probability schemes are consistent with the
axioms of probability theory, but avoid the need for precise numerical
probabilities. Using qualitative probabilities could substantially reduce the
effort for knowledge engineering and improve the robustness of results. We
examine experimentally how well infinitesimal probabilities (the kappa-calculus
of Goldszmidt and Pearl) perform a diagnostic task - troubleshooting a car that
will not start by comparison with a conventional numerical belief network. We
found the infinitesimal scheme to be as good as the numerical scheme in
identifying the true fault. The performance of the infinitesimal scheme worsens
significantly for prior fault probabilities greater than 0.03. These results
suggest that infinitesimal probability methods may be of substantial practical
value for machine diagnosis with small prior fault probabilities.
","An Experimental Comparison of Numerical and Qualitative Probabilistic
  Reasoning"
"  Possibilistic logic, an extension of first-order logic, deals with
uncertainty that can be estimated in terms of possibility and necessity
measures. Syntactically, this means that a first-order formula is equipped with
a possibility degree or a necessity degree that expresses to what extent the
formula is possibly or necessarily true. Possibilistic resolution yields a
calculus for possibilistic logic which respects the semantics developed for
possibilistic logic. A drawback, which possibilistic resolution inherits from
classical resolution, is that it may not terminate if applied to formulas
belonging to decidable fragments of first-order logic. Therefore we propose an
alternative proof method for possibilistic logic. The main feature of this
method is that it completely abstracts from a concrete calculus but uses as
basic operation a test for classical entailment. We then instantiate
possibilistic logic with a terminological logic, which is a decidable subclass
o f first-order logic but nevertheless much more expressive than propositional
logic. This yields an extension of terminological logics towards the
representation of uncertain knowledge which is satisfactory from a semantic as
well as algorithmic point of view.
","An Alternative Proof Method for Possibilistic Logic and its Application
  to Terminological Logics"
"  We give an axiomatization of confidence transfer - a known conditioning
scheme - from the perspective of expectation-based inference in the sense of
Gardenfors and Makinson. Then, we use the notion of belief independence to
""filter out"" different proposal s of possibilistic conditioning rules, all are
variations of confidence transfer. Among the three rules that we consider, only
Dempster's rule of conditioning passes the test of supporting the notion of
belief independence. With the use of this conditioning rule, we then show that
we can use local computation for computing desired conditional marginal
possibilities of the joint possibility satisfying the given constraints. It
turns out that our local computation scheme is already proposed by Shenoy.
However, our intuitions are completely different from that of Shenoy. While
Shenoy just defines a local computation scheme that fits his framework of
valuation-based systems, we derive that local computation scheme from II(,8) =
tI(,8 I a) * II(a) and appropriate independence assumptions, just like how the
Bayesians derive their local computation scheme.
",Possibilistic Conditioning and Propagation
"  To coordinate with other agents in its environment, an agent needs models of
what the other agents are trying to do. When communication is impossible or
expensive, this information must be acquired indirectly via plan recognition.
Typical approaches to plan recognition start with a specification of the
possible plans the other agents may be following, and develop special
techniques for discriminating among the possibilities. Perhaps more desirable
would be a uniform procedure for mapping plans to general structures supporting
inference based on uncertain and incomplete observations. In this paper, we
describe a set of methods for converting plans represented in a flexible
procedural language to observation models represented as probabilistic belief
networks.
",The Automated Mapping of Plans for Plan Recognition
"  A logic is defined that allows to express information about statistical
probabilities and about degrees of belief in specific propositions. By
interpreting the two types of probabilities in one common probability space,
the semantics given are well suited to model the influence of statistical
information on the formation of subjective beliefs. Cross entropy minimization
is a key element in these semantics, the use of which is justified by showing
that the resulting logic exhibits some very reasonable properties.
",A Logic for Default Reasoning About Probabilities
"  The paper deals with optimality issues in connection with updating beliefs in
networks. We address two processes: triangulation and construction of junction
trees. In the first part, we give a simple algorithm for constructing an
optimal junction tree from a triangulated network. In the second part, we argue
that any exact method based on local calculations must either be less efficient
than the junction tree method, or it has an optimality problem equivalent to
that of triangulation.
",Optimal Junction Trees
"  We present an approach to the solution of decision problems formulated as
influence diagrams. This approach involves a special triangulation of the
underlying graph, the construction of a junction tree with special properties,
and a message passing algorithm operating on the junction tree for computation
of expected utilities and optimal decision policies.
",From Influence Diagrams to Junction Trees
"  The paper presents a method for reducing the computational complexity of
Bayesian networks through identification and removal of weak dependencies
(removal of links from the (moralized) independence graph). The removal of a
small number of links may reduce the computational complexity dramatically,
since several fill-ins and moral links may be rendered superfluous by the
removal. The method is described in terms of impact on the independence graph,
the junction tree, and the potential functions associated with these. An
empirical evaluation of the method using large real-world networks demonstrates
the applicability of the method. Further, the method, which has been
implemented in Hugin, complements the approximation method suggested by Jensen
& Andersen (1990).
","Reduction of Computational Complexity in Bayesian Networks through
  Removal of Weak Dependencies"
"  We explore the issue of refining an existent Bayesian network structure using
new data which might mention only a subset of the variables. Most previous
works have only considered the refinement of the network's conditional
probability parameters, and have not addressed the issue of refining the
network's structure. We develop a new approach for refining the network's
structure. Our approach is based on the Minimal Description Length (MDL)
principle, and it employs an adapted version of a Bayesian network learning
algorithm developed in our previous work. One of the adaptations required is to
modify the previous algorithm to account for the structure of the existent
network. The learning algorithm generates a partial network structure which can
then be used to improve the existent network. We also present experimental
evidence demonstrating the effectiveness of our approach.
",Using New Data to Refine a Bayesian Network
"  We view the syntax-based approaches to default reasoning as a model-based
diagnosis problem, where each source giving a piece of information is
considered as a component. It is formalized in the ATMS framework (each source
corresponds to an assumption). We assume then that all sources are independent
and ""fail"" with a very small probability. This leads to a probability
assignment on the set of candidates, or equivalently on the set of consistent
environments. This probability assignment induces a Dempster-Shafer belief
function which measures the probability that a proposition can be deduced from
the evidence. This belief function can be used in several different ways to
define a non-monotonic consequence relation. We study and compare these
consequence relations. The -case of prioritized knowledge bases is briefly
considered.
",Syntax-based Default Reasoning as Probabilistic Model-based Diagnosis
"  A model to represent spatial information is presented in this paper. It is
based on fuzzy constraints represented as fuzzy geometric relations that can be
hierarchically structured. The concept of spatial template is introduced to
capture the idea of interrelated objects in two-dimensional space. The
representation model is used to specify imprecise or vague information
consisting in relative locations and orientations of template objects. It is
shown in this paper how a template represented by this model can be matched
against a crisp situation to recognize a particular instance of this template.
Furthermore, the proximity measure (fuzzy measure) between the instance and the
template is worked out - this measure can be interpreted as a degree of
similarity. In this context, template recognition can be viewed as a case of
fuzzy pattern recognition. The results of this work have been implemented and
applied to a complex military problem from which this work originated.
",Fuzzy Geometric Relations to Represent Hierarchical Spatial Information
"  This paper examines the problem of constructing belief networks to evaluate
plans produced by an knowledge-based planner. Techniques are presented for
handling various types of complicating plan features. These include plans with
context-dependent consequences, indirect consequences, actions with
preconditions that must be true during the execution of an action,
contingencies, multiple levels of abstraction multiple execution agents with
partially-ordered and temporally overlapping actions, and plans which reference
specific times and time durations.
",Constructing Belief Networks to Evaluate Plans
"  This paper describes the best first search strategy used by U-Plan (Mansell
1993a), a planning system that constructs quantitatively ranked plans given an
incomplete description of an uncertain environment. U-Plan uses uncertain and
incomplete evidence de scribing the environment, characterizes it using a
Dempster-Shafer interval, and generates a set of possible world states. Plan
construction takes place in an abstraction hierarchy where strategic decisions
are made before tactical decisions. Search through this abstraction hierarchy
is guided by a quantitative measure (expected fulfillment) based on decision
theory. The search strategy is best first with the provision to update expected
fulfillment and review previous decisions in the light of planning
developments. U-Plan generates multiple plans for multiple possible worlds, and
attempts to use existing plans for new world situations. A super-plan is then
constructed, based on merging the set of plans and appropriately timed
knowledge acquisition operators, which are used to decide between plan
alternatives during plan execution.
",Operator Selection While Planning Under Uncertainty
"  In this paper we describe a framework for model-based diagnosis of dynamic
systems, which extends previous work in this field by using and expressing
temporal uncertainty in the form of qualitative interval relations a la Allen.
Based on a logical framework extended by qualitative and quantitative temporal
constraints we show how to describe behavioral models (both consistency- and
abductive-based), discuss how to use abstract observations and show how
abstract temporal diagnoses are computed. This yields an expressive framework,
which allows the representation of complex temporal behavior allowing us to
represent temporal uncertainty. Due to its abstraction capabilities computation
is made independent of the number of observations and time points in a temporal
setting. An example of hepatitis diagnosis is used throughout the paper.
",Model-Based Diagnosis with Qualitative Temporal Uncertainty
"  Certain classes of problems, including perceptual data understanding,
robotics, discovery, and learning, can be represented as incremental,
dynamically constructed belief networks. These automatically constructed
networks can be dynamically extended and modified as evidence of new
individuals becomes available. The main result of this paper is the incremental
extension of the singly connected polytree network in such a way that the
network retains its singly connected polytree structure after the changes. The
algorithm is deterministic and is guaranteed to have a complexity of single
node addition that is at most of order proportional to the number of nodes (or
size) of the network. Additional speed-up can be achieved by maintaining the
path information. Despite its incremental and dynamic nature, the algorithm can
also be used for probabilistic inference in belief networks in a fashion
similar to other exact inference algorithms.
",Incremental Dynamic Construction of Layered Polytree Networks
"  We present a symbolic machinery that admits both probabilistic and causal
information about a given domain and produces probabilistic statements about
the effect of actions and the impact of observations. The calculus admits two
types of conditioning operators: ordinary Bayes conditioning, P(y|X = x), which
represents the observation X = x, and causal conditioning, P(y|do(X = x)), read
the probability of Y = y conditioned on holding X constant (at x) by deliberate
action. Given a mixture of such observational and causal sentences, together
with the topology of the causal graph, the calculus derives new conditional
probabilities of both types, thus enabling one to quantify the effects of
actions (and policies) from partially specified knowledge bases, such as
Bayesian networks in which some conditional probabilities may not be available.
",A Probabilistic Calculus of Actions
"  This paper describes a novel approach to planning which takes advantage of
decision theory to greatly improve robustness in an uncertain environment. We
present an algorithm which computes conditional plans of maximum expected
utility. This algorithm relies on a representation of the search space as an
AND/OR tree and employs a depth-limit to control computation costs. A numeric
robustness factor, which parameterizes the utility function, allows the user to
modulate the degree of risk-aversion employed by the planner. Via a look-ahead
search, the planning algorithm seeks to find an optimal plan using expected
utility as its optimization criterion. We present experimental results obtained
by applying our algorithm to a non-deterministic extension of the blocks world
domain. Our results demonstrate that the robustness factor governs the degree
of risk embodied in the conditional plans computed by our algorithm.
",Robust Planning in Uncertain Environments
"  This paper examines methods of decision making that are able to accommodate
limitations on both the form in which uncertainty pertaining to a decision
problem can be realistically represented and the amount of computing time
available before a decision must be made. The methods are anytime algorithms in
the sense of Boddy and Dean 1991. Techniques are presented for use with Frisch
and Haddawy's [1992] anytime deduction system, with an anytime adaptation of
Nilsson's [1986] probabilistic logic, and with a probabilistic database model.
",Anytime Decision Making with Imprecise Probabilities
"  We present several techniques for knowledge engineering of large belief
networks (BNs) based on the our experiences with a network derived from a large
medical knowledge base. The noisyMAX, a generalization of the noisy-OR gate, is
used to model causal in dependence in a BN with multi-valued variables. We
describe the use of leak probabilities to enforce the closed-world assumption
in our model. We present Netview, a visualization tool based on causal
independence and the use of leak probabilities. The Netview software allows
knowledge engineers to dynamically view sub-networks for knowledge engineering,
and it provides version control for editing a BN. Netview generates
sub-networks in which leak probabilities are dynamically updated to reflect the
missing portions of the network.
",Knowledge Engineering for Large Belief Networks
"  While influence diagrams have many advantages as a representation framework
for Bayesian decision problems, they have a serious drawback in handling
asymmetric decision problems. To be represented in an influence diagram, an
asymmetric decision problem must be symmetrized. A considerable amount of
unnecessary computation may be involved when a symmetrized influence diagram is
evaluated by conventional algorithms. In this paper we present an approach for
avoiding such unnecessary computation in influence diagram evaluation.
",Solving Asymmetric Decision Problems with Influence Diagrams
"  Bayesian Belief Networks (BBNs) are a powerful formalism for reasoning under
uncertainty but bear some severe limitations: they require a large amount of
information before any reasoning process can start, they have limited
contradiction handling capabilities, and their ability to provide explanations
for their conclusion is still controversial. There exists a class of reasoning
systems, called Truth Maintenance Systems (TMSs), which are able to deal with
partially specified knowledge, to provide well-founded explanation for their
conclusions, and to detect and handle contradictions. TMSs incorporating
measure of uncertainty are called Belief Maintenance Systems (BMSs). This paper
describes how a BMS based on probabilistic logic can be applied to BBNs, thus
introducing a new class of BBNs, called Ignorant Belief Networks, able to
incrementally deal with partially specified conditional dependencies, to
provide explanations, and to detect and handle contradictions.
",Belief Maintenance in Bayesian Networks
"  Independence-based (IB) assignments to Bayesian belief networks were
originally proposed as abductive explanations. IB assignments assign fewer
variables in abductive explanations than do schemes assigning values to all
evidentially supported variables. We use IB assignments to approximate marginal
probabilities in Bayesian belief networks. Recent work in belief updating for
Bayes networks attempts to approximate posterior probabilities by finding a
small number of the highest probability complete (or perhaps evidentially
supported) assignments. Under certain assumptions, the probability mass in the
union of these assignments is sufficient to obtain a good approximation. Such
methods are especially useful for highly-connected networks, where the maximum
clique size or the cutset size make the standard algorithms intractable. Since
IB assignments contain fewer assigned variables, the probability mass in each
assignment is greater than in the respective complete assignment. Thus, fewer
IB assignments are sufficient, and a good approximation can be obtained more
efficiently. IB assignments can be used for efficiently approximating posterior
node probabilities even in cases which do not obey the rather strict skewness
assumptions used in previous research. Two algorithms for finding the high
probability IB assignments are suggested: one by doing a best-first heuristic
search, and another by special-purpose integer linear programming. Experimental
results show that this approach is feasible for highly connected belief
networks.
","Belief Updating by Enumerating High-Probability Independence-Based
  Assignments"
"  In this paper we propose a new approach to probabilistic inference on belief
networks, global conditioning, which is a simple generalization of Pearl's
(1986b) method of loopcutset conditioning. We show that global conditioning, as
well as loop-cutset conditioning, can be thought of as a special case of the
method of Lauritzen and Spiegelhalter (1988) as refined by Jensen et al (199Oa;
1990b). Nonetheless, this approach provides new opportunities for parallel
processing and, in the case of sequential processing, a tradeoff of time for
memory. We also show how a hybrid method (Suermondt and others 1990) combining
loop-cutset conditioning with Jensen's method can be viewed within our
framework. By exploring the relationships between these methods, we develop a
unifying framework in which the advantages of each approach can be combined
successfully.
",Global Conditioning for Probabilistic Inference in Belief Networks
"  We construct the belief function that quantifies the agent, beliefs about
which event of Q will occurred when he knows that the event is selected by a
chance set-up and that the probability function associated to the chance set up
is only partially known.
",Belief Induced by the Partial Knowledge of the Probabilities
"  Over time, there have hen refinements in the way that probability
distributions are used for representing beliefs. Models which rely on single
probability distributions depict a complete ordering among the propositions of
interest, yet human beliefs are sometimes not completely ordered. Non-singleton
sets of probability distributions can represent partially ordered beliefs.
Convex sets are particularly convenient and expressive, but it is known that
there are reasonable patterns of belief whose faithful representation require
less restrictive sets. The present paper shows that prior ignorance about three
or more exclusive alternatives and the emergence of partially ordered beliefs
when evidence is obtained defy representation by any single set of
distributions, but yield to a representation baud on several uts. The partial
order is shown to be a partial qualitative probability which shares some
intuitively appealing attributes with probability distributions.
","Ignorance and the Expressiveness of Single- and Set-Valued Probability
  Models of Belief"
"  Model-based diagnosis reasons backwards from a functional schematic of a
system to isolate faults given observations of anomalous behavior. We develop a
fully probabilistic approach to model based diagnosis and extend it to support
hierarchical models. Our scheme translates the functional schematic into a
Bayesian network and diagnostic inference takes place in the Bayesian network.
A Bayesian network diagnostic inference algorithm is modified to take advantage
of the hierarchy to give computational gains.
",A Probabilistic Approach to Hierarchical Model-based Diagnosis
"  The semigraphoid closure of every couple of CI-statements (GI=conditional
independence) is a stochastic CI-model. As a consequence of this result it is
shown that every probabilistically sound inference rule for CI-model, having at
most two antecedents, is derivable from the semigraphoid inference rules. This
justifies the use of semigraphoids as approximations of stochastic CI-models in
probabilistic reasoning. The list of all 19 potential dominant elements of the
mentioned semigraphoid closure is given as a byproduct.
","Semigraphoids Are Two-Antecedental Approximations of Stochastic
  Conditional Independence Models"
"  System Z+ [Goldszmidt and Pearl, 1991, Goldszmidt, 1992] is a formalism for
reasoning with normality defaults of the form ""typically if phi then + (with
strength cf)"" where 6 is a positive integer. The system has a critical
shortcoming in that it does not sanction inheritance across exceptional
subclasses. In this paper we propose an extension to System Z+ that rectifies
this shortcoming by extracting additional conditions between worlds from the
defaults database. We show that the additional constraints do not change the
notion of the consistency of a database. We also make comparisons with
competing default reasoning systems.
",Exceptional Subclasses in Qualitative Probability
"  By analyzing the relationships among chance, weight of evidence and degree of
beliefwe show that the assertion ""probability functions are special cases of
belief functions"" and the assertion ""Dempster's rule can be used to combine
belief functions based on distinct bodies of evidence"" together lead to an
inconsistency in Dempster-Shafer theory. To solve this problem, we must reject
some fundamental postulates of the theory. We introduce a new approach for
uncertainty management that shares many intuitive ideas with D-S theory, while
avoiding this problem.
",A Defect in Dempster-Shafer Theory
"  One important factor determining the computational complexity of evaluating a
probabilistic network is the cardinality of the state spaces of the nodes. By
varying the granularity of the state spaces, one can trade off accuracy in the
result for computational efficiency. We present an anytime procedure for
approximate evaluation of probabilistic networks based on this idea. On
application to some simple networks, the procedure exhibits a smooth
improvement in approximation quality as computation time increases. This
suggests that state-space abstraction is one more useful control parameter for
designing real-time probabilistic reasoners.
",State-space Abstraction for Anytime Evaluation of Probabilistic Networks
"  Probability measures by themselves, are known to be inappropriate for
modeling the dynamics of plain belief and their excessively strong
measurability constraints make them unsuitable for some representational tasks,
e.g. in the context of firstorder knowledge. In this paper, we are therefore
going to look for possible alternatives and extensions. We begin by delimiting
the general area of interest, proposing a minimal list of assumptions to be
satisfied by any reasonable quasi-probabilistic valuation concept. Within this
framework, we investigate two particularly interesting kinds of quasi-measures
which are not or much less affected by the traditional problems. * Ranking
measures, which generalize Spohn-type and possibility measures. * Cumulative
measures, which combine the probabilistic and the ranking philosophy, allowing
thereby a fine-grained account of static and dynamic belief.
",General Belief Measures
"  We take a general approach to uncertainty on product spaces, and give
sufficient conditions for the independence structures of uncertainty measures
to satisfy graphoid properties. Since these conditions are arguably more
intuitive than some of the graphoid properties, they can be viewed as
explanations why probability and certain other formalisms generate graphoids.
The conditions include a sufficient condition for the Intersection property
which can still apply even if there is a strong logical relations hip between
the variables. We indicate how these results can be used to produce theories of
qualitative conditional probability which are semi-graphoids and graphoids.
",Generating Graphoids from Generalised Conditional Probability
"  This paper studies the connection between probabilistic conditional
independence in uncertain reasoning and data dependency in relational
databases. As a demonstration of the usefulness of this preliminary
investigation, an alternate proof is presented for refuting the conjecture
suggested by Pearl and Paz that probabilistic conditional independencies have a
complete axiomatization.
",On Axiomatization of Probabilistic Conditional Independencies
"  In the existing evidential networks with belief functions, the relations
among the variables are always represented by joint belief functions on the
product space of the involved variables. In this paper, we use conditional
belief functions to represent such relations in the network and show some
relations of these two kinds of representations. We also present a propagation
algorithm for such networks. By analyzing the properties of some special
evidential networks with conditional belief functions, we show that the
reasoning process can be simplified in such kinds of networks.
",Evidential Reasoning with Conditional Belief Functions
"  It is well known that conditional independence can be used to factorize a
joint probability into a multiplication of conditional probabilities. This
paper proposes a constructive definition of inter-causal independence, which
can be used to further factorize a conditional probability. An inference
algorithm is developed, which makes use of both conditional independence and
inter-causal independence to reduce inference complexity in Bayesian networks.
",Inter-causal Independence and Heterogeneous Factorization
"  We address the problem of causal interpretation of the graphical structure of
Bayesian belief networks (BBNs). We review the concept of causality explicated
in the domain of structural equations models and show that it is applicable to
BBNs. In this view, which we call mechanism-based, causality is defined within
models and causal asymmetries arise when mechanisms are placed in the context
of a system. We lay the link between structural equations models and BBNs
models and formulate the conditions under which the latter can be given causal
interpretation.
",Causality in Bayesian Belief Networks
"  The primary theme of this investigation is a decision theoretic account of
conditional ought statements (e.g., ""You ought to do A, if C"") that rectifies
glaring deficiencies in classical deontic logic. The resulting account forms a
sound basis for qualitative decision theory, thus providing a framework for
qualitative planning under uncertainty. In particular, we show that adding
causal relationships (in the form of a single graph) as part of an epistemic
state is sufficient to facilitate the analysis of action sequences, their
consequences, their interaction with observations, their expected utilities
and, hence, the synthesis of plans and strategies under uncertainty.
",From Conditional Oughts to Qualitative Decision Theory
"  We have developed a general Bayesian algorithm for determining the
coordinates of points in a three-dimensional space. The algorithm takes as
input a set of probabilistic constraints on the coordinates of the points, and
an a priori distribution for each point location. The output is a
maximum-likelihood estimate of the location of each point. We use the extended,
iterated Kalman filter, and add a search heuristic for optimizing its solution
under nonlinear conditions. This heuristic is based on the same principle as
the simulated annealing heuristic for other optimization problems. Our method
uses any probabilistic constraints that can be expressed as a function of the
point coordinates (for example, distance, angles, dihedral angles, and
planarity). It assumes that all constraints have Gaussian noise. In this paper,
we describe the algorithm and show its performance on a set of synthetic data
to illustrate its convergence properties, and its applicability to domains such
ng molecular structure determination.
","A Probabilistic Algorithm for Calculating Structure: Borrowing from
  Simulated Annealing"
"  The problems associated with scaling involve active and challenging research
topics in the area of artificial intelligence. The purpose is to solve real
world problems by means of AI technologies, in cases where the complexity of
representation of the real world problem is potentially combinatorial. In this
paper, we present a novel approach to cope with the scaling issues in Bayesian
belief networks for ship classification. The proposed approach divides the
conceptual model of a complex ship classification problem into a set of small
modules that work together to solve the classification problem while preserving
the functionality of the original model. The possible ways of explaining sensor
returns (e.g., the evidence) for some features, such as portholes along the
length of a ship, are sometimes combinatorial. Thus, using an exhaustive
approach, which entails the enumeration of all possible explanations, is
impractical for larger problems. We present a network structure (referred to as
Sequential Decomposition, SD) in which each observation is associated with a
set of legitimate outcomes which are consistent with the explanation of each
observed piece of evidence. The results show that the SD approach allows one to
represent feature-observation relations in a manageable way and achieve the
same explanatory power as an exhaustive approach.
","A Study of Scaling Issues in Bayesian Belief Networks for Ship
  Classification"
"  This paper addresses the tradeoffs which need to be considered in reasoning
using probabilistic network representations, such as Influence Diagrams (IDs).
In particular, we examine the tradeoffs entailed in using Temporal Influence
Diagrams (TIDs) which adequately capture the temporal evolution of a dynamic
system without prohibitive data and computational requirements. Three
approaches for TID construction which make different tradeoffs are examined:
(1) tailoring the network at each time interval to the data available (rather
then just copying the original Bayes Network for all time intervals); (2)
modeling the evolution of a parsimonious subset of variables (rather than all
variables); and (3) model selection approaches, which seek to minimize some
measure of the predictive accuracy of the model without introducing too many
parameters, which might cause ""overfitting"" of the model. Methods of evaluating
the accuracy/efficiency of the tradeoffs are proposed.
",Tradeoffs in Constructing and Evaluating Temporal Influence Diagrams
"  Influence diagrams are ideal knowledge representations for Bayesian
statistical models. However, these diagrams are difficult for end users to
interpret and to manipulate. We present a user-based architecture that enables
end users to create and to manipulate the knowledge representation. We use the
problem of physicians' interpretation of two-arm parallel randomized clinical
trials (TAPRCT) to illustrate the architecture and its use. There are three
primary data structures. Elements of statistical models are encoded as
subgraphs of a restricted class of influence diagram. The interpretations of
those elements are mapped into users' language in a domain-specific, user-based
semantic interface, called a patient-flow diagram, in the TAPRCT problem.
Pennitted transformations of the statistical model that maintain the semantic
relationships of the model are encoded in a metadata-state diagram, called the
cohort-state diagram, in the TAPRCT problem. The algorithm that runs the system
uses modular actions called construction steps. This framework has been
implemented in a system called THOMAS, that allows physicians to interpret the
data reported from a TAPRCT.
",End-User Construction of Influence Diagrams for Bayesian Statistics
"  Dynamic network models (DNMs) are belief networks for temporal reasoning. The
DNM methodology combines techniques from time series analysis and probabilistic
reasoning to provide (1) a knowledge representation that integrates
noncontemporaneous and contemporaneous dependencies and (2) methods for
iteratively refining these dependencies in response to the effects of exogenous
influences. We use belief-network inference algorithms to perform forecasting,
control, and discrete event simulation on DNMs. The belief network formulation
allows us to move beyond the traditional assumptions of linearity in the
relationships among time-dependent variables and of normality in their
probability distributions. We demonstrate the DNM methodology on an important
forecasting problem in medicine. We conclude with a discussion of how the
methodology addresses several limitations found in traditional time series
analyses.
",Forecasting Sleep Apnea with Dynamic Network Models
"  This paper describes a normative system design that incorporates diagnosis,
dynamic evolution, decision making, and information gathering. A single
influence diagram demonstrates the design's coherence, yet each activity is
more effectively modeled and evaluated separately. Application to offshore oil
platforms illustrates the design. For this application, the normative system is
embedded in a real-time expert system.
",Normative Engineering Risk Management Systems
"  We compare the diagnostic accuracy of three diagnostic inference models: the
simple Bayes model, the multimembership Bayes model, which is isomorphic to the
parallel combination function in the certainty-factor model, and a model that
incorporates the noisy OR-gate interaction. The comparison is done on 20
clinicopathological conference (CPC) cases from the American Journal of
Medicine-challenging cases describing actual patients often with multiple
disorders. We find that the distributions produced by the noisy OR model agree
most closely with the gold-standard diagnoses, although substantial differences
exist between the distributions and the diagnoses. In addition, we find that
the multimembership Bayes model tends to significantly overestimate the
posterior probabilities of diseases, whereas the simple Bayes model tends to
significantly underestimate the posterior probabilities. Our results suggest
that additional work to refine the noisy OR model for internal medicine will be
worthwhile.
",Diagnosis of Multiple Faults: A Sensitivity Analysis
"  The inherent intractability of probabilistic inference has hindered the
application of belief networks to large domains. Noisy OR-gates [30] and
probabilistic similarity networks [18, 17] escape the complexity of inference
by restricting model expressiveness. Recent work in the application of
belief-network models to time-series analysis and forecasting [9, 10] has given
rise to the additive belief network model (ABNM). We (1) discuss the nature and
implications of the approximations made by an additive decomposition of a
belief network, (2) show greater efficiency in the induction of additive models
when available data are scarce, (3) generalize probabilistic inference
algorithms to exploit the additive decomposition of ABNMs, (4) show greater
efficiency of inference, and (5) compare results on inference with a simple
additive belief network.
",Additive Belief-Network Models
"  Spiegelhalter and Lauritzen [15] studied sequential learning in Bayesian
networks and proposed three models for the representation of conditional
probabilities. A forth model, shown here, assumes that the parameter
distribution is given by a product of Gaussian functions and updates them from
the _ and _r messages of evidence propagation. We also generalize the noisy
OR-gate for multivalued variables, develop the algorithm to compute probability
in time proportional to the number of parents (even in networks with loops) and
apply the learning model to this gate.
",Parameter Adjustment in Bayes Networks. The generalized noisy OR-gate
"  Relational models for diagnosis are based on a direct description of the
association between disorders and manifestations. This type of model has been
specially used and developed by Reggia and his co-workers in the late eighties
as a basic starting point for approaching diagnosis problems. The paper
proposes a new relational model which includes Reggia's model as a particular
case and which allows for a more expressive representation of the observations
and of the manifestations associated with disorders. The model distinguishes,
i) between manifestations which are certainly absent and those which are not
(yet) observed, and ii) between manifestations which cannot be caused by a
given disorder and manifestations for which we do not know if they can or
cannot be caused by this disorder. This new model, which can handle uncertainty
in a non-probabilistic way, is based on possibility theory and so-called
twofold fuzzy sets, previously introduced by the authors.
","A fuzzy relation-based extension of Reggia's relational model for
  diagnosis handling uncertain and incomplete information"
"  From an inconsistent database non-trivial arguments may be constructed both
for a proposition, and for the contrary of that proposition. Therefore,
inconsistency in a logical database causes uncertainty about which conclusions
to accept. This kind of uncertainty is called logical uncertainty. We define a
concept of ""acceptability"", which induces a means for differentiating
arguments. The more acceptable an argument, the more confident we are in it. A
specific interest is to use the acceptability classes to assign linguistic
qualifiers to propositions, such that the qualifier assigned to a propositions
reflects its logical uncertainty. A more general interest is to understand how
classes of acceptability can be defined for arguments constructed from an
inconsistent database, and how this notion of acceptability can be devised to
reflect different criteria. Whilst concentrating on the aspects of assigning
linguistic qualifiers to propositions, we also indicate the more general
significance of the notion of acceptability.
",Dialectic Reasoning with Inconsistent Information
"  I introduce a temporal belief-network representation of causal independence
that a knowledge engineer can use to elicit probabilistic models. Like the
current, atemporal belief-network representation of causal independence, the
new representation makes knowledge acquisition tractable. Unlike the atemproal
representation, however, the temporal representation can simplify inference,
and does not require the use of unobservable variables. The representation is
less general than is the atemporal representation, but appears to be useful for
many practical applications.
",Causal Independence for Knowledge Acquisition and Inference
"  We take a utility-based approach to categorization. We construct
generalizations about events and actions by considering losses associated with
failing to distinguish among detailed distinctions in a decision model. The
utility-based methods transform detailed states of the world into more abstract
categories comprised of disjunctions of the states. We show how we can cluster
distinctions into groups of distinctions at progressively higher levels of
abstraction, and describe rules for decision making with the abstractions. The
techniques introduce a utility-based perspective on the nature of concepts, and
provide a means of simplifying decision models used in automated reasoning
systems. We demonstrate the techniques by describing the capabilities and
output of TUBA, a program for utility-based abstraction.
",Utility-Based Abstraction and Categorization
"  When eliciting probability models from experts, knowledge engineers may
compare the results of the model with expert judgment on test scenarios, then
adjust model parameters to bring the behavior of the model more in line with
the expert's intuition. This paper presents a methodology for analytic
computation of sensitivity values to measure the impact of small changes in a
network parameter on a target probability value or distribution. These values
can be used to guide knowledge elicitation. They can also be used in a gradient
descent algorithm to estimate parameter values that maximize a measure of
goodness-of-fit to both local and holistic probability assessments.
",Sensitivity Analysis for Probability Assessments in Bayesian Networks
"  Causal Models are like Dependency Graphs and Belief Nets in that they provide
a structure and a set of assumptions from which a joint distribution can, in
principle, be computed. Unlike Dependency Graphs, Causal Models are models of
hierarchical and/or parallel processes, rather than models of distributions
(partially) known to a model builder through some sort of gestalt. As such,
Causal Models are more modular, easier to build, more intuitive, and easier to
understand than Dependency Graph Models. Causal Models are formally defined and
Dependency Graph Models are shown to be a special case of them. Algorithms
supporting inference are presented. Parsimonious methods for eliciting
dependent probabilities are presented.
",Causal Modeling
"  One topic that is likely to attract an increasing amount of attention within
the Knowledge-base systems research community is the coordination of
information provided by multiple experts. We envision a situation in which
several experts independently encode information as belief networks. A
potential user must then coordinate the conclusions and recommendations of
these networks to derive some sort of consensus. One approach to such a
consensus is the fusion of the contributed networks into a single, consensus
model prior to the consideration of any case-specific data (specific
observations, test results). This approach requires two types of combination
procedures, one for probabilities, and one for graphs. Since the combination of
probabilities is relatively well understood, the key barriers to this approach
lie in the realm of graph theory. This paper provides formal definitions of
some of the operations necessary to effect the necessary graphical
combinations, and provides complexity analyses of these procedures. The paper's
key result is that most of these operations are NP-hard, and its primary
message is that the derivation of ?good? consensus networks must be done
heuristically.
",Some Complexity Considerations in the Combination of Belief Networks
"  This paper identifies and solves a new optimization problem: Given a belief
network (BN) and a target ordering on its variables, how can we efficiently
derive its minimal I-map whose arcs are consistent with the target ordering? We
present three solutions to this problem, all of which lead to directed acyclic
graphs based on the original BN's recursive basis relative to the specified
ordering (such a DAG is sometimes termed the boundary DAG drawn from the given
BN relative to the said ordering [5]). Along the way, we also uncover an
important general principal about arc reversals: when reordering a BN according
to some target ordering, (while attempting to minimize the number of arcs
generated), the sequence of arc reversals should follow the topological
ordering induced by the original belief network's arcs to as great an extent as
possible. These results promise to have a significant impact on the derivation
of consensus models, as well as on other algorithms that require the
reconfiguration and/or combination of BN's.
","Deriving a Minimal I-map of a Belief Network Relative to a Target
  Ordering of its Nodes"
"  Probabilistic conceptual network is a knowledge representation scheme
designed for reasoning about concepts and categorical abstractions in
utility-based categorization. The scheme combines the formalisms of abstraction
and inheritance hierarchies from artificial intelligence, and probabilistic
networks from decision analysis. It provides a common framework for
representing conceptual knowledge, hierarchical knowledge, and uncertainty. It
facilitates dynamic construction of categorization decision models at varying
levels of abstraction. The scheme is applied to an automated machining problem
for reasoning about the state of the machine at varying levels of abstraction
in support of actions for maintaining competitiveness of the plant.
","Probabilistic Conceptual Network: A Belief Representation Scheme for
  Utility-Based Categorization"
"  We investigate the value of extending the completeness of a decision model
along different dimensions of refinement. Specifically, we analyze the expected
value of quantitative, conceptual, and structural refinement of decision
models. We illustrate the key dimensions of refinement with examples. The
analyses of value of model refinement can be used to focus the attention of an
analyst or an automated reasoning system on extensions of a decision model
associated with the greatest expected value.
","Reasoning about the Value of Decision-Model Refinement: Methods and
  Application"
"  Problems of probabilistic inference and decision making under uncertainty
commonly involve continuous random variables. Often these are discretized to a
few points, to simplify assessments and computations. An alternative
approximation is to fit analytically tractable continuous probability
distributions. This approach has potential simplicity and accuracy advantages,
especially if variables can be transformed first. This paper shows how a
minimum relative entropy criterion can drive both transformation and fitting,
illustrating with a power and logarithm family of transformations and mixtures
of Gaussian (normal) distributions, which allow use of efficient influence
diagram methods. The fitting procedure in this case is the well-known EM
algorithm. The selection of the number of components in a fitted mixture
distribution is automated with an objective that trades off accuracy and
computational cost.
","Mixtures of Gaussians and Minimum Relative Entropy Techniques for
  Modeling Continuous Uncertainties"
"  Valuation networks have been proposed as graphical representations of
valuation-based systems (VBSs). The VBS framework is able to capture many
uncertainty calculi including probability theory, Dempster-Shafer's
belief-function theory, Spohn's epistemic belief theory, and Zadeh's
possibility theory. In this paper, we show how valuation networks encode
conditional independence relations. For the probabilistic case, the class of
probability models encoded by valuation networks includes undirected graph
models, directed acyclic graph models, directed balloon graph models, and
recursive causal graph models.
",Valuation Networks and Conditional Independence
"  Relevance-based explanation is a scheme in which partial assignments to
Bayesian belief network variables are explanations (abductive conclusions). We
allow variables to remain unassigned in explanations as long as they are
irrelevant to the explanation, where irrelevance is defined in terms of
statistical independence. When multiple-valued variables exist in the system,
especially when subsets of values correspond to natural types of events, the
over specification problem, alleviated by independence-based explanation,
resurfaces. As a solution to that, as well as for addressing the question of
explanation specificity, it is desirable to collapse such a subset of values
into a single value on the fly. The equivalent method, which is adopted here,
is to generalize the notion of assignments to allow disjunctive assignments. We
proceed to define generalized independence based explanations as maximum
posterior probability independence based generalized assignments (GIB-MAPs).
GIB assignments are shown to have certain properties that ease the design of
algorithms for computing GIB-MAPs. One such algorithm is discussed here, as
well as suggestions for how other algorithms may be adapted to compute
GIB-MAPs. GIB-MAP explanations still suffer from instability, a problem which
may be addressed using ?approximate? conditional independence as a condition
for irrelevance.
",Relevant Explanations: Allowing Disjunctive Assignments
"  The Noisy-Or model is convenient for describing a class of uncertain
relationships in Bayesian networks [Pearl 1988]. Pearl describes the Noisy-Or
model for Boolean variables. Here we generalize the model to nary input and
output variables and to arbitrary functions other than the Boolean OR function.
This generalization is a useful modeling aid for construction of Bayesian
networks. We illustrate with some examples including digital circuit diagnosis
and network reliability analysis.
",A Generalization of the Noisy-Or Model
"  We present a mechanism for constructing graphical models, specifically
Bayesian networks, from a knowledge base of general probabilistic information.
The unique feature of our approach is that it uses a powerful first-order
probabilistic logic for expressing the general knowledge base. This logic
allows for the representation of a wide range of logical and probabilistic
information. The model construction procedure we propose uses notions from
direct inference to identify pieces of local statistical information from the
knowledge base that are most appropriate to the particular event we want to
reason about. These pieces are composed to generate a joint probability
distribution specified as a Bayesian network. Although there are fundamental
difficulties in dealing with fully general knowledge, our procedure is
practical for quite rich knowledge bases and it supports the construction of a
far wider range of networks than allowed for by current template technology.
","Using First-Order Probability Logic for the Construction of Bayesian
  Networks"
"  PAGODA (Probabilistic Autonomous Goal-Directed Agent) is a model for
autonomous learning in probabilistic domains [desJardins, 1992] that
incorporates innovative techniques for using the agent's existing knowledge to
guide and constrain the learning process and for representing, reasoning with,
and learning probabilistic knowledge. This paper describes the probabilistic
representation and inference mechanism used in PAGODA. PAGODA forms theories
about the effects of its actions and the world state on the environment over
time. These theories are represented as conditional probability distributions.
A restriction is imposed on the structure of the theories that allows the
inference mechanism to find a unique predicted distribution for any action and
world state description. These restricted theories are called uniquely
predictive theories. The inference mechanism, Probability Combination using
Independence (PCI), uses minimal independence assumptions to combine the
probabilities in a theory to make probabilistic predictions.
","Representing and Reasoning With Probabilistic Knowledge: A Bayesian
  Approach"
"  One of the most difficult aspects of modeling complex dilemmas in
decision-analytic terms is composing a diagram of relevance relations from a
set of domain concepts. Decision models in domains such as medicine, however,
exhibit certain prototypical patterns that can guide the modeling process.
Medical concepts can be classified according to semantic types that have
characteristic positions and typical roles in an influence-diagram model. We
have developed a graph-grammar production system that uses such inherent
interrelationships among medical terms to facilitate the modeling of medical
decisions.
",Graph-Grammar Assistance for Automated Generation of Influence Diagrams
"  In previous work we developed a method of learning Bayesian Network models
from raw data. This method relies on the well known minimal description length
(MDL) principle. The MDL principle is particularly well suited to this task as
it allows us to tradeoff, in a principled way, the accuracy of the learned
network against its practical usefulness. In this paper we present some new
results that have arisen from our work. In particular, we present a new local
way of computing the description length. This allows us to make significant
improvements in our search algorithm. In addition, we modify our algorithm so
that it can take into account partial domain information that might be provided
by a domain expert. The local computation of description length also opens the
door for local refinement of an existent network. The feasibility of our
approach is demonstrated by experiments involving networks of a practical size.
",Using Causal Information and Local Measures to Learn Bayesian Networks
"  As belief networks are used to model increasingly complex situations, the
need to automatically construct them from large databases will become
paramount. This paper concentrates on solving a part of the belief network
induction problem: that of learning the quantitative structure (the conditional
probabilities), given the qualitative structure. In particular, a theory is
presented that shows how to propagate inference distributions in a belief
network, with the only assumption being that the given qualitative structure is
correct. Most inference algorithms must make at least this assumption. The
theory is based on four network transformations that are sufficient for any
inference in a belief network. Furthermore, the claim is made that contrary to
popular belief, error will not necessarily grow as the inference chain grows.
Instead, for QBN belief nets induced from large enough samples, the error is
more likely to decrease as the size of the inference chain increases.
",Minimal Assumption Distribution Propagation in Belief Networks
"  Previous algorithms for the construction of Bayesian belief network
structures from data have been either highly dependent on conditional
independence (CI) tests, or have required an ordering on the nodes to be
supplied by the user. We present an algorithm that integrates these two
approaches - CI tests are used to generate an ordering on the nodes from the
database which is then used to recover the underlying Bayesian network
structure using a non CI based method. Results of preliminary evaluation of the
algorithm on two networks (ALARM and LED) are presented. We also discuss some
algorithm performance issues and open problems.
","An Algorithm for the Construction of Bayesian Network Structures from
  Data"
"  This paper addresses learning stochastic rules especially on an
inter-attribute relation based on a Minimum Description Length (MDL) principle
with a finite number of examples, assuming an application to the design of
intelligent relational database systems. The stochastic rule in this paper
consists of a model giving the structure like the dependencies of a Bayesian
Belief Network (BBN) and some stochastic parameters each indicating a
conditional probability of an attribute value given the state determined by the
other attributes' values in the same record. Especially, we propose the
extended version of the algorithm of Chow and Liu in that our learning
algorithm selects the model in the range where the dependencies among the
attributes are represented by some general plural number of trees.
","A Construction of Bayesian Networks from Databases Based on an MDL
  Principle"
"  Numerous methods for probabilistic reasoning in large, complex belief or
decision networks are currently being developed. There has been little research
on automating the dynamic, incremental construction of decision models. A
uniform value-driven method of decision model construction is proposed for the
hierarchical complete diagnosis. Hierarchical complete diagnostic reasoning is
formulated as a stochastic process and modeled using influence diagrams. Given
observations, this method creates decision models in order to obtain the best
actions sequentially for locating and repairing a fault at minimum cost. This
method construct decision models incrementally, interleaving probe actions with
model construction and evaluation. The method treats meta-level and baselevel
tasks uniformly. That is, the method takes a decision-theoretic look at the
control of search in causal pathways and structural hierarchies.
","Knowledge-Based Decision Model Construction for Hierarchical Diagnosis:
  A Preliminary Report"
"  We describe the integration of logical and uncertain reasoning methods to
identify the likely source and location of software problems. To date, software
engineers have had few tools for identifying the sources of error in complex
software packages. We describe a method for diagnosing software problems
through combining logical and uncertain reasoning analyses. Our preliminary
results suggest that such methods can be of value in directing the attention of
software engineers to paths of an algorithm that have the highest likelihood of
harboring a programming error.
","A Synthesis of Logical and Probabilistic Reasoning for Program
  Understanding and Debugging"
"  In recent years the belief network has been used increasingly to model
systems in Al that must perform uncertain inference. The development of
efficient algorithms for probabilistic inference in belief networks has been a
focus of much research in AI. Efficient algorithms for certain classes of
belief networks have been developed, but the problem of reporting the
uncertainty in inferred probabilities has received little attention. A system
should not only be capable of reporting the values of inferred probabilities
and/or the favorable choices of a decision; it should report the range of
possible error in the inferred probabilities and/or choices. Two methods have
been developed and implemented for determining the variance in inferred
probabilities in belief networks. These methods, the Approximate Propagation
Method and the Monte Carlo Integration Method are discussed and compared in
this paper.
","An Implementation of a Method for Computing the Uncertainty in Inferred
  Probabilities in Belief Networks"
"  Propositional representation services such as truth maintenance systems offer
powerful support for incremental, interleaved, problem-model construction and
evaluation. Probabilistic inference systems, in contrast, have lagged behind in
supporting this incrementality typically demanded by problem solvers. The
problem, we argue, is that the basic task of probabilistic inference is
typically formulated at too large a grain-size. We show how a system built
around a smaller grain-size inference task can have the desired incrementality
and serve as the basis for a low-level (propositional) probabilistic
representation service.
",Incremental Probabilistic Inference
"  We describe a method for time-critical decision making involving sequential
tasks and stochastic processes. The method employs several iterative refinement
routines for solving different aspects of the decision making problem. This
paper concentrates on the meta-level control problem of deliberation
scheduling, allocating computational resources to these routines. We provide
different models corresponding to optimization problems that capture the
different circumstances and computational strategies for decision making under
time constraints. We consider precursor models in which all decision making is
performed prior to execution and recurrent models in which decision making is
performed in parallel with execution, accounting for the states observed during
execution and anticipating future states. We describe algorithms for precursor
and recurrent models and provide the results of our empirical investigations to
date.
",Deliberation Scheduling for Time-Critical Sequential Decision Making
"  Intercausal reasoning is a common inference pattern involving probabilistic
dependence of causes of an observed common effect. The sign of this dependence
is captured by a qualitative property called product synergy. The current
definition of product synergy is insufficient for intercausal reasoning where
there are additional uninstantiated causes of the common effect. We propose a
new definition of product synergy and prove its adequacy for intercausal
reasoning with direct and indirect evidence for the common effect. The new
definition is based on a new property matrix half positive semi-definiteness, a
weakened form of matrix positive semi-definiteness.
",Intercausal Reasoning with Uninstantiated Ancestor Nodes
"  We examine two types of similarity networks each based on a distinct notion
of relevance. For both types of similarity networks we present an efficient
inference algorithm that works under the assumption that every event has a
nonzero probability of occurrence. Another inference algorithm is developed for
type 1 similarity networks that works under no restriction, albeit less
efficiently.
",Inference Algorithms for Similarity Networks
"  Two algorithms are presented for ""compiling"" influence diagrams into a set of
simple decision rules. These decision rules define simple-to-execute, complete,
consistent, and near-optimal decision procedures. These compilation algorithms
can be used to derive decision procedures for human teams solving time
constrained decision problems.
",Two Procedures for Compiling Influence Diagrams
"  Given a belief network with evidence, the task of finding the I most probable
explanations (MPE) in the belief network is that of identifying and ordering
the I most probable instantiations of the non-evidence nodes of the belief
network. Although many approaches have been proposed for solving this problem,
most work only for restricted topologies (i.e., singly connected belief
networks). In this paper, we will present a new approach for finding I MPEs in
an arbitrary belief network. First, we will present an algorithm for finding
the MPE in a belief network. Then, we will present a linear time algorithm for
finding the next MPE after finding the first MPE. And finally, we will discuss
the problem of finding the MPE for a subset of variables of a belief network,
and show that the problem can be efficiently solved by this approach.
",An efficient approach for finding the MPE in belief networks
"  This paper describes ongoing research into planning in an uncertain
environment. In particular, it introduces U-Plan, a planning system that
constructs quantitatively ranked plans given an incomplete description of the
state of the world. U-Plan uses a DempsterShafer interval to characterise
uncertain and incomplete information about the state of the world. The planner
takes as input what is known about the world, and constructs a number of
possible initial states with representations at different abstraction levels. A
plan is constructed for the initial state with the greatest support, and this
plan is tested to see if it will work for other possible initial states. All,
part, or none of the existing plans may be used in the generation of the plans
for the remaining possible worlds. Planning takes place in an abstraction
hierarchy where strategic decisions are made before tactical decisions. A
super-plan is then constructed, based on merging the set of plans and the
appropriately timed acquisition of essential knowledge, which is used to decide
between plan alternatives. U-Plan usually produces a super-plan in less time
than a classical planner would take to produce a set of plans, one for each
possible world.
",A Method for Planning Given Uncertain and Incomplete Information
"  This paper discusses how conflicts (as used by the consistency-based
diagnosis community) can be adapted to be used in a search-based algorithm for
computing prior and posterior probabilities in discrete Bayesian Networks. This
is an ""anytime"" algorithm, that at any stage can estimate the probabilities and
give an error bound. Whereas the most popular Bayesian net algorithms exploit
the structure of the network for efficiency, we exploit probability
distributions for efficiency; this algorithm is most suited to the case with
extreme probabilities. This paper presents a solution to the inefficiencies
found in naive algorithms, and shows how the tools of the consistency-based
diagnosis community (namely conflicts) can be used effectively to improve the
efficiency. Empirical results with networks having tens of thousands of nodes
are presented.
",The use of conflicts in searching Bayesian networks
"  Bayesian belief networks can be used to represent and to reason about complex
systems with uncertain, incomplete and conflicting information. Belief networks
are graphs encoding and quantifying probabilistic dependence and conditional
independence among variables. One type of reasoning of interest in diagnosis is
called abductive inference (determination of the global most probable system
description given the values of any partial subset of variables). In some
cases, abductive inference can be performed with exact algorithms using
distributed network computations but it is an NP-hard problem and complexity
increases drastically with the presence of undirected cycles, number of
discrete states per variable, and number of variables in the network. This
paper describes an approximate method based on genetic algorithms to perform
abductive inference in large, multiply connected networks for which complexity
is a concern when using most exact methods and for which systematic search
methods are not feasible. The theoretical adequacy of the method is discussed
and preliminary experimental results are presented.
","GALGO: A Genetic ALGOrithm Decision Support Tool for Complex Uncertain
  Systems Modeled with Bayesian Belief Networks"
"  Tree structures have been shown to provide an efficient framework for
propagating beliefs [Pearl,1986]. This paper studies the problem of finding an
optimal approximating tree. The star decomposition scheme for sets of three
binary variables [Lazarsfeld,1966; Pearl,1986] is shown to enhance the class of
probability distributions that can support tree structures; such structures are
called tree-decomposable structures. The logarithm scoring rule is found to be
an appropriate optimality criterion to evaluate different tree-decomposable
structures. Characteristics of such structures closest to the actual belief
network are identified using the logarithm rule, and greedy and exact
techniques are developed to find the optimal approximation.
",Using Tree-Decomposable Structures to Approximate Belief Networks
"  The potential influence diagram is a generalization of the standard
""conditional"" influence diagram, a directed network representation for
probabilistic inference and decision analysis [Ndilikilikesha, 1991]. It allows
efficient inference calculations corresponding exactly to those on undirected
graphs. In this paper, we explore the relationship between potential and
conditional influence diagrams and provide insight into the properties of the
potential influence diagram. In particular, we show how to convert a potential
influence diagram into a conditional influence diagram, and how to view the
potential influence diagram operations in terms of the conditional influence
diagram.
","Using Potential Influence Diagrams for Probabilistic Inference and
  Decision Making"
"  In order to find a causal explanation for data presented in the form of
covariance and concentration matrices it is necessary to decide if the graph
formed by such associations is a projection of a directed acyclic graph (dag).
We show that the general problem of deciding whether such a dag exists is
NP-complete.
",Deciding Morality of Graphs is NP-complete
"  To determine the value of perfect information in an influence diagram, one
needs first to modify the diagram to reflect the change in information
availability, and then to compute the optimal expected values of both the
original diagram and the modified diagram. The value of perfect information is
the difference between the two optimal expected values. This paper is about how
to speed up the computation of the optimal expected value of the modified
diagram by making use of the intermediate computation results obtained when
computing the optimal expected value of the original diagram.
","Incremental computation of the value of perfect information in
  stepwise-decomposable influence diagrams"
"  This paper presents and discusses several methods for reasoning from
inconsistent knowledge bases. A so-called argumentative-consequence relation
taking into account the existence of consistent arguments in favor of a
conclusion and the absence of consistent arguments in favor of its contrary, is
particularly investigated. Flat knowledge bases, i.e. without any priority
between their elements, as well as prioritized ones where some elements are
considered as more strongly entrenched than others are studied under different
consequence relations. Lastly a paraconsistent-like treatment of prioritized
knowledge bases is proposed, where both the level of entrenchment and the level
of paraconsistency attached to a formula are propagated. The priority levels
are handled in the framework of possibility theory.
",Argumentative inference in uncertain and inconsistent knowledge bases
"  A major reason behind the success of probability calculus is that it
possesses a number of valuable tools, which are based on the notion of
probabilistic independence. In this paper, I identify a notion of logical
independence that makes some of these tools available to a class of
propositional databases, called argument databases. Specifically, I suggest a
graphical representation of argument databases, called argument networks, which
resemble Bayesian networks. I also suggest an algorithm for reasoning with
argument networks, which resembles a basic algorithm for reasoning with
Bayesian networks. Finally, I show that argument networks have several
applications: Nonmonotonic reasoning, truth maintenance, and diagnosis.
",Argument Calculus and Networks
"  Argumentation is the process of constructing arguments about propositions,
and the assignment of statements of confidence to those propositions based on
the nature and relative strength of their supporting arguments. The process is
modelled as a labelled deductive system, in which propositions are doubly
labelled with the grounds on which they are based and a representation of the
confidence attached to the argument. Argument construction is captured by a
generalized argument consequence relation based on the ^,--fragment of minimal
logic. Arguments can be aggregated by a variety of numeric and symbolic
flattening functions. This approach appears to shed light on the common logical
structure of a variety of quantitative, qualitative and defeasible uncertainty
calculi.
",Argumentation as a General Framework for Uncertain Reasoning
"  In this paper some initial work towards a new approach to qualitative
reasoning under uncertainty is presented. This method is not only applicable to
qualitative probabilistic reasoning, as is the case with other methods, but
also allows the qualitative propagation within networks of values based upon
possibility theory and Dempster-Shafer evidence theory. The method is applied
to two simple networks from which a large class of directed graphs may be
constructed. The results of this analysis are used to compare the qualitative
behaviour of the three major quantitative uncertainty handling formalisms, and
to demonstrate that the qualitative integration of the formalisms is possible
under certain assumptions.
",On reasoning in networks with qualitative uncertainty
"  This paper introduces a qualitative measure of ambiguity and analyses its
relationship with other measures of uncertainty. Probability measures relative
likelihoods, while ambiguity measures vagueness surrounding those judgments.
Ambiguity is an important representation of uncertain knowledge. It deals with
a different, type of uncertainty modeled by subjective probability or belief.
",Qualitative Measures of Ambiguity
"  Shafer's theory of belief and the Bayesian theory of probability are two
alternative and mutually inconsistent approaches toward modelling uncertainty
in artificial intelligence. To help reduce the conflict between these two
approaches, this paper reexamines expected utility theory-from which Bayesian
probability theory is derived. Expected utility theory requires the decision
maker to assign a utility to each decision conditioned on every possible event
that might occur. But frequently the decision maker cannot foresee all the
events that might occur, i.e., one of the possible events is the occurrence of
an unforeseen event. So once we acknowledge the existence of unforeseen events,
we need to develop some way of assigning utilities to decisions conditioned on
unforeseen events. The commonsensical solution to this problem is to assign
similar utilities to events which are similar. Implementing this commonsensical
solution is equivalent to replacing Bayesian subjective probabilities over the
space of foreseen and unforeseen events by random set theory probabilities over
the space of foreseen events. This leads to an expected utility principle in
which normalized variants of Shafer's commonalities play the role of subjective
probabilities. Hence allowing for unforeseen events in decision analysis causes
Bayesian probability theory to become much more similar to Shaferian theory.
","A Bayesian Variant of Shafer's Commonalities For Modelling Unforeseen
  Events"
"  We present a semantics for adding uncertainty to conditional logics for
default reasoning and belief revision. We are able to treat conditional
sentences as statements of conditional probability, and express rules for
revision such as ""If A were believed, then B would be believed to degree p.""
This method of revision extends conditionalization by allowing meaningful
revision by sentences whose probability is zero. This is achieved through the
use of counterfactual probabilities. Thus, our system accounts for the best
properties of qualitative methods of update (in particular, the AGM theory of
revision) and probabilistic methods. We also show how our system can be viewed
as a unification of probability theory and possibility theory, highlighting
their orthogonality and providing a means for expressing the probability of a
possibility. We also demonstrate the connection to Lewis's method of imaging.
",The Probability of a Possibility: Adding Uncertainty to Default Rules
"  A key issue in the handling of temporal data is the treatment of persistence;
in most approaches it consists in inferring defeasible confusions by
extrapolating from the actual knowledge of the history of the world; we propose
here a gradual modelling of persistence, following the idea that persistence is
decreasing (the further we are from the last time point where a fluent is known
to be true, the less certainly true the fluent is); it is based on possibility
theory, which has strong relations with other well-known ordering-based
approaches to nonmonotonic reasoning. We compare our approach with Dean and
Kanazawa's probabilistic projection. We give a formal modelling of the
decreasing persistence problem. Lastly, we show how to infer nonmonotonic
conclusions using the principle of decreasing persistence.
",Possibilistic decreasing persistence
"  Evidential reasoning is now a leading topic in Artificial Intelligence.
Evidence is represented by a variety of evidential functions. Evidential
reasoning is carried out by certain kinds of fundamental operation on these
functions. This paper discusses two of the basic operations on evidential
functions, the discount operation and the well-known orthogonal sum operation.
We show that the discount operation is not commutative with the orthogonal sum
operation, and derive expressions for the two operations applied to the various
evidential function.
",Discounting and Combination Operations in Evidential Reasoning
"  The classical propositional assumption-based model is extended to incorporate
probabilities for the assumptions. Then it is placed into the framework of
evidence theory. Several authors like Laskey, Lehner (1989) and Provan (1990)
already proposed a similar point of view, but the first paper is not as much
concerned with mathematical foundations, and Provan's paper develops into a
different direction. Here we thoroughly develop and present the mathematical
foundations of this theory, together with computational methods adapted from
Reiter, De Kleer (1987) and Inoue (1992). Finally, recently proposed techniques
for computing degrees of support are presented.
",Probabilistic Assumption-Based Reasoning
"  This paper presents a procedure to determine a complete belief function from
the known values of belief for some of the subsets of the frame of discerment.
The method is based on the principle of minimum commitment and a new principle
called the focusing principle. This additional principle is based on the idea
that belief is specified for the most relevant sets: the focal elements. The
resulting procedure is compared with existing methods of building complete
belief functions: the minimum specificity principle and the least commitment
principle.
",Partially Specified Belief Functions
"  Jeffrey's rule of conditioning has been proposed in order to revise a
probability measure by another probability function. We generalize it within
the framework of the models based on belief functions. We show that several
forms of Jeffrey's conditionings can be defined that correspond to the
geometrical rule of conditioning and to Dempster's rule of conditioning,
respectively.
",Jeffrey's rule of conditioning generalized to belief functions
"  In this paper, the concept of possibilistic evidence which is a possibility
distribution as well as a body of evidence is proposed over an infinite
universe of discourse. The inference with possibilistic evidence is
investigated based on a unified inference framework maintaining both the
compatibility of concepts and the consistency of the probability logic.
",Inference with Possibilistic Evidence
"  An elaboration of Dempster's method of constructing belief functions suggests
a broadly applicable strategy for constructing lower probabilities under a
variety of evidentiary constraints.
",Constructing Lower Probabilities
"  In a probability-based reasoning system, Bayes' theorem and its variations
are often used to revise the system's beliefs. However, if the explicit
conditions and the implicit conditions of probability assignments `me properly
distinguished, it follows that Bayes' theorem is not a generally applicable
revision rule. Upon properly distinguishing belief revision from belief
updating, we see that Jeffrey's rule and its variations are not revision rules,
either. Without these distinctions, the limitation of the Bayesian approach is
often ignored or underestimated. Revision, in its general form, cannot be done
in the Bayesian approach, because a probability distribution function alone
does not contain the information needed by the operation.
",Belief Revision in Probability Theory
"  This paper examines the concept of a combination rule for belief functions.
It is shown that two fairly simple and apparently reasonable assumptions
determine Dempster's rule, giving a new justification for it.
",The Assumptions Behind Dempster's Rule
"  In this paper, we present a decision support system based on belief functions
and the pignistic transformation. The system is an integration of an evidential
system for belief function propagation and a valuation-based system for
Bayesian decision analysis. The two subsystems are connected through the
pignistic transformation. The system takes as inputs the user's ""gut feelings""
about a situation and suggests what, if any, are to be tested and in what
order, and it does so with a user friendly interface.
",A Belief-Function Based Decision Support System
"  This paper provides an overview of the SP theory of intelligence and its
central idea that artificial intelligence, mainstream computing, and much of
human perception and cognition, may be understood as information compression.
  The background and origins of the SP theory are described, and the main
elements of the theory, including the key concept of multiple alignment,
borrowed from bioinformatics but with important differences. Associated with
the SP theory is the idea that redundancy in information may be understood as
repetition of patterns, that compression of information may be achieved via the
matching and unification (merging) of patterns, and that computing and
information compression are both fundamentally probabilistic. It appears that
the SP system is Turing-equivalent in the sense that anything that may be
computed with a Turing machine may, in principle, also be computed with an SP
machine.
  One of the main strengths of the SP theory and the multiple alignment concept
is in modelling concepts and phenomena in artificial intelligence. Within that
area, the SP theory provides a simple but versatile means of representing
different kinds of knowledge, it can model both the parsing and production of
natural language, with potential for the understanding and translation of
natural languages, it has strengths in pattern recognition, with potential in
computer vision, it can model several kinds of reasoning, and it has
capabilities in planning, problem solving, and unsupervised learning.
  The paper includes two examples showing how alternative parsings of an
ambiguous sentence may be modelled as multiple alignments, and another example
showing how the concept of multiple alignment may be applied in medical
diagnosis.
",Computing as compression: the SP theory of intelligence
"  This paper presents capabilities of using genetic algorithms to find
approximations of function extrema, which cannot be found using analytic ways.
To enhance effectiveness of calculations, algorithm has been parallelized using
OpenMP library. We gained much increase in speed on platforms using
multithreaded processors with shared memory free access. During analysis we
used different modifications of genetic operator, using them we obtained varied
evolution process of potential solutions. Results allow to choose best methods
among many applied in genetic algorithms and observation of acceleration on
Yorkfield, Bloomfield, Westmere-EX and most recent Sandy Bridge cores.
","Generating extrema approximation of analytically incomputable functions
  through usage of parallel computer aided genetic algorithms"
"  Several algorithms have been proposed for discovering patterns from
trajectories of moving objects, but only a few have concentrated on outlier
detection. Existing approaches, in general, discover spatial outliers, and do
not provide any further analysis of the patterns. In this paper we introduce
semantic spatial and spatio-temporal outliers and propose a new algorithm for
trajectory outlier detection. Semantic outliers are computed between regions of
interest, where objects have similar movement intention, and there exist
standard paths which connect the regions. We show with experiments on real data
that the method finds semantic outliers from trajectory data that are not
discovered by similar approaches.
","Discovering Semantic Spatial and Spatio-Temporal Outliers from Moving
  Object Trajectories"
"  Hepatitis C virus (HCV) is a widely spread disease all over the world. HCV
has very high mutation rate that makes it resistant to antibodies. Modeling HCV
to identify the virus mutation process is essential to its detection and
predicting its evolution. This paper presents a model based framework for
estimating mutation rate of HCV in two steps. Firstly profile hidden Markov
model (PHMM) architecture was builder to select the sequences which represents
sequence per year. Secondly mutation rate was calculated by using pair-wise
distance method between sequences. A pilot study is conducted on NS5B zone of
HCV dataset of genotype 4 subtype a (HCV4a) in Egypt.
","Model Based Framework for Estimating Mutation Rate of Hepatitis C Virus
  in Egypt"
"  In this paper we describe a novel method for evidential reasoning [1]. It
involves modelling the process of evidential reasoning in three steps, namely,
evidence structure construction, evidence accumulation, and decision making.
The proposed method, called RES, is novel in that evidence strength is
associated with an evidential support relationship (an argument) between a pair
of statements and such strength is carried by comparison between arguments.
This is in contrast to the onventional approaches, where evidence strength is
represented numerically and is associated with a statement.
",RES - a Relative Method for Evidential Reasoning
"  An algorithm for generating the structure of a directed acyclic graph from
data using the notion of causal input lists is presented. The algorithm
manipulates the ordering of the variables with operations which very much
resemble arc reversal. Operations are only applied if the DAG after the
operation represents at least the independencies represented by the DAG before
the operation until no more arcs can be removed from the DAG. The resulting DAG
is a minimal l-map.
",Optimizing Causal Orderings for Generating DAGs from Data
"  Possibilistic logic has been proposed as a numerical formalism for reasoning
with uncertainty. There has been interest in developing qualitative accounts of
possibility, as well as an explanation of the relationship between possibility
and modal logics. We present two modal logics that can be used to represent and
reason with qualitative statements of possibility and necessity. Within this
modal framework, we are able to identify interesting relationships between
possibilistic logic, beliefs and conditionals. In particular, the most natural
conditional definable via possibilistic means for default reasoning is
identical to Pearl's conditional for e-semantics.
",Modal Logics for Qualitative Possibility and Beliefs
"  Influence diagram is a graphical representation of belief networks with
uncertainty. This article studies the structural properties of a probabilistic
model in an influence diagram. In particular, structural controllability
theorems and structural observability theorems are developed and algorithms are
formulated. Controllability and observability are fundamental concepts in
dynamic systems (Luenberger 1979). Controllability corresponds to the ability
to control a system while observability analyzes the inferability of its
variables. Both properties can be determined by the ranks of the system
matrices. Structural controllability and observability, on the other hand,
analyze the property of a system with its structure only, without the specific
knowledge of the values of its elements (tin 1974, Shields and Pearson 1976).
The structural analysis explores the connection between the structure of a
model and the functional dependence among its elements. It is useful in
comprehending problem and formulating solution by challenging the underlying
intuitions and detecting inconsistency in a model. This type of qualitative
reasoning can sometimes provide insight even when there is insufficient
numerical information in a model.
",Structural Controllability and Observability in Influence Diagrams
"  Experts do not always feel very, comfortable when they have to give precise
numerical estimations of certainty degrees. In this paper we present a
qualitative approach which allows for attaching partially ordered symbolic
grades to logical formulas. Uncertain information is expressed by means of
parameterized modal operators. We propose a semantics for this multimodal logic
and give a sound and complete axiomatization. We study the links with related
approaches and suggest how this framework might be used to manage both
uncertain and incomplere knowledge.
",Lattice-Based Graded Logic: a Multimodal Approach
"  We have developed a probabilistic forecasting methodology through a synthesis
of belief network models and classical time-series analysis. We present the
dynamic network model (DNM) and describe methods for constructing, refining,
and performing inference with this representation of temporal probabilistic
knowledge. The DNM representation extends static belief-network models to more
general dynamic forecasting models by integrating and iteratively refining
contemporaneous and time-lagged dependencies. We discuss key concepts in terms
of a model for forecasting U.S. car sales in Japan.
",Dynamic Network Models for Forecasting
"  We describe how we selectively reformulate portions of a belief network that
pose difficulties for solution with a stochastic-simulation algorithm. With
employ the selective conditioning approach to target specific nodes in a belief
network for decomposition, based on the contribution the nodes make to the
tractability of stochastic simulation. We review previous work on BNRAS
algorithms- randomized approximation algorithms for probabilistic inference. We
show how selective conditioning can be employed to reformulate a single BNRAS
problem into multiple tractable BNRAS simulation problems. We discuss how we
can use another simulation algorithm-logic sampling-to solve a component of the
inference problem that provides a means for knitting the solutions of
individual subproblems into a final result. Finally, we analyze tradeoffs among
the computational subtasks associated with the selective conditioning approach
to reformulation.
",Reformulating Inference Problems Through Selective Conditioning
"  The product expansion of conditional probabilities for belief nets is not
maximum entropy. This appears to deny a desirable kind of assurance for the
model. However, a kind of guarantee that is almost as strong as maximum entropy
can be derived. Surprisingly, a variant model also exhibits the guarantee, and
for many cases obtains a higher performance score than the product expansion.
",Entropy and Belief Networks
"  We report on an experimental investigation into opportunities for parallelism
in beliefnet inference. Specifically, we report on a study performed of the
available parallelism, on hypercube style machines, of a set of randomly
generated belief nets, using factoring (SPI) style inference algorithms. Our
results indicate that substantial speedup is available, but that it is
available only through parallelization of individual conformal product
operations, and depends critically on finding an appropriate factoring. We find
negligible opportunity for parallelism at the topological, or clustering tree,
level.
",Parallelizing Probabilistic Inference: Some Early Explorations
"  This paper introduces the notion of objection-based causal networks which
resemble probabilistic causal networks except that they are quantified using
objections. An objection is a logical sentence and denotes a condition under
which a, causal dependency does not exist. Objection-based causal networks
enjoy almost all the properties that make probabilistic causal networks
popular, with the added advantage that objections are, arguably more intuitive
than probabilities.
",Objection-Based Causal Networks
"  This paper investigates the possibility of performing automated reasoning in
probabilistic logic when probabilities are expressed by means of linguistic
quantifiers. Each linguistic term is expressed as a prescribed interval of
proportions. Then instead of propagating numbers, qualitative terms are
propagated in accordance with the numerical interpretation of these terms. The
quantified syllogism, modelling the chaining of probabilistic rules, is studied
in this context. It is shown that a qualitative counterpart of this syllogism
makes sense, and is relatively independent of the threshold defining the
linguistically meaningful intervals, provided that these threshold values
remain in accordance with the intuition. The inference power is less than that
of a full-fledged probabilistic con-quaint propagation device but better
corresponds to what could be thought of as commonsense probabilistic reasoning.
",A Symbolic Approach to Reasoning with Linguistic Quantifiers
"  Data fusion allows the elaboration and the evaluation of a situation
synthesized from low level informations provided by different kinds of sensors.
The fusion of the collected data will result in fewer and higher level
informations more easily assessed by a human operator and that will assist him
effectively in his decision process. In this paper we present the suitability
and the advantages of using a Possibilistic Assumption based Truth Maintenance
System (n-ATMS) in a data fusion military application. We first describe the
problem, the needed knowledge representation formalisms and problem solving
paradigms. Then we remind the reader of the basic concepts of ATMSs,
Possibilistic Logic and 11-ATMSs. Finally we detail the solution to the given
data fusion problem and conclude with the results and comparison with a
non-possibilistic solution.
","Possibilistic Assumption based Truth Maintenance System, Validation in a
  Data Fusion Application"
"  In the probabilistic approach to uncertainty management the input knowledge
is usually represented by means of some probability distributions. In this
paper we assume that the input knowledge is given by two discrete conditional
probability distributions, represented by two stochastic matrices P and Q. The
consistency of the knowledge base is analyzed. Coherence conditions and
explicit formulas for the extension to marginal distributions are obtained in
some special cases.
",Knowledge Integration for Conditional Probability Assessments
"  To date, most probabilistic reasoning systems have relied on a fixed belief
network constructed at design time. The network is used by an application
program as a representation of (in)dependencies in the domain. Probabilistic
inference algorithms operate over the network to answer queries. Recognizing
the inflexibility of fixed models has led researchers to develop automated
network construction procedures that use an expressive knowledge base to
generate a network that can answer a query. Although more flexible than fixed
model approaches, these construction procedures separate construction and
evaluation into distinct phases. In this paper we develop an approach to
combining incremental construction and evaluation of a partial probability
model. The combined method holds promise for improved methods for control of
model construction based on a trade-off between fidelity of results and cost of
construction.
",Integrating Model Construction and Evaluation
"  We recently described a formalism for reasoning with if-then rules that re
expressed with different levels of firmness [18]. The formalism interprets
these rules as extreme conditional probability statements, specifying orders of
magnitude of disbelief, which impose constraints over possible rankings of
worlds. It was shown that, once we compute a priority function Z+ on the rules,
the degree to which a given query is confirmed or denied can be computed in
O(log n`) propositional satisfiability tests, where n is the number of rules in
the knowledge base. In this paper, we show that computing Z+ requires O(n2 X
log n) satisfiability tests, not an exponential number as was conjectured in
[18], which reduces to polynomial complexity in the case of Horn expressions.
We also show how reasoning with imprecise observations can be incorporated in
our formalism and how the popular notions of belief revision and epistemic
entrenchment are embodied naturally and tractably.
",Reasoning With Qualitative Probabilities Can Be Tractable
"  A computational scheme for reasoning about dynamic systems using (causal)
probabilistic networks is presented. The scheme is based on the framework of
Lauritzen and Spiegelhalter (1988), and may be viewed as a generalization of
the inference methods of classical time-series analysis in the sense that it
allows description of non-linear, multivariate dynamic systems with complex
conditional independence structures. Further, the scheme provides a method for
efficient backward smoothing and possibilities for efficient, approximate
forecasting methods. The scheme has been implemented on top of the HUGIN shell.
",A computational scheme for Reasoning in Dynamic Probabilistic Networks
"  The fundamental updating process in the transferable belief model is related
to the concept of specialization and can be described by a specialization
matrix. The degree of belief in the truth of a proposition is a degree of
justified support. The Principle of Minimal Commitment implies that one should
never give more support to the truth of a proposition than justified. We show
that Dempster's rule of conditioning corresponds essentially to the least
committed specialization, and that Dempster's rule of combination results
essentially from commutativity requirements. The concept of generalization,
dual to thc concept of specialization, is described.
","The Dynamic of Belief in the Transferable Belief Model and
  Specialization-Generalization Matrices"
"  A new entropy-like measure as well as a new measure of total uncertainty
pertaining to the Dempster-Shafer theory are introduced. It is argued that
these measures are better justified than any of the previously proposed
candidates.
",A Note on the Measure of Discord
"  A number of writers(Joseph Halpern and Fahiem Bacchus among them) have
offered semantics for formal languages in which inferences concerning
probabilities can be made. Our concern is different. This paper provides a
formalization of nonmonotonic inferences in which the conclusion is supported
only to a certain degree. Such inferences are clearly 'invalid' since they must
allow the falsity of a conclusion even when the premises are true.
Nevertheless, such inferences can be characterized both syntactically and
semantically. The 'premises' of probabilistic arguments are sets of statements
(as in a database or knowledge base), the conclusions categorical statements in
the language. We provide standards for both this form of inference, for which
high probability is required, and for an inference in which the conclusion is
qualified by an intermediate interval of support.
",Semantics for Probabilistic Inference
"  We discuss problems for convex Bayesian decision making and uncertainty
representation. These include the inability to accommodate various natural and
useful constraints and the possibility of an analog of the classical Dutch Book
being made against an agent behaving in accordance with convex Bayesian
prescriptions. A more general set-based Bayesianism may be as tractable and
would avoid the difficulties we raise.
",Some Problems for Convex Bayesians
"  This paper presents a Bayesian framework for assessing the adequacy of a
model without the necessity of explicitly enumerating a specific alternate
model. A test statistic is developed for tracking the performance of the model
across repeated problem instances. Asymptotic methods are used to derive an
approximate distribution for the test statistic. When the model is rejected,
the individual components of the test statistic can be used to guide search for
an alternate model.
","Bayesian Meta-Reasoning: Determining Model Adequacy from Within a Small
  World"
"  The ideal Bayesian agent reasons from a global probability model, but real
agents are restricted to simplified models which they know to be adequate only
in restricted circumstances. Very little formal theory has been developed to
help fallibly rational agents manage the process of constructing and revising
small world models. The goal of this paper is to present a theoretical
framework for analyzing model management approaches. For a probability
forecasting problem, a search process over small world models is analyzed as an
approximation to a larger-world model which the agent cannot explicitly
enumerate or compute. Conditions are given under which the sequence of
small-world models converges to the larger-world probabilities.
",The Bounded Bayesian
"  Automated decision making is often complicated by the complexity of the
knowledge involved. Much of this complexity arises from the context sensitive
variations of the underlying phenomena. We propose a framework for representing
descriptive, context-sensitive knowledge. Our approach attempts to integrate
categorical and uncertain knowledge in a network formalism. This paper outlines
the basic representation constructs, examines their expressiveness and
efficiency, and discusses the potential applications of the framework.
","Representing Context-Sensitive Knowledge in a Network Formalism: A
  Preliminary Report"
"  Bayesian networks are directed acyclic graphs representing independence
relationships among a set of random variables. A random variable can be
regarded as a set of exhaustive and mutually exclusive propositions. We argue
that there are several drawbacks resulting from the propositional nature and
acyclic structure of Bayesian networks. To remedy these shortcomings, we
propose a probabilistic network where nodes represent unary predicates and
which may contain directed cycles. The proposed representation allows us to
represent domain knowledge in a single static network even though we cannot
determine the instantiations of the predicates before hand. The ability to deal
with cycles also enables us to handle cyclic causal tendencies and to recognize
recursive plans.
",A Probabilistic Network of Predicates
"  The Dempster-Shafer theory of evidence has been used intensively to deal with
uncertainty in knowledge-based systems. However the representation of uncertain
relationships between evidence and hypothesis groups (heuristic knowledge) is
still a major research problem. This paper presents an approach to representing
such heuristic knowledge by evidential mappings which are defined on the basis
of mass functions. The relationships between evidential mappings and multi
valued mappings, as well as between evidential mappings and Bayesian multi-
valued causal link models in Bayesian theory are discussed. Following this the
detailed procedures for constructing evidential mappings for any set of
heuristic rules are introduced. Several situations of belief propagation are
discussed.
",Representing Heuristic Knowledge in D-S Theory
"  Bayes nets are relatively recent innovations. As a result, most of their
theoretical development has focused on the simplest class of single-author
models. The introduction of more sophisticated multiple-author settings raises
a variety of interesting questions. One such question involves the nature of
compromise and consensus. Posterior compromises let each model process all data
to arrive at an independent response, and then split the difference. Prior
compromises, on the other hand, force compromise to be reached on all points
before data is observed. This paper introduces prior compromises in a Bayes net
setting. It outlines the problem and develops an efficient algorithm for fusing
two directed acyclic graphs into a single, consensus structure, which may then
be used as the basis of a prior compromise.
",The Topological Fusion of Bayes Nets
"  In Moral, Campos (1991) and Cano, Moral, Verdegay-Lopez (1991) a new method
of conditioning convex sets of probabilities has been proposed. The result of
it is a convex set of non-necessarily normalized probability distributions. The
normalizing factor of each probability distribution is interpreted as the
possibility assigned to it by the conditioning information. From this, it is
deduced that the natural value for the conditional probability of an event is a
possibility distribution. The aim of this paper is to study methods of
transforming this possibility distribution into a probability (or uncertainty)
interval. These methods will be based on the use of Sugeno and Choquet
integrals. Their behaviour will be compared in basis to some selected examples.
","Calculating Uncertainty Intervals From Conditional Convex Sets of
  Probabilities"
"  The trajectory of a robot is monitored in a restricted dynamic environment
using light beam sensor data. We have a Dynamic Belief Network (DBN), based on
a discrete model of the domain, which provides discrete monitoring analogous to
conventional quantitative filter techniques. Sensor observations are added to
the basic DBN in the form of specific evidence. However, sensor data is often
partially or totally incorrect. We show how the basic DBN, which infers only an
impossible combination of evidence, may be modified to handle specific types of
incorrect data which may occur in the domain. We then present an extension to
the DBN, the addition of an invalidating node, which models the status of the
sensor as working or defective. This node provides a qualitative explanation of
inconsistent data: it is caused by a defective sensor. The connection of
successive instances of the invalidating node models the status of a sensor
over time, allowing the DBN to handle both persistent and intermittent faults.
",Sensor Validation Using Dynamic Belief Networks
"  The paper describes aHUGIN, a tool for creating adaptive systems. aHUGIN is
an extension of the HUGIN shell, and is based on the methods reported by
Spiegelhalter and Lauritzen (1990a). The adaptive systems resulting from aHUGIN
are able to adjust the C011ditional probabilities in the model. A short
analysis of the adaptation task is given and the features of aHUGIN are
described. Finally a session with experiments is reported and the results are
discussed.
",aHUGIN: A System Creating Adaptive Causal Probabilistic Networks
"  Probabilistic reasoning systems combine different probabilistic rules and
probabilistic facts to arrive at the desired probability values of
consequences. In this paper we describe the MESA-algorithm (Maximum Entropy by
Simulated Annealing) that derives a joint distribution of variables or
propositions. It takes into account the reliability of probability values and
can resolve conflicts between contradictory statements. The joint distribution
is represented in terms of marginal distributions and therefore allows to
process large inference networks and to determine desired probability values
with high precision. The procedure derives a maximum entropy distribution
subject to the given constraints. It can be applied to inference networks of
arbitrary topology and may be extended into a number of directions.
",MESA: Maximum Entropy by Simulated Annealing
"  This paper describes some results of research on associate systems:
knowledge-based systems that flexibly and adaptively support their human users
in carrying out complex, time-dependent problem-solving tasks under
uncertainty. Based on principles derived from decision theory and decision
analysis, a problem-solving approach is presented which can overcome many of
the limitations of traditional expert-systems. This approach implements an
explicit model of the human user's problem-solving capabilities as an integral
element in the overall problem solving architecture. This integrated model,
represented as an influence diagram, is the basis for achieving adaptive task
sharing behavior between the associate system and the human user. This
associate system model has been applied toward ongoing research on a Mars Rover
Manager's Associate (MRMA). MRMA's role would be to manage a small fleet of
robotic rovers on the Martian surface. The paper describes results for a
specific scenario where MRMA examines the benefits and costs of consulting
human experts on Earth to assist a Mars rover with a complex resource
management decision.
",Decision Methods for Adaptive Task-Sharing in Associate Systems
"  Although the notion of diagnostic problem has been extensively investigated
in the context of static systems, in most practical applications the behavior
of the modeled system is significantly variable during time. The goal of the
paper is to propose a novel approach to the modeling of uncertainty about
temporal evolutions of time-varying systems and a characterization of
model-based temporal diagnosis. Since in most real world cases knowledge about
the temporal evolution of the system to be diagnosed is uncertain, we consider
the case when probabilistic temporal knowledge is available for each component
of the system and we choose to model it by means of Markov chains. In fact, we
aim at exploiting the statistical assumptions underlying reliability theory in
the context of the diagnosis of timevarying systems. We finally show how to
exploit Markov chain theory in order to discard, in the diagnostic process,
very unlikely diagnoses.
",Modeling Uncertain Temporal Evolutions in Model-Based Diagnosis
"  An expert classification system having statistical information about the
prior probabilities of the different classes should be able to use this
knowledge to reduce the amount of additional information that it must collect,
e.g., through questions, in order to make a correct classification. This paper
examines how best to use such prior information and additional
information-collection opportunities to reduce uncertainty about the class to
which a case belongs, thus minimizing the average cost or effort required to
correctly classify new cases.
","Guess-And-Verify Heuristics for Reducing Uncertainties in Expert
  Classification Systems"
"  This paper describes the architecture of R&D Analyst, a commercial
intelligent decision system for evaluating corporate research and development
projects and portfolios. In analyzing projects, R&D Analyst interactively
guides a user in constructing an influence diagram model for an individual
research project. The system's interactive approach can be clearly explained
from a blackboard system perspective. The opportunistic reasoning emphasis of
blackboard systems satisfies the flexibility requirements of model
construction, thereby suggesting that a similar architecture would be valuable
for developing normative decision systems in other domains. Current research is
aimed at extending the system architecture to explicitly consider of sequential
decisions involving limited temporal, financial, and physical resources.
","R&D Analyst: An Interactive Approach to Normative Decision System Model
  Construction"
"  Many AI synthesis problems such as planning or scheduling may be modelized as
constraint satisfaction problems (CSP). A CSP is typically defined as the
problem of finding any consistent labeling for a fixed set of variables
satisfying all given constraints between these variables. However, for many
real tasks such as job-shop scheduling, time-table scheduling, design?, all
these constraints have not the same significance and have not to be necessarily
satisfied. A first distinction can be made between hard constraints, which
every solution should satisfy and soft constraints, whose satisfaction has not
to be certain. In this paper, we formalize the notion of possibilistic
constraint satisfaction problems that allows the modeling of uncertainly
satisfied constraints. We use a possibility distribution over labelings to
represent respective possibilities of each labeling. Necessity-valued
constraints allow a simple expression of the respective certainty degrees of
each constraint. The main advantage of our approach is its integration in the
CSP technical framework. Most classical techniques, such as Backtracking (BT),
arcconsistency enforcing (AC) or Forward Checking have been extended to handle
possibilistics CSP and are effectively implemented. The utility of our approach
is demonstrated on a simple design problem.
","Possibilistic Constraint Satisfaction Problems or ""How to handle soft
  constraints?"""
"  The analysis of decision making under uncertainty is closely related to the
analysis of probabilistic inference. Indeed, much of the research into
efficient methods for probabilistic inference in expert systems has been
motivated by the fundamental normative arguments of decision theory. In this
paper we show how the developments underlying those efficient methods can be
applied immediately to decision problems. In addition to general approaches
which need know nothing about the actual probabilistic inference method, we
suggest some simple modifications to the clustering family of algorithms in
order to efficiently incorporate decision making capabilities.
",Decision Making Using Probabilistic Inference Methods
"  This paper introduces the notions of independence and conditional
independence in valuation-based systems (VBS). VBS is an axiomatic framework
capable of representing many different uncertainty calculi. We define
independence and conditional independence in terms of factorization of the
joint valuation. The definitions of independence and conditional independence
in VBS generalize the corresponding definitions in probability theory. Our
definitions apply not only to probability theory, but also to Dempster-Shafer's
belief-function theory, Spohn's epistemic-belief theory, and Zadeh's
possibility theory. In fact, they apply to any uncertainty calculi that fit in
the framework of valuation-based systems.
",Conditional Independence in Uncertainty Theories
"  Within the transferable belief model, positive basic belief masses can be
allocated to the empty set, leading to unnormalized belief functions. The
nature of these unnormalized beliefs is analyzed.
","The Nature of the Unnormalized Beliefs Encountered in the Transferable
  Belief Model"
"  The general use of subjective probabilities to model belief has been
justified using many axiomatic schemes. For example, ?consistent betting
behavior' arguments are well-known. To those not already convinced of the
unique fitness and generality of probability models, such justifications are
often unconvincing. The present paper explores another rationale for
probability models. ?Qualitative probability,' which is known to provide
stringent constraints on belief representation schemes, is derived from five
simple assumptions about relationships among beliefs. While counterparts of
familiar rationality concepts such as transitivity, dominance, and consistency
are used, the betting context is avoided. The gap between qualitative
probability and probability proper can be bridged by any of several additional
assumptions. The discussion here relies on results common in the recent AI
literature, introducing a sixth simple assumption. The narrative emphasizes
models based on unique complete orderings, but the rationale extends easily to
motivate set-valued representations of partial orderings as well.
",Intuitions about Ordered Beliefs Leading to Probabilistic Models
"  Bayesian networks have been used extensively in diagnostic tasks such as
medicine, where they represent the dependency relations between a set of
symptoms and a set of diseases. A criticism of this type of knowledge
representation is that it is restricted to this kind of task, and that it
cannot cope with the knowledge required in other artificial intelligence
applications. For example, in computer vision, we require the ability to model
complex knowledge, including temporal and relational factors. In this paper we
extend Bayesian networks to model relational and temporal knowledge for
high-level vision. These extended networks have a simple structure which
permits us to propagate probability efficiently. We have applied them to the
domain of endoscopy, illustrating how the general modelling principles can be
used in specific cases.
","Expressing Relational and Temporal Knowledge in Visual Probabilistic
  Networks"
"  This paper discusses a target tracking problem in which no dynamic
mathematical model is explicitly assumed. A nonlinear filter based on the fuzzy
If-then rules is developed. A comparison with a Kalman filter is made, and
empirical results show that the performance of the fuzzy filter is better.
Intensive simulations suggest that theoretical justification of the empirical
results is possible.
",A Fuzzy Logic Approach to Target Tracking
"  The DUCK-calculus presented here is a recent approach to cope with
probabilistic uncertainty in a sound and efficient way. Uncertain rules with
bounds for probabilities and explicit conditional independences can be
maintained incrementally. The basic inference mechanism relies on local bounds
propagation, implementable by deductive databases with a bottom-up fixpoint
evaluation. In situations, where no precise bounds are deducible, it can be
combined with simple operations research techniques on a local scope. In
particular, we provide new precise analytical bounds for probabilistic
entailment.
",Towards Precision of Probabilistic Bounds Propagation
"  In a previous paper [Pearl and Verma, 1991] we presented an algorithm for
extracting causal influences from independence information, where a causal
influence was defined as the existence of a directed arc in all minimal causal
models consistent with the data. In this paper we address the question of
deciding whether there exists a causal model that explains ALL the observed
dependencies and independencies. Formally, given a list M of conditional
independence statements, it is required to decide whether there exists a
directed acyclic graph (dag) D that is perfectly consistent with M, namely,
every statement in M, and no other, is reflected via dseparation in D. We
present and analyze an effective algorithm that tests for the existence of such
a day, and produces one, if it exists.
","An Algorithm for Deciding if a Set of Observed Independencies Has a
  Causal Explanation"
"  Jeffrey's rule has been generalized by Wagner to the case in which new
evidence bounds the possible revisions of a prior probability below by a
Dempsterian lower probability. Classical probability kinematics arises within
this generalization as the special case in which the evidentiary focal elements
of the bounding lower probability are pairwise disjoint. We discuss a twofold
extension of this generalization, first allowing the lower bound to be any
two-monotone capacity and then allowing the prior to be a lower envelope.
",Generalizing Jeffrey Conditionalization
"  In this paper, a unified framework for representing uncertain information
based on the notion of an interval structure is proposed. It is shown that the
lower and upper approximations of the rough-set model, the lower and upper
bounds of incidence calculus, and the belief and plausibility functions all
obey the axioms of an interval structure. An interval structure can be used to
synthesize the decision rules provided by the experts. An efficient algorithm
to find the desirable set of rules is developed from a set of sound and
complete inference axioms.
",Interval Structure: A Framework for Representing Uncertain Information
"  Current Bayesian net representations do not consider structure in the domain
and include all variables in a homogeneous network. At any time, a human
reasoner in a large domain may direct his attention to only one of a number of
natural subdomains, i.e., there is ?localization' of queries and evidence. In
such a case, propagating evidence through a homogeneous network is inefficient
since the entire network has to be updated each time. This paper presents
multiply sectioned Bayesian networks that enable a (localization preserving)
representation of natural subdomains by separate Bayesian subnets. The subnets
are transformed into a set of permanent junction trees such that evidential
reasoning takes place at only one of them at a time. Probabilities obtained are
identical to those that would be obtained from the homogeneous network. We
discuss attention shift to a different junction tree and propagation of
previously acquired evidence. Although the overall system can be large,
computational requirements are governed by the size of only one junction tree.
",Exploring Localization in Bayesian Networks for Large Expert Systems
"  Valuation-based system (VBS) provides a general framework for representing
knowledge and drawing inferences under uncertainty. Recent studies have shown
that the semantics of VBS can represent and solve Bayesian decision problems
(Shenoy, 1991a). The purpose of this paper is to propose a decision calculus
for Dempster-Shafer (D-S) theory in the framework of VBS. The proposed calculus
uses a weighting factor whose role is similar to the probabilistic
interpretation of an assumption that disambiguates decision problems
represented with belief functions (Strat 1990). It will be shown that with the
presented calculus, if the decision problems are represented in the valuation
network properly, we can solve the problems by using fusion algorithm (Shenoy
1991a). It will also be shown the presented decision calculus can be reduced to
the calculus for Bayesian probability theory when probabilities, instead of
belief functions, are given.
",A Decision Calculus for Belief Functions in Valuation-Based Systems
"  This paper presents a new approach for computing posterior probabilities in
Bayesian nets, which sidesteps the triangulation problem. The current state of
art is the clique tree propagation approach. When the underlying graph of a
Bayesian net is triangulated, this approach arranges its cliques into a tree
and computes posterior probabilities by appropriately passing around messages
in that tree. The computation in each clique is simply direct marginalization.
When the underlying graph is not triangulated, one has to first triangulated it
by adding edges. Referred to as the triangulation problem, the problem of
finding an optimal or even a ?good? triangulation proves to be difficult. In
this paper, we propose to first decompose a Bayesian net into smaller
components by making use of Tarjan's algorithm for decomposing an undirected
graph at all its minimal complete separators. Then, the components are arranged
into a tree and posterior probabilities are computed by appropriately passing
around messages in that tree. The computation in each component is carried out
by repeating the whole procedure from the beginning. Thus the triangulation
problem is sidestepped.
",Sidestepping the Triangulation Problem in Bayesian Net Computations
"  VT (Viterbi training), or hard EM, is an efficient way of parameter learning
for probabilistic models with hidden variables. Given an observation $y$, it
searches for a state of hidden variables $x$ that maximizes $p(x,y \mid
\theta)$ by coordinate ascent on parameters $\theta$ and $x$. In this paper we
introduce VT to PRISM, a logic-based probabilistic modeling system for
generative models. VT improves PRISM in three ways. First VT in PRISM converges
faster than EM in PRISM due to the VT's termination condition. Second,
parameters learned by VT often show good prediction performance compared to
those learned by EM. We conducted two parsing experiments with probabilistic
grammars while learning parameters by a variety of inference methods, i.e.\ VT,
EM, MAP and VB. The result is that VT achieved the best parsing accuracy among
them in both experiments. Also we conducted a similar experiment for
classification tasks where a hidden variable is not a prediction target unlike
probabilistic grammars. We found that in such a case VT does not necessarily
yield superior performance. Third since VT always deals with a single
probability of a single explanation, Viterbi explanation, the exclusiveness
condition that is imposed on PRISM programs is no more required if we learn
parameters by VT.
  Last but not least we can say that as VT in PRISM is general and applicable
to any PRISM program, it largely reduces the need for the user to develop a
specific VT algorithm for a specific model. Furthermore since VT in PRISM can
be used just by setting a PRISM flag appropriately, it makes VT easily
accessible to (probabilistic) logic programmers. To appear in Theory and
Practice of Logic Programming (TPLP).
",Viterbi training in PRISM
"  This paper examines the interdependence generated between two parent nodes
with a common instantiated child node, such as two hypotheses sharing common
evidence. The relation so generated has been termed ""intercausal."" It is shown
by construction that inter-causal independence is possible for binary
distributions at one state of evidence. For such ""CICI"" distributions, the two
measures of inter-causal effect, ""multiplicative synergy"" and ""additive
synergy"" are equal. The well known ""noisy-or"" model is an example of such a
distribution. This introduces novel semantics for the noisy-or, as a model of
the degree of conflict among competing hypotheses of a common observation.
","""Conditional Inter-Causally Independent"" Node Distributions, a Property
  of ""Noisy-Or"" Models"
"  The way experts manage uncertainty usually changes depending on the task they
are performing. This fact has lead us to consider the problem of communicating
modules (task implementations) in a large and structured knowledge based system
when modules have different uncertainty calculi. In this paper, the analysis of
the communication problem is made assuming that (i) each uncertainty calculus
is an inference mechanism defining an entailment relation, and therefore the
communication is considered to be inference-preserving, and (ii) we restrict
ourselves to the case which the different uncertainty calculi are given by a
class of truth functional Multiple-valued Logics.
",Combining Multiple-Valued Logics in Modular Expert Systems
"  An approach to reasoning with default rules where the proportion of
exceptions, or more generally the probability of encountering an exception, can
be at least roughly assessed is presented. It is based on local uncertainty
propagation rules which provide the best bracketing of a conditional
probability of interest from the knowledge of the bracketing of some other
conditional probabilities. A procedure that uses two such propagation rules
repeatedly is proposed in order to estimate any simple conditional probability
of interest from the available knowledge. The iterative procedure, that does
not require independence assumptions, looks promising with respect to the
linear programming method. Improved bounds for conditional probabilities are
given when independence assumptions hold.
",Constraint Propagation with Imprecise Conditional Probabilities
"  This paper presents a plausible reasoning system to illustrate some broad
issues in knowledge representation: dualities between different reasoning
forms, the difficulty of unifying complementary reasoning styles, and the
approximate nature of plausible reasoning. These issues have a common
underlying theme: there should be an underlying belief calculus of which the
many different reasoning forms are special cases, sometimes approximate. The
system presented allows reasoning about defaults, likelihood, necessity and
possibility in a manner similar to the earlier work of Adams. The system is
based on the belief calculus of subjective Bayesian probability which itself is
based on a few simple assumptions about how belief should be manipulated.
Approximations, semantics, consistency and consequence results are presented
for the system. While this puts these often discussed plausible reasoning forms
on a probabilistic footing, useful application to practical problems remains an
issue.
",Some Properties of Plausible Reasoning
"  Theory refinement is the task of updating a domain theory in the light of new
cases, to be done automatically or with some expert assistance. The problem of
theory refinement under uncertainty is reviewed here in the context of Bayesian
statistics, a theory of belief revision. The problem is reduced to an
incremental learning task as follows: the learning system is initially primed
with a partial theory supplied by a domain expert, and thereafter maintains its
own internal representation of alternative theories which is able to be
interrogated by the domain expert and able to be incrementally refined from
data. Algorithms for refinement of Bayesian networks are presented to
illustrate what is meant by ""partial theory"", ""alternative theory
representation"", etc. The algorithms are an incremental variant of batch
learning algorithms from the literature so can work well in batch and
incremental mode.
",Theory Refinement on Bayesian Networks
"  In this paper, we consider several types of information and methods of
combination associated with incomplete probabilistic systems. We discriminate
between 'a priori' and evidential information. The former one is a description
of the whole population, the latest is a restriction based on observations for
a particular case. Then, we propose different combination methods for each one
of them. We also consider conditioning as the heterogeneous combination of 'a
priori' and evidential information. The evidential information is represented
as a convex set of likelihood functions. These will have an associated
possibility distribution with behavior according to classical Possibility
Theory.
",Combination of Upper and Lower Probabilities
"  Useless paths are a chronic problem for marker-passing techniques. We use a
probabilistic analysis to justify a method for quickly identifying and
rejecting useless paths. Using the same analysis, we identify key conditions
and assumptions necessary for marker-passing to perform well.
","A Probabilistic Analysis of Marker-Passing Techniques for
  Plan-Recognition"
"  Research on Symbolic Probabilistic Inference (SPI) [2, 3] has provided an
algorithm for resolving general queries in Bayesian networks. SPI applies the
concept of dependency directed backward search to probabilistic inference, and
is incremental with respect to both queries and observations. Unlike
traditional Bayesian network inferencing algorithms, SPI algorithm is goal
directed, performing only those calculations that are required to respond to
queries. Research to date on SPI applies to Bayesian networks with
discrete-valued variables and does not address variables with continuous
values. In this papers, we extend the SPI algorithm to handle Bayesian networks
made up of continuous variables where the relationships between the variables
are restricted to be ?linear gaussian?. We call this variation of the SPI
algorithm, SPI Continuous (SPIC). SPIC modifies the three basic SPI operations:
multiplication, summation, and substitution. However, SPIC retains the
framework of the SPI algorithm, namely building the search tree and recursive
query mechanism and therefore retains the goal-directed and incrementality
features of SPI.
",Symbolic Probabilistic Inference with Continuous Variables
"  Recent research on the Symbolic Probabilistic Inference (SPI) algorithm[2]
has focused attention on the importance of resolving general queries in
Bayesian networks. SPI applies the concept of dependency-directed backward
search to probabilistic inference, and is incremental with respect to both
queries and observations. In response to this research we have extended the
evidence potential algorithm [3] with the same features. We call the extension
symbolic evidence potential inference (SEPI). SEPI like SPI can handle generic
queries and is incremental with respect to queries and observations. While in
SPI, operations are done on a search tree constructed from the nodes of the
original network, in SEPI, a clique-tree structure obtained from the evidence
potential algorithm [3] is the basic framework for recursive query processing.
In this paper, we describe the systematic query and caching procedure of SEPI.
SEPI begins with finding a clique tree from a Bayesian network-the standard
procedure of the evidence potential algorithm. With the clique tree, various
probability distributions are computed and stored in each clique. This is the
?pre-processing? step of SEPI. Once this step is done, the query can then be
computed. To process a query, a recursive process similar to the SPI algorithm
is used. The queries are directed to the root clique and decomposed into
queries for the clique's subtrees until a particular query can be answered at
the clique at which it is directed. The algorithm and the computation are
simple. The SEPI algorithm will be presented in this paper along with several
examples.
",Symbolic Probabilistic Inference with Evidence Potential
"  This paper presents a Bayesian method for constructing Bayesian belief
networks from a database of cases. Potential applications include
computer-assisted hypothesis testing, automated scientific discovery, and
automated construction of probabilistic expert systems. Results are presented
of a preliminary evaluation of an algorithm for constructing a belief network
from a database of cases. We relate the methods in this paper to previous work,
and we discuss open problems.
","A Bayesian Method for Constructing Bayesian Belief Networks from
  Databases"
"  We present a generalization of the local expression language used in the
Symbolic Probabilistic Inference (SPI) approach to inference in belief nets
[1l, [8]. The local expression language in SPI is the language in which the
dependence of a node on its antecedents is described. The original language
represented the dependence as a single monolithic conditional probability
distribution. The extended language provides a set of operators (*, +, and -)
which can be used to specify methods for combining partial conditional
distributions. As one instance of the utility of this extension, we show how
this extended language can be used to capture the semantics, representational
advantages, and inferential complexity advantages of the ""noisy or""
relationship.
","Local Expression Languages for Probabilistic Dependence: a Preliminary
  Report"
"  The ability to reason under uncertainty and with incomplete information is a
fundamental requirement of decision support technology. In this paper we argue
that the concentration on theoretical techniques for the evaluation and
selection of decision options has distracted attention from many of the wider
issues in decision making. Although numerical methods of reasoning under
uncertainty have strong theoretical foundations, they are representationally
weak and only deal with a small part of the decision process. Knowledge based
systems, on the other hand, offer greater flexibility but have not been
accompanied by a clear decision theory. We describe here work which is under
way towards providing a theoretical framework for symbolic decision procedures.
A central proposal is an extended form of inference which we call
argumentation; reasoning for and against decision options from generalised
domain theories. The approach has been successfully used in several decision
support applications, but it is argued that a comprehensive decision theory
must cover autonomous decision making, where the agent can formulate questions
as well as take decisions. A major theoretical challenge for this theory is to
capture the idea of reflection to permit decision agents to reason about their
goals, what they believe and why, and what they need to know or do in order to
achieve their goals.
",Symbolic Decision Theory and Autonomous Systems
"  A reason maintenance system which extends an ATMS through Mukaidono's fuzzy
logic is described. It supports a problem solver in situations affected by
incomplete information and vague data, by allowing nonmonotonic inferences and
the revision of previous conclusions when contradictions are detected.
",A Reason Maintenace System Dealing with Vague Data
"  This paper discuses multiple Bayesian networks representation paradigms for
encoding asymmetric independence assertions. We offer three contributions: (1)
an inference mechanism that makes explicit use of asymmetric independence to
speed up computations, (2) a simplified definition of similarity networks and
extensions of their theory, and (3) a generalized representation scheme that
encodes more types of asymmetric independence assertions than do similarity
networks.
",Advances in Probabilistic Reasoning
"  In this paper, we consider one aspect of the problem of applying decision
theory to the design of agents that learn how to make decisions under
uncertainty. This aspect concerns how an agent can estimate probabilities for
the possible states of the world, given that it only makes limited observations
before committing to a decision. We show that the naive application of
statistical tools can be improved upon if the agent can determine which of his
observations are truly relevant to the estimation problem at hand. We give a
framework in which such determinations can be made, and define an estimation
procedure to use them. Our framework also suggests several extensions, which
show how additional knowledge can be used to improve tile estimation procedure
still further.
",Probability Estimation in Face of Irrelevant Information
"  Value-of-information analyses provide a straightforward means for selecting
the best next observation to make, and for determining whether it is better to
gather additional information or to act immediately. Determining the next best
test to perform, given a state of uncertainty about the world, requires a
consideration of the value of making all possible sequences of observations. In
practice, decision analysts and expert-system designers have avoided the
intractability of exact computation of the value of information by relying on a
myopic approximation. Myopic analyses are based on the assumption that only one
additional test will be performed, even when there is an opportunity to make a
large number of observations. We present a nonmyopic approximation for value of
information that bypasses the traditional myopic analyses by exploiting the
statistical properties of large samples.
",An Approximate Nonmyopic Computation for Value of Information
"  Since exact probabilistic inference is intractable in general for large
multiply connected belief nets, approximate methods are required. A promising
approach is to use heuristic search among hypotheses (instantiations of the
network) to find the most probable ones, as in the TopN algorithm. Search is
based on the relative probabilities of hypotheses which are efficient to
compute. Given upper and lower bounds on the relative probability of partial
hypotheses, it is possible to obtain bounds on the absolute probabilities of
hypotheses. Best-first search aimed at reducing the maximum error progressively
narrows the bounds as more hypotheses are examined. Here, qualitative
probabilistic analysis is employed to obtain bounds on the relative probability
of partial hypotheses for the BN20 class of networks networks and a
generalization replacing the noisy OR assumption by negative synergy. The
approach is illustrated by application to a very large belief network, QMR-BN,
which is a reformulation of the Internist-1 system for diagnosis in internal
medicine.
","Search-based Methods to Bound Diagnostic Probabilities in Very Large
  Belief Nets"
"  We discuss representing and reasoning with knowledge about the time-dependent
utility of an agent's actions. Time-dependent utility plays a crucial role in
the interaction between computation and action under bounded resources. We
present a semantics for time-dependent utility and describe the use of
time-dependent information in decision contexts. We illustrate our discussion
with examples of time-pressured reasoning in Protos, a system constructed to
explore the ideal control of inference by reasoners with limit abilities.
",Time-Dependent Utility and Action Under Uncertainty
"  Traditional approaches to non-monotonic reasoning fail to satisfy a number of
plausible axioms for belief revision and suffer from conceptual difficulties as
well. Recent work on ranked preferential models (RPMs) promises to overcome
some of these difficulties. Here we show that RPMs are not adequate to handle
iterated belief change. Specifically, we show that RPMs do not always allow for
the reversibility of belief change. This result indicates the need for
numerical strengths of belief.
",Non-monotonic Reasoning and the Reversibility of Belief Change
"  We motivate and describe a theory of belief in this paper. This theory is
developed with the following view of human belief in mind. Consider the belief
that an event E will occur (or has occurred or is occurring). An agent either
entertains this belief or does not entertain this belief (i.e., there is no
""grade"" in entertaining the belief). If the agent chooses to exercise ""the will
to believe"" and entertain this belief, he/she/it is entitled to a degree of
confidence c (1 > c > 0) in doing so. Adopting this view of human belief, we
conjecture that whenever an agent entertains the belief that E will occur with
c degree of confidence, the agent will be surprised (to the extent c) upon
realizing that E did not occur.
",Belief and Surprise - A Belief-Function Formulation
"  The categorial approach to evidential reasoning can be seen as a combination
of the probability kinematics approach of Richard Jeffrey (1965) and the
maximum (cross-) entropy inference approach of E. T. Jaynes (1957). As a
consequence of that viewpoint, it is well known that category theory provides
natural definitions for logical connectives. In particular, disjunction and
conjunction are modelled by general categorial constructions known as products
and coproducts. In this paper, I focus mainly on Dempster-Shafer theory of
belief functions for which I introduce a category I call Dempster?s category. I
prove the existence of and give explicit formulas for conjunction and
disjunction in the subcategory of separable belief functions. In Dempster?s
category, the new defined conjunction can be seen as the most cautious
conjunction of beliefs, and thus no assumption about distinctness (of the
sources) of beliefs is needed as opposed to Dempster?s rule of combination,
which calls for distinctness (of the sources) of beliefs.
","Evidential Reasoning in a Categorial Perspective: Conjunction and
  Disjunction of Belief Functions"
"  The concept of movable evidence masses that flow from supersets to subsets as
specified by experts represents a suitable framework for reasoning under
uncertainty. The mass flow is controlled by specialization matrices. New
evidence is integrated into the frame of discernment by conditioning or
revision (Dempster's rule of conditioning), for which special specialization
matrices exist. Even some aspects of non-monotonic reasoning can be represented
by certain specialization matrices.
",Reasoning with Mass Distributions
"  Any probabilistic model of a problem is based on assumptions which, if
violated, invalidate the model. Users of probability based decision aids need
to be alerted when cases arise that are not covered by the aid's model.
Diagnosis of model failure is also necessary to control dynamic model
construction and revision. This paper presents a set of decision theoretically
motivated heuristics for diagnosing situations in which a model is likely to
provide an inadequate representation of the process being modeled.
",Conflict and Surprise: Heuristics for Model Revision
"  A series of monte carlo studies were performed to compare the behavior of
some alternative procedures for reasoning under uncertainty. The behavior of
several Bayesian, linear model and default reasoning procedures were examined
in the context of increasing levels of calibration error. The most interesting
result is that Bayesian procedures tended to output more extreme posterior
belief values (posterior beliefs near 0.0 or 1.0) than other techniques, but
the linear models were relatively less likely to output strong support for an
erroneous conclusion. Also, accounting for the probabilistic dependencies
between evidence items was important for both Bayesian and linear updating
procedures.
",Reasoning under Uncertainty: Some Monte Carlo Results
"  This paper outlines a methodology for analyzing the representational support
for knowledge-based decision-modeling in a broad domain. A relevant set of
inference patterns and knowledge types are identified. By comparing the
analysis results to existing representations, some insights are gained into a
design approach for integrating categorical and uncertain knowledge in a
context sensitive manner.
",Representation Requirements for Supporting Decision Model Formulation
"  When a planner must decide whether it has enough evidence to make a decision
based on probability, it faces the sample size problem. Current planners using
probabilities need not deal with this problem because they do not generate
their probabilities from observations. This paper presents an event based
language in which the planner's probabilities are calculated from the binomial
random variable generated by the observed ratio of one type of event to
another. Such probabilities are subject to error, so the planner must
introspect about their validity. Inferences about the probability of these
events can be made using statistics. Inferences about the validity of the
approximations can be made using interval estimation. Interval estimation
allows the planner to avoid making choices that are only weakly supported by
the planner's evidence.
",A Language for Planning with Statistics
"  Selecting the right reference class and the right interval when faced with
conflicting candidates and no possibility of establishing subset style
dominance has been a problem for Kyburg's Evidential Probability system.
Various methods have been proposed by Loui and Kyburg to solve this problem in
a way that is both intuitively appealing and justifiable within Kyburg's
framework. The scheme proposed in this paper leads to stronger statistical
assertions without sacrificing too much of the intuitive appeal of Kyburg's
latest proposal.
",A Modification to Evidential Probability
"  The belief network is a well-known graphical structure for representing
independences in a joint probability distribution. The methods, which perform
probabilistic inference in belief networks, often treat the conditional
probabilities which are stored in the network as certain values. However, if
one takes either a subjectivistic or a limiting frequency approach to
probability, one can never be certain of probability values. An algorithm
should not only be capable of reporting the probabilities of the alternatives
of remaining nodes when other nodes are instantiated; it should also be capable
of reporting the uncertainty in these probabilities relative to the uncertainty
in the probabilities which are stored in the network. In this paper a method
for determining the variances in inferred probabilities is obtained under the
assumption that a posterior distribution on the uncertainty variables can be
approximated by the prior distribution. It is shown that this assumption is
plausible if their is a reasonable amount of confidence in the probabilities
which are stored in the network. Furthermore in this paper, a surprising upper
bound for the prior variances in the probabilities of the alternatives of all
nodes is obtained in the case where the probability distributions of the
probabilities of the alternatives are beta distributions. It is shown that the
prior variance in the probability at an alternative of a node is bounded above
by the largest variance in an element of the conditional probability
distribution for that node.
",Investigation of Variances in Belief Networks
"  At last year?s Uncertainty in AI Conference, we reported the results of a
sensitivity analysis study of Pathfinder. Our findings were quite
unexpected-slight variations to Pathfinder?s parameters appeared to lead to
substantial degradations in system performance. A careful look at our first
analysis, together with the valuable feedback provided by the participants of
last year?s conference, led us to conduct a follow-up study. Our follow-up
differs from our initial study in two ways: (i) the probabilities 0.0 and 1.0
remained unchanged, and (ii) the variations to the probabilities that are close
to both ends (0.0 or 1.0) were less than the ones close to the middle (0.5).
The results of the follow-up study look more reasonable-slight variations to
Pathfinder?s parameters now have little effect on its performance. Taken
together, these two sets of results suggest a viable extension of a common
decision analytic sensitivity analysis to the larger, more complex settings
generally encountered in artificial intelligence.
",A Sensitivity Analysis of Pathfinder: A Follow-up Study
"  In this paper we study the uses and the semantics of non-monotonic negation
in probabilistic deductive data bases. Based on the stable semantics for
classical logic programming, we introduce the notion of stable formula,
functions. We show that stable formula, functions are minimal fixpoints of
operators associated with probabilistic deductive databases with negation.
Furthermore, since a. probabilistic deductive database may not necessarily have
a stable formula function, we provide a stable class semantics for such
databases. Finally, we demonstrate that the proposed semantics can handle
default reasoning naturally in the context of probabilistic deduction.
",Non-monotonic Negation in Probabilistic Deductive Databases
"  We present a general architecture for the monitoring and diagnosis of large
scale sensor-based systems with real time diagnostic constraints. This
architecture is multileveled, combining a single monitoring level based on
statistical methods with two model based diagnostic levels. At each level,
sources of uncertainty are identified, and integrated methodologies for
uncertainty management are developed. The general architecture was applied to
the monitoring and diagnosis of a specific nuclear physics detector at Lawrence
Berkeley National Laboratory that contained approximately 5000 components and
produced over 500 channels of output data. The general architecture is
scalable, and work is ongoing to apply it to detector systems one and two
orders of magnitude more complex.
","Management of Uncertainty in the Multi-Level Monitoring and Diagnosis of
  the Time of Flight Scintillation Array"
"  The EM-algorithm is a general procedure to get maximum likelihood estimates
if part of the observations on the variables of a network are missing. In this
paper a stochastic version of the algorithm is adapted to probabilistic neural
networks describing the associative dependency of variables. These networks
have a probability distribution, which is a special case of the distribution
generated by probabilistic inference networks. Hence both types of networks can
be combined allowing to integrate probabilistic rules as well as unspecified
associations in a sound way. The resulting network may have a number of
interesting features including cycles of probabilistic rules, hidden
'unobservable' variables, and uncertain and contradictory evidence.
","Integrating Probabilistic Rules into Neural Networks: A Stochastic EM
  Learning Algorithm"
"  This paper presents a simple framework for Horn clause abduction, with
probabilities associated with hypotheses. It is shown how this representation
can represent any probabilistic knowledge representable in a Bayesian belief
network. The main contributions are in finding a relationship between logical
and probabilistic notions of evidential reasoning. This can be used as a basis
for a new way to implement Bayesian Networks that allows for approximations to
the value of the posterior probabilities, and also points to a way that
Bayesian networks can be extended beyond a propositional language.
",Representing Bayesian Networks within Probabilistic Horn Abduction
"  A new probabilistic network construction system, DYNASTY, is proposed for
diagnostic reasoning given variables whose probabilities change over time.
Diagnostic reasoning is formulated as a sequential stochastic process, and is
modeled using influence diagrams. Given a set O of observations, DYNASTY
creates an influence diagram in order to devise the best action given O.
Sensitivity analyses are conducted to determine if the best network has been
created, given the uncertainty in network parameters and topology. DYNASTY uses
an equivalence class approach to provide decision thresholds for the
sensitivity analysis. This equivalence-class approach to diagnostic reasoning
differentiates diagnoses only if the required actions are different. A set of
network-topology updating algorithms are proposed for dynamically updating the
network when necessary.
",Dynamic Network Updating Techniques For Diagnostic Reasoning
"  For high level path planning, environments are usually modeled as distance
graphs, and path planning problems are reduced to computing the shortest path
in distance graphs. One major drawback of this modeling is the inability to
model uncertainties, which are often encountered in practice. In this paper, a
new tool, called U-yraph, is proposed for environment modeling. A U-graph is an
extension of distance graphs with the ability to handle a kind of uncertainty.
By modeling an uncertain environment as a U-graph, and a navigation problem as
a Markovian decision process, we can precisely define a new optimality
criterion for navigation plans, and more importantly, we can come up with a
general algorithm for computing optimal plans for navigation tasks.
",High Level Path Planning with Uncertainty
"  Given a universe of discourse X-a domain of possible outcomes-an experiment
may consist of selecting one of its elements, subject to the operation of
chance, or of observing the elements, subject to imprecision. A priori
uncertainty about the actual result of the experiment may be quantified,
representing either the likelihood of the choice of :r_X or the degree to which
any such X would be suitable as a description of the outcome. The former case
corresponds to a probability distribution, while the latter gives a possibility
assignment on X. The study of such assignments and their properties falls
within the purview of possibility theory [DP88, Y80, Z783. It, like probability
theory, assigns values between 0 and 1 to express likelihoods of outcomes.
Here, however, the similarity ends. Possibility theory uses the maximum and
minimum functions to combine uncertainties, whereas probability theory uses the
plus and times operations. This leads to very dissimilar theories in terms of
analytical framework, even though they share several semantic concepts. One of
the shared concepts consists of expressing quantitatively the uncertainty
associated with a given distribution. In probability theory its value
corresponds to the gain of information that would result from conducting an
experiment and ascertaining an actual result. This gain of information can
equally well be viewed as a decrease in uncertainty about the outcome of an
experiment. In this case the standard measure of information, and thus
uncertainty, is Shannon entropy [AD75, G77]. It enjoys several advantages-it is
characterized uniquely by a few, very natural properties, and it can be
conveniently used in decision processes. This application is based on the
principle of maximum entropy; it has become a popular method of relating
decisions to uncertainty. This paper demonstrates that an equally integrated
theory can be built on the foundation of possibility theory. We first show how
to define measures of in formation and uncertainty for possibility assignments.
Next we construct an information-based metric on the space of all possibility
distributions defined on a given domain. It allows us to capture the notion of
proximity in information content among the distributions. Lastly, we show that
all the above constructions can be carried out for continuous
distributions-possibility assignments on arbitrary measurable domains. We
consider this step very significant-finite domains of discourse are but
approximations of the real-life infinite domains. If possibility theory is to
represent real world situations, it must handle continuous distributions both
directly and through finite approximations. In the last section we discuss a
principle of maximum uncertainty for possibility distributions. We show how
such a principle could be formalized as an inference rule. We also suggest it
could be derived as a consequence of simple assumptions about combining
information. We would like to mention that possibility assignments can be
viewed as fuzzy sets and that every fuzzy set gives rise to an assignment of
possibilities. This correspondence has far reaching consequences in logic and
in control theory. Our treatment here is independent of any special
interpretation; in particular we speak of possibility distributions and
possibility measures, defining them as measurable mappings into the interval
[0, 1]. Our presentation is intended as a self-contained, albeit terse summary.
Topics discussed were selected with care, to demonstrate both the completeness
and a certain elegance of the theory. Proofs are not included; we only offer
illustrative examples.
",Formal Model of Uncertainty for Possibilistic Rules
"  Deliberation plays an important role in the design of rational agents
embedded in the real-world. In particular, deliberation leads to the formation
of intentions, i.e., plans of action that the agent is committed to achieving.
In this paper, we present a branching time possible-worlds model for
representing and reasoning about, beliefs, goals, intentions, time, actions,
probabilities, and payoffs. We compare this possible-worlds approach with the
more traditional decision tree representation and provide a transformation from
decision trees to possible worlds. Finally, we illustrate how an agent can
perform deliberation using a decision-tree representation and then use a
possible-worlds model to form and reason about his intentions.
",Deliberation and its Role in the Formation of Intentions
"  During interactions with human consultants, people are used to providing
partial and/or inaccurate information, and still be understood and assisted. We
attempt to emulate this capability of human consultants; in computer
consultation systems. In this paper, we present a mechanism for handling
uncertainty in plan recognition during task-oriented consultations. The
uncertainty arises while choosing an appropriate interpretation of a user?s
statements among many possible interpretations. Our mechanism handles this
uncertainty by using probability theory to assess the probabilities of the
interpretations, and complements this assessment by taking into account the
information content of the interpretations. The information content of an
interpretation is a measure of how well defined an interpretation is in terms
of the actions to be performed on the basis of the interpretation. This measure
is used to guide the inference process towards interpretations with a higher
information content. The information content for an interpretation depends on
the specificity and the strength of the inferences in it, where the strength of
an inference depends on the reliability of the information on which the
inference is based. Our mechanism has been developed for use in task-oriented
consultation systems. The domain that we have chosen for exploration is that of
a travel agency.
","Handling Uncertainty during Plan Recognition in Task-Oriented
  Consultation Systems"
"  This paper introduces conceptual relations that synthesize utilitarian and
logical concepts, extending the logics of preference of Rescher. We define
first, in the context of a possible worlds model, constraint-dependent measures
that quantify the relative quality of alternative solutions of reasoning
problems or the relative desirability of various policies in control, decision,
and planning problems. We show that these measures may be interpreted as truth
values in a multi valued logic and propose mechanisms for the representation of
complex constraints as combinations of simpler restrictions. These extended
logical operations permit also the combination and aggregation of goal-specific
quality measures into global measures of utility. We identify also relations
that represent differential preferences between alternative solutions and
relate them to the previously defined desirability measures. Extending
conventional modal logic formulations, we introduce structures for the
representation of ignorance about the utility of alternative solutions.
Finally, we examine relations between these concepts and similarity based
semantic models of fuzzy logic.
",Truth as Utility: A Conceptual Synthesis
"  We present PULCinella and its use in comparing uncertainty theories.
PULCinella is a general tool for Propagating Uncertainty based on the Local
Computation technique of Shafer and Shenoy. It may be specialized to different
uncertainty theories: at the moment, Pulcinella can propagate probabilities,
belief functions, Boolean values, and possibilities. Moreover, Pulcinella
allows the user to easily define his own specializations. To illustrate
Pulcinella, we analyze two examples by using each of the four theories above.
In the first one, we mainly focus on intrinsic differences between theories. In
the second one, we take a knowledge engineer viewpoint, and check the adequacy
of each theory to a given problem.
","Pulcinella: A General Tool for Propagating Uncertainty in Valuation
  Networks"
"  In this article we present two ways of structuring bodies of evidence, which
allow us to reduce the complexity of the operations usually performed in the
framework of evidence theory. The first structure just partitions the focal
elements in a body of evidence by their cardinality. With this structure we are
able to reduce the complexity on the calculation of the belief functions Bel,
Pl, and Q. The other structure proposed here, the Hierarchical Trees, permits
us to reduce the complexity of the calculation of Bel, Pl, and Q, as well as of
the Dempster's rule of combination in relation to the brute-force algorithm.
Both these structures do not require the generation of all the subsets of the
reference domain.
",Structuring Bodies of Evidence
"  In general, the best explanation for a given observation makes no promises on
how good it is with respect to other alternative explanations. A major
deficiency of message-passing schemes for belief revision in Bayesian networks
is their inability to generate alternatives beyond the second best. In this
paper, we present a general approach based on linear constraint systems that
naturally generates alternative explanations in an orderly and highly efficient
manner. This approach is then applied to cost-based abduction problems as well
as belief revision in Bayesian net works.
","On the Generation of Alternative Explanations with Implications for
  Belief Revision"
"  A control strategy for expert systems is presented which is based on Shafer's
Belief theory and the combination rule of Dempster. In contrast to well known
strategies it is not sequentially and hypotheses-driven, but parallel and self
organizing, determined by the concept of information gain. The information
gain, calculated as the maximal difference between the actual evidence
distribution in the knowledge base and the potential evidence determines each
consultation step. Hierarchically structured knowledge is an important
representation form and experts even use several hierarchies in parallel for
constituting their knowledge. Hence the control strategy is applied to a
layered set of distinct hierarchies. Depending on the actual data one of these
hierarchies is chosen by the control strategy for the next step in the
reasoning process. Provided the actual data are well matched to the structure
of one hierarchy, this hierarchy remains selected for a longer consultation
time. If no good match can be achieved, a switch from the actual hierarchy to a
competing one will result, very similar to the phenomenon of restructuring in
problem solving tasks. Up to now the control strategy is restricted to multi
hierarchical knowledge bases with disjunct hierarchies. It is implemented in
the expert system IBIG (inference by information gain), being presently applied
to acquired speech disorders (aphasia).
",Completing Knowledge by Competing Hierarchies
"  The graphoid axioms for conditional independence, originally described by
Dawid [1979], are fundamental to probabilistic reasoning [Pearl, 19881. Such
axioms provide a mechanism for manipulating conditional independence assertions
without resorting to their numerical definition. This paper explores a
representation for independence statements using multiple undirected graphs and
some simple graphical transformations. The independence statements derivable in
this system are equivalent to those obtainable by the graphoid axioms.
Therefore, this is a purely graphical proof technique for conditional
independence.
",A Graph-Based Inference Method for Conditional Independence
"  This paper proposes a new method for solving Bayesian decision problems. The
method consists of representing a Bayesian decision problem as a
valuation-based system and applying a fusion algorithm for solving it. The
fusion algorithm is a hybrid of local computational methods for computation of
marginals of joint probability distributions and the local computational
methods for discrete optimization problems.
",A Fusion Algorithm for Solving Bayesian Decision Problems
"  Irrelevance-based partial MAPs are useful constructs for domain-independent
explanation using belief networks. We look at two definitions for such partial
MAPs, and prove important properties that are useful in designing algorithms
for computing them effectively. We make use of these properties in modifying
our standard MAP best-first algorithm, so as to handle irrelevance-based
partial MAPs.
",Algorithms for Irrelevance-Based Partial MAPs
"  Survey of several forms of updating, with a practical illustrative example.
We study several updating (conditioning) schemes that emerge naturally from a
common scenarion to provide some insights into their meaning. Updating is a
subtle operation and there is no single method, no single 'good' rule. The
choice of the appropriate rule must always be given due consideration. Planchet
(1989) presents a mathematical survey of many rules. We focus on the practical
meaning of these rules. After summarizing the several rules for conditioning,
we present an illustrative example in which the various forms of conditioning
can be explained.
",About Updating
"  In probabilistic logic entailments, even moderate size problems can yield
linear constraint systems with so many variables that exact methods are
impractical. This difficulty can be remedied in many cases of interest by
introducing a three valued logic (true, false, and ""don't care""). The
three-valued approach allows the construction of ""compressed"" constraint
systems which have the same solution sets as their two-valued counterparts, but
which may involve dramatically fewer variables. Techniques to calculate point
estimates for the posterior probabilities of entailed sentences are discussed.
",Compressed Constraints in Probabilistic Logic and Their Revision
"  The presence of latent variables can greatly complicate inferences about
causal relations between measured variables from statistical data. In many
cases, the presence of latent variables makes it impossible to determine for
two measured variables A and B, whether A causes B, B causes A, or there is
some common cause. In this paper I present several theorems that state
conditions under which it is possible to reliably infer the causal relation
between two measured variables, regardless of whether latent variables are
acting or not.
",Detecting Causal Relations in the Presence of Unmeasured Variables
"  In mechanical design, there is often unavoidable uncertainty in estimates of
design performance. Evaluation of design alternatives requires consideration of
the impact of this uncertainty. Expert heuristics embody assumptions regarding
the designer's attitude towards risk and uncertainty that might be reasonable
in most cases but inaccurate in others. We present a technique to allow
designers to incorporate their own unique attitude towards uncertainty as
opposed to those assumed by the domain expert's rules. The general approach is
to eliminate aspects of heuristic rules which directly or indirectly include
assumptions regarding the user's attitude towards risk, and replace them with
explicit, user-specified probabilistic multi attribute utility and probability
distribution functions. We illustrate the method in a system for material
selection for automobile bumpers.
","A Method for Integrating Utility Analysis into an Expert System for
  Design Evaluation"
"  The relationship between belief networks and relational databases is
examined. Based on this analysis, a method to construct belief networks
automatically from statistical relational data is proposed. A comparison
between our method and other methods shows that our method has several
advantages when generalization or prediction is deeded.
",From Relational Databases to Belief Networks
"  A very computationally-efficient Monte-Carlo algorithm for the calculation of
Dempster-Shafer belief is described. If Bel is the combination using Dempster's
Rule of belief functions Bel, ..., Bel,7, then, for subset b of the frame C),
Bel(b) can be calculated in time linear in 1(31 and m (given that the weight of
conflict is bounded). The algorithm can also be used to improve the complexity
of the Shenoy-Shafer algorithms on Markov trees, and be generalised to
calculate Dempster-Shafer Belief over other logics.
",A Monte-Carlo Algorithm for Dempster-Shafer Belief
"  The compatibility of quantitative and qualitative representations of beliefs
was studied extensively in probability theory. It is only recently that this
important topic is considered in the context of belief functions. In this
paper, the compatibility of various quantitative belief measures and
qualitative belief structures is investigated. Four classes of belief measures
considered are: the probability function, the monotonic belief function,
Shafer's belief function, and Smets' generalized belief function. The analysis
of their individual compatibility with different belief structures not only
provides a sound b<msis for these quantitative measures, but also alleviates
some of the difficulties in the acquisition and interpretation of numeric
belief numbers. It is shown that the structure of qualitative probability is
compatible with monotonic belief functions. Moreover, a belief structure
slightly weaker than that of qualitative belief is compatible with Smets'
generalized belief functions.
",Compatibility of Quantitative and Qualitative Representations of Belief
"  The local computation technique (Shafer et al. 1987, Shafer and Shenoy 1988,
Shenoy and Shafer 1986) is used for propagating belief functions in so called a
Markov Tree. In this paper, we describe an efficient implementation of belief
function propagation on the basis of the local computation technique. The
presented method avoids all the redundant computations in the propagation
process, and so makes the computational complexity decrease with respect to
other existing implementations (Hsia and Shenoy 1989, Zarley et al. 1988). We
also give a combined algorithm for both propagation and re-propagation which
makes the re-propagation process more efficient when one or more of the prior
belief functions is changed.
",An Efficient Implementation of Belief Function Propagation
"  We describe a technique that can be used for the fusion of multiple sources
of information as well as for the evaluation and selection of alternatives
under multi-criteria. Three important properties contribute to the uniqueness
of the technique introduced. The first is the ability to do all necessary
operations and aggregations with information that is of a nonnumeric linguistic
nature. This facility greatly reduces the burden on the providers of
information, the experts. A second characterizing feature is the ability
assign, again linguistically, differing importance to the criteria or in the
case of information fusion to the individual sources of information. A third
significant feature of the approach is its ability to be used as method to find
a consensus of the opinion of multiple experts on the issue of concern. The
techniques used in this approach are base on ideas developed from the theory of
approximate reasoning. We illustrate the approach with a problem of project
selection.
","A Non-Numeric Approach to Multi-Criteria/Multi-Expert Aggregation Based
  on Approximate Reasoning"
"  Surely we want solid foundations. What kind of castle can we build on sand?
What is the point of devoting effort to balconies and minarets, if the
foundation may be so weak as to allow the structure to collapse of its own
weight? We want our foundations set on bedrock, designed to last for
generations. Who would want an architect who cannot certify the soundness of
the foundations of his buildings?
",Why Do We Need Foundations for Modelling Uncertainties?
"  Ontology Learning has been the subject of intensive study for the past
decade. Researchers in this field have been motivated by the possibility of
automatically building a knowledge base on top of text documents so as to
support reasoning based knowledge extraction. While most works in this field
have been primarily statistical (known as light-weight Ontology Learning) not
much attempt has been made in axiomatic Ontology Learning (called heavy-weight
Ontology Learning) from Natural Language text documents. Heavy-weight Ontology
Learning supports more precise formal logic-based reasoning when compared to
statistical ontology learning. In this paper we have proposed a sound Ontology
Learning tool DLOL_(IS-A) that maps English language IS-A sentences into their
equivalent Description Logic (DL) expressions in order to automatically
generate a consistent pair of T-box and A-box thereby forming both regular
(definitional form) and generalized (axiomatic form) DL ontology. The current
scope of the paper is strictly limited to IS-A sentences that exclude the
possible structures of: (i) implicative IS-A sentences, and (ii) ""Wh"" IS-A
questions. Other linguistic nuances that arise out of pragmatics and epistemic
of IS-A sentences are beyond the scope of this present work. We have adopted
Gold Standard based Ontology Learning evaluation on chosen IS-A rich Wikipedia
documents.
",DLOLIS-A: Description Logic based Text Ontology Learning
"  In this article, we combine the concept of a bipolar fuzzy set and a soft
set. We introduce the notion of bipolar fuzzy soft set and study fundamental
properties. We study basic operations on bipolar fuzzy soft set. We define
exdended union, intersection of two bipolar fuzzy soft set. We also give an
application of bipolar fuzzy soft set into decision making problem. We give a
general algorithm to solve decision making problems by using bipolar fuzzy soft
set.
",Bipolar Fuzzy Soft sets and its applications in decision making problem
"  The Bootstrap method application in simulation supposes that value of random
variables are not generated during the simulation process but extracted from
available sample populations. In the case of Hierarchical Bootstrap the
function of interest is calculated recurrently using the calculation tree. In
the present paper we consider the optimization of sample sizes in each vertex
of the calculation tree. The dynamic programming method is used for this aim.
Proposed method allows to decrease a variance of system characteristic
estimators.
","Discrete Optimization of Statistical Sample Sizes in Simulation by Using
  the Hierarchical Bootstrap Method"
"  The accumulation of adaptations in an open-ended manner during lifetime
learning is a holy grail in reinforcement learning, intrinsic motivation,
artificial curiosity, and developmental robotics. We present a specification
for a cognitive architecture that is capable of specifying an unlimited range
of behaviors. We then give examples of how it can stochastically explore an
interesting space of adjacent possible behaviors. There are two main novelties;
the first is a proper definition of the fitness of self-generated games such
that interesting games are expected to evolve. The second is a modular and
evolvable behavior language that has systematicity, productivity, and
compositionality, i.e. it is a physical symbol system. A part of the
architecture has already been implemented on a humanoid robot.
",Design for a Darwinian Brain: Part 2. Cognitive Architecture
"  A fundamental question in Computer Science is understanding when a specific
class of problems go from being computationally easy to hard. Because of its
generality and applications, the problem of Boolean Satisfiability (aka SAT) is
often used as a vehicle for investigating this question. A signal result from
these studies is that the hardness of SAT problems exhibits a dramatic
easy-to-hard phase transition with respect to the problem constrainedness. Past
studies have however focused mostly on SAT instances generated using uniform
random distributions, where all constraints are independently generated, and
the problem variables are all considered of equal importance. These assumptions
are unfortunately not satisfied by most real problems. Our project aims for a
deeper understanding of hardness of SAT problems that arise in practice. We
study two key questions: (i) How does easy-to-hard transition change with more
realistic distributions that capture neighborhood sensitivity and
rich-get-richer aspects of real problems and (ii) Can these changes be
explained in terms of the network properties (such as node centrality and
small-worldness) of the clausal networks of the SAT problems. Our results,
based on extensive empirical studies and network analyses, provide important
structural and computational insights into realistic SAT problems. Our
extensive empirical studies show that SAT instances from realistic
distributions do exhibit phase transition, but the transition occurs sooner (at
lower values of constrainedness) than the instances from uniform random
distribution. We show that this behavior can be explained in terms of their
clausal network properties such as eigenvector centrality and small-worldness
(measured indirectly in terms of the clustering coefficients and average node
distance).
",Phase Transition and Network Structure in Realistic SAT Problems
"  This paper focuses on the expressive power of disjunctive and normal logic
programs under the stable model semantics over finite, infinite, or arbitrary
structures. A translation from disjunctive logic programs into normal logic
programs is proposed and then proved to be sound over infinite structures. The
equivalence of expressive power of two kinds of logic programs over arbitrary
structures is shown to coincide with that over finite structures, and coincide
with whether or not NP is closed under complement. Over finite structures, the
intranslatability from disjunctive logic programs to normal logic programs is
also proved if arities of auxiliary predicates and functions are bounded in a
certain way.
",Disjunctive Logic Programs versus Normal Logic Programs
"  In this work, we present definition of intuitionistic fuzzy parameterized
(IFP) intuitionistic fuzzy soft set and its operations. Then we define
IFP-aggregation operator to form IFP-intuitionistic fuzzy soft-decision-making
method which allows constructing more efficient decision processes.
",IFP-Intuitionistic fuzzy soft set theory and its applications
"  We describe a duality mapping between STRIPS planning tasks. By exchanging
the initial and goal conditions, taking their respective complements, and
swapping for every action its precondition and delete list, one obtains for
every STRIPS task its dual version, which has a solution if and only if the
original does. This is proved by showing that the described transformation
essentially turns progression (forward search) into regression (backward
search) and vice versa.
  The duality sheds new light on STRIPS planning by allowing a transfer of
ideas from one search approach to the other. It can be used to construct new
algorithms from old ones, or (equivalently) to obtain new benchmarks from
existing ones. Experiments show that the dual versions of IPC benchmarks are in
general quite difficult for modern planners. This may be seen as a new
challenge. On the other hand, the cases where the dual versions are easier to
solve demonstrate that the duality can also be made useful in practice.
",Duality in STRIPS planning
"  Functional dependencies restrict the potential interactions among variables
connected in a probabilistic network. This restriction can be exploited in
qualitative probabilistic reasoning by introducing deterministic variables and
modifying the inference rules to produce stronger conclusions in the presence
of functional relations. I describe how to accomplish these modifications in
qualitative probabilistic networks by exhibiting the update procedures for
graphical transformations involving probabilistic and deterministic variables
and combinations. A simple example demonstrates that the augmented scheme can
reduce qualitative ambiguity that would arise without the special treatment of
functional dependency. Analysis of qualitative synergy reveals that new
higher-order relations are required to reason effectively about synergistic
interactions among deterministic variables.
","Exploiting Functional Dependencies in Qualitative Probabilistic
  Reasoning"
"  Comprehensible explanations of probabilistic reasoning are a prerequisite for
wider acceptance of Bayesian methods in expert systems and decision support
systems. A study of human reasoning under uncertainty suggests two different
strategies for explaining probabilistic reasoning: The first, qualitative
belief propagation, traces the qualitative effect of evidence through a belief
network from one variable to the next. This propagation algorithm is an
alternative to the graph reduction algorithms of Wellman (1988) for inference
in qualitative probabilistic networks. It is based on a qualitative analysis of
intercausal reasoning, which is a generalization of Pearl's ""explaining away"",
and an alternative to Wellman's definition of qualitative synergy. The other,
Scenario-based reasoning, involves the generation of alternative causal
""stories"" accounting for the evidence. Comparing a few of the most probable
scenarios provides an approximate way to explain the results of probabilistic
reasoning. Both schemes employ causal as well as probabilistic knowledge.
Probabilities may be presented as phrases and/or numbers. Users can control the
style, abstraction and completeness of explanations.
","Qualitative Propagation and Scenario-based Explanation of Probabilistic
  Reasoning"
"  An experiment replicated and extended recent findings on psychologically
realistic ways of modeling propagation of uncertainty in rule based reasoning.
Within a single production rule, the antecedent evidence can be summarized by
taking the maximum of disjunctively connected antecedents and the minimum of
conjunctively connected antecedents. The maximum certainty factor attached to
each of the rule's conclusions can be sealed down by multiplication with this
summarized antecedent certainty. Heckerman's modified certainty factor
technique can be used to combine certainties for common conclusions across
production rules.
",Managing Uncertainty in Rule Based Cognitive Models
"  Attribute weighting and differential weighting, two major mechanisms for
computing context-dependent similarity or dissimilarity measures are studied
and compared. A dissimilarity measure based on subset size in the context is
proposed and its metrization and application are given. It is also shown that
while all attribute weighting dissimilarity measures are metrics differential
weighting dissimilarity measures are usually non-metric.
",Context-Dependent Similarity
"  A similarity network is a tool for constructing belief networks for the
diagnosis of a single fault. In this paper, we examine modifications to the
similarity-network representation that facilitate the construction of belief
networks for the diagnosis of multiple coexisting faults.
","Similarity Networks for the Construction of Multiple-Faults Belief
  Networks"
"  We propose an abductive diagnosis theory that integrates probabilistic,
causal and taxonomic knowledge. Probabilistic knowledge allows us to select the
most likely explanation; causal knowledge allows us to make reasonable
independence assumptions; taxonomic knowledge allows causation to be modeled at
different levels of detail, and allows observations be described in different
levels of precision. Unlike most other approaches where a causal explanation is
a hypothesis that one or more causative events occurred, we define an
explanation of a set of observations to be an occurrence of a chain of
causation events. These causation events constitute a scenario where all the
observations are true. We show that the probabilities of the scenarios can be
computed from the conditional probabilities of the causation events. Abductive
reasoning is inherently complex even if only modest expressive power is
allowed. However, our abduction algorithm is exponential only in the number of
observations to be explained, and is polynomial in the size of the knowledge
base. This contrasts with many other abduction procedures that are exponential
in the size of the knowledge base.
","Integrating Probabilistic, Taxonomic and Causal Knowledge in Abductive
  Diagnosis"
"  Within diagnostic reasoning there have been a number of proposed definitions
of a diagnosis, and thus of the most likely diagnosis, including most probable
posterior hypothesis, most probable interpretation, most probable covering
hypothesis, etc. Most of these approaches assume that the most likely diagnosis
must be computed, and that a definition of what should be computed can be made
a priori, independent of what the diagnosis is used for. We argue that the
diagnostic problem, as currently posed, is incomplete: it does not consider how
the diagnosis is to be used, or the utility associated with the treatment of
the abnormalities. In this paper we analyze several well-known definitions of
diagnosis, showing that the different definitions of the most likely diagnosis
have different qualitative meanings, even given the same input data. We argue
that the most appropriate definition of (optimal) diagnosis needs to take into
account the utility of outcomes and what the diagnosis is used for.
",What is an Optimal Diagnosis?
"  Kutato is a system that takes as input a database of cases and produces a
belief network that captures many of the dependence relations represented by
those data. This system incorporates a module for determining the entropy of a
belief network and a module for constructing belief networks based on entropy
calculations. Kutato constructs an initial belief network in which all
variables in the database are assumed to be marginally independent. The entropy
of this belief network is calculated, and that arc is added that minimizes the
entropy of the resulting belief network. Conditional probabilities for an arc
are obtained directly from the database. This process continues until an
entropy-based threshold is reached. We have tested the system by generating
databases from networks using the probabilistic logic-sampling method, and then
using those databases as input to Kutato. The system consistently reproduces
the original belief networks with high fidelity.
","Kutato: An Entropy-Driven System for Construction of Probabilistic
  Expert Systems from Databases"
"  The intelligent reformulation or restructuring of a belief network can
greatly increase the efficiency of inference. However, time expended for
reformulation is not available for performing inference. Thus, under time
pressure, there is a tradeoff between the time dedicated to reformulating the
network and the time applied to the implementation of a solution. We
investigate this partition of resources into time applied to reformulation and
time used for inference. We shall describe first general principles for
computing the ideal partition of resources under uncertainty. These principles
have applicability to a wide variety of problems that can be divided into
interdependent phases of problem solving. After, we shall present results of
our empirical study of the problem of determining the ideal amount of time to
devote to searching for clusters in belief networks. In this work, we acquired
and made use of probability distributions that characterize (1) the performance
of alternative heuristic search methods for reformulating a network instance
into a set of cliques, and (2) the time for executing inference procedures on
various belief networks. Given a preference model describing the value of a
solution as a function of the delay required for its computation, the system
selects an ideal time to devote to reformulation.
",Ideal Reformulation of Belief Networks
"  This paper focuses on managing the cost of deliberation before action. In
many problems, the overall quality of the solution reflects costs incurred and
resources consumed in deliberation as well as the cost and benefit of
execution, when both the resource consumption in deliberation phase, and the
costs in deliberation and execution are uncertain and may be described by
probability distribution functions. A feasible (in terms of resource
consumption) strategy that minimizes the expected total cost is termed
computationally-optimal. For a situation with several independent,
uninterruptible methods to solve the problem, we develop a
pseudopolynomial-time algorithm to construct generate-and-test computationally
optimal strategy. We show this strategy-construction problem to be NP-complete,
and apply Bellman's Optimality Principle to solve it efficiently.
",Computationally-Optimal Real-Resource Strategies
"  In this paper, we extend the QMRDT probabilistic model for the domain of
internal medicine to include decisions about treatments. In addition, we
describe how we can use the comprehensive decision model to construct a simpler
decision model for a specific patient. In so doing, we transform the task of
problem formulation to that of narrowing of a larger problem.
",Problem Formulation as the Reduction of a Decision Model
"  We describe a method for incrementally constructing belief networks. We have
developed a network-construction language similar to a forward-chaining
language using data dependencies, but with additional features for specifying
distributions. Using this language, we can define parameterized classes of
probabilistic models. These parameterized models make it possible to apply
probabilistic reasoning to problems for which it is impractical to have a
single large static model.
",Dynamic Construction of Belief Networks
"  We present a new algorithm for finding maximum a-posterior) (MAP) assignments
of values to belief networks. The belief network is compiled into a network
consisting only of nodes with boolean (i.e. only 0 or 1) conditional
probabilities. The MAP assignment is then found using a best-first search on
the resulting network. We argue that, as one would anticipate, the algorithm is
exponential for the general case, but only linear in the size of the network
for poly trees.
",A New Algorithm for Finding MAP Assignments to Belief Networks
"  A significant problem in designing mobile robot control systems involves
coping with the uncertainty that arises in moving about in an unknown or
partially unknown environment and relying on noisy or ambiguous sensor data to
acquire knowledge about that environment. We describe a control system that
chooses what activity to engage in next on the basis of expectations about how
the information re- turned as a result of a given activity will improve 2 its
knowledge about the spatial layout of its environment. Certain of the
higher-level components of the control system are specified in terms of
probabilistic decision models whose output is used to mediate the behavior of
lower-level control components responsible for movement and sensing.
",Reducing Uncertainty in Navigation and Exploration
"  We describe an environment that considerably simplifies the process of
generating Bayesian belief networks. The system has been implemented on readily
available, inexpensive hardware, and provides clarity and high performance. We
present an introduction to Bayesian belief networks, discuss algorithms for
inference with these networks, and delineate the classes of problems that can
be solved with this paradigm. We then describe the hardware and software that
constitute the system, and illustrate Ergo's use with several example
",Ergo: A Graphical Environment for Constructing Bayesian
"  In previous work (Fertig and Breese, 1989; Fertig and Breese, 1990) we
defined a mechanism for performing probabilistic reasoning in influence
diagrams using interval rather than point-valued probabilities. In this paper
we extend these procedures to incorporate decision nodes and interval-valued
value functions in the diagram. We derive the procedures for chance node
removal (calculating expected value) and decision node removal (optimization)
in influence diagrams where lower bounds on probabilities are stored at each
chance node and interval bounds are stored on the value function associated
with the diagram's value node. The output of the algorithm are a set of
admissible alternatives for each decision variable and a set of bounds on
expected value based on the imprecision in the input. The procedure can be
viewed as an approximation to a full e-dimensional sensitivity analysis where n
are the number of imprecise probability distributions in the input. We show the
transformations are optimal and sound. The performance of the algorithm on an
influence diagrams is investigated and compared to an exact algorithm.
",Decision Making with Interval Influence Diagrams
"  In recent years, researchers in decision analysis and artificial intelligence
(AI) have used Bayesian belief networks to build models of expert opinion.
Using standard methods drawn from the theory of computational complexity,
workers in the field have shown that the problem of exact probabilistic
inference on belief networks almost certainly requires exponential computation
in the worst ease [3]. We have previously described a randomized approximation
scheme, called BN-RAS, for computation on belief networks [ 1, 2, 4]. We gave
precise analytic bounds on the convergence of BN-RAS and showed how to trade
running time for accuracy in the evaluation of posterior marginal
probabilities. We now extend our previous results and demonstrate the
generality of our framework by applying similar mathematical techniques to the
analysis of convergence for logic sampling [7], an alternative simulation
algorithm for probabilistic inference.
",A Randomized Approximation Algorithm of Logic Sampling
"  To operate intelligently in the world, an agent must reason about its
actions. The consequences of an action are a function of both the state of the
world and the action itself. Many aspects of the world are inherently
stochastic, so a representation for reasoning about actions must be able to
express chances of world states as well as indeterminacy in the effects of
actions and other events. This paper presents a propositional temporal
probability logic for representing and reasoning about actions. The logic can
represent the probability that facts hold and events occur at various times. It
can represent the probability that actions and other events affect the future.
It can represent concurrent actions and conditions that hold or change during
execution of an action. The model of probability relates probabilities over
time. The logical language integrates both modal and probabilistic constructs
and can thus represent and distinguish between possibility, probability, and
truth. Several examples illustrating the use of the logic are given.
","Time, Chance, and Action"
"  In this paper we present a framework for dynamically constructing Bayesian
networks. We introduce the notion of a background knowledge base of schemata,
which is a collection of parameterized conditional probability statements.
These schemata explicitly separate the general knowledge of properties an
individual may have from the specific knowledge of particular individuals that
may have these properties. Knowledge of individuals can be combined with this
background knowledge to create Bayesian networks, which can then be used in any
propagation scheme. We discuss the theory and assumptions necessary for the
implementation of dynamic Bayesian networks, and indicate where our approach
may be useful.
",A Dynamic Approach to Probabilistic Inference
"  When expert systems based on causal probabilistic networks (CPNs) reach a
certain size and complexity, the ""combinatorial explosion monster"" tends to be
present. We propose an approximation scheme that identifies rarely occurring
cases and excludes these from being processed as ordinary cases in a CPN-based
expert system. Depending on the topology and the probability distributions of
the CPN, the numbers (representing probabilities of state combinations) in the
underlying numerical representation can become very small. Annihilating these
numbers and utilizing the resulting sparseness through data structuring
techniques often results in several orders of magnitude of improvement in the
consumption of computer resources. Bounds on the errors introduced into a
CPN-based expert system through approximations are established. Finally,
reports on empirical studies of applying the approximation scheme to a
real-world CPN are given.
",Approximations in Bayesian Belief Universe for Knowledge Based Systems
"  A series of monte carlo studies were performed to assess the extent to which
different inference procedures robustly output reasonable belief values in the
context of increasing levels of judgmental imprecision. It was found that, when
compared to an equal-weights linear model, the Bayesian procedures are more
likely to deduce strong support for a hypothesis. But, the Bayesian procedures
are also more likely to strongly support the wrong hypothesis. Bayesian
techniques are more powerful, but are also more error prone.
",Robust Inference Policies
"  This paper describes a generalization of previous methods for constructing
tree-structured belief network with hidden variables. The major new feature of
the described method is the ability to produce a tree decomposition even when
there are errors in the correlation data among the input variables. This is an
important extension of existing methods since the correlational coefficients
usually cannot be measured with precision. The technique involves using a
greedy search algorithm that locally minimizes an error function.
",Minimum Error Tree Decomposition
"  A method of calculating probability values from a system of marginal
constraints is presented. Previous systems for finding the probability of a
single attribute have either made an independence assumption concerning the
evidence or have required, in the worst case, time exponential in the number of
attributes of the system. In this paper a closed form solution to the
probability of an attribute given the evidence is found. The closed form
solution, however does not enforce the (non-linear) constraint that all terms
in the underlying distribution be positive. The equation requires O(r^3) steps
to evaluate, where r is the number of independent marginal constraints
describing the system at the time of evaluation. Furthermore, a marginal
constraint may be exchanged with a new constraint, and a new solution
calculated in O(r^2) steps. This method is appropriate for calculating
probabilities in a real time expert system
","A Polynomial Time Algorithm for Finding Bayesian Probabilities from
  Marginal Constraints"
"  The causal (belief) network is a well-known graphical structure for
representing independencies in a joint probability distribution. The exact
methods and the approximation methods, which perform probabilistic inference in
causal networks, often treat the conditional probabilities which are stored in
the network as certain values. However, if one takes either a subjectivistic or
a limiting frequency approach to probability, one can never be certain of
probability values. An algorithm for probabilistic inference should not only be
capable of reporting the inferred probabilities; it should also be capable of
reporting the uncertainty in these probabilities relative to the uncertainty in
the probabilities which are stored in the network. In section 2 of this paper a
method is given for determining the prior variances of the probabilities of all
the nodes. Section 3 contains an approximation method for determining the
variances in inferred probabilities.
",Computation of Variances in Causal Networks
"  Knowledge elicitation is one of the major bottlenecks in expert system
design. Systems based on Bayes nets require two types of information--network
structure and parameters (or probabilities). Both must be elicited from the
domain expert. In general, parameters have greater opacity than structure, and
more time is spent in their refinement than in any other phase of elicitation.
Thus, it is important to determine the point of diminishing returns, beyond
which further refinements will promise little (if any) improvement. Sensitivity
analyses address precisely this issue--the sensitivity of a model to the
precision of its parameters. In this paper, we report the results of a
sensitivity analysis of Pathfinder, a Bayes net based system for diagnosing
pathologies of the lymph system. This analysis is intended to shed some light
on the relative importance of structure and parameters to system performance,
as well as the sensitivity of a system based on a Bayes net to noise in its
assessed parameters.
",A Sensitivity Analysis of Pathfinder
"  IDEAL (Influence Diagram Evaluation and Analysis in Lisp) is a software
environment for creation and evaluation of belief networks and influence
diagrams. IDEAL is primarily a research tool and provides an implementation of
many of the latest developments in belief network and influence diagram
evaluation in a unified framework. This paper describes IDEAL and some lessons
learned during its development.
",IDEAL: A Software Package for Analysis of Influence Diagrams
"  Scientists often use directed acyclic graphs (days) to model the qualitative
structure of causal theories, allowing the parameters to be estimated from
observational data. Two causal models are equivalent if there is no experiment
which could distinguish one from the other. A canonical representation for
causal models is presented which yields an efficient graphical criterion for
deciding equivalence, and provides a theoretical basis for extracting causal
structures from empirical data. This representation is then extended to the
more general case of an embedded causal model, that is, a dag in which only a
subset of the variables are observable. The canonical representation presented
here yields an efficient algorithm for determining when two embedded causal
models reflect the same dependency information. This algorithm leads to a model
theoretic definition of causation in terms of statistical dependencies.
",On the Equivalence of Causal Models
"  Objects in the world usually appear in context, participating in spatial
relationships and interactions that are predictable and expected. Knowledge of
these contexts can be used in the task of using a mobile camera to search for a
specified object in a room. We call this the object search task. This paper is
concerned with representing this knowledge in a manner facilitating its
application to object search while at the same time lending itself to
autonomous learning by a robot. The ability for the robot to learn such
knowledge without supervision is crucial due to the vast number of possible
relationships that can exist for any given set of objects. Moreover, since a
robot will not have an infinite amount of time to learn, it must be able to
determine an order in which to look for possible relationships so as to
maximize the rate at which new knowledge is gained. In effect, there must be a
""focus of interest"" operator that allows the robot to choose which examples are
likely to convey the most new information and should be examined first. This
paper demonstrates how a representation based on statistical confidence
intervals allows the construction of a system that achieves the above goals. An
algorithm, based on the Highest Impact First heuristic, is presented as a means
for providing a ""focus of interest"" with which to control the learning process,
and examples are given.
","Application of Confidence Intervals to the Autonomous Acquisition of
  High-level Spatial Knowledge"
"  In recent years, there have been intense research efforts to develop
efficient methods for probabilistic inference in probabilistic influence
diagrams or belief networks. Many people have concluded that the best methods
are those based on undirected graph structures, and that those methods are
inherently superior to those based on node reduction operations on the
influence diagram. We show here that these two approaches are essentially the
same, since they are explicitly or implicity building and operating on the same
underlying graphical structures. In this paper we examine those graphical
structures and show how this insight can lead to an improved class of directed
reduction methods.
",Directed Reduction Algorithms and Decomposable Graphs
"  In this paper, optimum decomposition of belief networks is discussed. Some
methods of decomposition are examined and a new method - the method of Minimum
Total Number of States (MTNS) - is proposed. The problem of optimum belief
network decomposition under our framework, as under all the other frameworks,
is shown to be NP-hard. According to the computational complexity analysis, an
algorithm of belief network decomposition is proposed in (Wee, 1990a) based on
simulated annealing.
",Optimal Decomposition of Belief Networks
"  This paper analyzes the circumstances under which Bayesian networks can be
pruned in order to reduce computational complexity without altering the
computation for variables of interest. Given a problem instance which consists
of a query and evidence for a set of nodes in the network, it is possible to
delete portions of the network which do not participate in the computation for
the query. Savings in computational complexity can be large when the original
network is not singly connected. Results analogous to those described in this
paper have been derived before [Geiger, Verma, and Pearl 89, Shachter 88] but
the implications for reducing complexity of the computations in Bayesian
networks have not been stated explicitly. We show how a preprocessing step can
be used to prune a Bayesian network prior to using standard algorithms to solve
a given problem instance. We also show how our results can be used in a
parallel distributed implementation in order to achieve greater savings. We
define a computationally equivalent subgraph of a Bayesian network. The
algorithm developed in [Geiger, Verma, and Pearl 89] is modified to construct
the subgraphs described in this paper with O(e) complexity, where e is the
number of edges in the Bayesian network. Finally, we define a minimal
computationally equivalent subgraph and prove that the subgraphs described are
minimal.
",Pruning Bayesian Networks for Efficient Computation
"  We introduce a new heuristic algorithm for the problem of finding minimum
size loop cutsets in multiply connected belief networks. We compare this
algorithm to that proposed in [Suemmondt and Cooper, 1988]. We provide lower
bounds on the performance of these algorithms with respect to one another and
with respect to optimal. We demonstrate that no heuristic algorithm for this
problem cam be guaranteed to produce loop cutsets within a constant difference
from optimal. We discuss experimental results based on randomly generated
networks, and discuss future work and open questions.
","On Heuristics for Finding Loop Cutsets in Multiply-Connected Belief
  Networks"
"  Cutset conditioning and clique-tree propagation are two popular methods for
performing exact probabilistic inference in Bayesian belief networks. Cutset
conditioning is based on decomposition of a subset of network nodes, whereas
clique-tree propagation depends on aggregation of nodes. We describe a means to
combine cutset conditioning and clique- tree propagation in an approach called
aggregation after decomposition (AD). We discuss the application of the AD
method in the Pathfinder system, a medical expert system that offers assistance
with diagnosis in hematopathology.
","A Combination of Cutset Conditioning with Clique-Tree Propagation in the
  Pathfinder System"
"  This paper addresses fundamental issues on the nature of the concepts and
structures of fuzzy logic, focusing, in particular, on the conceptual and
functional differences that exist between probabilistic and possibilistic
approaches. A semantic model provides the basic framework to define
possibilistic structures and concepts by means of a function that quantifies
proximity, closeness, or resemblance between pairs of possible worlds. The
resulting model is a natural extension, based on multiple conceivability
relations, of the modal logic concepts of necessity and possibility. By
contrast, chance-oriented probabilistic concepts and structures rely on
measures of set extension that quantify the proportion of possible worlds where
a proposition is true. Resemblance between possible worlds is quantified by a
generalized similarity relation: a function that assigns a number between O and
1 to every pair of possible worlds. Using this similarity relation, which is a
form of numerical complement of a classic metric or distance, it is possible to
define and interpret the major constructs and methods of fuzzy logic:
conditional and unconditioned possibility and necessity distributions and the
generalized modus ponens of Zadeh.
",Possibility as Similarity: the Semantics of Fuzzy Logic
"  Rule based reasoning (RBR) and case based reasoning (CBR) have emerged as two
important and complementary reasoning methodologies in artificial intelligence
(Al). For problem solving in complex, real world situations, it is useful to
integrate RBR and CBR. This paper presents an approach to achieve a compact and
seamless integration of RBR and CBR within the base architecture of rules. The
paper focuses on the possibilistic nature of the approximate reasoning
methodology common to both CBR and RBR. In CBR, the concept of similarity is
casted as the complement of the distance between cases. In RBR the transitivity
of similarity is the basis for the approximate deductions based on the
generalized modus ponens. It is shown that the integration of CBR and RBR is
possible without altering the inference engine of RBR. This integration is
illustrated in the financial domain of mergers and acquisitions. These ideas
have been implemented in a prototype system called MARS.
","Integrating Case-Based and Rule-Based Reasoning: the Possibilistic
  Connection"
"  We are concerned with the problem of introducing credibility type information
into reasoning systems. The concept of credibility allows us to discount
information provided by agents. An important characteristic of this kind of
procedure is that a complete lack of credibility rather than resulting in the
negation of the information provided results in the nullification of the
information provided. We suggest a representational scheme for credibility
qualification in the theory of approximate reasoning. We discuss the concept of
relative credibility. By this idea we mean to indicate situations in which the
credibility of a piece of evidence is determined by its compatibility with
higher priority evidence. This situation leads to structures very much in the
spirit of nonmonotonic reasoning.
",Credibility Discounting in the Theory of Approximate Reasoning
"  This paper discusses how a measure of uncertainty representing a state of
knowledge can be updated when a new information, which may be pervaded with
uncertainty, becomes available. This problem is considered in various
framework, namely: Shafer's evidence theory, Zadeh's possibility theory,
Spohn's theory of epistemic states. In the two first cases, analogues of
Jeffrey's rule of conditioning are introduced and discussed. The relations
between Spohn's model and possibility theory are emphasized and Spohn's
updating rule is contrasted with the Jeffrey-like rule of conditioning in
possibility theory. Recent results by Shenoy on the combination of ordinal
conditional functions are reinterpreted in the language of possibility theory.
It is shown that Shenoy's combination rule has a well-known possibilistic
counterpart.
","Updating with Belief Functions, Ordinal Conditioning Functions and
  Possibility Measures"
"  Dempster-Shafer's model aims at quantifying degrees of belief But there are
so many interpretations of Dempster-Shafer's theory in the literature that it
seems useful to present the various contenders in order to clarify their
respective positions. We shall successively consider the classical probability
model, the upper and lower probabilities model, Dempster's model, the
transferable belief model, the evidentiary value model, the provability or
necessity model. None of these models has received the qualification of
Dempster-Shafer. In fact the transferable belief model is our interpretation
not of Dempster's work but of Shafer's work as presented in his book (Shafer
1976, Smets 1988). It is a ?purified' form of Dempster-Shafer's model in which
any connection with probability concept has been deleted. Any model for belief
has at least two components: one static that describes our state of belief, the
other dynamic that explains how to update our belief given new pieces of
information. We insist on the fact that both components must be considered in
order to study these models. Too many authors restrict themselves to the static
component and conclude that Dempster-Shafer theory is the same as some other
theory. But once the dynamic component is considered, these conclusions break
down. Any comparison based only on the static component is too restricted. The
dynamic component must also be considered as the originality of the models
based on belief functions lies in its dynamic component.
","The Transferable Belief Model and Other Interpretations of
  Dempster-Shafer's Model"
"  This paper describes valuation-based systems for representing and solving
discrete optimization problems. In valuation-based systems, we represent
information in an optimization problem using variables, sample spaces of
variables, a set of values, and functions that map sample spaces of sets of
variables to the set of values. The functions, called valuations, represent the
factors of an objective function. Solving the optimization problem involves
using two operations called combination and marginalization. Combination tells
us how to combine the factors of the joint objective function. Marginalization
is either maximization or minimization. Solving an optimization problem can be
simply described as finding the marginal of the joint objective function for
the empty set. We state some simple axioms that combination and marginalization
need to satisfy to enable us to solve an optimization problem using local
computation. For optimization problems, the solution method of valuation-based
systems reduces to non-serial dynamic programming. Thus our solution method for
VBS can be regarded as an abstract description of dynamic programming. And our
axioms can be viewed as conditions that permit the use of dynamic programming.
",Valuation-Based Systems for Discrete Optimization
"  In this paper we associate with every (directed) graph G a transformation
called the Mobius transformation of the graph G. The Mobius transformation of
the graph (O) is of major significance for Dempster-Shafer theory of evidence.
However, because it is computationally very heavy, the Mobius transformation
together with Dempster's rule of combination is a major obstacle to the use of
Dempster-Shafer theory for handling uncertainty in expert systems. The major
contribution of this paper is the discovery of the 'fast Mobius
transformations' of (O). These 'fast Mobius transformations' are the fastest
algorithms for computing the Mobius transformation of (O). As an easy but
useful application, we provide, via the commonality function, an algorithm for
computing Dempster's rule of combination which is much faster than the usual
one.
",Computational Aspects of the Mobius Transform
"  In this paper, we suggest marrying Dempster-Shafer (DS) theory with Knowledge
Representation (KR). Born out of this marriage is the definition of
""Dempster-Shafer Belief Bases"", abstract data types representing uncertain
knowledge that use DS theory for representing strength of belief about our
knowledge, and the linguistic structures of an arbitrary KR system for
representing the knowledge itself. A formal result guarantees that both the
properties of the given KR system and of DS theory are preserved. The general
model is exemplified by defining DS Belief Bases where First Order Logic and
(an extension of) KRYPTON are used as KR systems. The implementation problem is
also touched upon.
",Using Dempster-Shafer Theory in Knowledge Representation
"  This paper presents a new technique for the design of approximate reasoning
based controllers for dynamic physical systems with interacting goals. In this
approach, goals are achieved based on a hierarchy defined by a control
knowledge base and remain highly interactive during the execution of the
control task. The approach has been implemented in a rule-based computer
program which is used in conjunction with a prototype hardware system to solve
the cart-pole balancing problem in real-time. It provides a complementary
approach to the conventional analytical control methodology, and is of
substantial use where a precise mathematical model of the process being
controlled is not available.
","A Hierarchical Approach to Designing Approximate Reasoning-Based
  Controllers for Dynamic Physical Systems"
"  In this paper a new mathematical procedure is presented for combining
different pieces of evidence which are represented in the interval form to
reflect our knowledge about the truth of a hypothesis. Evidences may be
correlated to each other (dependent evidences) or conflicting in supports
(conflicting evidences). First, assuming independent evidences, we propose a
methodology to construct combination rules which obey a set of essential
properties. The method is based on a geometric model. We compare results
obtained from Dempster-Shafer's rule and the proposed combination rules with
both conflicting and non-conflicting data and show that the values generated by
proposed combining rules are in tune with our intuition in both cases.
Secondly, in the case that evidences are known to be dependent, we consider
extensions of the rules derived for handling conflicting evidence. The
performance of proposed rules are shown by different examples. The results show
that the proposed rules reasonably make decision under dependent evidences
","Evidence Combination and Reasoning and Its Application to Real-World
  Problem-Solving"
"  Incidence Calculus and Dempster-Shafer Theory of Evidence are both theories
to describe agents' degrees of belief in propositions, thus being appropriate
to represent uncertainty in reasoning systems. This paper presents a
straightforward equivalence proof between some special cases of these theories.
","On Some Equivalence Relations between Incidence Calculus and
  Dempster-Shafer Theory of Evidence"
"  This paper describes recent work on an ongoing project in medical diagnosis
at the University of Guelph. A domain on which experts are not very good at
pinpointing a single disease outcome is explored. On-line medical data is
available over a relatively short period of time. Belief Functions
(Dempster-Shafer theory) are first extracted from data and then modified with
expert opinions. Several methods for doing this are compared and results show
that one formulation statistically outperforms the others, including a method
suggested by Shafer. Expert opinions and statistically derived information
about dependencies among symptoms are also compared. The benefits of using
uncertainty management techniques as methods for knowledge acquisition from
data are discussed.
","Using Belief Functions for Uncertainty Management and Knowledge
  Acquisition: An Expert Application"
"  While concept-based methods for information retrieval can provide improved
performance over more conventional techniques, they require large amounts of
effort to acquire the concepts and their qualitative and quantitative
relationships. This paper discusses an architecture for probabilistic
concept-based information retrieval which addresses the knowledge acquisition
problem. The architecture makes use of the probabilistic networks technology
for representing and reasoning about concepts and includes a knowledge
acquisition component which partially automates the construction of concept
knowledge bases from data. We describe two experiments that apply the
architecture to the task of retrieving documents about terrorism from a set of
documents from the Reuters news service. The experiments provide positive
evidence that the architecture design is feasible and that there are advantages
to concept-based methods.
",An Architecture for Probabilistic Concept-Based Information Retrieval
"  We point out the need to use probability amplitudes rather than probabilities
to model evidence accumulation in decision processes involving real physical
sensors. Optical information processing systems are given as typical examples
of systems that naturally gather evidence in this manner. We derive a new,
amplitude-based generalization of the Hough transform technique used for object
recognition in machine vision. We argue that one should use complex Hough
accumulators and square their magnitudes to get a proper probabilistic
interpretation of the likelihood that an object is present. Finally, we suggest
that probability amplitudes may have natural applications in connectionist
models, as well as in formulating knowledge-based reasoning problems.
",Amplitude-Based Approach to Evidence Accumulation
"  A framework is presented for a computational theory of probabilistic
argument. The Probabilistic Reasoning Environment encodes knowledge at three
levels. At the deepest level are a set of schemata encoding the system's domain
knowledge. This knowledge is used to build a set of second-level arguments,
which are structured for efficient recapture of the knowledge used to construct
them. Finally, at the top level is a Bayesian network constructed from the
arguments. The system is designed to facilitate not just propagation of beliefs
and assimilation of evidence, but also the dynamic process of constructing a
belief network, evaluating its adequacy, and revising it when necessary.
",A Probabilistic Reasoning Environment
"  This note is concerned with a formal analysis of the problem of non-monotonic
reasoning in intelligent systems, especially when the uncertainty is taken into
account in a quantitative way. A firm connection between logic and probability
is established by introducing conditioning notions by means of formal
structures that do not rely on quantitative measures. The associated
conditional logic, compatible with conditional probability evaluations, is
non-monotonic relative to additional evidence. Computational aspects of
conditional probability logic are mentioned. The importance of this development
lies on its role to provide a conceptual basis for various forms of evidence
combination and on its significance to unify multi-valued and non-monotonic
logics
",On Non-monotonic Conditional Reasoning
"  Probability estimation by maximum entropy reconstruction of an initial
relative frequency estimate from its projection onto a hypergraph model of the
approximate conditional independence relations exhibited by it is investigated.
The results of this study suggest that use of this estimation technique may
improve the quality of decisions that must be made on the basis of limited
observations over a decomposable finite product space.
","Decisions with Limited Observations over a Finite Product Space: the
  Klir Effect"
"  Decision-theoretic control of search has previously used as its basic unit.
of computation the generation and evaluation of a complete set of successors.
Although this simplifies analysis, it results in some lost opportunities for
pruning and satisficing. This paper therefore extends the analysis of the value
of computation to cover individual successor evaluations. The analytic
techniques used may prove useful for control of reasoning in more general
settings. A formula is developed for the expected value of a node, k of whose n
successors have been evaluated. This formula is used to estimate the value of
expanding further successors, using a general formula for the value of a
computation in game-playing developed in earlier work. We exhibit an improved
version of the MGSS* algorithm, giving empirical results for the game of
Othello.
",Fine-Grained Decision-Theoretic Search Control
"  This paper describes a natural framework for rules, based on belief
functions, which includes a repre- sentation of numerical rules, default rules
and rules allowing and rules not allowing contraposition. In particular it
justifies the use of the Dempster-Shafer Theory for representing a particular
class of rules, Belief calculated being a lower probability given certain
independence assumptions on an underlying space. It shows how a belief function
framework can be generalised to other logics, including a general Monte-Carlo
algorithm for calculating belief, and how a version of Reiter's Default Logic
can be seen as a limiting case of a belief function formalism.
","Rules, Belief Functions and Default Logic"
"  One of the most important aspects in any treatment of uncertain information
is the rule of combination for updating the degrees of uncertainty. The theory
of belief functions uses the Dempster rule to combine two belief functions
defined by independent bodies of evidence. However, with limited dependency
information about the accumulated belief the Dempster rule may lead to
unsatisfactory results. The present study suggests a method to determine the
accumulated belief based on the premise that the information gain from the
combination process should be minimum. This method provides a mechanism that is
equivalent to the Bayes rule when all the conditional probabilities are
available and to the Dempster rule when the normalization constant is equal to
one. The proposed principle of minimum information gain is shown to be
equivalent to the maximum entropy formalism, a special case of the principle of
minimum cross-entropy. The application of this principle results in a monotonic
increase in belief with accumulation of consistent evidence. The suggested
approach may provide a more reasonable criterion for identifying conflicts
among various bodies of evidence.
",Combination of Evidence Using the Principle of Minimum Information Gain
"  This paper derives a formula for computing the conditional probability of a
set of candidates, where a candidate is a set of disorders that explain a given
set of positive findings. Such candidate sets are produced by a recent method
for multidisorder diagnosis called symptom clustering. A symptom clustering
represents a set of candidates compactly as a cartesian product of differential
diagnoses. By evaluating the probability of a candidate set, then, a large set
of candidates can be validated or pruned simultaneously. The probability of a
candidate set is then specialized to obtain the probability of a single
candidate. Unlike earlier results, the equation derived here allows the
specification of positive, negative, and unknown symptoms and does not make
assumptions about disorders not in the candidate.
","Probabilistic Evaluation of Candidates and Symptom Clustering for
  Multidisorder Diagnosis"
"  A major difficulty in developing and maintaining very large knowledge bases
originates from the variety of forms in which knowledge is made available to
the KB builder. The objective of this research is to bring together two
complementary knowledge representation schemes: term subsumption languages,
which represent and reason about defining characteristics of concepts, and
proximate reasoning models, which deal with uncertain knowledge and data in
expert systems. Previous works in this area have primarily focused on
probabilistic inheritance. In this paper, we address two other important issues
regarding the integration of term subsumption-based systems and approximate
reasoning models. First, we outline a general architecture that specifies the
interactions between the deductive reasoner of a term subsumption system and an
approximate reasoner. Second, we generalize the semantics of terminological
language so that terminological knowledge can be used to make plausible
inferences. The architecture, combined with the generalized semantics, forms
the foundation of a synergistic tight integration of term subsumption systems
and approximate reasoning models.
",Extending Term Subsumption systems for Uncertainty Management
"  In almost all situation assessment problems, it is useful to dynamically
contract and expand the states under consideration as assessment proceeds.
Contraction is most often used to combine similar events or low probability
events together in order to reduce computation. Expansion is most often used to
make distinctions of interest which have significant probability in order to
improve the quality of the assessment. Although other uncertainty calculi,
notably Dempster-Shafer [Shafer, 1976], have addressed these operations, there
has not yet been any approach of refining and coarsening state spaces for the
Bayesian Network technology. This paper presents two operations for refining
and coarsening the state space in Bayesian Networks. We also discuss their
practical implications for knowledge acquisition.
",Refinement and Coarsening of Bayesian Networks
"  In this paper the elicitation of probabilities from human experts is
considered as a measurement process, which may be disturbed by random
'measurement noise'. Using Bayesian concepts a second order probability
distribution is derived reflecting the uncertainty of the input probabilities.
The algorithm is based on an approximate sample representation of the basic
probabilities. This sample is continuously modified by a stochastic simulation
procedure, the Metropolis algorithm, such that the sequence of successive
samples corresponds to the desired posterior distribution. The procedure is
able to combine inconsistent probabilities according to their reliability and
is applicable to general inference networks with arbitrary structure.
Dempster-Shafer probability mass functions may be included using specific
measurement distributions. The properties of the approach are demonstrated by
numerical experiments.
",Second Order Probabilities for Uncertain and Conflicting Evidence
"  Many AI researchers argue that probability theory is only capable of dealing
with uncertainty in situations where a full specification of a joint
probability distribution is available, and conclude that it is not suitable for
application in knowledge-based systems. Probability intervals, however,
constitute a means for expressing incompleteness of information. We present a
method for computing such probability intervals for probabilities of interest
from a partial specification of a joint probability distribution. Our method
improves on earlier approaches by allowing for independency relationships
between statistical variables to be exploited.
",Computing Probability Intervals Under Independency Constraints
"  We analyzed the convergence properties of likelihood- weighting algorithms on
a two-level, multiply connected, belief-network representation of the QMR
knowledge base of internal medicine. Specifically, on two difficult diagnostic
cases, we examined the effects of Markov blanket scoring, importance sampling,
demonstrating that the Markov blanket scoring and self-importance sampling
significantly improve the convergence of the simulation on our model.
","An Empirical Analysis of Likelihood-Weighting Simulation on a Large,
  Multiply-Connected Belief Network"
"  A scientific reasoning system makes decisions using objective evidence in the
form of independent experimental trials, propositional axioms, and constraints
on the probabilities of events. As a first step towards this goal, we propose a
system that derives probability intervals from objective evidence in those
forms. Our reasoning system can manage uncertainty about data and rules in a
rule based expert system. We expect that our system will be particularly
applicable to diagnosis and analysis in domains with a wealth of experimental
evidence such as medicine. We discuss limitations of this solution and propose
future directions for this research. This work can be considered a
generalization of Nilsson's ""probabilistic logic"" [Nil86] to intervals and
experimental observations.
",Towards a Normative Theory of Scientific Evidence
"  Considerable attention has been given to the problem of non-monotonic
reasoning in a belief function framework. Earlier work (M. Ginsberg) proposed
solutions introducing meta-rules which recognized conditional independencies in
a probabilistic sense. More recently an e-calculus formulation of default
reasoning (J. Pearl) shows that the application of Dempster's rule to a
non-monotonic situation produces erroneous results. This paper presents a new
belief function interpretation of the problem which combines the rules in a way
which is more compatible with probabilistic results and respects conditions of
independence necessary for the application of Dempster's combination rule. A
new general framework for combining conflicting evidence is also proposed in
which the normalization factor becomes modified. This produces more intuitively
acceptable results.
",A Model for Non-Monotonic Reasoning Using Dempster's Rule
"  Inappropriate use of Dempster's rule of combination has led some authors to
reject the Dempster-Shafer model, arguing that it leads to supposedly
unacceptable conclusions when defaults are involved. A most classic example is
about the penguin Tweety. This paper will successively present: the origin of
the miss-management of the Tweety example; two types of default; the correct
solution for both types based on the transferable belief model (our
interpretation of the Dempster-Shafer model (Shafer 1976, Smets 1988)); Except
when explicitly stated, all belief functions used in this paper are simple
support functions, i.e. belief functions for which only one proposition (the
focus) of the frame of discernment receives a positive basic belief mass with
the remaining mass being given to the tautology. Each belief function will be
described by its focus and the weight of the focus (e.g. m(A)=.9). Computation
of the basic belief masses are always performed by vacuously extending each
belief function to the product space built from all variables involved,
combining them on that space by Dempster's rule of combination, and projecting
the result to the space corresponding to each individual variable.
",Default Reasoning and the Transferable Belief Model
"  We examine three probabilistic formulations of the sentence a and b are
totally unrelated with respect to a given set of variables U. First, two
variables a and b are totally independent if they are independent given any
value of any subset of the variables in U. Second, two variables are totally
uncoupled if U can be partitioned into two marginally independent sets
containing a and b respectively. Third, two variables are totally disconnected
if the corresponding nodes are disconnected in every belief network
representation. We explore the relationship between these three formulations of
unrelatedness and explain their relevance to the process of acquiring
probabilistic knowledge from human experts.
",Separable and transitive graphoids
"  After a brief introduction to causal probabilistic networks and the HUGIN
approach, the problem of conflicting data is discussed. A measure of conflict
is defined, and it is used in the medical diagnostic system MUNIN. Finally, it
is discussed how to distinguish between conflicting data and a rare case.
",Analysis in HUGIN of Data Conflict
"  Rewriting-based approaches for answering queries over an OWL 2 DL ontology
have so far been developed mainly for Horn fragments of OWL 2 DL. In this
paper, we study the possibilities of answering queries over non-Horn ontologies
using datalog rewritings. We prove that this is impossible in general even for
very simple ontology languages, and even if PTIME = NP. Furthermore, we present
a resolution-based procedure for $\SHI$ ontologies that, in case it terminates,
produces a datalog rewriting of the ontology. Our procedure necessarily
terminates on DL-Lite_{bool}^H ontologies---an extension of OWL 2 QL with
transitive roles and Boolean connectives.
",Computing Datalog Rewritings beyond Horn Ontologies
"  This extended abstract presents a logic, called Lp, that is capable of
representing and reasoning with a wide variety of both qualitative and
quantitative statistical information. The advantage of this logical formalism
is that it offers a declarative representation of statistical knowledge;
knowledge represented in this manner can be used for a variety of reasoning
tasks. The logic differs from previous work in probability logics in that it
uses a probability distribution over the domain of discourse, whereas most
previous work (e.g., Nilsson [2], Scott et al. [3], Gaifinan [4], Fagin et al.
[5]) has investigated the attachment of probabilities to the sentences of the
logic (also, see Halpern [6] and Bacchus [7] for further discussion of the
differences). The logic Lp possesses some further important features. First, Lp
is a superset of first order logic, hence it can represent ordinary logical
assertions. This means that Lp provides a mechanism for integrating statistical
information and reasoning about uncertainty into systems based solely on logic.
Second, Lp possesses transparent semantics, based on sets and probabilities of
those sets. Hence, knowledge represented in Lp can be understood in terms of
the simple primative concepts of sets and probabilities. And finally, the there
is a sound proof theory that has wide coverage (the proof theory is complete
for certain classes of models). The proof theory captures a sufficient range of
valid inferences to subsume most previous probabilistic uncertainty reasoning
systems. For example, the linear constraints like those generated by Nilsson's
probabilistic entailment [2] can be generated by the proof theory, and the
Bayesian inference underlying belief nets [8] can be performed. In addition,
the proof theory integrates quantitative and qualitative reasoning as well as
statistical and logical reasoning. In the next section we briefly examine
previous work in probability logics, comparing it to Lp. Then we present some
of the varieties of statistical information that Lp is capable of expressing.
After this we present, briefly, the syntax, semantics, and proof theory of the
logic. We conclude with a few examples of knowledge representation and
reasoning in Lp, pointing out the advantages of the declarative representation
offered by Lp. We close with a brief discussion of probabilities as degrees of
belief, indicating how such probabilities can be generated from statistical
knowledge encoded in Lp. The reader who is interested in a more complete
treatment should consult Bacchus [7].
",Lp : A Logic for Statistical Information
"  Nearly all spatial reasoning problems involve uncertainty of one sort or
another. Uncertainty arises due to the inaccuracies of sensors used in
measuring distances and angles. We refer to this as directional uncertainty.
Uncertainty also arises in combining spatial information when one location is
mistakenly identified with another. We refer to this as recognition
uncertainty. Most problems in constructing spatial representations (maps) for
the purpose of navigation involve both directional and recognition uncertainty.
In this paper, we show that a particular class of spatial reasoning problems
involving the construction of representations of large-scale space can be
solved efficiently even in the presence of directional and recognition
uncertainty. We pay particular attention to the problems that arise due to
recognition uncertainty.
",Map Learning with Indistinguishable Locations
"  In this paper we explore representations of temporal knowledge based upon the
formalism of Causal Probabilistic Networks (CPNs). Two different
?continuous-time? representations are proposed. In the first, the CPN includes
variables representing ?event-occurrence times?, possibly on different time
scales, and variables representing the ?state? of the system at these times. In
the second, the CPN describes the influences between random variables with
values in () representing dates, i.e. time-points associated with the
occurrence of relevant events. However, structuring a system of inter-related
dates as a network where all links commit to a single specific notion of cause
and effect is in general far from trivial and leads to severe difficulties. We
claim that we should recognize explicitly different kinds of relation between
dates, such as ?cause?, ?inhibition?, ?competition?, etc., and propose a method
whereby these relations are coherently embedded in a CPN using additional
auxiliary nodes corresponding to ""instrumental"" variables. Also discussed,
though not covered in detail, is the topic concerning how the quantitative
specifications to be inserted in a temporal CPN can be learned from specific
data.
",Temporal Reasoning with Probabilities
"  Rather than discussing the isolated merits of a nominative theory of
uncertainty, this paper focuses on a class of problems, referred to as Dynamic
Classification Problem (DCP), which requires the integration of many theories,
including a prescriptive theory of uncertainty. We start by analyzing the
Dynamic Classification Problem and by defining its induced requirements on a
supporting (plausible) reasoning system. We provide a summary of the underlying
theory (based on the semantics of many-valed logics) and illustrate the
constraints imposed upon it to ensure the modularity and computational
performance required by the applications. We describe the technologies used for
knowledge engineering (such as object-based simulator to exercise requirements,
and development tools to build the Knowledge Base and functionally validate
it). We emphasize the difference between development environment and run-time
system, describe the rule cross-compiler, and the real-time inference engine
with meta-reasoning capabilities. Finally, we illustrate how our proposed
technology satisfies the pop's requirements and analyze some of the lessons
reamed from its applications to situation assessment problems for Pilot's
Associate and Submarine Commander Associate.
","Now that I Have a Good Theory of Uncertainty, What Else Do I Need?"
"  Two major difficulties in using default logics are their intractability and
the problem of selecting among multiple extensions. We propose an approach to
these problems based on integrating nommonotonic reasoning with plausible
reasoning based on triangular norms. A previously proposed system for reasoning
with uncertainty (RUM) performs uncertain monotonic inferences on an acyclic
graph. We have extended RUM to allow nommonotonic inferences and cycles within
nonmonotonic rules. By restricting the size and complexity of the nommonotonic
cycles we can still perform efficient inferences. Uncertainty measures provide
a basis for deciding among multiple defaults. Different algorithms and
heuristics for finding the optimal defaults are discussed.
",Uncertainty and Incompleteness
"  As the technology for building knowledge based systems has matured, important
lessons have been learned about the relationship between the architecture of a
system and the nature of the problems it is intended to solve. We are
implementing a knowledge engineering tool called BART that is designed with
these lessons in mind. BART is a Bayesian reasoning tool that makes belief
networks and other probabilistic techniques available to knowledge engineers
building classificatory problem solvers. BART has already been used to develop
a decision aid for classifying ship images, and it is currently being used to
manage uncertainty in systems concerned with analyzing intelligence reports.
This paper discusses how state-of-the-art probabilistic methods fit naturally
into a knowledge based approach to classificatory problem solving, and
describes the current capabilities of BART.
",BaRT: A Bayesian Reasoning Tool for Knowledge Based Systems
"  Plan recognition does not work the same way in stories and in ""real life""
(people tend to jump to conclusions more in stories). We present a theory of
this, for the particular case of how objects in stories (or in life) influence
plan recognition decisions. We provide a Bayesian network formalization of a
simple first-order theory of plans, and show how a particular network parameter
seems to govern the difference between ""life-like"" and ""story-like"" response.
We then show why this parameter would be influenced (in the desired way) by a
model of speaker (or author) topic selection which assumes that facts in
stories are typically ""relevant"".
",Plan Recognition in Stories and in Life
"  In recent years, researchers in decision analysis and artificial intelligence
(Al) have used Bayesian belief networks to build models of expert opinion.
Using standard methods drawn from the theory of computational complexity,
workers in the field have shown that the problem of probabilistic inference in
belief networks is difficult and almost certainly intractable. K N ET, a
software environment for constructing knowledge-based systems within the
axiomatic framework of decision theory, contains a randomized approximation
scheme for probabilistic inference. The algorithm can, in many circumstances,
perform efficient approximate inference in large and richly interconnected
models of medical diagnosis. Unlike previously described stochastic algorithms
for probabilistic inference, the randomized approximation scheme computes a
priori bounds on running time by analyzing the structure and contents of the
belief network. In this article, we describe a randomized algorithm for
probabilistic inference and analyze its performance mathematically. Then, we
devote the major portion of the paper to a discussion of the algorithm's
empirical behavior. The results indicate that the generation of good trials
(that is, trials whose distribution closely matches the true distribution),
rather than the computation of numerous mediocre trials, dominates the
performance of stochastic simulation. Key words: probabilistic inference,
belief networks, stochastic simulation, computational complexity theory,
randomized algorithms.
","An Empirical Evaluation of a Randomized Algorithm for Probabilistic
  Inference"
"  Unaided human decision making appears to systematically violate consistency
constraints imposed by normative theories; these biases in turn appear to
justify the application of formal decision-analytic models. It is argued that
both claims are wrong. In particular, we will argue that the ""confirmation
bias"" is premised on an overly narrow view of how conflicting evidence is and
ought to be handled. Effective decision aiding should focus on supporting the
contral processes by means of which knowledge is extended into novel situations
and in which assumptions are adopted, utilized, and revised. The Non- Monotonic
Probabilist represents initial work toward such an aid.
","Decision Making ""Biases"" and Support for Assumption-Based Higher-Order
  Reasoning"
"  In this paper an approach to automated deduction under uncertainty,based on
possibilistic logic, is proposed ; for that purpose we deal with clauses
weighted by a degree which is a lower bound of a necessity or a possibility
measure, according to the nature of the uncertainty. Two resolution rules are
used for coping with the different situations, and the refutation method can be
generalized. Besides the lower bounds are allowed to be functions of variables
involved in the clause, which gives hypothetical reasoning capabilities. The
relation between our approach and the idea of minimizing abnormality is briefly
discussed. In case where only lower bounds of necessity measures are involved,
a semantics is proposed, in which the completeness of the extended resolution
principle is proved. Moreover deduction from a partially inconsistent knowledge
base can be managed in this approach and displays some form of
non-monotonicity.
","Automated Reasoning Using Possibilistic Logic: Semantics, Belief
  Revision and Variable Certainty Weights"
"  Bayesian inference systems should be able to explain their reasoning to
users, translating from numerical to natural language. Previous empirical work
has investigated the correspondence between absolute probabilities and
linguistic phrases. This study extends that work to the correspondence between
changes in probabilities (updates) and relative probability phrases, such as
""much more likely"" or ""a little less likely."" Subjects selected such phrases to
best describe numerical probability updates. We examined three hypotheses about
the correspondence, and found the most descriptively accurate of these three to
be that each such phrase corresponds to a fixed difference in probability
(rather than fixed ratio of probabilities or of odds). The empirically derived
phrase selection function uses eight phrases and achieved a 72% accuracy in
correspondence with the subjects' actual usage.
","How Much More Probable is ""Much More Probable""? Verbal Expressions for
  Probability Updates"
"  This paper presents an approach for developing the explanation capabilities
of rule-based expert systems managing imprecise and uncertain knowledge. The
treatment of uncertainty takes place in the framework of possibility theory
where the available information concerning the value of a logical or numerical
variable is represented by a possibility distribution which restricts its more
or less possible values. We first discuss different kinds of queries asking for
explanations before focusing on the two following types : i) how, a particular
possibility distribution is obtained (emphasizing the main reasons only) ; ii)
why in a computed possibility distribution, a particular value has received a
possibility degree which is so high, so low or so contrary to the expectation.
The approach is based on the exploitation of equations in max-min algebra. This
formalism includes the limit case of certain and precise information.
","Positive and Negative Explanations of Uncertain Reasoning in the
  Framework of Possibility Theory"
"  We describe a mechanism for performing probabilistic reasoning in influence
diagrams using interval rather than point valued probabilities. We derive the
procedures for node removal (corresponding to conditional expectation) and arc
reversal (corresponding to Bayesian conditioning) in influence diagrams where
lower bounds on probabilities are stored at each node. The resulting bounds for
the transformed diagram are shown to be optimal within the class of constraints
on probability distributions that can be expressed exclusively as lower bounds
on the component probabilities of the diagram. Sequences of these operations
can be performed to answer probabilistic queries with indeterminacies in the
input and for performing sensitivity analysis on an influence diagram. The
storage requirements and computational complexity of this approach are
comparable to those for point-valued probabilistic inference mechanisms, making
the approach attractive for performing sensitivity analysis and where
probability information is not available. Limited empirical data on an
implementation of the methodology are provided.
",Interval Influence Diagrams
"  Stochastic simulation approaches perform probabilistic inference in Bayesian
networks by estimating the probability of an event based on the frequency that
the event occurs in a set of simulation trials. This paper describes the
evidence weighting mechanism, for augmenting the logic sampling stochastic
simulation algorithm [Henrion, 1986]. Evidence weighting modifies the logic
sampling algorithm by weighting each simulation trial by the likelihood of a
network's evidence given the sampled state node values for that trial. We also
describe an enhancement to the basic algorithm which uses the evidential
integration technique [Chin and Cooper, 1987]. A comparison of the basic
evidence weighting mechanism with the Markov blanket algorithm [Pearl, 1987],
the logic sampling algorithm, and the evidence integration algorithm is
presented. The comparison is aided by analyzing the performance of the
algorithms in a simple example network.
","Weighing and Integrating Evidence for Stochastic Simulation in Bayesian
  Networks"
"  An efficient algorithm is developed that identifies all independencies
implied by the topology of a Bayesian network. Its correctness and maximality
stems from the soundness and completeness of d-separation with respect to
probability theory. The algorithm runs in time O (l E l) where E is the number
of edges in the network.
",d-Separation: From Theorems to Algorithms
"  In this paper, we first consider a Bayesian framework and model the ""utility
function"" in terms of fuzzy random variables. On the basis of this model, we
define the ""prior (fuzzy) expected utility"" associated with each action, and
the corresponding ""posterior (fuzzy) expected utility given sample information
from a random experiment"". The aim of this paper is to analyze how sample
information can affect the expected utility. In this way, by using some fuzzy
preference relations, we conclude that sample information allows a decision
maker to increase the expected utility on the average. The upper bound on the
value of the expected utility is when the decision maker has perfect
information. Applications of this work to the field of artificial intelligence
are presented through two examples.
","The Effects of Perfect and Sample Information on Fuzzy Utilities in
  Decision-Making"
"  We propose a norm of consistency for a mixed set of defeasible and strict
sentences, based on a probabilistic semantics. This norm establishes a clear
distinction between knowledge bases depicting exceptions and those containing
outright contradictions. We then define a notion of entailment based also on
probabilistic considerations and provide a characterization of the relation
between consistency and entailment. We derive necessary and sufficient
conditions for consistency, and provide a simple decision procedure for testing
consistency and deciding whether a sentence is entailed by a database. Finally,
it is shown that if al1 sentences are Horn clauses, consistency and entailment
can be tested in polynomial time.
","Deciding Consistency of Databases Containing Defeasible and Strict
  Information"
"  We consider the relation between knowledge and certainty, where a fact is
known if it is true at all worlds an agent considers possible and is certain if
it holds with probability 1. We identify certainty with probabilistic belief.
We show that if we assume one fixed probability assignment, then the logic
KD45, which has been identified as perhaps the most appropriate for belief,
provides a complete axiomatization for reasoning about certainty. Just as an
agent may believe a fact although phi is false, he may be certain that a fact
phi, is true although phi is false. However, it is easy to see that an agent
can have such false (probabilistic) beliefs only at a set of worlds of
probability 0. If we restrict attention to structures where all worlds have
positive probability, then S5 provides a complete axiomatization. If we
consider a more general setting, where there might be a different probability
assignment at each world, then by placing appropriate conditions on the support
of the probability function (the set of worlds which have non-zero
probability), we can capture many other well-known modal logics, such as T and
S4. Finally, we consider which axioms characterize structures satisfying
Miller's principle.
","The Relationship between Knowledge, Belief and Certainty"
"  BPS, the Bayesian Problem Solver, applies probabilistic inference and
decision-theoretic control to flexible, resource-constrained problem-solving.
This paper focuses on the Bayesian inference mechanism in BPS, and contrasts it
with those of traditional heuristic search techniques. By performing sound
inference, BPS can outperform traditional techniques with significantly less
computational effort. Empirical tests on the Eight Puzzle show that after only
a few hundred node expansions, BPS makes better decisions than does the best
existing algorithm after several million node expansions
",Heuristic Search as Evidential Reasoning
"  We introduce and analyze the problem of the compilation of decision models
from a decision-theoretic perspective. The techniques described allow us to
evaluate various configurations of compiled knowledge given the nature of
evidential relationships in a domain, the utilities associated with alternative
actions, the costs of run-time delays, and the costs of memory. We describe
procedures for selecting a subset of the total observations available to be
incorporated into a compiled situation-action mapping, in the context of a
binary decision with conditional independence of evidence. The methods allow us
to incrementally select the best pieces of evidence to add to the set of
compiled knowledge in an engineering setting. After presenting several
approaches to compilation, we exercise one of the methods to provide insight
into the relationship between the distribution over weights of evidence and the
preferred degree of compilation.
",The Compilation of Decision Models
"  We examine a probabilistic model for the diagnosis of multiple diseases. In
the model, diseases and findings are represented as binary variables. Also,
diseases are marginally independent, features are conditionally independent
given disease instances, and diseases interact to produce findings via a noisy
OR-gate. An algorithm for computing the posterior probability of each disease,
given a set of observed findings, called quickscore, is presented. The time
complexity of the algorithm is O(nm-2m+), where n is the number of diseases, m+
is the number of positive findings and m- is the number of negative findings.
Although the time complexity of quickscore i5 exponential in the number of
positive findings, the algorithm is useful in practice because the number of
observed positive findings is usually far less than the number of diseases
under consideration. Performance results for quickscore applied to a
probabilistic version of Quick Medical Reference (QMR) are provided.
",A Tractable Inference Algorithm for Diagnosing Multiple Diseases
"  We introduce a graceful approach to probabilistic inference called bounded
conditioning. Bounded conditioning monotonically refines the bounds on
posterior probabilities in a belief network with computation, and converges on
final probabilities of interest with the allocation of a complete resource
fraction. The approach allows a reasoner to exchange arbitrary quantities of
computational resource for incremental gains in inference quality. As such,
bounded conditioning holds promise as a useful inference technique for
reasoning under the general conditions of uncertain and varying reasoning
resources. The algorithm solves a probabilistic bounding problem in complex
belief networks by breaking the problem into a set of mutually exclusive,
tractable subproblems and ordering their solution by the expected effect that
each subproblem will have on the final answer. We introduce the algorithm,
discuss its characterization, and present its performance on several belief
networks, including a complex model for reasoning about problems in
intensive-care medicine.
","Bounded Conditioning: Flexible Inference for Decisions under Scarce
  Resources"
"  In this paper, we will review the process of evidence accumulation in the
PSEIKI system for expectation-driven interpretation of images of 3-D scenes.
Expectations are presented to PSEIKI as a geometrical hierarchy of
abstractions. PSEIKI's job is then to construct abstraction hierarchies in the
perceived image taking cues from the abstraction hierarchies in the
expectations. The Dempster-Shafer formalism is used for associating belief
values with the different possible labels for the constructed abstractions in
the perceived image. This system has been used successfully for autonomous
navigation of a mobile robot in indoor environments.
","Hierarchical Evidence Accumulation in the Pseiki System and Experiments
  in Model-Driven Mobile Robot Navigation"
"  Many Artificial Intelligence systems depend on the agent's updating its
beliefs about the world on the basis of experience. Experiments constitute one
type of experience, so scientific methodology offers a natural environment for
examining the issues attendant to using this class of evidence. This paper
presents a framework which structures the process of using scientific data from
research reports for the purpose of making decisions, using decision analysis
as the basis for the structure and using medical research as the general
scientific domain. The structure extends the basic influence diagram for
updating belief in an object domain parameter of interest by expanding the
parameter into four parts: those of the patient, the population, the study
sample, and the effective study sample. The structure uses biases to perform
the transformation of one parameter into another, so that, for instance,
selection biases, in concert with the population parameter, yield the study
sample parameter. The influence diagram structure provides decision theoretic
justification for practices of good clinical research such as randomized
assignment and blindfolding of care providers. The model covers most research
designs used in medicine: case-control studies, cohort studies, and controlled
clinical trials, and provides an architecture to separate clearly between
statistical knowledge and domain knowledge. The proposed general model can be
the basis for clinical epidemiological advisory systems, when coupled with
heuristic pruning of irrelevant biases; of statistical workstations, when the
computational machinery for calculation of posterior distributions is added;
and of meta-analytic reviews, when multiple studies may impact on a single
population parameter.
",A Decision-Theoretic Model for Using Scientific Data
"  This paper argues that the principal difference between decision aids and
most other types of information systems is the greater reliance of decision
aids on fallible algorithms--algorithms that sometimes generate incorrect
advice. It is shown that interactive problem solving with a decision aid that
is based on a fallible algorithm can easily result in aided performance which
is poorer than unaided performance, even if the algorithm, by itself, performs
significantly better than the unaided decision maker. This suggests that unless
certain conditions are satisfied, using a decision aid as an aid is
counterproductive. Some conditions under which a decision aid is best used as
an aid are derived.
",When Should a Decision Maker Ignore the Advice of a Decision Aid?
"  It is suggested that an AI inference system should reflect an inference
policy that is tailored to the domain of problems to which it is applied -- and
furthermore that an inference policy need not conform to any general theory of
rational inference or induction. We note, for instance, that Bayesian reasoning
about the probabilistic characteristics of an inference domain may result in
the specification of an nonBayesian procedure for reasoning within the
inference domain. In this paper, the idea of an inference policy is explored in
some detail. To support this exploration, the characteristics of some standard
and nonstandard inference policies are examined.
",Inference Policies
"  In two recent papers, I have proposed a description of decision analysis that
differs from the Bayesian picture painted by Savage, Jeffrey and other classic
authors. Response to this view has been either overly enthusiastic or unduly
pessimistic. In this paper I try to place the idea in its proper place, which
must be somewhere in between. Looking at decision analysis as defeasible
reasoning produces a framework in which planning and decision theory can be
integrated, but work on the details has barely begun. It also produces a
framework in which the meta-decision regress can be stopped in a reasonable
way, but it does not allow us to ignore meta-level decisions. The heuristics
for producing arguments that I have presented are only supposed to be
suggestive; but they are not open to the egregious errors about which some have
worried. And though the idea is familiar to those who have studied heuristic
search, it is somewhat richer because the control of dialectic is more
interesting than the deepening of search.
",Defeasible Decisions: What the Proposal is and isn't
"  This paper presents some ideas and results of using uncertainty management
methods in the presence of data in preference to other statistical and machine
learning methods. A medical domain is used as a test-bed with data available
from a large hospital database system which collects symptom and outcome
information about patients. Data is often missing, of many variable types and
sample sizes for particular outcomes is not large. Uncertainty management
methods are useful for such domains and have the added advantage of allowing
for expert modification of belief values originally obtained from data.
Methodological considerations for using belief functions on statistical data
are dealt with in some detail. Expert opinions are Incorporated at various
levels of the project development and results are reported on an application to
liver disease diagnosis. Recent results contrasting the use of weights of
evidence and logistic regression on another medical domain are also presented.
","Experiments Using Belief Functions and Weights of Evidence incorporating
  Statistical Data and Expert Opinions"
"  During the summer of 1989, the Forecast Systems Laboratory of the National
Oceanic and Atmospheric Administration sponsored an evaluation of artificial
intelligence-based systems that forecast severe convective storms. The
evaluation experiment, called Shootout-89, took place in Boulder, and focussed
on storms over the northeastern Colorado foothills and plains (Moninger, et
al., 1990). Six systems participated in Shootout-89. These included traditional
expert systems, an analogy-based system, and a system developed using methods
from the cognitive science/judgment analysis tradition. Each day of the
exercise, the systems generated 2 to 9 hour forecasts of the probabilities of
occurrence of: non significant weather, significant weather, and severe
weather, in each of four regions in northeastern Colorado. A verification
coordinator working at the Denver Weather Service Forecast Office gathered
ground-truth data from a network of observers. Systems were evaluated on the
basis of several measures of forecast skill, and on other metrics such as
timeliness, ease of learning, and ease of use. Systems were generally easy to
operate, however the various systems required substantially different levels of
meteorological expertise on the part of their users--reflecting the various
operational environments for which the systems had been designed. Systems
varied in their statistical behavior, but on this difficult forecast problem,
the systems generally showed a skill approximately equal to that of persistence
forecasts and climatological (historical frequency) forecasts. The two systems
that appeared best able to discriminate significant from non significant
weather events were traditional expert systems. Both of these systems required
the operator to make relatively sophisticated meteorological judgments. We are
unable, based on only one summer's worth of data, to determine the extent to
which the greater skill of the two systems was due to the content of their
knowledge bases, or to the subjective judgments of the operator. A follow-on
experiment, Shootout-91, is currently being planned. Interested potential
participants are encouraged to contact the author at the address above.
","Shootout-89: A Comparative Evaluation of Knowledge-based Systems that
  Forecast Severe Weather"
"  Many writers have observed that default logics appear to contain the ""lottery
paradox"" of probability theory. This arises when a default ""proof by
contradiction"" lets us conclude that a typical X is not a Y where Y is an
unusual subclass of X. We show that there is a similar problem with default
""proof by cases"" and construct a setting where we might draw a different
conclusion knowing a disjunction than we would knowing any particular disjunct.
Though Reiter's original formalism is capable of representing this distinction,
other approaches are not. To represent and reason about this case, default
logicians must specify how a ""typical"" individual is selected. The problem is
closely related to Simpson's paradox of probability theory. If we accept a
simple probabilistic account of defaults based on the notion that one
proposition may favour or increase belief in another, the ""multiple extension
problem"" for both conjunctive and disjunctive knowledge vanishes.
",Conditioning on Disjunctive Knowledge: Defaults and Probabilities
"  Measures of uncertainty and divergence are introduced for interval-valued
probability distributions and are shown to have desirable mathematical
properties. A maximum uncertainty inference procedure for marginal interval
distributions is presented. A technique for reconstruction of interval
distributions from projections is developed based on this inference procedure
","Maximum Uncertainty Procedures for Interval-Valued Probability
  Distributions"
"  We formulate Dempster Shafer Belief functions in terms of Propositional Logic
using the implicit notion of provability underlying Dempster Shafer Theory.
Given a set of propositional clauses, assigning weights to certain
propositional literals enables the Belief functions to be explicitly computed
using Network Reliability techniques. Also, the logical procedure corresponding
to updating Belief functions using Dempster's Rule of Combination is shown.
This analysis formalizes the implementation of Belief functions within an
Assumption-based Truth Maintenance System (ATMS). We describe the extension of
an ATMS-based visual recognition system, VICTORS, with this logical formulation
of Dempster Shafer theory. Without Dempster Shafer theory, VICTORS computes all
possible visual interpretations (i.e. all logical models) without determining
the best interpretation(s). Incorporating Dempster Shafer theory enables
optimal visual interpretations to be computed and a logical semantics to be
maintained.
","A Logical Interpretation of Dempster-Shafer Theory, with Application to
  Visual Recognition"
"  Bayesian Belief Networks have been largely overlooked by Expert Systems
practitioners on the grounds that they do not correspond to the human inference
mechanism. In this paper, we introduce an explanation mechanism designed to
generate intuitive yet probabilistically sound explanations of inferences drawn
by a Bayesian Belief Network. In particular, our mechanism accounts for the
results obtained due to changes in the causal and the evidential support of a
node.
","Strategies for Generating Micro Explanations for Bayesian Belief
  Networks"
"  The arc reversal/node reduction approach to probabilistic inference is
extended to include the case of instantiated evidence by an operation called
""evidence reversal."" This not only provides a technique for computing posterior
joint distributions on general belief networks, but also provides insight into
the methods of Pearl [1986b] and Lauritzen and Spiegelhalter [1988]. Although
it is well understood that the latter two algorithms are closely related, in
fact all three algorithms are identical whenever the belief network is a
forest.
",Evidence Absorption and Propagation through Evidence Reversals
"  A number of algorithms have been developed to solve probabilistic inference
problems on belief networks. These algorithms can be divided into two main
groups: exact techniques which exploit the conditional independence revealed
when the graph structure is relatively sparse, and probabilistic sampling
techniques which exploit the ""conductance"" of an embedded Markov chain when the
conditional probabilities have non-extreme values. In this paper, we
investigate a family of ""forward"" Monte Carlo sampling techniques similar to
Logic Sampling [Henrion, 1988] which appear to perform well even in some
multiply connected networks with extreme conditional probabilities, and thus
would be generally applicable. We consider several enhancements which reduce
the posterior variance using this approach and propose a framework and criteria
for choosing when to use those enhancements.
","Simulation Approaches to General Probabilistic Inference on Belief
  Networks"
"  We derive axiomatically the probability function that should be used to make
decisions given any form of underlying uncertainty.
",Decision under Uncertainty
"  This paper discusses a new measure that is adaptable to certain intervalic
probability frameworks, possibility theory, and belief theory. As such, it has
the potential for wide use in knowledge engineering, expert systems, and
related problems in the human sciences. This measure (denoted here by F) has
been introduced in Smithson (1988) and is more formally discussed in Smithson
(1989a)o Here, I propose to outline the conceptual basis for F and compare its
properties with other measures of second-order uncertainty. I will argue that F
is an indicator of nonspecificity or alternatively, of freedom, as
distinguished from either ambiguity or vagueness.
","Freedom: A Measure of Second-order Uncertainty for Intervalic
  Probability Schemes"
"  Three paediatric cardiologists assessed nearly 1000 imprecise subjective
conditional probabilities for a simple belief network representing congenital
heart disease, and the quality of the assessments has been measured using
prospective data on 200 babies. Quality has been assessed by a Brier scoring
rule, which decomposes into terms measuring lack of discrimination and
reliability. The results are displayed for each of 27 diseases and 24
questions, and generally the assessments are reliable although there was a
tendency for the probabilities to be too extreme. The imprecision allows the
judgements to be converted to implicit samples, and by combining with the
observed data the probabilities naturally adapt with experience. This appears
to be a practical procedure even for reasonably large expert systems.
","Assessment, Criticism and Improvement of Imprecise Subjective
  Probabilities for a Medical Expert System"
"  An algorithm for automated construction of a sparse Bayesian network given an
unstructured probabilistic model and causal domain information from an expert
has been developed and implemented. The goal is to obtain a network that
explicitly reveals as much information regarding conditional independence as
possible. The network is built incrementally adding one node at a time. The
expert's information and a greedy heuristic that tries to keep the number of
arcs added at each step to a minimum are used to guide the search for the next
node to add. The probabilistic model is a predicate that can answer queries
about independencies in the domain. In practice the model can be implemented in
various ways. For example, the model could be a statistical independence test
operating on empirical data or a deductive prover operating on a set of
independence statements about the domain.
","Automated Construction of Sparse Bayesian Networks from Unstructured
  Probabilistic Models and Domain Information"
"  A primary motivation for reasoning under uncertainty is to derive decisions
in the face of inconclusive evidence. However, Shafer's theory of belief
functions, which explicitly represents the underconstrained nature of many
reasoning problems, lacks a formal procedure for making decisions. Clearly,
when sufficient information is not available, no theory can prescribe actions
without making additional assumptions. Faced with this situation, some
assumption must be made if a clearly superior choice is to emerge. In this
paper we offer a probabilistic interpretation of a simple assumption that
disambiguates decision problems represented with belief functions. We prove
that it yields expected values identical to those obtained by a probabilistic
analysis that makes the same assumption. In addition, we show how the decision
analysis methodology frequently employed in probabilistic reasoning can be
extended for use with belief functions.
",Making Decisions with Belief Functions
"  We present a new, deterministic, distributed MAP estimation algorithm for
Markov Random Fields called Local Highest Confidence First (Local HCF). The
algorithm has been applied to segmentation problems in computer vision and its
performance compared with stochastic algorithms. The experiments show that
Local HCF finds better estimates than stochastic algorithms with much less
computation.
",Efficient Parallel Estimation for Markov Random Fields
"  This study compares the inherent intuitiveness or usability of the most
prominent methods for managing uncertainty in expert systems, including those
of EMYCIN, PROSPECTOR, Dempster-Shafer theory, fuzzy set theory, simplified
probability theory (assuming marginal independence), and linear regression
using probability estimates. Participants in the study gained experience in a
simple, hypothetical problem domain through a series of learning trials. They
were then randomly assigned to develop an expert system using one of the six
Uncertain Inference Systems (UISs) listed above. Performance of the resulting
systems was then compared. The results indicate that the systems based on the
PROSPECTOR and EMYCIN models were significantly less accurate for certain types
of problems compared to systems based on the other UISs. Possible reasons for
these differences are discussed.
","Comparing Expert Systems Built Using Different Uncertain Inference
  Systems"
"  The most difficult task in probabilistic reasoning may be handling directed
cycles in belief networks. To the best knowledge of this author, there is no
serious discussion of this problem at all in the literature of probabilistic
reasoning so far.
",Directed Cycles in Belief Networks
"  In this paper, the feasibility of using finite totally ordered probability
models under Alelinnas's Theory of Probabilistic Logic [Aleliunas, 1988] is
investigated. The general form of the probability algebra of these models is
derived and the number of possible algebras with given size is deduced. Based
on this analysis, we discuss problems of denominator-indifference and
ambiguity-generation that arise in reasoning by cases and abductive reasoning.
An example is given that illustrates how these problems arise. The
investigation shows that a finite probability model may be of very limited
usage.
","Can Uncertainty Management be Realized in a Finite Totally Ordered
  Probability Algebra?"
"  We discuss the Dempster-Shafer theory of evidence. We introduce a concept of
monotonicity which is related to the diminution of the range between belief and
plausibility. We show that the accumulation of knowledge in this framework
exhibits a nonmonotonic property. We show how the belief structure can be used
to represent typical or commonsense knowledge.
","Normalization and the Representation of Nonmonotonic Knowledge in the
  Theory of Evidence"
"  Probability answer set programming is a declarative programming that has been
shown effective for representing and reasoning about a variety of probability
reasoning tasks. However, the lack of probability aggregates, e.g. {\em
expected values}, in the language of disjunctive hybrid probability logic
programs (DHPP) disallows the natural and concise representation of many
interesting problems. In this paper, we extend DHPP to allow arbitrary
probability aggregates. We introduce two types of probability aggregates; a
type that computes the expected value of a classical aggregate, e.g., the
expected value of the minimum, and a type that computes the probability of a
classical aggregate, e.g, the probability of sum of values. In addition, we
define a probability answer set semantics for DHPP with arbitrary probability
aggregates including monotone, antimonotone, and nonmonotone probability
aggregates. We show that the proposed probability answer set semantics of DHPP
subsumes both the original probability answer set semantics of DHPP and the
classical answer set semantics of classical disjunctive logic programs with
classical aggregates, and consequently subsumes the classical answer set
semantics of the original disjunctive logic programs. We show that the proposed
probability answer sets of DHPP with probability aggregates are minimal
probability models and hence incomparable, which is an important property for
nonmonotonic probability reasoning.
",Probability Aggregates in Probability Answer Set Programming
"  Reinforcement learning methods are increasingly used to optimise dialogue
policies from experience. Most current techniques are model-free: they directly
estimate the utility of various actions, without explicit model of the
interaction dynamics. In this paper, we investigate an alternative strategy
grounded in model-based Bayesian reinforcement learning. Bayesian inference is
used to maintain a posterior distribution over the model parameters, reflecting
the model uncertainty. This parameter distribution is gradually refined as more
data is collected and simultaneously used to plan the agent's actions. Within
this learning framework, we carried out experiments with two alternative
formalisations of the transition model, one encoded with standard multinomial
distributions, and one structured with probabilistic rules. We demonstrate the
potential of our approach with empirical results on a user simulator
constructed from Wizard-of-Oz data in a human-robot interaction scenario. The
results illustrate in particular the benefits of capturing prior domain
knowledge with high-level rules.
",Model-based Bayesian Reinforcement Learning for Dialogue Management
"  Fuzzy answer set programming is a declarative framework for representing and
reasoning about knowledge in fuzzy environments. However, the unavailability of
fuzzy aggregates in disjunctive fuzzy logic programs, DFLP, with fuzzy answer
set semantics prohibits the natural and concise representation of many
interesting problems. In this paper, we extend DFLP to allow arbitrary fuzzy
aggregates. We define fuzzy answer set semantics for DFLP with arbitrary fuzzy
aggregates including monotone, antimonotone, and nonmonotone fuzzy aggregates.
We show that the proposed fuzzy answer set semantics subsumes both the original
fuzzy answer set semantics of DFLP and the classical answer set semantics of
classical disjunctive logic programs with classical aggregates, and
consequently subsumes the classical answer set semantics of classical
disjunctive logic programs. We show that the proposed fuzzy answer sets of DFLP
with fuzzy aggregates are minimal fuzzy models and hence incomparable, which is
an important property for nonmonotonic fuzzy reasoning.
",Fuzzy Aggregates in Fuzzy Answer Set Programming
"  This paper is part of a study whose goal is to show the effciency of using
Bayes networks to carry out model based vision calculations. [Binford et al.
1987] Recognition proceeds by drawing up a network model from the object's
geometric and functional description that predicts the appearance of an object.
Then this network is used to find the object within a photographic image. Many
existing and proposed techniques for vision recognition resemble the
uncertainty calculations of a Bayes net. In contrast, though, they lack a
derivation from first principles, and tend to rely on arbitrary parameters that
we hope to avoid by a network model. The connectedness of the network depends
on what independence considerations can be identified in the vision problem.
Greater independence leads to easier calculations, at the expense of the net's
expressiveness. Once this trade-off is made and the structure of the network is
determined, it should be possible to tailor a solution technique for it. This
paper explores the use of a network with multiply connected paths, drawing on
both techniques of belief networks [Pearl 86] and influence diagrams. We then
demonstrate how one formulation of a multiply connected network can be solved.
",The structure of Bayes nets for vision recognition
"  By probabilistic logic I mean a normative theory of belief that explains how
a body of evidence affects one's degree of belief in a possible hypothesis. A
new axiomatization of such a theory is presented which avoids a finite
additivity axiom, yet which retains many useful inference rules. Many of the
examples of this theory--its models do not use numerical probabilities. Put
another way, this article gives sharper answers to the two questions: 1.What
kinds of sets can used as the range of a probability function? 2.Under what
conditions is the range set of a probability function isomorphic to the set of
real numbers in the interval 10,1/ with the usual arithmetical operations?
",Summary of A New Normative Theory of Probabilistic Logic
"  In Probabilistic Logic Nilsson uses the device of a probability distribution
over a set of possible worlds to assign probabilities to the sentences of a
logical language. In his paper Nilsson concentrated on inference and associated
computational issues. This paper, on the other hand, examines the probabilistic
semantics in more detail, particularly for the case of first-order languages,
and attempts to explain some of the features and limitations of this form of
probability logic. It is pointed out that the device of assigning probabilities
to logical sentences has certain expressive limitations. In particular,
statistical assertions are not easily expressed by such a device. This leads to
certain difficulties with attempts to give probabilistic semantics to default
reasoning using probabilities assigned to logical sentences.
",Probability Distributions Over Possible Worlds
"  Dempster/Shafer (D/S) theory has been advocated as a way of representing
incompleteness of evidence in a system's knowledge base. Methods now exist for
propagating beliefs through chains of inference. This paper discusses how rules
with attached beliefs, a common representation for knowledge in automated
reasoning systems, can be transformed into the joint belief functions required
by propagation algorithms. A rule is taken as defining a conditional belief
function on the consequent given the antecedents. It is demonstrated by example
that different joint belief functions may be consistent with a given set of
rules. Moreover, different representations of the same rules may yield
different beliefs on the consequent hypotheses.
",Hierarchical Evidence and Belief Functions
"  This paper presents an approach to the design of autonomous, real-time
systems operating in uncertain environments. We address issues of problem
solving and reflective control of reasoning under uncertainty in terms of two
fundamental elements: l) a set of decision-theoretic models for selecting among
alternative problem-solving methods and 2) a general computational architecture
for resource-bounded problem solving. The decisiontheoretic models provide a
set of principles for choosing among alternative problem-solving methods based
on their relative costs and benefits, where benefits are characterized in terms
of the value of information provided by the output of a reasoning activity. The
output may be an estimate of some uncertain quantity or a recommendation for
action. The computational architecture, called Schemer-ll, provides for
interleaving of and communication among various problem-solving subsystems.
These subsystems provide alternative approaches to information gathering,
belief refinement, solution construction, and solution execution. In
particular, the architecture provides a mechanism for interrupting the
subsystems in response to critical events. We provide a decision theoretic
account for scheduling problem-solving elements and for critical-event-driven
interruption of activities in an architecture such as Schemer-II.
","Decision-Theoretic Control of Problem Solving: Principles and
  Architecture"
"  This paper discusses a project undertaken between the Departments of
Computing Science, Statistics, and the College of Veterinary Medicine to design
a medical diagnostic system. On-line medical data has been collected in the
hospital database system for several years. A number of induction methods are
being used to extract knowledge from the data in an attempt to improve upon
simple diagnostic charts used by the clinicians. They also enhance the results
of classical statistical methods - finding many more significant variables. The
second part of the paper describes an essentially Bayesian method of evidence
combination using fuzzy events at an initial step. Results are presented and
comparisons are made with other methods.
","Induction and Uncertainty Management Techniques Applied to Veterinary
  Medical Diagnosis"
"  KNET is a general-purpose shell for constructing expert systems based on
belief networks and decision networks. Such networks serve as graphical
representations for decision models, in which the knowledge engineer must
define clearly the alternatives, states, preferences, and relationships that
constitute a decision basis. KNET contains a knowledge-engineering core written
in Object Pascal and an interface that tightly integrates HyperCard, a
hypertext authoring tool for the Apple Macintosh computer, into a novel
expert-system architecture. Hypertext and hypermedia have become increasingly
important in the storage management, and retrieval of information. In broad
terms, hypermedia deliver heterogeneous bits of information in dynamic,
extensively cross-referenced packages. The resulting KNET system features a
coherent probabilistic scheme for managing uncertainty, an objectoriented
graphics editor for drawing and manipulating decision networks, and HyperCard's
potential for quickly constructing flexible and friendly user interfaces. We
envision KNET as a useful prototyping tool for our ongoing research on a
variety of Bayesian reasoning problems, including tractable representation,
inference, and explanation.
",KNET: Integrating Hypermedia and Bayesian Modeling
"  This paper demonstrates a method for using belief-network algorithms to solve
influence diagram problems. In particular, both exact and approximation
belief-network algorithms may be applied to solve influence-diagram problems.
More generally, knowing the relationship between belief-network and
influence-diagram problems may be useful in the design and development of more
efficient influence diagram algorithms.
",A Method for Using Belief Networks as Influence Diagrams
"  Computational mechanisms for uncertainty management must support interactive
and incremental problem formulation, inference, hypothesis testing, and
decision making. However, most current uncertainty inference systems
concentrate primarily on inference, and provide no support for the larger
issues. We present a computational approach to uncertainty management which
provides direct support for the dynamic, incremental aspect of this task, while
at the same time permitting direct representation of the structure of
evidential relationships. At the same time, we show that this approach responds
to the modularity concerns of Heckerman and Horvitz [Heck87]. This paper
emphasizes examples of the capabilities of this approach. Another paper
[D'Am89] details the representations and algorithms involved.
","Process, Structure, and Modularity in Reasoning with Uncertainty"
"  Predicting the future is an important component of decision making. In most
situations, however, there is not enough information to make accurate
predictions. In this paper, we develop a theory of causal reasoning for
predictive inference under uncertainty. We emphasize a common type of
prediction that involves reasoning about persistence: whether or not a
proposition once made true remains true at some later time. We provide a
decision procedure with a polynomial-time algorithm for determining the
probability of the possible consequences of a set events and initial
conditions. The integration of simple probability theory with temporal
projection enables us to circumvent problems that nonmonotonic temporal
reasoning schemes have in dealing with persistence. The ideas in this paper
have been implemented in a prototype system that refines a database of causal
rules in the course of applying those rules to construct and carry out plans in
a manufacturing domain.
",Probabilistic Causal Reasoning
"  This paper advocates the usefulness of new theories of uncertainty for the
purpose of modeling some facets of uncertain knowledge, especially vagueness,
in AI. It can be viewed as a partial reply to Cheeseman's (among others)
defense of probability.
","Modeling uncertain and vague knowledge in possibility and evidence
  theories"
"  There is uncertainty associated with the occurrence of many events in real
life. In this paper we develop a temporal logic to deal with such uncertain
events and outline a possible implementation in an extension of PROLOG. Events
are represented as fuzzy sets with the membership function giving the
possibility of occurrence of the event in a given interval of time. The
developed temporal logic is simple but powerful. It can determine effectively
the various temporal relations between uncertain events or their combinations.
PROLOG provides a uniform substrate on which to effectively implement such a
temporal logic for uncertain events
","A Temporal Logic for Uncertain Events and An Outline of A Possible
  Implementation in An Extension of PROLOG"
"  A new approach for uncertainty management for fuzzy, rule based decision
support systems is proposed: The domain expert's knowledge is expressed by a
set of rules that frequently refer to vague and uncertain propositions. The
certainty of propositions is represented using intervals [a, b] expressing that
the proposition's probability is at least a and at most b. Methods and
techniques for computing the overall certainty of fuzzy compound propositions
that have been defined by using logical connectives 'and', 'or' and 'not' are
introduced. Different inference schemas for applying fuzzy rules by using modus
ponens are discussed. Different algorithms for combining evidence that has been
received from different rules for the same proposition are provided. The
relationship of the approach to other approaches is analyzed and its problems
of knowledge acquisition and knowledge representation are discussed in some
detail. The basic concepts of a rule-based programming language called PICASSO,
for which the approach is a theoretical foundation, are outlined.
",Uncertainty Management for Fuzzy Decision Support Systems
"  This paper argues for a modal view of probability. The syntax and semantics
of one particularly strong probability logic are discussed and some examples of
the use of the logic are provided. We show that it is both natural and useful
to think of probability as a modal operator. Contrary to popular belief in AI,
a probability ranging between 0 and 1 represents a continuum between
impossibility and necessity, not between simple falsity and truth. The present
work provides a clear semantics for quantification into the scope of the
probability operator and for higher-order probabilities. Probability logic is a
language for expressing both probabilistic and logical concepts.
",Probability as a Modal Operator
"  This paper addresses the problem of resolving errors under uncertainty in a
rule-based system. A new approach has been developed that reformulates this
problem as a neural-network learning problem. The strength and the fundamental
limitations of this approach are explored and discussed. The main result is
that neural heuristics can be applied to solve some but not all problems in
rule-based systems.
",Truth Maintenance Under Uncertainty
"  A previous paper [2] showed how to generate a linear discriminant network
(LDN) that computes likely faults for a noisy fault detection problem by using
a modification of the perceptron learning algorithm called the pocket
algorithm. Here we compare the performance of this connectionist model with
performance of the optimal Bayesian decision rule for the example that was
previously described. We find that for this particular problem the
connectionist model performs about 97% as well as the optimal Bayesian
procedure. We then define a more general class of noisy single-pattern boolean
(NSB) fault detection problems where each fault corresponds to a single
:pattern of boolean instrument readings and instruments are independently
noisy. This is equivalent to specifying that instrument readings are
probabilistic but conditionally independent given any particular fault. We
prove:
  1. The optimal Bayesian decision rule for every NSB fault detection problem
is representable by an LDN containing no intermediate nodes. (This slightly
extends a result first published by Minsky & Selfridge.) 2. Given an NSB fault
detection problem, then with arbitrarily high probability after sufficient
iterations the pocket algorithm will generate an LDN that computes an optimal
Bayesian decision rule for that problem. In practice we find that a reasonable
number of iterations of the pocket algorithm produces a network with good, but
not optimal, performance.
",Bayesian Assessment of a Connectionist Model for Fault Detection
"  This paper explores the role of Directed Acyclic Graphs (DAGs) as a
representation of conditional independence relationships. We show that DAGs
offer polynomially sound and complete inference mechanisms for inferring
conditional independence relationships from a given causal set of such
relationships. As a consequence, d-separation, a graphical criterion for
identifying independencies in a DAG, is shown to uncover more valid
independencies then any other criterion. In addition, we employ the Armstrong
property of conditional independence to show that the dependence relationships
displayed by a DAG are inherently consistent, i.e. for every DAG D there exists
some probability distribution P that embodies all the conditional
independencies displayed in D and none other.
",On the Logic of Causal Models
"  This paper addresses a prevailing assumption in single-agent heuristic search
theory- that problem-solving algorithms should guarantee shortest-path
solutions, which are typically called optimal. Optimality implies a metric for
judging solution quality, where the optimal solution is the solution with the
highest quality. When path-length is the metric, we will distinguish such
solutions as p-optimal.
",The Optimality of Satisficing Solutions
"  In this paper, an empirical evaluation of three inference methods for
uncertain reasoning is presented in the context of Pathfinder, a large expert
system for the diagnosis of lymph-node pathology. The inference procedures
evaluated are (1) Bayes' theorem, assuming evidence is conditionally
independent given each hypothesis; (2) odds-likelihood updating, assuming
evidence is conditionally independent given each hypothesis and given the
negation of each hypothesis; and (3) a inference method related to the
Dempster-Shafer theory of belief. Both expert-rating and decision-theoretic
metrics are used to compare the diagnostic accuracy of the inference methods.
",An Empirical Comparison of Three Inference Methods
"  This paper describes a formal system of belief revision developed by Wolfgang
Spohn and shows that this system has a parallel implementation that can be
derived from an influence diagram in a manner similar to that in which Bayesian
networks are derived. The proof rests upon completeness results for an
axiomatization of the notion of conditional independence, with the Spohn system
being used as a semantics for the relation of conditional independence.
",Parallel Belief Revision
"  The practice of stochastic sensitivity analysis described in the decision
analysis literature is a testimonial to the need for considering deviations
from precise point estimates of uncertainty. We propose the use of Bayesian
fuzzy probabilities within an influence diagram computational scheme for
performing sensitivity analysis during the solution of probabilistic inference
and decision problems. Unlike other parametric approaches, the proposed scheme
does not require resolving the problem for the varying probability point
estimates. We claim that the solution to fuzzy influence diagrams provides as
much information as the classical point estimate approach plus additional
information concerning stochastic sensitivity. An example based on diagnostic
decision making in microcomputer assembly is used to illustrate this idea. We
claim that the solution to fuzzy influence diagrams provides as much
information as the classical point estimate approach plus additional interval
information that is useful for stochastic sensitivity analysis.
",Stochastic Sensitivity Analysis Using Fuzzy Influence Diagrams
"  Many real world models can be characterized as weak, meaning that there is
significant uncertainty in both the data input and inferences. This lack of
determinism makes it especially difficult for users of computer decision aids
to understand and have confidence in the models. This paper presents a
representation for uncertainty and utilities that serves as a framework for
graphical summary and computer-generated explanation of decision models. The
application described that tests the methodology is a computer decision aid
designed to enhance the clinician-patient consultation process for patients
with angina (chest pain due to lack of blood flow to the heart muscle). The
angina model is represented as a Bayesian decision network. Additionally, the
probabilities and utilities are treated as random variables with probability
distributions on their range of possible values. The initial distributions
represent information on all patients with anginal symptoms, and the approach
allows for rapid tailoring to more patientspecific distributions. This
framework provides a metric for judging the importance of each variable in the
model dynamically.
",A Representation of Uncertainty to Aid Insight into Decision Models
"  Nonmonotonic reasoning is a pattern of reasoning that allows an agent to make
and retract (tentative) conclusions from inconclusive evidence. This paper
gives a possible-worlds interpretation of the nonmonotonic reasoning problem
based on standard decision theory and the emerging probability logic. The
system's central principle is that a tentative conclusion is a decision to make
a bet, not an assertion of fact. The system is rational, and as sound as the
proof theory of its underlying probability log.
",Rational Nonmonotonic Reasoning
"  There has long been debate about the relative merits of decision theoretic
methods and heuristic rule-based approaches for reasoning under uncertainty. We
report an experimental comparison of the performance of the two approaches to
troubleshooting, specifically to test selection for fault diagnosis. We use as
experimental testbed the problem of diagnosing motorcycle engines. The first
approach employs heuristic test selection rules obtained from expert mechanics.
We compare it with the optimal decision analytic algorithm for test selection
which employs estimated component failure probabilities and test costs. The
decision analytic algorithm was found to reduce the expected cost (i.e. time)
to arrive at a diagnosis by an average of 14% relative to the expert rules.
Sensitivity analysis shows the results are quite robust to inaccuracy in the
probability and cost estimates. This difference suggests some interesting
implications for knowledge acquisition.
","A Comparison of Decision Analysis and Expert Rules for Sequential
  Diagnosis"
"  Uncertainty enters into human reasoning and inference in at least two ways.
It is reasonable to suppose that there will be roles for these distinct uses of
uncertainty also in automated reasoning.
",Probabilistic Inference and Probabilistic Reasoning
"  (l) I have enough evidence to render the sentence S probable. (la) So,
relative to what I know, it is rational of me to believe S. (2) Now that I have
more evidence, S may no longer be probable. (2a) So now, relative to what I
know, it is not rational of me to believe S. These seem a perfectly ordinary,
common sense, pair of situations. Generally and vaguely, I take them to embody
what I shall call probabilistic inference. This form of inference is clearly
non-monotonic. Relatively few people have taken this form of inference, based
on high probability, to serve as a foundation for non-monotonic logic or for a
logical or defeasible inference. There are exceptions: Jane Nutter [16] thinks
that sometimes probability has something to do with non-monotonic reasoning.
Judea Pearl [ 17] has recently been exploring the possibility. There are any
number of people whom one might call probability enthusiasts who feel that
probability provides all the answers by itself, with no need of help from
logic. Cheeseman [1], Henrion [5] and others think it useful to look at a
distribution of probabilities over a whole algebra of statements, to update
that distribution in the light of new evidence, and to use the latest updated
distribution of probability over the algebra as a basis for planning and
decision making. A slightly weaker form of this approach is captured by Nilsson
[15], where one assumes certain probabilities for certain statements, and
infers the probabilities, or constraints on the probabilities of other
statement. None of this corresponds to what I call probabilistic inference. All
of the inference that is taking place, either in Bayesian updating, or in
probabilistic logic, is strictly deductive. Deductive inference, particularly
that concerned with the distribution of classical probabilities or chances, is
of great importance. But this is not to say that there is no important role for
what earlier logicians have called ""ampliative"" or ""inductive"" or ""scientific""
inference, in which the conclusion goes beyond the premises, asserts more than
do the premises. This depends on what David Israel [6] has called ""real rules
of inference"". It is characteristic of any such logic or inference procedure
that it can go wrong: that statements accepted at one point may be rejected at
a later point. Research underlying the results reported here has been partially
supported by the Signals Warfare Center of the United States Army.
",Probabilistic and Non-Monotonic Inference
"  For many years, at least since McCarthy and Hayes (1969), writers have
lamented, and attempted to compensate for, the alleged fact that we often do
not have adequate statistical knowledge for governing the uncertainty of
belief, for making uncertain inferences, and the like. It is hardly ever
spelled out what ""adequate statistical knowledge"" would be, if we had it, and
how adequate statistical knowledge could be used to control and regulate
epistemic uncertainty.
",Epistemological Relevance and Statistical Knowledge
"  This paper reports on empirical work aimed at comparing evidential reasoning
techniques. While there is prima facie evidence for some conclusions, this i6
work in progress; the present focus is methodology, with the goal that
subsequent results be meaningful. The domain is a network of UNIX* cycle
servers, and the task is to predict properties of the state of the network from
partial descriptions of the state. Actual data from the network are taken and
used for blindfold testing in a betting game that allows abstention. The focal
technique has been Kyburg's method for reasoning with data of varying relevance
to a particular query, though the aim is to be able eventually to compare
various uncertainty calculi. The conclusions are not novel, but are
instructive. 1. All of the calculi performed better than human subjects, so
unbiased access to sample experience is apparently of value. 2. Performance
depends on metric: (a) when trials are repeated, net = gains - losses favors
methods that place many bets, if the probability of placing a correct bet is
sufficiently high; that is, it favors point-valued formalisms; (b) yield =
gains/(gains + lossee) favors methods that bet only when sure to bet correctly;
that is, it favors interval-valued formalisms. 3. Among the calculi, there were
no clear winners or losers. Methods are identified for eliminating the bias of
the net as a performance criterion and for separating the calculi effectively:
in both cases by posting odds for the betting game in the appropriate way.
",Evidential Reasoning in a Network Usage Prediction Testbed
"  When knowledge is obtained from a database, it is only possible to deduce
confidence intervals for probability values. With confidence intervals
replacing point values, the results in the set covering model include interval
constraints for the probabilities of mutually exclusive and exhaustive
explanations. The Principle of Interval Constraints ranks these explanations by
determining the expected values of the probabilities based on distributions
determined from the interval, constraints. This principle was developed using
the Classical Approach to probability. This paper justifies the Principle of
Interval Constraints with a more rigorous statement of the Classical Approach
and by defending the concept of probabilities of probabilities.
",Justifying the Principle of Interval Constraints
"  There is much interest in providing probabilistic semantics for defaults but
most approaches seem to suffer from one of two problems: either they require
numbers, a problem defaults were intended to avoid, or they generate peculiar
side effects. Rather than provide semantics for defaults, we address the
problem defaults were intended to solve: that of reasoning under uncertainty
where numeric probability distributions are not available. We describe a
non-numeric formalism called an inference graph based on standard probability
theory, conditional independence and sentences of favouring where a favours b -
favours(a, b) - p(a|b) > p(a). The formalism seems to handle the examples from
the nonmonotonic literature. Most importantly, the sentences of our system can
be verified by performing an appropriate experiment in the semantic domain.
",Probabilistic Semantics and Defaults
"  Techniques for decision making with knowledge of linear constraints on
condition probabilities are examined. These constraints arise naturally in many
situations: upper and lower condition probabilities are known; an ordering
among the probabilities is determined; marginal probabilities or bounds on such
probabilities are known, e.g., data are available in the form of a
probabilistic database (Cavallo and Pittarelli, 1987a); etc. Standard
situations of decision making under risk and uncertainty may also be
characterized by linear constraints. Each of these types of information may be
represented by a convex polyhedron of numerically determinate condition
probabilities. A uniform approach to decision making under risk, uncertainty,
and partial uncertainty based on a generalized version of a criterion of
Hurwicz is proposed, Methods for processing marginal probabilities to improve
decision making using any of the criteria discussed are presented.
",Decision Making with Linear Constraints on Probabilities
"  Recent developments using directed acyclical graphs (i.e., influence diagrams
and Bayesian networks) for knowledge representation have lessened the problems
of using probability in knowledge-based systems (KBS). Most current research
involves the efficient propagation of new evidence, but little has been done
concerning the maintenance of domain-specific knowledge, which includes the
probabilistic information about the problem domain. By making use of
conditional independencies represented in she graphs, however, probability
assessments are required only for certain variables when the knowledge base is
updated. The purpose of this study was to investigate, for those variables
which require probability assessments, ways to reduce the amount of new
knowledge required from the expert when updating probabilistic information in a
probabilistic knowledge-based system. Three special cases (ignored outcome,
split outcome, and assumed constraint outcome) were identified under which many
of the original probabilities (those already in the knowledge-base) do not need
to be reassessed when maintenance is required.
",Maintenance in Probabilistic Knowledge-Based Systems
"  An approximation method is presented for probabilistic inference with
continuous random variables. These problems can arise in many practical
problems, in particular where there are ""second order"" probabilities. The
approximation, based on the Gaussian influence diagram, iterates over linear
approximations to the inference problem.
",A Linear Approximation Method for Probabilistic Inference
"  In this paper, we describe an abstract framework and axioms under which exact
local computation of marginals is possible. The primitive objects of the
framework are variables and valuations. The primitive operators of the
framework are combination and marginalization. These operate on valuations. We
state three axioms for these operators and we derive the possibility of local
computation from the axioms. Next, we describe a propagation scheme for
computing marginals of a valuation when we have a factorization of the
valuation on a hypertree. Finally we show how the problem of computing
marginals of joint probability distributions and joint belief functions fits
the general framework.
",An Axiomatic Framework for Bayesian and Belief-function Propagation
"  Probability theory, epistemically interpreted, provides an excellent, if not
the best available account of inductive reasoning. This is so because there are
general and definite rules for the change of subjective probabilities through
information or experience; induction and belief change are one and same topic,
after all. The most basic of these rules is simply to conditionalize with
respect to the information received; and there are similar and more general
rules. 1 Hence, a fundamental reason for the epistemological success of
probability theory is that there at all exists a well-behaved concept of
conditional probability. Still, people have, and have reasons for, various
concerns over probability theory. One of these is my starting point:
Intuitively, we have the notion of plain belief; we believe propositions2 to be
true (or to be false or neither). Probability theory, however, offers no formal
counterpart to this notion. Believing A is not the same as having probability 1
for A, because probability 1 is incorrigible3; but plain belief is clearly
corrigible. And believing A is not the same as giving A a probability larger
than some 1 - c, because believing A and believing B is usually taken to be
equivalent to believing A & B.4 Thus, it seems that the formal representation
of plain belief has to take a non-probabilistic route. Indeed, representing
plain belief seems easy enough: simply represent an epistemic state by the set
of all propositions believed true in it or, since I make the common assumption
that plain belief is deductively closed, by the conjunction of all propositions
believed true in it. But this does not yet provide a theory of induction, i.e.
an answer to the question how epistemic states so represented are changed
tbrough information or experience. There is a convincing partial answer: if the
new information is compatible with the old epistemic state, then the new
epistemic state is simply represented by the conjunction of the new information
and the old beliefs. This answer is partial because it does not cover the quite
common case where the new information is incompatible with the old beliefs. It
is, however, important to complete the answer and to cover this case, too;
otherwise, we would not represent plain belief as conigible. The crucial
problem is that there is no good completion. When epistemic states are
represented simply by the conjunction of all propositions believed true in it,
the answer cannot be completed; and though there is a lot of fruitful work, no
other representation of epistemic states has been proposed, as far as I know,
which provides a complete solution to this problem. In this paper, I want to
suggest such a solution. In [4], I have more fully argued that this is the only
solution, if certain plausible desiderata are to be satisfied. Here, in section
2, I will be content with formally defining and intuitively explaining my
proposal. I will compare my proposal with probability theory in section 3. It
will turn out that the theory I am proposing is structurally homomorphic to
probability theory in important respects and that it is thus equally easily
implementable, but moreover computationally simpler. Section 4 contains a very
brief comparison with various kinds of logics, in particular conditional logic,
with Shackle's functions of potential surprise and related theories, and with
the Dempster - Shafer theory of belief functions.
",A General Non-Probabilistic Theory of Inductive Reasoning
"  This paper examines two related problems that are central to developing an
autonomous decision-making agent, such as a robot. Both problems require
generating structured representafions from a database of unstructured
declarative knowledge that includes many facts and rules that are irrelevant in
the problem context. The first problem is how to generate a well structured
decision problem from such a database. The second problem is how to generate,
from the same database, a well-structured explanation of why some possible
world occurred. In this paper it is shown that the problem of generating the
appropriate decision structure or explanation is intractable without
introducing further constraints on the knowledge in the database. The paper
proposes that the problem search space can be constrained by adding knowledge
to the database about causal relafions between events. In order to determine
the causal knowledge that would be most useful, causal theories for
deterministic and indeterministic universes are proposed. A program that uses
some of these causal constraints has been used to generate explanations about
faulty plans. The program shows the expected increase in efficiency as the
causal constraints are introduced.
","Generating Decision Structures and Causal Explanations for Decision
  Making"
"  This paper focuses on probability updates in multiply-connected belief
networks. Pearl has designed the method of conditioning, which enables us to
apply his algorithm for belief updates in singly-connected networks to
multiply-connected belief networks by selecting a loop-cutset for the network
and instantiating these loop-cutset nodes. We discuss conditions that need to
be satisfied by the selected nodes. We present a heuristic algorithm for
finding a loop-cutset that satisfies these conditions.
",Updating Probabilities in Multiply-Connected Belief Networks
"  In pattern analysis, information regarding an object can often be drawn from
its surroundings. This paper presents a method for handling uncertainty when
using context of symbols and texts for analyzing technical drawings. The method
is based on Dempster-Shafer theory and possibility theory.
",Handling uncertainty in a system for text-symbol context analysis
"  Dependency knowledge of the form ""x is independent of y once z is known""
invariably obeys the four graphoid axioms, examples include probabilistic and
database dependencies. Often, such knowledge can be represented efficiently
with graphical structures such as undirected graphs and directed acyclic graphs
(DAGs). In this paper we show that the graphical criterion called d-separation
is a sound rule for reading independencies from any DAG based on a causal input
list drawn from a graphoid. The rule may be extended to cover DAGs that
represent functional dependencies as well as conditional dependencies.
",Causal Networks: Semantics and Expressiveness
"  A probabilistic method of reasoning under uncertainty is proposed based on
the principle of Minimum Cross Entropy (MCE) and concept of Recursive Causal
Model (RCM). The dependency and correlations among the variables are described
in a special language BNDL (Belief Networks Description Language). Beliefs are
propagated among the clauses of the BNDL programs representing the underlying
probabilistic distributions. BNDL interpreters in both Prolog and C has been
developed and the performance of the method is compared with those of the
others.
",MCE Reasoning in Recursive Causal Networks
"  We introduce the operation of possibility qualification and show how. this
modal-like operator can be used to represent ""typical"" or default knowledge in
a theory of nonmonotonic reasoning. We investigate the representational power
of this approach by looking at a number of prototypical problems from the
nonmonotonic reasoning literature. In particular we look at the so called Yale
shooting problem and its relation to priority in default reasoning.
",Nonmonotonic Reasoning via Possibility Theory
"  With the desire to apply the Dempster-Shafer theory to complex real world
problems where the evidential strength is often imprecise and vague, several
attempts have been made to generalize the theory. However, the important
concept in the D-S theory that the belief and plausibility functions are lower
and upper probabilities is no longer preserved in these generalizations. In
this paper, we describe a generalized theory of evidence where the degree of
belief in a fuzzy set is obtained by minimizing the probability of the fuzzy
set under the constraints imposed by a basic probability assignment. To
formulate the probabilistic constraint of a fuzzy focal element, we decompose
it into a set of consonant non-fuzzy focal elements. By generalizing the
compatibility relation to a possibility theory, we are able to justify our
generalization to Dempster's rule based on possibility distribution. Our
generalization not only extends the application of the D-S theory but also
illustrates a way that probability theory and fuzzy set theory can be combined
to deal with different kinds of uncertain information in AI systems.
",Generalizing the Dempster-Shafer Theory to Fuzzy Sets
"  We present a logical framework to represent and reason about fuzzy
optimization problems based on fuzzy answer set optimization programming. This
is accomplished by allowing fuzzy optimization aggregates, e.g., minimum and
maximum in the language of fuzzy answer set optimization programming to allow
minimization or maximization of some desired criteria under fuzzy environments.
We show the application of the proposed logical fuzzy optimization framework
under the fuzzy answer set optimization programming to the fuzzy water
allocation optimization problem.
",Logical Fuzzy Optimization
"  This article addresses the problem of expressing preferences in flexible
queries while basing on a combination of the fuzzy logic theory and Conditional
Preference Networks or CP-Nets.
",Mod\`ele flou d'expression des pr\'ef\'erences bas\'e sur les CP-Nets
"  The Rao-Blackwell theorem is utilized to analyze and improve the scalability
of inference in large probabilistic models that exhibit symmetries. A novel
marginal density estimator is introduced and shown both analytically and
empirically to outperform standard estimators by several orders of magnitude.
The developed theory and algorithms apply to a broad class of probabilistic
models including statistical relational models considered not susceptible to
lifted probabilistic inference.
",Symmetry-Aware Marginal Density Estimation
"  This paper examines the relationship between Shafer's belief functions and
convex sets of probability distributions. Kyburg's (1986) result showed that
belief function models form a subset of the class of closed convex probability
distributions. This paper emphasizes the importance of Kyburg's result by
looking at simple examples involving Bernoulli trials. Furthermore, it is shown
that many convex sets of probability distributions generate the same belief
function in the sense that they support the same lower and upper values. This
has implications for a decision theoretic extension. Dempster's rule of
combination is also compared with Bayes' rule of conditioning.
",Is Shafer General Bayes?
"  Modifiable combining functions are a synthesis of two common approaches to
combining evidence. They offer many of the advantages of these approaches and
avoid some disadvantages. Because they facilitate the acquisition,
representation, explanation, and modification of knowledge about combinations
of evidence, they are proposed as a tool for knowledge engineers who build
systems that reason under uncertainty, not as a normative theory of evidence.
",Modifiable Combining Functions
"  The combination of evidence in Dempster-Shafer theory is compared with the
combination of evidence in probabilistic logic. Sufficient conditions are
stated for these two methods to agree. It is then shown that these conditions
are minimal in the sense that disagreement can occur when any one of them is
removed. An example is given in which the traditional assumption of conditional
independence of evidence on hypotheses holds and a uniform prior is assumed,
but probabilistic logic and Dempster's rule give radically different results
for the combination of two evidence events.
",Dempster-Shafer vs. Probabilistic Logic
"  A number of writers have supposed that for the full specification of belief,
higher order probabilities are required. Some have even supposed that there may
be an unending sequence of higher order probabilities of probabilities of
probabilities.... In the present paper we show that higher order probabilities
can always be replaced by the marginal distributions of joint probability
distributions. We consider both the case in which higher order probabilities
are of the same sort as lower order probabilities and that in which higher
order probabilities are distinct in character, as when lower order
probabilities are construed as frequencies and higher order probabilities are
construed as subjective degrees of belief. In neither case do higher order
probabilities appear to offer any advantages, either conceptually or
computationally.
",Higher Order Probabilities
"  In the canonical examples underlying Shafer-Dempster theory, beliefs over the
hypotheses of interest are derived from a probability model for a set of
auxiliary hypotheses. Beliefs are derived via a compatibility relation
connecting the auxiliary hypotheses to subsets of the primary hypotheses. A
belief function differs from a Bayesian probability model in that one does not
condition on those parts of the evidence for which no probabilities are
specified. The significance of this difference in conditioning assumptions is
illustrated with two examples giving rise to identical belief functions but
different Bayesian probability distributions.
","Belief in Belief Functions: An Examination of Shafer's Canonical
  Examples"
"  The apparent failure of individual probabilistic expressions to distinguish
uncertainty about truths from uncertainty about probabilistic assessments have
prompted researchers to seek formalisms where the two types of uncertainties
are given notational distinction. This paper demonstrates that the desired
distinction is already a built-in feature of classical probabilistic models,
thus, specialized notations are unnecessary.
","Do We Need Higher-Order Probabilities and, If So, What Do They Mean?"
"  This paper shows that the common method used for making predictions under
uncertainty in A1 and science is in error. This method is to use currently
available data to select the best model from a given class of models-this
process is called abduction-and then to use this model to make predictions
about future data. The correct method requires averaging over all the models to
make a prediction-we call this method transduction. Using transduction, an AI
system will not give misleading results when basing predictions on small
amounts of data, when no model is clearly best. For common classes of models we
show that the optimal solution can be given in closed form.
",Bayesian Prediction for Artificial Intelligence
"  Dempster's rule of combination has been the most controversial part of the
Dempster-Shafer (D-S) theory. In particular, Zadeh has reached a conjecture on
the noncombinability of evidence from a relational model of the D-S theory. In
this paper, we will describe another relational model where D-S masses are
represented as conditional granular distributions. By comparing it with Zadeh's
relational model, we will show how Zadeh's conjecture on combinability does not
affect the applicability of Dempster's rule in our model.
",Can Evidence Be Combined in the Dempster-Shafer Theory
"  The domain of spare parts forecasting is examined, and is found to present
unique uncertainty based problems in the architectural design of a
knowledge-based system. A mixture of different uncertainty paradigms is
required for the solution, with an intriguing combinatoric problem arising from
an uncertain choice of inference engines. Thus, uncertainty in the system is
manifested in two different meta-levels. The different uncertainty paradigms
and meta-levels must be integrated into a functioning whole. FRED is an example
of a difficult real-world domain to which no existing uncertainty approach is
completely appropriate. This paper discusses the architecture of FRED,
highlighting: the points of uncertainty and other interesting features of the
domain, the specific implications of those features on the system design
(including the combinatoric explosions), their current implementation & future
plans,and other problems and issues with the architecture.
","An Interesting Uncertainty-Based Combinatoric Problem in Spare Parts
  Forecasting: The FRED System"
"  This is a preliminary version of visual interpretation integrating multiple
sensors in SUCCESSOR, an intelligent, model-based vision system. We pursue a
thorough integration of hierarchical Bayesian inference with comprehensive
physical representation of objects and their relations in a system for
reasoning with geometry, surface materials and sensor models in machine vision.
Bayesian inference provides a framework for accruing_ probabilities to rank
order hypotheses.
",Bayesian Inference in Model-Based Machine Vision
"  This paper discusses an expert system shell that integrates rule-based
reasoning and the Dempster-Shafer evidence combination scheme. Domain knowledge
is stored as rules with associated belief functions. The reasoning component
uses a combination of forward and backward inferencing mechanisms to allow
interaction with users in a mixed-initiative format.
",Using the Dempster-Shafer Scheme in a Diagnostic Expert System Shell
"  This paper examines Bayesian belief network inference using simulation as a
method for computing the posterior probabilities of network variables.
Specifically, it examines the use of a method described by Henrion, called
logic sampling, and a method described by Pearl, called stochastic simulation.
We first review the conditions under which logic sampling is computationally
infeasible. Such cases motivated the development of the Pearl's stochastic
simulation algorithm. We have found that this stochastic simulation algorithm,
when applied to certain networks, leads to much slower than expected
convergence to the true posterior probabilities. This behavior is a result of
the tendency for local areas in the network to become fixed through many
simulation cycles. The time required to obtain significant convergence can be
made arbitrarily long by strengthening the probabilistic dependency between
nodes. We propose the use of several forms of graph modification, such as graph
pruning, arc reversal, and node reduction, in order to convert some networks
into formats that are computationally more efficient for simulation.
",Stochastic Simulation of Bayesian Belief Networks
"  We present a program that manages a database of temporally scoped beliefs.
The basic functionality of the system includes maintaining a network of
constraints among time points, supporting a variety of fetches, mediating the
application of causal rules, monitoring intervals of time for the addition of
new facts, and managing data dependencies that keep the database consistent. At
this level the system operates independent of any measure of belief or belief
calculus. We provide an example of how an application program mi9ght use this
functionality to implement a belief calculus.
",Temporal Reasoning About Uncertain Worlds
"  We present a representation of partial confidence in belief and preference
that is consistent with the tenets of decision-theory. The fundamental insight
underlying the representation is that if a person is not completely confident
in a probability or utility assessment, additional modeling of the assessment
may improve decisions to which it is relevant. We show how a traditional
decision-analytic approach can be used to balance the benefits of additional
modeling with associated costs. The approach can be used during knowledge
acquisition to focus the attention of a knowledge engineer or expert on parts
of a decision model that deserve additional refinement.
","A Perspective on Confidence and Its Use in Focusing Attention During
  Knowledge Acquisition"
"  Bayes belief networks and influence diagrams are tools for constructing
coherent probabilistic representations of uncertain knowledge. The process of
constructing such a network to represent an expert's knowledge is used to
illustrate a variety of techniques which can facilitate the process of
structuring and quantifying uncertain relationships. These include some
generalizations of the ""noisy OR gate"" concept. Sensitivity analysis of generic
elements of Bayes' networks provides insight into when rough probability
assessments are sufficient and when greater precision may be important.
",Practical Issues in Constructing a Bayes' Belief Network
"  This paper describes NAIVE, a low-level knowledge representation language and
inferencing process. NAIVE has been designed for reasoning about
nondeterministic dynamic systems like those found in medicine. Knowledge is
represented in a graph structure consisting of nodes, which correspond to the
variables describing the system of interest, and arcs, which correspond to the
procedures used to infer the value of a variable from the values of other
variables. The value of a variable can be determined at an instant in time,
over a time interval or for a series of times. Information about the value of a
variable is expressed as a probability density function which quantifies the
likelihood of each possible value. The inferencing process uses these
probability density functions to propagate uncertainty. NAIVE has been used to
develop medical knowledge bases including over 100 variables.
","NAIVE: A Method for Representing Uncertainty and Temporal Relationships
  in an Automated Reasoner"
"  A distinction is sometimes made between ""statistical"" and ""subjective""
probabilities. This is based on a distinction between ""unique"" events and
""repeatable"" events. We argue that this distinction is untenable, since all
events are ""unique"" and all events belong to ""kinds"", and offer a conception of
probability for A1 in which (1) all probabilities are based on -- possibly
vague -- statistical knowledge, and (2) every statement in the language has a
probability. This conception of probability can be applied to very rich
languages.
",Objective Probability
"  Definitions and notations with historical references are given for some
numerical coefficients commonly used to quantify relations among collections of
objects for the purpose of expressing approximate knowledge and probabilistic
reasoning.
",Coefficients of Relations for Probabilistic Reasoning
"  This paper demonstrates a methodology for examining the accuracy of uncertain
inference systems (UIS), after their parameters have been optimized, and does
so for several common UIS's. This methodology may be used to test the accuracy
when either the prior assumptions or updating formulae are not exactly
satisfied. Surprisingly, these UIS's were revealed to be no more accurate on
the average than a simple linear regression. Moreover, even on prior
distributions which were deliberately biased so as give very good accuracy,
they were less accurate than the simple probabilistic model which assumes
marginal independence between inputs. This demonstrates that the importance of
updating formulae can outweigh that of prior assumptions. Thus, when UIS's are
judged by their final accuracy after optimization, we get completely different
results than when they are judged by whether or not their prior assumptions are
perfectly satisfied.
",Satisfaction of Assumptions is a Weak Predictor of Performance
"  This paper considers the problem of invoking auxiliary, unobservable
variables to facilitate the structuring of causal tree models for a given set
of continuous variables. Paralleling the treatment of bi-valued variables in
[Pearl 1986], we show that if a collection of coupled variables are governed by
a joint normal distribution and a tree-structured representation exists, then
both the topology and all internal relationships of the tree can be uncovered
by observing pairwise dependencies among the observed variables (i.e., the
leaves of the tree). Furthermore, the conditions for normally distributed
variables are less restrictive than those governing bi-valued variables. The
result extends the applications of causal tree models which were found useful
in evidential reasoning tasks.
",Structuring Causal Tree Models with Continuous Variables
"  The Dempster-Shafer theory has been extended recently for its application to
expert systems. However, implementing the extended D-S reasoning model in
rule-based systems greatly complicates the task of generating informative
explanations. By implementing GERTIS, a prototype system for diagnosing
rheumatoid arthritis, we show that two kinds of knowledge are essential for
explanation generation: (l) taxonomic class relationships between hypotheses
and (2) pointers to the rules that significantly contribute to belief in the
hypothesis. As a result, the knowledge represented in GERTIS is richer and more
complex than that of conventional rule-based systems. GERTIS not only
demonstrates the feasibility of rule-based evidential-reasoning systems, but
also suggests ways to generate better explanations, and to explicitly represent
various useful relationships among hypotheses and rules.
",Implementing Evidential Reasoning in Expert Systems
"  Decision tree induction systems are being used for knowledge acquisition in
noisy domains. This paper develops a subjective Bayesian interpretation of the
task tackled by these systems and the heuristic methods they use. It is argued
that decision tree systems implicitly incorporate a prior belief that the
simpler (in terms of decision tree complexity) of two hypotheses be preferred,
all else being equal, and that they perform a greedy search of the space of
decision rules to find one in which there is strong posterior belief. A number
of improvements to these systems are then suggested.
",Decision Tree Induction Systems: A Bayesian Analysis
"  The use of numerical uncertainty representations allows better modeling of
some aspects of human evidential reasoning. It also makes knowledge acquisition
and system development, test, and modification more difficult. We propose that
where possible, the assignment and/or refinement of rule weights should be
performed automatically. We present one approach to performing this training -
numerical optimization - and report on the results of some preliminary tests in
training rule bases. We also show that truth maintenance can be used to make
training more efficient and ask some epistemological questions raised by
training rule weights.
","The Automatic Training of Rule Bases that Use Numerical Uncertainty
  Representations"
"  An inductive logic can be formulated in which the elements are not
propositions or probability distributions, but information systems. The logic
is complete for information systems with binary hypotheses, i.e., it applies to
all such systems. It is not complete for information systems with more than two
hypotheses, but applies to a subset of such systems. The logic is inductive in
that conclusions are more informative than premises. Inferences using the
formalism have a strong justification in terms of the expected value of the
derived information system.
",The Inductive Logic of Information Systems
"  When creating an expert system, the most difficult and expensive task is
constructing a knowledge base. This is particularly true if the problem
involves noisy data and redundant measurements. This paper shows how to modify
the MACIE process for generating connectionist expert systems from training
examples so that it can accommodate noisy and redundant data. The basic idea is
to dynamically generate appropriate training examples by constructing both a
'deep' model and a noise model for the underlying problem. The use of
winner-take-all groups of variables is also discussed. These techniques are
illustrated with a small example that would be very difficult for standard
expert system approaches.
","Automated Generation of Connectionist Expert Systems for Problems
  Involving Noise and Redundancy"
"  Poly-trees are singly connected causal networks in which variables may arise
from multiple causes. This paper develops a method of recovering ply-trees from
empirically measured probability distributions of pairs of variables. The
method guarantees that, if the measured distributions are generated by a causal
process structured as a ply-tree then the topological structure of such tree
can be recovered precisely and, in addition, the causal directionality of the
branches can be determined up to the maximum extent possible. The method also
pinpoints the minimum (if any) external semantics required to determine the
causal relationships among the variables considered.
",The Recovery of Causal Poly-Trees from Statistical Data
"  This paper describes a heuristic Bayesian method for computing probability
distributions from experimental data, based upon the multivariate normal form
of the influence diagram. An example illustrates its use in medical technology
assessment. This approach facilitates the integration of results from different
studies, and permits a medical expert to make proper assessments without
considerable statistical training.
","A Heuristic Bayesian Approach to Knowledge Acquisition: Application to
  Analysis of Tissue-Type Plasminogen Activator"
"  The objective of this paper is to propose a method that will generate a
causal explanation of observed events in an uncertain world and then make
decisions based on that explanation. Feedback can cause the explanation and
decisions to be modified. I call the method Theory-Based Inductive Learning
(T-BIL). T-BIL integrates deductive learning, based on a technique called
Explanation-Based Generalization (EBG) from the field of machine learning, with
inductive learning methods from Bayesian decision theory. T-BIL takes as inputs
(1) a decision problem involving a sequence of related decisions over time, (2)
a training example of a solution to the decision problem in one period, and (3)
the domain theory relevant to the decision problem. T-BIL uses these inputs to
construct a probabilistic explanation of why the training example is an
instance of a solution to one stage of the sequential decision problem. This
explanation is then generalized to cover a more general class of instances and
is used as the basis for making the next-stage decisions. As the outcomes of
each decision are observed, the explanation is revised, which in turn affects
the subsequent decisions. A detailed example is presented that uses T-BIL to
solve a very general stochastic adaptive control problem for an autonomous
mobile robot.
","Theory-Based Inductive Learning: An Integration of Symbolic and
  Quantitative Methods"
"  RUM (Reasoning with Uncertainty Module), is an integrated software tool based
on a KEE, a frame system implemented in an object oriented language. RUM's
architecture is composed of three layers: representation, inference, and
control. The representation layer is based on frame-like data structures that
capture the uncertainty information used in the inference layer and the
uncertainty meta-information used in the control layer. The inference layer
provides a selection of five T-norm based uncertainty calculi with which to
perform the intersection, detachment, union, and pooling of information. The
control layer uses the meta-information to select the appropriate calculus for
each context and to resolve eventual ignorance or conflict in the information.
This layer also provides a context mechanism that allows the system to focus on
the relevant portion of the knowledge base, and an uncertain-belief revision
system that incrementally updates the certainty values of well-formed formulae
(wffs) in an acyclic directed deduction graph. RUM has been tested and
validated in a sequence of experiments in both naval and aerial situation
assessment (SA), consisting of correlating reports and tracks, locating and
classifying platforms, and identifying intents and threats. An example of naval
situation assessment is illustrated. The testbed environment for developing
these experiments has been provided by LOTTA, a symbolic simulator implemented
in Flavors. This simulator maintains time-varying situations in a multi-player
antagonistic game where players must make decisions in light of uncertain and
incomplete data. RUM has been used to assist one of the LOTTA players to
perform the SA task.
","Using T-Norm Based Uncertainty Calculi in a Naval Situation Assessment
  Application"
"  Evidential reasoning is cast as the problem of simplifying the
evidence-hypothesis relation and constructing combination formulas that possess
certain testable properties. Important classes of evidence as identifiers,
annihilators, and idempotents and their roles in determining binary operations
on intervals of reals are discussed. The appropriate way of constructing
formulas for combining evidence and their limitations, for instance, in
robustness, are presented.
",A Study of Associative Evidential Reasoning
"  In an earlier paper, a new theory of measurefree ""conditional"" objects was
presented. In this paper, emphasis is placed upon the motivation of the theory.
The central part of this motivation is established through an example involving
a knowledge-based system. In order to evaluate combination of evidence for this
system, using observed data, auxiliary at tribute and diagnosis variables, and
inference rules connecting them, one must first choose an appropriate algebraic
logic description pair (ALDP): a formal language or syntax followed by a
compatible logic or semantic evaluation (or model). Three common choices- for
this highly non-unique choice - are briefly discussed, the logics being
Classical Logic, Fuzzy Logic, and Probability Logic. In all three,the key
operator representing implication for the inference rules is interpreted as the
often-used disjunction of a negation (b => a) = (b'v a), for any events a,b.
  However, another reasonable interpretation of the implication operator is
through the familiar form of probabilistic conditioning. But, it can be shown -
quite surprisingly - that the ALDP corresponding to Probability Logic cannot be
used as a rigorous basis for this interpretation! To fill this gap, a new ALDP
is constructed consisting of ""conditional objects"", extending ordinary
Probability Logic, and compatible with the desired conditional probability
interpretation of inference rules. It is shown also that this choice of ALDP
leads to feasible computations for the combination of evidence evaluation in
the example. In addition, a number of basic properties of conditional objects
and the resulting Conditional Probability Logic are given, including a
characterization property and a developed calculus of relations.
",A Measure-Free Approach to Conditioning
"  This paper discusses the semantics and proof theory of Nilsson's
probabilistic logic, outlining both the benefits of its well-defined model
theory and the drawbacks of its proof theory. Within Nilsson's semantic
framework, we derive a set of inference rules which are provably sound. The
resulting proof system, in contrast to Nilsson's approach, has the important
feature of convergence - that is, the inference process proceeds by computing
increasingly narrow probability intervals which converge from above and below
on the smallest entailed probability interval. Thus the procedure can be
stopped at any time to yield partial information concerning the smallest
entailed interval.
",Convergent Deduction for Probabilistic Logic
"  The comparisons of uncertainty calculi from the last two Uncertainty
Workshops have all used theoretical probabilistic accuracy as the sole metric.
While mathematical correctness is important, there are other factors which
should be considered when developing reasoning systems. These other factors
include, among other things, the error in uncertainty measures obtainable for
the problem and the effect of this error on the performance of the resulting
system.
",A Knowledge Engineer's Comparison of Three Evidence Aggregation Methods
"  The multiple extension problem arises frequently in diagnostic and default
inference. That is, we can often use any of a number of sets of defaults or
possible hypotheses to explain observations or make Predictions. In default
inference, some extensions seem to be simply wrong and we use qualitative
techniques to weed out the unwanted ones. In the area of diagnosis, however,
the multiple explanations may all seem reasonable, however improbable. Choosing
among them is a matter of quantitative preference. Quantitative preference
works well in diagnosis when knowledge is modelled causally. Here we suggest a
framework that combines probabilities and defaults in a single unified
framework that retains the semantics of diagnosis as construction of
explanations from a fixed set of possible hypotheses. We can then compute
probabilities incrementally as we construct explanations. Here we describe a
branch and bound algorithm that maintains a set of all partial explanations
while exploring a most promising one first. A most probable explanation is
found first if explanations are partially ordered.
","Towards Solving the Multiple Extension Problem: Combining Defaults and
  Probabilities"
"  In our previous series of studies to investigate the role of evidential
reasoning in the RUBRIC system for full-text document retrieval (Tong et al.,
1985; Tong and Shapiro, 1985; Tong and Appelbaum, 1987), we identified the
important role that problem structure plays in the overall performance of the
system. In this paper, we focus on these structural elements (which we now call
""semantic structure"") and show how explicit consideration of their properties
reduces what previously were seen as difficult evidential reasoning problems to
more tractable questions.
",Problem Structure and Evidential Reasoning
"  Much of the controversy about methods for automated decision making has
focused on specific calculi for combining beliefs or propagating uncertainty.
We broaden the debate by (1) exploring the constellation of secondary tasks
surrounding any primary decision problem, and (2) identifying knowledge
engineering concerns that present additional representational tradeoffs. We
argue on pragmatic grounds that the attempt to support all of these tasks
within a single calculus is misguided. In the process, we note several
uncertain reasoning objectives that conflict with the Bayesian ideal of
complete specification of probabilities and utilities. In response, we advocate
treating the uncertainty calculus as an object language for reasoning
mechanisms that support the secondary tasks. Arguments against Bayesian
decision theory are weakened when the calculus is relegated to this role.
Architectures for uncertainty handling that take statements in the calculus as
objects to be reasoned about offer the prospect of retaining normative status
with respect to decision making while supporting the other tasks in uncertain
reasoning.
",The Role of Calculi in Uncertain Inference Systems
"  This study examined the effects of ""tuning"" the parameters of the incremental
function of MYCIN, the independent function of PROSPECTOR, a probability model
that assumes independence, and a simple additive linear equation. me parameters
of each of these models were optimized to provide solutions which most nearly
approximated those from a full probability model for a large set of simple
networks. Surprisingly, MYCIN, PROSPECTOR, and the linear equation performed
equivalently; the independence model was clearly more accurate on the networks
studied.
",The Role of Tuning Uncertain Inference Systems
"  Our previous work on classifying complex ship images [1,2] has evolved into
an effort to develop software tools for building and solving generic
classification problems. Managing the uncertainty associated with feature data
and other evidence is an important issue in this endeavor. Bayesian techniques
for managing uncertainty [7,12,13] have proven to be useful for managing
several of the belief maintenance requirements of classification problem
solving. One such requirement is the need to give qualitative explanations of
what is believed. Pearl [11] addresses this need by computing what he calls a
belief commitment-the most probable instantiation of all hypothesis variables
given the evidence available. Before belief commitments can be computed, the
straightforward implementation of Pearl's procedure involves finding an
analytical solution to some often difficult optimization problems. We describe
an efficient implementation of this procedure using tensor products that solves
these problems enumeratively and avoids the need for case by case analysis. The
procedure is thereby made more practical to use in the general case.
",Implementing a Bayesian Scheme for Revising Belief Commitments
"  We describe a representation and a set of inference methods that combine
logic programming techniques with probabilistic network representations for
uncertainty (influence diagrams). The techniques emphasize the dynamic
construction and solution of probabilistic and decision-theoretic models for
complex and uncertain domains. Given a query, a logical proof is produced if
possible; if not, an influence diagram based on the query and the knowledge of
the decision domain is produced and subsequently solved. A uniform declarative,
first-order, knowledge representation is combined with a set of integrated
inference procedures for logical, probabilistic, and decision-theoretic
reasoning.
",Integrating Logical and Probabilistic Reasoning for Decision Making
"  A major aspect of human reasoning involves the use of approximations.
Particularly in situations where the decision-making process is under stringent
time constraints, decisions are based largely on approximate, qualitative
assessments of the situations. Our work is concerned with the application of
approximate reasoning to real-time control. Because of the stringent processing
speed requirements in such applications, hardware implementations of fuzzy
logic inferencing are being pursued. We describe a programming environment for
translating fuzzy control rules into hardware realizations. Two methods of
hardware realizations are possible. The First is based on a special purpose
chip for fuzzy inferencing. The second is based on a simple memory chip. The
ability to directly translate a set of decision rules into hardware
implementations is expected to make fuzzy control an increasingly practical
approach to the control of complex systems.
",Compiling Fuzzy Logic Control Rules to Hardware Implementations
"  Reasoning under uncertainty in Al hats come to mean assessing the credibility
of hypotheses inferred from evidence. But techniques for assessing credibility
do not tell a problem solver what to do when it is uncertain. This is the focus
of our current research. We have developed a medical expert system called MUM,
for Managing Uncertainty in Medicine, that plans diagnostic sequences of
questions, tests, and treatments. This paper describes the kinds of problems
that MUM was designed to solve and gives a brief description of its
architecture. More recently, we have built an empty version of MUM called MU,
and used it to reimplement MUM and a small diagnostic system for plant
pathology. The latter part of the paper describes the features of MU that make
it appropriate for building expert systems that manage uncertainty.
",Steps Towards Programs that Manage Uncertainty
"  A method for computing probabilistic propositions is presented. It assumes
the availability of a single external routine for computing the probability of
one instantiated variable, given a conjunction of other instantiated variables.
In particular, the method allows belief network algorithms to calculate general
probabilistic propositions over nodes in the network. Although in the worst
case the time complexity of the method is exponential in the size of a query,
it is polynomial in the size of a number of common types of queries.
",An Algorithm for Computing Probabilistic Propositions
"  A complete approach to reasoning under uncertainty requires support for
incremental and interactive formulation and revision of, as well as reasoning
with, models of the problem domain capable of representing our uncertainty. We
present a hybrid reasoning scheme which combines symbolic and numeric methods
for uncertainty management to provide efficient and effective support for each
of these tasks. The hybrid is based on symbolic techniques adapted from
Assumption-based Truth Maintenance systems (ATMS), combined with numeric
methods adapted from the Dempster/Shafer theory of evidence, as extended in
Baldwin's Support Logic Programming system. The hybridization is achieved by
viewing an ATMS as a symbolic algebra system for uncertainty calculations. This
technique has several major advantages over conventional methods for performing
inference with numeric certainty estimates in addition to the ability to
dynamically determine hypothesis spaces, including improved management of
dependent and partially independent evidence, faster run-time evaluation of
propositional certainties, the ability to query the certainty value of a
proposition from multiple perspectives, and the ability to incrementally extend
or revise domain models.
",Combining Symbolic and Numeric Approaches to Uncertainty Management
"  An automated explanation facility for Bayesian conditioning aimed at
improving user acceptance of probability-based decision support systems has
been developed. The domain-independent facility is based on an information
processing perspective on reasoning about conditional evidence that accounts
both for biased and normative inferences. Experimental results indicate that
the facility is both acceptable to naive users and effective in improving
understanding.
",Explanation of Probabilistic Inference for Decision Support Systems
"  The generalized fault diagram, a data structure for failure analysis based on
the influence diagram, is defined. Unlike the fault tree, this structure allows
for dependence among the basic events and replicated logical elements. A
heuristic procedure is developed for efficient processing of these structures.
",Efficient Inference on Generalized Fault Diagrams
"  Although many investigators affirm a desire to build reasoning systems that
behave consistently with the axiomatic basis defined by probability theory and
utility theory, limited resources for engineering and computation can make a
complete normative analysis impossible. We attempt to move discussion beyond
the debate over the scope of problems that can be handled effectively to cases
where it is clear that there are insufficient computational resources to
perform an analysis deemed as complete. Under these conditions, we stress the
importance of considering the expected costs and benefits of applying
alternative approximation procedures and heuristics for computation and
knowledge acquisition. We discuss how knowledge about the structure of user
utility can be used to control value tradeoffs for tailoring inference to
alternative contexts. We address the notion of real-time rationality, focusing
on the application of knowledge about the expected timewise-refinement
abilities of reasoning strategies to balance the benefits of additional
computation with the costs of acting with a partial result. We discuss the
benefits of applying decision theory to control the solution of difficult
problems given limitations and uncertainty in reasoning resources.
","Reasoning About Beliefs and Actions Under Computational Resource
  Constraints"
"  After experimenting with a number of non-probabilistic methods for dealing
with uncertainty many researchers reaffirm a preference for probability methods
[1] [2], although this remains controversial. The importance of being able to
form decisions from incomplete data in diagnostic problems has highlighted
probabilistic methods [5] which compute posterior probabilities from prior
distributions in a way similar to Bayes Rule, and thus are called Bayesian
methods. This paper documents the use of a Bayesian method in a real time
problem which is similar to medical diagnosis in that there is a need to form
decisions and take some action without complete knowledge of conditions in the
problem domain. This particular method has a limitation which is discussed.
",Advantages and a Limitation of Using LEG Nets in a Real-TIme Problem
"  We present a unified logical framework for representing and reasoning about
both quantitative and qualitative preferences in fuzzy answer set programming,
called fuzzy answer set optimization programs. The proposed framework is vital
to allow defining quantitative preferences over the possible outcomes of
qualitative preferences. We show the application of fuzzy answer set
optimization programs to the course scheduling with fuzzy preferences problem.
To the best of our knowledge, this development is the first to consider a
logical framework for reasoning about quantitative preferences, in general, and
reasoning about both quantitative and qualitative preferences in particular.
",Logical Fuzzy Preferences
"  We allow representing and reasoning in the presence of nested multiple
aggregates over multiple variables and nested multiple aggregates over
functions involving multiple variables in answer sets, precisely, in answer set
optimization programming and in answer set programming. We show the
applicability of the answer set optimization programming with nested multiple
aggregates and the answer set programming with nested multiple aggregates to
the Probabilistic Traveling Salesman Problem, a fundamental a priori
optimization problem in Operation Research.
","Nested Aggregates in Answer Sets: An Application to a Priori
  Optimization"
"  During the ongoing debate over the representation of uncertainty in
Artificial Intelligence, Cheeseman, Lemmer, Pearl, and others have argued that
probability theory, and in particular the Bayesian theory, should be used as
the basis for the inference mechanisms of Expert Systems dealing with
uncertainty. In order to pursue the issue in a practical setting, sophisticated
tools for knowledge engineering are needed that allow flexible and
understandable interaction with the underlying knowledge representation
schemes. This paper describes a Generalized Bayesian framework for building
expert systems which function in uncertain domains, using algorithms proposed
by Lemmer. It is neither rule-based nor frame-based, and requires a new system
of knowledge engineering tools. The framework we describe provides a
knowledge-based system architecture with an inference engine, explanation
capability, and a unique aid for building consistent knowledge bases.
",Knowledge Engineering Within A Generalized Bayesian Framework
"  The fundamental elements of evidential reasoning problems are described,
followed by a discussion of the structure of various types of problems.
Bayesian inference networks and state space formalism are used as the tool for
problem representation.
  A human-oriented decision making cycle for solving evidential reasoning
problems is described and illustrated for a military situation assessment
problem. The implementation of this cycle may serve as the basis for an expert
system shell for evidential reasoning; i.e. a situation assessment processor.
","Taxonomy, Structure, and Implementation of Evidential Reasoning"
"  One of the most important aspects of current expert systems technology is the
ability to make causal inferences about the impact of new evidence. When the
domain knowledge and problem knowledge are uncertain and incomplete Bayesian
reasoning has proven to be an effective way of forming such inferences [3,4,8].
While several reasoning schemes have been developed based on Bayes Rule, there
has been very little work examining the comparative effectiveness of these
schemes in a real application. This paper describes a knowledge based system
for ship classification [1], originally developed using the PROSPECTOR updating
method [2], that has been reimplemented to use the inference procedure
developed by Pearl and Kim [4,5]. We discuss our reasons for making this
change, the implementation of the new inference engine, and the comparative
performance of the two versions of the system.
",Probabilistic Reasoning About Ship Images
"  The ability to predict the future in a given domain can be acquired by
discovering empirically from experience certain temporal patterns that tend to
repeat unerringly. Previous works in time series analysis allow one to make
quantitative predictions on the likely values of certain linear variables.
Since certain types of knowledge are better expressed in symbolic forms, making
qualitative predictions based on symbolic representations require a different
approach. A domain independent methodology called TIM (Time based Inductive
Machine) for discovering potentially uncertain temporal patterns from real time
observations using the technique of inductive inference is described here.
",Towards The Inductive Acquisition of Temporal Knowledge
"  In [12], Nilsson proposed the probabilistic logic in which the truth values
of logical propositions are probability values between 0 and 1. It is
applicable to any logical system for which the consistency of a finite set of
propositions can be established. The probabilistic inference scheme reduces to
the ordinary logical inference when the probabilities of all propositions are
either 0 or 1. This logic has the same limitations of other probabilistic
reasoning systems of the Bayesian approach. For common sense reasoning,
consistency is not a very natural assumption. We have some well known examples:
{Dick is a Quaker, Quakers are pacifists, Republicans are not pacifists, Dick
is a Republican}and {Tweety is a bird, birds can fly, Tweety is a penguin}. In
this paper, we shall propose some extensions of the probabilistic logic. In the
second section, we shall consider the space of all interpretations, consistent
or not. In terms of frames of discernment, the basic probability assignment
(bpa) and belief function can be defined. Dempster's combination rule is
applicable. This extension of probabilistic logic is called the evidential
logic in [ 1]. For each proposition s, its belief function is represented by an
interval [Spt(s), Pls(s)]. When all such intervals collapse to single points,
the evidential logic reduces to probabilistic logic (in the generalized version
of not necessarily consistent interpretations). Certainly, we get Nilsson's
probabilistic logic by further restricting to consistent interpretations. In
the third section, we shall give a probabilistic interpretation of
probabilistic logic in terms of multi-dimensional random variables. This
interpretation brings the probabilistic logic into the framework of probability
theory. Let us consider a finite set S = {sl, s2, ..., Sn) of logical
propositions. Each proposition may have true or false values; and may be
considered as a random variable. We have a probability distribution for each
proposition. The e-dimensional random variable (sl,..., Sn) may take values in
the space of all interpretations of 2n binary vectors. We may compute absolute
(marginal), conditional and joint probability distributions. It turns out that
the permissible probabilistic interpretation vector of Nilsson [12] consists of
the joint probabilities of S. Inconsistent interpretations will not appear, by
setting their joint probabilities to be zeros. By summing appropriate joint
probabilities, we get probabilities of individual propositions or subsets of
propositions. Since the Bayes formula and other techniques are valid for
e-dimensional random variables, the probabilistic logic is actually very close
to the Bayesian inference schemes. In the last section, we shall consider a
relaxation scheme for probabilistic logic. In this system, not only new
evidences will update the belief measures of a collection of propositions, but
also constraint satisfaction among these propositions in the relational network
will revise these measures. This mechanism is similar to human reasoning which
is an evaluative process converging to the most satisfactory result. The main
idea arises from the consistent labeling problem in computer vision. This
method is originally applied to scene analysis of line drawings. Later, it is
applied to matching, constraint satisfaction and multi sensor fusion by several
authors [8], [16] (and see references cited there). Recently, this method is
used in knowledge aggregation by Landy and Hummel [9].
",Some Extensions of Probabilistic Logic
"  The discovery that the minimax decision rule performs poorly in some games
has sparked interest in possible alternatives to minimax. Until recently, the
only games in which minimax was known to perform poorly were games which were
mainly of theoretical interest. However, this paper reports results showing
poor performance of minimax in a more common game called kalah. For the kalah
games tested, a non-minimax decision rule called the product rule performs
significantly better than minimax.
  This paper also discusses a possible way to predict whether or not minimax
will perform well in a game when compared to product. A parameter called the
rate of heuristic flaw (rhf) has been found to correlate positively with the.
performance of product against minimax. Both analytical and experimental
results are given that appear to support the predictive power of rhf.
",Predicting The Performance of Minimax and Product in Game-Tree
"  A model of knowledge representation is described in which propositional facts
and the relationships among them can be supported by other facts. The set of
knowledge which can be supported is called the set of cognitive units, each
having associated descriptions of their explicit and implicit support
structures, summarizing belief and reliability of belief. This summary is
precise enough to be useful in a computational model while remaining
descriptive of the underlying symbolic support structure. When a fact supports
another supportive relationship between facts we call this meta-support. This
facilitates reasoning about both the propositional knowledge. and the support
structures underlying it.
",Reasoning With Uncertain Knowledge
"  Two different approaches to dealing with probabilistic knowledge are examined
-models and inductive inference. Examples of the first are: influence diagrams
[1], Bayesian networks [2], log-linear models [3, 4]. Examples of the second
are: games-against nature [5, 6] varieties of maximum-entropy methods [7, 8,
9], and the author's min-score induction [10]. In the modeling approach, the
basic issue is manageability, with respect to data elicitation and computation.
Thus, it is assumed that the pertinent set of users in some sense knows the
relevant probabilities, and the problem is to format that knowledge in a way
that is convenient to input and store and that allows computation of the
answers to current questions in an expeditious fashion. The basic issue for the
inductive approach appears at first sight to be very different. In this
approach it is presumed that the relevant probabilities are only partially
known, and the problem is to extend that incomplete information in a reasonable
way to answer current questions. Clearly, this approach requires that some form
of induction be invoked. Of course, manageability is an important additional
concern. Despite their seeming differences, the two approaches have a fair
amount in common, especially with respect to the structural framework they
employ. Roughly speaking, this framework involves identifying clusters of
variables which strongly interact, establishing marginal probability
distributions on the clusters, and extending the subdistributions to a more
complete distribution, usually via a product formalism. The product extension
is justified on the modeling approach in terms of assumed conditional
independence; in the inductive approach the product form arises from an
inductive rule.
",Models vs. Inductive Inference for Dealing With Probabilistic Knowledge
"  There currently exists a gap between the theories proposed by the probability
and uncertainty and the needs of Artificial Intelligence research. These
theories primarily address the needs of expert systems, using knowledge
structures which must be pre-compiled and remain static in structure during
runtime. Many Al systems require the ability to dynamically add and remove
parts of the current knowledge structure (e.g., in order to examine what the
world would be like for different causal theories). This requires more
flexibility than existing uncertainty systems display. In addition, many Al
researchers are only interested in using ""probabilities"" as a means of
obtaining an ordering, rather than attempting to derive an accurate
probabilistic account of a situation. This indicates the need for systems which
stress ease of use and don't require extensive probability information when one
cannot (or doesn't wish to) provide such information. This paper attempts to
help reconcile the gap between approaches to uncertainty and the needs of many
AI systems by examining the control issues which arise, independent of a
particular uncertainty calculus. when one tries to satisfy these needs. Truth
Maintenance Systems have been used extensively in problem solving tasks to help
organize a set of facts and detect inconsistencies in the believed state of the
world. These systems maintain a set of true/false propositions and their
associated dependencies. However, situations often arise in which we are unsure
of certain facts or in which the conclusions we can draw from available
information are somewhat uncertain. The non-monotonic TMS 12] was an attempt at
reasoning when all the facts are not known, but it fails to take into account
degrees of belief and how available evidence can combine to strengthen a
particular belief. This paper addresses the problem of probabilistic reasoning
as it applies to Truth Maintenance Systems. It describes a belief Maintenance
System that manages a current set of beliefs in much the same way that a TMS
manages a set of true/false propositions. If the system knows that belief in
fact is dependent in some way upon belief in fact2, then it automatically
modifies its belief in facts when new information causes a change in belief of
fact2. It models the behavior of a TMS, replacing its 3-valued logic (true,
false, unknown) with an infinite valued logic, in such a way as to reduce to a
standard TMS if all statements are given in absolute true/false terms. Belief
Maintenance Systems can, therefore, be thought of as a generalization of Truth
Maintenance Systems, whose possible reasoning tasks are a superset of those for
a TMS.
",Towards a General-Purpose Belief Maintenance System
"  Scheduling in the factory setting is compounded by computational complexity
and temporal uncertainty. Together, these two factors guarantee that the
process of constructing an optimal schedule will be costly and the chances of
executing that schedule will be slight. Temporal uncertainty in the task
execution time can be offset by several methods: eliminate uncertainty by
careful engineering, restore certainty whenever it is lost, reduce the
uncertainty by using more accurate sensors, and quantify and circumscribe the
remaining uncertainty. Unfortunately, these methods focus exclusively on the
sources of uncertainty and fail to apply knowledge of the tasks which are to be
scheduled. A complete solution must adapt the schedule of activities to be
performed according to the evolving state of the production world. The example
of vision-directed assembly is presented to illustrate that the principle of
least commitment, in the creation of a plan, in the representation of a
schedule, and in the execution of a schedule, enables a robot to operate
intelligently and efficiently, even in the presence of considerable uncertainty
in the sequence of future events.
","Planning, Scheduling, and Uncertainty in the Sequence of Future Events"
"  To develop an approach to utilizing continuous statistical information within
the Dempster- Shafer framework, we combine methods proposed by Strat and by
Shafero We first derive continuous possibility and mass functions from
probability-density functions. Then we propose a rule for combining such
evidence that is simpler and more efficiently computed than Dempster's rule. We
discuss the relationship between Dempster's rule and our proposed rule for
combining evidence over continuous frames.
","Deriving And Combining Continuous Possibility Functions in the Framework
  of Evidential Reasoning"
"  We start by defining an approach to non-monotonic probabilistic reasoning in
terms of non-monotonic categorical (true-false) reasoning. We identify a type
of non-monotonic probabilistic reasoning, akin to default inheritance, that is
commonly found in practice, especially in ""evidential"" and ""Bayesian""
reasoning. We formulate this in terms of the Maximization of Conditional
Independence (MCI), and identify a variety of applications for this sort of
default. We propose a formalization using Pointwise Circumscription. We compare
MCI to Maximum Entropy, another kind of non-monotonic principle, and conclude
by raising a number of open questions
",Non-Monotonicity in Probabilistic Reasoning
"  The investigations reported in this paper center on the process of dynamic
uncertainty assessment during interpretation tasks in real domain. In
particular, we are interested here in the nature of the control structure of
computer programs that can support multiple interpretation and smooth
transitions between them, in real time. Each step of the processing involves
the interpretation of one input item and the appropriate re-establishment of
the system's confidence of the correctness of its interpretation(s).
","Flexible Interpretations: A Computational Model for Dynamic Uncertainty
  Assessment"
"  In this paper, we examine the concept of modularity, an often cited advantage
of the ruled-based representation methodology. We argue that the notion of
modularity consists of two distinct concepts which we call syntactic modularity
and semantic modularity. We argue that when reasoning under certainty, it is
reasonable to regard the rule-based approach as both syntactically and
semantically modular. However, we argue that in the case of plausible
reasoning, rules are syntactically modular but are rarely semantically modular.
To illustrate this point, we examine a particular approach for managing
uncertainty in rule-based systems called the MYCIN certainty factor model. We
formally define the concept of semantic modularity with respect to the
certainty factor model and discuss logical consequences of the definition. We
show that the assumption of semantic modularity imposes strong restrictions on
rules in a knowledge base. We argue that such restrictions are rarely valid in
practical applications. Finally, we suggest how the concept of semantic
modularity can be relaxed in a manner that makes it appropriate for plausible
reasoning.
",The Myth of Modularity in Rule-Based Systems
"  In the 1940's, a physicist named Cox provided the first formal justification
for the axioms of probability based on the subjective or Bayesian
interpretation. He showed that if a measure of belief satisfies several
fundamental properties, then the measure must be some monotonic transformation
of a probability. In this paper, measures of change in belief or belief updates
are examined. In the spirit of Cox, properties for a measure of change in
belief are enumerated. It is shown that if a measure satisfies these
properties, it must satisfy other restrictive conditions. For example, it is
shown that belief updates in a probabilistic context must be equal to some
monotonic transformation of a likelihood ratio. It is hoped that this formal
explication of the belief update paradigm will facilitate critical discussion
and useful extensions of the approach.
",An Axiomatic Framework for Belief Updates
"  We describe a viewpoint on the Dempster/Shafer 'Theory of Evidence', and
provide an interpretation which regards the combination formulas as statistics
of the opinions of ""experts"". This is done by introducing spaces with binary
operations that are simpler to interpret or simpler to implement than the
standard combination formula, and showing that these spaces can be mapped
homomorphically onto the Dempster/Shafer theory of evidence space. The experts
in the space of ""opinions of experts"" combine information in a Bayesian
fashion. We present alternative spaces for the combination of evidence
suggested by this viewpoint.
",Evidence as Opinions of Experts
"  This paper describes the incorporation of uncertainty in diagnostic reasoning
based on the set covering model of Reggia et. al. extended to what in the
Artificial Intelligence dichotomy between deep and compiled (shallow, surface)
knowledge based diagnosis may be viewed as the generic form at the compiled end
of the spectrum. A major undercurrent in this is advocating the need for a
strong underlying model and an integrated set of support tools for carrying
such a model in order to deal with uncertainty.
",Decision Under Uncertainty in Diagnosis
"  One purpose -- quite a few thinkers would say the main purpose -- of seeking
knowledge about the world is to enhance our ability to make good decisions. An
item of knowledge that can make no conceivable difference with regard to
anything we might do would strike many as frivolous. Whether or not we want to
be philosophical pragmatists in this strong sense with regard to everything we
might want to enquire about, it seems a perfectly appropriate attitude to adopt
toward artificial knowledge systems. If is granted that we are ultimately
concerned with decisions, then some constraints are imposed on our measures of
uncertainty at the level of decision making. If our measure of uncertainty is
real-valued, then it isn't hard to show that it must satisfy the classical
probability axioms. For example, if an act has a real-valued utility U(E) if
the event E obtains, and the same real-valued utility if the denial of E
obtains, so that U(E) = U(-E), then the expected utility of that act must be
U(E), and that must be the same as the uncertainty-weighted average of the
returns of the act, p-U(E) + q-U('E), where p and q represent the uncertainty
of E and-E respectively. But then we must have p + q = 1.
",Knowledge and Uncertainty
"  Current approaches to expert systems' reasoning under uncertainty fail to
capture the iterative revision process characteristic of intelligent human
reasoning. This paper reports on a system, called the Non-monotonic
Probabilist, or NMP (Cohen, et al., 1985). When its inferences result in
substantial conflict, NMP examines and revises the assumptions underlying the
inferences until conflict is reduced to acceptable levels. NMP has been
implemented in a demonstration computer-based system, described below, which
supports threat correlation and in-flight route replanning by Air Force pilots.
","An Application of Non-Monotonic Probabilistic Reasoning to Air Force
  Threat Correlation"
"  We are interested in creating an automated or semi-automated system with the
capability of taking a set of radar imagery, collection parameters and a priori
map and other tactical data, and producing likely interpretations of the
possible military situations given the available evidence. This paper is
concerned with the problem of the interpretation and computation of certainty
or belief in the conclusions reached by such a system.
",Bayesian Inference for Radar Imagery Based Surveillance
"  For any system with limited statistical knowledge, the combination of
evidence and the interpretation of sampling information require the
determination of the right reference class (or of an adequate one). The present
note (1) discusses the use of reference classes in evidential reasoning, and
(2) discusses implementations of Kyburg's rules for reference classes. This
paper contributes the first frank discussion of how much of Kyburg's system is
needed to be powerful, how much can be computed effectively, and how much is
philosophical fat.
",Computing Reference Classes
"  MINDS is a distributed system of cooperating query engines that customize,
document retrieval for each user in a dynamic environment. It improves its
performance and adapts to changing patterns of document distribution by
observing system-user interactions and modifying the appropriate certainty
factors, which act as search control parameters. It argued here that the
uncertainty management calculus must account for temporal precedence,
reliability of evidence, degree of support for a proposition, and saturation
effects. The calculus presented here possesses these features. Some results
obtained with this scheme are discussed.
","An Uncertainty Management Calculus for Ordering Searches in Distributed
  Dynamic Databases"
"  Explanation facilities are a particularly important feature of expert system
frameworks. It is an area in which traditional rule-based expert system
frameworks have had mixed results. While explanations about control are well
handled, facilities are needed for generating better explanations concerning
knowledge base content. This paper approaches the explanation problem by
examining the effect an event has on a variable of interest within a symmetric
Bayesian inferencing system. We argue that any effect measure operating in this
context must satisfy certain properties. Such a measure is proposed. It forms
the basis for an explanation facility which allows the user of the Generalized
Bayesian Inferencing System to question the meaning of the knowledge base. That
facility is described in detail.
",An Explanation Mechanism for Bayesian Inferencing Systems
"  This paper extends the applications of belief-networks to include the
revision of belief commitments, i.e., the categorical acceptance of a subset of
hypotheses which, together, constitute the most satisfactory explanation of the
evidence at hand. A coherent model of non-monotonic reasoning is established
and distributed algorithms for belief revision are presented. We show that, in
singly connected networks, the most satisfactory explanation can be found in
linear time by a message-passing algorithm similar to the one used in belief
updating. In multiply-connected networks, the problem may be exponentially hard
but, if the network is sparse, topological considerations can be used to render
the interpretation task tractable. In general, finding the most probable
combination of hypotheses is no more complex than computing the degree of
belief for any individual hypothesis. Applications to medical diagnosis are
illustrated.
","Distributed Revision of Belief Commitment in Multi-Hypothesis
  Interpretations"
"  A learning algorithm is presented which given the structure of a causal tree,
will estimate its link probabilities by sequential measurements on the leaves
only. Internal nodes of the tree represent conceptual (hidden) variables
inaccessible to observation. The method described is incremental, local,
efficient, and remains robust to measurement imprecisions.
",Learning Link-Probabilities in Causal Trees
"  Results on approximate deduction in the context of the calculus of evidence
of Dempster-Shafer and the theory of interval probabilities are reported.
Approximate conditional knowledge about the truth of conditional propositions
was assumed available and expressed as sets of possible values (actually
numeric intervals) of conditional probabilities. Under different
interpretations of this conditional knowledge, several formulas were produced
to integrate unconditioned estimates (assumed given as sets of possible values
of unconditioned probabilities) with conditional estimates. These formulas are
discussed together with the computational characteristics of the methods
derived from them. Of particular importance is one such evidence integration
formulation, produced under a belief oriented interpretation, which
incorporates both modus ponens and modus tollens inferential mechanisms, allows
integration of conditioned and unconditioned knowledge without resorting to
iterative or sequential approximations, and produces elementary mass
distributions as outputs using similar distributions as inputs.
",Approximate Deduction in Single Evidential Bodies
"  Belief updating schemes in artificial intelligence may be viewed as three
dimensional languages, consisting of a syntax (e.g. probabilities or certainty
factors), a calculus (e.g. Bayesian or CF combination rules), and a semantics
(i.e. cognitive interpretations of competing formalisms). This paper studies
the rational scope of those languages on the syntax and calculus grounds. In
particular, the paper presents an endomorphism theorem which highlights the
limitations imposed by the conditional independence assumptions implicit in the
CF calculus. Implications of the theorem to the relationship between the CF and
the Bayesian languages and the Dempster-Shafer theory of evidence are
presented. The paper concludes with a discussion of some implications on
rule-based knowledge engineering in uncertain domains.
","The Rational and Computational Scope of Probabilistic Rule-Based Expert
  Systems"
"  The causal Bayesian approach is based on the assumption that effects (e.g.,
symptoms) that are not conditionally independent with respect to some causal
agent (e.g., a disease) are conditionally independent with respect to some
intermediate state caused by the agent, (e.g., a pathological condition). This
paper describes the development of a causal Bayesian model for the diagnosis of
appendicitis. The paper begins with a description of the standard Bayesian
approach to reasoning about uncertainty and the major critiques it faces. The
paper then lays the theoretical groundwork for the causal extension of the
Bayesian approach, and details specific improvements we have developed. The
paper then goes on to describe our knowledge engineering and implementation and
the results of a test of the system. The paper concludes with a discussion of
how the causal Bayesian approach deals with the criticisms of the standard
Bayesian model and why it is superior to alternative approaches to reasoning
about uncertainty popular in the Al community.
",A Causal Bayesian Model for the Diagnosis of Appendicitis
"  Much artificial intelligence research focuses on the problem of deducing the
validity of unobservable propositions or hypotheses from observable evidence.!
Many of the knowledge representation techniques designed for this problem
encode the relationship between evidence and hypothesis in a directed manner.
Moreover, the direction in which evidence is stored is typically from evidence
to hypothesis.
",A Backwards View for Assessment
"  Influence diagrams are a directed graph representation for uncertainties as
probabilities. The graph distinguishes between those variables which are under
the control of a decision maker (decisions, shown as rectangles) and those
which are not (chances, shown as ovals), as well as explicitly denoting a goal
for solution (value, shown as a rounded rectangle.
",DAVID: Influence Diagram Processing System for the Macintosh
"  In this paper, we describe a scheme for propagating belief functions in
certain kinds of trees using only local computations. This scheme generalizes
the computational scheme proposed by Shafer and Logan1 for diagnostic trees of
the type studied by Gordon and Shortliffe, and the slightly more general scheme
given by Shafer for hierarchical evidence. It also generalizes the scheme
proposed by Pearl for Bayesian causal trees (see Shenoy and Shafer). Pearl's
causal trees and Gordon and Shortliffe's diagnostic trees are both ways of
breaking the evidence that bears on a large problem down into smaller items of
evidence that bear on smaller parts of the problem so that these smaller
problems can be dealt with one at a time. This localization of effort is often
essential in order to make the process of probability judgment feasible, both
for the person who is making probability judgments and for the machine that is
combining them. The basic structure for our scheme is a type of tree that
generalizes both Pearl's and Gordon and Shortliffe's trees. Trees of this
general type permit localized computation in Pearl's sense. They are based on
qualitative judgments of conditional independence. We believe that the scheme
we describe here will prove useful in expert systems. It is now clear that the
successful propagation of probabilities or certainty factors in expert systems
requires much more structure than can be provided in a pure production-system
framework. Bayesian schemes, on the other hand, often make unrealistic demands
for structure. The propagation of belief functions in trees and more general
networks stands on a middle ground where some sensible and useful things can be
done. We would like to emphasize that the basic idea of local computation for
propagating probabilities is due to Judea Pearl. It is a very innovative idea;
we do not believe that it can be found in the Bayesian literature prior to
Pearl's work. We see our contribution as extending the usefulness of Pearl's
idea by generalizing it from Bayesian probabilities to belief functions. In the
next section, we give a brief introduction to belief functions. The notions of
qualitative independence for partitions and a qualitative Markov tree are
introduced in Section III. Finally, in Section IV, we describe a scheme for
propagating belief functions in qualitative Markov trees.
",Propagation of Belief Functions: A Distributed Approach
"  Mode {also called MAP} estimation, mean estimation and median estimation are
examined here to determine when they can be safely used to derive {posterior)
cost minimizing estimates. (These are all Bayes procedures, using the mode.
mean. or median of the posterior distribution). It is found that modal
estimation only returns cost minimizing estimates when the cost function is
0-t. If the cost function is a function of distance then mean estimation only
returns cost minimizing estimates when the cost function is squared distance
from the true value and median estimation only returns cost minimizing
estimates when the cost function ts the distance from the true value. Results
are presented on the goodness or modal estimation with non 0-t cost functions
",Appropriate and Inappropriate Estimation Techniques
"  In this paper, we describe a representation for spatial information, called
the stochastic map, and associated procedures for building it, reading
information from it, and revising it incrementally as new information is
obtained. The map contains the estimates of relationships among objects in the
map, and their uncertainties, given all the available information. The
procedures provide a general solution to the problem of estimating uncertain
relative spatial relationships. The estimates are probabilistic in nature, an
advance over the previous, very conservative, worst-case approaches to the
problem. Finally, the procedures are developed in the context of
state-estimation and filtering theory, which provides a solid basis for
numerous extensions.
",Estimating Uncertain Spatial Relationships in Robotics
"  The role of inferencing with uncertainty is becoming more important in
rule-based expert systems (ES), since knowledge given by a human expert is
often uncertain or imprecise. We have succeeded in designing a VLSI chip which
can perform an entire inference process based on fuzzy logic. The design of the
VLSI fuzzy inference engine emphasizes simplicity, extensibility, and
efficiency (operational speed and layout area). It is fabricated in 2.5 um CMOS
technology. The inference engine consists of three major components; a rule set
memory, an inference processor, and a controller. In this implementation, a
rule set memory is realized by a read only memory (ROM). The controller
consists of two counters. In the inference processor, one data path is laid out
for each rule. The number of the inference rule can be increased adding more
data paths to the inference processor. All rules are executed in parallel, but
each rule is processed serially. The logical structure of fuzzy inference
proposed in the current paper maps nicely onto the VLSI structure. A two-phase
nonoverlapping clocking scheme is used. Timing tests indicate that the
inference engine can operate at approximately 20.8 MHz. This translates to an
execution speed of approximately 80,000 Fuzzy Logical Inferences Per Second
(FLIPS), and indicates that the inference engine is suitable for a demanding
real-time application. The potential applications include decision-making in
the area of command and control for intelligent robot systems, process control,
missile and aircraft guidance, and other high performance machines.
",A VLSI Design and Implementation for a Real-Time Approximate Reasoning
"  The purpose of this paper is to report on the most recent developments in our
ongoing investigation of the representation and manipulation of uncertainty in
automated reasoning systems. In our earlier studies (Tong and Shapiro, 1985) we
described a series of experiments with RUBRIC (Tong et al., 1985), a system for
full-text document retrieval, that generated some interesting insights into the
effects of choosing among a class of scalar valued uncertainty calculi. [n
order to extend these results we have begun a new series of experiments with a
larger class of representations and calculi, and to help perform these
experiments we have developed a general purpose inference engine.
",A General Purpose Inference Engine for Evidential Reasoning Research
"  Linear representations for a subclass of boolean symmetric functions selected
by a parity condition are shown to constitute a generalization of the linear
constraints on probabilities introduced by Boole. These linear constraints are
necessary to compute probabilities of events with relations between the.
arbitrarily specified with propositional calculus boolean formulas.
",Generalizing Fuzzy Logic Probabilistic Inferences
"  Bayesian networks provide a probabilistic semantics for qualitative
assertions about likelihood. A qualitative reasoner based on an algebra over
these assertions can derive further conclusions about the influence of actions.
While the conclusions are much weaker than those computed from complete
probability distributions, they are still valuable for suggesting potential
actions, eliminating obviously inferior plans, identifying important tradeoffs,
and explaining probabilistic models.
",Qualitative Probabilistic Networks for Planning Under Uncertainty
"  This paper examines the biases and performance of several uncertain inference
systems: Mycin, a variant of Mycin. and a simplified version of probability
using conditional independence assumptions. We present axiomatic arguments for
using Minimum Cross Entropy inference as the best way to do uncertain
inference. For Mycin and its variant we found special situations where its
performance was very good, but also situations where performance was worse than
random guessing, or where data was interpreted as having the opposite of its
true import We have found that all three of these systems usually gave accurate
results, and that the conditional independence assumptions gave the most robust
results. We illustrate how the Importance of biases may be quantitatively
assessed and ranked. Considerations of robustness might be a critical factor is
selecting UlS's for a given application.
",Experimentally Comparing Uncertain Inference Systems to Probability
"  This paper examines the accuracy of the PROSPECTOR model for uncertain
reasoning. PROSPECTOR's solutions for a large number of computer-generated
inference networks were compared to those obtained from probability theory and
minimum cross-entropy calculations. PROSPECTOR's answers were generally
accurate for a restricted subset of problems that are consistent with its
assumptions. However, even within this subset, we identified conditions under
which PROSPECTOR's performance deteriorates.
",Evaluation of Uncertain Inference Models I: PROSPECTOR
"  In many cases commonsense knowledge consists of knowledge of what is usual.
In this paper we develop a system for reasoning with usual information. This
system is based upon the fact that these pieces of commonsense information
involve both a probabilistic aspect and a granular aspect. We implement this
system with the aid of possibility-probability granules.
",On Implementing Usual Values
"  In the current versions of the Dempster-Shafer theory, the only essential
restriction on the validity of the rule of combination is that the sources of
evidence must be statistically independent. Under this assumption, it is
permissible to apply the Dempster-Shafer rule to two or mere distinct
probability distributions.
",On the Combinality of Evidence in the Dempster-Shafer Theory
"  We present a unified logical framework for representing and reasoning about
both probability quantitative and qualitative preferences in probability answer
set programming, called probability answer set optimization programs. The
proposed framework is vital to allow defining probability quantitative
preferences over the possible outcomes of qualitative preferences. We show the
application of probability answer set optimization programs to a variant of the
well-known nurse restoring problem, called the nurse restoring with probability
preferences problem. To the best of our knowledge, this development is the
first to consider a logical framework for reasoning about probability
quantitative preferences, in general, and reasoning about both probability
quantitative and qualitative preferences in particular.
",Logical Probability Preferences
"  Many real world problems naturally appear as constraints satisfaction
problems (CSP), for which very efficient algorithms are known. Most of these
involve the combination of two techniques: some direct propagation of
constraints between variables (with the goal of reducing their sets of possible
values) and some kind of structured search (depth-first, breadth-first,...).
But when such blind search is not possible or not allowed or when one wants a
'constructive' or a 'pattern-based' solution, one must devise more complex
propagation rules instead. In this case, one can introduce the notion of a
candidate (a 'still possible' value for a variable). Here, we give this
intuitive notion a well defined logical status, from which we can define the
concepts of a resolution rule and a resolution theory. In order to keep our
analysis as concrete as possible, we illustrate each definition with the well
known Sudoku example. Part I proposes a general conceptual framework based on
first order logic; with the introduction of chains and braids, Part II will
give much deeper results.
","From Constraints to Resolution Rules, Part I: Conceptual Framework"
"  In this Part II, we apply the general theory developed in Part I to a
detailed analysis of the Constraint Satisfaction Problem (CSP). We show how
specific types of resolution rules can be defined. In particular, we introduce
the general notions of a chain and a braid. As in Part I, these notions are
illustrated in detail with the Sudoku example - a problem known to be
NP-complete and which is therefore typical of a broad class of hard problems.
For Sudoku, we also show how far one can go in 'approximating' a CSP with a
resolution theory and we give an empirical statistical analysis of how the
various puzzles, corresponding to different sets of entries, can be classified
along a natural scale of complexity. For any CSP, we also prove the confluence
property of some Resolution Theories based on braids and we show how it can be
used to define different resolution strategies. Finally, we prove that, in any
CSP, braids have the same solving capacity as Trial-and-Error (T&E) with no
guessing and we comment this result in the Sudoku case.
","From Constraints to Resolution Rules, Part II: chains, braids,
  confluence and T&E"
"  We propose an inequality paradigm for probabilistic reasoning based on a
logic of upper and lower bounds on conditional probabilities. We investigate a
family of probabilistic logics, generalizing the work of Nilsson [14]. We
develop a variety of logical notions for probabilistic reasoning, including
soundness, completeness justification; and convergence: reduction of a theory
to a simpler logical class. We argue that a bound view is especially useful for
describing the semantics of probabilistic knowledge representation and for
describing intermediate states of probabilistic inference and updating. We show
that the Dempster-Shafer theory of evidence is formally identical to a special
case of our generalized probabilistic logic. Our paradigm thus incorporates
both Bayesian ""rule-based"" approaches and avowedly non-Bayesian ""evidential""
approaches such as MYCIN and DempsterShafer. We suggest how to integrate the
two ""schools"", and explore some possibilities for novel synthesis of a variety
of ideas in probabilistic reasoning.
",An Inequality Paradigm for Probabilistic Knowledge
"  This paper examines the quantities used by MYCIN to reason with uncertainty,
called certainty factors. It is shown that the original definition of certainty
factors is inconsistent with the functions used in MYCIN to combine the
quantities. This inconsistency is used to argue for a redefinition of certainty
factors in terms of the intuitively appealing desiderata associated with the
combining functions. It is shown that this redefinition accommodates an
unlimited number of probabilistic interpretations. These interpretations are
shown to be monotonic transformations of the likelihood ratio p(EIH)/p(El H).
The construction of these interpretations provides insight into the assumptions
implicit in the certainty factor model. In particular, it is shown that if
uncertainty is to be propagated through an inference network in accordance with
the desiderata, evidence must be conditionally independent given the hypothesis
and its negation and the inference network must have a tree structure. It is
emphasized that assumptions implicit in the model are rarely true in practical
applications. Methods for relaxing the assumptions are suggested.
",Probabilistic Interpretations for MYCIN's Certainty Factors
"  The use of maximum entropy inference in reasoning with uncertain information
is commonly justified by an information-theoretic argument. This paper
discusses a possible objection to this information-theoretic justification and
shows how it can be met. I then compare maximum entropy inference with certain
other currently popular methods for uncertain reasoning. In making such a
comparison, one must distinguish between static and dynamic theories of degrees
of belief: a static theory concerns the consistency conditions for degrees of
belief at a given time; whereas a dynamic theory concerns how one's degrees of
belief should change in the light of new information. It is argued that maximum
entropy is a dynamic theory and that a complete theory of uncertain reasoning
can be gotten by combining maximum entropy inference with probability theory,
which is a static theory. This total theory, I argue, is much better grounded
than are other theories of uncertain reasoning.
",Uncertain Reasoning Using Maximum Entropy Inference
"  Duda, Hart, and Nilsson have set forth a method for rule-based inference
systems to use in updating the probabilities of hypotheses on the basis of
multiple items of new evidence. Pednault, Zucker, and Muresan claimed to give
conditions under which independence assumptions made by Duda et al. preclude
updating-that is, prevent the evidence from altering the probabilities of the
hypotheses. Glymour refutes Pednault et al.'s claim with a counterexample of a
rather special form (one item of evidence is incompatible with all but one of
the hypotheses); he raises, but leaves open, the question whether their result
would be true with an added assumption to rule out such special cases. We show
that their result does not hold even with the added assumption, but that it can
nevertheless be largely salvaged. Namely, under the conditions assumed by
Pednault et al., at most one of the items of evidence can alter the probability
of any given hypothesis; thus, although updating is possible, multiple updating
for any of the hypotheses is precluded.
",Independence and Bayesian Updating Methods
"  The paper demonstrates that strict adherence to probability theory does not
preclude the use of concurrent, self-activated constraint-propagation
mechanisms for managing uncertainty. Maintaining local records of
sources-of-belief allows both predictive and diagnostic inferences to be
activated simultaneously and propagate harmoniously towards a stable
equilibrium.
",A Constraint Propagation Approach to Probabilistic Reasoning
"  Various properties of relative entropy have led to its widespread use in
information theory. These properties suggest that relative entropy has a role
to play in systems that attempt to perform inference in terms of probability
distributions. In this paper, I will review some basic properties of relative
entropy as well as its role in probabilistic inference. I will also mention
briefly a few existing and potential applications of relative entropy to
so-called artificial intelligence (AI).
","Relative Entropy, Probabilistic Inference and AI"
"  This paper covers two topics: first an introduction to Algorithmic Complexity
Theory: how it defines probability, some of its characteristic properties and
past successful applications. Second, we apply it to problems in A.I. - where
it promises to give near optimum search procedures for two very broad classes
of problems.
","Foundations of Probability Theory for AI - The Application of
  Algorithmic Probability to Problems in Artificial Intelligence"
"  The management of uncertainty in expert systems has usually been left to ad
hoc representations and rules of combinations lacking either a sound theory or
clear semantics. The objective of this paper is to establish a theoretical
basis for defining the syntax and semantics of a small subset of calculi of
uncertainty operating on a given term set of linguistic statements of
likelihood. Each calculus is defined by specifying a negation, a conjunction
and a disjunction operator. Families of Triangular norms and conorms constitute
the most general representations of conjunction and disjunction operators.
These families provide us with a formalism for defining an infinite number of
different calculi of uncertainty. The term set will define the uncertainty
granularity, i.e. the finest level of distinction among different
quantifications of uncertainty. This granularity will limit the ability to
differentiate between two similar operators. Therefore, only a small finite
subset of the infinite number of calculi will produce notably different
results. This result is illustrated by two experiments where nine and eleven
different calculi of uncertainty are used with three term sets containing five,
nine, and thirteen elements, respectively. Finally, the use of context
dependent rule set is proposed to select the most appropriate calculus for any
given situation. Such a rule set will be relatively small since it must only
describe the selection policies for a small number of calculi (resulting from
the analyzed trade-off between complexity and precision).
","Selecting Uncertainty Calculi and Granularity: An Experiment in
  Trading-Off Precision and Complexity"
"  Attempts to replicate probabilistic reasoning in expert systems have
typically overlooked a critical ingredient of that process. Probabilistic
analysis typically requires extensive judgments regarding interdependencies
among hypotheses and data, and regarding the appropriateness of various
alternative models. The application of such models is often an iterative
process, in which the plausibility of the results confirms or disconfirms the
validity of assumptions made in building the model. In current expert systems,
by contrast, probabilistic information is encapsulated within modular rules
(involving, for example, ""certainty factors""), and there is no mechanism for
reviewing the overall form of the probability argument or the validity of the
judgments entering into it.
",A Framework for Non-Monotonic Reasoning About Probabilistic Assumptions
"  Evidential reasoning in expert systems has often used ad-hoc uncertainty
calculi. Although it is generally accepted that probability theory provides a
firm theoretical foundation, researchers have found some problems with its use
as a workable uncertainty calculus. Among these problems are representation of
ignorance, consistency of probabilistic judgements, and adjustment of a priori
judgements with experience. The application of metaprobability theory to
evidential reasoning is a new approach to solving these problems.
Metaprobability theory can be viewed as a way to provide soft or hard
constraints on beliefs in much the same manner as the Dempster-Shafer theory
provides constraints on probability masses on subsets of the state space. Thus,
we use the Dempster-Shafer theory, an alternative theory of evidential
reasoning to illuminate metaprobability theory as a theory of evidential
reasoning. The goal of this paper is to compare how metaprobability theory and
Dempster-Shafer theory handle the adjustment of beliefs with evidence with
respect to a particular thought experiment. Sections 2 and 3 give brief
descriptions of the metaprobability and Dempster-Shafer theories.
Metaprobability theory deals with higher order probabilities applied to
evidential reasoning. Dempster-Shafer theory is a generalization of probability
theory which has evolved from a theory of upper and lower probabilities.
Section 4 describes a thought experiment and the metaprobability and
DempsterShafer analysis of the experiment. The thought experiment focuses on
forming beliefs about a population with 6 types of members {1, 2, 3, 4, 5, 6}.
A type is uniquely defined by the values of three features: A, B, C. That is,
if the three features of one member of the population were known then its type
could be ascertained. Each of the three features has two possible values, (e.g.
A can be either ""a0"" or ""al""). Beliefs are formed from evidence accrued from
two sensors: sensor A, and sensor B. Each sensor senses the corresponding
defining feature. Sensor A reports that half of its observations are ""a0"" and
half the observations are 'al'. Sensor B reports that half of its observations
are ``b0,' and half are ""bl"". Based on these two pieces of evidence, what
should be the beliefs on the distribution of types in the population? Note that
the third feature is not observed by any sensor.
",Metaprobability and Dempster-Shafer in Evidential Reasoning
"  General problems in analyzing information in a probabilistic database are
considered. The practical difficulties (and occasional advantages) of storing
uncertain data, of using it conventional forward- or backward-chaining
inference engines, and of working with a probabilistic version of resolution
are discussed. The background for this paper is the incorporation of uncertain
reasoning facilities in MRS, a general-purpose expert system building tool.
",Implementing Probabilistic Reasoning
"  This paper is concerned with two theories of probability judgment: the
Bayesian theory and the theory of belief functions. It illustrates these
theories with some simple examples and discusses some of the issues that arise
when we try to implement them in expert systems. The Bayesian theory is well
known; its main ideas go back to the work of Thomas Bayes (1702-1761). The
theory of belief functions, often called the Dempster-Shafer theory in the
artificial intelligence community, is less well known, but it has even older
antecedents; belief-function arguments appear in the work of George Hooper
(16401723) and James Bernoulli (1654-1705). For elementary expositions of the
theory of belief functions, see Shafer (1976, 1985).
",Probability Judgement in Artificial Intelligence
"  Several different uncertain inference systems (UISs) have been developed for
representing uncertainty in rule-based expert systems. Some of these, such as
Mycin's Certainty Factors, Prospector, and Bayes' Networks were designed as
approximations to probability, and others, such as Fuzzy Set Theory and
DempsterShafer Belief Functions were not. How different are these UISs in
practice, and does it matter which you use? When combining and propagating
uncertain information, each UIS must, at least by implication, make certain
assumptions about correlations not explicily specified. The maximum entropy
principle with minimum cross-entropy updating, provides a way of making
assumptions about the missing specification that minimizes the additional
information assumed, and thus offers a standard against which the other UISs
can be compared. We describe a framework for the experimental comparison of the
performance of different UISs, and provide some illustrative results.
",A Framework for Comparing Uncertain Inference Systems to Probability
"  The form and justification of inductive inference rules depend strongly on
the representation of uncertainty. This paper examines one generic
representation, namely, incomplete information. The notion can be formalized by
presuming that the relevant probabilities in a decision problem are known only
to the extent that they belong to a class K of probability distributions. The
concept is a generalization of a frequent suggestion that uncertainty be
represented by intervals or ranges on probabilities. To make the representation
useful for decision making, an inductive rule can be formulated which
determines, in a well-defined manner, a best approximation to the unknown
probability, given the set K. In addition, the knowledge set notion entails a
natural procedure for updating -- modifying the set K given new evidence.
Several non-intuitive consequences of updating emphasize the differences
between inference with complete and inference with incomplete information.
",Inductive Inference and the Representation of Uncertainty
"  This paper examines some methods and ideas underlying the author's successful
probabilistic learning systems(PLS), which have proven uniquely effective and
efficient in generalization learning or induction. While the emerging
principles are generally applicable, this paper illustrates them in heuristic
search, which demands noise management and incremental learning. In our
approach, both task performance and learning are guided by probability.
Probabilities are incrementally normalized and revised, and their errors are
located and corrected.
","Induction, of and by Probability"
"  Expert systems applications that involve uncertain inference can be
represented by a multidimensional contingency table. These tables offer a
general approach to inferring with uncertain evidence, because they can embody
any form of association between any number of pieces of evidence and
conclusions. (Simpler models may be required, however, if the number of pieces
of evidence bearing on a conclusion is large.) This paper presents a method of
using these tables to make uncertain inferences without assumptions of
conditional independence among pieces of evidence or heuristic combining rules.
As evidence is accumulated, new joint probabilities are calculated so as to
maintain any dependencies among the pieces of evidence that are found in the
contingency table. The new conditional probability of the conclusion is then
calculated directly from these new joint probabilities and the conditional
probabilities in the contingency table.
",An Odds Ratio Based Inference Engine
"  Control Strategies for hierarchical tree-like probabilistic inference
networks are formulated and investigated. Strategies that utilize staged
look-ahead and temporary focus on subgoals are formalized and refined using the
Depth Vector concept that serves as a tool for defining the 'virtual tree'
regarded by the control strategy. The concept is illustrated by four types of
control strategies for three-level trees that are characterized according to
their Depth Vector, and according to the way they consider intermediate nodes
and the role that they let these nodes play. INFERENTI is a computerized
inference system written in Prolog, which provides tools for exercising a
variety of control strategies. The system also provides tools for simulating
test data and for comparing the relative average performance under different
strategies.
",A Framework for Control Strategies in Uncertain Inference Networks
"  In a real expert system, one may have unreliable, unconfident, conflicting
estimates of the value for a particular parameter. It is important for decision
making that the information present in this aggregate somehow find its way into
use. We cast the problem of representing and combining uncertain estimates as
selection of two kinds of functions, one to determine an estimate, the other
its uncertainty. The paper includes a long list of properties that such
functions should satisfy, and it presents one method that satisfies them.
",Combining Uncertain Estimates
"  The issue of confidence factors in Knowledge Based Systems has become
increasingly important and Dempster-Shafer (DS) theory has become increasingly
popular as a basis for these factors. This paper discusses the need for an
empirical lnterpretatlon of any theory of confidence factors applied to
Knowledge Based Systems and describes an empirical lnterpretatlon of DS theory
suggesting that the theory has been extensively misinterpreted. For the
essentially syntactic DS theory, a model is developed based on sample spaces,
the traditional semantic model of probability theory. This model is used to
show that, if belief functions are based on reasonably accurate sampling or
observation of a sample space, then the beliefs and upper probabilities as
computed according to DS theory cannot be interpreted as frequency ratios.
Since many proposed applications of DS theory use belief functions in
situations with statistically derived evidence (Wesley [1]) and seem to appeal
to statistical intuition to provide an lnterpretatlon of the results as has
Garvey [2], it may be argued that DS theory has often been misapplied.
","Confidence Factors, Empiricism and the Dempster-Shafer Theory of
  Evidence"
"  Mechanisms for the automation of uncertainty are required for expert systems.
Sometimes these mechanisms need to obey the properties of probabilistic
reasoning. A purely numeric mechanism, like those proposed so far, cannot
provide a probabilistic logic with truth functional connectives. We propose an
alternative mechanism, Incidence Calculus, which is based on a representation
of uncertainty using sets of points, which might represent situations, models
or possible worlds. Incidence Calculus does provide a probabilistic logic with
truth functional connectives.
",Incidence Calculus: A Mechanism for Probabilistic Reasoning
"  A considerable body of work in AI has been concerned with aggregating
measures of confirmatory and disconfirmatory evidence for a common set of
propositions. Claiming classical probability to be inadequate or inappropriate,
several researchers have gone so far as to invent new formalisms and methods.
We show how to represent two major such alternative approaches to evidential
confirmation not only in terms of transformed (Bayesian) probability, but also
in terms of each other. This unifies two of the leading approaches to
confirmation theory, by showing that a revised MYCIN Certainty Factor method
[12] is equivalent to a special case of Dempster-Shafer theory. It yields a
well-understood axiomatic basis, i.e. conditional independence, to interpret
previous work on quantitative confirmation theory. It substantially resolves
the ""taxe-them-or-leave-them"" problem of priors: MYCIN had to leave them out,
while PROSPECTOR had to have them in. It recasts some of confirmation theory's
advantages in terms of the psychological accessibility of probabilistic
information in different (transformed) formats. Finally, it helps to unify the
representation of uncertain reasoning (see also [11]).
",Evidential Confirmation as Transformed Probability
"  This essay looks at decision-making with interval-valued probability
measures. Existing decision methods have either supplemented expected utility
methods with additional criteria of optimality, or have attempted to supplement
the interval-valued measures. We advocate a new approach, which makes the
following questions moot: 1. which additional criteria to use, and 2. how wide
intervals should be. In order to implement the approach, we need more
epistemological information. Such information can be generated by a rule of
acceptance with a parameter that allows various attitudes toward error, or can
simply be declared. In sketch, the argument is: 1. probability intervals are
useful and natural in All. systems; 2. wide intervals avoid error, but are
useless in some risk sensitive decision-making; 3. one may obtain narrower
intervals if one is less cautious; 4. if bodies of knowledge can be ordered by
their caution, one should perform the decision analysis with the acceptable
body of knowledge that is the most cautious, of those that are useful. The
resulting behavior differs from that of a behavioral probabilist (a Bayesian)
because in the proposal, 5. intervals based on successive bodies of knowledge
are not always nested; 6. if the agent uses a probability for a particular
decision, she need not commit to that probability for credence or future
decision; and 7. there may be no acceptable body of knowledge that is useful;
hence, sometimes no decision is mandated.
",Interval-Based Decisions for Reasoning Systems
"  In designing an intelligent system that must be able to explain its reasoning
to a human user, or to provide generalizations that the human user finds
reasonable, it may be useful to take into consideration psychological data on
what types of concepts and categories people naturally use. The psychological
literature on concept learning and categorization provides strong evidence that
certain categories are more easily learned, recalled, and recognized than
others. We show here how a measure of the informational value of a category
predicts the results of several important categorization experiments better
than standard alternative explanations. This suggests that information-based
approaches to machine generalization may prove particularly useful and natural
for human users of the systems.
","Machine Generalization and Human Categorization: An
  Information-Theoretic View"
"  This paper focuses on designing expert systems to support decision making in
complex, uncertain environments. In this context, our research indicates that
strictly probabilistic representations, which enable the use of
decision-theoretic reasoning, are highly preferable to recently proposed
alternatives (e.g., fuzzy set theory and Dempster-Shafer theory). Furthermore,
we discuss the language of influence diagrams and a corresponding methodology
-decision analysis -- that allows decision theory to be used effectively and
efficiently as a decision-making aid. Finally, we use RACHEL, a system that
helps infertile couples select medical treatments, to illustrate the
methodology of decision analysis as basis for expert decision systems.
",Exact Reasoning Under Uncertainty
"  Theoretically as well as experimentally it is investigated how people
represent their knowledge in order to make decisions or to share their
knowledge with others. Experiment 1 probes into the ways how people 6ather
information about the frequencies of events and how the requested response
mode, that is, numerical vs. verbal estimates interferes with this knowledge.
The least interference occurs if the subjects are allowed to give verbal
responses. From this it is concluded that processing knowledge about
uncertainty categorically, that is, by means of verbal expressions, imposes
less mental work load on the decision matter than numerical processing.
Possibility theory is used as a framework for modeling the individual usage of
verbal categories for grades of uncertainty. The 'elastic' constraints on the
verbal expressions for every sing1e subject are determined in Experiment 2 by
means of sequential calibration. In further experiments it is shown that the
superiority of the verbal processing of knowledge about uncertainty guise
generally reduces persistent biases reported in the literature: conservatism
(Experiment 3) and neg1igence of regression (Experiment 4). The reanalysis of
Hormann's data reveal that in verbal Judgments people exhibit sensitivity for
base rates and are not prone to the conjunction fallacy. In a final experiment
(5) about predictions in a real-life situation it turns out that in a numerical
forecasting task subjects restricted themselves to those parts of their
knowledge which are numerical. On the other hand subjects in a verbal
forecasting task accessed verbally as well as numerically stated knowledge.
Forecasting is structurally related to the estimation of probabilities for rare
events insofar as supporting and contradicting arguments have to be evaluated
and the choice of the final Judgment has to be Justified according to the
evidence brought forward. In order to assist people in such choice situations a
formal model for the interactive checking of arguments has been developed. The
model transforms the normal-language quantifiers used in the arguments into
fuzzy numbers and evaluates the given train of arguments by means of fuzzy
numerica1 operations. Ambiguities in the meanings of quantifiers are resolved
interactively.
","The Estimation of Subjective Probabilities via Categorical Judgments of
  Uncertainty"
"  The traditional approach to choosing moves in game-playing programs is the
minimax procedure. The general belief underlying its use is that increasing
search depth improves play. Recent research has shown that given certain
simplifying assumptions about a game tree's structure, this belief is
erroneous: searching deeper decreases the probability of making a correct move.
This phenomenon is called game tree pathology. Among these simplifying
assumptions is uniform depth of win/loss (terminal) nodes, a condition which is
not true for most real games. Analytic studies in [10] have shown that if every
node in a pathological game tree is made terminal with probability exceeding a
certain threshold, the resulting tree is nonpathological. This paper considers
a new evaluation function which recognizes increasing densities of forced wins
at deeper levels in the tree. This property raises two points that strengthen
the hypothesis that uniform win depth causes pathology. First, it proves
mathematically that as search deepens, an evaluation function that does not
explicitly check for certain forced win patterns becomes decreasingly likely to
force wins. This failing predicts the pathological behavior of the original
evaluation function. Second, it shows empirically that despite recognizing
fewer mid-game wins than the theoretically predicted minimum, the new function
is nonpathological.
",A Cure for Pathological Behavior in Games that Use Minimax
"  In the field of Artificial Intelligence, traditional approaches to choosing
moves in games involve the we of the minimax algorithm. However, recent
research results indicate that minimizing may not always be the best approach.
In this paper we summarize the results of some measurements on several model
games with several different evaluation functions. These measurements, which
are presented in detail in [NPT], show that there are some new algorithms that
can make significantly better use of evaluation function values than the
minimax algorithm does.
",An Evaluation of Two Alternatives to Minimax
"  The analysis of practical probabilistic models on the computer demands a
convenient representation for the available knowledge and an efficient
algorithm to perform inference. An appealing representation is the influence
diagram, a network that makes explicit the random variables in a model and
their probabilistic dependencies. Recent advances have developed solution
procedures based on the influence diagram. In this paper, we examine the
fundamental properties that underlie those techniques, and the information
about the probabilistic structure that is available in the influence diagram
representation. The influence diagram is a convenient representation for
computer processing while also being clear and non-mathematical. It displays
probabilistic dependence precisely, in a way that is intuitive for decision
makers and experts to understand and communicate. As a result, the same
influence diagram can be used to build, assess and analyze a model,
facilitating changes in the formulation and feedback from sensitivity analysis.
The goal in this paper is to determine arbitrary conditional probability
distributions from a given probabilistic model. Given qualitative information
about the dependence of the random variables in the model we can, for a
specific conditional expression, specify precisely what quantitative
information we need to be able to determine the desired conditional probability
distribution. It is also shown how we can find that probability distribution by
performing operations locally, that is, over subspaces of the joint
distribution. In this way, we can exploit the conditional independence present
in the model to avoid having to construct or manipulate the full joint
distribution. These results are extended to include maximal processing when the
information available is incomplete, and optimal decision making in an
uncertain environment. Influence diagrams as a computer-aided modeling tool
were developed by Miller, Merkofer, and Howard [5] and extended by Howard and
Matheson [2]. Good descriptions of how to use them in modeling are in Owen [7]
and Howard and Matheson [2]. The notion of solving a decision problem through
influence diagrams was examined by Olmsted [6] and such an algorithm was
developed by Shachter [8]. The latter paper also shows how influence diagrams
can be used to perform a variety of sensitivity analyses. This paper extends
those results by developing a theory of the properties of the diagram that are
used by the algorithm, and the information needed to solve arbitrary
probability inference problems. Section 2 develops the notation and the
framework for the paper and the relationship between influence diagrams and
joint probability distributions. The general probabilistic inference problem is
posed in Section 3. In Section 4 the transformations on the diagram are
developed and then put together into a solution procedure in Section 5. In
Section 6, this procedure is used to calculate the information requirement to
solve an inference problem and the maximal processing that can be performed
with incomplete information. Section 7 contains a summary of results.
",Intelligent Probabilistic Inference
"  The last few years has seen a growing debate about techniques for managing
uncertainty in AI systems. Unfortunately this debate has been cast as a rivalry
between AI methods and classical probability based ones. Three arguments for
extending the probability framework of uncertainty are presented, none of which
imply a challenge to classical methods. These are (1) explicit representation
of several types of uncertainty, specifically possibility and plausibility, as
well as probability, (2) the use of weak methods for uncertainty management in
problems which are poorly defined, and (3) symbolic representation of different
uncertainty calculi and methods for choosing between them.
",Strong & Weak Methods: A Logical View of Uncertainty
"  Artificial intelligence applications such as industrial robotics, military
surveillance, and hazardous environment clean-up, require situation
understanding based on partial, uncertain, and ambiguous or erroneous evidence.
It is necessary to evaluate the relative likelihood of multiple possible
hypotheses of the (current) situation faced by the decision making program.
Often, the evidence and hypotheses are hierarchical in nature. In image
understanding tasks, for example, evidence begins with raw imagery, from which
ambiguous features are extracted which have multiple possible aggregations
providing evidential support for the presence of multiple hypothesis of objects
and terrain, which in turn aggregate in multiple ways to provide partial
evidence for different interpretations of the ambient scene. Information fusion
for military situation understanding has a similar evidence/hypothesis
hierarchy from multiple sensor through message level interpretations, and also
provides evidence at multiple levels of the doctrinal hierarchy of military
forces.
",Probabilistic Conflict Resolution in Hierarchical Hypothesis Spaces
"  The roles played by decision factors in making complex subject are decisions
are characterized by how these factors affect the overall decision. Evidence
that partially matches a factor is evaluated, and then effective computational
rules are applied to these roles to form an appropriate aggregation of the
evidence. The use of this technique supports the expression of deeper levels of
causality, and may also preserve the cognitive structure of the decision maker
better than the usual weighting methods, certainty-factor or other
probabilistic models can.
",Knowledge Structures and Evidential Reasoning in Decision Analysis
"  We present a logical framework to represent and reason about stochastic
optimization problems based on probability answer set programming. This is
established by allowing probability optimization aggregates, e.g., minimum and
maximum in the language of probability answer set programming to allow
minimization or maximization of some desired criteria under the probabilistic
environments. We show the application of the proposed logical stochastic
optimization framework under the probability answer set programming to two
stages stochastic optimization problems with recourse.
",Logical Stochastic Optimization
"  One of the roots of evolutionary computation was the idea of Turing about
unorganized machines. The goal of this work is the development of foundations
for evolutionary computations, connecting Turing's ideas and the contemporary
state of art in evolutionary computations. To achieve this goal, we develop a
general approach to evolutionary processes in the computational context,
building mathematical models of computational systems, functioning of which is
based on evolutionary processes, and studying properties of such systems.
Operations with evolutionary machines are described and it is explored when
definite classes of evolutionary machines are closed with respect to basic
operations with these machines. We also study such properties as linguistic and
functional equivalence of evolutionary machines and their classes, as well as
computational power of evolutionary machines and their classes, comparing of
evolutionary machines to conventional automata, such as finite automata or
Turing machines.
",Evolutionary Turing in the Context of Evolutionary Machines
"  This is the Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence, which was held in San Francisco, CA, June 30 - July 3,
2000
","Proceedings of the Sixteenth Conference on Uncertainty in Artificial
  Intelligence (2000)"
"  This is the Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence, which was held in Stockholm Sweden, July 30 - August
1, 1999
","Proceedings of the Fifteenth Conference on Uncertainty in Artificial
  Intelligence (1999)"
"  This is the Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence, which was held in Madison, WI, July 24-26, 1998
","Proceedings of the Fourteenth Conference on Uncertainty in Artificial
  Intelligence (1998)"
"  This is the Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence, which was held in Providence, RI, August 1-3, 1997
","Proceedings of the Thirteenth Conference on Uncertainty in Artificial
  Intelligence (1997)"
"  This is the Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence, which was held in Portland, OR, August 1-4, 1996
","Proceedings of the Twelfth Conference on Uncertainty in Artificial
  Intelligence (1996)"
"  This is the Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence, which was held in Montreal, QU, August 18-20, 1995
","Proceedings of the Eleventh Conference on Uncertainty in Artificial
  Intelligence (1995)"
"  This is the Proceedings of the Tenth Conference on Uncertainty in Artificial
Intelligence, which was held in Seattle, WA, July 29-31, 1994
","Proceedings of the Tenth Conference on Uncertainty in Artificial
  Intelligence (1994)"
"  This is the Proceedings of the Ninth Conference on Uncertainty in Artificial
Intelligence, which was held in Washington, DC, July 9-11, 1993
","Proceedings of the Ninth Conference on Uncertainty in Artificial
  Intelligence (1993)"
"  This is the Proceedings of the Eighth Conference on Uncertainty in Artificial
Intelligence, which was held in Stanford, CA, July 17-19, 1992
","Proceedings of the Eighth Conference on Uncertainty in Artificial
  Intelligence (1992)"
"  This is the Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence, which was held in Los Angeles, CA, July 13-15, 1991
","Proceedings of the Seventh Conference on Uncertainty in Artificial
  Intelligence (1991)"
"  This is the Proceedings of the Sixth Conference on Uncertainty in Artificial
Intelligence, which was held in Cambridge, MA, Jul 27 - Jul 29, 1990
","Proceedings of the Sixth Conference on Uncertainty in Artificial
  Intelligence (1990)"
"  This is the Proceedings of the Fifth Conference on Uncertainty in Artificial
Intelligence, which was held in Windsor, ON, August 18-20, 1989
","Proceedings of the Fifth Conference on Uncertainty in Artificial
  Intelligence (1989)"
"  This is the Proceedings of the Fourth Conference on Uncertainty in Artificial
Intelligence, which was held in Minneapolis, MN, July 10-12, 1988
","Proceedings of the Fourth Conference on Uncertainty in Artificial
  Intelligence (1988)"
"  This is the Proceedings of the Third Conference on Uncertainty in Artificial
Intelligence, which was held in Seattle, WA, July 10-12, 1987
","Proceedings of the Third Conference on Uncertainty in Artificial
  Intelligence (1987)"
"  This is the Proceedings of the Second Conference on Uncertainty in Artificial
Intelligence, which was held in Philadelphia, PA, August 8-10, 1986
","Proceedings of the Second Conference on Uncertainty in Artificial
  Intelligence (1986)"
"  In the interaction between agents we can have an explicative discourse, when
communicating preferences or intentions, and a normative discourse, when
considering normative knowledge. For justifying their actions our agents are
endowed with a Justification and Explanation Logic (JEL), capable to cover both
the justification for their commitments and explanations why they had to act in
that way, due to the current situation in the environment. Social commitments
are used to formalise justificatory and explanatory patterns. The combination
of ex- planation, justification, and commitments
",Justificatory and Explanatory Argumentation for Committing Agents
"  This is the Proceedings of the First Conference on Uncertainty in Artificial
Intelligence, which was held in Los Angeles, CA, July 10-12, 1985
","Proceedings of the First Conference on Uncertainty in Artificial
  Intelligence (1985)"
"  RockIt is a maximum a-posteriori (MAP) query engine for statistical
relational models. MAP inference in graphical models is an optimization problem
which can be compiled to integer linear programs (ILPs). We describe several
advances in translating MAP queries to ILP instances and present the novel
meta-algorithm cutting plane aggregation (CPA). CPA exploits local
context-specific symmetries and bundles up sets of linear constraints. The
resulting counting constraints lead to more compact ILPs and make the symmetry
of the ground model more explicit to state-of-the-art ILP solvers. Moreover,
RockIt parallelizes most parts of the MAP inference pipeline taking advantage
of ubiquitous shared-memory multi-core architectures.
  We report on extensive experiments with Markov logic network (MLN) benchmarks
showing that RockIt outperforms the state-of-the-art systems Alchemy, Markov
TheBeast, and Tuffy both in terms of efficiency and quality of results.
","RockIt: Exploiting Parallelism and Symmetry for MAP Inference in
  Statistical Relational Models"
"  In this paper, we propose a first application of data mining techniques to
propositional satisfiability. Our proposed Mining4SAT approach aims to discover
and to exploit hidden structural knowledge for reducing the size of
propositional formulae in conjunctive normal form (CNF). Mining4SAT combines
both frequent itemset mining techniques and Tseitin's encoding for a compact
representation of CNF formulae. The experiments of our Mining4SAT approach show
interesting reductions of the sizes of many application instances taken from
the last SAT competitions.
",Mining to Compact CNF Propositional Formulae
"  We propose an approximation of the Possible Worlds Semantics (PWS) for action
planning. A corresponding planning system is implemented by a transformation of
the action specification to an Answer-Set Program. A novelty is support for
postdiction wrt. (a) the plan existence problem in our framework can be solved
in NP, as compared to $\Sigma_2^P$ for non-approximated PWS of Baral(2000); and
(b) the planner generates optimal plans wrt. a minimal number of actions in
$\Delta_2^P$. We demo the planning system with standard problems, and
illustrate its integration in a larger software framework for robot control in
a smart home.
","h-approximation: History-Based Approximation of Possible World Semantics
  as ASP"
"  The paper describes development (improvement/extension) approaches for
composite (modular) systems (as combinatorial reengineering). The following
system improvement/extension actions are considered: (a) improvement of systems
component(s) (e.g., improvement of a system component, replacement of a system
component); (b) improvement of system component interconnection
(compatibility); (c) joint improvement improvement of system components(s) and
their interconnection; (d) improvement of system structure (replacement of
system part(s), addition of a system part, deletion of a system part,
modification of system structure). The study of system improvement approaches
involve some crucial issues: (i) scales for evaluation of system components and
component compatibility (quantitative scale, ordinal scale, poset-like scale,
scale based on interval multiset estimate), (ii) evaluation of integrated
system quality, (iii) integration methods to obtain the integrated system
quality. The system improvement/extension strategies can be examined as
seleciton/combination of the improvement action(s) above and as modification of
system structure. The strategies are based on combinatorial optimization
problems (e.g., multicriteria selection, knapsack problem, multiple choice
problem, combinatorial synthesis based on morphological clique problem,
assignment/reassignment problem, graph recoloring problem, spanning problems,
hotlink assignment). Here, heuristics are used. Various system
improvement/extension strategies are presented including illustrative numerical
examples.
","Improvement/Extension of Modular Systems as Combinatorial Reengineering
  (Survey)"
"  Usual techniques to solve WCSP are based on cost transfer operations coupled
with a branch and bound algorithm. In this paper, we focus on an approach
integrating extraction and relaxation of Minimal Unsatisfiable Cores in order
to solve this problem. We decline our approach in two ways: an incomplete,
greedy, algorithm and a complete one.
",Solving WCSP by Extraction of Minimal Unsatisfiable Cores
"  This paper presents the OntoRich framework, a support tool for semi-automatic
ontology enrichment and evaluation. The WordNet is used to extract candidates
for dynamic ontology enrichment from RSS streams. With the integration of
OpenNLP the system gains access to syntactic analysis of the RSS news. The
enriched ontologies are evaluated against several qualitative metrics.
","OntoRich - A Support Tool for Semi-Automatic Ontology Enrichment and
  Evaluation"
"  This research advocates the idea of combining argumentation theory with the
social web technology, aiming to enact large scale or mass argumentation. The
proposed framework allows mass-collaborative editing of structured arguments in
the style of semantic wikipedia. The long term goal is to apply the abstract
machinery of argumentation theory to more practical applications based on human
generated arguments, such as deliberative democracy, business negotiation, or
self-care. The ARGNET system was developed based on ther Semantic MediaWiki
framework and on the Argument Interchange Format (AIF) ontology.
",Enacting Social Argumentative Machines in Semantic Wikipedia
"  Modeling emotional-cognition is in a nascent stage and therefore wide-open
for new ideas and discussions. In this paper the author looks at the modeling
problem by bringing in ideas from axiomatic mathematics, information theory,
computer science, molecular biology, non-linear dynamical systems and quantum
computing and explains how ideas from these disciplines may have applications
in modeling emotional-cognition.
",A novice looks at emotional cognition
"  Knowledge base exchange is an important problem in the area of data exchange
and knowledge representation, where one is interested in exchanging information
between a source and a target knowledge base connected through a mapping. In
this paper, we study this fundamental problem for knowledge bases and mappings
expressed in OWL 2 QL, the profile of OWL 2 based on the description logic
DL-Lite_R. More specifically, we consider the problem of computing universal
solutions, identified as one of the most desirable translations to be
materialized, and the problem of computing UCQ-representations, which optimally
capture in a target TBox the information that can be extracted from a source
TBox and a mapping by means of unions of conjunctive queries. For the former we
provide a novel automata-theoretic technique, and complexity results that range
from NP to EXPTIME, while for the latter we show NLOGSPACE-completeness.
",Exchanging OWL 2 QL Knowledge Bases
"  In the domain of Computing with words (CW), fuzzy linguistic approaches are
known to be relevant in many decision-making problems. Indeed, they allow us to
model the human reasoning in replacing words, assessments, preferences,
choices, wishes... by ad hoc variables, such as fuzzy sets or more
sophisticated variables.
  This paper focuses on a particular model: Herrera & Martinez' 2-tuple
linguistic model and their approach to deal with unbalanced linguistic term
sets. It is interesting since the computations are accomplished without loss of
information while the results of the decision-making processes always refer to
the initial linguistic term set. They propose a fuzzy partition which
distributes data on the axis by using linguistic hierarchies to manage the
non-uniformity. However, the required input (especially the density around the
terms) taken by their fuzzy partition algorithm may be considered as too much
demanding in a real-world application, since density is not always easy to
determine. Moreover, in some limit cases (especially when two terms are very
closed semantically to each other), the partition doesn't comply with the data
themselves, it isn't close to the reality. Therefore we propose to modify the
required input, in order to offer a simpler and more faithful partition. We
have added an extension to the package jFuzzyLogic and to the corresponding
script language FCL. This extension supports both 2-tuple models: Herrera &
Martinez' and ours. In addition to the partition algorithm, we present two
aggregation algorithms: the arithmetic means and the addition. We also discuss
these kinds of 2-tuple models.
","Towards an Extension of the 2-tuple Linguistic Model to Deal With
  Unbalanced Linguistic Term sets"
"  The FOCUS constraint expresses the notion that solutions are concentrated. In
practice, this constraint suffers from the rigidity of its semantics. To tackle
this issue, we propose three generalizations of the FOCUS constraint. We
provide for each one a complete filtering algorithm as well as discussing
decompositions.
",Three Generalizations of the FOCUS Constraint
"  When perturbation or unexpected events do occur, agents need protocols for
repairing or reforming the supply chain. Unfortunate contingency could increase
too much the cost of performance, while breaching the current contract may be
more efficient. In our framework the principles of contract law are applied to
set penalties: expectation damages, opportunity cost, reliance damages, and
party design remedies, and they are introduced in the task dependency model
",Automating the Dispute Resolution in Task Dependency Network
"  Description Logic Knowledge and Action Bases (KABs) have been recently
introduced as a mechanism that provides a semantically rich representation of
the information on the domain of interest in terms of a DL KB and a set of
actions to change such information over time, possibly introducing new objects.
In this setting, decidability of verification of sophisticated temporal
properties over KABs, expressed in a variant of first-order mu-calculus, has
been shown. However, the established framework treats inconsistency in a
simplistic way, by rejecting inconsistent states produced through action
execution. We address this problem by showing how inconsistency handling based
on the notion of repairs can be integrated into KABs, resorting to
inconsistency-tolerant semantics. In this setting, we establish decidability
and complexity of verification.
","Verification of Inconsistency-Aware Knowledge and Action Bases (Extended
  Version)"
"  Non deterministic applications arise in many domains, including, stochastic
optimization, multi-objectives optimization, stochastic planning, contingent
stochastic planning, reinforcement learning, reinforcement learning in
partially observable Markov decision processes, and conditional planning. We
present a logic programming framework called non deterministic logic programs,
along with a declarative semantics and fixpoint semantics, to allow
representing and reasoning about inherently non deterministic real-world
applications. The language of non deterministic logic programs framework is
extended with non-monotonic negation, and two alternative semantics are
defined: the stable non deterministic model semantics and the well-founded non
deterministic model semantics as well as their relationship is studied. These
semantics subsume the deterministic stable model semantics and the
deterministic well-founded semantics of deterministic normal logic programs,
and they reduce to the semantics of deterministic definite logic programs
without negation. We show the application of the non deterministic logic
programs framework to a conditional planning problem.
",Non Deterministic Logic Programs
"  The Fuzzy Modeling has been applied in a wide variety of fields such as
Engineering and Management Sciences and Social Sciences to solve a number
Decision Making Problems which involve impreciseness, uncertainty and vagueness
in data. In particular, applications of this Modeling technique in Decision
Making Problems have remarkable significance. These problems have been tackled
using various theories such as Probability theory, Fuzzy Set Theory, Rough Set
Theory, Vague Set Theory, Approximate Reasoning Theory etc. which lack in
parameterization of the tools due to which they could not be applied
successfully to such problems. The concept of Soft Set has a promising
potential for giving an optimal solution for these problems. With the
motivation of this new concept, in this paper we define the concepts of Soft
Relation and Fuzzy Soft Relation and then apply them to solve a number of
Decision Making Problems. The advantages of Fuzzy Soft Relation compared to
other paradigms are discussed. To the best of our knowledge this is the first
work on the application of Fuzzy Soft Relation to the Decision Making Problems.
",Solution of the Decision Making Problems using Fuzzy Soft Relations
"  Neuro-Fuzzy Modeling has been applied in a wide variety of fields such as
Decision Making, Engineering and Management Sciences etc. In particular,
applications of this Modeling technique in Decision Making by involving complex
Systems of Linear Algebraic Equations have remarkable significance. In this
Paper, we present Polak-Ribiere Conjugate Gradient based Neural Network with
Fuzzy rules to solve System of Simultaneous Linear Algebraic Equations. This is
achieved using Fuzzy Backpropagation Learning Rule. The implementation results
show that the proposed Neuro-Fuzzy Network yields effective solutions for
exactly determined, underdetermined and over-determined Systems of Linear
Equations. This fact is demonstrated by the Computational Complexity analysis
of the Neuro-Fuzzy Algorithm. The proposed Algorithm is simulated effectively
using MATLAB software. To the best of our knowledge this is the first work of
the Systems of Linear Algebraic Equations using Neuro-Fuzzy Modeling.
",Solution of System of Linear Equations - A Neuro-Fuzzy Approach
"  In this paper, we address the problem of enumerating all models of a Boolean
formula in conjunctive normal form (CNF). We propose an extension of CDCL-based
SAT solvers to deal with this fundamental problem. Then, we provide an
experimental evaluation of our proposed SAT model enumeration algorithms on
both satisfiable SAT instances taken from the last SAT challenge and on
instances from the SAT-based encoding of sequence mining problems.
",Extending Modern SAT Solvers for Enumerating All Models
"  We define the notion of rational closure in the context of Description Logics
extended with a tipicality operator. We start from ALC+T, an extension of ALC
with a typicality operator T: intuitively allowing to express concepts of the
form T(C), meant to select the ""most normal"" instances of a concept C. The
semantics we consider is based on rational model. But we further restrict the
semantics to minimal models, that is to say, to models that minimise the rank
of domain elements. We show that this semantics captures exactly a notion of
rational closure which is a natural extension to Description Logics of Lehmann
and Magidor's original one. We also extend the notion of rational closure to
the Abox component. We provide an ExpTime algorithm for computing the rational
closure of an Abox and we show that it is sound and complete with respect to
the minimal model semantics.
",On Rational Closure in Description Logics of Typicality
"  Most real-world Planning problems are multi-objective, trying to minimize
both the makespan of the solution plan, and some cost of the actions involved
in the plan. But most, if not all existing approaches are based on
single-objective planners, and use an aggregation of the objectives to remain
in the single-objective context. Divide and Evolve (DaE) is an evolutionary
planner that won the temporal deterministic satisficing track at the last
International Planning Competitions (IPC). Like all Evolutionary Algorithms
(EA), it can easily be turned into a Pareto-based Multi-Objective EA. It is
however important to validate the resulting algorithm by comparing it with the
aggregation approach: this is the goal of this paper. The comparative
experiments on a recently proposed benchmark set that are reported here
demonstrate the usefulness of going Pareto-based in AI Planning.
",Multi-Objective AI Planning: Comparing Aggregation and Pareto Approaches
"  There has been an increasing interest in inferring some personality traits
from users and players in social networks and games, respectively. This goes
beyond classical sentiment analysis, and also much further than customer
profiling. The purpose here is to have a characterisation of users in terms of
personality traits, such as openness, conscientiousness, extraversion,
agreeableness, and neuroticism. While this is an incipient area of research, we
ask the question of whether cognitive abilities, and intelligence in
particular, are also measurable from user profiles. However, we pose the
question as broadly as possible in terms of subjects, in the context of
universal psychometrics, including humans, machines and hybrids. Namely, in
this paper we analyse the following question: is it possible to measure the
intelligence of humans and (non-human) bots in a social network or a game just
from their user profiles, i.e., by observation, without the use of interactive
tests, such as IQ tests, the Turing test or other more principled machine
intelligence tests?
","A short note on estimating intelligence from user profiles in the
  context of universal psychometrics: prospects and caveats"
"  The analysis of the adaptive behaviour of many different kinds of systems
such as humans, animals and machines, requires more general ways of assessing
their cognitive abilities. This need is strengthened by increasingly more tasks
being analysed for and completed by a wider diversity of systems, including
swarms and hybrids. The notion of universal test has recently emerged in the
context of machine intelligence evaluation as a way to define and use the same
cognitive test for a variety of systems, using some principled tasks and
adapting the interface to each particular subject. However, how far can
universal tests be taken? This paper analyses this question in terms of
subjects, environments, space-time resolution, rewards and interfaces. This
leads to a number of findings, insights and caveats, according to several
levels where universal tests may be progressively more difficult to conceive,
implement and administer. One of the most significant contributions is given by
the realisation that more universal tests are defined as maximisations of less
universal tests for a variety of configurations. This means that universal
tests must be necessarily adaptive.
",On the universality of cognitive tests
"  In many probabilistic first-order representation systems, inference is
performed by ""grounding""---i.e., mapping it to a propositional representation,
and then performing propositional inference. With a large database of facts,
groundings can be very large, making inference and learning computationally
expensive. Here we present a first-order probabilistic language which is
well-suited to approximate ""local"" grounding: every query $Q$ can be
approximately grounded with a small graph. The language is an extension of
stochastic logic programs where inference is performed by a variant of
personalized PageRank. Experimentally, we show that the approach performs well
without weight learning on an entity resolution task; that supervised
weight-learning improves accuracy; and that grounding time is independent of DB
size. We also show that order-of-magnitude speedups are possible by
parallelizing learning.
","Programming with Personalized PageRank: A Locally Groundable First-Order
  Probabilistic Logic"
"  Parameter tuning is recognized today as a crucial ingredient when tackling an
optimization problem. Several meta-optimization methods have been proposed to
find the best parameter set for a given optimization algorithm and (set of)
problem instances. When the objective of the optimization is some scalar
quality of the solution given by the target algorithm, this quality is also
used as the basis for the quality of parameter sets. But in the case of
multi-objective optimization by aggregation, the set of solutions is given by
several single-objective runs with different weights on the objectives, and it
turns out that the hypervolume of the final population of each single-objective
run might be a better indicator of the global performance of the aggregation
method than the best fitness in its population. This paper discusses this issue
on a case study in multi-objective temporal planning using the evolutionary
planner DaE-YAHSP and the meta-optimizer ParamILS. The results clearly show how
ParamILS makes a difference between both approaches, and demonstrate that
indeed, in this context, using the hypervolume indicator as ParamILS target is
the best choice. Other issues pertaining to parameter tuning in the proposed
context are also discussed.
","Quality Measures of Parameter Tuning for Aggregated Multi-Objective
  Temporal Planning"
"  We present Exponentiated Gradient LINUCB, an algorithm for con-textual
multi-armed bandits. This algorithm uses Exponentiated Gradient to find the
optimal exploration of the LINUCB. Within a deliberately designed offline
simulation framework we conduct evaluations with real online event log data.
The experimental results demonstrate that our algorithm outperforms surveyed
algorithms.
",Exponentiated Gradient LINUCB for Contextual Multi-Armed Bandits
"  A popular current research trend deals with expanding the Monte-Carlo tree
search sampling methodologies to the environments with uncertainty and
incomplete information. Recently a finite population version of Geiringer
theorem with nonhomologous recombination has been adopted to the setting of
Monte-Carlo tree search to cope with randomness and incomplete information by
exploiting the entrinsic similarities within the state space of the problem.
The only limitation of the new theorem is that the similarity relation was
assumed to be an equivalence relation on the set of states. In the current
paper we lift this ""curtain of limitation"" by allowing the similarity relation
to be modeled in terms of an arbitrary set cover of the set of state-action
pairs.
","A Further Generalization of the Finite-Population Geiringer-like Theorem
  for POMDPs to Allow Recombination Over Arbitrary Set Covers"
"  As network traffic monitoring software for cybersecurity, malware detection,
and other critical tasks becomes increasingly automated, the rate of alerts and
supporting data gathered, as well as the complexity of the underlying model,
regularly exceed human processing capabilities. Many of these applications
require complex models and constituent rules in order to come up with decisions
that influence the operation of entire systems. In this paper, we motivate the
novel ""strategic planning"" problem -- one of gathering data from the world and
applying the underlying model of the domain in order to come up with decisions
that will monitor the system in an automated manner. We describe our use of
automated planning methods to this problem, including the technique that we
used to solve it in a manner that would scale to the demands of a real-time,
real world scenario. We then present a PDDL model of one such application
scenario related to network administration and monitoring, followed by a
description of a novel integrated system that was built to accept generated
plans and to continue the execution process. Finally, we present evaluations of
two different automated planners and their different capabilities with our
integrated system, both on a six-month window of network data, and using a
simulator.
",Strategic Planning for Network Data Analysis
"  In this paper we present a new concept called generalized neutrosophic soft
set. This concept incorporates the beneficial properties of both generalized
neutrosophic set introduced by A.A. Salama [7]and soft set techniques proposed
by Molodtsov [4]. We also study some properties of this concept. Some
definitions and operations have been introduced on generalized neutrosophic
soft set. Finally we present an application of generalized neuutrosophic soft
set in decision making problem.
",Generalized Neutrosophic Soft Set
"  In this paper, we propose an extension of our Mining for SAT framework to
Constraint satisfaction Problem (CSP). We consider n-ary extensional
constraints (table constraints). Our approach aims to reduce the size of the
CSP by exploiting the structure of the constraints graph and of its associated
microstructure. More precisely, we apply itemset mining techniques to search
for closed frequent itemsets on these two representation. Using Tseitin
extension, we rewrite the whole CSP to another compressed CSP equivalent with
respect to satisfiability. Our approach contrast with previous proposed
approach by Katsirelos and Walsh, as we do not change the structure of the
constraints.
",A Mining-Based Compression Approach for Constraint Satisfaction Problems
"  The extraction of logically-independent fragments out of an ontology ABox can
be useful for solving the tractability problem of querying ontologies with
large ABoxes. In this paper, we propose a formal definition of an ABox module,
such that it guarantees complete preservation of facts about a given set of
individuals, and thus can be reasoned independently w.r.t. the ontology TBox.
With ABox modules of this type, isolated or distributed (parallel) ABox
reasoning becomes feasible, and more efficient data retrieval from ontology
ABoxes can be attained. To compute such an ABox module, we present a
theoretical approach and also an approximation for $\mathcal{SHIQ}$ ontologies.
Evaluation of the module approximation on different types of ontologies shows
that, on average, extracted ABox modules are significantly smaller than the
entire ABox, and the time for ontology reasoning based on ABox modules can be
improved significantly.
",Extract ABox Modules for Efficient Ontology Querying
"  The obvious way to use several admissible heuristics in A* is to take their
maximum. In this paper we aim to reduce the time spent on computing heuristics.
We discuss Lazy A*, a variant of A* where heuristics are evaluated lazily: only
when they are essential to a decision to be made in the A* search process. We
present a new rational meta-reasoning based scheme, rational lazy A*, which
decides whether to compute the more expensive heuristics at all, based on a
myopic value of information estimate. Both methods are examined theoretically.
Empirical evaluation on several domains supports the theoretical results, and
shows that lazy A* and rational lazy A* are state-of-the-art heuristic
combination methods.
",Towards Rational Deployment of Multiple Heuristics in A*
"  This is a purely pedagogical paper with no new results. The goal of the paper
is to give a fairly self-contained introduction to Judea Pearl's do-calculus,
including proofs of his 3 rules.
",Introduction to Judea Pearl's Do-Calculus
"  The bipartite boolean quadratic programming problem (BBQP) is a
generalization of the well studied boolean quadratic programming problem. The
model has a variety of real life applications; however, empirical studies of
the model are not available in the literature, except in a few isolated
instances. In this paper, we develop efficient heuristic algorithms based on
tabu search, very large scale neighborhood (VLSN) search, and a hybrid
algorithm that integrates the two. The computational study establishes that
effective integration of simple tabu search with VLSN search results in
superior outcomes, and suggests the value of such an integration in other
settings. Complexity analysis and implementation details are provided along
with conclusions drawn from experimental analysis. In addition, we obtain
solutions better than the best previously known for almost all medium and large
size benchmark instances.
","Integrating tabu search and VLSN search to develop enhanced algorithms:
  A case study using bipartite boolean quadratic programs"
"  Objective: The main objective of this study was to assess the diagnostic
performances of an alert system integrated into the CPOE/EMR system for renally
cleared drug dosing control. The generated alerts were compared with the daily
routine practice of pharmacists as part of the analysis of medication orders.
Materials and Methods: The pharmacists performed their analysis of medication
orders as usual and were not aware of the alert system interventions that were
not displayed for the purpose of the study neither to the physician nor to the
pharmacist but kept with associate recommendations in a log file. A senior
pharmacist analyzed the results of medication order analysis with and without
the alert system. The unit of analysis was the drug prescription line. The
primary study endpoints were the detection of drug-dose prescription errors and
inter-rater reliability between the alert system and the pharmacists in the
detection of drug dose error. Results: The alert system fired alerts in 8.41%
(421/5006) of cases: 5.65% (283/5006) exceeds max daily dose alerts and 2.76%
(138/5006) under dose alerts. The alert system and the pharmacists showed a
relatively poor concordance: 0.106 (CI 95% [0.068, 0.144]). According to the
senior pharmacist review, the alert system fired more appropriate alerts than
pharmacists, and made fewer errors than pharmacists in analyzing drug dose
prescriptions: 143 for the alert system and 261 for the pharmacists. Unlike the
alert system, most diagnostic errors made by the pharmacists were false
negatives. The pharmacists were not able to analyze a significant number (2097;
25.42%) of drug prescription lines because understaffing. Conclusion: This
study strongly suggests that an alert system would be complementary to the
pharmacists activity and contribute to drug prescription safety.
","Validity of a clinical decision rule based alert system for drug dose
  adjustment in patients with renal failure intended to improve pharmacists'
  analysis of medication orders in hospitals"
"  The Low Autocorrelation Binary Sequence problem has applications in
telecommunications, is of theoretical interest to physicists, and has inspired
many optimisation researchers. Metaheuristics for the problem have progressed
greatly in recent years but complete search has not progressed since a
branch-and-bound method of 1996. In this paper we find four ways of improving
branch-and-bound, leading to a tighter relaxation, faster convergence to
optimality, and better empirical scalability.
",Improved Branch-and-Bound for Low Autocorrelation Binary Sequences
"  This Ontologies are widely used as a means for solving the information
heterogeneity problems on the web because of their capability to provide
explicit meaning to the information. They become an efficient tool for
knowledge representation in a structured manner. There is always more than one
ontology for the same domain. Furthermore, there is no standard method for
building ontologies, and there are many ontology building tools using different
ontology languages. Because of these reasons, interoperability between the
ontologies is very low. Current ontology tools mostly use functions to build,
edit and inference the ontology. Methods for merging heterogeneous domain
ontologies are not included in most tools. This paper presents ontology merging
methodology for building a single global ontology from heterogeneous eXtensible
Markup Language (XML) data sources to capture and maintain all the knowledge
which XML data sources can contain
",Towards an Ontology based integrated Framework for Semantic Web
"  This article first lists reasons why - in the long term or when creating a
new knowledge base (KB) for general knowledge sharing purposes -
collaboratively building a well-organized KB does/can provide more
possibilities, with on the whole no more costs, than the mainstream approach
where knowledge creation and re-use involves searching, merging and creating
(semi-)independent (relatively small) ontologies or semi-formal documents. The
article lists elements required to achieve this and describes the main one: a
KB editing protocol that keeps the KB free of automatically/manually detected
inconsistencies while not forcing them to discuss or agree on terminology and
beliefs nor requiring a selection committee.
",Collaborative ontology sharing and editing
"  This paper presents an adaptation of the harmony search algorithm to solve
the storage allocation problem for inbound and outbound containers. This
problem is studied considering multiple container type (regular, open side,
open top, tank, empty and refrigerated) which lets the situation more
complicated, as various storage constraints appeared. The objective is to find
an optimal container arrangement which respects their departure dates, and
minimize the re-handle operations of containers. The performance of the
proposed approach is verified comparing to the results generated by genetic
algorithm and LIFO algorithm.
","Harmony search to solve the container storage problem with different
  container types"
"  Qualitative spatial and temporal reasoning is based on so-called qualitative
calculi. Algebraic properties of these calculi have several implications on
reasoning algorithms. But what exactly is a qualitative calculus? And to which
extent do the qualitative calculi proposed meet these demands? The literature
provides various answers to the first question but only few facts about the
second. In this paper we identify the minimal requirements to binary
spatio-temporal calculi and we discuss the relevance of the according axioms
for representation and reasoning. We also analyze existing qualitative calculi
and provide a classification involving different notions of a relation algebra.
",Algebraic Properties of Qualitative Spatio-Temporal Calculi
"  Universal induction is a crucial issue in AGI. Its practical applicability
can be achieved by the choice of the reference machine or representation of
algorithms agreed with the environment. This machine should be updatable for
solving subsequent tasks more efficiently. We study this problem on an example
of combinatory logic as the very simple Turing-complete reference machine,
which enables modifying program representations by introducing different sets
of primitive combinators. Genetic programming system is used to search for
combinator expressions, which are easily decomposed into sub-expressions being
recombined in crossover. Our experiments show that low-complexity induction or
prediction tasks can be solved by the developed system (much more efficiently
than using brute force); useful combinators can be revealed and included into
the representation simplifying more difficult tasks. However, optimal sets of
combinators depend on the specific task, so the reference machine should be
adaptively chosen in coordination with the search engine.
",Universal Induction with Varying Sets of Combinators
"  Lifting attempts to speed up probabilistic inference by exploiting symmetries
in the model. Exact lifted inference methods, like their propositional
counterparts, work by recursively decomposing the model and the problem. In the
propositional case, there exist formal structures, such as decomposition trees
(dtrees), that represent such a decomposition and allow us to determine the
complexity of inference a priori. However, there is currently no equivalent
structure nor analogous complexity results for lifted inference. In this paper,
we introduce FO-dtrees, which upgrade propositional dtrees to the first-order
level. We show how these trees can characterize a lifted inference solution for
a probabilistic logical model (in terms of a sequence of lifted operations),
and make a theoretical analysis of the complexity of lifted inference in terms
of the novel notion of lifted width for the tree.
",First-Order Decomposition Trees
"  Algorithm portfolio and selection approaches have achieved remarkable
improvements over single solvers. However, the implementation of such systems
is often highly customised and specific to the problem domain. This makes it
difficult for researchers to explore different techniques for their specific
problems. We present LLAMA, a modular and extensible toolkit implemented as an
R package that facilitates the exploration of a range of different portfolio
techniques on any problem domain. It implements the algorithm selection
approaches most commonly used in the literature and leverages the extensive
library of machine learning algorithms and techniques in R. We describe the
current capabilities and limitations of the toolkit and illustrate its usage on
a set of example SAT problems.
",LLAMA: Leveraging Learning to Automatically Manage Algorithms
"  Optimal probabilistic approach in reinforcement learning is computationally
infeasible. Its simplification consisting in neglecting difference between true
environment and its model estimated using limited number of observations causes
exploration vs exploitation problem. Uncertainty can be expressed in terms of a
probability distribution over the space of environment models, and this
uncertainty can be propagated to the action-value function via Bellman
iterations, which are computationally insufficiently efficient though. We
consider possibility of directly measuring uncertainty of the action-value
function, and analyze sufficiency of this facilitated approach.
",Direct Uncertainty Estimation in Reinforcement Learning
"  Solomonoff induction is known to be universal, but incomputable. Its
approximations, namely, the Minimum Description (or Message) Length (MDL)
principles, are adopted in practice in the efficient, but non-universal form.
Recent attempts to bridge this gap leaded to development of the
Representational MDL principle that originates from formal decomposition of the
task of induction. In this paper, possible extension of the RMDL principle in
the context of universal intelligence agents is considered, for which
introduction of representations is shown to be an unavoidable meta-heuristic
and a step toward efficient general intelligence. Hierarchical representations
and model optimization with the use of information-theoretic interpretation of
the adaptive resonance are also discussed.
","Extending Universal Intelligence Models with Formal Notion of
  Representation"
"  In this paper the theory of flexibly-bounded rationality which is an
extension to the theory of bounded rationality is revisited. Rational decision
making involves using information which is almost always imperfect and
incomplete together with some intelligent machine which if it is a human being
is inconsistent to make decisions. In bounded rationality, this decision is
made irrespective of the fact that the information to be used is incomplete and
imperfect and that the human brain is inconsistent and thus this decision that
is to be made is taken within the bounds of these limitations. In the theory of
flexibly-bounded rationality, advanced information analysis is used, the
correlation machine is applied to complete missing information and artificial
intelligence is used to make more consistent decisions. Therefore
flexibly-bounded rationality expands the bounds within which rationality is
exercised. Because human decision making is essentially irrational, this paper
proposes the theory of marginalization of irrationality in decision making to
deal with the problem of satisficing in the presence of irrationality.
","Flexibly-bounded Rationality and Marginalization of Irrationality
  Theories for Decision Making"
"  In recent studies of political decision-making, apparently anomalous behavior
has been observed on the part of voters, in which negative information about a
candidate strengthens, rather than weakens, a prior positive opinion about the
candidate. This behavior appears to run counter to rational models of decision
making, and it is sometimes interpreted as evidence of non-rational ""motivated
reasoning"". We consider scenarios in which this effect arises in a model of
rational decision making which includes the possibility of deceptive
information. In particular, we will consider a model in which there are two
classes of voters, which we will call trusting voters and suspicious voters,
and two types of information sources, which we will call unbiased sources and
biased sources. In our model, new data about a candidate can be efficiently
incorporated by a trusting voter, and anomalous updates are impossible;
however, anomalous updates can be made by suspicious voters, if the information
source mistakenly plans for an audience of trusting voters, and if the partisan
goals of the information source are known by the suspicious voter to be
""opposite"" to his own. Our model is based on a formalism introduced by the
artificial intelligence community called ""multi-agent influence diagrams"",
which generalize Bayesian networks to settings involving multiple agents with
distinct goals.
","The Effect of Biased Communications On Both Trusting and Suspicious
  Voters"
"  In this paper I present a new approach for regression of time series using
their own samples. This is a celebrated problem known as Auto-Regression.
Dealing with outlier or missed samples in a time series makes the problem of
estimation difficult, so it should be robust against them. Moreover for coding
purposes I will show that it is desired the residual of auto-regression be
sparse. To these aims, I first assume a multivariate Gaussian prior on the
residual and then obtain the estimation. Two simple simulations have been done
on spectrum estimation and speech coding.
",Sparse Auto-Regressive: Robust Estimation of AR Parameters
"  One of our long term research goals is to develop systems to answer realistic
questions (e.g., some mentioned in textbooks) about biological pathways that a
biologist may ask. To answer such questions we need formalisms that can model
pathways, simulate their execution, model intervention to those pathways, and
compare simulations under different circumstances. We found Petri Nets to be
the starting point of a suitable formalism for the modeling and simulation
needs. However, we need to make extensions to the Petri Net model and also
reason with multiple simulation runs and parallel state evolutions. Towards
that end Answer Set Programming (ASP) implementation of Petri Nets would allow
us to do both. In this paper we show how ASP can be used to encode basic Petri
Nets in an intuitive manner. We then show how we can modify this encoding to
model several Petri Net extensions by making small changes. We then highlight
some of the reasoning capabilities that we will use to accomplish our ultimate
research goal.
","Encoding Petri Nets in Answer Set Programming for Simulation Based
  Reasoning"
"  Answering realistic questions about biological systems and pathways similar
to the ones used by text books to test understanding of students about
biological systems is one of our long term research goals. Often these
questions require simulation based reasoning. To answer such questions, we need
formalisms to build pathway models, add extensions, simulate, and reason with
them. We chose Petri Nets and Answer Set Programming (ASP) as suitable
formalisms, since Petri Net models are similar to biological pathway diagrams;
and ASP provides easy extension and strong reasoning abilities. We found that
certain aspects of biological pathways, such as locations and substance types,
cannot be represented succinctly using regular Petri Nets. As a result, we need
higher level constructs like colored tokens. In this paper, we show how Petri
Nets with colored tokens can be encoded in ASP in an intuitive manner, how
additional Petri Net extensions can be added by making small code changes, and
how this work furthers our long term research goals. Our approach can be
adapted to other domains with similar modeling needs.
",Encoding Higher Level Extensions of Petri Nets in Answer Set Programming
"  Logic programs under the stable model semantics, or answer-set programs,
provide an expressive rule-based knowledge representation framework, featuring
a formal, declarative and well-understood semantics. However, handling the
evolution of rule bases is still a largely open problem. The AGM framework for
belief change was shown to give inappropriate results when directly applied to
logic programs under a non-monotonic semantics such as the stable models. The
approaches to address this issue, developed so far, proposed update semantics
based on manipulating the syntactic structure of programs and rules.
  More recently, AGM revision has been successfully applied to a significantly
more expressive semantic characterisation of logic programs based on SE-models.
This is an important step, as it changes the focus from the evolution of a
syntactic representation of a rule base to the evolution of its semantic
content.
  In this paper, we borrow results from the area of belief update to tackle the
problem of updating (instead of revising) answer-set programs. We prove a
representation theorem which makes it possible to constructively define any
operator satisfying a set of postulates derived from Katsuno and Mendelzon's
postulates for belief update. We define a specific operator based on this
theorem, examine its computational complexity and compare the behaviour of this
operator with syntactic rule update semantics from the literature. Perhaps
surprisingly, we uncover a serious drawback of all rule update operators based
on Katsuno and Mendelzon's approach to update and on SE-models.
",The Rise and Fall of Semantic Rule Updates Based on SE-Models
"  This article is an overview of the ""SP theory of intelligence"". The theory
aims to simplify and integrate concepts across artificial intelligence,
mainstream computing and human perception and cognition, with information
compression as a unifying theme. It is conceived as a brain-like system that
receives 'New' information and stores some or all of it in compressed form as
'Old' information. It is realised in the form of a computer model -- a first
version of the SP machine. The concept of ""multiple alignment"" is a powerful
central idea. Using heuristic techniques, the system builds multiple alignments
that are 'good' in terms of information compression. For each multiple
alignment, probabilities may be calculated. These provide the basis for
calculating the probabilities of inferences. The system learns new structures
from partial matches between patterns. Using heuristic techniques, the system
searches for sets of structures that are 'good' in terms of information
compression. These are normally ones that people judge to be 'natural', in
accordance with the 'DONSVIC' principle -- the discovery of natural structures
via information compression. The SP theory may be applied in several areas
including 'computing', aspects of mathematics and logic, representation of
knowledge, natural language processing, pattern recognition, several kinds of
reasoning, information storage and retrieval, planning and problem solving,
information compression, neuroscience, and human perception and cognition.
Examples include the parsing and production of language including discontinuous
dependencies in syntax, pattern recognition at multiple levels of abstraction
and its integration with part-whole relations, nonmonotonic reasoning and
reasoning with default values, reasoning in Bayesian networks including
'explaining away', causal diagnosis, and the solving of a geometric analogy
problem.
",The SP theory of intelligence: an overview
"  This article is about how the ""SP theory of intelligence"" and its realisation
in the ""SP machine"" may, with advantage, be applied to the management and
analysis of big data. The SP system -- introduced in the article and fully
described elsewhere -- may help to overcome the problem of variety in big data:
it has potential as ""a universal framework for the representation and
processing of diverse kinds of knowledge"" (UFK), helping to reduce the
diversity of formalisms and formats for knowledge and the different ways in
which they are processed. It has strengths in the unsupervised learning or
discovery of structure in data, in pattern recognition, in the parsing and
production of natural language, in several kinds of reasoning, and more. It
lends itself to the analysis of streaming data, helping to overcome the problem
of velocity in big data. Central in the workings of the system is lossless
compression of information: making big data smaller and reducing problems of
storage and management. There is potential for substantial economies in the
transmission of data, for big cuts in the use of energy in computing, for
faster processing, and for smaller and lighter computers. The system provides a
handle on the problem of veracity in big data, with potential to assist in the
management of errors and uncertainties in data. It lends itself to the
visualisation of knowledge structures and inferential processes. A
high-parallel, open-source version of the SP machine would provide a means for
researchers everywhere to explore what can be done with the system and to
create new versions of it.
",Big data and the SP theory of intelligence
"  The broader goal of our research is to formulate answers to why and how
questions with respect to knowledge bases, such as AURA. One issue we face when
reasoning with many available knowledge bases is that at times needed
information is missing. Examples of this include partially missing information
about next sub-event, first sub-event, last sub-event, result of an event,
input to an event, destination of an event, and raw material involved in an
event. In many cases one can recover part of the missing knowledge through
reasoning. In this paper we give a formal definition about how such missing
information can be recovered and then give an ASP implementation of it. We then
discuss the implication of this with respect to answering why and how
questions.
","Event-Object Reasoning with Curated Knowledge Bases: Deriving Missing
  Information"
"  Nogood learning is a powerful approach to reducing search in Constraint
Programming (CP) solvers. The current state of the art, called Lazy Clause
Generation (LCG), uses resolution to derive nogoods expressing the reasons for
each search failure. Such nogoods can prune other parts of the search tree,
producing exponential speedups on a wide variety of problems. Nogood learning
solvers can be seen as resolution proof systems. The stronger the proof system,
the faster it can solve a CP problem. It has recently been shown that the proof
system used in LCG is at least as strong as general resolution. However,
stronger proof systems such as \emph{extended resolution} exist. Extended
resolution allows for literals expressing arbitrary logical concepts over
existing variables to be introduced and can allow exponentially smaller proofs
than general resolution. The primary problem in using extended resolution is to
figure out exactly which literals are useful to introduce. In this paper, we
show that we can use the structural information contained in a CP model in
order to introduce useful literals, and that this can translate into
significant speedups on a range of problems.
",Structure Based Extended Resolution for Constraint Programming
"  In real-time strategy games like StarCraft, skilled players often block the
entrance to their base with buildings to prevent the opponent's units from
getting inside. This technique, called ""walling-in"", is a vital part of
player's skill set, allowing him to survive early aggression. However, current
artificial players (bots) do not possess this skill, due to numerous
inconveniences surfacing during its implementation in imperative languages like
C++ or Java. In this text, written as a guide for bot programmers, we address
the problem of finding an appropriate building placement that would block the
entrance to player's base, and present a ready to use declarative solution
employing the paradigm of answer set programming (ASP). We also encourage the
readers to experiment with different declarative approaches to this problem.
","Implementing a Wall-In Building Placement in StarCraft with Declarative
  Programming"
"  We propose a decomposition of the max-min fair curriculum-based course
timetabling (MMF-CB-CTT) problem. The decomposition models the room assignment
subproblem as a generalized lexicographic bottleneck optimization problem
(LBOP). We show that the generalized LBOP can be solved efficiently if the
corresponding sum optimization problem can be solved efficiently. As a
consequence, the room assignment subproblem of the MMF-CB-CTT problem can be
solved efficiently. We use this insight to improve a previously proposed
heuristic algorithm for the MMF-CB-CTT problem. Our experimental results
indicate that using the new decomposition improves the performance of the
algorithm on most of the 21 ITC2007 test instances with respect to the quality
of the best solution found. Furthermore, we introduce a measure of the quality
of a solution to a max-min fair optimization problem. This measure helps to
overcome some limitations imposed by the qualitative nature of max-min fairness
and aids the statistical evaluation of the performance of randomized algorithms
for such problems. We use this measure to show that using the new decomposition
the algorithm outperforms the original one on most instances with respect to
the average solution quality.
","A Decomposition of the Max-min Fair Curriculum-based Course Timetabling
  Problem"
"  In recent years, portfolio approaches to solving SAT problems and CSPs have
become increasingly common. There are also a number of different encodings for
representing CSPs as SAT instances. In this paper, we leverage advances in both
SAT and CSP solving to present a novel hierarchical portfolio-based approach to
CSP solving, which we call Proteus, that does not rely purely on CSP solvers.
Instead, it may decide that it is best to encode a CSP problem instance into
SAT, selecting an appropriate encoding and a corresponding SAT solver. Our
experimental evaluation used an instance of Proteus that involved four CSP
solvers, three SAT encodings, and six SAT solvers, evaluated on the most
challenging problem instances from the CSP solver competitions, involving
global and intensional constraints. We show that significant performance
improvements can be achieved by Proteus obtained by exploiting alternative
view-points and solvers for combinatorial problem-solving.
",Proteus: A Hierarchical Portfolio of Solvers and Transformations
"  Determination of dietary food consumed a day for patients with diseases in
general, greatly affect the health of the body and the healing process, is no
exception for people with kidney disease and urinary tract. This paper presents
the determination of diet composition in the form of food subtance for people
with kidney and urinary tract diseases with a genetic fuzzy approach. This
approach combines fuzzy logic and genetic algorithms, which utilizing fuzzy
logic fuzzy tools and techniques to model the components of the genetic
algorithm and adapting genetic algorithm control parameters, with the aim of
improving system performance. The Mamdani fuzzy inference model and fuzzy rules
based on population parameters and generation are used to determine the
probability of crossover and mutation, and was using In this study, 400 food
survey data along with their substances was used as test material. From the
data, a varying amount of population is established. Each chromosome has 10
genes in which the value of each gene indicates the index number of foodstuffs
in the database. The fuzzy genetic approach produces 10 best food substance and
their compositions. The composition of these foods has nutritional value in
accordance with the number of calories needed by people with kidney and urinary
tract diseases by type of food.
","Computation of Diet Composition for Patients Suffering from Kidney and
  Urinary Tract Diseases with the Fuzzy Genetic System"
"  A conceptual area is divided into units or barangays, each was allowed to
evolve under a physical constraint. A risk assessment method was then used to
identify the flood risk in each community using the following risk factors: the
area's urbanized area ratio, literacy rate, mortality rate, poverty incidence,
radio/TV penetration, and state of structural and non-structural measures.
Vulnerability is defined as a weighted-sum of these components. A penalty was
imposed for reduced vulnerability. Optimization comparison was done with
MatLab's Genetic Algorithms and Simulated Annealing; results showed 'extreme'
solutions and realistic designs, for simulated annealing and genetic algorithm,
respectively.
",Metaheuristics in Flood Disaster Management and Risk Assessment
"  As the education fees are becoming more expensive, more students apply for
scholarships. Consequently, hundreds and even thousands of applications need to
be handled by the sponsor. To solve the problems, some alternatives based on
several attributes (criteria) need to be selected. In order to make a decision
on such fuzzy problems, Fuzzy Multiple Attribute Decision Making (FMDAM) can be
applied. In this study, Unified Modeling Language (UML) in FMADM with TOPSIS
and Weighted Product (WP) methods is applied to select the candidates for
academic and non-academic scholarships at Universitas Islam Negeri Sunan
Kalijaga. Data used were a crisp and fuzzy data. The results show that TOPSIS
and Weighted Product FMADM methods can be used to select the most suitable
candidates to receive the scholarships since the preference values applied in
this method can show applicants with the highest eligibility
","A Fuzzy Topsis Multiple-Attribute Decision Making for Scholarship
  Selection"
"  Pairwise comparisons are a well-known method for the representation of the
subjective preferences of a decision maker. Evaluating their inconsistency has
been a widely studied and discussed topic and several indices have been
proposed in the literature to perform this task. Since an acceptable level of
consistency is closely related with the reliability of preferences, a suitable
choice of an inconsistency index is a crucial phase in decision making
processes. The use of different methods for measuring consistency must be
carefully evaluated, as it can affect the decision outcome in practical
applications. In this paper, we present five axioms aimed at characterizing
inconsistency indices. In addition, we prove that some of the indices proposed
in the literature satisfy these axioms, while others do not, and therefore, in
our view, they may fail to correctly evaluate inconsistency.
",Axiomatic properties of inconsistency indices for pairwise comparisons
"  This work uses the L-system to construct a tree structure for the text
sequence and derives its complexity. It serves as a measure of structural
complexity of the text. It is applied to anomaly detection in data
transmission.
",Syntactic sensitive complexity for symbol-free sequence
"  This article describes existing and expected benefits of the ""SP theory of
intelligence"", and some potential applications. The theory aims to simplify and
integrate ideas across artificial intelligence, mainstream computing, and human
perception and cognition, with information compression as a unifying theme. It
combines conceptual simplicity with descriptive and explanatory power across
several areas of computing and cognition. In the ""SP machine"" -- an expression
of the SP theory which is currently realized in the form of a computer model --
there is potential for an overall simplification of computing systems,
including software. The SP theory promises deeper insights and better solutions
in several areas of application including, most notably, unsupervised learning,
natural language processing, autonomous robots, computer vision, intelligent
databases, software engineering, information compression, medical diagnosis and
big data. There is also potential in areas such as the semantic web,
bioinformatics, structuring of documents, the detection of computer viruses,
data fusion, new kinds of computer, and the development of scientific theories.
The theory promises seamless integration of structures and functions within and
between different areas of application. The potential value, worldwide, of
these benefits and applications is at least $190 billion each year. Further
development would be facilitated by the creation of a high-parallel,
open-source version of the SP machine, available to researchers everywhere.
",The SP theory of intelligence: benefits and applications
"  A famous biologically inspired hierarchical model firstly proposed by
Riesenhuber and Poggio has been successfully applied to multiple visual
recognition tasks. The model is able to achieve a set of position- and
scale-tolerant recognition, which is a central problem in pattern recognition.
In this paper, based on some other biological experimental results, we
introduce the Memory and Association Mechanisms into the above biologically
inspired model. The main motivations of the work are (a) to mimic the active
memory and association mechanism and add the 'top down' adjustment to the above
biologically inspired hierarchical model and (b) to build up an algorithm which
can save the space and keep a good recognition performance. The new model is
also applied to object recognition processes. The primary experimental results
show that our method is efficient with much less memory requirement.
","Introducing Memory and Association Mechanism into a Biologically
  Inspired Visual Model"
"  In this paper we present an interface between a symbolic planner and a
geometric task planner, which is different to a standard trajectory planner in
that the former is able to perform geometric reasoning on abstract
entities---tasks. We believe that this approach facilitates a more principled
interface to symbolic planning, while also leaving more room for the geometric
planner to make independent decisions. We show how the two planners could be
interfaced, and how their planning and backtracking could be interleaved. We
also provide insights for a methodology for using the combined system, and
experimental results to use as a benchmark with future extensions to both the
combined system, as well as to the geometric task planner.
",Towards Combining HTN Planning and Geometric Task Planning
"  Ontologies provide a formal description of concepts and their relationships
in a knowledge domain. The goal of ontology alignment is to identify
semantically matching concepts and relationships across independently developed
ontologies that purport to describe the same knowledge. In order to handle the
widest possible class of ontologies, many alignment algorithms rely on
terminological and structural meth- ods, but the often fuzzy nature of concepts
complicates the matching process. However, one area that should provide clear
matching solutions due to its mathematical nature, is units of measurement.
Several on- tologies for units of measurement are available, but there has been
no attempt to align them, notwithstanding the obvious importance for tech-
nical interoperability. We propose a general strategy to map these (and
similar) ontologies by introducing MathML to accurately capture the semantic
description of concepts specified therein. We provide mapping results for three
ontologies, and show that our approach improves on lexical comparisons.
","Using MathML to Represent Units of Measurement for Improved Ontology
  Alignment"
"  A wide range of problems can be modelled as constraint satisfaction problems
(CSPs), that is, a set of constraints that must be satisfied simultaneously.
Constraints can either be represented extensionally, by explicitly listing
allowed combinations of values, or implicitly, by special-purpose algorithms
provided by a solver. Such implicitly represented constraints, known as global
constraints, are widely used; indeed, they are one of the key reasons for the
success of constraint programming in solving real-world problems.
  In recent years, a variety of restrictions on the structure of CSP instances
that yield tractable classes have been identified. However, many such
restrictions fail to guarantee tractability for CSPs with global constraints.
In this paper, we investigate the properties of extensionally represented
constraints that these restrictions exploit to achieve tractability, and show
that there are large classes of global constraints that also possess these
properties. This allows us to lift these restrictions to the global case, and
identify new tractable classes of CSPs with global constraints.
",Lifting Structural Tractability to CSP with Global Constraints
"  Fuzzy Set Theory has been applied in many fields such as Operations Research,
Control Theory, and Management Sciences etc. In particular, an application of
this theory in Managerial Decision Making Problems has a remarkable
significance. In this Paper, we consider a solution of Rectangular Fuzzy game
with pay-off as imprecise numbers instead of crisp numbers viz., interval and
LR-type Trapezoidal Fuzzy Numbers. The solution of such Fuzzy games with pure
strategies by minimax-maximin principle is discussed. The Algebraic Method to
solve Fuzzy games without saddle point by using mixed strategies is also
illustrated. Here, pay-off matrix is reduced to pay-off matrix by Dominance
Method. This fact is illustrated by means of Numerical Example.
","Solution of Rectangular Fuzzy Games by Principle of Dominance Using
  LR-type Trapezoidal Fuzzy Numbers"
"  Transportation Problem is an important aspect which has been widely studied
in Operations Research domain. It has been studied to simulate different real
life problems. In particular, application of this Problem in NP- Hard Problems
has a remarkable significance. In this Paper, we present a comparative study of
Transportation Problem through Probabilistic and Fuzzy Uncertainties. Fuzzy
Logic is a computational paradigm that generalizes classical two-valued logic
for reasoning under uncertainty. In order to achieve this, the notation of
membership in a set needs to become a matter of degree. By doing this we
accomplish two things viz., (i) ease of describing human knowledge involving
vague concepts and (ii) enhanced ability to develop cost-effective solution to
real-world problem. The multi-valued nature of Fuzzy Sets allows handling
uncertain and vague information. It is a model-less approach and a clever
disguise of Probability Theory. We give comparative simulation results of both
approaches and discuss the Computational Complexity. To the best of our
knowledge, this is the first work on comparative study of Transportation
Problem using Probabilistic and Fuzzy Uncertainties.
","A Comparative study of Transportation Problem under Probabilistic and
  Fuzzy Uncertainties"
"  Transportation Problem is an important problem which has been widely studied
in Operations Research domain. It has been often used to simulate different
real life problems. In particular, application of this Problem in NP Hard
Problems has a remarkable significance. In this Paper, we present the closed,
bounded and non empty feasible region of the transportation problem using fuzzy
trapezoidal numbers which ensures the existence of an optimal solution to the
balanced transportation problem. The multivalued nature of Fuzzy Sets allows
handling of uncertainty and vagueness involved in the cost values of each cells
in the transportation table. For finding the initial solution of the
transportation problem we use the Fuzzy Vogel Approximation Method and for
determining the optimality of the obtained solution Fuzzy Modified Distribution
Method is used. The fuzzification of the cost of the transportation problem is
discussed with the help of a numerical example. Finally, we discuss the
computational complexity involved in the problem. To the best of our knowledge,
this is the first work on obtaining the solution of the transportation problem
using fuzzy trapezoidal numbers.
",Trapezoidal Fuzzy Numbers for the Transportation Problem
"  In India financial markets have existed for many years. A functionally
accented, diverse, efficient and flexible financial system is vital to the
national objective of creating a market driven, productive and competitive
economy. Today markets of varying maturity exist in equity, debt, commodities
and foreign exchange. In this work we attempt to generate prediction rules
scheme for stock price movement at Bombay Stock Exchange using an important
Soft Computing paradigm viz., Rough Fuzzy Multi Layer Perception. The use of
Computational Intelligence Systems such as Neural Networks, Fuzzy Sets, Genetic
Algorithms, etc. for Stock Market Predictions has been widely established. The
process is to extract knowledge in the form of rules from daily stock
movements. These rules can then be used to guide investors. To increase the
efficiency of the prediction process, Rough Sets is used to discretize the
data. The methodology uses a Genetic Algorithm to obtain a structured network
suitable for both classification and rule extraction. The modular concept,
based on divide and conquer strategy, provides accelerated training and a
compact network suitable for generating a minimum number of rules with high
certainty values. The concept of variable mutation operator is introduced for
preserving the localized structure of the constituting Knowledge Based
sub-networks, while they are integrated and evolved. Rough Set Dependency Rules
are generated directly from the real valued attribute table containing Fuzzy
membership values. The paradigm is thus used to develop a rule extraction
algorithm. The extracted rules are compared with some of the related rule
extraction techniques on the basis of some quantitative performance indices.
The proposed methodology extracts rules which are less in number, are accurate,
have high certainty factor and have low confusion with less computation time.
","Discovering Stock Price Prediction Rules of Bombay Stock Exchange Using
  Rough Fuzzy Multi Layer Perception Networks"
"  ETP is NP Hard combinatorial optimization problem. It has received tremendous
research attention during the past few years given its wide use in
universities. In this Paper, we develop three mathematical models for NSOU,
Kolkata, India using FILP technique. To deal with impreciseness and vagueness
we model various allocation variables through fuzzy numbers. The solution to
the problem is obtained using Fuzzy number ranking method. Each feasible
solution has fuzzy number obtained by Fuzzy objective function. The different
FILP technique performance are demonstrated by experimental data generated
through extensive simulation from NSOU, Kolkata, India in terms of its
execution times. The proposed FILP models are compared with commonly used
heuristic viz. ILP approach on experimental data which gives an idea about
quality of heuristic. The techniques are also compared with different
Artificial Intelligence based heuristics for ETP with respect to best and mean
cost as well as execution time measures on Carter benchmark datasets to
illustrate its effectiveness. FILP takes an appreciable amount of time to
generate satisfactory solution in comparison to other heuristics. The
formulation thus serves as good benchmark for other heuristics. The
experimental study presented here focuses on producing a methodology that
generalizes well over spectrum of techniques that generates significant results
for one or more datasets. The performance of FILP model is finally compared to
the best results cited in literature for Carter benchmarks to assess its
potential. The problem can be further reduced by formulating with lesser number
of allocation variables it without affecting optimality of solution obtained.
FLIP model for ETP can also be adapted to solve other ETP as well as
combinatorial optimization problems.
","Fuzzy Integer Linear Programming Mathematical Models for Examination
  Timetable Problem"
"  Fuzzy regression models have been applied to several Operations Research
applications viz., forecasting and prediction. Earlier works on fuzzy
regression analysis obtain crisp regression coefficients for eliminating the
problem of increasing spreads for the estimated fuzzy responses as the
magnitude of the independent variable increases. But they cannot deal with the
problem of non-uniform spreads. In this work, a three-phase approach is
discussed to construct the fuzzy regression model with non-uniform spreads to
deal with this problem. The first phase constructs the membership functions of
the least-squares estimates of regression coefficients based on extension
principle to completely conserve the fuzziness of observations. They are then
defuzzified by the centre of area method to obtain crisp regression
coefficients in the second phase. Finally, the error terms of the method are
determined by setting each estimated spread equal to its corresponding observed
spread. The Tagaki-Sugeno inference system is used for improving the accuracy
of forecasts. The simulation example demonstrates the strength of fuzzy linear
regression model in terms of higher explanatory power and forecasting
performance.
","Achieving greater Explanatory Power and Forecasting Accuracy with
  Non-uniform spread Fuzzy Linear Regression"
"  We present a dynamic algorithm for solving the Longest Common Subsequence
Problem using Ant Colony Optimization Technique. The Ant Colony Optimization
Technique has been applied to solve many problems in Optimization Theory,
Machine Learning and Telecommunication Networks etc. In particular, application
of this theory in NP-Hard Problems has a remarkable significance. Given two
strings, the traditional technique for finding Longest Common Subsequence is
based on Dynamic Programming which consists of creating a recurrence relation
and filling a table of size . The proposed algorithm draws analogy with
behavior of ant colonies function and this new computational paradigm is known
as Ant System. It is a viable new approach to Stochastic Combinatorial
Optimization. The main characteristics of this model are positive feedback,
distributed computation, and the use of constructive greedy heuristic. Positive
feedback accounts for rapid discovery of good solutions, distributed
computation avoids premature convergence and greedy heuristic helps find
acceptable solutions in minimum number of stages. We apply the proposed
methodology to Longest Common Subsequence Problem and give the simulation
results. The effectiveness of this approach is demonstrated by efficient
Computational Complexity. To the best of our knowledge, this is the first Ant
Colony Optimization Algorithm for Longest Common Subsequence Problem.
","A Dynamic Algorithm for the Longest Common Subsequence Problem using Ant
  Colony Optimization Technique"
"  Many studies in heuristic search suggest that the accuracy of the heuristic
used has a positive impact on improving the performance of the search. In
another direction, historical research perceives that the performance of
heuristic search algorithms, such as A* and IDA*, can be improved by requiring
the heuristics to be consistent -- a property satisfied by any perfect
heuristic. However, a few recent studies show that inconsistent heuristics can
also be used to achieve a large improvement in these heuristic search
algorithms. These results leave us a natural question: which property of
heuristics, accuracy or consistency/inconsistency, should we focus on when
building heuristics? While there are studies on the heuristic accuracy with the
assumption of consistency, no studies on both the inconsistency and the
accuracy of heuristics are known to our knowledge.
  In this study, we investigate the relationship between the inconsistency and
the accuracy of heuristics with A* search. Our analytical result reveals a
correlation between these two properties. We then run experiments on the domain
for the Knapsack problem with a family of practical heuristics. Our empirical
results show that in many cases, the more accurate heuristics also have higher
level of inconsistency and result in fewer node expansions by A*.
",Inconsistency and Accuracy of Heuristics with A* Search
"  In covering based rough sets, the neighborhood of an element is the
intersection of all the covering blocks containing the element. All the
neighborhoods form a new covering called a covering of neighborhoods. In the
course of studying under what condition a covering of neighborhoods is a
partition, the concept of repeat degree is proposed, with the help of which the
issue is addressed. This paper studies further the application of repeat degree
on coverings of neighborhoods. First, we investigate under what condition a
covering of neighborhoods is the reduct of the covering inducing it. As a
preparation for addressing this issue, we give a necessary and sufficient
condition for a subset of a set family to be the reduct of the set family. Then
we study under what condition two coverings induce a same relation and a same
covering of neighborhoods. Finally, we give the method of calculating the
covering according to repeat degree.
",Applications of repeat degree on coverings of neighborhoods
"  Philosophers writing about the ravens paradox often note that Nicod's
Condition (NC) holds given some set of background information, and fails to
hold against others, but rarely go any further. That is, it is usually not
explored which background information makes NC true or false. The present paper
aims to fill this gap. For us, ""(objective) background knowledge"" is restricted
to information that can be expressed as probability events. Any other
configuration is regarded as being subjective and a property of the a priori
probability distribution. We study NC in two specific settings. In the first
case, a complete description of some individuals is known, e.g. one knows of
each of a group of individuals whether they are black and whether they are
ravens. In the second case, the number of individuals having a particular
property is given, e.g. one knows how many ravens or how many black things
there are (in the relevant population). While some of the most famous answers
to the paradox are measure-dependent, our discussion is not restricted to any
particular probability measure. Our most interesting result is that in the
second setting, NC violates a simple kind of inductive inference (namely
projectability). Since relative to NC, this latter rule is more closely related
to, and more directly justified by our intuitive notion of inductive reasoning,
this tension makes a case against the plausibility of NC. In the end, we
suggest that the informal representation of NC may seem to be intuitively
plausible because it can easily be mistaken for reasoning by analogy.
","On Nicod's Condition, Rules of Induction and the Raven Paradox"
"  ExtractingMUCs(MinimalUnsatisfiableCores)fromanunsatisfiable constraint
network is a useful process when causes of unsatisfiability must be understood
so that the network can be re-engineered and relaxed to become sat- isfiable.
Despite bad worst-case computational complexity results, various MUC- finding
approaches that appear tractable for many real-life instances have been
proposed. Many of them are based on the successive identification of so-called
transition constraints. In this respect, we show how local search can be used
to possibly extract additional transition constraints at each main iteration
step. The approach is shown to outperform a technique based on a form of model
rotation imported from the SAT-related technology and that also exhibits
additional transi- tion constraints. Our extensive computational
experimentations show that this en- hancement also boosts the performance of
state-of-the-art DC(WCORE)-like MUC extractors.
",Improving MUC extraction thanks to local search
"  Complete tree search is a highly effective method for tackling MIP problems,
and over the years, a plethora of branching heuristics have been introduced to
further refine the technique for varying problems. Recently, portfolio
algorithms have taken the process a step further, trying to predict the best
heuristic for each instance at hand. However, the motivation behind algorithm
selection can be taken further still, and used to dynamically choose the most
appropriate algorithm for each encountered subproblem. In this paper we
identify a feature space that captures both the evolution of the problem in the
branching tree and the similarity among subproblems of instances from the same
MIP models. We show how to exploit these features to decide the best time to
switch the branching heuristic and then show how such a system can be trained
efficiently. Experiments on a highly heterogeneous collection of MIP instances
show significant gains over the pure algorithm selection approach that for a
given instance uses only a single heuristic throughout the search.
",DASH: Dynamic Approach for Switching Heuristics
"  Ontology Matching aims to find a set of semantic correspondences, called an
alignment, between related ontologies. In recent years, there has been a
growing interest in efficient and effective matching methods for large
ontologies. However, most of the alignments produced for large ontologies are
logically incoherent. It was only recently that the use of repair techniques to
improve the quality of ontology alignments has been explored. In this paper we
present a novel technique for detecting incoherent concepts based on ontology
modularization, and a new repair algorithm that minimizes the incoherence of
the resulting alignment and the number of matches removed from the input
alignment. An implementation was done as part of a lightweight version of
AgreementMaker system, a successful ontology matching platform, and evaluated
using a set of four benchmark biomedical ontology matching tasks. Our results
show that our implementation is efficient and produces better alignments with
respect to their coherence and f-measure than the state of the art repairing
tools. They also show that our implementation is a better alternative for
producing coherent silver standard alignments.
","Ontology alignment repair through modularization and confidence-based
  heuristics"
"  Rational agents are usually built to maximize rewards. However, AGI agents
can find undesirable ways of maximizing any prior reward function. Therefore
value learning is crucial for safe AGI. We assume that generalized states of
the world are valuable - not rewards themselves, and propose an extension of
AIXI, in which rewards are used only to bootstrap hierarchical value learning.
The modified AIXI agent is considered in the multi-agent environment, where
other agents can be either humans or other ""mature"" agents, which values should
be revealed and adopted by the ""infant"" AGI agent. General framework for
designing such empathic agent with ethical bias is proposed also as an
extension of the universal intelligence model. Moreover, we perform experiments
in the simple Markov environment, which demonstrate feasibility of our approach
to value learning in safe AGI.
",Universal Empathy and Ethical Bias for Artificial General Intelligence
"  We introduce stratified labelings as a novel semantical approach to abstract
argumentation frameworks. Compared to standard labelings, stratified labelings
provide a more fine-grained assessment of the controversiality of arguments
using ranks instead of the usual labels in, out, and undecided. We relate the
framework of stratified labelings to conditional logic and, in particular, to
the System Z ranking functions.
",Stratified Labelings for Abstract Argumentation
"  MaLeS is an automatic tuning framework for automated theorem provers. It
provides solutions for both the strategy finding as well as the strategy
scheduling problem. This paper describes the tool and the methods used in it,
and evaluates its performance on three automated theorem provers: E, LEO-II and
Satallax. An evaluation on a subset of the TPTP library problems shows that on
average a MaLeS-tuned prover solves 8.67% more problems than the prover with
its default settings.
",MaLeS: A Framework for Automatic Tuning of Automated Theorem Provers
"  Analogy has been shown to be important in many key cognitive abilities,
including learning, problem solving, creativity and language change. For
cognitive models of analogy, the fundamental computational question is how its
inherent complexity (its NP-hardness) is solved by the human cognitive system.
Indeed, different models of analogical processing can be categorized by the
simplification strategies they adopt to make this computational problem more
tractable. In this paper, I deconstruct several of these models in terms of the
simplification-strategies they use; a deconstruction that provides some
interesting perspectives on the relative differences between them. Later, I
consider whether any of these computational simplifications reflect the actual
strategies used by people and sketch a new cognitive model that tries to
present a closer fit to the psychological evidence.
",Deconstructing analogy
"  The question of the nature of space around us has occupied thinkers since the
dawn of humanity, with scientists and philosophers today implicitly assuming
that space is something that exists objectively. Here we show that this does
not have to be the case: the notion of space could emerge when biological
organisms seek an economic representation of their sensorimotor flow. The
emergence of spatial notions does not necessitate the existence of real
physical space, but only requires the presence of sensorimotor invariants
called `compensable' sensory changes. We show mathematically and then in
simulations that na\""ive agents making no assumptions about the existence of
space are able to learn these invariants and to build the abstract notion that
physicists call rigid displacement, which is independent of what is being
displaced. Rigid displacements may underly perception of space as an unchanging
medium within which objects are described by their relative positions. Our
findings suggest that the question of the nature of space, currently exclusive
to philosophy and physics, should also be addressed from the standpoint of
neuroscience and artificial intelligence.
",Space as an invention of biological organisms
"  In this paper, we propose a new methodology based on the Negative Selection
Algorithm that belongs to the field of Computational Intelligence,
specifically, Artificial Immune Systems to identify takeover targets. Although
considerable research based on customary statistical techniques and some
contemporary Computational Intelligence techniques have been devoted to
identify takeover targets, most of the existing studies are based upon multiple
previous mergers and acquisitions. Contrary to previous research, the novelty
of this proposal lies in its ability to suggest takeover targets for novice
firms that are at the beginning of their merger and acquisition spree. We first
discuss the theoretical perspective and then provide a case study with details
for practical implementation, both capitalizing from unique generalization
capabilities of artificial immune systems algorithms.
","Applying the Negative Selection Algorithm for Merger and Acquisition
  Target Identification"
"  In this paper, a new structure of cooperative learning automata so-called
extended learning automata (eDLA) is introduced. Based on the proposed
structure, a new iterative randomized heuristic algorithm for finding optimal
sub-graph in a stochastic edge-weighted graph through sampling is proposed. It
has been shown that the proposed algorithm based on new networked-structure can
be to solve the optimization problems on stochastic graph through less number
of sampling in compare to standard sampling. Stochastic graphs are graphs in
which the edges have an unknown distribution probability weights. Proposed
algorithm uses an eDLA to find a policy that leads to an induced sub-graph that
satisfies some restrictions such as minimum or maximum weight (length). At each
stage of the proposed algorithm, eDLA determines which edges to be sampled.
This eDLA-based proposed sampling method may result in decreasing unnecessary
samples and hence decreasing the time that algorithm requires for finding the
optimal sub-graph. It has been shown that proposed method converge to optimal
solution, furthermore the probability of this convergence can be made
arbitrarily close to 1 by using a sufficiently small learning rate. A new
variance-aware threshold value was proposed that can be improving significantly
convergence rate of the proposed eDLA-based algorithm. It has been shown that
the proposed algorithm is competitive in terms of the quality of the solution
","Extended Distributed Learning Automata:A New Method for Solving
  Stochastic Graph Optimization Problems"
"  Recent real-time heuristic search algorithms have demonstrated outstanding
performance in video-game pathfinding. However, their applications have been
thus far limited to that domain. We proceed with the aim of facilitating wider
applications of real-time search by fostering a greater understanding of the
performance of recent algorithms. We first introduce eight
algorithm-independent complexity measures for search spaces and correlate their
values with algorithm performance. The complexity measures are statistically
shown to be significant predictors of algorithm performance across a set of
commercial video-game maps. We then extend this analysis to a wider variety of
search spaces in the first application of database-driven real-time search to
domains outside of video-game pathfinding. In doing so, we gain insight into
algorithm performance and possible enhancement as well as into search space
complexity.
",Search-Space Characterization for Real-time Heuristic Search
"  We consider partially observable Markov decision processes (POMDPs) with
limit-average payoff, where a reward value in the interval [0,1] is associated
to every transition, and the payoff of an infinite path is the long-run average
of the rewards. We consider two types of path constraints: (i) quantitative
constraint defines the set of paths where the payoff is at least a given
threshold {\lambda} in (0, 1]; and (ii) qualitative constraint which is a
special case of quantitative constraint with {\lambda} = 1. We consider the
computation of the almost-sure winning set, where the controller needs to
ensure that the path constraint is satisfied with probability 1. Our main
results for qualitative path constraint are as follows: (i) the problem of
deciding the existence of a finite-memory controller is EXPTIME-complete; and
(ii) the problem of deciding the existence of an infinite-memory controller is
undecidable. For quantitative path constraint we show that the problem of
deciding the existence of a finite-memory controller is undecidable.
",POMDPs under Probabilistic Semantics
"  In the middle of the 1980s, David Poole introduced a semantical,
model-theoretic notion of specificity to the artificial-intelligence community.
Since then it has found further applications in non-monotonic reasoning, in
particular in defeasible reasoning. Poole tried to approximate the intuitive
human concept of specificity, which seems to be essential for reasoning in
everyday life with its partial and inconsistent information. His notion,
however, turns out to be intricate and problematic, which --- as we show ---
can be overcome to some extent by a closer approximation of the intuitive human
concept of specificity. Besides the intuitive advantages of our novel
specificity ordering over Poole's specificity relation in the classical
examples of the literature, we also report some hard mathematical facts:
Contrary to what was claimed before, we show that Poole's relation is not
transitive. The present means to decide our novel specificity relation,
however, show only a slight improvement over the known ones for Poole's
relation, and further work is needed in this aspect.
",David Poole's Specificity Revised
"  Modern SAT solvers have experienced a remarkable progress on solving
industrial instances. Most of the techniques have been developed after an
intensive experimental testing process. Recently, there have been some attempts
to analyze the structure of these formulas in terms of complex networks, with
the long-term aim of explaining the success of these SAT solving techniques,
and possibly improving them.
  We study the fractal dimension of SAT formulas, and show that most industrial
families of formulas are self-similar, with a small fractal dimension. We also
show that this dimension is not affected by the addition of learnt clauses. We
explore how the dimension of a formula, together with other graph properties
can be used to characterize SAT instances. Finally, we give empirical evidence
that these graph properties can be used in state-of-the-art portfolios.
",The Fractal Dimension of SAT Formulas
"  Similarity measures provide one of the core tools that enable reasoning about
fuzzy sets. While many types of similarity measures exist for type-1 and
interval type-2 fuzzy sets, there are very few similarity measures that enable
the comparison of general type-2 fuzzy sets. In this paper, we introduce a
general method for extending existing interval type-2 similarity measures to
similarity measures for general type-2 fuzzy sets. Specifically, we show how
similarity measures for interval type-2 fuzzy sets can be employed in
conjunction with the zSlices based general type-2 representation for fuzzy sets
to provide measures of similarity which preserve all the common properties
(i.e. reflexivity, symmetry, transitivity and overlapping) of the original
interval type-2 similarity measure. We demonstrate examples of such extended
fuzzy measures and provide comparisons between (different types of) interval
and general type-2 fuzzy measures.
","Extending Similarity Measures of Interval Type-2 Fuzzy Sets to General
  Type-2 Fuzzy Sets"
"  The measure of distance between two fuzzy sets is a fundamental tool within
fuzzy set theory. However, current distance measures within the literature do
not account for the direction of change between fuzzy sets; a useful concept in
a variety of applications, such as Computing With Words. In this paper, we
highlight this utility and introduce a distance measure which takes the
direction between sets into account. We provide details of its application for
normal and non-normal, as well as convex and non-convex fuzzy sets. We
demonstrate the new distance measure using real data from the MovieLens dataset
and establish the benefits of measuring the direction between fuzzy sets.
",Measuring the Directional Distance Between Fuzzy Sets
"  The present study gives a mathematical framework for self-evolution within
autonomous problem solving systems. Special attention is set on universal
abstraction, thereof generation by net block homomorphism, consequently
multiple order solving systems and the overall decidability of the set of the
solutions. By overlapping presentation of nets new abstraction relation among
nets is formulated alongside with consequent alphabetical net block renetting
system proportional to normal forms of renetting systems regarding the
operational power. A new structure in self-evolving problem solving is
established via saturation by groups of equivalence relations and iterative
closures of generated quotient transducer algebras over the whole evolution.
",Evolution Theory of Self-Evolving Autonomous Problem Solving Systems
"  Artifact-Centric systems have emerged in the last years as a suitable
framework to model business-relevant entities, by combining their static and
dynamic aspects. In particular, the Guard-Stage-Milestone (GSM) approach has
been recently proposed to model artifacts and their lifecycle in a declarative
way. In this paper, we enhance GSM with a Semantic Layer, constituted by a
full-fledged OWL 2 QL ontology linked to the artifact information models
through mapping specifications. The ontology provides a conceptual view of the
domain under study, and allows one to understand the evolution of the artifact
system at a higher level of abstraction. In this setting, we present a
technique to specify temporal properties expressed over the Semantic Layer, and
verify them according to the evolution in the underlying GSM model. This
technique has been implemented in a tool that exploits state-of-the-art
ontology-based data access technologies to manipulate the temporal properties
according to the ontology and the mappings, and that relies on the GSMC model
checker for verification.
","Verification of Semantically-Enhanced Artifact Systems (Extended
  Version)"
"  Recent work in psychology and experimental philosophy has shown that
judgments of actual causation are often influenced by consideration of
defaults, typicality, and normality. A number of philosophers and computer
scientists have also suggested that an appeal to such factors can help deal
with problems facing existing accounts of actual causation. This paper develops
a flexible formal framework for incorporating defaults, typicality, and
normality into an account of actual causation. The resulting account takes
actual causation to be both graded and comparative. We then show how our
account would handle a number of standard cases.
",Graded Causation and Defaults
"  Judea Pearl was the first to propose a definition of actual causation using
causal models. A number of authors have suggested that an adequate account of
actual causation must appeal not only to causal structure, but also to
considerations of normality. In earlier work, we provided a definition of
actual causation using extended causal models, which include information about
both causal structure and normality. Extended causal models are potentially
very complex. In this paper, we show how it is possible to achieve a compact
representation of extended causal models.
",Compact Representations of Extended Causal Models
"  Recently, Halpern and Leung suggested representing uncertainty by a weighted
set of probability measures, and suggested a way of making decisions based on
this representation of uncertainty: maximizing weighted regret. Their paper
does not answer an apparently simpler question: what it means, according to
this representation of uncertainty, for an event E to be more likely than an
event E'. In this paper, a notion of comparative likelihood when uncertainty is
represented by a weighted set of probability measures is defined. It
generalizes the ordering defined by probability (and by lower probability) in a
natural way; a generalization of upper probability can also be defined. A
complete axiomatic characterization of this notion of regret-based likelihood
is given.
","Weighted regret-based likelihood: a new approach to describing
  uncertainty"
"  Many multi-agent coordination problems can be represented as DCOPs. Motivated
by task allocation in disaster response, we extend standard DCOP models to
consider uncertain task rewards where the outcome of completing a task depends
on its current state, which is randomly drawn from unknown distributions. The
goal of solving this problem is to find a solution for all agents that
minimizes the overall worst-case loss. This is a challenging problem for
centralized algorithms because the search space grows exponentially with the
number of agents and is nontrivial for standard DCOP algorithms we have. To
address this, we propose a novel decentralized algorithm that incorporates
Max-Sum with iterative constraint generation to solve the problem by passing
messages among agents. By so doing, our approach scales well and can solve
instances of the task allocation problem with hundreds of agents and tasks.
",Regret-Based Multi-Agent Coordination with Uncertain Task Rewards
"  By introducing elements of information mining to tax analysis, by means of
data mining software and advanced computational concepts of artificial
intelligence, the problem of tax evader's crime against public property has
been addressed. Through an empirical approach from a hypothetical case of use,
induction algorithms, neural networks and bayesian networks are applied to
determine the feasibility of its heuristic application by the tax public
administrator. Different strategies are explored to facilitate the work of
local and regional federal tax inspectors, considering their limited
computational capabilities, but equally effective for those social scientist
committed to handcrafting tax research.
  -----
  Apresentando a introdu\c{c}\~ao de elementos de explora\c{c}\~ao de
informa\c{c}\~oes para an\'alise fiscal, por meio de software de
minera\c{c}\~ao de dados e conceitos avan\c{c}ados computacionais de
intelig\^encia artificial, foi abordado o problema do crime de sonegador fiscal
contra o patrim\^onio p\'ublico. Atrav\'es de uma abordagem emp\'irica a partir
de um caso hipot\'etico de uso, os algoritmos de indu\c{c}\~ao, redes neurais e
redes bayesianas s\~ao aplicados para determinar a viabilidade de sua
aplica\c{c}\~ao heur\'istica pelo administrador p\'ublico tribut\'ario.
Diferentes estrat\'egias s\~ao exploradas para facilitar o trabalho dos
inspectores tribut\'arios federais locais e regionais, tendo em conta as suas
capacidades computacionais limitados, mas igualmente eficaz para aqueles
cientista social comprometido com a investiga\c{c}\~ao fiscal.
","Elementos de ingenier\'ia de explotaci\'on de la informaci\'on aplicados
  a la investigaci\'on tributaria fiscal"
"  We propose a new approximate method for counting the number of the solutions
for constraint satisfaction problem (CSP). The method derives from the
partition function based on introducing the free energy and capturing the
relationship of probabilities of variables and constraints, which requires the
marginal probabilities. It firstly obtains the marginal probabilities using the
belief propagation, and then computes the number of solutions according to the
partition function. This allows us to directly plug the marginal probabilities
into the partition function and efficiently count the number of solutions for
CSP. The experimental results show that our method can solve both random
problems and structural problems efficiently.
",Approximate Counting CSP Solutions Using Partition Function
"  Composition conventions are guidelines used by human composers in composing
chess problems. They are particularly significant in composition tournaments.
Examples include, not having any check in the first move of the solution and
not dressing up the board with unnecessary pieces. Conventions are often
associated or even directly conflated with the overall aesthetics or beauty of
a composition. Using an existing experimentally-validated computational
aesthetics model for three-move mate problems, we analyzed sets of
computer-generated compositions adhering to at least 2, 3 and 4 comparable
conventions to test if simply conforming to more conventions had a positive
effect on their aesthetics, as is generally believed by human composers. We
found slight but statistically significant evidence that it does, but only to a
point. We also analyzed human judge scores of 145 three-move mate problems
composed by humans to see if they had any positive correlation with the
computational aesthetic scores of those problems. We found that they did not.
These seemingly conflicting findings suggest two main things. First, the right
amount of adherence to composition conventions in a composition has a positive
effect on its perceived aesthetics. Second, human judges either do not look at
the same conventions related to aesthetics in the model used or emphasize
others that have less to do with beauty as perceived by the majority of
players, even though they may mistakenly consider their judgements beautiful in
the traditional, non-esoteric sense. Human judges may also be relying
significantly on personal tastes as we found no correlation between their
individual scores either.
",How Relevant Are Chess Composition Conventions?
"  Although fuzzy techniques promise fast meanwhile accurate modeling and
control abilities for complicated systems, different difficulties have been
re-vealed in real situation implementations. Usually there is no escape of
it-erative optimization based on crisp domain algorithms. Recently memristor
structures appeared promising to implement neural network structures and fuzzy
algorithms. In this paper a novel adaptive real-time fuzzy modeling algorithm
is proposed which uses active learning method concept to mimic recent
understandings of right brain processing techniques. The developed method is
based on processing fuzzy numbers to provide the ability of being sensitive to
each training data point to expand the knowledge tree leading to plasticity
while used defuzzification technique guaranties enough stability. An
outstanding characteristic of the proposed algorithm is its consistency to
memristor crossbar hardware processing concepts. An analog implemen-tation of
the proposed algorithm on memristor crossbars structure is also introduced in
this paper. The effectiveness of the proposed algorithm in modeling and pattern
recognition tasks is verified by means of computer simulations
","Using memristor crossbar structure to implement a novel adaptive real
  time fuzzy modeling algorithm"
"  The school timetabling problem can be described as scheduling a set of
lessons (combination of classes, teachers, subjects and rooms) in a weekly
timetable. This paper presents a novel way to generate timetables for high
schools. The algorithm has three phases. Pre-scheduling, initial phase and
optimization through tabu search. In the first phase, a graph based algorithm
used to create groups of lessons to be scheduled simultaneously; then an
initial solution is built by a sequential greedy heuristic. Finally, the
solution is optimized using tabu search algorithm based on frequency based
diversification. The algorithm has been tested on a set of real problems
gathered from Iranian high schools. Experiments show that the proposed
algorithm can effectively build acceptable timetables.
","A tabu search algorithm with efficient diversification strategy for high
  school timetabling problem"
"  We review the theory and practice of determining what parts of a data set are
ultrametric. It is assumed that the data set, to begin with, is endowed with a
metric, and we include discussion of how this can be brought about if a
dissimilarity, only, holds. The basis for part of the metric-endowed data set
being ultrametric is to consider triplets of the observables (vectors). We
develop a novel consensus of hierarchical clusterings. We do this in order to
have a framework (including visualization and supporting interpretation) for
the parts of the data that are determined to be ultrametric. Furthermore a
major objective is to determine locally ultrametric relationships as opposed to
non-local ultrametric relationships. As part of this work, we also study a
particular property of our ultrametricity coefficient, namely, it being a
function of the difference of angles of the base angles of the isosceles
triangle. This work is completed by a review of related work, on consensus
hierarchies, and of a major new application, namely quantifying and
interpreting the emotional content of narrative.
","Ultrametric Component Analysis with Application to Analysis of Text and
  of Emotion"
"  With the objective of handling the airspace sector congestion subject to
continuously growing air traffic, we suggest to create a collaborative working
plan during the strategic phase of air traffic control. The plan obtained via a
new decision support tool presented in this article consists in a schedule for
controllers, which specifies time of overflight on the different waypoints of
the flight plans. In order to do it, we believe that the decision-support tool
shall model directly the uncertainty at a trajectory level in order to
propagate the uncertainty to the sector level. Then, the probability of
congestion for any sector in the airspace can be computed. Since air traffic
regulations and sector congestion are antagonist, we designed and implemented a
multi-objective optimization algorithm for determining the best trade-off
between these two criteria. The solution comes up as a set of alternatives for
the multi-sector planner where the severity of the congestion cost is
adjustable. In this paper, the Non-dominated Sorting Genetic Algorithm
(NSGA-II) was used to solve an artificial benchmark problem involving 24
aircraft and 11 sectors, and is able to provide a good approximation of the
Pareto front.
","Strategic Planning in Air Traffic Control as a Multi-objective
  Stochastic Optimization Problem"
"  This article addresses the issue of computing the expected cost functions
from a probabilistic model of the air traffic flow and capacity management. The
Clenshaw-Curtis quadrature is compared to Monte-Carlo algorithms defined
specifically for this problem. By tailoring the algorithms to this model, we
reduce the computational burden in order to simulate real instances. The study
shows that the Monte-Carlo algorithm is more sensible to the amount of
uncertainty in the system, but has the advantage to return a result with the
associated accuracy on demand. The performances for both approaches are
comparable for the computation of the expected cost of delay and the expected
cost of congestion. Finally, this study shows some evidences that the
simulation of the proposed probabilistic model is tractable for realistic
instances.
","Computational Methods for Probabilistic Inference of Sector Congestion
  in Air Traffic Management"
"  We investigate a method to deal with congestion of sectors and delays in the
tactical phase of air traffic flow and capacity management. It relies on
temporal objectives given for every point of the flight plans and shared among
the controllers in order to create a collaborative environment. This would
enhance the transition from the network view of the flow management to the
local view of air traffic control. Uncertainty is modeled at the trajectory
level with temporal information on the boundary points of the crossed sectors
and then, we infer the probabilistic occupancy count. Therefore, we can model
the accuracy of the trajectory prediction in the optimization process in order
to fix some safety margins. On the one hand, more accurate is our prediction;
more efficient will be the proposed solutions, because of the tighter safety
margins. On the other hand, when uncertainty is not negligible, the proposed
solutions will be more robust to disruptions. Furthermore, a multiobjective
algorithm is used to find the tradeoff between the delays and congestion, which
are antagonist in airspace with high traffic density. The flow management
position can choose manually, or automatically with a preference-based
algorithm, the adequate solution. This method is tested against two instances,
one with 10 flights and 5 sectors and one with 300 flights and 16 sectors.
","Multiobjective Tactical Planning under Uncertainty for Air Traffic Flow
  and Capacity Management"
"  This short note presents a new formal language, lambda dependency-based
compositional semantics (lambda DCS) for representing logical forms in semantic
parsing. By eliminating variables and making existential quantification
implicit, lambda DCS logical forms are generally more compact than those in
lambda calculus.
",Lambda Dependency-Based Compositional Semantics
"  This paper describes a program that solves elementary mathematical problems,
mostly in metric space theory, and presents solutions that are hard to
distinguish from solutions that might be written by human mathematicians. The
program is part of a more general project, which we also discuss.
",A fully automatic problem solver with human-style output
"  This work proposes a framework using temporal data and domain knowledge in
order to analyze complex agronomical features. The expertise is first
formalized in an ontology, under the form of concepts and relationships between
them, and then used in conjunction with raw data and mathematical models to
design a software sensor. Next the software sensor outputs are put in relation
to product quality, assessed by quantitative measurements. This requires the
use of advanced data analysis methods, such as functional regression. The
methodology is applied to a case study involving an experimental design in
French vineyards. The temporal data consist of sap flow measurements, and the
goal is to explain fruit quality (sugar concentration and weight), using vine's
water courses through the various vine phenological stages. The results are
discussed, as well as the method genericity and robustness.
","A modeling approach to design a software sensor and analyze agronomical
  features - Application to sap flow and grape quality relationship"
"  Background: Understanding the distinction between function and role is vexing
and difficult. While it appears to be useful, in practice this distinction is
hard to apply, particularly within biology.
  Results: I take an evolutionary approach, considering a series of examples,
to develop and generate definitions for these concepts. I test them in practice
against the Ontology for Biomedical Investigations (OBI). Finally, I give an
axiomatisation and discuss methods for applying these definitions in practice.
  Conclusions: The definitions in this paper are applicable, formalizing
current practice. As such, they make a significant contribution to the use of
these concepts within biomedical ontologies.
",An evolutionary approach to Function
"  We review the history of the automation of mathematical induction
",Automation of Mathematical Induction as part of the History of Logic
"  Goalkeeper (GK) is an expert in soccer and goalkeeping is a complete
professional job. In fact, achieving success seems impossible without a
reliable GK. His effect in successes and failures is more dominant than other
players. The most visible mistakes in a game are those of goalkeeper's. In this
paper the expert fuzzy system is used as a suitable tool to study the quality
of a goalkeeper and compare it with others. Previously done researches are used
to find the goalkeepers' indexes in soccer. Soccer experts have found that a
successful GK should have some qualifications. A new pattern is offered here
which is called ""Soccer goalkeeper quality recognition using fuzzy expert
systems"". This pattern has some important capabilities. Firstly, among some
goalkeepers the one with the best quality for the main team arrange can be
chosen. Secondly, the need to expert coaches for choosing a GK using their
senses and experiences decreases a lot. Thirdly, in the survey of a GK,
quantitative criteria can be included, and finally this pattern is simple and
easy to understand.
",A Fuzzy expert system for goalkeeper quality recognition
"  Reasoning about degrees of belief in uncertain dynamic worlds is fundamental
to many applications, such as robotics and planning, where actions modify state
properties and sensors provide measurements, both of which are prone to noise.
With the exception of limited cases such as Gaussian processes over linear
phenomena, belief state evolution can be complex and hard to reason with in a
general way. This paper proposes a framework with new results that allows the
reduction of subjective probabilities after sensing and acting to questions
about the initial state only. We build on an expressive probabilistic
first-order logical account by Bacchus, Halpern and Levesque, resulting in a
methodology that, in principle, can be coupled with a variety of existing
inference solutions.
",Reasoning about Probabilities in Dynamic Systems using Goal Regression
"  In order to represent the preferences of a group of individuals, we introduce
Probabilistic CP-nets (PCP-nets). PCP-nets provide a compact language for
representing probability distributions over preference orderings. We argue that
they are useful for aggregating preferences or modelling noisy preferences.
Then we give efficient algorithms for the main reasoning problems, namely for
computing the probability that a given outcome is preferred to another one, and
the probability that a given outcome is optimal. As a by-product, we obtain an
unexpected linear-time algorithm for checking dominance in a standard,
tree-structured CP-net.
",Probabilistic Conditional Preference Networks
"  Using the theory of group action, we first introduce the concept of the
automorphism group of an exponential family or a graphical model, thus
formalizing the general notion of symmetry of a probabilistic model. This
automorphism group provides a precise mathematical framework for lifted
inference in the general exponential family. Its group action partitions the
set of random variables and feature functions into equivalent classes (called
orbits) having identical marginals and expectations. Then the inference problem
is effectively reduced to that of computing marginals or expectations for each
class, thus avoiding the need to deal with each individual variable or feature.
We demonstrate the usefulness of this general framework in lifting two classes
of variational approximation for maximum a posteriori (MAP) inference: local
linear programming (LP) relaxation and local LP relaxation with cycle
constraints; the latter yields the first lifted variational inference algorithm
that operates on a bound tighter than the local constraints.
",Automorphism Groups of Graphical Models and Lifted Variational Inference
"  This paper shows that causal model discovery is not an NP-hard problem, in
the sense that for sparse graphs bounded by node degree k the sound and
complete causal model can be obtained in worst case order N^{2(k+2)}
independence tests, even when latent variables and selection bias may be
present. We present a modification of the well-known FCI algorithm that
implements the method for an independence oracle, and suggest improvements for
sample/real-world data versions. It does not contradict any known hardness
results, and does not solve an NP-hard problem: it just proves that sparse
causal discovery is perhaps more complicated, but not as hard as learning
minimal Bayesian networks.
",Learning Sparse Causal Models is not NP-hard
"  We consider the problem of learning Bayesian networks (BNs) from complete
discrete data. This problem of discrete optimisation is formulated as an
integer program (IP). We describe the various steps we have taken to allow
efficient solving of this IP. These are (i) efficient search for cutting
planes, (ii) a fast greedy algorithm to find high-scoring (perhaps not optimal)
BNs and (iii) tightening the linear relaxation of the IP. After relating this
BN learning problem to set covering and the multidimensional 0-1 knapsack
problem, we present our empirical results. These show improvements, sometimes
dramatic, over earlier results.
",Advances in Bayesian Network Learning using Integer Programming
"  Possibilistic and qualitative POMDPs (pi-POMDPs) are counterparts of POMDPs
used to model situations where the agent's initial belief or observation
probabilities are imprecise due to lack of past experiences or insufficient
data collection. However, like probabilistic POMDPs, optimally solving
pi-POMDPs is intractable: the finite belief state space exponentially grows
with the number of system's states. In this paper, a possibilistic version of
Mixed-Observable MDPs is presented to get around this issue: the complexity of
solving pi-POMDPs, some state variables of which are fully observable, can be
then dramatically reduced. A value iteration algorithm for this new formulation
under infinite horizon is next proposed and the optimality of the returned
policy (for a specified criterion) is shown assuming the existence of a ""stay""
action in some goal states. Experimental work finally shows that this
possibilistic model outperforms probabilistic POMDPs commonly used in robotics,
for a target recognition problem where the agent's observations are imprecise.
",Qualitative Possibilistic Mixed-Observable MDPs
"  Many probabilistic inference tasks involve summations over exponentially
large sets. Recently, it has been shown that these problems can be reduced to
solving a polynomial number of MAP inference queries for a model augmented with
randomly generated parity constraints. By exploiting a connection with
max-likelihood decoding of binary codes, we show that these optimizations are
computationally hard. Inspired by iterative message passing decoding
algorithms, we propose an Integer Linear Programming (ILP) formulation for the
problem, enhanced with new sparsification techniques to improve decoding
performance. By solving the ILP through a sequence of LP relaxations, we get
both lower and upper bounds on the partition function, which hold with high
probability and are much tighter than those obtained with variational methods.
","Optimization With Parity Constraints: From Binary Codes to Discrete
  Integration"
"  Popular Monte-Carlo tree search (MCTS) algorithms for online planning, such
as epsilon-greedy tree search and UCT, aim at rapidly identifying a reasonably
good action, but provide rather poor worst-case guarantees on performance
improvement over time. In contrast, a recently introduced MCTS algorithm BRUE
guarantees exponential-rate improvement over time, yet it is not geared towards
identifying reasonably good choices right at the go. We take a stand on the
individual strengths of these two classes of algorithms, and show how they can
be effectively connected. We then rationalize a principle of ""selective tree
expansion"", and suggest a concrete implementation of this principle within
MCTS. The resulting algorithm,s favorably compete with other MCTS algorithms
under short planning times, while preserving the attractive convergence
properties of BRUE.
","Monte-Carlo Planning: Theoretically Fast Convergence Meets Practical
  Efficiency"
"  In this paper, we present structured message passing (SMP), a unifying
framework for approximate inference algorithms that take advantage of
structured representations such as algebraic decision diagrams and sparse hash
tables. These representations can yield significant time and space savings over
the conventional tabular representation when the message has several identical
values (context-specific independence) or zeros (determinism) or both in its
range. Therefore, in order to fully exploit the power of structured
representations, we propose to artificially introduce context-specific
independence and determinism in the messages. This yields a new class of
powerful approximate inference algorithms which includes popular algorithms
such as cluster-graph Belief propagation (BP), expectation propagation and
particle BP as special cases. We show that our new algorithms introduce several
interesting bias-variance trade-offs. We evaluate these trade-offs empirically
and demonstrate that our new algorithms are more accurate and scalable than
state-of-the-art techniques.
",Structured Message Passing
"  We present a very general approach to learning the structure of causal models
based on d-separation constraints, obtained from any given set of overlapping
passive observational or experimental data sets. The procedure allows for both
directed cycles (feedback loops) and the presence of latent variables. Our
approach is based on a logical representation of causal pathways, which permits
the integration of quite general background knowledge, and inference is
performed using a Boolean satisfiability (SAT) solver. The procedure is
complete in that it exhausts the available information on whether any given
edge can be determined to be present or absent, and returns ""unknown""
otherwise. Many existing constraint-based causal discovery algorithms can be
seen as special cases, tailored to circumstances in which one or more
restricting assumptions apply. Simulations illustrate the effect of these
assumptions on discovery and how the present algorithm scales.
","Discovering Cyclic Causal Models with Latent Variables: A General
  SAT-Based Procedure"
"  A limited-memory influence diagram (LIMID) generalizes a traditional
influence diagram by relaxing the assumptions of regularity and no-forgetting,
allowing a wider range of decision problems to be modeled. Algorithms for
solving traditional influence diagrams are not easily generalized to solve
LIMIDs, however, and only recently have exact algorithms for solving LIMIDs
been developed. In this paper, we introduce an exact algorithm for solving
LIMIDs that is based on branch-and-bound search. Our approach is related to the
approach of solving an influence diagram by converting it to an equivalent
decision tree, with the difference that the LIMID is converted to a much
smaller decision graph that can be searched more efficiently.
",Solving Limited-Memory Influence Diagrams Using Branch-and-Bound Search
"  We introduce z-transportability, the problem of estimating the causal effect
of a set of variables X on another set of variables Y in a target domain from
experiments on any subset of controllable variables Z where Z is an arbitrary
subset of observable variables V in a source domain. z-Transportability
generalizes z-identifiability, the problem of estimating in a given domain the
causal effect of X on Y from surrogate experiments on a set of variables Z such
that Z is disjoint from X;. z-Transportability also generalizes
transportability which requires that the causal effect of X on Y in the target
domain be estimable from experiments on any subset of all observable variables
in the source domain. We first generalize z-identifiability to allow cases
where Z is not necessarily disjoint from X. Then, we establish a necessary and
sufficient condition for z-transportability in terms of generalized
z-identifiability and transportability. We provide a correct and complete
algorithm that determines whether a causal effect is z-transportable; and if it
is, produces a transport formula, that is, a recipe for estimating the causal
effect of X on Y in the target domain using information elicited from the
results of experimental manipulations of Z in the source domain and
observational data from the target domain. Our results also show that
do-calculus is complete for z-transportability.
","Causal Transportability of Experiments on Controllable Subsets of
  Variables: z-Transportability"
"  The PC algorithm learns maximally oriented causal Bayesian networks. However,
there is no equivalent complete algorithm for learning the structure of
relational models, a more expressive generalization of Bayesian networks.
Recent developments in the theory and representation of relational models
support lifted reasoning about conditional independence. This enables a
powerful constraint for orienting bivariate dependencies and forms the basis of
a new algorithm for learning structure. We present the relational causal
discovery (RCD) algorithm that learns causal relational models. We prove that
RCD is sound and complete, and we present empirical results that demonstrate
effectiveness.
","A Sound and Complete Algorithm for Learning Causal Models from
  Relational Data"
"  Exact algorithms for learning Bayesian networks guarantee to find provably
optimal networks. However, they may fail in difficult learning tasks due to
limited time or memory. In this research we adapt several anytime heuristic
search-based algorithms to learn Bayesian networks. These algorithms find
high-quality solutions quickly, and continually improve the incumbent solution
or prove its optimality before resources are exhausted. Empirical results show
that the anytime window A* algorithm usually finds higher-quality, often
optimal, networks more quickly than other approaches. The results also show
that, surprisingly, while generating networks with few parents per variable are
structurally simpler, they are harder to learn than complex generating networks
with more parents per variable.
",Evaluating Anytime Algorithms for Learning Optimal Bayesian Networks
"  Credal networks are graph-based statistical models whose parameters take
values in a set, instead of being sharply specified as in traditional
statistical models (e.g., Bayesian networks). The computational complexity of
inferences on such models depends on the irrelevance/independence concept
adopted. In this paper, we study inferential complexity under the concepts of
epistemic irrelevance and strong independence. We show that inferences under
strong independence are NP-hard even in trees with ternary variables. We prove
that under epistemic irrelevance the polynomial time complexity of inferences
in credal trees is not likely to extend to more general models (e.g. singly
connected networks). These results clearly distinguish networks that admit
efficient inferences and those where inferences are most likely hard, and
settle several open questions regarding computational complexity.
",On the Complexity of Strong and Epistemic Credal Networks
"  In many developing countries, half the population lives in rural locations,
where access to essentials such as school materials, mosquito nets, and medical
supplies is restricted. We propose an alternative method of distribution (to
standard road delivery) in which the existing mobility habits of a local
population are leveraged to deliver aid, which raises two technical challenges
in the areas optimisation and learning. For optimisation, a standard Markov
decision process applied to this problem is intractable, so we provide an exact
formulation that takes advantage of the periodicities in human location
behaviour. To learn such behaviour models from sparse data (i.e., cell tower
observations), we develop a Bayesian model of human mobility. Using real cell
tower data of the mobility behaviour of 50,000 individuals in Ivory Coast, we
find that our model outperforms the state of the art approaches in mobility
prediction by at least 25% (in held-out data likelihood). Furthermore, when
incorporating mobility prediction with our MDP approach, we find a 81.3%
reduction in total delivery time versus routine planning that minimises just
the number of participants in the solution path.
","Learning Periodic Human Behaviour Models from Sparse Data for
  Crowdsourcing Aid Delivery in Developing Countries"
"  Graphical models with High Order Potentials (HOPs) have received considerable
interest in recent years. While there are a variety of approaches to inference
in these models, nearly all of them amount to solving a linear program (LP)
relaxation with unary consistency constraints between the HOP and the
individual variables. In many cases, the resulting relaxations are loose, and
in these cases the results of inference can be poor. It is thus desirable to
look for more accurate ways of performing inference in these models. In this
work, we study the LP relaxations that result from enforcing additional
consistency constraints between the HOP and the rest of the model. We address
theoretical questions about the strength of the resulting relaxations compared
to the relaxations that arise in standard approaches, and we develop practical
and efficient message passing algorithms for optimizing the LPs. Empirically,
we show that the LPs with additional consistency constraints lead to more
accurate inference on some challenging problems that include a combination of
low order and high order terms.
",Tighter Linear Program Relaxations for High Order Graphical Models
"  We evaluate four computational models of explanation in Bayesian networks by
comparing model predictions to human judgments. In two experiments, we present
human participants with causal structures for which the models make divergent
predictions and either solicit the best explanation for an observed event
(Experiment 1) or have participants rate provided explanations for an observed
event (Experiment 2). Across two versions of two causal structures and across
both experiments, we find that the Causal Explanation Tree and Most Relevant
Explanation models provide better fits to human data than either Most Probable
Explanation or Explanation Tree models. We identify strengths and shortcomings
of these models and what they can reveal about human explanation. We conclude
by suggesting the value of pursuing computational and psychological
investigations of explanation in parallel.
",Evaluating computational models of explanation using human judgments
"  This paper is devoted to fair optimization in Multiobjective Markov Decision
Processes (MOMDPs). A MOMDP is an extension of the MDP model for planning under
uncertainty while trying to optimize several reward functions simultaneously.
This applies to multiagent problems when rewards define individual utility
functions, or in multicriteria problems when rewards refer to different
features. In this setting, we study the determination of policies leading to
Lorenz-non-dominated tradeoffs. Lorenz dominance is a refinement of Pareto
dominance that was introduced in Social Choice for the measurement of
inequalities. In this paper, we introduce methods to efficiently approximate
the sets of Lorenz-non-dominated solutions of infinite-horizon, discounted
MOMDPs. The approximations are polynomial-sized subsets of those solutions.
","Approximation of Lorenz-Optimal Solutions in Multiobjective Markov
  Decision Processes"
"  We propose solution methods for previously-unsolved constrained MDPs in which
actions can continuously modify the transition probabilities within some
acceptable sets. While many methods have been proposed to solve regular MDPs
with large state sets, there are few practical approaches for solving
constrained MDPs with large action sets. In particular, we show that the
continuous action sets can be replaced by their extreme points when the rewards
are linear in the modulation. We also develop a tractable optimization
formulation for concave reward functions and, surprisingly, also extend it to
non- concave reward functions by using their concave envelopes. We evaluate the
effectiveness of the approach on the problem of managing delinquencies in a
portfolio of loans.
","Solution Methods for Constrained Markov Decision Process with Continuous
  Probability Modulation"
"  This paper discusses {General Random Utility Models (GRUMs)}. These are a
class of parametric models that generate partial ranks over alternatives given
attributes of agents and alternatives. We propose two preference elicitation
scheme for GRUMs developed from principles in Bayesian experimental design, one
for social choice and the other for personalized choice. We couple this with a
general Monte-Carlo-Expectation-Maximization (MC-EM) based algorithm for MAP
inference under GRUMs. We also prove uni-modality of the likelihood functions
for a class of GRUMs. We examine the performance of various criteria by
experimental studies, which show that the proposed elicitation scheme increases
the precision of estimation.
",Preference Elicitation For General Random Utility Models
"  In this paper, we investigate combining blocking and collapsing -- two widely
used strategies for improving the accuracy of Gibbs sampling -- in the context
of probabilistic graphical models (PGMs). We show that combining them is not
straight-forward because collapsing (or eliminating variables) introduces new
dependencies in the PGM and in computation-limited settings, this may adversely
affect blocking. We therefore propose a principled approach for tackling this
problem. Specifically, we develop two scoring functions, one each for blocking
and collapsing, and formulate the problem of partitioning the variables in the
PGM into blocked and collapsed subsets as simultaneously maximizing both
scoring functions (i.e., a multi-objective optimization problem). We propose a
dynamic, greedy algorithm for approximately solving this intractable
optimization problem. Our dynamic algorithm periodically updates the
partitioning into blocked and collapsed variables by leveraging correlation
statistics gathered from the generated samples and enables rapid mixing by
blocking together and collapsing highly correlated variables. We demonstrate
experimentally the clear benefit of our dynamic approach: as more samples are
drawn, our dynamic approach significantly outperforms static graph-based
approaches by an order of magnitude in terms of accuracy.
",Dynamic Blocking and Collapsing for Gibbs Sampling
"  Recent advances in symbolic dynamic programming (SDP) combined with the
extended algebraic decision diagram (XADD) data structure have provided exact
solutions for mixed discrete and continuous (hybrid) MDPs with piecewise linear
dynamics and continuous actions. Since XADD-based exact solutions may grow
intractably large for many problems, we propose a bounded error compression
technique for XADDs that involves the solution of a constrained bilinear saddle
point problem. Fortuitously, we show that given the special structure of this
problem, it can be expressed as a bilevel linear programming problem and solved
to optimality in finite time via constraint generation, despite having an
infinite set of constraints. This solution permits the use of efficient linear
program solvers for XADD compression and enables a novel class of bounded
approximate SDP algorithms for hybrid MDPs that empirically offers
order-of-magnitude speedups over the exact solution in exchange for a small
approximation error.
",Bounded Approximate Symbolic Dynamic Programming for Hybrid MDPs
"  One of the main challenges in the field of embodied artificial intelligence
is the open-ended autonomous learning of complex behaviours. Our approach is to
use task-independent, information-driven intrinsic motivation(s) to support
task-dependent learning. The work presented here is a preliminary step in which
we investigate the predictive information (the mutual information of the past
and future of the sensor stream) as an intrinsic drive, ideally supporting any
kind of task acquisition. Previous experiments have shown that the predictive
information (PI) is a good candidate to support autonomous, open-ended learning
of complex behaviours, because a maximisation of the PI corresponds to an
exploration of morphology- and environment-dependent behavioural regularities.
The idea is that these regularities can then be exploited in order to solve any
given task. Three different experiments are presented and their results lead to
the conclusion that the linear combination of the one-step PI with an external
reward function is not generally recommended in an episodic policy gradient
setting. Only for hard tasks a great speed-up can be achieved at the cost of an
asymptotic performance lost.
","Linear combination of one-step predictive information with an external
  reward in an episodic policy gradient setting: a critical analysis"
"  This is the Proceedings of the Twenty-Ninth Conference on Uncertainty in
Artificial Intelligence, which was held in Bellevue, WA, August 11-15, 2013
","Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial
  Intelligence (2013)"
"  The resource constrained multi-mode multi-project scheduling problem
(RCMMMPSP) is a notoriously difficult combinatorial optimization problem. For a
given set of activities, feasible execution mode assignments and execution
starting times must be found such that some optimization function, e.g. the
makespan, is optimized. When determining an optimal (or at least feasible)
assignment of decision variable values, a set of side constraints, such as
resource availabilities, precedence constraints, etc., has to be respected.
  In 2013, the MISTA 2013 Challenge stipulated research in the RCMMMPSP. It's
goal was the solution of a given set of instances under running time
restrictions. We have contributed to this challenge with the here presented
approach.
","Iterated Variable Neighborhood Search for the resource constrained
  multi-mode multi-project scheduling problem"
"  We investigate the problem of learning the structure of a Markov network from
data. It is shown that the structure of such networks can be described in terms
of constraints which enables the use of existing solver technology with
optimization capabilities to compute optimal networks starting from initial
scores computed from the data. To achieve efficient encodings, we develop a
novel characterization of Markov network structure using a balancing condition
on the separators between cliques forming the network. The resulting
translations into propositional satisfiability and its extensions such as
maximum satisfiability, satisfiability modulo theories, and answer set
programming, enable us to prove optimal certain network structures which have
been previously found by stochastic search.
",Learning Chordal Markov Networks by Constraint Satisfaction
"  A number of well-known theorems, such as Cox's theorem and de Finetti's
theorem. prove that any model of reasoning with uncertain information that
satisfies specified conditions of ""rationality"" must satisfy the axioms of
probability theory. I argue here that these theorems do not in themselves
demonstrate that probabilistic models are in fact suitable for any specific
task in automated reasoning or plausible for cognitive models. First, the
theorems only establish that there exists some probabilistic model; they do not
establish that there exists a useful probabilistic model, i.e. one with a
tractably small number of numerical parameters and a large number of
independence assumptions. Second, there are in general many different
probabilistic models for a given situation, many of which may be far more
irrational, in the usual sense of the term, than a model that violates the
axioms of probability theory. I illustrate this second point with an extended
examples of two tasks of induction, of a similar structure, where the
reasonable probabilistic models are very different.
","The Relevance of Proofs of the Rationality of Probability Theory to
  Automated Reasoning and Cognitive Models"
"  In this paper, a new method for dynamic balancing of double four-bar crank
slider mechanism by meta- heuristic-based optimization algorithms is proposed.
For this purpose, a proper objective function which is necessary for balancing
of this mechanism and corresponding constraints has been obtained by dynamic
modeling of the mechanism. Then PSO, ABC, BGA and HGAPSO algorithms have been
applied for minimizing the defined cost function in optimization step. The
optimization results have been studied completely by extracting the cost
function, fitness, convergence speed and runtime values of applied algorithms.
It has been shown that PSO and ABC are more efficient than BGA and HGAPSO in
terms of convergence speed and result quality. Also, a laboratory scale
experimental doublefour-bar crank-slider mechanism was provided for validating
the proposed balancing method practically.
","Double four-bar crank-slider mechanism dynamic balancing by
  meta-heuristic algorithms"
"  State-of-the-art algorithms for industrial instances of MaxSAT problem rely
on iterative calls to a SAT solver. Preprocessing is crucial for the
acceleration of SAT solving, and the key preprocessing techniques rely on the
application of resolution and subsumption elimination. Additionally,
satisfiability-preserving clause elimination procedures are often used. Since
MaxSAT computation typically involves a large number of SAT calls, we are
interested in whether an input instance to a MaxSAT problem can be preprocessed
up-front, i.e. prior to running the MaxSAT solver, rather than (or, in addition
to) during each iterative SAT solver call. The key requirement in this setting
is that the preprocessing has to be sound, i.e. so that the solution can be
reconstructed correctly and efficiently after the execution of a MaxSAT
algorithm on the preprocessed instance. While, as we demonstrate in this paper,
certain clause elimination procedures are sound for MaxSAT, it is well-known
that this is not the case for resolution and subsumption elimination. In this
paper we show how to adapt these preprocessing techniques to MaxSAT. To achieve
this we recast the MaxSAT problem in a recently introduced labelled-CNF
framework, and show that within the framework the preprocessing techniques can
be applied soundly. Furthermore, we show that MaxSAT algorithms restated in the
framework have a natural implementation on top of an incremental SAT solver. We
evaluate the prototype implementation of a MaxSAT algorithm WMSU1 in this
setting, demonstrate the effectiveness of preprocessing, and show overall
improvement with respect to non-incremental versions of the algorithm on some
classes of problems.
",SAT-based Preprocessing for MaxSAT (extended version)
"  In Pawlak rough sets, the structure of the definable set families is simple
and clear, but in generalizing rough sets, the structure of the definable set
families is a bit more complex. There has been much research work focusing on
this topic. However, as a fundamental issue in relation based rough sets, under
what condition two relations induce the same definable set family has not been
discussed. In this paper, based on the concept of the closure of relations, we
present a necessary and sufficient condition for two relations to induce the
same definable set family.
","A necessary and sufficient condition for two relations to induce the
  same definable set family"
"  Combining knowledge and beliefs of autonomous peers in distributed settings,
is a ma- jor challenge. In this paper we consider peers that combine ontologies
and reason jointly with their coupled knowledge. Ontologies are within the SHIQ
fragment of Description Logics. Although there are several representation
frameworks for modular Description Log- ics, each one makes crucial assumptions
concerning the subjectivity of peers' knowledge, the relation between the
domains over which ontologies are interpreted, the expressivity of the
constructors used for combining knowledge, and the way peers share their
knowledge. However in settings where autonomous peers can evolve and extend
their knowledge and beliefs independently from others, these assumptions may
not hold. In this article, we moti- vate the need for a representation
framework that allows peers to combine their knowledge in various ways,
maintaining the subjectivity of their own knowledge and beliefs, and that
reason collaboratively, constructing a tableau that is distributed among them,
jointly. The paper presents the proposed E-SHIQ representation framework, the
implementation of the E-SHIQ distributed tableau reasoner, and discusses the
efficiency of this reasoner.
","Combining Ontologies with Correspondences and Link Relations: The E-SHIQ
  Representation Framework"
"  This paper proposes an approach for the adaptation of spatial or temporal
cases in a case-based reasoning system. Qualitative algebras are used as
spatial and temporal knowledge representation languages. The intuition behind
this adaptation approach is to apply a substitution and then repair potential
inconsistencies, thanks to belief revision on qualitative algebras. A temporal
example from the cooking domain is given. (The paper on which this extended
abstract is based was the recipient of the best paper award of the 2012
International Conference on Case-Based Reasoning.)
",Case Adaptation with Qualitative Algebras
"  We present an approach to Intelligent Tutoring Systems which adaptively
personalizes sequences of learning activities to maximize skills acquired by
students, taking into account the limited time and motivational resources. At a
given point in time, the system proposes to the students the activity which
makes them progress faster. We introduce two algorithms that rely on the
empirical estimation of the learning progress, RiARiT that uses information
about the difficulty of each exercise and ZPDES that uses much less knowledge
about the problem.
  The system is based on the combination of three approaches. First, it
leverages recent models of intrinsically motivated learning by transposing them
to active teaching, relying on empirical estimation of learning progress
provided by specific activities to particular students. Second, it uses
state-of-the-art Multi-Arm Bandit (MAB) techniques to efficiently manage the
exploration/exploitation challenge of this optimization process. Third, it
leverages expert knowledge to constrain and bootstrap initial exploration of
the MAB, while requiring only coarse guidance information of the expert and
allowing the system to deal with didactic gaps in its knowledge. The system is
evaluated in a scenario where 7-8 year old schoolchildren learn how to
decompose numbers while manipulating money. Systematic experiments are
presented with simulated students, followed by results of a user study across a
population of 400 school children.
",Multi-Armed Bandits for Intelligent Tutoring Systems
"  We tested the computational feasibility of the proposal that open-ended
cultural evolution was made possible by two cognitive transitions: (1) onset of
the capacity to chain thoughts together, followed by (2) onset of contextual
focus (CF): the capacity to shift between a divergent mode of thought conducive
to 'breaking out of a rut' and a convergent mode of thought conducive to minor
modifications. These transitions were simulated in EVOC, an agent-based model
of cultural evolution, in which the fitness of agents' actions increases as
agents invent ideas for new actions, and imitate the fittest of their
neighbors' actions. Both mean fitness and diversity of actions across the
society increased with chaining, and even more so with CF, as hypothesized. CF
was only effective when the fitness function changed, which supports its
hypothesized role in generating and refining ideas.
","A Computational Model of Two Cognitive Transitions Underlying Cultural
  Evolution"
"  This paper presents a novel SAT-based approach for the computation of
extensions in abstract argumentation, with focus on preferred semantics, and an
empirical evaluation of its performances. The approach is based on the idea of
reducing the problem of computing complete extensions to a SAT problem and then
using a depth-first search method to derive preferred extensions. The proposed
approach has been tested using two distinct SAT solvers and compared with three
state-of-the-art systems for preferred extension computation. It turns out that
the proposed approach delivers significantly better performances in the large
majority of the considered cases.
","Computing Preferred Extensions in Abstract Argumentation: a SAT-based
  Approach"
"  In this extended abstract, we carefully examine a purported counterexample to
a postulate of iterated belief revision. We suggest that the example is better
seen as a failure to apply the theory of belief revision in sufficient detail.
The main contribution is conceptual aiming at the literature on the
philosophical foundations of the AGM theory of belief revision [1]. Our
discussion is centered around the observation that it is often unclear whether
a specific example is a ""genuine"" counterexample to an abstract theory or a
misapplication of that theory to a concrete case.
",When is an Example a Counterexample?
"  The tasks of semantic web service (discovery, selection, composition, and
execution) are supposed to enable seamless interoperation between systems,
whereby human intervention is kept at a minimum. In the field of Web service
description research, the exploitation of descriptions of services through
semantics is a better support for the life-cycle of Web services. The large
number of developed ontologies, languages of representations, and integrated
frameworks supporting the discovery, composition and invocation of services is
a good indicator that research in the field of Semantic Web Services (SWS) has
been considerably active. We provide in this paper a detailed classification of
the approaches and solutions, indicating their core characteristics and
objectives required and provide indicators for the interested reader to follow
up further insights and details about these solutions and related software.
",Semantic Description of Web Services
"  Dempster-Shafer theory (D-S theory) is widely used in decision making under
the uncertain environment. Ranking basic belief assignments (BBAs) now is an
open issue. Existing evidence distance measures cannot rank the BBAs in the
situations when the propositions have their own ranking order or their inherent
measure of closeness. To address this issue, a new ranking evidence distance
(RED) measure is proposed. Compared with the existing evidence distance
measures including the Jousselme's distance and the distance between betting
commitments, the proposed RED measure is much more general due to the fact that
the order of the propositions in the systems is taken into consideration. If
there is no order or no inherent measure of closeness in the propositions, our
proposed RED measure is reduced to the existing evidence distance. Numerical
examples show that the proposed RED measure is an efficient alternative to rank
BBAs in decision making under uncertain environment.
","Ranking basic belief assignments in decision making under uncertain
  environment"
"  The aim of this work is to present a meta-heuristically approach of the
spatial assignment problem of human resources in multi-sites enterprise.
Usually, this problem consists to move employees from one site to another based
on one or more criteria. Our goal in this new approach is to improve the
quality of service and performance of all sites with maximizing an objective
function under some managers imposed constraints. The formulation presented
here of this problem coincides perfectly with a Combinatorial Optimization
Problem (COP) which is in the most cases NP-hard to solve optimally. To avoid
this difficulty, we have opted to use a meta-heuristic popular method, which is
the genetic algorithm, to solve this problem in concrete cases. The results
obtained have shown the effectiveness of our approach, which remains until now
very costly in time. But the reduction of the time can be obtained by different
ways that we plan to do in the next work.
","A Meta-heuristically Approach of the Spatial Assignment Problem of Human
  Resources in Multi-sites Enterprise"
"  This paper presents evidence for the idea that much of artificial
intelligence, human perception and cognition, mainstream computing, and
mathematics, may be understood as compression of information via the matching
and unification of patterns. This is the basis for the ""SP theory of
intelligence"", outlined in the paper and fully described elsewhere. Relevant
evidence may be seen: in empirical support for the SP theory; in some
advantages of information compression (IC) in terms of biology and engineering;
in our use of shorthands and ordinary words in language; in how we merge
successive views of any one thing; in visual recognition; in binocular vision;
in visual adaptation; in how we learn lexical and grammatical structures in
language; and in perceptual constancies. IC via the matching and unification of
patterns may be seen in both computing and mathematics: in IC via equations; in
the matching and unification of names; in the reduction or removal of
redundancy from unary numbers; in the workings of Post's Canonical System and
the transition function in the Universal Turing Machine; in the way computers
retrieve information from memory; in systems like Prolog; and in the
query-by-example technique for information retrieval. The chunking-with-codes
technique for IC may be seen in the use of named functions to avoid repetition
of computer code. The schema-plus-correction technique may be seen in functions
with parameters and in the use of classes in object-oriented programming. And
the run-length coding technique may be seen in multiplication, in division, and
in several other devices in mathematics and computing. The SP theory resolves
the apparent paradox of ""decompression by compression"". And computing and
cognition as IC is compatible with the uses of redundancy in such things as
backup copies to safeguard data and understanding speech in a noisy
environment.
","Information Compression, Intelligence, Computing, and Mathematics"
"  The introduction of covering-based rough sets has made a substantial
contribution to the classical rough sets. However, many vital problems in rough
sets, including attribution reduction, are NP-hard and therefore the algorithms
for solving them are usually greedy. Matroid, as a generalization of linear
independence in vector spaces, it has a variety of applications in many fields
such as algorithm design and combinatorial optimization. An excellent
introduction to the topic of rough matroids is due to Zhu and Wang. On the
basis of their work, we study the rough matroids based on coverings in this
paper. First, we investigate some properties of the definable sets with respect
to a covering. Specifically, it is interesting that the set of all definable
sets with respect to a covering, equipped with the binary relation of inclusion
$\subseteq$, constructs a lattice. Second, we propose the rough matroids based
on coverings, which are a generalization of the rough matroids based on
relations. Finally, some properties of rough matroids based on coverings are
explored. Moreover, an equivalent formulation of rough matroids based on
coverings is presented. These interesting and important results exhibit many
potential connections between rough sets and matroids.
",Rough matroids based on coverings
"  Nature can be seen as informational structure with computational dynamics
(info-computationalism), where an (info-computational) agent is needed for the
potential information of the world to actualize. Starting from the definition
of information as the difference in one physical system that makes a difference
in another physical system, which combines Bateson and Hewitt definitions, the
argument is advanced for natural computation as a computational model of the
dynamics of the physical world where information processing is constantly going
on, on a variety of levels of organization. This setting helps elucidating the
relationships between computation, information, agency and cognition, within
the common conceptual framework, which has special relevance for biology and
robotics.
","Information, Computation, Cognition. Agency-based Hierarchies of Levels"
"  In this paper, I put forward that in many instances, thinking mechanisms are
equivalent to artificial intelligence modules programmed into the human mind.
",Artificial Intelligence in Humans
"  In mathematics and computer science, connectivity is one of the basic
concepts of matroid theory: it asks for the minimum number of elements which
need to be removed to disconnect the remaining nodes from each other. It is
closely related to the theory of network flow problems. The connectivity of a
matroid is an important measure of its robustness as a network. Therefore, it
is very necessary to investigate the conditions under which a matroid is
connected. In this paper, the connectivity for matroids is studied through
relation-based rough sets. First, a symmetric and transitive relation is
introduced from a general matroid and its properties are explored from the
viewpoint of matroids. Moreover, through the relation introduced by a general
matroid, an undirected graph is generalized. Specifically, the connection of
the graph can be investigated by the relation-based rough sets. Second, we
study the connectivity for matroids by means of relation-based rough sets and
some conditions under which a general matroid is connected are presented.
Finally, it is easy to prove that the connectivity for a general matroid with
some special properties and its induced undirected graph is equivalent. These
results show an important application of relation-based rough sets to matroids.
",Connectivity for matroids based on rough sets
"  In this paper we discuss various problems, associated to temporal phenomena.
These problems include persistence and change, the integration of objects and
processes, and truth-makers for temporal propositions. We propose an approach
which interprets persistence as a phenomenon emanating from the activity of the
mind, and which, additionally, postulates that persistence, finally, rests on
personal identity. The General Formal Ontology (GFO) is a top level ontology
being developed at the University of Leipzig. Top level ontologies can be
roughly divided into 3D-ontologies, and 4D-ontologies. GFO is the only top
level ontology, used in applications, which is a 4D-ontology admitting
additionally 3D objects. Objects and processes are integrated in a natural way.
","Persistence, Change, and the Integration of Objects and Processes in the
  Framework of the General Formal Ontology"
"  Suuplier selection is one of the most important functions of a purchasing
department. Since by deciding the best supplier, companies can save material
costs and increase competitive advantage.However this decision becomes
compilcated in case of multiple suppliers, multiple conflicting criteria, and
imprecise parameters. In addition the uncertainty and vagueness of the experts'
opinion is the prominent characteristic of the problem. therefore an
extensively used multi criteria decision making tool Fuzzy AHP can be utilized
as an approach for supplier selection problem. This paper reveals the
application of Fuzzy AHP in a gear motor company determining the best supplier
with respect to selected criteria. the contribution of this study is not only
the application of the Fuzzy AHP methodology for supplier selection problem,
but also releasing a comprehensive literature review of multi criteria decision
making problems. In addition by stating the steps of Fuzzy AHP clearly and
numerically, this study can be a guide of the methodology to be implemented to
other multiple criteria decision making problems.
","A Fuzzy AHP Approach for Supplier Selection Problem: A Case Study in a
  Gear Motor Company"
"  The chinese room problem asks if computers can think; I ask here if most
humans can.
",A Misanthropic Reinterpretation of the Chinese Room Problem
"  *** To appear in Theory and Practice of Logic Programming (TPLP) ***
  Within the context of constraint solving, a portfolio approach allows one to
exploit the synergy between different solvers in order to create a globally
better solver. In this paper we present SUNNY: a simple and flexible algorithm
that takes advantage of a portfolio of constraint solvers in order to compute
--- without learning an explicit model --- a schedule of them for solving a
given Constraint Satisfaction Problem (CSP). Motivated by the performance
reached by SUNNY vs. different simulations of other state of the art
approaches, we developed sunny-csp, an effective portfolio solver that exploits
the underlying SUNNY algorithm in order to solve a given CSP. Empirical tests
conducted on exhaustive benchmarks of MiniZinc models show that the actual
performance of SUNNY conforms to the predictions. This is encouraging both for
improving the power of CSP portfolio solvers and for trying to export them to
fields such as Answer Set Programming and Constraint Logic Programming.
",SUNNY: a Lazy Portfolio Approach for Constraint Solving
"  In Artificial Intelligence, planning refers to an area of research that
proposes to develop systems that can automatically generate a result set, in
the form of an integrated decision-making system through a formal procedure,
known as plan. Instead of resorting to the scheduling algorithms to generate
plans, it is proposed to operate the automatic learning by decision tree to
optimize time. In this paper, we propose to build a classification model by
induction graph from a learning sample containing plans that have an associated
set of descriptors whose values change depending on each plan. This model will
then operate for classifying new cases by assigning the appropriate plan.
",Planning based on classification by induction graph
"  In this paper we consider optimization as an approach for quickly and
flexibly developing hybrid cognitive capabilities that are efficient, scalable,
and can exploit knowledge to improve solution speed and quality. In this
context, we focus on the Three-Weight Algorithm, which aims to solve general
optimization problems. We propose novel methods by which to integrate knowledge
with this algorithm to improve expressiveness, efficiency, and scaling, and
demonstrate these techniques on two example problems (Sudoku and circle
packing).
","Methods for Integrating Knowledge with the Three-Weight Optimization
  Algorithm for Hybrid Cognitive Processing"
"  The problem of aggregation is considerable importance in many disciplines. In
this paper, a new type of operator called visibility graph averaging (VGA)
aggregation operator is proposed. This proposed operator is based on the
visibility graph which can convert a time series into a graph. The weights are
obtained according to the importance of the data in the visibility graph.
Finally, the VGA operator is used in the analysis of the TAIEX database to
illustrate that it is practical and compared with the classic aggregation
operators, it shows its advantage that it not only implements the aggregation
of the data purely, but also conserves the time information, and meanwhile, the
determination of the weights is more reasonable.
",A Visibility Graph Averaging Aggregation Operator
"  The treatment of complex systems often requires the manipulation of vague,
imprecise and uncertain information. Indeed, the human being is competent in
handling of such systems in a natural way. Instead of thinking in mathematical
terms, humans describes the behavior of the system by language proposals. In
order to represent this type of information, Zadeh proposed to model the
mechanism of human thought by approximate reasoning based on linguistic
variables. He introduced the theory of fuzzy sets in 1965, which provides an
interface between language and digital worlds. In this paper, we propose a
Boolean modeling of the fuzzy reasoning that we baptized Fuzzy-BML and uses the
characteristics of induction graph classification. Fuzzy-BML is the process by
which the retrieval phase of a CBR is modelled not in the conventional form of
mathematical equations, but in the form of a database with membership functions
of fuzzy rules.
",Planning by case-based reasoning based on fuzzy logic
"  Reasoning, the most important human brain operation, is charactrized by a
degree fuzziness. In the present paper we construct a fuzzy model for the
reasoning process giving through the calculation of the possibilities of all
possible individuals' profiles a quantitative/qualitative view of their
behaviour during the above process and we use the centroid defuzzification
technique for measuring the reasoning skills. We also present a number of
classroom experiments illustrating our results in practice.
",Dealing with the Fuzziness of Human Reasoning
"  To know which operators to apply and in which order, as well as attributing
good values to their parameters is a challenge for users of computer vision.
This paper proposes a solution to this problem as a multi-agent system modeled
according to the Vowel approach and using the Q-learning algorithm to optimize
its choice. An implementation is given to test and validate this method.
",Q-learning optimization in a multi-agents system for image segmentation
"  Lifted inference algorithms exploit symmetries in probabilistic models to
speed up inference. They show impressive performance when calculating
unconditional probabilities in relational models, but often resort to
non-lifted inference when computing conditional probabilities. The reason is
that conditioning on evidence breaks many of the model's symmetries, which can
preempt standard lifting techniques. Recent theoretical results show, for
example, that conditioning on evidence which corresponds to binary relations is
#P-hard, suggesting that no lifting is to be expected in the worst case. In
this paper, we balance this negative result by identifying the Boolean rank of
the evidence as a key parameter for characterizing the complexity of
conditioning in lifted inference. In particular, we show that conditioning on
binary evidence with bounded Boolean rank is efficient. This opens up the
possibility of approximating evidence by a low-rank Boolean matrix
factorization, which we investigate both theoretically and empirically.
","On the Complexity and Approximation of Binary Evidence in Lifted
  Inference"
"  Number of web services available on Internet and its usage are increasing
very fast. In many cases, one service is not enough to complete the business
requirement; composition of web services is carried out. Autonomous composition
of web services to achieve new functionality is generating considerable
attention in semantic web domain. Development time and effort for new
applications can be reduced with service composition. Various approaches to
carry out automated composition of web services are discussed in literature.
Web service composition using ontologies is one of the effective approaches. In
this paper we demonstrate how the ontology based composition can be made faster
for each customer. We propose a framework to provide precomposed web services
to fulfil user requirements. We detail how ontology merging can be used for
composition which expedites the whole process. We discuss how framework
provides customer specific ontology merging and repository. We also elaborate
on how merging of ontologies is carried out.
",A Framework for Semi-automated Web Service Composition in Semantic Web
"  In this paper, we introduce for the first time the notions of neutrosophic
measure and neutrosophic integral, and we develop the 1995 notion of
neutrosophic probability. We present many practical examples. It is possible to
define the neutrosophic measure and consequently the neutrosophic integral and
neutrosophic probability in many ways, because there are various types of
indeterminacies, depending on the problem we need to solve. Neutrosophics study
the indeterminacy. Indeterminacy is different from randomness. It can be caused
by physical space materials and type of construction, by items involved in the
space, etc.
","Introduction to Neutrosophic Measure, Neutrosophic Integral, and
  Neutrosophic Probability"
"  Knowing whether a proposition is true means knowing that it is true or
knowing that it is false. In this paper, we study logics with a modal operator
Kw for knowing whether but without a modal operator K for knowing that. This
logic is not a normal modal logic, because we do not have Kw (phi -> psi) ->
(Kw phi -> Kw psi). Knowing whether logic cannot define many common frame
properties, and its expressive power less than that of basic modal logic over
classes of models without reflexivity. These features make axiomatizing knowing
whether logics non-trivial. We axiomatize knowing whether logic over various
frame classes. We also present an extension of knowing whether logic with
public announcement operators and we give corresponding reduction axioms for
that. We compare our work in detail to two recent similar proposals.
",Knowing Whether
"  Well-designed medical decision support system (DSS) have been shown to
improve health care quality. However, before they can be used in real clinical
situations, these systems must be extensively tested, to ensure that they
conform to the clinical guidelines (CG) on which they are based. Existing
methods cannot be used for the systematic testing of all possible test cases.
We describe here a new exhaustive dynamic verification method. In this method,
the DSS is considered to be a black box, and the Quinlan C4.5 algorithm is used
to build a decision tree from an exhaustive set of DSS input vectors and
outputs. This method was successfully used for the testing of a medical DSS
relating to chronic diseases: the ASTI critiquing module for type 2 diabetes.
","Use of the C4.5 machine learning algorithm to test a clinical
  guideline-based decision support system"
"  Clinical decision support systems have been developed to help physicians to
take clinical guidelines into account during consultations. The ASTI critiquing
module is one such systems; it provides the physician with automatic criticisms
when a drug prescription does not follow the guidelines. It was initially
developed for hypertension and type 2 diabetes, but is designed to be generic
enough for application to all chronic diseases. We present here the results of
usability and satisfaction evaluations for the ASTI critiquing module, obtained
with GPs for a newly implemented guideline concerning dyslipaemia, and we
discuss the lessons learnt and the difficulties encountered when building a
generic DSS for critiquing physicians' prescriptions.
","A generic system for critiquing physicians' prescriptions: usability,
  satisfaction and lessons learnt"
"  In many applications of computer algebra large expressions must be simplified
to make repeated numerical evaluations tractable. Previous works presented
heuristically guided improvements, e.g., for Horner schemes. The remaining
expression is then further reduced by common subexpression elimination. A
recent approach successfully applied a relatively new algorithm, Monte Carlo
Tree Search (MCTS) with UCT as the selection criterion, to find better variable
orderings. Yet, this approach is fit for further improvements since it is
sensitive to the so-called exploration-exploitation constant $C_p$ and the
number of tree updates $N$. In this paper we propose a new selection criterion
called Simulated Annealing UCT (SA-UCT) that has a dynamic
exploration-exploitation parameter, which decreases with the iteration number
$i$ and thus reduces the importance of exploration over time. First, we provide
an intuitive explanation in terms of the exploration-exploitation behavior of
the algorithm. Then, we test our algorithm on three large expressions of
different origins. We observe that SA-UCT widens the interval of good initial
values $C_p$ where best results are achieved. The improvement is large (more
than a tenfold) and facilitates the selection of an appropriate $C_p$.
","Combining Simulated Annealing and Monte Carlo Tree Search for Expression
  Simplification"
"  Case-based planning can take advantage of former problem-solving experiences
by storing in a plan library previously generated plans that can be reused to
solve similar planning problems in the future. Although comparative worst-case
complexity analyses of plan generation and reuse techniques reveal that it is
not possible to achieve provable efficiency gain of reuse over generation, we
show that the case-based planning approach can be an effective alternative to
plan generation when similar reuse candidates can be chosen.
",Case-Based Merging Techniques in OAKPLAN
"  Drawing from research on computational models of argumentation (particularly
the Carneades Argumentation System), we explore the graphical representation of
arguments in a dispute; then, comparing two different traditions on the limits
of the justification of decisions, and devising an intermediate, semi-formal,
model, we also show that it can shed light on the theory of dispute resolution.
  We conclude our paper with an observation on the usefulness of highly
constrained reasoning for Online Dispute Resolution systems. Restricting the
search space of arguments exclusively to reasons proposed by the parties
(vetoing the introduction of new arguments by the human or artificial
arbitrator) is the only way to introduce some kind of decidability -- together
with foreseeability -- in the argumentation system.
",Constraints on the search space of argumentation
"  The paper presents a suspicious email detection model which incorporates
enhanced feature selection. In the paper we proposed the use of feature
selection strategies along with classification technique for terrorists email
detection. The presented model focuses on the evaluation of machine learning
algorithms such as decision tree (ID3), logistic regression, Na\""ive Bayes
(NB), and Support Vector Machine (SVM) for detecting emails containing
suspicious content. In the literature, various algorithms achieved good
accuracy for the desired task. However, the results achieved by those
algorithms can be further improved by using appropriate feature selection
mechanisms. We have identified the use of a specific feature selection scheme
that improves the performance of the existing algorithms.
",Modeling Suspicious Email Detection using Enhanced Feature Selection
"  Traditional Artificial Cognitive Systems (for example, intelligent robots)
share a number of limitations. First, they are usually made up only of machine
components; humans are only playing the role of user or supervisor. And yet,
there are tasks in which the current state of the art of AI has much worse
performance or is more expensive than humans: thus, it would be highly
beneficial to have a systematic way of creating systems with both human and
machine components, possibly with remote non-expert humans providing
short-duration real-time services. Second, their components are often dedicated
to only one system, and underutilized for a big part of their lifetime. Third,
there is no inherent support for robust operation, and if a new better
component becomes available, one cannot easily replace the old component.
Fourth, they are viewed as a resource to be developed and owned, not as a
utility. Thus, we are presenting CLIC: a framework for constructing cognitive
systems that overcome the above limitations. The architecture of CLIC provides
specific mechanisms for creating and operating cognitive systems that fulfill a
set of desiderata: First, that are distributed yet situated, interacting with
the physical world though sensing and actuation services, and that are also
combining human as well as machine services. Second, that are made up of
components that are time-shared and re-usable. Third, that provide increased
robustness through self-repair. Fourth, that are constructed and reconstructed
on the fly, with components that dynamically enter and exit the system during
operation, on the basis of availability, pricing, and need. Importantly, fifth,
the cognitive systems created and operated by CLIC do not need to be owned and
can be provided on demand, as a utility; thus transforming human-machine
situated intelligence to a service, and opening up many interesting
opportunities.
","CLIC: A Framework for Distributed, On-Demand, Human-Machine Cognitive
  Systems"
"  This paper explores the contributions of Answer Set Programming (ASP) to the
study of an established theory from the field of Second Language Acquisition:
Input Processing. The theory describes default strategies that learners of a
second language use in extracting meaning out of a text, based on their
knowledge of the second language and their background knowledge about the
world. We formalized this theory in ASP, and as a result we were able to
determine opportunities for refining its natural language description, as well
as directions for future theory development. We applied our model to automating
the prediction of how learners of English would interpret sentences containing
the passive voice. We present a system, PIas, that uses these predictions to
assist language instructors in designing teaching materials. To appear in
Theory and Practice of Logic Programming (TPLP).
","An Application of Answer Set Programming to the Field of Second Language
  Acquisition"
"  Phishing is a common online weapon, used against users, by Phishers for
acquiring a confidential information through deception. Since the inception of
internet, nearly everything, ranging from money transaction to sharing
information, is done online in most parts of the world. This has also given
rise to malicious activities such as Phishing. Detecting Phishing is an
intricate process due to complexity, ambiguity and copious amount of
possibilities of factors responsible for phishing . Rough sets can be a
powerful tool, when working on such kind of Applications containing vague or
imprecise data. This paper proposes an approach towards Phishing Detection
Using Rough Set Theory. The Thirteen basic factors, directly responsible
towards Phishing, are grouped into four Strata. Reliability Factor is
determined on the basis of the outcome of these strata, using Rough Set Theory
. Reliability Factor determines the possibility of a suspected site to be Valid
or Fake. Using Rough set Theory most and the least influential factors towards
Phishing are also determined.
","Phishing Detection by determining reliability factor using rough set
  theory"
"  Ontology development is a non-trivial task requiring expertise in the chosen
ontological language. We propose a method for making the content of ontologies
more transparent by presenting, through the use of natural language generation,
naturalistic descriptions of ontology classes as textual paragraphs. The method
has been implemented in a proof-of- concept system, OntoVerbal, that
automatically generates paragraph-sized textual descriptions of ontological
classes expressed in OWL. OntoVerbal has been applied to ontologies that can be
loaded into Prot\'eg\'e and been evaluated with SNOMED CT, showing that it
provides coherent, well-structured and accurate textual descriptions of
ontology classes.
",OntoVerbal: a Generic Tool and Practical Application to SNOMED CT
"  This paper reviews related work and state-of-the-art publications for
recognizing motor symptoms of Parkinson's Disease (PD). It presents research
efforts that were undertaken to inform on how well traditional machine learning
algorithms can handle this task. In particular, four PD related motor symptoms
are highlighted (i.e. tremor, bradykinesia, freezing of gait and dyskinesia)
and their details summarized. Thus the primary objective of this research is to
provide a literary foundation for development and improvement of algorithms for
detecting PD related motor symptoms.
",Parkinson's Disease Motor Symptoms in Machine Learning: A Review
"  Bike sharing systems are a very popular means to provide bikes to citizens in
a simple and cheap way. The idea is to install bike stations at various points
in the city, from which a registered user can easily loan a bike by removing it
from a specialized rack. After the ride, the user may return the bike at any
station (if there is a free rack). Services of this kind are mainly public or
semi-public, often aimed at increasing the attractiveness of non-motorized
means of transportation, and are usually free, or almost free, of charge for
the users. Depending on their location, bike stations have specific patterns
regarding when they are empty or full. For instance, in cities where most jobs
are located near the city centre, the commuters cause certain peaks in the
morning: the central bike stations are filled, while the stations in the
outskirts are emptied. Furthermore, stations located on top of a hill are more
likely to be empty, since users are less keen on cycling uphill to return the
bike, and often leave their bike at a more reachable station. These issues
result in substantial user dissatisfaction which may eventually cause the users
to abandon the service. This is why nowadays most bike sharing system providers
take measures to rebalance them. Over the last few years, balancing bike
sharing systems (BBSS) has become increasingly studied in optimization. As
such, generating meaningful instance to serve as a benchmark for the proposed
approaches is an important task. In this technical report we describe the
procedure we used to generate BBSS problem instances from data of the CitiBike
NYC bike sharing system.
","Balancing bike sharing systems (BBSS): instance generation from the
  CitiBike NYC data"
"  Attribute reduction is a basic issue in knowledge representation and data
mining. Rough sets provide a theoretical foundation for the issue. Matroids
generalized from matrices have been widely used in many fields, particularly
greedy algorithm design, which plays an important role in attribute reduction.
Therefore, it is meaningful to combine matroids with rough sets to solve the
optimization problems. In this paper, we introduce an existing algebraic
structure called dependence space to study the reduction problem in terms of
matroids. First, a dependence space of matroids is constructed. Second, the
characterizations for the space such as consistent sets and reducts are studied
through matroids. Finally, we investigate matroids by the means of the space
and present two expressions for their bases. In a word, this paper provides new
approaches to study attribute reduction.
",Dependence space of matroids and its application to attribute reduction
"  The reduction of covering decision systems is an important problem in data
mining, and covering-based rough sets serve as an efficient technique to
process the problem. Geometric lattices have been widely used in many fields,
especially greedy algorithm design which plays an important role in the
reduction problems. Therefore, it is meaningful to combine coverings with
geometric lattices to solve the optimization problems. In this paper, we obtain
geometric lattices from coverings through matroids and then apply them to the
issue of attribute reduction. First, a geometric lattice structure of a
covering is constructed through transversal matroids. Then its atoms are
studied and used to describe the lattice. Second, considering that all the
closed sets of a finite matroid form a geometric lattice, we propose a
dependence space through matroids and study the attribute reduction issues of
the space, which realizes the application of geometric lattices to attribute
reduction. Furthermore, a special type of information system is taken as an
example to illustrate the application. In a word, this work points out an
interesting view, namely, geometric lattice to study the attribute reduction
issues of information systems.
","Geometric lattice structure of covering and its application to attribute
  reduction through matroids"
"  Graph theoretical ideas are highly utilized by computer science fields
especially data mining. In this field, a data structure can be designed in the
form of tree. Covering is a widely used form of data representation in data
mining and covering-based rough sets provide a systematic approach to this type
of representation. In this paper, we study the connectedness of graphs through
covering-based rough sets and apply it to connected matroids. First, we present
an approach to inducing a covering by a graph, and then study the connectedness
of the graph from the viewpoint of the covering approximation operators.
Second, we construct a graph from a matroid, and find the matroid and the graph
have the same connectedness, which makes us to use covering-based rough sets to
study connected matroids. In summary, this paper provides a new approach to
studying graph theory and matroid theory.
","Connectedness of graphs and its application to connected matroids
  through covering-based rough sets"
"  In this paper we describe a decision process framework allowing an agent to
decide what information it should reveal to its neighbours within a
communication graph in order to maximise its utility. We assume that these
neighbours can pass information onto others within the graph. The inferences
made by agents receiving the messages can have a positive or negative impact on
the information providing agent, and our decision process seeks to identify how
a message should be modified in order to be most beneficial to the information
producer. Our decision process is based on the provider's subjective beliefs
about others in the system, and therefore makes extensive use of the notion of
trust. Our core contributions are therefore the construction of a model of
information propagation; the description of the agent's decision procedure; and
an analysis of some of its properties.
",Reasoning about the Impacts of Information Sharing
"  Video games can be used as an excellent test bed for Artificial Intelligence
(AI) techniques. They are challenging and non-deterministic, this makes it very
difficult to write strong AI players. An example of such a video game is Ms.
Pac-Man. In this paper we will outline some of the previous techniques used to
build AI controllers for Ms. Pac-Man as well as presenting a new and novel
solution. My technique utilises a Cellular Automaton (CA) to build a
representation of the environment at each time step of the game. The basis of
the representation is a 2-D grid of cells. Each cell has a state and a set of
rules which determine whether or not that cell will dominate (i.e. pass its
state value onto) adjacent cells at each update. Once a certain number of
update iterations have been completed, the CA represents the state of the
environment in the game, the goals and the dangers. At this point, Ms. Pac-Man
will decide her next move based only on her adjacent cells, that is to say, she
has no knowledge of the state of the environment as a whole, she will simply
follow the strongest path. This technique shows promise and allows the
controller to achieve high scores in a live game, the behaviour it exhibits is
interesting and complex. Moreover, this behaviour is produced by using very
simple rules which are applied many times to each cell in the grid. Simple
local interactions with complex global results are truly achieved.
",A Cellular Automaton Based Controller for a Ms. Pac-Man Agent
"  BP3TKI Palembang is the government agencies that coordinate, execute and
selection of prospective migrants registration and placement. To simplify the
existing procedures and improve decision-making is necessary to build a
decision support system (DSS) to determine eligibility for employment abroad by
applying Fuzzy Multiple Attribute Decision Making (FMADM), using the linear
sequential systems development methods. The system is built using Microsoft
Visual Basic. Net 2010 and SQL Server 2008 database. The design of the system
using use case diagrams and class diagrams to identify the needs of users and
systems as well as systems implementation guidelines. This Decision Support
System able to rank and produce the prospective migrants, making it easier for
parties to take decision BP3TKI the workers who will be working out of the
country.
","Sistem pendukung keputusan kelayakan TKI ke luar negeri menggunakan
  FMADM"
"  First-order model counting emerged recently as a novel reasoning task, at the
core of efficient algorithms for probabilistic logics. We present a
Skolemization algorithm for model counting problems that eliminates existential
quantifiers from a first-order logic theory without changing its weighted model
count. For certain subsets of first-order logic, lifted model counters were
shown to run in time polynomial in the number of objects in the domain of
discourse, where propositional model counters require exponential time.
However, these guarantees apply only to Skolem normal form theories (i.e., no
existential quantifiers) as the presence of existential quantifiers reduces
lifted model counters to propositional ones. Since textbook Skolemization is
not sound for model counting, these restrictions precluded efficient model
counting for directed models, such as probabilistic logic programs, which rely
on existential quantification. Our Skolemization procedure extends the
applicability of first-order model counters to these representations. Moreover,
it simplifies the design of lifted model counting algorithms.
",Skolemization for Weighted First-Order Model Counting
"  Information discounting plays an important role in the theory of belief
functions and, generally, in information fusion. Nevertheless, neither
classical uniform discounting nor contextual cannot model certain use cases,
notably temporal discounting. In this article, new contextual discounting
schemes, conservative, proportional and optimistic, are proposed. Some
properties of these discounting operations are examined. Classical discounting
is shown to be a special case of these schemes. Two motivating cases are
discussed: modelling of source reliability and application to temporal
discounting.
","Conservative, Proportional and Optimistic Contextual Discounting in the
  Belief Functions Theory"
"  Artificial Intelligence - what is this? That is the question! In earlier
papers we already gave a formal definition for AI, but if one desires to build
an actual AI implementation, the following issues require attention and are
treated here: the data format to be used, the idea of Undef and Nothing
symbols, various ways for defining the ""meaning of life"", and finally, a new
notion of ""incorrect move"". These questions are of minor importance in the
theoretical discussion, but we already know the answer of the question ""Does AI
exist?"" Now we want to make the next step and to create this program.
",Giving the AI definition a form suitable for the engineer
"  In reinforcement learning, the goal is to seek rewards and avoid punishments.
A simple scalar captures the value of a state or of taking an action, where
expected future rewards increase and punishments decrease this quantity.
Naturally an agent should learn to predict this quantity to take beneficial
actions, and many value function approximators exist for this purpose. In the
present work, however, we show how value function approximators can cause
confusion between predictors of an outcome of one valence (e.g., a signal of
reward) and the inhibitor of the opposite valence (e.g., a signal canceling
expectation of punishment). We show this to be a problem for both linear and
non-linear value function approximators, especially when the amount of data (or
experience) is limited. We propose and evaluate a simple resolution: to instead
predict reward and punishment values separately, and rectify and add them to
get the value needed for decision making. We evaluate several function
approximators in this slightly different value function approximation
architecture and show that this approach is able to circumvent the confusion
and thereby achieve lower value-prediction errors.
","Avoiding Confusion between Predictors and Inhibitors in Value Function
  Approximation"
"  In recent years, Answer Set Programming (ASP), logic programming under the
stable model or answer set semantics, has seen several extensions by
generalizing the notion of an atom in these programs: be it aggregate atoms,
HEX atoms, generalized quantifiers, or abstract constraints, the idea is to
have more complicated satisfaction patterns in the lattice of Herbrand
interpretations than traditional, simple atoms. In this paper we refer to any
of these constructs as generalized atoms. Several semantics with differing
characteristics have been proposed for these extensions, rendering the big
picture somewhat blurry. In this paper, we analyze the class of programs that
have convex generalized atoms (originally proposed by Liu and Truszczynski in
[10]) in rule bodies and show that for this class many of the proposed
semantics coincide. This is an interesting result, since recently it has been
shown that this class is the precise complexity boundary for the FLP semantics.
We investigate whether similar results also hold for other semantics, and
discuss the implications of our findings.
",Properties of Answer Set Programming with Convex Generalized Atoms
"  Recently, researchers in answer set programming and constraint programming
spent significant efforts in the development of hybrid languages and solving
algorithms combining the strengths of these traditionally separate fields.
These efforts resulted in a new research area: constraint answer set
programming (CASP). CASP languages and systems proved to be largely successful
at providing efficient solutions to problems involving hybrid reasoning tasks,
such as scheduling problems with elements of planning. Yet, the development of
CASP systems is difficult, requiring non-trivial expertise in multiple areas.
This suggests a need for a study identifying general development principles of
hybrid systems. Once these principles and their implications are well
understood, the development of hybrid languages and systems may become a
well-established and well-understood routine process. As a step in this
direction, in this paper we conduct a case study aimed at evaluating various
integration schemas of CASP methods.
","Hybrid Automated Reasoning Tools: from Black-box to Clear-box
  Integration"
"  Encoding finite linear CSPs as Boolean formulas and solving them by using
modern SAT solvers has proven to be highly effective, as exemplified by the
award-winning sugar system. We here develop an alternative approach based on
ASP. This allows us to use first-order encodings providing us with a high
degree of flexibility for easy experimentation with different implementations.
The resulting system aspartame re-uses parts of sugar for parsing and
normalizing CSPs. The obtained set of facts is then combined with an ASP
encoding that can be grounded and solved by off-the-shelf ASP systems. We
establish the competitiveness of our approach by empirically contrasting
aspartame and sugar.
","Aspartame: Solving Constraint Satisfaction Problems with Answer Set
  Programming"
"  The distinction between strong negation and default negation has been useful
in answer set programming. We present an alternative account of strong
negation, which lets us view strong negation in terms of the functional stable
model semantics by Bartholomew and Lee. More specifically, we show that, under
complete interpretations, minimizing both positive and negative literals in the
traditional answer set semantics is essentially the same as ensuring the
uniqueness of Boolean function values under the functional stable model
semantics. The same account lets us view Lifschitz's two-valued logic programs
as a special case of the functional stable model semantics. In addition, we
show how non-Boolean intensional functions can be eliminated in favor of
Boolean intensional functions, and furthermore can be represented using strong
negation, which provides a way to compute the functional stable model semantics
using existing ASP solvers. We also note that similar results hold with the
functional stable model semantics by Cabalar.
",A Functional View of Strong Negation in Answer Set Programming
"  In this work we propose a multi-valued extension of logic programs under the
stable models semantics where each true atom in a model is associated with a
set of justifications, in a similar spirit than a set of proof trees. The main
contribution of this paper is that we capture justifications into an algebra of
truth values with three internal operations: an addition '+' representing
alternative justifications for a formula, a commutative product '*'
representing joint interaction of causes and a non-commutative product '.'
acting as a concatenation or proof constructor. Using this multi-valued
semantics, we obtain a one-to-one correspondence between the syntactic proof
tree of a standard (non-causal) logic program and the interpretation of each
true atom in a model. Furthermore, thanks to this algebraic characterization we
can detect semantic properties like redundancy and relevance of the obtained
justifications. We also identify a lattice-based characterization of this
algebra, defining a direct consequences operator, proving its continuity and
that its least fix point can be computed after a finite number of iterations.
Finally, we define the concept of causal stable model by introducing an
analogous transformation to Gelfond and Lifschitz's program reduct.
",An Algebra of Causal Chains
"  Research on developing efficient and scalable ASP solvers can substantially
benefit by the availability of data sets to experiment with. KB_Bio_101
contains knowledge from a biology textbook, has been developed as part of
Project Halo, and has recently become available for research use. KB_Bio_101 is
one of the largest KBs available in ASP and the reasoning with it is
undecidable in general. We give a description of this KB and ASP programs for a
suite of queries that have been of practical interest. We explain why these
queries pose significant practical challenges for the current ASP solvers.
","Query Answering in Object Oriented Knowledge Bases in Logic Programming:
  Description and Challenge for ASP"
"  Abstract dialectical frameworks (ADFs) are a powerful generalisation of
Dung's abstract argumentation frameworks. In this paper we present an answer
set programming based software system, called DIAMOND (DIAlectical MOdels
eNcoDing). It translates ADFs into answer set programs whose stable models
correspond to models of the ADF with respect to several semantics (i.e.
admissible, complete, stable, grounded).
",The DIAMOND System for Argumentation: Preliminary Report
"  Reactive answer set programming has paved the way for incorporating online
information into operative solving processes. Although this technology was
originally devised for dealing with data streams in dynamic environments, like
assisted living and cognitive robotics, it can likewise be used to incorporate
facts, rules, or queries provided by a user. As a result, we present the design
and implementation of a system for interactive query answering with reactive
answer set programming. Our system quontroller is based on the reactive solver
oclingo and implemented as a dedicated front-end. We describe its functionality
and implementation, and we illustrate its features by some selected use cases.
",A System for Interactive Query Answering with Answer Set Programming
"  For a finite state automaton, a synchronizing sequence is an input sequence
that takes all the states to the same state. Checking the existence of a
synchronizing sequence and finding a synchronizing sequence, if one exists, can
be performed in polynomial time. However, the problem of finding a shortest
synchronizing sequence is known to be NP-hard. In this work, the usefulness of
Answer Set Programming to solve this optimization problem is investigated, in
comparison with brute-force algorithms and SAT-based approaches.
  Keywords: finite automata, shortest synchronizing sequence, ASP
",Generating Shortest Synchronizing Sequences using Answer Set Programming
"  Integrating diverse formalisms into modular knowledge representation systems
offers increased expressivity, modeling convenience and computational benefits.
We introduce concepts of abstract modules and abstract modular systems to study
general principles behind the design and analysis of model-finding programs, or
solvers, for integrated heterogeneous multi-logic systems. We show how abstract
modules and abstract modular systems give rise to transition systems, which are
a natural and convenient representation of solvers pioneered by the SAT
community. We illustrate our approach by showing how it applies to answer set
programming and propositional logic, and to multi-logic systems based on these
two formalisms.
",Abstract Modular Systems and Solvers
"  CP-logic is a probabilistic extension of the logic FO(ID). Unlike ASP, both
of these logics adhere to a Tarskian informal semantics, in which
interpretations represent objective states-of-affairs. In other words, these
logics lack the epistemic component of ASP, in which interpretations represent
the beliefs or knowledge of a rational agent. Consequently, neither CP-logic
nor FO(ID) have the need for two kinds of negations: there is only one
negation, and its meaning is that of objective falsehood. Nevertheless, the
formal semantics of this objective negation is mathematically more similar to
ASP's negation-as-failure than to its classical negation. The reason is that
both CP-logic and FO(ID) have a constructive semantics in which all atoms start
out as false, and may only become true as the result of a rule application.
This paper investigates the possibility of adding the well-known ASP feature of
allowing negation in the head of rules to CP-logic. Because CP-logic only has
one kind of negation, it is of necessity this ''negation-as-failure like''
negation that will be allowed in the head. We investigate the intuitive meaning
of such a construct and the benefits that arise from it.
",Negation in the Head of CP-logic Rules
"  Different machine learning techniques have been proposed and used for
modeling individual and group user needs, interests and preferences. In the
traditional predictive modeling instances are described by observable
variables, called attributes. The goal is to learn a model for predicting the
target variable for unseen instances. For example, for marketing purposes a
company consider profiling a new user based on her observed web browsing
behavior, referral keywords or other relevant information. In many real world
applications the values of some attributes are not only observable, but can be
actively decided by a decision maker. Furthermore, in some of such applications
the decision maker is interested not only to generate accurate predictions, but
to maximize the probability of the desired outcome. For example, a direct
marketing manager can choose which type of a special offer to send to a client
(actionable attribute), hoping that the right choice will result in a positive
response with a higher probability. We study how to learn to choose the value
of an actionable attribute in order to maximize the probability of a desired
outcome in predictive modeling. We emphasize that not all instances are equally
sensitive to changes in actions. Accurate choice of an action is critical for
those instances, which are on the borderline (e.g. users who do not have a
strong opinion one way or the other). We formulate three supervised learning
approaches for learning to select the value of an actionable attribute at an
instance level. We also introduce a focused training procedure which puts more
emphasis on the situations where varying the action is the most likely to take
the effect. The proof of concept experimental validation on two real-world case
studies in web analytics and e-learning domains highlights the potential of the
proposed approaches.
",Predictive User Modeling with Actionable Attributes
"  A perfectly rational decision-maker chooses the best action with the highest
utility gain from a set of possible actions. The optimality principles that
describe such decision processes do not take into account the computational
costs of finding the optimal action. Bounded rational decision-making addresses
this problem by specifically trading off information-processing costs and
expected utility. Interestingly, a similar trade-off between energy and entropy
arises when describing changes in thermodynamic systems. This similarity has
been recently used to describe bounded rational agents. Crucially, this
framework assumes that the environment does not change while the decision-maker
is computing the optimal policy. When this requirement is not fulfilled, the
decision-maker will suffer inefficiencies in utility, that arise because the
current policy is optimal for an environment in the past. Here we borrow
concepts from non-equilibrium thermodynamics to quantify these inefficiencies
and illustrate with simulations its relationship with computational resources.
",Bounded Rational Decision-Making in Changing Environments
"  We have designed a machine that becomes increasingly better at behaving in
underspecified circumstances, in a goal-directed way, on the job, by modeling
itself and its environment as experience accumulates. Based on principles of
autocatalysis, endogeny, and reflectivity, the work provides an architectural
blueprint for constructing systems with high levels of operational autonomy in
underspecified circumstances, starting from a small seed. Through value-driven
dynamic priority scheduling controlling the parallel execution of a vast number
of reasoning threads, the system achieves recursive self-improvement after it
leaves the lab, within the boundaries imposed by its designers. A prototype
system has been implemented and demonstrated to learn a complex real-world
task, real-time multimodal dialogue with humans, by on-line observation. Our
work presents solutions to several challenges that must be solved for achieving
artificial general intelligence.
",Bounded Recursive Self-Improvement
"  A Constraint Satisfaction Problem (CSP) is a framework used for modeling and
solving constrained problems. Tree-search algorithms like backtracking try to
construct a solution to a CSP by selecting the variables of the problem one
after another. The order in which these algorithm select the variables
potentially have significant impact on the search performance. Various
heuristics have been proposed for choosing good variable ordering. Many
powerful variable ordering heuristics weigh the constraints first and then
utilize the weights for selecting good order of the variables. Constraint
weighting are basically employed to identify global bottlenecks in a CSP.
  In this paper, we propose a new approach for learning weights for the
constraints using competitive coevolutionary Genetic Algorithm (GA). Weights
learned by the coevolutionary GA later help to make better choices for the
first few variables in a search. In the competitive coevolutionary GA,
constraints and candidate solutions for a CSP evolve together through an
inverse fitness interaction process. We have conducted experiments on several
random, quasi-random and patterned instances to measure the efficiency of the
proposed approach. The results and analysis show that the proposed approach is
good at learning weights to distinguish the hard constraints for quasi-random
instances and forced satisfiable random instances generated with the Model RB.
For other type of instances, RNDI still seems to be the best approach as our
experiments show.
","A New Approach to Constraint Weight Learning for Variable Ordering in
  CSPs"
"  We present a newly developed Replica Exchange algorithm using q -Gaussian
Swarm Quantum Particle Optimization (REX@q-GSQPO) method for solving the
problem of finding the global optimum. The basis of the algorithm is to run
multiple copies of independent swarms at different values of q parameter. Based
on an energy criterion, chosen to satisfy the detailed balance, we are swapping
the particle coordinates of neighboring swarms at regular iteration intervals.
The swarm replicas with high q values are characterized by high diversity of
particles allowing escaping local minima faster, while the low q replicas,
characterized by low diversity of particles, are used to sample more
efficiently the local basins. We compare the new algorithm with the standard
Gaussian Swarm Quantum Particle Optimization (GSQPO) and q-Gaussian Swarm
Quantum Particle Optimization (q-GSQPO) algorithms, and we found that the new
algorithm is more robust in terms of the number of fitness function calls, and
more efficient in terms ability convergence to the global minimum. In
additional, we also provide a method of optimally allocating the swarm replicas
among different q values. Our algorithm is tested for three benchmark
functions, which are known to be multimodal problems, at different
dimensionalities. In addition, we considered a polyalanine peptide of 12
residues modeled using a G\=o coarse-graining potential energy function.
","Replica Exchange using q-Gaussian Swarm Quantum Particle Intelligence
  Method"
"  This volume contains the papers presented at the sixth workshop on Answer Set
Programming and Other Computing Paradigms (ASPOCP 2013) held on August 25th,
2013 in Istanbul, co-located with the 29th International Conference on Logic
Programming (ICLP 2013). It thus continues a series of previous events
co-located with ICLP, aiming at facilitating the discussion about crossing the
boundaries of current ASP techniques in theory, solving, and applications, in
combination with or inspired by other computing paradigms.
","Proceedings of Answer Set Programming and Other Computing Paradigms
  (ASPOCP 2013), 6th International Workshop, August 25, 2013, Istanbul, Turkey"
"  Today, with respect to the increasing growth of demand to get credit from the
customers of banks and finance and credit institutions, using an effective and
efficient method to decrease the risk of non-repayment of credit given is very
necessary. Assessment of customers' credit is one of the most important and the
most essential duties of banks and institutions, and if an error occurs in this
field, it would leads to the great losses for banks and institutions. Thus,
using the predicting computer systems has been significantly progressed in
recent decades. The data that are provided to the credit institutions' managers
help them to make a straight decision for giving the credit or not-giving it.
In this paper, we will assess the customer credit through a combined
classification using artificial neural networks, genetics algorithm and
Bayesian probabilities simultaneously, and the results obtained from three
methods mentioned above would be used to achieve an appropriate and final
result. We use the K_folds cross validation test in order to assess the method
and finally, we compare the proposed method with the methods such as
Clustering-Launched Classification (CLC), Support Vector Machine (SVM) as well
as GA+SVM where the genetics algorithm has been used to improve them.
","Assessment of Customer Credit through Combined Clustering of Artificial
  Neural Networks, Genetics Algorithm and Bayesian Probabilities"
"  A computer Program Capable of performing at a human-expert level in a narrow
problem domain area is called an expert system. Management of uncertainty is an
intrinsically important issue in the design of expert systems because much of
the information in the knowledge base of a typical expert system is imprecise,
incomplete or not totally reliable. In this paper, the author present s the
review of past work that has been carried out by various researchers based on
development of expert systems for the diagnosis of cardiac disease
",A Review: Expert System for Diagnosis of Myocardial Infarction
"  Exchangeability is a central notion in statistics and probability theory. The
assumption that an infinite sequence of data points is exchangeable is at the
core of Bayesian statistics. However, finite exchangeability as a statistical
property that renders probabilistic inference tractable is less
well-understood. We develop a theory of finite exchangeability and its relation
to tractable probabilistic inference. The theory is complementary to that of
independence and conditional independence. We show that tractable inference in
probabilistic models with high treewidth and millions of variables can be
understood using the notion of finite (partial) exchangeability. We also show
that existing lifted inference algorithms implicitly utilize a combination of
conditional independence and partial exchangeability.
","Tractability through Exchangeability: A New Perspective on Efficient
  Probabilistic Inference"
"  I think that the main reason why we do not understand the general principles
of how knowledge works (and probably also the reason why we have not yet
designed and built efficient machines capable of artificial intelligence), is
not the excessive complexity of cognitive phenomena, but the lack of the
conceptual and methodological tools to properly address the problem. It is like
trying to build up Physics without the concept of number, or to understand the
origin of species without including the mechanism of natural selection. In this
paper I propose some new conceptual and methodological tools, which seem to
offer a real opportunity to understand the logic of cognitive processes. I
propose a new method to properly treat the concepts of structure and schema,
and to perform on them operations of structural analysis. These operations
allow to move straightforwardly from concrete to more abstract representations.
With these tools I will suggest a definition for the concept of rule, of
regularity and of emergent phenomena. From the analysis of some important
aspects of the rules, I suggest to distinguish them in operational and
associative rules. I propose that associative rules assume a dominant role in
cognition. I also propose a definition for the concept of problem. At the end I
will briefly illustrate a possible general model for cognitive systems.
","Proposta di nuovi strumenti per comprendere come funziona la cognizione
  (Novel tools to understand how cognition works)"
"  These notes describe how the ""SP theory of intelligence"", and its embodiment
in the ""SP machine"", may help to realise cognitive computing, as described in
the book ""Smart Machines"". In the SP system, information compression and a
concept of ""multiple alignment"" are centre stage. The system is designed to
integrate such things as unsupervised learning, pattern recognition,
probabilistic reasoning, and more. It may help to overcome the problem of
variety in big data, it may serve in pattern recognition and in the
unsupervised learning of structure in data, and it may facilitate the
management and transmission of big data. There is potential, via information
compression, for substantial gains in computational efficiency, especially in
the use of energy. The SP system may help to realise data-centric computing,
perhaps via a development of Hebb's concept of a ""cell assembly"", or via the
use of light or DNA for the processing of information. It has potential in the
management of errors and uncertainty in data, in medical diagnosis, in
processing streams of data, and in promoting adaptability in robots.
",Smart machines and the SP theory of intelligence
"  The interaction between business models is used in consumer centric manner
instead of using a producer centric approach for customizing the business
process in cloud environment. The knowledge based human semantic web is used
for customizing the business process It introduces the Human Semantic Web as a
conceptual interface, providing human-understandable semantics on top of the
ordinary Semantic Web, which provides machine-readable semantics based on RDF
in this mismatching is a major problem. To overcome this following technique
automatic customization detection is an automated process of detecting possible
elements or variables of a business process that needto be especially treated
in order to suit the requirement of the other process. To the business
processto be customized as the primary business process and those that it
collaborates with as secondary business process or SBP Automatic customization
enactment is an automated process of taking actions to perform the
customization on the PBP according to the detected customization spots and the
automatic reasoning on the customization conceptualization knowledge framework.
The process of customizing businessprocesses by composite the web pages by
using web service.
",Ontology - Based Dynamic Business Process Customization
"  Instance-specific algorithm configuration and algorithm portfolios have been
shown to offer significant improvements over single algorithm approaches in a
variety of application domains. In the SAT and CSP domains algorithm portfolios
have consistently dominated the main competitions in these fields for the past
five years. For a portfolio approach to be effective there are two crucial
conditions that must be met. First, there needs to be a collection of
complementary solvers with which to make a portfolio. Second, there must be a
collection of problem features that can accurately identify structural
differences between instances. This paper focuses on the latter issue: feature
representation, because, unlike SAT, not every problem has well-studied
features. We employ the well-known SATzilla feature set, but compute
alternative sets on different SAT encodings of CSPs. We show that regardless of
what encoding is used to convert the instances, adequate structural information
is maintained to differentiate between problem instances, and that this can be
exploited to make an effective portfolio-based CSP solver.
",Transformation-based Feature Computation for Algorithm Portfolios
"  This paper presents Dempster-Shafer theory for move prediction in start
kicking of the bicycle kick of sepak takraw game. Sepak takraw is a highly
complex net-barrier kicking sport that involves dazzling displays of quick
reflexes, acrobatic twists, turns and swerves of the agile human body movement.
A Bicycle kick or Scissor kick is a physical move made by throwing the body up
into the air, making a shearing movement with the legs to get one leg in front
of the other without holding on to the ground. Specifically, this paper
considers bicycle kick of sepak takraw game in start kicking of the ball with
uncertainty where player has different awareness regarding the contingencies.
We have chosen Dempster-Shafer theory because the advantages of the
Dempster-Shafer theory which include the ability to model information in a
flexible way without requiring a probability to be assigned to each element in
a set, providing a convenient and simple mechanism for combining two or more
pieces of evidence under certain conditions, it can model ignorance explicitly,
rejection of the law of additivity for belief in disjoint propositions.
","Dempster-Shafer Theory for Move Prediction in Start Kicking of The
  Bicycle Kick of Sepak Takraw Game"
"  We consider the problem of optimal planning in stochastic domains with
resource constraints, where the resources are continuous and the choice of
action at each step depends on resource availability. We introduce the HAO*
algorithm, a generalization of the AO* algorithm that performs search in a
hybrid state space that is modeled using both discrete and continuous state
variables, where the continuous variables represent monotonic resources. Like
other heuristic search algorithms, HAO* leverages knowledge of the start state
and an admissible heuristic to focus computational effort on those parts of the
state space that could be reached from the start state by following an optimal
policy. We show that this approach is especially effective when resource
constraints limit how much of the state space is reachable. Experimental
results demonstrate its effectiveness in the domain that motivates our
research: automated planning for planetary exploration rovers.
","A Heuristic Search Approach to Planning with Continuous Resources in
  Stochastic Domains"
"  In this paper we explore a class of belief update operators, in which the
definition of the operator is compositional with respect to the sentence to be
added. The goal is to provide an update operator that is intuitive, in that its
definition is based on a recursive decomposition of the update sentences
structure, and that may be reasonably implemented. In addressing update, we
first provide a definition phrased in terms of the models of a knowledge base.
While this operator satisfies a core group of the benchmark Katsuno-Mendelzon
update postulates, not all of the postulates are satisfied. Other
Katsuno-Mendelzon postulates can be obtained by suitably restricting the
syntactic form of the sentence for update, as we show. In restricting the
syntactic form of the sentence for update, we also obtain a hierarchy of update
operators with Winsletts standard semantics as the most basic interesting
approach captured. We subsequently give an algorithm which captures this
approach; in the general case the algorithm is exponential, but with some
not-unreasonable assumptions we obtain an algorithm that is linear in the size
of the knowledge base. Hence the resulting approach has much better complexity
characteristics than other operators in some situations. We also explore other
compositional belief change operators: erasure is developed as a dual operator
to update; we show that a forget operator is definable in terms of update; and
we give a definition of the compositional revision operator. We obtain that
compositional revision, under the most natural definition, yields the Satoh
revision operator.
",Compositional Belief Update
"  Partially Observable Markov Decision Processes (POMDPs) provide a rich
framework for sequential decision-making under uncertainty in stochastic
domains. However, solving a POMDP is often intractable except for small
problems due to their complexity. Here, we focus on online approaches that
alleviate the computational complexity by computing good local policies at each
decision step during the execution. Online algorithms generally consist of a
lookahead search to find the best action to execute at each time step in an
environment. Our objectives here are to survey the various existing online
POMDP methods, analyze their properties and discuss their advantages and
disadvantages; and to thoroughly evaluate these online approaches in different
environments under various metrics (return, error bound reduction, lower bound
improvement). Our experimental results indicate that state-of-the-art online
heuristic search methods can handle large POMDP domains efficiently.
",Online Planning Algorithms for POMDPs
"  We present exact algorithms for identifying deterministic-actions effects and
preconditions in dynamic partially observable domains. They apply when one does
not know the action model(the way actions affect the world) of a domain and
must learn it from partial observations over time. Such scenarios are common in
real world applications. They are challenging for AI tasks because traditional
domain structures that underly tractability (e.g., conditional independence)
fail there (e.g., world features become correlated). Our work departs from
traditional assumptions about partial observations and action models. In
particular, it focuses on problems in which actions are deterministic of simple
logical structure and observation models have all features observed with some
frequency. We yield tractable algorithms for the modified problem for such
domains.
  Our algorithms take sequences of partial observations over time as input, and
output deterministic action models that could have lead to those observations.
The algorithms output all or one of those models (depending on our choice), and
are exact in that no model is misclassified given the observations. Our
algorithms take polynomial time in the number of time steps and state features
for some traditional action classes examined in the AI-planning literature,
e.g., STRIPS actions. In contrast, traditional approaches for HMMs and
Reinforcement Learning are inexact and exponentially intractable for such
domains. Our experiments verify the theoretical tractability guarantees, and
show that we identify action models exactly. Several applications in planning,
autonomous exploration, and adventure-game playing already use these results.
They are also promising for probabilistic settings, partially observable
reinforcement learning, and diagnosis.
",Learning Partially Observable Deterministic Action Models
"  A phylogenetic tree shows the evolutionary relationships among species.
Internal nodes of the tree represent speciation events and leaf nodes
correspond to species. A goal of phylogenetics is to combine such trees into
larger trees, called supertrees, whilst respecting the relationships in the
original trees. A rooted tree exhibits an ultrametric property; that is, for
any three leaves of the tree it must be that one pair has a deeper most recent
common ancestor than the other pairs, or that all three have the same most
recent common ancestor. This inspires a constraint programming encoding for
rooted trees. We present an efficient constraint that enforces the ultrametric
property over a symmetric array of constrained integer variables, with the
inevitable property that the lower bounds of any three variables are mutually
supportive. We show that this allows an efficient constraint-based solution to
the supertree construction problem. We demonstrate that the versatility of
constraint programming can be exploited to allow solutions to variants of the
supertree construction problem.
",The Ultrametric Constraint and its Application to Phylogenetics
"  We present Confidence-Based Autonomy (CBA), an interactive algorithm for
policy learning from demonstration. The CBA algorithm consists of two
components which take advantage of the complimentary abilities of humans and
computer agents. The first component, Confident Execution, enables the agent to
identify states in which demonstration is required, to request a demonstration
from the human teacher and to learn a policy based on the acquired data. The
algorithm selects demonstrations based on a measure of action selection
confidence, and our results show that using Confident Execution the agent
requires fewer demonstrations to learn the policy than when demonstrations are
selected by a human teacher. The second algorithmic component, Corrective
Demonstration, enables the teacher to correct any mistakes made by the agent
through additional demonstrations in order to improve the policy and future
task performance. CBA and its individual components are compared and evaluated
in a complex simulated driving domain. The complete CBA algorithm results in
the best overall learning performance, successfully reproducing the behavior of
the teacher while balancing the tradeoff between number of demonstrations and
number of incorrect actions during learning.
",Interactive Policy Learning through Confidence-Based Autonomy
"  A new search algorithm for solving distributed constraint optimization
problems (DisCOPs) is presented. Agents assign variables sequentially and
compute bounds on partial assignments asynchronously. The asynchronous bounds
computation is based on the propagation of partial assignments. The
asynchronous forward-bounding algorithm (AFB) is a distributed optimization
search algorithm that keeps one consistent partial assignment at all times. The
algorithm is described in detail and its correctness proven. Experimental
evaluation shows that AFB outperforms synchronous branch and bound by many
orders of magnitude, and produces a phase transition as the tightness of the
problem increases. This is an analogous effect to the phase transition that has
been observed when local consistency maintenance is applied to MaxCSPs. The AFB
algorithm is further enhanced by the addition of a backjumping mechanism,
resulting in the AFB-BJ algorithm. Distributed backjumping is based on
accumulated information on bounds of all values and on processing concurrently
a queue of candidate goals for the next move back. The AFB-BJ algorithm is
compared experimentally to other DisCOP algorithms (ADOPT, DPOP, OptAPO) and is
shown to be a very efficient algorithm for DisCOPs.
",Asynchronous Forward Bounding for Distributed COPs
"  This paper presents the computational logic foundations of a model of agency
called the KGP (Knowledge, Goals and Plan model. This model allows the
specification of heterogeneous agents that can interact with each other, and
can exhibit both proactive and reactive behaviour allowing them to function in
dynamic environments by adjusting their goals and plans when changes happen in
such environments. KGP provides a highly modular agent architecture that
integrates a collection of reasoning and physical capabilities, synthesised
within transitions that update the agents state in response to reasoning,
sensing and acting. Transitions are orchestrated by cycle theories that specify
the order in which transitions are executed while taking into account the
dynamic context and agent preferences, as well as selection operators for
providing inputs to transitions.
",Computational Logic Foundations of KGP Agents
"  Making a decision is often a matter of listing and comparing positive and
negative arguments. In such cases, the evaluation scale for decisions should be
considered bipolar, that is, negative and positive values should be explicitly
distinguished. That is what is done, for example, in Cumulative Prospect
Theory. However, contraryto the latter framework that presupposes genuine
numerical assessments, human agents often decide on the basis of an ordinal
ranking of the pros and the cons, and by focusing on the most salient
arguments. In other terms, the decision process is qualitative as well as
bipolar. In this article, based on a bipolar extension of possibility theory,
we define and axiomatically characterize several decision rules tailored for
the joint handling of positive and negative arguments in an ordinal setting.
The simplest rules can be viewed as extensions of the maximin and maximax
criteria to the bipolar case, and consequently suffer from poor decisive power.
More decisive rules that refine the former are also proposed. These refinements
agree both with principles of efficiency and with the spirit of
order-of-magnitude reasoning, that prevails in qualitative decision theory. The
most refined decision rule uses leximin rankings of the pros and the cons, and
the ideas of counting arguments of equal strength and cancelling pros by cons.
It is shown to come down to a special case of Cumulative Prospect Theory, and
to subsume the Take the Best heuristic studied by cognitive psychologists.
","On the Qualitative Comparison of Decisions Having Positive and Negative
  Features"
"  Inspired by the recently introduced framework of AND/OR search spaces for
graphical models, we propose to augment Multi-Valued Decision Diagrams (MDD)
with AND nodes, in order to capture function decomposition structure and to
extend these compiled data structures to general weighted graphical models
(e.g., probabilistic models). We present the AND/OR Multi-Valued Decision
Diagram (AOMDD) which compiles a graphical model into a canonical form that
supports polynomial (e.g., solution counting, belief updating) or constant time
(e.g. equivalence of graphical models) queries. We provide two algorithms for
compiling the AOMDD of a graphical model. The first is search-based, and works
by applying reduction rules to the trace of the memory intensive AND/OR search
algorithm. The second is inference-based and uses a Bucket Elimination schedule
to combine the AOMDDs of the input functions via the the APPLY operator. For
both algorithms, the compilation time and the size of the AOMDD are, in the
worst case, exponential in the treewidth of the graphical model, rather than
pathwidth as is known for ordered binary decision diagrams (OBDDs). We
introduce the concept of semantic treewidth, which helps explain why the size
of a decision diagram is often much smaller than the worst case bound. We
provide an experimental evaluation that demonstrates the potential of AOMDDs.
",AND/OR Multi-Valued Decision Diagrams (AOMDDs) for Graphical Models
"  Asynchronous Partial Overlay (APO) is a search algorithm that uses
cooperative mediation to solve Distributed Constraint Satisfaction Problems
(DisCSPs). The algorithm partitions the search into different subproblems of
the DisCSP. The original proof of completeness of the APO algorithm is based on
the growth of the size of the subproblems. The present paper demonstrates that
this expected growth of subproblems does not occur in some situations, leading
to a termination problem of the algorithm. The problematic parts in the APO
algorithm that interfere with its completeness are identified and necessary
modifications to the algorithm that fix these problematic parts are given. The
resulting version of the algorithm, Complete Asynchronous Partial Overlay
(CompAPO), ensures its completeness. Formal proofs for the soundness and
completeness of CompAPO are given. A detailed performance evaluation of CompAPO
comparing it to other DisCSP algorithms is presented, along with an extensive
experimental evaluation of the algorithm's unique behavior. Additionally, an
optimization version of the algorithm, CompOptAPO, is presented, discussed, and
evaluated.
",Completeness and Performance Of The APO Algorithm
"  We investigate the computational complexity of testing dominance and
consistency in CP-nets. Previously, the complexity of dominance has been
determined for restricted classes in which the dependency graph of the CP-net
is acyclic. However, there are preferences of interest that define cyclic
dependency graphs; these are modeled with general CP-nets. In our main results,
we show here that both dominance and consistency for general CP-nets are
PSPACE-complete. We then consider the concept of strong dominance, dominance
equivalence and dominance incomparability, and several notions of optimality,
and identify the complexity of the corresponding decision problems. The
reductions used in the proofs are from STRIPS planning, and thus reinforce the
earlier established connections between both areas.
",The Computational Complexity of Dominance and Consistency in CP-Nets
"  Partially observable Markov decision processes (POMDPs) provide a principled
framework for sequential planning in uncertain single agent settings. An
extension of POMDPs to multiagent settings, called interactive POMDPs
(I-POMDPs), replaces POMDP belief spaces with interactive hierarchical belief
systems which represent an agent's belief about the physical world, about
beliefs of other agents, and about their beliefs about others' beliefs. This
modification makes the difficulties of obtaining solutions due to complexity of
the belief and policy spaces even more acute. We describe a general method for
obtaining approximate solutions of I-POMDPs based on particle filtering (PF).
We introduce the interactive PF, which descends the levels of the interactive
belief hierarchies and samples and propagates beliefs at each level. The
interactive PF is able to mitigate the belief space complexity, but it does not
address the policy space complexity. To mitigate the policy space complexity --
sometimes also called the curse of history -- we utilize a complementary method
based on sampling likely observations while building the look ahead
reachability tree. While this approach does not completely address the curse of
history, it beats back the curse's impact substantially. We provide
experimental results and chart future work.
",Monte Carlo Sampling Methods for Approximating Interactive POMDPs
"  Inference in Bayes Nets (BAYES) is an important problem with numerous
applications in probabilistic reasoning. Counting the number of satisfying
assignments of a propositional formula (#SAT) is a closely related problem of
fundamental theoretical importance. Both these problems, and others, are
members of the class of sum-of-products (SUMPROD) problems. In this paper we
show that standard backtracking search when augmented with a simple memoization
scheme (caching) can solve any sum-of-products problem with time complexity
that is at least as good any other state-of-the-art exact algorithm, and that
it can also achieve the best known time-space tradeoff. Furthermore,
backtracking's ability to utilize more flexible variable orderings allows us to
prove that it can achieve an exponential speedup over other standard algorithms
for SUMPROD on some instances.
  The ideas presented here have been utilized in a number of solvers that have
been applied to various types of sum-of-product problems. These system's have
exploited the fact that backtracking can naturally exploit more of the
problem's structure to achieve improved performance on a range of
probleminstances. Empirical evidence of this performance gain has appeared in
published works describing these solvers, and we provide references to these
works.
",Solving #SAT and Bayesian Inference with Backtracking Search
"  Various tasks in decision making and decision support systems require
selecting a preferred subset of a given set of items. Here we focus on problems
where the individual items are described using a set of characterizing
attributes, and a generic preference specification is required, that is, a
specification that can work with an arbitrary set of items. For example,
preferences over the content of an online newspaper should have this form: At
each viewing, the newspaper contains a subset of the set of articles currently
available. Our preference specification over this subset should be provided
offline, but we should be able to use it to select a subset of any currently
available set of articles, e.g., based on their tags. We present a general
approach for lifting formalisms for specifying preferences over objects with
multiple attributes into ones that specify preferences over subsets of such
objects. We also show how we can compute an optimal subset given such a
specification in a relatively efficient manner. We provide an empirical
evaluation of the approach as well as some worst-case complexity results.
",Generic Preferences over Subsets of Structured Objects
"  Coordination of distributed agents is required for problems arising in many
areas, including multi-robot systems, networking and e-commerce. As a formal
framework for such problems, we use the decentralized partially observable
Markov decision process (DEC-POMDP). Though much work has been done on optimal
dynamic programming algorithms for the single-agent version of the problem,
optimal algorithms for the multiagent case have been elusive. The main
contribution of this paper is an optimal policy iteration algorithm for solving
DEC-POMDPs. The algorithm uses stochastic finite-state controllers to represent
policies. The solution can include a correlation device, which allows agents to
correlate their actions without communicating. This approach alternates between
expanding the controller and performing value-preserving transformations, which
modify the controller without sacrificing value. We present two efficient
value-preserving transformations: one can reduce the size of the controller and
the other can improve its value while keeping the size fixed. Empirical results
demonstrate the usefulness of value-preserving transformations in increasing
value while keeping controller size to a minimum. To broaden the applicability
of the approach, we also present a heuristic version of the policy iteration
algorithm, which sacrifices convergence to optimality. This algorithm further
reduces the size of the controllers at each step by assuming that probability
distributions over the other agents actions are known. While this assumption
may not hold in general, it helps produce higher quality solutions in our test
problems.
",Policy Iteration for Decentralized Control of Markov Decision Processes
"  Multiagent planning and coordination problems are common and known to be
computationally hard. We show that a wide range of two-agent problems can be
formulated as bilinear programs. We present a successive approximation
algorithm that significantly outperforms the coverage set algorithm, which is
the state-of-the-art method for this class of multiagent problems. Because the
algorithm is formulated for bilinear programs, it is more general and simpler
to implement. The new algorithm can be terminated at any time and-unlike the
coverage set algorithm-it facilitates the derivation of a useful online
performance bound. It is also much more efficient, on average reducing the
computation time of the optimal solution by about four orders of magnitude.
Finally, we introduce an automatic dimensionality reduction method that
improves the effectiveness of the algorithm, extending its applicability to new
domains and providing a new way to analyze a subclass of bilinear programs.
",A Bilinear Programming Approach for Multiagent Planning
"  Conformant planning is the problem of finding a sequence of actions for
achieving a goal in the presence of uncertainty in the initial state or action
effects. The problem has been approached as a path-finding problem in belief
space where good belief representations and heuristics are critical for scaling
up. In this work, a different formulation is introduced for conformant problems
with deterministic actions where they are automatically converted into
classical ones and solved by an off-the-shelf classical planner. The
translation maps literals L and sets of assumptions t about the initial
situation, into new literals KL/t that represent that L must be true if t is
initially true. We lay out a general translation scheme that is sound and
establish the conditions under which the translation is also complete. We show
that the complexity of the complete translation is exponential in a parameter
of the problem called the conformant width, which for most benchmarks is
bounded. The planner based on this translation exhibits good performance in
comparison with existing planners, and is the basis for T0, the best performing
planner in the Conformant Track of the 2006 International Planning Competition.
","Compiling Uncertainty Away in Conformant Planning Problems with Bounded
  Width"
"  Symmetries in discrete constraint satisfaction problems have been explored
and exploited in the last years, but symmetries in continuous constraint
problems have not received the same attention. Here we focus on permutations of
the variables consisting of one single cycle. We propose a procedure that takes
advantage of these symmetries by interacting with a continuous constraint
solver without interfering with it. A key concept in this procedure are the
classes of symmetric boxes formed by bisecting a n-dimensional cube at the same
point in all dimensions at the same time. We analyze these classes and quantify
them as a function of the cube dimensionality. Moreover, we propose a simple
algorithm to generate the representatives of all these classes for any number
of variables at very high rates. A problem example from the chemical
and#64257;eld and the cyclic n-roots problem are used to show the performance
of the approach in practice.
",Exploiting Single-Cycle Symmetries in Continuous Constraint Problems
"  Thanks to recent advances, AI Planning has become the underlying technique
for several applications. Figuring prominently among these is automated Web
Service Composition (WSC) at the ""capability"" level, where services are
described in terms of preconditions and effects over ontological concepts. A
key issue in addressing WSC as planning is that ontologies are not only formal
vocabularies; they also axiomatize the possible relationships between concepts.
Such axioms correspond to what has been termed ""integrity constraints"" in the
actions and change literature, and applying a web service is essentially a
belief update operation. The reasoning required for belief update is known to
be harder than reasoning in the ontology itself. The support for belief update
is severely limited in current planning tools.
  Our first contribution consists in identifying an interesting special case of
WSC which is both significant and more tractable. The special case, which we
term ""forward effects"", is characterized by the fact that every ramification of
a web service application involves at least one new constant generated as
output by the web service. We show that, in this setting, the reasoning
required for belief update simplifies to standard reasoning in the ontology
itself. This relates to, and extends, current notions of ""message-based"" WSC,
where the need for belief update is removed by a strong (often implicit or
informal) assumption of ""locality"" of the individual messages. We clarify the
computational properties of the forward effects case, and point out a strong
relation to standard notions of planning under uncertainty, suggesting that
effective tools for the latter can be successfully adapted to address the
former.
  Furthermore, we identify a significant sub-case, named ""strictly forward
effects"", where an actual compilation into planning under uncertainty exists.
This enables us to exploit off-the-shelf planning tools to solve message-based
WSC in a general form that involves powerful ontologies, and requires reasoning
about partial matches between concepts. We provide empirical evidence that this
approach may be quite effective, using Conformant-FF as the underlying planner.
","Message-Based Web Service Composition, Integrity Constraints, and
  Planning under Uncertainty: A New Connection"
"  In this paper we formulate the problem of inference under incomplete
information in very general terms. This includes modelling the process
responsible for the incompleteness, which we call the incompleteness process.
We allow the process behaviour to be partly unknown. Then we use Walleys theory
of coherent lower previsions, a generalisation of the Bayesian theory to
imprecision, to derive the rule to update beliefs under incompleteness that
logically follows from our assumptions, and that we call conservative inference
rule. This rule has some remarkable properties: it is an abstract rule to
update beliefs that can be applied in any situation or domain; it gives us the
opportunity to be neither too optimistic nor too pessimistic about the
incompleteness process, which is a necessary condition to draw reliable while
strong enough conclusions; and it is a coherent rule, in the sense that it
cannot lead to inconsistencies. We give examples to show how the new rule can
be applied in expert systems, in parametric statistical inference, and in
pattern classification, and discuss more generally the view of incompleteness
processes defended here as well as some of its consequences.
",Conservative Inference Rule for Uncertain Reasoning under Incompleteness
"  Many real-world decision making tasks require us to choose among several
expensive observations. In a sensor network, for example, it is important to
select the subset of sensors that is expected to provide the strongest
reduction in uncertainty. In medical decision making tasks, one needs to select
which tests to administer before deciding on the most effective treatment. It
has been general practice to use heuristic-guided procedures for selecting
observations. In this paper, we present the first efficient optimal algorithms
for selecting observations for a class of probabilistic graphical models. For
example, our algorithms allow to optimally label hidden variables in Hidden
Markov Models (HMMs). We provide results for both selecting the optimal subset
of observations, and for obtaining an optimal conditional observation plan.
  Furthermore we prove a surprising result: In most graphical models tasks, if
one designs an efficient algorithm for chain graphs, such as HMMs, this
procedure can be generalized to polytree graphical models. We prove that the
optimizing value of information is $NP^{PP}$-hard even for polytrees. It also
follows from our results that just computing decision theoretic value of
information objective functions, which are commonly used in practice, is a
#P-complete problem even on Naive Bayes models (a simple special case of
polytrees).
  In addition, we consider several extensions, such as using our algorithms for
scheduling observation selection for multiple sensors. We demonstrate the
effectiveness of our approach on several real-world datasets, including a
prototype sensor network deployment for energy conservation in buildings.
",Optimal Value of Information in Graphical Models
"  A weighted constraint satisfaction problem (WCSP) is a constraint
satisfaction problem in which preferences among solutions can be expressed.
Bucket elimination is a complete technique commonly used to solve this kind of
constraint satisfaction problem. When the memory required to apply bucket
elimination is too high, a heuristic method based on it (denominated
mini-buckets) can be used to calculate bounds for the optimal solution.
Nevertheless, the curse of dimensionality makes these techniques impractical on
large scale problems. In response to this situation, we present a memetic
algorithm for WCSPs in which bucket elimination is used as a mechanism for
recombining solutions, providing the best possible child from the parental set.
Subsequently, a multi-level model in which this exact/metaheuristic hybrid is
further hybridized with branch-and-bound techniques and mini-buckets is
studied. As a case study, we have applied these algorithms to the resolution of
the maximum density still life problem, a hard constraint optimization problem
based on Conways game of life. The resulting algorithm consistently finds
optimal patterns for up to date solved instances in less time than current
approaches. Moreover, it is shown that this proposal provides new best known
solutions for very large instances.
","Solving Weighted Constraint Satisfaction Problems with Memetic/Exact
  Hybrid Algorithms"
"  The Weighted Constraint Satisfaction Problem (WCSP) framework allows
representing and solving problems involving both hard constraints and cost
functions. It has been applied to various problems, including resource
allocation, bioinformatics, scheduling, etc. To solve such problems, solvers
usually rely on branch-and-bound algorithms equipped with local consistency
filtering, mostly soft arc consistency. However, these techniques are not well
suited to solve problems with very large domains. Motivated by the resolution
of an RNA gene localization problem inside large genomic sequences, and in the
spirit of bounds consistency for large domains in crisp CSPs, we introduce soft
bounds arc consistency, a new weighted local consistency specifically designed
for WCSP with very large domains. Compared to soft arc consistency, BAC
provides significantly improved time and space asymptotic complexity. In this
paper, we show how the semantics of cost functions can be exploited to further
improve the time complexity of BAC. We also compare both in theory and in
practice the efficiency of BAC on a WCSP with bounds consistency enforced on a
crisp CSP using cost variables. On two different real problems modeled as WCSP,
including our RNA gene localization problem, we observe that maintaining bounds
arc consistency outperforms arc consistency and also improves over bounds
consistency enforced on a constraint model with cost variables.
",Bounds Arc Consistency for Weighted CSPs
"  The survey propagation (SP) algorithm has been shown to work well on large
instances of the random 3-SAT problem near its phase transition. It was shown
that SP estimates marginals over covers that represent clusters of solutions.
The SP-y algorithm generalizes SP to work on the maximum satisfiability
(Max-SAT) problem, but the cover interpretation of SP does not generalize to
SP-y. In this paper, we formulate the relaxed survey propagation (RSP)
algorithm, which extends the SP algorithm to apply to the weighted Max-SAT
problem. We show that RSP has an interpretation of estimating marginals over
covers violating a set of clauses with minimal weight. This naturally
generalizes the cover interpretation of SP. Empirically, we show that RSP
outperforms SP-y and other state-of-the-art Max-SAT solvers on random Max-SAT
instances. RSP also outperforms state-of-the-art weighted Max-SAT solvers on
random weighted Max-SAT instances.
","Relaxed Survey Propagation for The Weighted Maximum Satisfiability
  Problem"
"  This paper presents several new tractability results for planning based on
macros. We describe an algorithm that optimally solves planning problems in a
class that we call inverted tree reducible, and is provably tractable for
several subclasses of this class. By using macros to store partial plans that
recur frequently in the solution, the algorithm is polynomial in time and space
even for exponentially long plans. We generalize the inverted tree reducible
class in several ways and describe modifications of the algorithm to deal with
these new classes. Theoretical results are validated in experiments.
",The Role of Macros in Tractable Planning
"  The paper investigates parameterized approximate message-passing schemes that
are based on bounded inference and are inspired by Pearl's belief propagation
algorithm (BP). We start with the bounded inference mini-clustering algorithm
and then move to the iterative scheme called Iterative Join-Graph Propagation
(IJGP), that combines both iteration and bounded inference. Algorithm IJGP
belongs to the class of Generalized Belief Propagation algorithms, a framework
that allowed connections with approximate algorithms from statistical physics
and is shown empirically to surpass the performance of mini-clustering and
belief propagation, as well as a number of other state-of-the-art algorithms on
several classes of networks. We also provide insight into the accuracy of
iterative BP and IJGP by relating these algorithms to well known classes of
constraint propagation schemes.
",Join-Graph Propagation Algorithms
"  Distributed constraint optimization (DCOP) problems are a popular way of
formulating and solving agent-coordination problems. A DCOP problem is a
problem where several agents coordinate their values such that the sum of the
resulting constraint costs is minimal. It is often desirable to solve DCOP
problems with memory-bounded and asynchronous algorithms. We introduce
Branch-and-Bound ADOPT (BnB-ADOPT), a memory-bounded asynchronous DCOP search
algorithm that uses the message-passing and communication framework of ADOPT
(Modi, Shen, Tambe, and Yokoo, 2005), a well known memory-bounded asynchronous
DCOP search algorithm, but changes the search strategy of ADOPT from best-first
search to depth-first branch-and-bound search. Our experimental results show
that BnB-ADOPT finds cost-minimal solutions up to one order of magnitude faster
than ADOPT for a variety of large DCOP problems and is as fast as NCBB, a
memory-bounded synchronous DCOP search algorithm, for most of these DCOP
problems. Additionally, it is often desirable to find bounded-error solutions
for DCOP problems within a reasonable amount of time since finding cost-minimal
solutions is NP-hard. The existing bounded-error approximation mechanism allows
users only to specify an absolute error bound on the solution cost but a
relative error bound is often more intuitive. Thus, we present two new
bounded-error approximation mechanisms that allow for relative error bounds and
implement them on top of BnB-ADOPT.
",BnB-ADOPT: An Asynchronous Branch-and-Bound DCOP Algorithm
"  Soft goals extend the classical model of planning with a simple model of
preferences. The best plans are then not the ones with least cost but the ones
with maximum utility, where the utility of a plan is the sum of the utilities
of the soft goals achieved minus the plan cost. Finding plans with high utility
appears to involve two linked problems: choosing a subset of soft goals to
achieve and finding a low-cost plan to achieve them. New search algorithms and
heuristics have been developed for planning with soft goals, and a new track
has been introduced in the International Planning Competition (IPC) to test
their performance. In this note, we show however that these extensions are not
needed: soft goals do not increase the expressive power of the basic model of
planning with action costs, as they can easily be compiled away. We apply this
compilation to the problems of the net-benefit track of the most recent IPC,
and show that optimal and satisficing cost-based planners do better on the
compiled problems than optimal and satisficing net-benefit planners on the
original problems with explicit soft goals. Furthermore, we show that
penalties, or negative preferences expressing conditions to avoid, can also be
compiled away using a similar idea.
",Soft Goals Can Be Compiled Away
"  The identification of performance-optimizing parameter settings is an
important part of the development and application of algorithms. We describe an
automatic framework for this algorithm configuration problem. More formally, we
provide methods for optimizing a target algorithm's performance on a given
class of problem instances by varying a set of ordinal and/or categorical
parameters. We review a family of local-search-based algorithm configuration
procedures and present novel techniques for accelerating them by adaptively
limiting the time spent for evaluating individual configurations. We describe
the results of a comprehensive experimental evaluation of our methods, based on
the configuration of prominent complete and incomplete algorithms for SAT. We
also present what is, to our knowledge, the first published work on
automatically configuring the CPLEX mixed integer programming solver. All the
algorithms we considered had default parameter settings that were manually
identified with considerable effort. Nevertheless, using our automated
algorithm configuration procedures, we achieved substantial and consistent
performance improvements.
",ParamILS: An Automatic Algorithm Configuration Framework
"  Korf, Reid, and Edelkamp introduced a formula to predict the number of nodes
IDA* will expand on a single iteration for a given consistent heuristic, and
experimentally demonstrated that it could make very accurate predictions. In
this paper we show that, in addition to requiring the heuristic to be
consistent, their formulas predictions are accurate only at levels of the
brute-force search tree where the heuristic values obey the unconditional
distribution that they defined and then used in their formula. We then propose
a new formula that works well without these requirements, i.e., it can make
accurate predictions of IDA*s performance for inconsistent heuristics and if
the heuristic values in any level do not obey the unconditional distribution.
In order to achieve this we introduce the conditional distribution of heuristic
values which is a generalization of their unconditional heuristic distribution.
We also provide extensions of our formula that handle individual start states
and the augmentation of IDA* with bidirectional pathmax (BPMX), a technique for
propagating heuristic values when inconsistent heuristics are used.
Experimental results demonstrate the accuracy of our new method and all its
variations.
",Predicting the Performance of IDA* using Conditional Distributions
"  Deciding how to act in partially observable environments remains an active
area of research. Identifying good sequences of decisions is particularly
challenging when good control performance requires planning multiple steps into
the future in domains with many states. Towards addressing this challenge, we
present an online, forward-search algorithm called the Posterior Belief
Distribution (PBD). PBD leverages a novel method for calculating the posterior
distribution over beliefs that result after a sequence of actions is taken,
given the set of observation sequences that could be received during this
process. This method allows us to efficiently evaluate the expected reward of a
sequence of primitive actions, which we refer to as macro-actions. We present a
formal analysis of our approach, and examine its performance on two very large
simulation experiments: scientific exploration and a target monitoring domain.
We also demonstrate our algorithm being used to control a real robotic
helicopter in a target monitoring experiment, which suggests that our approach
has practical potential for planning in real-world, large partially observable
domains where a multi-step lookahead is required to achieve good performance.
",Efficient Planning under Uncertainty with Macro-actions
"  In many AI domains such as product configuration, a user should interactively
specify a solution that must satisfy a set of constraints. In such scenarios,
offline compilation of feasible solutions into a tractable representation is an
important approach to delivering efficient backtrack-free user interaction
online. In particular,binary decision diagrams (BDDs) have been successfully
used as a compilation target for product and service configuration. In this
paper we discuss how to extend BDD-based configuration to scenarios involving
cost functions which express user preferences.
  We first show that an efficient, robust and easy to implement extension is
possible if the cost function is additive, and feasible solutions are
represented using multi-valued decision diagrams (MDDs). We also discuss the
effect on MDD size if the cost function is non-additive or if it is encoded
explicitly into MDD. We then discuss interactive configuration in the presence
of multiple cost functions. We prove that even in its simplest form,
multiple-cost configuration is NP-hard in the input MDD. However, for solving
two-cost configuration we develop a pseudo-polynomial scheme and a fully
polynomial approximation scheme. The applicability of our approach is
demonstrated through experiments over real-world configuration models and
product-catalogue datasets. Response times are generally within a fraction of a
second even for very large instances.
",Interactive Cost Configuration Over Decision Diagrams
"  Decentralized planning in uncertain environments is a complex task generally
dealt with by using a decision-theoretic approach, mainly through the framework
of Decentralized Partially Observable Markov Decision Processes (DEC-POMDPs).
Although DEC-POMDPS are a general and powerful modeling tool, solving them is a
task with an overwhelming complexity that can be doubly exponential. In this
paper, we study an alternate formulation of DEC-POMDPs relying on a
sequence-form representation of policies. From this formulation, we show how to
derive Mixed Integer Linear Programming (MILP) problems that, once solved, give
exact optimal solutions to the DEC-POMDPs. We show that these MILPs can be
derived either by using some combinatorial characteristics of the optimal
solutions of the DEC-POMDPs or by using concepts borrowed from game theory.
Through an experimental validation on classical test problems from the
DEC-POMDP literature, we compare our approach to existing algorithms. Results
show that mathematical programming outperforms dynamic programming but is less
efficient than forward search, except for some particular problems. The main
contributions of this work are the use of mathematical programming for
DEC-POMDPs and a better understanding of DEC-POMDPs and of their solutions.
Besides, we argue that our alternate representation of DEC-POMDPs could be
helpful for designing novel algorithms looking for approximate solutions to
DEC-POMDPs.
","An Investigation into Mathematical Programming for Finite Horizon
  Decentralized POMDPs"
"  The paper presents a scheme for computing lower and upper bounds on the
posterior marginals in Bayesian networks with discrete variables. Its power
lies in its ability to use any available scheme that bounds the probability of
evidence or posterior marginals and enhance its performance in an anytime
manner. The scheme uses the cutset conditioning principle to tighten existing
bounding schemes and to facilitate anytime behavior, utilizing a fixed number
of cutset tuples. The accuracy of the bounds improves as the number of used
cutset tuples increases and so does the computation time. We demonstrate
empirically the value of our scheme for bounding posterior marginals and
probability of evidence using a variant of the bound propagation algorithm as a
plug-in scheme.
",Active Tuples-based Scheme for Bounding Posterior Beliefs
"  As historically acknowledged in the Reasoning about Actions and Change
community, intuitiveness of a logical domain description cannot be fully
automated. Moreover, like any other logical theory, action theories may also
evolve, and thus knowledge engineers need revision methods to help in
accommodating new incoming information about the behavior of actions in an
adequate manner. The present work is about changing action domain descriptions
in multimodal logic. Its contribution is threefold: first we revisit the
semantics of action theory contraction proposed in previous work, giving more
robust operators that express minimal change based on a notion of distance
between Kripke-models. Second we give algorithms for syntactical action theory
contraction and establish their correctness with respect to our semantics for
those action theories that satisfy a principle of modularity investigated in
previous work. Since modularity can be ensured for every action theory and, as
we show here, needs to be computed at most once during the evolution of a
domain description, it does not represent a limitation at all to the method
here studied. Finally we state AGM-like postulates for action theory
contraction and assess the behavior of our operators with respect to them.
Moreover, we also address the revision counterpart of action theory change,
showing that it benefits from our semantics for contraction.
",On Action Theory Change
"  In this paper, we address the problem of change in an abstract argumentation
system. We focus on a particular change: the addition of a new argument which
interacts with previous arguments. We study the impact of such an addition on
the outcome of the argumentation system, more particularly on the set of its
extensions. Several properties for this change operation are defined by
comparing the new set of extensions to the initial one, these properties are
called structural when the comparisons are based on set-cardinality or
set-inclusion relations. Several other properties are proposed where
comparisons are based on the status of some particular arguments: the accepted
arguments; these properties refer to the evolution of this status during the
change, e.g., Monotony and Priority to Recency. All these properties may be
more or less desirable according to specific applications. They are studied
under two particular semantics: the grounded and preferred semantics.
",Change in Abstract Argumentation Frameworks: Adding an Argument
"  LAMA is a classical planning system based on heuristic forward search. Its
core feature is the use of a pseudo-heuristic derived from landmarks,
propositional formulas that must be true in every solution of a planning task.
LAMA builds on the Fast Downward planning system, using finite-domain rather
than binary state variables and multi-heuristic search. The latter is employed
to combine the landmark heuristic with a variant of the well-known FF
heuristic. Both heuristics are cost-sensitive, focusing on high-quality
solutions in the case where actions have non-uniform cost. A weighted A* search
is used with iteratively decreasing weights, so that the planner continues to
search for plans of better quality until the search is terminated. LAMA showed
best performance among all planners in the sequential satisficing track of the
International Planning Competition 2008. In this paper we present the system in
detail and investigate which features of LAMA are crucial for its performance.
We present individual results for some of the domains used at the competition,
demonstrating good and bad cases for the techniques implemented in LAMA.
Overall, we find that using landmarks improves performance, whereas the
incorporation of action costs into the heuristic estimators proves not to be
beneficial. We show that in some domains a search that ignores cost solves far
more problems, raising the question of how to deal with action costs more
effectively in the future. The iterated weighted A* search greatly improves
results, and shows synergy effects with the use of landmarks.
",The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks
"  Narrative, and in particular storytelling, is an important part of the human
experience. Consequently, computational systems that can reason about narrative
can be more effective communicators, entertainers, educators, and trainers. One
of the central challenges in computational narrative reasoning is narrative
generation, the automated creation of meaningful event sequences. There are
many factors -- logical and aesthetic -- that contribute to the success of a
narrative artifact. Central to this success is its understandability. We argue
that the following two attributes of narratives are universal: (a) the logical
causal progression of plot, and (b) character believability. Character
believability is the perception by the audience that the actions performed by
characters do not negatively impact the audiences suspension of disbelief.
Specifically, characters must be perceived by the audience to be intentional
agents. In this article, we explore the use of refinement search as a technique
for solving the narrative generation problem -- to find a sound and believable
sequence of character actions that transforms an initial world state into a
world state in which goal propositions hold. We describe a novel refinement
search planning algorithm -- the Intent-based Partial Order Causal Link (IPOCL)
planner -- that, in addition to creating causally sound plot progression,
reasons about character intentionality by identifying possible character goals
that explain their actions and creating plan structures that explain why those
characters commit to their goals. We present the results of an empirical
evaluation that demonstrates that narrative plans generated by the IPOCL
algorithm support audience comprehension of character intentions better than
plans generated by conventional partial-order planners.
",Narrative Planning: Balancing Plot and Character
"  Call control features (e.g., call-divert, voice-mail) are primitive options
to which users can subscribe off-line to personalise their service. The
configuration of a feature subscription involves choosing and sequencing
features from a catalogue and is subject to constraints that prevent
undesirable feature interactions at run-time. When the subscription requested
by a user is inconsistent, one problem is to find an optimal relaxation, which
is a generalisation of the feedback vertex set problem on directed graphs, and
thus it is an NP-hard task. We present several constraint programming
formulations of the problem. We also present formulations using partial
weighted maximum Boolean satisfiability and mixed integer linear programming.
We study all these formulations by experimentally comparing them on a variety
of randomly generated instances of the feature subscription problem.
","Developing Approaches for Solving a Telecommunications Feature
  Subscription Problem"
"  Binary Decision Diagram (BDD) based set bounds propagation is a powerful
approach to solving set-constraint satisfaction problems. However, prior BDD
based techniques in- cur the significant overhead of constructing and
manipulating graphs during search. We present a set-constraint solver which
combines BDD-based set-bounds propagators with the learning abilities of a
modern SAT solver. Together with a number of improvements beyond the basic
algorithm, this solver is highly competitive with existing propagation based
set constraint solvers.
",Fast Set Bounds Propagation Using a BDD-SAT Hybrid
"  Domain-specific features are important in representing problem structure
throughout machine learning and decision-theoretic planning. In planning, once
state features are provided, domain-independent algorithms such as approximate
value iteration can learn weighted combinations of those features that often
perform well as heuristic estimates of state value (e.g., distance to the
goal). Successful applications in real-world domains often require features
crafted by human experts. Here, we propose automatic processes for learning
useful domain-specific feature sets with little or no human intervention. Our
methods select and add features that describe state-space regions of high
inconsistency in the Bellman equation (statewise Bellman error) during
approximate value iteration. Our method can be applied using any
real-valued-feature hypothesis space and corresponding learning method for
selecting features from training sets of state-value pairs. We evaluate the
method with hypothesis spaces defined by both relational and propositional
feature languages, using nine probabilistic planning domains. We show that
approximate value iteration using a relational feature space performs at the
state-of-the-art in domain-independent stochastic relational planning. Our
method provides the first domain-independent approach that plays Tetris
successfully (without human-engineered features).
",Automatic Induction of Bellman-Error Features for Probabilistic Planning
"  We propose a StochAstic Fault diagnosis AlgoRIthm, called SAFARI, which
trades off guarantees of computing minimal diagnoses for computational
efficiency. We empirically demonstrate, using the 74XXX and ISCAS-85 suites of
benchmark combinatorial circuits, that SAFARI achieves several
orders-of-magnitude speedup over two well-known deterministic algorithms, CDA*
and HA*, for multiple-fault diagnoses; further, SAFARI can compute a range of
multiple-fault diagnoses that CDA* and HA* cannot. We also prove that SAFARI is
optimal for a range of propositional fault models, such as the widely-used
weak-fault models (models with ignorance of abnormal behavior). We discuss the
optimality of SAFARI in a class of strong-fault circuit models with stuck-at
failure modes. By modeling the algorithm itself as a Markov chain, we provide
exact bounds on the minimality of the diagnosis computed. SAFARI also displays
strong anytime behavior, and will return a diagnosis after any non-trivial
inference time.
",Approximate Model-Based Diagnosis Using Greedy Stochastic Search
"  Model-based diagnostic reasoning often leads to a large number of diagnostic
hypotheses. The set of diagnoses can be reduced by taking into account extra
observations (passive monitoring), measuring additional variables (probing) or
executing additional tests (sequential diagnosis/test sequencing). In this
paper we combine the above approaches with techniques from Automated Test
Pattern Generation (ATPG) and Model-Based Diagnosis (MBD) into a framework
called FRACTAL (FRamework for ACtive Testing ALgorithms). Apart from the inputs
and outputs that connect a system to its environment, in active testing we
consider additional input variables to which a sequence of test vectors can be
supplied. We address the computationally hard problem of computing optimal
control assignments (as defined in FRACTAL) in terms of a greedy approximation
algorithm called FRACTAL-G. We compare the decrease in the number of remaining
minimal cardinality diagnoses of FRACTAL-G to that of two more FRACTAL
algorithms: FRACTAL-ATPG and FRACTAL-P. FRACTAL-ATPG is based on ATPG and
sequential diagnosis while FRACTAL-P is based on probing and, although not an
active testing algorithm, provides a baseline for comparing the lower bound on
the number of reachable diagnoses for the FRACTAL algorithms. We empirically
evaluate the trade-offs of the three FRACTAL algorithms by performing extensive
experimentation on the ISCAS85/74XXX benchmark of combinational circuits.
",A Model-Based Active Testing Approach to Sequential Diagnosis
"  State-space search with explicit abstraction heuristics is at the state of
the art of cost-optimal planning. These heuristics are inherently limited,
nonetheless, because the size of the abstract space must be bounded by some,
even if a very large, constant. Targeting this shortcoming, we introduce the
notion of (additive) implicit abstractions, in which the planning task is
abstracted by instances of tractable fragments of optimal planning. We then
introduce a concrete setting of this framework, called fork-decomposition, that
is based on two novel fragments of tractable cost-optimal planning. The induced
admissible heuristics are then studied formally and empirically. This study
testifies for the accuracy of the fork decomposition heuristics, yet our
empirical evaluation also stresses the tradeoff between their accuracy and the
runtime complexity of computing them. Indeed, some of the power of the explicit
abstraction heuristics comes from precomputing the heuristic function offline
and then determining h(s) for each evaluated state s by a very fast lookup in a
database. By contrast, while fork-decomposition heuristics can be calculated in
polynomial time, computing them is far from being fast. To address this
problem, we show that the time-per-node complexity bottleneck of the
fork-decomposition heuristics can be successfully overcome. We demonstrate that
an equivalent of the explicit abstraction notion of a database exists for the
fork-decomposition abstractions as well, despite their exponential-size
abstract spaces. We then verify empirically that heuristic search with the
databased"" fork-decomposition heuristics favorably competes with the state of
the art of cost-optimal planning.
",Implicit Abstraction Heuristics
"  Diagrammatic reasoning (DR) is pervasive in human problem solving as a
powerful adjunct to symbolic reasoning based on language-like representations.
The research reported in this paper is a contribution to building a general
purpose DR system as an extension to a SOAR-like problem solving architecture.
The work is in a framework in which DR is modeled as a process where subtasks
are solved, as appropriate, either by inference from symbolic representations
or by interaction with a diagram, i.e., perceiving specified information from a
diagram or modifying/creating objects in a diagram in specified ways according
to problem solving needs. The perceptions and actions in most DR systems built
so far are hand-coded for the specific application, even when the rest of the
system is built using the general architecture. The absence of a general
framework for executing perceptions/actions poses as a major hindrance to using
them opportunistically -- the essence of open-ended search in problem solving.
Our goal is to develop a framework for executing a wide variety of specified
perceptions and actions across tasks/domains without human intervention. We
observe that the domain/task-specific visual perceptions/actions can be
transformed into domain/task-independent spatial problems. We specify a spatial
problem as a quantified constraint satisfaction problem in the real domain
using an open-ended vocabulary of properties, relations and actions involving
three kinds of diagrammatic objects -- points, curves, regions. Solving a
spatial problem from this specification requires computing the equivalent
simplified quantifier-free expression, the complexity of which is inherently
doubly exponential. We represent objects as configuration of simple elements to
facilitate decomposition of complex problems into simpler and similar
subproblems. We show that, if the symbolic solution to a subproblem can be
expressed concisely, quantifiers can be eliminated from spatial problems in
low-order polynomial time using similar previously solved subproblems. This
requires determining the similarity of two problems, the existence of a mapping
between them computable in polynomial time, and designing a memory for storing
previously solved problems so as to facilitate search. The efficacy of the idea
is shown by time complexity analysis. We demonstrate the proposed approach by
executing perceptions and actions involved in DR tasks in two army
applications.
","A Constraint Satisfaction Framework for Executing Perceptions and
  Actions in Diagrammatic Reasoning"
"  Real-time heuristic search algorithms satisfy a constant bound on the amount
of planning per action, independent of problem size. As a result, they scale up
well as problems become larger. This property would make them well suited for
video games where Artificial Intelligence controlled agents must react quickly
to user commands and to other agents actions. On the downside, real-time search
algorithms employ learning methods that frequently lead to poor solution
quality and cause the agent to appear irrational by re-visiting the same
problem states repeatedly. The situation changed recently with a new algorithm,
D LRTA*, which attempted to eliminate learning by automatically selecting
subgoals. D LRTA* is well poised for video games, except it has a complex and
memory-demanding pre-computation phase during which it builds a database of
subgoals. In this paper, we propose a simpler and more memory-efficient way of
pre-computing subgoals thereby eliminating the main obstacle to applying
state-of-the-art real-time search methods in video games. The new algorithm
solves a number of randomly chosen problems off-line, compresses the solutions
into a series of subgoals and stores them in a database. When presented with a
novel problem on-line, it queries the database for the most similar previously
solved case and uses its subgoals to solve the problem. In the domain of
pathfinding on four large video game maps, the new algorithm delivers solutions
eight times better while using 57 times less memory and requiring 14% less
pre-computation time.
","Case-Based Subgoaling in Real-Time Heuristic Search for Video Game
  Pathfinding"
"  Noisy probabilistic relational rules are a promising world model
representation for several reasons. They are compact and generalize over world
instantiations. They are usually interpretable and they can be learned
effectively from the action experiences in complex worlds. We investigate
reasoning with such rules in grounded relational domains. Our algorithms
exploit the compactness of rules for efficient and flexible decision-theoretic
planning. As a first approach, we combine these rules with the Upper Confidence
Bounds applied to Trees (UCT) algorithm based on look-ahead trees. Our second
approach converts these rules into a structured dynamic Bayesian network
representation and predicts the effects of action sequences using approximate
inference and beliefs over world states. We evaluate the effectiveness of our
approaches for planning in a simulated complex 3D robot manipulation scenario
with an articulated manipulator and realistic physics and in domains of the
probabilistic planning competition. Empirical results show that our methods can
solve problems where existing methods fail.
",Planning with Noisy Probabilistic Relational Rules
"  The Hamiltonian cycle problem (HCP) is an important combinatorial problem
with applications in many areas. It is among the first problems used for
studying intrinsic properties, including phase transitions, of combinatorial
problems. While thorough theoretical and experimental analyses have been made
on the HCP in undirected graphs, a limited amount of work has been done for the
HCP in directed graphs (DHCP). The main contribution of this work is an
effective algorithm for the DHCP. Our algorithm explores and exploits the close
relationship between the DHCP and the Assignment Problem (AP) and utilizes a
technique based on Boolean satisfiability (SAT). By combining effective
algorithms for the AP and SAT, our algorithm significantly outperforms previous
exact DHCP algorithms, including an algorithm based on the award-winning
Concorde TSP algorithm. The second result of the current study is an
experimental analysis of phase transitions of the DHCP, verifying and refining
a known phase transition of the DHCP.
","An Effective Algorithm for and Phase Transitions of the Directed
  Hamiltonian Cycle Problem"
"  In action domains where agents may have erroneous beliefs, reasoning about
the effects of actions involves reasoning about belief change. In this paper,
we use a transition system approach to reason about the evolution of an agents
beliefs as actions are executed. Some actions cause an agent to perform belief
revision while others cause an agent to perform belief update, but the
interaction between revision and update can be non-elementary. We present a set
of rationality properties describing the interaction between revision and
update, and we introduce a new class of belief change operators for reasoning
about alternating sequences of revisions and updates. Our belief change
operators can be characterized in terms of a natural shifting operation on
total pre-orderings over interpretations. We compare our approach with related
work on iterated belief change due to action, and we conclude with some
directions for future research.
",Iterated Belief Change Due to Actions and Observations
"  In this paper, we propose a comprehensive study of second-order consistencies
(i.e., consistencies identifying inconsistent pairs of values) for constraint
satisfaction. We build a full picture of the relationships existing between
four basic second-order consistencies, namely path consistency (PC),
3-consistency (3C), dual consistency (DC) and 2-singleton arc consistency
(2SAC), as well as their conservative and strong variants. Interestingly, dual
consistency is an original property that can be established by using the
outcome of the enforcement of generalized arc consistency (GAC), which makes it
rather easy to obtain since constraint solvers typically maintain GAC during
search. On binary constraint networks, DC is equivalent to PC, but its
restriction to existing constraints, called conservative dual consistency
(CDC), is strictly stronger than traditional conservative consistencies derived
from path consistency, namely partial path consistency (PPC) and conservative
path consistency (CPC). After introducing a general algorithm to enforce strong
(C)DC, we present the results of an experimentation over a wide range of
benchmarks that demonstrate the interest of (conservative) dual consistency. In
particular, we show that enforcing (C)DC before search clearly improves the
performance of MAC (the algorithm that maintains GAC during search) on several
binary and non-binary structured problems.
",Second-Order Consistencies
"  We present a case study of artificial intelligence techniques applied to the
control of production printing equipment. Like many other real-world
applications, this complex domain requires high-speed autonomous
decision-making and robust continual operation. To our knowledge, this work
represents the first successful industrial application of embedded
domain-independent temporal planning. Our system handles execution failures and
multi-objective preferences. At its heart is an on-line algorithm that combines
techniques from state-space planning and partial-order scheduling. We suggest
that this general architecture may prove useful in other applications as more
intelligent systems operate in continual, on-line settings. Our system has been
used to drive several commercial prototypes and has enabled a new product
architecture for our industrial partner. When compared with state-of-the-art
off-line planners, our system is hundreds of times faster and often finds
better plans. Our experience demonstrates that domain-independent AI planning
based on heuristic search can flexibly handle time, resources, replanning, and
multiple objectives in a high-speed practical application without requiring
hand-coded control knowledge.
","On-line Planning and Scheduling: An Application to Controlling Modular
  Printers"
"  We address the cost-sensitive feature acquisition problem, where
misclassifying an instance is costly but the expected misclassification cost
can be reduced by acquiring the values of the missing features. Because
acquiring the features is costly as well, the objective is to acquire the right
set of features so that the sum of the feature acquisition cost and
misclassification cost is minimized. We describe the Value of Information
Lattice (VOILA), an optimal and efficient feature subset acquisition framework.
Unlike the common practice, which is to acquire features greedily, VOILA can
reason with subsets of features. VOILA efficiently searches the space of
possible feature subsets by discovering and exploiting conditional independence
properties between the features and it reuses probabilistic inference
computations to further speed up the process. Through empirical evaluation on
five medical datasets, we show that the greedy strategy is often reluctant to
acquire features, as it cannot forecast the benefit of acquiring multiple
features in combination.
","Value of Information Lattice: Exploiting Probabilistic Independence for
  Effective Feature Subset Acquisition"
"  Dynamic programming algorithms have been successfully applied to
propositional stochastic planning problems by using compact representations, in
particular algebraic decision diagrams, to capture domain dynamics and value
functions. Work on symbolic dynamic programming lifted these ideas to first
order logic using several representation schemes. Recent work introduced a
first order variant of decision diagrams (FODD) and developed a value iteration
algorithm for this representation. This paper develops several improvements to
the FODD algorithm that make the approach practical. These include, new
reduction operators that decrease the size of the representation, several
speedup techniques, and techniques for value approximation. Incorporating
these, the paper presents a planning system, FODD-Planner, for solving
relational stochastic planning problems. The system is evaluated on several
domains, including problems from the recent international planning competition,
and shows competitive performance with top ranking systems. This is the first
demonstration of feasibility of this approach and it shows that abstraction
through compact representation is a promising approach to stochastic planning.
",Probabilistic Relational Planning with First Order Decision Diagrams
"  Current evaluation functions for heuristic planning are expensive to compute.
In numerous planning problems these functions provide good guidance to the
solution, so they are worth the expense. However, when evaluation functions are
misguiding or when planning problems are large enough, lots of node evaluations
must be computed, which severely limits the scalability of heuristic planners.
In this paper, we present a novel solution for reducing node evaluations in
heuristic planning based on machine learning. Particularly, we define the task
of learning search control for heuristic planning as a relational
classification task, and we use an off-the-shelf relational classification tool
to address this learning task. Our relational classification task captures the
preferred action to select in the different planning contexts of a specific
planning domain. These planning contexts are defined by the set of helpful
actions of the current state, the goals remaining to be achieved, and the
static predicates of the planning task. This paper shows two methods for
guiding the search of a heuristic planner with the learned classifiers. The
first one consists of using the resulting classifier as an action policy. The
second one consists of applying the classifier to generate lookahead states
within a Best First Search algorithm. Experiments over a variety of domains
reveal that our heuristic planner using the learned classifiers solves larger
problems than state-of-the-art planners.
",Scaling up Heuristic Planning with Relational Decision Trees
"  Previous studies have demonstrated that encoding a Bayesian network into a
SAT formula and then performing weighted model counting using a backtracking
search algorithm can be an effective method for exact inference. In this paper,
we present techniques for improving this approach for Bayesian networks with
noisy-OR and noisy-MAX relations---two relations that are widely used in
practice as they can dramatically reduce the number of probabilities one needs
to specify. In particular, we present two SAT encodings for noisy-OR and two
encodings for noisy-MAX that exploit the structure or semantics of the
relations to improve both time and space efficiency, and we prove the
correctness of the encodings. We experimentally evaluated our techniques on
large-scale real and randomly generated Bayesian networks. On these benchmarks,
our techniques gave speedups of up to two orders of magnitude over the best
previous approaches for networks with noisy-OR/MAX relations and scaled up to
larger networks. As well, our techniques extend the weighted model counting
approach for exact inference to networks that were previously intractable for
the approach.
","Exploiting Structure in Weighted Model Counting Approaches to
  Probabilistic Inference"
"  The ignoring delete lists relaxation is of paramount importance for both
satisficing and optimal planning. In earlier work, it was observed that the
optimal relaxation heuristic h+ has amazing qualities in many classical
planning benchmarks, in particular pertaining to the complete absence of local
minima. The proofs of this are hand-made, raising the question whether such
proofs can be lead automatically by domain analysis techniques. In contrast to
earlier disappointing results -- the analysis method has exponential runtime
and succeeds only in two extremely simple benchmark domains -- we herein answer
this question in the affirmative. We establish connections between causal graph
structure and h+ topology. This results in low-order polynomial time analysis
methods, implemented in a tool we call TorchLight. Of the 12 domains where the
absence of local minima has been proved, TorchLight gives strong success
guarantees in 8 domains. Empirically, its analysis exhibits strong performance
in a further 2 of these domains, plus in 4 more domains where local minima may
exist but are rare. In this way, TorchLight can distinguish easy domains from
hard ones. By summarizing structural reasons for analysis failure, TorchLight
also provides diagnostic output indicating domain aspects that may cause local
minima.
","Analyzing Search Topology Without Running Any Search: On the Connection
  Between Causal Graphs and h+"
"  When a system behaves abnormally, sequential diagnosis takes a sequence of
measurements of the system until the faults causing the abnormality are
identified, and the goal is to reduce the diagnostic cost, defined here as the
number of measurements. To propose measurement points, previous work employs a
heuristic based on reducing the entropy over a computed set of diagnoses. This
approach generally has good performance in terms of diagnostic cost, but can
fail to diagnose large systems when the set of diagnoses is too large. Focusing
on a smaller set of probable diagnoses scales the approach but generally leads
to increased average diagnostic costs. In this paper, we propose a new
diagnostic framework employing four new techniques, which scales to much larger
systems with good performance in terms of diagnostic cost. First, we propose a
new heuristic for measurement point selection that can be computed efficiently,
without requiring the set of diagnoses, once the system is modeled as a
Bayesian network and compiled into a logical form known as d-DNNF. Second, we
extend hierarchical diagnosis, a technique based on system abstraction from our
previous work, to handle probabilities so that it can be applied to sequential
diagnosis to allow larger systems to be diagnosed. Third, for the largest
systems where even hierarchical diagnosis fails, we propose a novel method that
converts the system into one that has a smaller abstraction and whose diagnoses
form a superset of those of the original system; the new system can then be
diagnosed and the result mapped back to the original system. Finally, we
propose a novel cost estimation function which can be used to choose an
abstraction of the system that is more likely to provide optimal average cost.
Experiments with ISCAS-85 benchmark circuits indicate that our approach scales
to all circuits in the suite except one that has a flat structure not
susceptible to useful abstraction.
",Sequential Diagnosis by Abstraction
"  A major inference task in Bayesian networks is explaining why some variables
are observed in their particular states using a set of target variables.
Existing methods for solving this problem often generate explanations that are
either too simple (underspecified) or too complex (overspecified). In this
paper, we introduce a method called Most Relevant Explanation (MRE) which finds
a partial instantiation of the target variables that maximizes the generalized
Bayes factor (GBF) as the best explanation for the given evidence. Our study
shows that GBF has several theoretical properties that enable MRE to
automatically identify the most relevant target variables in forming its
explanation. In particular, conditional Bayes factor (CBF), defined as the GBF
of a new explanation conditioned on an existing explanation, provides a soft
measure on the degree of relevance of the variables in the new explanation in
explaining the evidence given the existing explanation. As a result, MRE is
able to automatically prune less relevant variables from its explanation. We
also show that CBF is able to capture well the explaining-away phenomenon that
is often represented in Bayesian networks. Moreover, we define two dominance
relations between the candidate solutions and use the relations to generalize
MRE to find a set of top explanations that is both diverse and representative.
Case studies on several benchmark diagnostic Bayesian networks show that MRE is
often able to find explanatory hypotheses that are not only precise but also
concise.
",Most Relevant Explanation in Bayesian Networks
"  Translations between different nonmonotonic formalisms always have been an
important topic in the field, in particular to understand the
knowledge-representation capabilities those formalisms offer. We provide such
an investigation in terms of different semantics proposed for abstract
argumentation frameworks, a nonmonotonic yet simple formalism which received
increasing interest within the last decade. Although the properties of these
different semantics are nowadays well understood, there are no explicit results
about intertranslatability. We provide such translations wrt. different
properties and also give a few novel complexity results which underlie some
negative results.
",On the Intertranslatability of Argumentation Semantics
"  Many applications, e.g., Web service composition, complex system design, team
formation, etc., rely on methods for identifying collections of objects or
entities satisfying some functional requirement. Among the collections that
satisfy the functional requirement, it is often necessary to identify one or
more collections that are optimal with respect to user preferences over a set
of attributes that describe the non-functional properties of the collection.
  We develop a formalism that lets users express the relative importance among
attributes and qualitative preferences over the valuations of each attribute.
We define a dominance relation that allows us to compare collections of objects
in terms of preferences over attributes of the objects that make up the
collection. We establish some key properties of the dominance relation. In
particular, we show that the dominance relation is a strict partial order when
the intra-attribute preference relations are strict partial orders and the
relative importance preference relation is an interval order.
  We provide algorithms that use this dominance relation to identify the set of
most preferred collections. We show that under certain conditions, the
algorithms are guaranteed to return only (sound), all (complete), or at least
one (weakly complete) of the most preferred collections. We present results of
simulation experiments comparing the proposed algorithms with respect to (a)
the quality of solutions (number of most preferred solutions) produced by the
algorithms, and (b) their performance and efficiency. We also explore some
interesting conjectures suggested by the results of our experiments that relate
the properties of the user preferences, the dominance relation, and the
algorithms.
","Representing and Reasoning with Qualitative Preferences for
  Compositional Systems"
"  Multi-agent path planning is a challenging problem with numerous real-life
applications. Running a centralized search such as A* in the combined state
space of all units is complete and cost-optimal, but scales poorly, as the
state space size is exponential in the number of mobile units. Traditional
decentralized approaches, such as FAR and WHCA*, are faster and more scalable,
being based on problem decomposition. However, such methods are incomplete and
provide no guarantees with respect to the running time or the solution quality.
They are not necessarily able to tell in a reasonable time whether they would
succeed in finding a solution to a given instance. We introduce MAPP, a
tractable algorithm for multi-agent path planning on undirected graphs. We
present a basic version and several extensions. They have low-polynomial
worst-case upper bounds for the running time, the memory requirements, and the
length of solutions. Even though all algorithmic versions are incomplete in the
general case, each provides formal guarantees on problems it can solve. For
each version, we discuss the algorithms completeness with respect to clearly
defined subclasses of instances. Experiments were run on realistic game grid
maps. MAPP solved 99.86% of all mobile units, which is 18--22% better than the
percentage of FAR and WHCA*. MAPP marked 98.82% of all units as provably
solvable during the first stage of plan computation. Parts of MAPPs computation
can be re-used across instances on the same map. Speed-wise, MAPP is
competitive or significantly faster than WHCA*, depending on whether MAPP
performs all computations from scratch. When data that MAPP can re-use are
preprocessed offline and readily available, MAPP is slower than the very fast
FAR algorithm by a factor of 2.18 on average. MAPPs solutions are on average
20% longer than FARs solutions and 7--31% longer than WHCA*s solutions.
","MAPP: a Scalable Multi-Agent Path Planning Algorithm with Tractability
  and Completeness Guarantees"
"  Value iteration is a powerful yet inefficient algorithm for Markov decision
processes (MDPs) because it puts the majority of its effort into backing up the
entire state space, which turns out to be unnecessary in many cases. In order
to overcome this problem, many approaches have been proposed. Among them, ILAO*
and variants of RTDP are state-of-the-art ones. These methods use reachability
analysis and heuristic search to avoid some unnecessary backups. However, none
of these approaches build the graphical structure of the state transitions in a
pre-processing step or use the structural information to systematically
decompose a problem, whereby generating an intelligent backup sequence of the
state space. In this paper, we present two optimal MDP algorithms. The first
algorithm, topological value iteration (TVI), detects the structure of MDPs and
backs up states based on topological sequences. It (1) divides an MDP into
strongly-connected components (SCCs), and (2) solves these components
sequentially. TVI outperforms VI and other state-of-the-art algorithms vastly
when an MDP has multiple, close-to-equal-sized SCCs. The second algorithm,
focused topological value iteration (FTVI), is an extension of TVI. FTVI
restricts its attention to connected components that are relevant for solving
the MDP. Specifically, it uses a small amount of heuristic search to eliminate
provably sub-optimal actions; this pruning allows FTVI to find smaller
connected components, thus running faster. We demonstrate that FTVI outperforms
TVI by an order of magnitude, averaged across several domains. Surprisingly,
FTVI also significantly outperforms popular heuristically-informed MDP
algorithms such as ILAO*, LRTDP, BRTDP and Bayesian-RTDP in many domains,
sometimes by as much as two orders of magnitude. Finally, we characterize the
type of domains where FTVI excels --- suggesting a way to an informed choice of
solver.
",Topological Value Iteration Algorithms
"  We investigate an approach to reasoning about causes through argumentation.
We consider a causal model for a physical system, and look for arguments about
facts. Some arguments are meant to provide explanations of facts whereas some
challenge these explanations and so on. At the root of argumentation here, are
causal links ({A_1, ... ,A_n} causes B) and ontological links (o_1 is_a o_2).
We present a system that provides a candidate explanation ({A_1, ... ,A_n}
explains {B_1, ... ,B_m}) by resorting to an underlying causal link
substantiated with appropriate ontological links. Argumentation is then at work
from these various explaining links. A case study is developed: a severe storm
Xynthia that devastated part of France in 2010, with an unaccountably high
number of casualties.
",Arguments using ontological and causal knowledge
"  In this paper, we consider the problem of finding a minimum common partition
of two strings. The problem has its application in genome comparison. As it is
an NP-hard, discrete combinatorial optimization problem, we employ a
metaheuristic technique, namely, MAX-MIN ant system to solve this problem. To
achieve better efficiency we first map the problem instance into a special kind
of graph. Subsequently, we employ a MAX-MIN ant system to achieve high quality
solutions for the problem. Experimental results show the superiority of our
algorithm in comparison with the state of art algorithm in the literature. The
improvement achieved is also justified by standard statistical test.
","Solving the Minimum Common String Partition Problem with the Help of
  Ants"
"  In a deterministic world, a planning agent can be certain of the consequences
of its planned sequence of actions. Not so, however, in dynamic, stochastic
domains where Markov decision processes are commonly used. Unfortunately these
suffer from the curse of dimensionality: if the state space is a Cartesian
product of many small sets (dimensions), planning is exponential in the number
of those dimensions.
  Our new technique exploits the intuitive strategy of selectively ignoring
various dimensions in different parts of the state space. The resulting
non-uniformity has strong implications, since the approximation is no longer
Markovian, requiring the use of a modified planner. We also use a spatial and
temporal proximity measure, which responds to continued planning as well as
movement of the agent through the state space, to dynamically adapt the
abstraction as planning progresses.
  We present qualitative and quantitative results across a range of
experimental domains showing that an agent exploiting this novel approximation
method successfully finds solutions to the planning problem using much less
than the full state space. We assess and analyse the features of domains which
our method can exploit.
",Proximity-Based Non-uniform Abstractions for Approximate Planning
"  Scheduling problems in manufacturing, logistics and project management have
frequently been modeled using the framework of Resource Constrained Project
Scheduling Problems with minimum and maximum time lags (RCPSP/max). Due to the
importance of these problems, providing scalable solution schedules for
RCPSP/max problems is a topic of extensive research. However, all existing
methods for solving RCPSP/max assume that durations of activities are known
with certainty, an assumption that does not hold in real world scheduling
problems where unexpected external events such as manpower availability,
weather changes, etc. lead to delays or advances in completion of activities.
Thus, in this paper, our focus is on providing a scalable method for solving
RCPSP/max problems with durational uncertainty. To that end, we introduce the
robust local search method consisting of three key ideas: (a) Introducing and
studying the properties of two decision rule approximations used to compute
start times of activities with respect to dynamic realizations of the
durational uncertainty; (b) Deriving the expression for robust makespan of an
execution strategy based on decision rule approximations; and (c) A robust
local search mechanism to efficiently compute activity execution strategies
that are robust against durational uncertainty. Furthermore, we also provide
enhancements to local search that exploit temporal dependencies between
activities. Our experimental results illustrate that robust local search is
able to provide robust execution strategies efficiently.
",Robust Local Search for Solving RCPSP/max with Durational Uncertainty
"  We describe Dr.Fill, a program that solves American-style crossword puzzles.
From a technical perspective, Dr.Fill works by converting crosswords to
weighted CSPs, and then using a variety of novel techniques to find a solution.
These techniques include generally applicable heuristics for variable and value
selection, a variant of limited discrepancy search, and postprocessing and
partitioning ideas. Branch and bound is not used, as it was incompatible with
postprocessing and was determined experimentally to be of little practical
value. Dr.Fillls performance on crosswords from the American Crossword Puzzle
Tournament suggests that it ranks among the top fifty or so crossword solvers
in the world.
",Dr.Fill: Crosswords and an Implemented Solver for Singly Weighted CSPs
"  Planning as satisfiability is a principal approach to planning with many
eminent advantages. The existing planning as satisfiability techniques usually
use encodings compiled from STRIPS. We introduce a novel SAT encoding scheme
(SASE) based on the SAS+ formalism. The new scheme exploits the structural
information in SAS+, resulting in an encoding that is both more compact and
efficient for planning. We prove the correctness of the new encoding by
establishing an isomorphism between the solution plans of SASE and that of
STRIPS based encodings. We further analyze the transition variables newly
introduced in SASE to explain why it accommodates modern SAT solving algorithms
and improves performance. We give empirical statistical results to support our
analysis. We also develop a number of techniques to further reduce the encoding
size of SASE, and conduct experimental studies to show the strength of each
individual technique. Finally, we report extensive experimental results to
demonstrate significant improvements of SASE over the state-of-the-art STRIPS
based encoding schemes in terms of both time and memory efficiency.
",SAS+ Planning as Satisfiability
"  We focus on the problem of sequential decision making in partially observable
environments shared with other agents of uncertain types having similar or
conflicting objectives. This problem has been previously formalized by multiple
frameworks one of which is the interactive dynamic influence diagram (I-DID),
which generalizes the well-known influence diagram to the multiagent setting.
I-DIDs are graphical models and may be used to compute the policy of an agent
given its belief over the physical state and others models, which changes as
the agent acts and observes in the multiagent setting.
  As we may expect, solving I-DIDs is computationally hard. This is
predominantly due to the large space of candidate models ascribed to the other
agents and its exponential growth over time. We present two methods for
reducing the size of the model space and stemming its exponential growth. Both
these methods involve aggregating individual models into equivalence classes.
Our first method groups together behaviorally equivalent models and selects
only those models for updating which will result in predictive behaviors that
are distinct from others in the updated model space. The second method further
compacts the model space by focusing on portions of the behavioral predictions.
Specifically, we cluster actionally equivalent models that prescribe identical
actions at a single time step. Exactly identifying the equivalences would
require us to solve all models in the initial set. We avoid this by selectively
solving some of the models, thereby introducing an approximation. We discuss
the error introduced by the approximation, and empirically demonstrate the
improved efficiency in solving I-DIDs due to the equivalences.
","Exploiting Model Equivalences for Solving Interactive Dynamic Influence
  Diagrams"
"  Designing a search heuristic for constraint programming that is reliable
across problem domains has been an important research topic in recent years.
This paper concentrates on one family of candidates: counting-based search.
Such heuristics seek to make branching decisions that preserve most of the
solutions by determining what proportion of solutions to each individual
constraint agree with that decision. Whereas most generic search heuristics in
constraint programming rely on local information at the level of the individual
variable, our search heuristics are based on more global information at the
constraint level. We design several algorithms that are used to count the
number of solutions to specific families of constraints and propose some search
heuristics exploiting such information. The experimental part of the paper
considers eight problem domains ranging from well-established benchmark puzzles
to rostering and sport scheduling. An initial empirical analysis identifies
heuristic maxSD as a robust candidate among our proposals.eWe then evaluate the
latter against the state of the art, including the latest generic search
heuristics, restarts, and discrepancy-based tree traversals. Experimental
results show that counting-based search generally outperforms other generic
heuristics.
","Counting-Based Search: Branching Heuristics for Constraint Satisfaction
  Problems"
"  This work presents Drake, a dynamic executive for temporal plans with choice.
Dynamic plan execution strategies allow an autonomous agent to react quickly to
unfolding events, improving the robustness of the agent. Prior work developed
methods for dynamically dispatching Simple Temporal Networks, and further
research enriched the expressiveness of the plans executives could handle,
including discrete choices, which are the focus of this work. However, in some
approaches to date, these additional choices induce significant storage or
latency requirements to make flexible execution possible.
  Drake is designed to leverage the low latency made possible by a
preprocessing step called compilation, while avoiding high memory costs through
a compact representation. We leverage the concepts of labels and environments,
taken from prior work in Assumption-based Truth Maintenance Systems (ATMS), to
concisely record the implications of the discrete choices, exploiting the
structure of the plan to avoid redundant reasoning or storage. Our labeling and
maintenance scheme, called the Labeled Value Set Maintenance System, is
distinguished by its focus on properties fundamental to temporal problems, and,
more generally, weighted graph algorithms. In particular, the maintenance
system focuses on maintaining a minimal representation of non-dominated
constraints. We benchmark Drakes performance on random structured problems, and
find that Drake reduces the size of the compiled representation by a factor of
over 500 for large problems, while incurring only a modest increase in run-time
latency, compared to prior work in compiled executives for temporal plans with
discrete choices.
",Drake: An Efficient Executive for Temporal Plans with Choice
"  This paper addresses the open question formulated as: Which levels of
abstraction are appropriate in the synthetic modelling of life and cognition?
within the framework of info-computational constructivism, treating natural
phenomena as computational processes on informational structures. At present we
lack the common understanding of the processes of life and cognition in living
organisms with the details of co-construction of informational structures and
computational processes in embodied, embedded cognizing agents, both living and
artifactual ones. Starting with the definition of an agent as an entity capable
of acting on its own behalf, as an actor in Hewitt Actor model of computation,
even so simple systems as molecules can be modelled as actors exchanging
messages (information). We adopt Kauffmans view of a living agent as something
that can reproduce and undergoes at least one thermodynamic work cycle. This
definition of living agents leads to the Maturana and Varelas identification of
life with cognition. Within the info-computational constructive approach to
living beings as cognizing agents, from the simplest to the most complex living
systems, mechanisms of cognition can be studied in order to construct synthetic
model classes of artifactual cognizing agents on different levels of
organization.
",Info-computational constructivism in modelling of life as cognition
"  In this paper, harmony search algorithm is applied to curriculum-based course
timetabling. The implementation, specifically the process of improvisation
consists of memory consideration, random consideration and pitch adjustment. In
memory consideration, the value of the course number for new solution was
selected from all other course number located in the same column of the Harmony
Memory. This research used the highest occurrence of the course number to be
scheduled in a new harmony. The remaining courses that have not been scheduled
by memory consideration will go through random consideration, i.e. will select
any feasible location available to be scheduled in the new harmony solution.
Each course scheduled out of memory consideration is examined as to whether it
should be pitch adjusted with probability of eight procedures. However, the
algorithm produced results that were not comparatively better than those
previously known as best solution. With proper modification in terms of the
approach in this algorithm would make the algorithm perform better on
curriculum-based course timetabling.
",Harmony Search Algorithm for Curriculum-Based Course Timetabling Problem
"  We present a skill analysis with time series image data using data mining
methods, focused on table tennis. We do not use body model, but use only
hi-speed movies, from which time series data are obtained and analyzed using
data mining methods such as C4.5 and so on. We identify internal models for
technical skills as evaluation skillfulness for the forehand stroke of table
tennis, and discuss mono and meta-functional skills for improving skills.
",Skill Analysis with Time Series Image Data
"  Real Time Strategy (RTS) games provide complex domain to test the latest
artificial intelligence (AI) research. In much of the literature, AI systems
have been limited to playing one game. Although, this specialization has
resulted in stronger AI gaming systems it does not address the key concerns of
AI researcher. AI researchers seek the development of AI agents that can
autonomously interpret learn, and apply new knowledge. To achieve human level
performance, current AI systems rely on game specific knowledge of an expert.
The paper presents the full RTS language in hopes of shifting the current
research focus to the development of general RTS agents. General RTS agents are
AI gaming systems that can play any RTS games, defined in the RTS language.
This prevents game specific knowledge from being hard coded into the system,
thereby facilitating research that addresses the fundamental concerns of
artificial intelligence.
",Real Time Strategy Language
"  Quality of General Game Playing (GGP) matches suffers from slow
state-switching and weak knowledge modules. Instantiation and Propositional
Networks offer great performance gains over Prolog-based reasoning, but do not
scale well. In this publication mGDL, a variant of GDL stripped of function
constants, has been defined as a basis for simple reasoning machines. mGDL
allows to easily map rules to C++ functions. 253 out of 270 tested GDL rule
sheets conformed to mGDL without any modifications; the rest required minor
changes. A revised (m)GDL to C++ translation scheme has been reevaluated; it
brought gains ranging from 28% to 7300% over YAP Prolog, managing to compile
even demanding rule sheets under few seconds. For strengthening game knowledge,
spatial features inspired by similar successful techniques from computer Go
have been proposed. For they required an Euclidean metric, a small board
extension to GDL has been defined through a set of ground atomic sentences. An
SGA-based genetic algorithm has been designed for tweaking game parameters and
conducting self-plays, so the features could be mined from meaningful game
records. The approach has been tested on a small cluster, giving performance
gains up to 20% more wins against the baseline UCT player. Implementations of
proposed ideas constitutes the core of GGP Spatium - a small C++/Python GGP
framework, created for developing compact GGP Players and problem solvers.
",GGP with Advanced Reasoning and Board Knowledge Discovery
"  Compact representations of objects is a common concept in computer science.
Automated planning can be viewed as a case of this concept: a planning instance
is a compact implicit representation of a graph and the problem is to find a
path (a plan) in this graph. While the graphs themselves are represented
compactly as planning instances, the paths are usually represented explicitly
as sequences of actions. Some cases are known where the plans always have
compact representations, for example, using macros. We show that these results
do not extend to the general case, by proving a number of bounds for compact
representations of plans under various criteria, like efficient sequential or
random access of actions. In addition to this, we show that our results have
consequences for what can be gained from reformulating planning into some other
problem. As a contrast to this we also prove a number of positive results,
demonstrating restricted cases where plans do have useful compact
representations, as well as proving that macro plans have favourable access
properties. Our results are finally discussed in relation to other relevant
contexts.
",Algorithms and Limits for Compact Plan Representations
"  Heuristics used for solving hard real-time search problems have regions with
depressions. Such regions are bounded areas of the search space in which the
heuristic function is inaccurate compared to the actual cost to reach a
solution. Early real-time search algorithms, like LRTA*, easily become trapped
in those regions since the heuristic values of their states may need to be
updated multiple times, which results in costly solutions. State-of-the-art
real-time search algorithms, like LSS-LRTA* or LRTA*(k), improve LRTA*s
mechanism to update the heuristic, resulting in improved performance. Those
algorithms, however, do not guide search towards avoiding depressed regions.
This paper presents depression avoidance, a simple real-time search principle
to guide search towards avoiding states that have been marked as part of a
heuristic depression. We propose two ways in which depression avoidance can be
implemented: mark-and-avoid and move-to-border. We implement these strategies
on top of LSS-LRTA* and RTAA*, producing 4 new real-time heuristic search
algorithms: aLSS-LRTA*, daLSS-LRTA*, aRTAA*, and daRTAA*. When the objective is
to find a single solution by running the real-time search algorithm once, we
show that daLSS-LRTA* and daRTAA* outperform their predecessors sometimes by
one order of magnitude. Of the four new algorithms, daRTAA* produces the best
solutions given a fixed deadline on the average time allowed per planning
episode. We prove all our algorithms have good theoretical properties: in
finite search spaces, they find a solution if one exists, and converge to an
optimal after a number of trials.
",Avoiding and Escaping Depressions in Real-Time Heuristic Search
"  A model of story generation recently proposed by Riedl and Young casts it as
planning, with the additional condition that story characters behave
intentionally. This means that characters have perceivable motivation for the
actions they take. I show that this condition can be compiled away (in more
ways than one) to produce a classical planning problem that can be solved by an
off-the-shelf classical planner, more efficiently than by Riedl and Youngs
specialised planner.
",Narrative Planning: Compilations to Classical Planning
"  In this paper we describe COLIN, a forward-chaining heuristic search planner,
capable of reasoning with COntinuous LINear numeric change, in addition to the
full temporal semantics of PDDL. Through this work we make two advances to the
state-of-the-art in terms of expressive reasoning capabilities of planners: the
handling of continuous linear change, and the handling of duration-dependent
effects in combination with duration inequalities, both of which require
tightly coupled temporal and numeric reasoning during planning. COLIN combines
FF-style forward chaining search, with the use of a Linear Program (LP) to
check the consistency of the interacting temporal and numeric constraints at
each state. The LP is used to compute bounds on the values of variables in each
state, reducing the range of actions that need to be considered for
application. In addition, we develop an extension of the Temporal Relaxed
Planning Graph heuristic of CRIKEY3, to support reasoning directly with
continuous change. We extend the range of task variables considered to be
suitable candidates for specifying the gradient of the continuous numeric
change effected by an action. Finally, we explore the potential for employing
mixed integer programming as a tool for optimising the timestamps of the
actions in the plan, once a solution has been found. To support this, we
further contribute a selection of extended benchmark domains that include
continuous numeric effects. We present results for COLIN that demonstrate its
scalability on a range of benchmarks, and compare to existing state-of-the-art
planners.
",COLIN: Planning with Continuous Linear Numeric Change
"  Efficient use of multiple batteries is a practical problem with wide and
growing application. The problem can be cast as a planning problem under
uncertainty. We describe the approach we have adopted to modelling and solving
this problem, seen as a Markov Decision Problem, building effective policies
for battery switching in the face of stochastic load profiles.
  Our solution exploits and adapts several existing techniques: planning for
deterministic mixed discrete-continuous problems and Monte Carlo sampling for
policy learning. The paper describes the development of planning techniques to
allow solution of the non-linear continuous dynamic models capturing the
battery behaviours. This approach depends on carefully handled discretisation
of the temporal dimension. The construction of policies is performed using a
classification approach and this idea offers opportunities for wider
exploitation in other problems. The approach and its generality are described
in the paper.
  Application of the approach leads to construction of policies that, in
simulation, significantly outperform those that are currently in use and the
best published solutions to the battery management problem. We achieve
solutions that achieve more than 99% efficiency in simulation compared with the
theoretical limit and do so with far fewer battery switches than existing
policies. Behaviour of physical batteries does not exactly match the simulated
models for many reasons, so to confirm that our theoretical results can lead to
real measured improvements in performance we also conduct and report
experiments using a physical test system. These results demonstrate that we can
obtain 5%-15% improvement in lifetimes in the case of a two battery system.
",Plan-based Policies for Efficient Multiple Battery Load Management
"  Pseudo-Boolean constraints are omnipresent in practical applications, and
thus a significant effort has been devoted to the development of good SAT
encoding techniques for them. Some of these encodings first construct a Binary
Decision Diagram (BDD) for the constraint, and then encode the BDD into a
propositional formula. These BDD-based approaches have some important
advantages, such as not being dependent on the size of the coefficients, or
being able to share the same BDD for representing many constraints.
  We first focus on the size of the resulting BDDs, which was considered to be
an open problem in our research community. We report on previous work where it
was proved that there are Pseudo-Boolean constraints for which no polynomial
BDD exists. We also give an alternative and simpler proof assuming that NP is
different from Co-NP. More interestingly, here we also show how to overcome the
possible exponential blowup of BDDs by phcoefficient decomposition. This allows
us to give the first polynomial generalized arc-consistent ROBDD-based encoding
for Pseudo-Boolean constraints.
  Finally, we focus on practical issues: we show how to efficiently construct
such ROBDDs, how to encode them into SAT with only 2 clauses per node, and
present experimental results that confirm that our approach is competitive with
other encodings and state-of-the-art Pseudo-Boolean solvers.
",A New Look at BDDs for Pseudo-Boolean Constraints
"  Domain-independent planning is one of the foundational areas in the field of
Artificial Intelligence. A description of a planning task consists of an
initial world state, a goal, and a set of actions for modifying the world
state. The objective is to find a sequence of actions, that is, a plan, that
transforms the initial world state into a goal state. In optimal planning, we
are interested in finding not just a plan, but one of the cheapest plans. A
prominent approach to optimal planning these days is heuristic state-space
search, guided by admissible heuristic functions. Numerous admissible
heuristics have been developed, each with its own strengths and weaknesses, and
it is well known that there is no single ""best heuristic for optimal planning
in general. Thus, which heuristic to choose for a given planning task is a
difficult question. This difficulty can be avoided by combining several
heuristics, but that requires computing numerous heuristic estimates at each
state, and the tradeoff between the time spent doing so and the time saved by
the combined advantages of the different heuristics might be high. We present a
novel method that reduces the cost of combining admissible heuristics for
optimal planning, while maintaining its benefits. Using an idealized search
space model, we formulate a decision rule for choosing the best heuristic to
compute at each state. We then present an active online learning approach for
learning a classifier with that decision rule as the target concept, and employ
the learned classifier to decide which heuristic to compute at each state. We
evaluate this technique empirically, and show that it substantially outperforms
the standard method for combining several heuristics via their pointwise
maximum.
",Online Speedup Learning for Optimal Planning
"  The talent scheduling problem is a simplified version of the real-world film
shooting problem, which aims to determine a shooting sequence so as to minimize
the total cost of the actors involved. In this article, we first formulate the
problem as an integer linear programming model. Next, we devise a
branch-and-bound algorithm to solve the problem. The branch-and-bound algorithm
is enhanced by several accelerating techniques, including preprocessing,
dominance rules and caching search states. Extensive experiments over two sets
of benchmark instances suggest that our algorithm is superior to the current
best exact algorithm. Finally, the impacts of different parameter settings are
disclosed by some additional experiments.
",An Enhanced Branch-and-bound Algorithm for the Talent Scheduling Problem
"  Replanning via determinization is a recent, popular approach for online
planning in MDPs. In this paper we adapt this idea to classical, non-stochastic
domains with partial information and sensing actions, presenting a new planner:
SDR (Sample, Determinize, Replan). At each step we generate a solution plan to
a classical planning problem induced by the original problem. We execute this
plan as long as it is safe to do so. When this is no longer the case, we
replan. The classical planning problem we generate is based on the
translation-based approach for conformant planning introduced by Palacios and
Geffner. The state of the classical planning problem generated in this approach
captures the belief state of the agent in the original problem. Unfortunately,
when this method is applied to planning problems with sensing, it yields a
non-deterministic planning problem that is typically very large. Our main
contribution is the introduction of state sampling techniques for overcoming
these two problems. In addition, we introduce a novel, lazy, regression-based
method for querying the agents belief state during run-time. We provide a
comprehensive experimental evaluation of the planner, showing that it scales
better than the state-of-the-art CLG planner on existing benchmark problems,
but also highlighting its weaknesses with new domains. We also discuss its
theoretical guarantees.
",Replanning in Domains with Partial Information and Sensing Actions
"  In some domestic professional sports leagues, the home stadiums are located
in cities connected by a common train line running in one direction. For these
instances, we can incorporate this geographical information to determine
optimal or nearly-optimal solutions to the n-team Traveling Tournament Problem
(TTP), an NP-hard sports scheduling problem whose solution is a double
round-robin tournament schedule that minimizes the sum total of distances
traveled by all n teams. We introduce the Linear Distance Traveling Tournament
Problem (LD-TTP), and solve it for n=4 and n=6, generating the complete set of
possible solutions through elementary combinatorial techniques. For larger n,
we propose a novel ""expander construction"" that generates an approximate
solution to the LD-TTP. For n congruent to 4 modulo 6, we show that our
expander construction produces a feasible double round-robin tournament
schedule whose total distance is guaranteed to be no worse than 4/3 times the
optimal solution, regardless of where the n teams are located. This
4/3-approximation for the LD-TTP is stronger than the currently best-known
ratio of 5/3 + epsilon for the general TTP. We conclude the paper by applying
this linear distance relaxation to general (non-linear) n-team TTP instances,
where we develop fast approximate solutions by simply ""assuming"" the n teams
lie on a straight line and solving the modified problem. We show that this
technique surprisingly generates the distance-optimal tournament on all
benchmark sets on 6 teams, as well as close-to-optimal schedules for larger n,
even when the teams are located around a circle or positioned in
three-dimensional space.
","Generating Approximate Solutions to the TTP using a Linear Distance
  Relaxation"
"  One of the defining characteristic of human being is their ability to walk
upright. Loss or restriction of such ability whether due to the accident, spine
problem, stroke or other neurological injuries can cause tremendous stress on
the patients and hence will contribute negatively to their quality of life.
Modern research shows that physical exercise is very important for maintaining
physical fitness and adopting a healthier life style. In modern days treadmill
is widely used for physical exercises and training which enables the user to
set up an exercise regime that can be adhered to irrespective of the weather
conditions. Among the users of treadmills today are medical facilities such as
hospitals, rehabilitation centres, medical and physiotherapy clinics etc. The
process of assisted training or doing rehabilitation exercise through treadmill
is referred to as treadmill therapy. A modern treadmill is an automated machine
having built in functions and predefined features. Most of the treadmills used
today are one dimensional and user can only walk in one direction. This paper
presents the idea of using omnidirectional treadmills which will be more
appealing to the patients as they can walk in any direction, hence encouraging
them to do exercises more frequently. This paper proposes a fuzzy control
design and possible implementation strategy to assist patients in treadmill
therapy. By intelligently controlling the safety belt attached to the treadmill
user, one can help them steering left, right or in any direction. The use of
intelligent treadmill therapy can help patients to improve their walking
ability without being continuously supervised by the specialists. The patients
can walk freely within a limited space and the support system will provide
continuous evaluation of their position and can adjust the control parameters
of treadmill accordingly to provide best possible assistance.
",Fuzzy Controller Design for Assisted Omni-Directional Treadmill Therapy
"  Airspace sectorisation provides a partition of a given airspace into sectors,
subject to geometric constraints and workload constraints, so that some cost
metric is minimised. We make a study of the constraints that arise in airspace
sectorisation. For each constraint, we give an analysis of what algorithms and
properties are required under systematic search and stochastic local search.
","Propagators and Violation Functions for Geometric and Workload
  Constraints Arising in Airspace Sectorisation"
"  Dynamic Bayesian networks (DBNs) are a general model for stochastic processes
with partially observed states. Belief filtering in DBNs is the task of
inferring the belief state (i.e. the probability distribution over process
states) based on incomplete and noisy observations. This can be a hard problem
in complex processes with large state spaces. In this article, we explore the
idea of accelerating the filtering task by automatically exploiting causality
in the process. We consider a specific type of causal relation, called
passivity, which pertains to how state variables cause changes in other
variables. We present the Passivity-based Selective Belief Filtering (PSBF)
method, which maintains a factored belief representation and exploits passivity
to perform selective updates over the belief factors. PSBF produces exact
belief states under certain assumptions and approximate belief states
otherwise, where the approximation error is bounded by the degree of
uncertainty in the process. We show empirically, in synthetic processes with
varying sizes and degrees of passivity, that PSBF is faster than several
alternative methods while achieving competitive accuracy. Furthermore, we
demonstrate how passivity occurs naturally in a complex system such as a
multi-robot warehouse, and how PSBF can exploit this to accelerate the
filtering task.
","Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian
  Networks"
"  We study a probability distribution d on the truth assignments to a uniform
binary AND-OR tree. Liu and Tanaka [2007, Inform. Process. Lett.] showed the
following: If d achieves the equilibrium among independent distributions (ID)
then d is an independent identical distribution (IID). We show a stronger form
of the above result. Given a real number r such that 0 < r < 1, we consider a
constraint that the probability of the root node having the value 0 is r. Our
main result is the following: When we restrict ourselves to IDs satisfying this
constraint, the above result of Liu and Tanaka still holds. The proof employs
clever tricks of induction. In particular, we show two fundamental
relationships between expected cost and probability in an IID on an OR-AND
tree: (1) The ratio of the cost to the probability (of the root having the
value 0) is a decreasing function of the probability x of the leaf. (2) The
ratio of derivative of the cost to the derivative of the probability is a
decreasing function of x, too.
",Equilibrium Points of an AND-OR Tree: under Constraints on Probability
"  Special-purpose constraint propagation algorithms frequently make implicit
use of short supports -- by examining a subset of the variables, they can infer
support (a justification that a variable-value pair may still form part of an
assignment that satisfies the constraint) for all other variables and values
and save substantial work -- but short supports have not been studied in their
own right. The two main contributions of this paper are the identification of
short supports as important for constraint propagation, and the introduction of
HaggisGAC, an efficient and effective general purpose propagation algorithm for
exploiting short supports. Given the complexity of HaggisGAC, we present it as
an optimised version of a simpler algorithm ShortGAC. Although experiments
demonstrate the efficiency of ShortGAC compared with other general-purpose
propagation algorithms where a compact set of short supports is available, we
show theoretically and experimentally that HaggisGAC is even better. We also
find that HaggisGAC performs better than GAC-Schema on full-length supports. We
also introduce a variant algorithm HaggisGAC-Stable, which is adapted to avoid
work on backtracking and in some cases can be faster and have significant
reductions in memory use. All the proposed algorithms are excellent for
propagating disjunctions of constraints. In all experiments with disjunctions
we found our algorithms to be faster than Constructive Or and GAC-Schema by at
least an order of magnitude, and up to three orders of magnitude.
",Short and Long Supports for Constraint Propagation
"  The results in this paper add useful tools to the theory of sets of desirable
gambles, a growing toolbox for reasoning with partial probability assessments.
We investigate how to combine a number of marginal coherent sets of desirable
gambles into a joint set using the properties of epistemic irrelevance and
independence. We provide formulas for the smallest such joint, called their
independent natural extension, and study its main properties. The independent
natural extension of maximal coherent sets of desirable gambles allows us to
define the strong product of sets of desirable gambles. Finally, we explore an
easy way to generalise these results to also apply for the conditional versions
of epistemic irrelevance and independence. Having such a set of tools that are
easily implemented in computer programs is clearly beneficial to fields, like
AI, with a clear interest in coherent reasoning under uncertainty using general
and robust uncertainty models that require no full specification.
","Irrelevant and independent natural extension for sets of desirable
  gambles"
"  Although the use of metric fluents is fundamental to many practical planning
problems, the study of heuristics to support fully automated planners working
with these fluents remains relatively unexplored. The most widely used
heuristic is the relaxation of metric fluents into interval-valued variables
--- an idea first proposed a decade ago. Other heuristics depend on domain
encodings that supply additional information about fluents, such as capacity
constraints or other resource-related annotations. A particular challenge to
these approaches is in handling interactions between metric fluents that
represent exchange, such as the transformation of quantities of raw materials
into quantities of processed goods, or trading of money for materials. The
usual relaxation of metric fluents is often very poor in these situations,
since it does not recognise that resources, once spent, are no longer available
to be spent again. We present a heuristic for numeric planning problems
building on the propositional relaxed planning graph, but using a mathematical
program for numeric reasoning. We define a class of producer--consumer planning
problems and demonstrate how the numeric constraints in these can be modelled
in a mixed integer program (MIP). This MIP is then combined with a metric
Relaxed Planning Graph (RPG) heuristic to produce an integrated hybrid
heuristic. The MIP tracks resource use more accurately than the usual
relaxation, but relaxes the ordering of actions, while the RPG captures the
causal propositional aspects of the problem. We discuss how these two
components interact to produce a single unified heuristic and go on to explore
how further numeric features of planning problems can be integrated into the
MIP. We show that encoding a limited subset of the propositional problem to
augment the MIP can yield more accurate guidance, partly by exploiting
structure such as propositional landmarks and propositional resources. Our
results show that the use of this heuristic enhances scalability on problems
where numeric resource interaction is key in finding a solution.
","A Hybrid LP-RPG Heuristic for Modelling Numeric Resource Flows in
  Planning"
"  Lifted probabilistic inference algorithms exploit regularities in the
structure of graphical models to perform inference more efficiently. More
specifically, they identify groups of interchangeable variables and perform
inference once per group, as opposed to once per variable. The groups are
defined by means of constraints, so the flexibility of the grouping is
determined by the expressivity of the constraint language. Existing approaches
for exact lifted inference use specific languages for (in)equality constraints,
which often have limited expressivity. In this article, we decouple lifted
inference from the constraint language. We define operators for lifted
inference in terms of relational algebra operators, so that they operate on the
semantic level (the constraints extension) rather than on the syntactic level,
making them language-independent. As a result, lifted inference can be
performed using more powerful constraint languages, which provide more
opportunities for lifting. We empirically demonstrate that this can improve
inference efficiency by orders of magnitude, allowing exact inference where
until now only approximate inference was feasible.
","Lifted Variable Elimination: Decoupling the Operators from the
  Constraint Language"
"  This article presents the state-of-the-art in optimal solution methods for
decentralized partially observable Markov decision processes (Dec-POMDPs),
which are general models for collaborative multiagent planning under
uncertainty. Building off the generalized multiagent A* (GMAA*) algorithm,
which reduces the problem to a tree of one-shot collaborative Bayesian games
(CBGs), we describe several advances that greatly expand the range of
Dec-POMDPs that can be solved optimally. First, we introduce lossless
incremental clustering of the CBGs solved by GMAA*, which achieves exponential
speedups without sacrificing optimality. Second, we introduce incremental
expansion of nodes in the GMAA* search tree, which avoids the need to expand
all children, the number of which is in the worst case doubly exponential in
the nodes depth. This is particularly beneficial when little clustering is
possible. In addition, we introduce new hybrid heuristic representations that
are more compact and thereby enable the solution of larger Dec-POMDPs. We
provide theoretical guarantees that, when a suitable heuristic is used, both
incremental clustering and incremental expansion yield algorithms that are both
complete and search equivalent. Finally, we present extensive empirical results
demonstrating that GMAA*-ICE, an algorithm that synthesizes these advances, can
optimally solve Dec-POMDPs of unprecedented size.
","Incremental Clustering and Expansion for Faster Optimal Planning in
  Dec-POMDPs"
"  We present an approach to propagation-based SAT encoding of combinatorial
problems, Boolean equi-propagation, where constraints are modeled as Boolean
functions which propagate information about equalities between Boolean
literals. This information is then applied to simplify the CNF encoding of the
constraints. A key factor is that considering only a small fragment of a
constraint model at one time enables us to apply stronger, and even complete,
reasoning to detect equivalent literals in that fragment. Once detected,
equivalences apply to simplify the entire constraint model and facilitate
further reasoning on other fragments. Equi-propagation in combination with
partial evaluation and constraint simplification provide the foundation for a
powerful approach to SAT-based finite domain constraint solving. We introduce a
tool called BEE (Ben-Gurion Equi-propagation Encoder) based on these ideas and
demonstrate for a variety of benchmarks that our approach leads to a
considerable reduction in the size of CNF encodings and subsequent speed-ups in
SAT solving times.
","Boolean Equi-propagation for Concise and Efficient SAT Encodings of
  Combinatorial Problems"
"  Major advances in Question Answering technology were needed for IBM Watson to
play Jeopardy! at championship level -- the show requires rapid-fire answers to
challenging natural language questions, broad general knowledge, high
precision, and accurate confidence estimates. In addition, Jeopardy! features
four types of decision making carrying great strategic importance: (1) Daily
Double wagering; (2) Final Jeopardy wagering; (3) selecting the next square
when in control of the board; (4) deciding whether to attempt to answer, i.e.,
""buzz in."" Using sophisticated strategies for these decisions, that properly
account for the game state and future event probabilities, can significantly
boost a players overall chances to win, when compared with simple ""rule of
thumb"" strategies. This article presents our approach to developing Watsons
game-playing strategies, comprising development of a faithful simulation model,
and then using learning and Monte-Carlo methods within the simulator to
optimize Watsons strategic decision-making. After giving a detailed description
of each of our game-strategy algorithms, we then focus in particular on
validating the accuracy of the simulators predictions, and documenting
performance improvements using our methods. Quantitative performance benefits
are shown with respect to both simple heuristic strategies, and actual human
contestant performance in historical episodes. We further extend our analysis
of human play to derive a number of valuable and counterintuitive examples
illustrating how human contestants may improve their performance on the show.
",Analysis of Watson's Strategies for Playing Jeopardy!
"  Dung's abstract argumentation theory can be seen as a general framework for
non-monotonic reasoning. An important question is then: what is the class of
logics that can be subsumed as instantiations of this theory? The goal of this
paper is to identify and study the large class of logic-based instantiations of
Dung's theory which correspond to the maxi-consistent operator, i.e. to the
function which returns maximal consistent subsets of an inconsistent knowledge
base. In other words, we study the class of instantiations where very extension
of the argumentation system corresponds to exactly one maximal consistent
subset of the knowledge base. We show that an attack relation belonging to this
class must be conflict-dependent, must not be valid, must not be
conflict-complete, must not be symmetric etc. Then, we show that some attack
relations serve as lower or upper bounds of the class (e.g. if an attack
relation contains canonical undercut then it is not a member of this class). By
using our results, we show for all existing attack relations whether or not
they belong to this class. We also define new attack relations which are
members of this class. Finally, we interpret our results and discuss more
general questions, like: what is the added value of argumentation in such a
setting? We believe that this work is a first step towards achieving our
long-term goal, which is to better understand the role of argumentation and,
particularly, the expressivity of logic-based instantiations of Dung-style
argumentation frameworks.
",Identifying the Class of Maxi-Consistent Operators in Argumentation
"  This paper presents a model-based planner called the Probabilistic Sulu
Planner or the p-Sulu Planner, which controls stochastic systems in a goal
directed manner within user-specified risk bounds. The objective of the p-Sulu
Planner is to allow users to command continuous, stochastic systems, such as
unmanned aerial and space vehicles, in a manner that is both intuitive and
safe. To this end, we first develop a new plan representation called a
chance-constrained qualitative state plan (CCQSP), through which users can
specify the desired evolution of the plant state as well as the acceptable
level of risk. An example of a CCQSP statement is go to A through B within 30
minutes, with less than 0.001% probability of failure."" We then develop the
p-Sulu Planner, which can tractably solve a CCQSP planning problem. In order to
enable CCQSP planning, we develop the following two capabilities in this paper:
1) risk-sensitive planning with risk bounds, and 2) goal-directed planning in a
continuous domain with temporal constraints. The first capability is to ensures
that the probability of failure is bounded. The second capability is essential
for the planner to solve problems with a continuous state space such as vehicle
path planning. We demonstrate the capabilities of the p-Sulu Planner by
simulations on two real-world scenarios: the path planning and scheduling of a
personal aerial vehicle as well as the space rendezvous of an autonomous cargo
spacecraft.
",Probabilistic Planning for Continuous Dynamic Systems under Bounded Risk
"  This paper presents a structured power and energy-flow-based qualitative
modelling approach that is applicable to a variety of system types including
electrical and fluid flow. The modelling is split into two parts. Power flow is
a global phenomenon and is therefore naturally represented and analysed by a
network comprised of the relevant structural elements from the components of a
system. The power flow analysis is a platform for higher-level behaviour
prediction of energy related aspects using local component behaviour models to
capture a state-based representation with a global time. The primary
application is Failure Modes and Effects Analysis (FMEA) and a form of
exaggeration reasoning is used, combined with an order of magnitude
representation to derive the worst case failure modes. The novel aspects of the
work are an order of magnitude(OM) qualitative network analyser to represent
any power domain and topology, including multiple power sources, a feature that
was not required for earlier specialised electrical versions of the approach.
Secondly, the representation of generalised energy related behaviour as
state-based local models is presented as a modelling strategy that can be more
vivid and intuitive for a range of topologically complex applications than
qualitative equation-based representations.The two-level modelling strategy
allows the broad system behaviour coverage of qualitative simulation to be
exploited for the FMEA task, while limiting the difficulties of qualitative
ambiguity explanation that can arise from abstracted numerical models. We have
used the method to support an automated FMEA system with examples of an
aircraft fuel system and domestic a heating system discussed in this paper.
","Qualitative Order of Magnitude Energy-Flow-Based Failure Modes and
  Effects Analysis"
"  We address a dynamic repair shop scheduling problem in the context of
military aircraft fleet management where the goal is to maintain a full
complement of aircraft over the long-term. A number of flights, each with a
requirement for a specific number and type of aircraft, are already scheduled
over a long horizon. We need to assign aircraft to flights and schedule repair
activities while considering the flights requirements, repair capacity, and
aircraft failures. The number of aircraft awaiting repair dynamically changes
over time due to failures and it is therefore necessary to rebuild the repair
schedule online. To solve the problem, we view the dynamic repair shop as
successive static repair scheduling sub-problems over shorter time periods. We
propose a complete approach based on the logic-based Benders decomposition to
solve the static sub-problems, and design different rescheduling policies to
schedule the dynamic repair shop. Computational experiments demonstrate that
the Benders model is able to find and prove optimal solutions on average four
times faster than a mixed integer programming model. The rescheduling approach
having both aspects of scheduling over a longer horizon and quickly adjusting
the schedule increases aircraft available in the long term by 10% compared to
the approaches having either one of the aspects alone.
",Scheduling a Dynamic Aircraft Repair Shop with Limited Repair Resources
"  Algorithmic composition is the partial or total automation of the process of
music composition by using computers. Since the 1950s, different computational
techniques related to Artificial Intelligence have been used for algorithmic
composition, including grammatical representations, probabilistic methods,
neural networks, symbolic rule-based systems, constraint programming and
evolutionary algorithms. This survey aims to be a comprehensive account of
research on algorithmic composition, presenting a thorough view of the field
for researchers in Artificial Intelligence.
",AI Methods in Algorithmic Composition: A Comprehensive Survey
"  Distributed Constraint Optimization (DCOP) is a powerful framework for
representing and solving distributed combinatorial problems, where the
variables of the problem are owned by different agents. Many multi-agent
problems include constraints that produce different gains (or costs) for the
participating agents. Asymmetric gains of constrained agents cannot be
naturally represented by the standard DCOP model. The present paper proposes a
general framework for Asymmetric DCOPs (ADCOPs). In ADCOPs different agents may
have different valuations for constraints that they are involved in. The new
framework bridges the gap between multi-agent problems which tend to have
asymmetric structure and the standard symmetric DCOP model. The benefits of the
proposed model over previous attempts to generalize the DCOP model are
discussed and evaluated. Innovative algorithms that apply to the special
properties of the proposed ADCOP model are presented in detail. These include
complete algorithms that have a substantial advantage in terms of runtime and
network load over existing algorithms (for standard DCOPs) which use
alternative representations. Moreover, standard incomplete algorithms (i.e.,
local search algorithms) are inapplicable to the existing DCOP representations
of asymmetric constraints and when they are applied to the new ADCOP framework
they often fail to converge to a local optimum and yield poor results. The
local search algorithms proposed in the present paper converge to high quality
solutions. The experimental evidence that is presented reveals that the
proposed local search algorithms for ADCOPs achieve high quality solutions
while preserving a high level of privacy.
",Asymmetric Distributed Constraint Optimization Problems
"  Sequential decision-making problems with multiple objectives arise naturally
in practice and pose unique challenges for research in decision-theoretic
planning and learning, which has largely focused on single-objective settings.
This article surveys algorithms designed for sequential decision-making
problems with multiple objectives. Though there is a growing body of literature
on this subject, little of it makes explicit under what circumstances special
methods are needed to solve multi-objective problems. Therefore, we identify
three distinct scenarios in which converting such a problem to a
single-objective one is impossible, infeasible, or undesirable. Furthermore, we
propose a taxonomy that classifies multi-objective methods according to the
applicable scenario, the nature of the scalarization function (which projects
multi-objective values to scalar ones), and the type of policies considered. We
show how these factors determine the nature of an optimal solution, which can
be a single policy, a convex hull, or a Pareto front. Using this taxonomy, we
survey the literature on multi-objective methods for planning and learning.
Finally, we discuss key applications of such methods and outline opportunities
for future work.
",A Survey of Multi-Objective Sequential Decision-Making
"  Combining efficiency with reliability within CP systems is one of the main
concerns of CP developers. This paper presents a simple and efficient way to
connect Choco and Ibex, two CP solvers respectively specialised on finite and
continuous domains. This enables to take advantage of the most recent advances
of the continuous community within Choco while saving development and
maintenance resources, hence ensuring a better software quality.
",Combining finite and continuous solvers
"  The paper focuses on mining patterns that are characterized by a fuzzy lagged
relationship between the data objects forming them. Such a regulatory mechanism
is quite common in real life settings. It appears in a variety of fields:
finance, gene expression, neuroscience, crowds and collective movements are but
a limited list of examples. Mining such patterns not only helps in
understanding the relationship between objects in the domain, but assists in
forecasting their future behavior. For most interesting variants of this
problem, finding an optimal fuzzy lagged co-cluster is an NP-complete problem.
We thus present a polynomial-time Monte-Carlo approximation algorithm for
mining fuzzy lagged co-clusters. We prove that for any data matrix, the
algorithm mines a fuzzy lagged co-cluster with fixed probability, which
encompasses the optimal fuzzy lagged co-cluster by a maximum 2 ratio columns
overhead and completely no rows overhead. Moreover, the algorithm handles
noise, anti-correlations, missing values and overlapping patterns. The
algorithm was extensively evaluated using both artificial and real datasets.
The results not only corroborate the ability of the algorithm to efficiently
mine relevant and accurate fuzzy lagged co-clusters, but also illustrate the
importance of including the fuzziness in the lagged-pattern model.
",Co-clustering of Fuzzy Lagged Data
"  In this paper, we revisit an important issue of CDCL-based SAT solvers,
namely the learned clauses database management policies. Our motivation takes
its source from a simple observation on the remarkable performances of both
random and size-bounded reduction strategies. We first derive a simple
reduction strategy, called Size-Bounded Randomized strategy (in short SBR),
that combines maintaing short clauses (of size bounded by k), while deleting
randomly clauses of size greater than k. The resulting strategy outperform the
state-of-the-art, namely the LBD based one, on SAT instances taken from the
last SAT competition. Reinforced by the interest of keeping short clauses, we
propose several new dynamic variants, and we discuss their performances.
",Revisiting the Learned Clauses Database Reduction Strategies
"  We introduce in this paper an algorithm named Contextuel-E-Greedy that
tackles the dynamicity of the user's content. It is based on dynamic
exploration/exploitation tradeoff and can adaptively balance the two aspects by
deciding which situation is most relevant for exploration or exploitation. The
experimental results demonstrate that our algorithm outperforms surveyed
algorithms.
","Recommandation mobile, sensible au contexte de contenus \'evolutifs:
  Contextuel-E-Greedy"
"  Dempster-Shafer theory is widely applied to uncertainty modelling and
knowledge reasoning due to its ability of expressing uncertain information.
However, some conditions, such as exclusiveness hypothesis and completeness
constraint, limit its development and application to a large extend. To
overcome these shortcomings in Dempster-Shafer theory and enhance its
capability of representing uncertain information, a novel theory called D
numbers theory is systematically proposed in this paper. Within the proposed
theory, uncertain information is expressed by D numbers, reasoning and
synthesization of information are implemented by D numbers combination rule.
The proposed D numbers theory is an generalization of Dempster-Shafer theory,
which inherits the advantage of Dempster-Shafer theory and strengthens its
capability of uncertainty modelling.
",D numbers theory: a generalization of Dempster-Shafer theory
"  Parameter estimation based on uncertain data represented as belief structures
is one of the latest problems in the Dempster-Shafer theory. In this paper, a
novel method is proposed for the parameter estimation in the case where belief
structures are uncertain and represented as interval-valued belief structures.
Within our proposed method, the maximization of likelihood criterion and
minimization of estimated parameter's uncertainty are taken into consideration
simultaneously. As an illustration, the proposed method is employed to estimate
parameters for deterministic and uncertain belief structures, which
demonstrates its effectiveness and versatility.
",Parameter estimation based on interval-valued belief structures
"  Collecting and maintaining accurate world knowledge in a dynamic, complex,
adversarial, and stochastic environment such as the RoboCup 3D Soccer
Simulation is a challenging task. Knowledge should be learned in real-time with
time constraints. We use recently introduced Off-Policy Gradient Descent
algorithms within Reinforcement Learning that illustrate learnable knowledge
representations for dynamic role assignments. The results show that the agents
have learned competitive policies against the top teams from the RoboCup 2012
competitions for three vs three, five vs five, and seven vs seven agents. We
have explicitly used subsets of agents to identify the dynamics and the
semantics for which the agents learn to maximize their performance measures,
and to gather knowledge about different objectives, so that all agents
participate effectively and efficiently within the group.
","Off-Policy General Value Functions to Represent Dynamic Role Assignments
  in RoboCup 3D Soccer Simulation"
"  As digital games continue to be explored as solutions to educational and
behavioural challenges, the need for evaluation methodologies which support
both the unique nature of the format and the need for comparison with other
approaches continues to increase. In this workshop paper, a range of challenges
are described related specifically to the case of cultural learning using
digital games, in terms of how it may best be assessed, understood, and
sustained through an iterative process supported by research. An evaluation
framework is proposed, identifying metrics for reach and impact and their
associated challenges, as well as presenting ethical considerations and the
means to utilize evaluation outcomes within an iterative cycle, and to provide
feedback to learners. Presenting as a case study a serious game from the Mobile
Assistance for Social Inclusion and Empowerment of Immigrants with Persuasive
Learning Technologies and Social Networks (MASELTOV) project, the use of the
framework in the context of an integrative project is discussed, with emphasis
on the need to view game-based learning as a blended component of the cultural
learning process, rather than a standalone solution. The particular case of
mobile gaming is also considered within this case study, providing a platform
by which to deliver and update content in response to evaluation outcomes.
Discussion reflects upon the general challenges related to the assessment of
cultural learning, and behavioural change in more general terms, suggesting
future work should address the need to provide sustainable, research-driven
platforms for game-based learning content.
","Assessing the Reach and Impact of Game-Based Learning Approaches to
  Cultural Competency and Behavioural Change"
"  Job interview simulation with a virtual agents aims at improving people's
social skills and supporting professional inclusion. In such simulators, the
virtual agent must be capable of representing and reasoning about the user's
mental state based on social cues that inform the system about his/her affects
and social attitude. In this paper, we propose a formal model of Theory of Mind
(ToM) for virtual agent in the context of human-agent interaction that focuses
on the affective dimension. It relies on a hybrid ToM that combines the two
major paradigms of the domain. Our framework is based on modal logic and
inference rules about the mental states, emotions and social relations of both
actors. Finally, we present preliminary results regarding the impact of such a
model on natural interaction in the context of job interviews simulation.
","A logical model of Theory of Mind for virtual agents in the context of
  job interview simulation"
"  The task of artificial intelligence is to provide representation techniques
for describing problems, as well as search algorithms that can be used to
answer our questions. A widespread and elaborated model is state-space
representation, which, however, has some shortcomings. Classical search
algorithms are not applicable in practice when the state space contains even
only a few tens of thousands of states. We can give remedy to this problem by
defining some kind of heuristic knowledge. In case of classical state-space
representation, heuristic must be defined so that it qualifies an arbitrary
state based on its ""goodness,"" which is obviously not trivial. In our paper, we
introduce an algorithm that gives us the ability to handle huge state spaces
and to use a heuristic concept which is easier to embed into search algorithms.
",Extended Breadth-First Search Algorithm
"  We frame the question of what kind of subjective experience a brain
simulation would have in contrast to a biological brain. We discuss the brain
prosthesis thought experiment. We evaluate how the experience of the brain
simulation might differ from the biological, according to a number of
hypotheses about experience and the properties of simulation. Then, we identify
finer questions relating to the original inquiry, and answer them from both a
general physicalist, and panexperientialist perspective.
",What Is It Like to Be a Brain Simulation?
"  It is hypothesized by some thinkers that benign looking AI objectives may
result in powerful AI drives that may pose an existential risk to human
society. We analyze this scenario and find the underlying assumptions to be
unlikely. We examine the alternative scenario of what happens when universal
goals that are not human-centric are used for designing AI agents. We follow a
design approach that tries to exclude malevolent motivations from AI agents,
however, we see that objectives that seem benevolent may pose significant risk.
We consider the following meta-rules: preserve and pervade life and culture,
maximize the number of free minds, maximize intelligence, maximize wisdom,
maximize energy production, behave like human, seek pleasure, accelerate
evolution, survive, maximize control, and maximize capital. We also discuss
various solution approaches for benevolent behavior including selfless goals,
hybrid designs, Darwinism, universal constraints, semi-autonomy, and
generalization of robot laws. A ""prime directive"" for AI may help in
formulating an encompassing constraint for avoiding malicious behavior. We
hypothesize that social instincts for autonomous robots may be effective such
as attachment learning. We mention multiple beneficial scenarios for an
advanced semi-autonomous AGI agent in the near future including space
exploration, automation of industries, state functions, and cities. We conclude
that a beneficial AI agent with intelligence beyond human-level is possible and
has many practical use cases.
",Godseed: Benevolent or Malevolent?
"  This paper presents an analysis of data from a gift-exchange-game experiment.
The experiment was described in `The Impact of Social Comparisons on
Reciprocity' by G\""achter et al. 2012. Since this paper uses state-of-art data
science techniques, the results provide a different point of view on the
problem. As already shown in relevant literature from experimental economics,
human decisions deviate from rational payoff maximization. The average gift
rate was $31$%. Gift rate was under no conditions zero. Further, we derive some
special findings and calculate their significance.
",Reciprocity in Gift-Exchange-Games
"  Valuation algebras abstract a large number of formalisms for automated
reasoning and enable the definition of generic inference procedures. Many of
these formalisms provide some notions of solutions. Typical examples are
satisfying assignments in constraint systems, models in logics or solutions to
linear equation systems.
  Recently, formal requirements for the presence of solutions and a generic
algorithm for solution construction based on the results of a previously
executed inference scheme have been proposed in the literature. Unfortunately,
the formalization of Pouly and Kohlas relies on a theorem for which we provide
a counter example. In spite of that, the mainline of the theory described is
correct, although some of the necessary conditions to apply some of the
algorithms have to be revised. To fix the theory, we generalize some of their
definitions and provide correct sufficient conditions for the algorithms. As a
result, we get a more general and corrected version of the already existing
theory.
",Even more generic solution construction in Valuation-Based Systems
"  We present an epistemic action theory for tractable epistemic reasoning as an
extension to the h-approximation (HPX) theory. In contrast to existing
tractable approaches, the theory supports functional fluents and postdictive
reasoning with static causal laws. We argue that this combination is
particularly synergistic because it allows one not only to perform direct
postdiction about the conditions of actions, but also indirect postdiction
about the conditions of static causal laws. We show that despite the richer
expressiveness, the temporal projection problem remains tractable (polynomial),
and therefore the planning problem remains in NP. We present the operational
semantics of our theory as well as its formulation as Answer Set Programming.
","Tractable Epistemic Reasoning with Functional Fluents, Static Causal
  Laws and Postdiction"
"  The Distributed Cooperative Modeling System (DCMS) solves complex decision
problems involving a lot of participants with different viewpoints by network
based distributed modeling and multi-template aggregation.
  This thesis aims at extending the system with support for dynamic decision
making process. First, the thesis presents a discussion of characteristics and
optimal policy finding Markov Decision Process as well as a brief introduction
to dynamic Bayesian decision network, which is inherently equal to MDP. After
that, discussion and implementation of prediction in Markov process for both
discrete and continuous random variable are given, as well as several different
kinds of correlation analysis among multiple indices which could help
decision-makers to realize the interaction of indices and design appropriate
policy.
  Appending history data of Macau industry, as the foundation of extending
DCMS, is introduced. Additional works include rearrangement of graphical class
hierarchy in DCMS, which in turn allows convenient implementation of curve
relation-line, which makes template modeling clearer and friendlier.
","Dynamic Decision Process Modeling and Relation-line Handling in
  Distributed Cooperative Modeling System"
"  The thyroid, an endocrine gland that secretes hormones in the blood,
circulates its products to all tissues of the body, where they control vital
functions in every cell. Normal levels of thyroid hormone help the brain,
heart, intestines, muscles and reproductive system function normally. Thyroid
hormones control the metabolism of the body. Abnormalities of thyroid function
are usually related to production of too little thyroid hormone
(hypothyroidism) or production of too much thyroid hormone (hyperthyroidism).
Therefore, the correct diagnosis of these diseases is very important topic. In
this study, Linguistic Hedges Neural-Fuzzy Classifier with Selected Features
(LHNFCSF) is presented for diagnosis of thyroid diseases. The performance
evaluation of this system is estimated by using classification accuracy and
k-fold cross-validation. The results indicated that the classification accuracy
without feature selection was 98.6047% and 97.6744% during training and testing
phases, respectively with RMSE of 0.02335. After applying feature selection
algorithm, LHNFCSF achieved 100% for all cluster sizes during training phase.
However, in the testing phase LHNFCSF achieved 88.3721% using one cluster for
each class, 90.6977% using two clusters, 91.8605% using three clusters and
97.6744% using four clusters for each class and 12 fuzzy rules. The obtained
classification accuracy was very promising with regard to the other
classification applications in literature for this problem.
",Expert System Based On Neural-Fuzzy Rules for Thyroid Diseases Diagnosis
"  The Region Connection Calculus (RCC) is a well-known calculus for
representing part-whole and topological relations. It plays an important role
in qualitative spatial reasoning, geographical information science, and
ontology. The computational complexity of reasoning with RCC5 and RCC8 (two
fragments of RCC) as well as other qualitative spatial/temporal calculi has
been investigated in depth in the literature. Most of these works focus on the
consistency of qualitative constraint networks. In this paper, we consider the
important problem of redundant qualitative constraints. For a set $\Gamma$ of
qualitative constraints, we say a constraint $(x R y)$ in $\Gamma$ is redundant
if it is entailed by the rest of $\Gamma$. A prime subnetwork of $\Gamma$ is a
subset of $\Gamma$ which contains no redundant constraints and has the same
solution set as $\Gamma$. It is natural to ask how to compute such a prime
subnetwork, and when it is unique.
  In this paper, we show that this problem is in general intractable, but
becomes tractable if $\Gamma$ is over a tractable subalgebra $\mathcal{S}$ of a
qualitative calculus. Furthermore, if $\mathcal{S}$ is a subalgebra of RCC5 or
RCC8 in which weak composition distributes over nonempty intersections, then
$\Gamma$ has a unique prime subnetwork, which can be obtained in cubic time by
removing all redundant constraints simultaneously from $\Gamma$. As a
byproduct, we show that any path-consistent network over such a distributive
subalgebra is weakly globally consistent and minimal. A thorough empirical
analysis of the prime subnetwork upon real geographical data sets demonstrates
the approach is able to identify significantly more redundant constraints than
previously proposed algorithms, especially in constraint networks with larger
proportions of partial overlap relations.
",On Redundant Topological Constraints
"  This paper describes a process for clustering concepts into chains from data
presented randomly to an evaluating system. There are a number of rules or
guidelines that help the system to determine more accurately what concepts
belong to a particular chain and what ones do not, but it should be possible to
write these in a generic way. This mechanism also uses a flat structure without
any hierarchical path information, where the link between two concepts is made
at the level of the concept itself. It does not require related metadata, but
instead, a simple counting mechanism is used. Key to this is a count for both
the concept itself and also the group or chain that it belongs to. To test the
possible success of the mechanism, concept chain parts taken randomly from a
larger ontology were presented to the system, but only at a depth of 2 concepts
each time. That is - root concept plus a concept that it is linked to. The
results show that this can still lead to very variable structures being formed
and can also accommodate some level of randomness.
",Clustering Concept Chains from Ordered Data without Path Descriptions
"  Our understanding of intelligence is directed primarily at the human level.
This paper attempts to give a more unifying definition that can be applied to
the natural world in general and then Artificial Intelligence. The definition
would be used more to verify a relative intelligence, not to quantify it and
might help when making judgements on the matter. While correct behaviour is the
preferred definition, a metric that is grounded in Kolmogorov's Complexity
Theory is suggested, which leads to a measurement about entropy. A version of
an accepted AI test is then put forward as the 'acid test' and might be what a
free-thinking program would try to achieve. Recent work by the author has been
more from a direction of mechanical processes, or ones that might operate
automatically. This paper agrees that intelligence is a pro-active event, but
also notes a second aspect to it that is in the background and mechanical. The
paper suggests looking at intelligence and the conscious as being slightly
different, where consciousness is this more mechanical aspect. In fact, a
surprising conclusion can be a passive but intelligent brain being invoked by
active and less intelligent senses.
",Is Intelligence Artificial?
"  These notes pose a ""proof challenge"": a proof, or disproof, of the
proposition that ""For any given body of information, I, expressed as a
one-dimensional sequence of atomic symbols, a multiple alignment concept,
described in the document, provides a means of encoding all the redundancy that
may exist in I. Aspects of the challenge are described.
",A proof challenge: multiple alignment and information compression
"  In this survey we present different approaches that allow an intelligent
agent to explore autonomous its environment to gather information and learn
multiple tasks. Different communities proposed different solutions, that are in
many cases, similar and/or complementary. These solutions include active
learning, exploration/exploitation, online-learning and social learning. The
common aspect of all these approaches is that it is the agent to selects and
decides what information to gather next. Applications for these approaches
already include tutoring systems, autonomous grasping learning, navigation and
mapping and human-robot interaction. We discuss how these approaches are
related, explaining their similarities and their differences in terms of
problem assumptions and metrics of success. We consider that such an integrated
discussion will improve inter-disciplinary research and applications.
","Active Learning for Autonomous Intelligent Agents: Exploration,
  Curiosity, and Interaction"
"  Real-time strategy (RTS) games make heavy use of artificial intelligence
(AI), especially in the design of computerized opponents. Because of the
computational complexity involved in managing all aspects of these games, many
AI opponents are designed to optimize only a few areas of playing style. In
games like StarCraft 2, a very popular and recently released RTS, most AI
strategies revolve around economic and building efficiency: AI opponents try to
gather and spend all resources as quickly and effectively as possible while
ensuring that no units are idle. The aim of this work was to help address the
need for AI combat strategies that are not computationally intensive. Our goal
was to produce a computationally efficient model that is accurate at predicting
the results of complex battles between diverse armies, including which army
will win and how many units will remain. Our results suggest it may be possible
to develop a relatively simple approximation model of combat that can
accurately predict many battles that do not involve micromanagement. Future
designs of AI opponents may be able to incorporate such an approximation model
into their decision and planning systems to provide a challenge that is
strategically balanced across all aspects of play.
",Approximation Models of Combat in StarCraft 2
"  Current research on Internet of Things (IoT) mainly focuses on how to enable
general objects to see, hear, and smell the physical world for themselves, and
make them connected to share the observations. In this paper, we argue that
only connected is not enough, beyond that, general objects should have the
capability to learn, think, and understand both physical and social worlds by
themselves. This practical need impels us to develop a new paradigm, named
Cognitive Internet of Things (CIoT), to empower the current IoT with a `brain'
for high-level intelligence. Specifically, we first present a comprehensive
definition for CIoT, primarily inspired by the effectiveness of human
cognition. Then, we propose an operational framework of CIoT, which mainly
characterizes the interactions among five fundamental cognitive tasks:
perception-action cycle, massive data analytics, semantic derivation and
knowledge discovery, intelligent decision-making, and on-demand service
provisioning. Furthermore, we provide a systematic tutorial on key enabling
techniques involved in the cognitive tasks. In addition, we also discuss the
design of proper performance metrics on evaluating the enabling techniques.
Last but not least, we present the research challenges and open issues ahead.
Building on the present work and potentially fruitful future studies, CIoT has
the capability to bridge the physical world (with objects, resources, etc.) and
the social world (with human demand, social behavior, etc.), and enhance smart
resource allocation, automatic network operation, and intelligent service
provisioning.
",Cognitive Internet of Things: A New Paradigm beyond Connection
"  This paper looks at Turing's postulations about Artificial Intelligence in
his paper 'Computing Machinery and Intelligence', published in 1950. It notes
how accurate they were and how relevant they still are today. This paper notes
the arguments and mechanisms that he suggested and tries to expand on them
further. The paper however is mostly about describing the essential ingredients
for building an intelligent model and the problems related with that. The
discussion includes recent work by the author himself, who adds his own
thoughts on the matter that come from a purely technical investigation into the
problem. These are personal and quite speculative, but provide an interesting
insight into the mechanisms that might be used for building an intelligent
system.
","Turing: Then, Now and Still Key"
"  The creation of fictional stories is a very complex task that usually implies
a creative process where the author has to combine characters, conflicts and
plots to create an engaging narrative. This work presents a simulated
environment with hundreds of characters that allows the study of coherent and
interesting literary archetypes (or behaviours), plots and sub-plots. We will
use this environment to perform a study about the number of profiles
(parameters that define the personality of a character) needed to create two
emergent scenes of archetypes: ""natality control"" and ""revenge"". A Genetic
Algorithm (GA) will be used to find the fittest number of profiles and
parameter configuration that enables the existence of the desired archetypes
(played by the characters without their explicit knowledge). The results show
that parametrizing this complex system is possible and that these kind of
archetypes can emerge in the given environment.
","Emerging archetypes in massive artificial societies for literary
  purposes using genetic algorithms"
"  Broad application of answer set programming (ASP) for declarative problem
solving requires the development of tools supporting the coding process.
Program debugging is one of the crucial activities within this process.
Recently suggested ASP debugging approaches allow efficient computation of
possible explanations of a fault. However, even for a small program a debugger
might return a large number of possible explanations and selection of the
correct one must be done manually. In this paper we present an interactive
query-based ASP debugging method which extends previous approaches and finds a
preferred explanation by means of observations. The system queries a programmer
whether a set of ground atoms must be true in all (cautiously) or some
(bravely) answer sets of the program. Since some queries can be more
informative than the others, we discuss query selection strategies which, given
user's preferences for an explanation, can find the best query. That is, the
query an answer of which reduces the overall number of queries required for the
identification of a preferred explanation.
",Interactive Debugging of ASP Programs
"  Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is widely
used in many real applications. With the desirable property of efficient
handling with the uncertain information in decision making, the fuzzy DEMATEL
is heavily studied. Recently, Dytczak and Ginda suggested to defuzzify the
fuzzy numbers firstly and then use the classical DEMATEL to obtain the final
result. In this short paper, we show that it is not reasonable in some
situations. The results of defuzzification at the first step are not coincide
with the results of defuzzification at the final step.It seems that the
alternative is to defuzzification in the final step in fuzzy DEMATEL.
","Defuzzify firstly or finally: Dose it matter in fuzzy DEMATEL under
  uncertain environment?"
"  In this paper we present the new logic programming language DALI, aimed at
defining agents and agent systems. A main design objective for DALI has been
that of introducing in a declarative fashion all the essential features, while
keeping the language as close as possible to the syntax and semantics of the
plain Horn--clause language. Special atoms and rules have been introduced, for
representing: external events, to which the agent is able to respond
(reactivity); actions (reactivity and proactivity); internal events (previous
conclusions which can trigger further activity); past and present events (to be
aware of what has happened). An extended resolution is provided, so that a DALI
agent is able to answer queries like in the plain Horn--clause language, but is
also able to cope with the different kinds of events, and exhibit a (rational)
reactive and proactive behaviour.
",Towards Active Logic Programming
"  In the dawn of computer science and the eve of neuroscience we participate in
rebirth of neuroscience due to new technology that allows us to deeply and
precisely explore whole new world that dwells in our brains.
","Cortex simulation system proposal using distributed computer network
  environments"
"  How to express an expert's or a decision maker's preference for alternatives
is an open issue. Consistent fuzzy preference relation (CFPR) is with big
advantages to handle this problem due to it can be construed via a smaller
number of pairwise comparisons and satisfies additive transitivity property.
However, the CFPR is incapable of dealing with the cases involving uncertain
and incomplete information. In this paper, a D numbers extended consistent
fuzzy preference relation (D-CFPR) is proposed to overcome the weakness. The
D-CFPR extends the classical CFPR by using a new model of expressing uncertain
information called D numbers. The D-CFPR inherits the merits of classical CFPR
and can be totally reduced to the classical CFPR. This study can be integrated
into our previous study about D-AHP (D numbers extended AHP) model to provide a
systematic solution for multi-criteria decision making (MCDM).
",D-CFPR: D numbers extended consistent fuzzy preference relations
"  Probabilistic Logic Programming (PLP) languages enable programmers to specify
systems that combine logical models with statistical knowledge. The inference
problem, to determine the probability of query answers in PLP, is intractable
in general, thereby motivating the need for approximate techniques. In this
paper, we present a technique for approximate inference of conditional
probabilities for PLP queries. It is an Adaptive Markov Chain Monte Carlo
(MCMC) technique, where the distribution from which samples are drawn is
modified as the Markov Chain is explored. In particular, the distribution is
progressively modified to increase the likelihood that a generated sample is
consistent with evidence. In our context, each sample is uniquely characterized
by the outcomes of a set of random variables. Inspired by reinforcement
learning, our technique propagates rewards to random variable/outcome pairs
used in a sample based on whether the sample was consistent or not. The
cumulative rewards of each outcome is used to derive a new ""adapted
distribution"" for each random variable. For a sequence of samples, the
distributions are progressively adapted after each sample. For a query with
""Markovian evaluation structure"", we show that the adapted distribution of
samples converges to the query's conditional probability distribution. For
Markovian queries, we present a modified adaptation process that can be used in
adaptive MCMC as well as adaptive independent sampling. We empirically evaluate
the effectiveness of the adaptive sampling methods for queries with and without
Markovian evaluation structure.
",Adaptive MCMC-Based Inference in Probabilistic Logic Programs
"  The fundamental step in measuring the robustness of a system is the synthesis
of the so called Process Map.This is generally based on the user raw data
material.Process Maps are of fundamental importance towards the understanding
of the nature of a system in that they indicate which variables are causally
related and which are particularly important.This paper represent the system
Map or business structure map to understand business criteria studying the
various aspects of the company.The business structure map or knowledge map or
Process map are used to increase the growth of the company by giving some
useful measures according to the business criteria.This paper also deals with
the different company strategy to reduce the risk factors.Process Map is
helpful for building such knowledge successfully.Making decisions from such map
in a highly complex situation requires more knowledge and resources.
",A Mining Method to Create Knowledge Map by Analysing the Data Resource
"  How can we predict the difficulty of a Sudoku puzzle? We give an overview of
difficulty rating metrics and evaluate them on extensive dataset on human
problem solving (more then 1700 Sudoku puzzles, hundreds of solvers). The best
results are obtained using a computational model of human solving activity.
Using the model we show that there are two sources of the problem difficulty:
complexity of individual steps (logic operations) and structure of dependency
among steps. We also describe metrics based on analysis of solutions under
relaxed constraints -- a novel approach inspired by phase transition phenomenon
in the graph coloring problem. In our discussion we focus not just on the
performance of individual metrics on the Sudoku puzzle, but also on their
generalizability and applicability to other problems.
",Difficulty Rating of Sudoku Puzzles: An Overview and Evaluation
"  Hierarchies are the most common structure used to understand the world
better. In galaxies, for instance, multiple-star systems are organised in a
hierarchical system. Then, governmental and company organisations are
structured using a hierarchy, while the Internet, which is used on a daily
basis, has a space of domain names arranged hierarchically. Since Artificial
Intelligence (AI) planning portrays information about the world and reasons to
solve some of world's problems, Hierarchical Task Network (HTN) planning has
been introduced almost 40 years ago to represent and deal with hierarchies. Its
requirement for rich domain knowledge to characterise the world enables HTN
planning to be very useful, but also to perform well. However, the history of
almost 40 years obfuscates the current understanding of HTN planning in terms
of accomplishments, planning models, similarities and differences among
hierarchical planners, and its current and objective image. On top of these
issues, attention attracts the ability of hierarchical planning to truly cope
with the requirements of applications from the real world. We propose a
framework-based approach to remedy this situation. First, we provide a basis
for defining different formal models of hierarchical planning, and define two
models that comprise a large portion of HTN planners. Second, we provide a set
of concepts that helps to interpret HTN planners from the aspect of their
search space. Then, we analyse and compare the planners based on a variety of
properties organised in five segments, namely domain authoring, expressiveness,
competence, performance and applicability. Furthermore, we select Web service
composition as a real-world and current application, and classify and compare
the approaches that employ HTN planning to solve the problem of service
composition. Finally, we conclude with our findings and present directions for
future work.
",An Overview of Hierarchical Task Network Planning
"  Since long, corporations are looking for knowledge sources which can provide
structured description of data and can focus on meaning and shared
understanding. Structures which can facilitate open world assumptions and can
be flexible enough to incorporate and recognize more than one name for an
entity. A source whose major purpose is to facilitate human communication and
interoperability. Clearly, databases fail to provide these features and
ontologies have emerged as an alternative choice, but corporations working on
same domain tend to make different ontologies. The problem occurs when they
want to share their data/knowledge. Thus we need tools to merge ontologies into
one. This task is termed as ontology matching. This is an emerging area and
still we have to go a long way in having an ideal matcher which can produce
good results. In this paper we have shown a framework to matching ontologies
using graphs.
",Shiva: A Framework for Graph Based Ontology Matching
"  The current paper offers a perspective on what we term conceptive
intelligence - the capacity of an agent to continuously think of new object
definitions (tasks, problems, physical systems, etc.) and to look for methods
to realize them. The framework, called a Brouwer machine, is inspired by
previous research in design theory and modeling, with its roots in the
constructivist mathematics of intuitionism. The dual constructivist perspective
we describe offers the possibility to create novelty both in terms of the types
of objects and the methods for constructing objects. More generally, the
theoretical work on which Brouwer machines are based is called imaginative
constructivism. Based on the framework and the theory, we discuss many
paradigms and techniques omnipresent in AI research and their merits and
shortcomings for modeling aspects of design, as described by imaginative
constructivism. To demonstrate and explain the type of creative process
expressed by the notion of a Brouwer machine, we compare this concept with a
system using genetic algorithms for scientific law discovery.
",Conceptive Artificial Intelligence: Insights from design theory
"  MTD(f) is a new minimax search algorithm, simpler and more efficient than
previous algorithms. In tests with a number of tournament game playing programs
for chess, checkers and Othello it performed better, on average, than
NegaScout/PVS (the AlphaBeta variant used in practically all good chess,
checkers, and Othello programs). One of the strongest chess programs of the
moment, MIT's parallel chess program Cilkchess uses MTD(f) as its search
algorithm, replacing NegaScout, which was used in StarSocrates, the previous
version of the program.
","MTD(f), A Minimax Algorithm Faster Than NegaScout"
"  This paper introduces a new paradigm for minimax game-tree search algo-
rithms. MT is a memory-enhanced version of Pearls Test procedure. By changing
the way MT is called, a number of best-first game-tree search algorithms can be
simply and elegantly constructed (including SSS*). Most of the assessments of
minimax search algorithms have been based on simulations. However, these
simulations generally do not address two of the key ingredients of high
performance game-playing programs: iterative deepening and memory usage. This
paper presents experimental data from three game-playing programs (checkers,
Othello and chess), covering the range from low to high branching factor. The
improved move ordering due to iterative deepening and memory usage results in
significantly different results from those portrayed in the literature. Whereas
some simulations show Alpha-Beta expanding almost 100% more leaf nodes than
other algorithms [12], our results showed variations of less than 20%. One new
instance of our framework (MTD-f) out-performs our best alpha- beta searcher
(aspiration NegaScout) on leaf nodes, total nodes and execution time. To our
knowledge, these are the first reported results that compare both depth-first
and best-first algorithms given the same amount of memory
",A New Paradigm for Minimax Search
"  In 1979 Stockman introduced the SSS* minimax search algorithm that domi-
nates Alpha-Beta in the number of leaf nodes expanded. Further investigation of
the algorithm showed that it had three serious drawbacks, which prevented its
use by practitioners: it is difficult to understand, it has large memory
requirements, and it is slow. This paper presents an alternate formulation of
SSS*, in which it is implemented as a series of Alpha-Beta calls that use a
transposition table (AB- SSS*). The reformulation solves all three perceived
drawbacks of SSS*, making it a practical algorithm. Further, because the search
is now based on Alpha-Beta, the extensive research on minimax search
enhancements can be easily integrated into AB-SSS*. To test AB-SSS* in
practise, it has been implemented in three state-of-the- art programs: for
checkers, Othello and chess. AB-SSS* is comparable in performance to Alpha-Beta
on leaf node count in all three games, making it a viable alternative to
Alpha-Beta in practise. Whereas SSS* has usually been regarded as being
entirely different from Alpha-Beta, it turns out to be just an Alpha-Beta
enhancement, like null-window searching. This runs counter to published
simulation results. Our research leads to the surprising result that iterative
deepening versions of Alpha-Beta can expand fewer leaf nodes than iterative
deepening versions of SSS* due to dynamic move re-ordering.
",SSS* = Alpha-Beta + TT
"  Knuth and Moore presented a theoretical lower bound on the number of leaves
that any fixed-depth minimax tree-search algorithm traversing a uniform tree
must explore, the so-called minimal tree. Since real-life minimax trees are not
uniform, the exact size of this tree is not known for most applications.
Further, most games have transpositions, implying that there exists a minimal
graph which is smaller than the minimal tree. For three games (chess, Othello
and checkers) we compute the size of the minimal tree and the minimal graph.
Empirical evidence shows that in all three games, enhanced Alpha-Beta search is
capable of building a tree that is close in size to that of the minimal graph.
Hence, it appears game-playing programs build nearly optimal search trees.
However, the conventional definition of the minimal graph is wrong. There are
ways in which the size of the minimal graph can be reduced: by maximizing the
number of transpositions in the search, and generating cutoffs using branches
that lead to smaller search trees. The conventional definition of the minimal
graph is just a left-most approximation. Calculating the size of the real
minimal graph is too computationally intensive. However, upper bound
approximations show it to be significantly smaller than the left-most minimal
graph. Hence, it appears that game-playing programs are not searching as
efficiently as is widely believed. Understanding the left-most and real minimal
search graphs leads to some new ideas for enhancing Alpha-Beta search. One of
them, enhanced transposition cutoffs, is shown to significantly reduce search
tree size.
",Nearly Optimal Minimax Tree Search?
"  This paper presents formulae that can solve various seemingly hopeless
philosophical conundrums. We discuss the simulation argument, teleportation,
mind-uploading, the rationality of utilitarianism, and the ethics of exploiting
artificial general intelligence. Our approach arises from combining the
essential ideas of formalisms such as algorithmic probability, the universal
intelligence measure, space-time-embedded intelligence, and Hutter's observer
localization. We argue that such universal models can yield the ultimate
solutions, but a novel research direction would be required in order to find
computationally efficient approximations thereof.
",Applications of Algorithmic Probability to the Philosophy of Mind
"  Autopilot is a system designed to guide a vehicle without aid. Due to
increase in flight hours and complexity of modern day flight it has become
imperative to equip the aircrafts with autopilot. Thus reliability and
consistency of an Autopilot system becomes a crucial role in a flight. But the
increased complexity and demand for better accuracy has made the process of
evaluating the autopilot for consistency a difficult process .A vast amount of
imprecise data has been involved. Rough sets can be a potent tool for such kind
of Applications containing vague data. This paper proposes an approach towards
Consistency factor determination using Rough Set Theory. The seventeen basic
factors, that are crucial in determining the consistency of an Autopilot
system, are grouped into five Payloads based on their functionality.
Consistency Factor is evaluated through these payloads, using Rough Set Theory.
Consistency Factor determines the consistency and reliability of an autopilot
system and the conditions under which manual override becomes imperative. Using
Rough set Theory the most and the least influential factors towards Autopilot
system are also determined.
",Determining the Consistency factor of Autopilot using Rough Set Theory
"  Energy disaggregation is to discover the energy consumption of individual
appliances from their aggregated energy values. To solve the problem, most
existing approaches rely on either appliances' signatures or their state
transition patterns, both hard to obtain in practice. Aiming at developing a
simple, universal model that works without depending on sophisticated machine
learning techniques or auxiliary equipments, we make use of easily accessible
knowledge of appliances and the sparsity of the switching events to design a
Sparse Switching Event Recovering (SSER) method. By minimizing the total
variation (TV) of the (sparse) event matrix, SSER can effectively recover the
individual energy consumption values from the aggregated ones. To speed up the
process, a Parallel Local Optimization Algorithm (PLOA) is proposed to solve
the problem in active epochs of appliance activities in parallel. Using
real-world trace data, we compare the performance of our method with that of
the state-of-the-art solutions, including Least Square Estimation (LSE) and
iterative Hidden Markov Model (HMM). The results show that our approach has an
overall higher detection accuracy and a smaller overhead.
","Plug and Play! A Simple, Universal Model for Energy Disaggregation"
"  This paper introduces the concept of rational countefactuals which is an idea
of identifying a counterfactual from the factual (whether perceived or real)
that maximizes the attainment of the desired consequent. In counterfactual
thinking if we have a factual statement like: Saddam Hussein invaded Kuwait and
consequently George Bush declared war on Iraq then its counterfactuals is: If
Saddam Hussein did not invade Kuwait then George Bush would not have declared
war on Iraq. The theory of rational counterfactuals is applied to identify the
antecedent that gives the desired consequent necessary for rational decision
making. The rational countefactual theory is applied to identify the values of
variables Allies, Contingency, Distance, Major Power, Capability, Democracy, as
well as Economic Interdependency that gives the desired consequent Peace.
",Rational Counterfactuals
"  Due to an ageing society, it can be expected that less nursing personnel will
be responsible for an increasing number of patients in the future. One way to
address this challenge is to provide system-based support for nursing personnel
in creating, executing, and adapting patient care processes. In care practice,
these processes are following the general care process definition and
individually specified according to patient-specific data as well as diagnoses
and guidelines from the NANDA, NIC, and NOC (NNN) standards. In addition,
adaptations to running patient processes become necessary frequently and are to
be conducted by nursing personnel including NNN knowledge. In order to provide
semi-automatic support for design and adaption of care processes, a
formalization of NNN knowledge is indispensable. This technical report presents
the NNN formalization that is developed targeting at goals such as
completeness, flexibility, and later exploitation for creating and adapting
patient care processes. The formalization also takes into consideration an
extensive evaluation of existing formalization standards for clinical
guidelines. The NNN formalization as well as its usage are evaluated based on
case study FATIGUE.
","The NNN Formalization: Review and Development of Guideline Specification
  in the Care Domain"
"  Inspired by the extraordinary computing power promised by quantum computers,
the quantum mind hypothesis postulated that quantum mechanical phenomena are
the source of neuronal synchronization, which, in turn, might underlie
consciousness. Here, I present an alternative inspired by a classical computing
method with quantum power. This method relies on special distributed
representations called hyperstrings. Hyperstrings are superpositions of up to
an exponential number of strings, which -- by a single-processor classical
computer -- can be evaluated in a transparallel fashion, that is,
simultaneously as if only one string were concerned. Building on a neurally
plausible model of human visual perceptual organization, in which hyperstrings
are formal counterparts of transient neural assemblies, I postulate that
synchronization in such assemblies is a manifestation of transparallel
information processing. This accounts for the high combinatorial capacity and
speed of human visual perceptual organization and strengthens ideas that
self-organizing cognitive architecture bridges the gap between neurons and
consciousness.
",Transparallel mind: Classical computing with quantum power
"  It is important to find optimal solutions for structural errors in rule-based
expert systems .Solutions to discovering such errors by using model checking
techniques have already been proposed, but these solutions have problems such
as state space explosion. In this paper, to overcome these problems, we model
the rule-based systems as finite state transition systems and express
confliction and unreachability as Computation Tree Logic (CTL) logic formula
and then use the technique of model checking to detect confliction and
unreachability in rule-based systems with the model checker UPPAAL.
","Verification of confliction and unreachability in rule-based expert
  systems with model checking"
"  In this paper, we address the dynamic Emergency Medical Service (EMS)
systems. A dynamic location model is presented that tries to locate and
relocate the ambulances. The proposed model controls the movements and
locations of ambulances in order to provide a better coverage of the demand
points under different fluctuation patterns that may happen during a given
period of time. Some numerical experiments have been carried out by using some
real-world data sets that have been collected through the French EMS system.
","An Integer Programming Model for the Dynamic Location and Relocation of
  Emergency Vehicles: A Case Study"
"  One important challenge for probabilistic logics is reasoning with very large
knowledge bases (KBs) of imperfect information, such as those produced by
modern web-scale information extraction systems. One scalability problem shared
by many probabilistic logics is that answering queries involves ""grounding"" the
query---i.e., mapping it to a propositional representation---and the size of a
""grounding"" grows with database size. To address this bottleneck, we present a
first-order probabilistic language called ProPPR in which that approximate
""local groundings"" can be constructed in time independent of database size.
Technically, ProPPR is an extension to stochastic logic programs (SLPs) that is
biased towards short derivations; it is also closely related to an earlier
relational learning algorithm called the path ranking algorithm (PRA). We show
that the problem of constructing proofs for this logic is related to
computation of personalized PageRank (PPR) on a linearized version of the proof
space, and using on this connection, we develop a proveably-correct approximate
grounding scheme, based on the PageRank-Nibble algorithm. Building on this, we
develop a fast and easily-parallelized weight-learning algorithm for ProPPR. In
experiments, we show that learning for ProPPR is orders magnitude faster than
learning for Markov logic networks; that allowing mutual recursion (joint
learning) in KB inference leads to improvements in performance; and that ProPPR
can learn weights for a mutually recursive program with hundreds of clauses,
which define scores of interrelated predicates, over a KB containing one
million entities.
","Efficient Inference and Learning in a Large Knowledge Base: Reasoning
  with Extracted Information using a Locally Groundable First-Order
  Probabilistic Logic"
"  Dempster-Shafer theory is widely applied in uncertainty modelling and
knowledge reasoning due to its ability of expressing uncertain information. A
distance between two basic probability assignments(BPAs) presents a measure of
performance for identification algorithms based on the evidential theory of
Dempster-Shafer. However, some conditions lead to limitations in practical
application for Dempster-Shafer theory, such as exclusiveness hypothesis and
completeness constraint. To overcome these shortcomings, a novel theory called
D numbers theory is proposed. A distance function of D numbers is proposed to
measure the distance between two D numbers. The distance function of D numbers
is an generalization of distance between two BPAs, which inherits the advantage
of Dempster-Shafer theory and strengthens the capability of uncertainty
modeling. An illustrative case is provided to demonstrate the effectiveness of
the proposed function.
",Distance function of D numbers
"  We propose a number of heuristics that can be used for identifying when
intransitive choice behaviour is likely to occur in choice situations. We also
suggest two methods for avoiding undesired choice behaviour, namely transparent
communication and adaptive choice-set generation. We believe that these two
ways can contribute to the avoidance of decision biases in choice situations
that may often be regretted.
",Avoiding Undesired Choices Using Intelligent Adaptive Systems
"  We consider the problem of bottom-up compilation of knowledge bases, which is
usually predicated on the existence of a polytime function for combining
compilations using Boolean operators (usually called an Apply function). While
such a polytime Apply function is known to exist for certain languages (e.g.,
OBDDs) and not exist for others (e.g., DNNF), its existence for certain
languages remains unknown. Among the latter is the recently introduced language
of Sentential Decision Diagrams (SDDs), for which a polytime Apply function
exists for unreduced SDDs, but remains unknown for reduced ones (i.e. canonical
SDDs). We resolve this open question in this paper and consider some of its
theoretical and practical implications. Some of the findings we report question
the common wisdom on the relationship between bottom-up compilation, language
canonicity and the complexity of the Apply function.
",On the Role of Canonicity in Bottom-up Knowledge Compilation
"  Recent interest in the use of $L_1$ regularization in the use of value
function approximation includes Petrik et al.'s introduction of
$L_1$-Regularized Approximate Linear Programming (RALP). RALP is unique among
$L_1$-regularized approaches in that it approximates the optimal value function
using off-policy samples. Additionally, it produces policies which outperform
those of previous methods, such as LSPI. RALP's value function approximation
quality is affected heavily by the choice of state-relevance weights in the
objective function of the linear program, and by the distribution from which
samples are drawn; however, there has been no discussion of these
considerations in the previous literature. In this paper, we discuss and
explain the effects of choices in the state-relevance weights and sampling
distribution on approximation quality, using both theoretical and experimental
illustrations. The results provide insight not only onto these effects, but
also provide intuition into the types of MDPs which are especially well suited
for approximation with RALP.
","An Analysis of State-Relevance Weights and Sampling Distributions on
  L1-Regularized Approximate Linear Programming Approximation Accuracy"
"  This paper discloses the potential of OWL (Web Ontology Language) ontologies
for generation of rules. The main purpose of this paper is to identify new
types of rules, which may be generated from OWL ontologies. Rules, generated
from OWL ontologies, are necessary for the functioning of the Semantic Web
Expert System. It is expected that the Semantic Web Expert System (SWES) will
be able to process ontologies from the Web with the purpose to supplement or
even to develop its knowledge base.
",Ontology as a Source for Rule Generation
"  Dempster-Shafer evidence theory is a powerful tool in information fusion.
When the evidence are highly conflicting, the counter-intuitive results will be
presented. To adress this open issue, a new method based on evidence distance
of Jousselme and Hausdorff distance is proposed. Weight of each evidence can be
computed, preprocess the original evidence to generate a new evidence. The
Dempster's combination rule is used to combine the new evidence. Comparing with
the existing methods, the new proposed method is efficient.
",A new combination approach based on improved evidence distance
"  Conflict management is still an open issue in the application of Dempster
Shafer evidence theory. A lot of works have been presented to address this
issue. In this paper, a new theory, called as generalized evidence theory
(GET), is proposed. Compared with existing methods, GET assumes that the
general situation is in open world due to the uncertainty and incomplete
knowledge. The conflicting evidence is handled under the framework of GET. It
is shown that the new theory can explain and deal with the conflicting evidence
in a more reasonable way.
",Generalized Evidence Theory
"  With the web getting bigger and assimilating knowledge about different
concepts and domains, it is becoming very difficult for simple database driven
applications to capture the data for a domain. Thus developers have come out
with ontology based systems which can store large amount of information and can
apply reasoning and produce timely information. Thus facilitating effective
knowledge management. Though this approach has made our lives easier, but at
the same time has given rise to another problem. Two different ontologies
assimilating same knowledge tend to use different terms for the same concepts.
This creates confusion among knowledge engineers and workers, as they do not
know which is a better term then the other. Thus we need to merge ontologies
working on same domain so that the engineers can develop a better application
over it. This paper shows the development of one such matcher which merges the
concepts available in two ontologies at two levels; 1) at string level and 2)
at semantic level; thus producing better merged ontologies. We have used a
graph matching technique which works at the core of the system. We have also
evaluated the system and have tested its performance with its predecessor which
works only on string matching. Thus current approach produces better results.
",Shiva++: An Enhanced Graph based Ontology Matcher
"  TurKontrol, and algorithm presented in (Dai et al. 2010), uses a POMDP to
model and control an iterative workflow for crowdsourced work. Here, TurKontrol
is re-implemented as ""TurKPF,"" which uses a Particle Filter to reduce
computation time & memory usage. Most importantly, in our experimental
environment with default parameter settings, the action is chosen nearly
instantaneously. Through a series of experiments we see that TurKPF and
TurKontrol perform similarly.
",TurKPF: TurKontrol as a Particle Filter
"  Online services such as web search and e-commerce applications typically rely
on the collection of data about users, including details of their activities on
the web. Such personal data is used to enhance the quality of service via
personalization of content and to maximize revenues via better targeting of
advertisements and deeper engagement of users on sites. To date, service
providers have largely followed the approach of either requiring or requesting
consent for opting-in to share their data. Users may be willing to share
private information in return for better quality of service or for incentives,
or in return for assurances about the nature and extend of the logging of data.
We introduce \emph{stochastic privacy}, a new approach to privacy centering on
a simple concept: A guarantee is provided to users about the upper-bound on the
probability that their personal data will be used. Such a probability, which we
refer to as \emph{privacy risk}, can be assessed by users as a preference or
communicated as a policy by a service provider. Service providers can work to
personalize and to optimize revenues in accordance with preferences about
privacy risk. We present procedures, proofs, and an overall system for
maximizing the quality of services, while respecting bounds on allowable or
communicated privacy risk. We demonstrate the methodology with a case study and
evaluation of the procedures applied to web search personalization. We show how
we can achieve near-optimal utility of accessing information with provable
guarantees on the probability of sharing data.
",Stochastic Privacy
"  Recently, there has been a growing interest in modeling planning with
information constraints. Accordingly, an agent maximizes a regularized expected
utility known as the free energy, where the regularizer is given by the
information divergence from a prior to a posterior policy. While this approach
can be justified in various ways, including from statistical mechanics and
information theory, it is still unclear how it relates to decision-making
against adversarial environments. This connection has previously been suggested
in work relating the free energy to risk-sensitive control and to extensive
form games. Here, we show that a single-agent free energy optimization is
equivalent to a game between the agent and an imaginary adversary. The
adversary can, by paying an exponential penalty, generate costs that diminish
the decision maker's payoffs. It turns out that the optimal strategy of the
adversary consists in choosing costs so as to render the decision maker
indifferent among its choices, which is a definining property of a Nash
equilibrium, thus tightening the connection between free energy optimization
and game theory.
","An Adversarial Interpretation of Information-Theoretic Bounded
  Rationality"
"  Data clustering is an important area of data mining. This is an unsupervised
study where data of similar types are put into one cluster while data of
another types are put into different cluster. Fuzzy C means is a very important
clustering technique based on fuzzy logic. Also we have some hard clustering
techniques available like K-means among the popular ones. In this paper a
comparative study is done between Fuzzy clustering algorithm and hard
clustering algorithm
","A Comparative study Between Fuzzy Clustering Algorithm and Hard
  Clustering Algorithm"
"  In the last 20 years many proposals have been made to incorporate
non-monotonic reasoning into description logics, ranging from approaches based
on default logic and circumscription to those based on preferential semantics.
In particular, the non-monotonic description logic
$\mathcal{ALC}$+T$_{\mathsf{min}}$ uses a combination of the preferential
semantics with minimization of a certain kind of concepts, which represent
atypical instances of a class of elements. One of its drawbacks is that it
suffers from the problem known as the \emph{property blocking inheritance},
which can be seen as a weakness from an inferential point of view. In this
paper we propose an extension of $\mathcal{ALC}$+T$_{\mathsf{min}}$, namely
$\mathcal{ALC}$+T$^+_{\mathsf{min}}$, with the purpose to solve the mentioned
problem. In addition, we show the close connection that exists between
$\mathcal{ALC}$+T$^+_{\mathsf{min}}$ and concept-circumscribed knowledge bases.
Finally, we study the complexity of deciding the classical reasoning tasks in
$\mathcal{ALC}$+T$^+_{\mathsf{min}}$.
","On the Non-Monotonic Description Logic
  $\mathcal{ALC}$+T$_{\mathsf{min}}$"
"  The Clustered Vehicle Routing Problem (CluVRP) is a variant of the
Capacitated Vehicle Routing Problem in which customers are grouped into
clusters. Each cluster has to be visited once, and a vehicle entering a cluster
cannot leave it until all customers have been visited. This article presents
two alternative hybrid metaheuristic algorithms for the CluVRP. The first
algorithm is based on an Iterated Local Search algorithm, in which only
feasible solutions are explored and problem-specific local search moves are
utilized. The second algorithm is a Hybrid Genetic Search, for which the
shortest Hamiltonian path between each pair of vertices within each cluster
should be precomputed. Using this information, a sequence of clusters can be
used as a solution representation and large neighborhoods can be efficiently
explored by means of bi-directional dynamic programming, sequence
concatenations, by using appropriate data structures. Extensive computational
experiments are performed on benchmark instances from the literature, as well
as new large scale ones. Recommendations on promising algorithm choices are
provided relatively to average cluster size.
",Hybrid Metaheuristics for the Clustered Vehicle Routing Problem
"  Existing semantics for answer-set program updates fall into two categories:
either they consider only strong negation in heads of rules, or they primarily
rely on default negation in heads of rules and optionally provide support for
strong negation by means of a syntactic transformation. In this paper we
pinpoint the limitations of both these approaches and argue that both types of
negation should be first-class citizens in the context of updates. We identify
principles that plausibly constrain their interaction but are not
simultaneously satisfied by any existing rule update semantics. Then we extend
one of the most advanced semantics with direct support for strong negation and
show that it satisfies the outlined principles as well as a variety of other
desirable properties.
","On Strong and Default Negation in Logic Program Updates (Extended
  Version)"
"  Argumentation is one of the most popular approaches of defining
a~non-monotonic formalism and several argumentation based semantics were
proposed for defeasible logic programs. Recently, a new approach based on
notions of conflict resolutions was proposed, however with declarative
semantics only. This paper gives a more procedural counterpart by developing
skeptical and credulous argument games for complete semantics and soundness and
completeness theorems for both games are provided. After that, distribution of
defeasible logic program into several contexts is investigated and both
argument games are adapted for multi-context system.
","Credulous and Skeptical Argument Games for Complete Semantics in
  Conflict Resolution based Argumentation"
"  Deontic logic is shown to be applicable for modelling human reasoning. For
this the Wason selection task and the suppression task are discussed in detail.
Different versions of modelling norms with deontic logic are introduced and in
the case of the Wason selection task it is demonstrated how differences in the
performance of humans in the abstract and in the social contract case can be
explained. Furthermore it is shown that an automated theorem prover can be used
as a reasoning tool for deontic logic.
",Deontic Logic for Human Reasoning
"  Answer Set Programming (ASP) is a declarative programming paradigm. The
intrinsic complexity of the evaluation of ASP programs makes the development of
more effective and faster systems a challenging research topic. This paper
reports on the recent improvements of the ASP solver WASP. WASP is undergoing a
refactoring process which will end up in the release of a new and more
performant version of the software. In particular the paper focus on the
improvements to the core evaluation algorithms working on normal programs. A
preliminary experiment on benchmarks from the 3rd ASP competition belonging to
the NP class is reported. The previous version of WASP was often not
competitive with alternative solutions on this class. The new version of WASP
shows a substantial increase in performance.
",Preliminary Report on WASP 2.0
"  A {\it dynamic reasoning system} (DRS) is an adaptation of a conventional
formal logical system that explicitly portrays reasoning as a temporal
activity, with each extralogical input to the system and each inference rule
application being viewed as occurring at a distinct time step. Every DRS
incorporates some well-defined logic together with a controller that serves to
guide the reasoning process in response to user inputs. Logics are generic,
whereas controllers are application-specific. Every controller does,
nonetheless, provide an algorithm for nonmonotonic belief revision. The general
notion of a DRS comprises a framework within which one can formulate the logic
and algorithms for a given application and prove that the algorithms are
correct, i.e., that they serve to (i) derive all salient information and (ii)
preserve the consistency of the belief set. This paper illustrates the idea
with ordinary first-order predicate calculus, suitably modified for the present
purpose, and an example. The example revisits some classic nonmonotonic
reasoning puzzles (Opus the Penguin, Nixon Diamond) and shows how these can be
resolved in the context of a DRS, using an expanded version of first-order
logic that incorporates typed predicate symbols. All concepts are rigorously
defined and effectively computable, thereby providing the foundation for a
future software implementation.
",Nonmonotonic Reasoning as a Temporal Activity
"  Contract bridge occupies nowadays a position of great prestige being,
together with chess, the only mind games officially recognized by the
International Olympic Committee. In the present paper an innovative method for
assessing the total performance of bridge- players' belonging to groups of
special interest(e.g. different bridge clubs during a tournament, men and
women, new and old players, etc) is introduced, which is based on principles of
fuzzy logic. For this, the cohorts under assessment are represented as fuzzy
subsets of a set of linguistic labels characterizing their performance and the
centroid defuzzification method is used to convert the fuzzy data collected
from the game to a crisp number. This new method of assessment could be used
informally as a complement of the official bridge-scoring methods for
statistical and other obvious reasons. Two real applications related to
simultaneous tournaments with pre-dealt boards, organized by the Hellenic
Bridge Federation, are also presented, illustrating the importance of our
results in practice.
","Assessing the players'performance in the game of bridge: A fuzzy logic
  approach"
"  Dialogical argumentation is an important cognitive activity by which agents
exchange arguments and counterarguments as part of some process such as
discussion, debate, persuasion and negotiation. Whilst numerous formal systems
have been proposed, there is a lack of frameworks for implementing and
evaluating these proposals. First-order executable logic has been proposed as a
general framework for specifying and analysing dialogical argumentation. In
this paper, we investigate how we can implement systems for dialogical
argumentation using propositional executable logic. Our approach is to present
and evaluate an algorithm that generates a finite state machine that reflects a
propositional executable logic specification for a dialogical argumentation
together with an initial state. We also consider how the finite state machines
can be analysed, with the minimax strategy being used as an illustration of the
kinds of empirical analysis that can be undertaken.
",Analysis of Dialogical Argumentation via Finite State Machines
"  The semantic web is an open and distributed environment in which it is hard
to guarantee consistency of knowledge and information. Under the standard
two-valued semantics everything is entailed if knowledge and information is
inconsistent. The semantics of the paraconsistent logic LP offers a solution.
However, if the available knowledge and information is consistent, the set of
conclusions entailed under the three-valued semantics of the paraconsistent
logic LP is smaller than the set of conclusions entailed under the two-valued
semantics. Preferring conflict-minimal three-valued interpretations eliminates
this difference.
  Preferring conflict-minimal interpretations introduces non-monotonicity. To
handle the non-monotonicity, this paper proposes an assumption-based
argumentation system. Assumptions needed to close branches of a semantic
tableaux form the arguments. Stable extensions of the set of derived arguments
correspond to conflict minimal interpretations and conclusions entailed by all
conflict-minimal interpretations are supported by arguments in all stable
extensions.
","An argumentation system for reasoning with conflict-minimal
  paraconsistent ALC"
"  Abstract argumentation frameworks (AFs) are one of the most studied
formalisms in AI. In this work, we introduce a certain subclass of AFs which we
call compact. Given an extension-based semantics, the corresponding compact AFs
are characterized by the feature that each argument of the AF occurs in at
least one extension. This not only guarantees a certain notion of fairness;
compact AFs are thus also minimal in the sense that no argument can be removed
without changing the outcome. We address the following questions in the paper:
(1) How are the classes of compact AFs related for different semantics? (2)
Under which circumstances can AFs be transformed into equivalent compact ones?
(3) Finally, we show that compact AFs are indeed a non-trivial subclass, since
the verification problem remains coNP-hard for certain semantics.
",Compact Argumentation Frameworks
"  Belief revision is the process in which an agent incorporates a new piece of
information together with a pre-existing set of beliefs. When the new
information comes in the form of a report from another agent, then it is clear
that we must first determine whether or not that agent should be trusted. In
this paper, we provide a formal approach to modeling trust as a pre-processing
step before belief revision. We emphasize that trust is not simply a relation
between agents; the trust that one agent has in another is often restricted to
a particular domain of expertise. We demonstrate that this form of trust can be
captured by associating a state-partition with each agent, then relativizing
all reports to this state partition before performing belief revision. In this
manner, we incorporate only the part of a report that falls under the perceived
domain of expertise of the reporting agent. Unfortunately, state partitions
based on expertise do not allow us to compare the relative strength of trust
held with respect to different agents. To address this problem, we introduce
pseudometrics over states to represent differing degrees of trust. This allows
us to incorporate simultaneous reports from multiple agents in a way that
ensures the most trusted reports will be believed.
",Belief Revision and Trust
"  One of the most prominent tools for abstract argumentation is the Dung's
framework, AF for short. It is accompanied by a variety of semantics including
grounded, complete, preferred and stable. Although powerful, AFs have their
shortcomings, which led to development of numerous enrichments. Among the most
general ones are the abstract dialectical frameworks, also known as the ADFs.
They make use of the so-called acceptance conditions to represent arbitrary
relations. This level of abstraction brings not only new challenges, but also
requires addressing existing problems in the field. One of the most
controversial issues, recognized not only in argumentation, concerns the
support cycles. In this paper we introduce a new method to ensure acyclicity of
the chosen arguments and present a family of extension-based semantics built on
it. We also continue our research on the semantics that permit cycles and fill
in the gaps from the previous works. Moreover, we provide ADF versions of the
properties known from the Dung setting. Finally, we also introduce a
classification of the developed sub-semantics and relate them to the existing
labeling-based approaches.
",Extension-based Semantics of Abstract Dialectical Frameworks
"  A semantic net can be used to represent a sentence. A sentence in a language
contains semantics which are polar in nature, that is, semantics which are
positive, neutral and negative. Neutrosophy is a relatively new field of
science which can be used to mathematically represent triads of concepts. These
triads include truth, indeterminacy and falsehood, and so also positivity,
neutrality and negativity. Thus a conventional semantic net has been extended
in this paper using neutrosophy into a Polar Fuzzy Neutrosophic Semantic Net. A
Polar Fuzzy Neutrosophic Semantic Net has been implemented in MATLAB and has
been used to illustrate a polar sentence in English language. The paper
demonstrates a method for the representation of polarity in a computers memory.
Thus, polar concepts can be applied to imbibe a machine such as a robot, with
emotions, making machine emotion representation possible.
","Representation of a Sentence using a Polar Fuzzy Neutrosophic Semantic
  Net"
"  We propose a new formal language for the expressive representation of
probabilistic knowledge based on Answer Set Programming (ASP). It allows for
the annotation of first-order formulas as well as ASP rules and facts with
probabilities and for learning of such weights from data (parameter
estimation). Weighted formulas are given a semantics in terms of soft and hard
constraints which determine a probability distribution over answer sets. In
contrast to related approaches, we approach inference by optionally utilizing
so-called streamlining XOR constraints, in order to reduce the number of
computed answer sets. Our approach is prototypically implemented. Examples
illustrate the introduced concepts and point at issues and topics for future
research.
","Probabilistic Inductive Logic Programming Based on Answer Set
  Programming"
"  Belief revision is an operation that aims at modifying old beliefs so that
they become consistent with new ones. The issue of belief revision has been
studied in various formalisms, in particular, in qualitative algebras (QAs) in
which the result is a disjunction of belief bases that is not necessarily
representable in a QA. This motivates the study of belief revision in
formalisms extending QAs, namely, their propositional closures: in such a
closure, the result of belief revision belongs to the formalism. Moreover, this
makes it possible to define a contraction operator thanks to the Harper
identity. Belief revision in the propositional closure of QAs is studied, an
algorithm for a family of revision operators is designed, and an open-source
implementation is made freely available on the web. (This is the extended
version of an article originally presented at the 14th International Conference
on Principles of Knowledge Representation and Reasoning.)
","Belief revision in the propositional closure of a qualitative algebra
  (extended version)"
"  Outlier detection in a large-scale database is a significant and complex
issue in knowledge discovering field. As the data distributions are obscure and
uncertain in high dimensional space, most existing solutions try to solve the
issue taking into account the two intuitive points: first, outliers are
extremely far away from other points in high dimensional space; second,
outliers are detected obviously different in projected-dimensional subspaces.
However, for a complicated case that outliers are hidden inside the normal
points in all dimensions, existing detection methods fail to find such inner
outliers. In this paper, we propose a method with twice dimension-projections,
which integrates primary subspace outlier detection and secondary
point-projection between subspaces, and sums up the multiple weight values for
each point. The points are computed with local density ratio separately in
twice-projected dimensions. After the process, outliers are those points
scoring the largest values of weight. The proposed method succeeds to find all
inner outliers on the synthetic test datasets with the dimension varying from
100 to 10000. The experimental results also show that the proposed algorithm
can work in low dimensional space and can achieve perfect performance in high
dimensional space. As for this reason, our proposed approach has considerable
potential to apply it in multimedia applications helping to process images or
video with large-scale attributes.
",Finding Inner Outliers in High Dimensional Space
"  MEASP is a multi-engine solver for ground ASP programs. It exploits algorithm
selection techniques based on classification to select one among a set of
out-of-the-box heterogeneous ASP solvers used as black-box engines. In this
paper we report on (i) a new optimized implementation of MEASP; and (ii) an
attempt of applying algorithm selection to non-ground programs. An experimental
analysis reported in the paper shows that (i) the new implementation of \measp
is substantially faster than the previous version; and (ii) the multi-engine
recipe can be applied to the evaluation of non-ground programs with some
benefits.
",The Multi-engine ASP Solver ME-ASP: Progress Report
"  The interest in the combination of probability with logics for modeling the
world has rapidly increased in the last few years. One of the most effective
approaches is the Distribution Semantics which was adopted by many logic
programming languages and in Descripion Logics. In this paper, we illustrate
the work we have done in this research field by presenting a probabilistic
semantics for description logics and reasoning and learning algorithms. In
particular, we present in detail the system TRILL P, which computes the
probability of queries w.r.t. probabilistic knowledge bases, which has been
implemented in Prolog. Note: An extended abstract / full version of a paper
accepted to be presented at the Doctoral Consortium of the 30th International
Conference on Logic Programming (ICLP 2014), July 19-22, Vienna, Austria
",Reasoning with Probabilistic Logics
"  In this essay the stance on robots is discussed. The attitude against robots
in history, starting in Ancient Greek culture until the industrial revolution
is described. The uncanny valley and some possible explanations are given. Some
differences in Western and Asian understanding of robots are listed and finally
we answer the question raised with the title.
",Do we need Asimov's Laws?
"  This paper describes an architecture that combines the complementary
strengths of declarative programming and probabilistic graphical models to
enable robots to represent, reason with, and learn from, qualitative and
quantitative descriptions of uncertainty and knowledge. An action language is
used for the low-level (LL) and high-level (HL) system descriptions in the
architecture, and the definition of recorded histories in the HL is expanded to
allow prioritized defaults. For any given goal, tentative plans created in the
HL using default knowledge and commonsense reasoning are implemented in the LL
using probabilistic algorithms, with the corresponding observations used to
update the HL history. Tight coupling between the two levels enables automatic
selection of relevant variables and generation of suitable action policies in
the LL for each HL action, and supports reasoning with violation of defaults,
noisy observations and unreliable actions in large and complex domains. The
architecture is evaluated in simulation and on physical robots transporting
objects in indoor domains; the benefit on robots is a reduction in task
execution time of 39% compared with a purely probabilistic, but still
hierarchical, approach.
","KR$^3$: An Architecture for Knowledge Representation and Reasoning in
  Robotics"
"  Existential rules have been proposed for representing ontological knowledge,
specifically in the context of Ontology- Based Data Access. Entailment with
existential rules is undecidable. We focus in this paper on conditions that
ensure the termination of a breadth-first forward chaining algorithm known as
the chase. Several variants of the chase have been proposed. In the first part
of this paper, we propose a new tool that allows to extend existing acyclicity
conditions ensuring chase termination, while keeping good complexity
properties. In the second part, we study the extension to existential rules
with nonmonotonic negation under stable model semantics, discuss the relevancy
of the chase variants for these rules and further extend acyclicity results
obtained in the positive case.
","Revisiting Chase Termination for Existential Rules and their Extension
  to Nonmonotonic Negation"
"  Traditional AI reasoning techniques have been used successfully in many
domains, including logistics, scheduling and game playing. This paper is part
of a project aimed at investigating how such techniques can be extended to
coordinate teams of unmanned aerial vehicles (UAVs) in dynamic environments.
Specifically challenging are real-world environments where UAVs and other
network-enabled devices must communicate to coordinate---and communication
actions are neither reliable nor free. Such network-centric environments are
common in military, public safety and commercial applications, yet most
research (even multi-agent planning) usually takes communications among
distributed agents as a given. We address this challenge by developing an agent
architecture and reasoning algorithms based on Answer Set Programming (ASP).
ASP has been chosen for this task because it enables high flexibility of
representation, both of knowledge and of reasoning tasks. Although ASP has been
used successfully in a number of applications, and ASP-based architectures have
been studied for about a decade, to the best of our knowledge this is the first
practical application of a complete ASP-based agent architecture. It is also
the first practical application of ASP involving a combination of centralized
reasoning, decentralized reasoning, execution monitoring, and reasoning about
network communications. This work has been empirically validated using a
distributed network-centric software evaluation testbed and the results provide
guidance to designers in how to understand and control intelligent systems that
operate in these environments.
","An ASP-Based Architecture for Autonomous UAVs in Dynamic Environments:
  Progress Report"
"  The NMR community would like to build a repository of benchmarks to push
forward the design of systems implementing NMR as it has been the case for many
other areas in AI. There are a number of lessons which can be learned from the
experience of other communi- ties. Here are a few thoughts about the
requirements and choices to make before building such a repository.
",Some thoughts about benchmarks for NMR
"  Answer Set Programming (ASP) is logic programming under the stable model or
answer set semantics. During the last decade, this paradigm has seen several
extensions by generalizing the notion of atom used in these programs. Among
these, there are aggregate atoms, HEX atoms, generalized quantifiers, and
abstract constraints. In this paper we refer to these constructs collectively
as generalized atoms. The idea common to all of these constructs is that their
satisfaction depends on the truth values of a set of (non-generalized) atoms,
rather than the truth value of a single (non-generalized) atom. Motivated by
several examples, we argue that for some of the more intricate generalized
atoms, the previously suggested semantics provide unintuitive results and
provide an alternative semantics, which we call supportedly stable or SFLP
answer sets. We show that it is equivalent to the major previously proposed
semantics for programs with convex generalized atoms, and that it in general
admits more intended models than other semantics in the presence of non-convex
generalized atoms. We show that the complexity of supportedly stable models is
on the second level of the polynomial hierarchy, similar to previous proposals
and to stable models of disjunctive logic programs. Given these complexity
results, we provide a compilation method that compactly transforms programs
with generalized atoms in disjunctive normal form to programs without
generalized atoms. Variants are given for the new supportedly stable and the
existing FLP semantics, for which a similar compilation technique has not been
known so far.
","Semantics and Compilation of Answer Set Programming with Generalized
  Atoms"
"  The fundamental issue in knowledge representation is to provide a precise
definition of the knowledge that they possess in a manner that is independent
of procedural considerations, context free and easy to manipulate, exchange and
reason about. Knowledge must be accessible to everyone regardless of their
native languages. Universal Networking Language (UNL) is a declarative formal
language and a generalized form of human language in a machine independent
digital platform for defining, recapitulating, amending, storing and
dissipating knowledge among people of different affiliations. UNL extracts
semantic data from a native language for Interlingua machine translation. This
paper presents the development of a graphical tool that incorporates UNL to
provide a visual mean to represent the semantic data available in a native
text. UNL represents the semantics of a sentence as a conceptual hyper-graph.
We translate this information into XML format and create a graph from XML,
representing the actual concepts available in the native language
",Analysis Tool for UNL-Based Knowledge Representation
"  To appear in Theory and Practice of Logic Programming (TPLP). Building on the
award-winning, portfolio-based ASP solver claspfolio, we present claspfolio 2,
a modular and open solver architecture that integrates several different
portfolio-based algorithm selection approaches and techniques. The claspfolio 2
solver framework supports various feature generators, solver selection
approaches, solver portfolios, as well as solver-schedule-based pre-solving
techniques. The default configuration of claspfolio 2 relies on a light-weight
version of the ASP solver clasp to generate static and dynamic instance
features. The flexible open design of claspfolio 2 is a distinguishing factor
even beyond ASP. As such, it provides a unique framework for comparing and
combining existing portfolio-based algorithm selection approaches and
techniques in a single, unified framework. Taking advantage of this, we
conducted an extensive experimental study to assess the impact of different
feature sets, selection approaches and base solver portfolios. In addition to
gaining substantial insights into the utility of the various approaches and
techniques, we identified a default configuration of claspfolio 2 that achieves
substantial performance gains not only over clasp's default configuration and
the earlier version of claspfolio 2, but also over manually tuned
configurations of clasp.
",claspfolio 2: Advances in Algorithm Selection for Answer Set Programming
"  Expert systems prove to be suitable replacement for human experts when human
experts are unavailable for different reasons. Various expert system has been
developed for wide range of application. Although some expert systems in the
field of fishery and aquaculture has been developed but a system that aids user
in process of selecting a new addition to their aquarium tank never been
designed. This paper proposed an expert system that suggests new addition to an
aquarium tank based on current environmental condition of aquarium and
currently existing fishes in aquarium. The system suggest the best fit for
aquarium condition and most compatible to other fishes in aquarium.
","An expert system for recommending suitable ornamental fish addition to
  an aquarium based on aquarium condition"
"  In this paper we present the Transalg system, designed to produce SAT
encodings for discrete functions, written as programs in a specific language.
Translation of such programs to SAT is based on propositional encoding methods
for formal computing models and on the concept of symbolic execution. We used
the Transalg system to make SAT encodings for a number of cryptographic
functions.
","Transalg: a Tool for Translating Procedural Descriptions of Discrete
  Functions to SAT"
"  Modelling problems containing a mixture of Boolean and numerical variables is
a long-standing interest of Artificial Intelligence. However, performing
inference and learning in hybrid domains is a particularly daunting task. The
ability to model this kind of domains is crucial in ""learning to design"" tasks,
that is, learning applications where the goal is to learn from examples how to
perform automatic {\em de novo} design of novel objects. In this paper we
present Structured Learning Modulo Theories, a max-margin approach for learning
in hybrid domains based on Satisfiability Modulo Theories, which allows to
combine Boolean reasoning and optimization over continuous linear arithmetical
constraints. The main idea is to leverage a state-of-the-art generalized
Satisfiability Modulo Theory solver for implementing the inference and
separation oracles of Structured Output SVMs. We validate our method on
artificial and real world scenarios.
",Structured Learning Modulo Theories
"  Abductive logic programs offer a formalism to declaratively represent and
reason about problems in a variety of areas: diagnosis, decision making,
hypothetical reasoning, etc. On the other hand, logic program updates allow us
to express knowledge changes, be they internal (or self) and external (or
world) changes. Abductive logic programs and logic program updates thus
naturally coexist in problems that are susceptible to hypothetical reasoning
about change. Taking this as a motivation, in this paper we integrate abductive
logic programs and logic program updates by jointly exploiting tabling features
of logic programming. The integration is based on and benefits from the two
implementation techniques we separately devised previously, viz., tabled
abduction and incremental tabling for query-driven propagation of logic program
updates. A prototype of the integrated system is implemented in XSB Prolog.
",Joint Tabling of Logic Program Abductions and Updates
"  Tabling has been used for some time to improve efficiency of Prolog programs
by memorizing answered queries. The same idea can be naturally used to memorize
visited states during search for planning. In this paper we present a planner
developed in the Picat language to solve the Petrobras planning problem. Picat
is a novel Prolog-like language that provides pattern matching, deterministic
and non-deterministic rules, and tabling as its core modelling and solving
features. We demonstrate these capabilities using the Petrobras problem, where
the goal is to plan transport of cargo items from ports to platforms using
vessels with limited capacity. Monte Carlo Tree Search has been so far the best
technique to tackle this problem and we will show that by using tabling we can
achieve much better runtime efficiency and better plan quality.
",Using Tabled Logic Programming to Solve the Petrobras Planning Problem
"  Data originating from the Web, sensor readings and social media result in
increasingly huge datasets. The so called Big Data comes with new scientific
and technological challenges while creating new opportunities, hence the
increasing interest in academia and industry. Traditionally, logic programming
has focused on complex knowledge structures/programs, so the question arises
whether and how it can work in the face of Big Data. In this paper, we examine
how the well-founded semantics can process huge amounts of data through mass
parallelization. More specifically, we propose and evaluate a parallel approach
using the MapReduce framework. Our experimental results indicate that our
approach is scalable and that well-founded semantics can be applied to billions
of facts. To the best of our knowledge, this is the first work that addresses
large scale nonmonotonic reasoning without the restriction of stratification
for predicates of arbitrary arity. To appear in Theory and Practice of Logic
Programming (TPLP).
",Efficient Computation of the Well-Founded Semantics over Big Data
"  Efficient modeling of uncertain information in real world is still an open
issue. Dempster-Shafer evidence theory is one of the most commonly used
methods. However, the Dempster-Shafer evidence theory has the assumption that
the hypothesis in the framework of discernment is exclusive of each other. This
condition can be violated in real applications, especially in linguistic
decision making since the linguistic variables are not exclusive of each others
essentially. In this paper, a new theory, called as D numbers theory (DNT), is
systematically developed to address this issue. The combination rule of two D
numbers is presented. An coefficient is defined to measure the exclusive degree
among the hypotheses in the framework of discernment. The combination rule of
two D numbers is presented. If the exclusive coefficient is one which means
that the hypothesis in the framework of discernment is exclusive of each other
totally, the D combination is degenerated as the classical Dempster combination
rule. Finally, a linguistic variables transformation of D numbers is presented
to make a decision. A numerical example on linguistic evidential decision
making is used to illustrate the efficiency of the proposed D numbers theory.
",D numbers theory: a generalization of Dempster-Shafer evidence theory
"  Lifted inference has been proposed for various probabilistic logical
frameworks in order to compute the probability of queries in a time that
depends on the size of the domains of the random variables rather than the
number of instances. Even if various authors have underlined its importance for
probabilistic logic programming (PLP), lifted inference has been applied up to
now only to relational languages outside of logic programming. In this paper we
adapt Generalized Counting First Order Variable Elimination (GC-FOVE) to the
problem of computing the probability of queries to probabilistic logic programs
under the distribution semantics. In particular, we extend the Prolog Factor
Language (PFL) to include two new types of factors that are needed for
representing ProbLog programs. These factors take into account the existing
causal independence relationships among random variables and are managed by the
extension to variable elimination proposed by Zhang and Poole for dealing with
convergent variables and heterogeneous factors. Two new operators are added to
GC-FOVE for treating heterogeneous factors. The resulting algorithm, called
LP$^2$ for Lifted Probabilistic Logic Programming, has been implemented by
modifying the PFL implementation of GC-FOVE and tested on three benchmarks for
lifted inference. A comparison with PITA and ProbLog2 shows the potential of
the approach.
",Lifted Variable Elimination for Probabilistic Logic Programming
"  In the event that a bacteriological or chemical toxin is intro- duced to a
water distribution network, a large population of consumers may become exposed
to the contaminant. A contamination event may be poorly predictable dynamic
process due to the interactions of consumers and utility managers during an
event. Consumers that become aware of a threat may select protective actions
that change their water demands from typical demand patterns, and new hydraulic
conditions can arise that differ from conditions that are predicted when
demands are considered as exogenous inputs. Consequently, the movement of the
contaminant plume in the pipe network may shift from its expected trajectory. A
sociotechnical model is developed here to integrate agent-based models of
consumers with an engineering water distribution system model and capture the
dynamics between consumer behaviors and the water distribution system for
predicting contaminant transport and public exposure. Consumers are simulated
as agents with behaviors defined for water use activities, mobility,
word-of-mouth communication, and demand reduction, based on a set of rules
representing an agents autonomy and reaction to health impacts, the
environment, and the actions of other agents. As consumers decrease their water
use, the demand exerted on the water distribution system is updated; as the
flow directions and volumes shift in response, the location of the contaminant
plume is updated and the amount of contaminant consumed by each agent is
calculated. The framework is tested through simulating realistic contamination
scenarios for a virtual city and water distribution system.
","An Agent-based Modeling Framework for Sociotechnical Simulation of Water
  Distribution Contamination Events"
"  To appear in Theory and Practice of Logic Programming (TPLP)
  Bound Founded Answer Set Programming (BFASP) is an extension of Answer Set
Programming (ASP) that extends stable model semantics to numeric variables.
While the theory of BFASP is defined on ground rules, in practice BFASP
programs are written as complex non-ground expressions. Flattening of BFASP is
a technique used to simplify arbitrary expressions of the language to a small
and well defined set of primitive expressions. In this paper, we first show how
we can flatten arbitrary BFASP rule expressions, to give equivalent BFASP
programs. Next, we extend the bottom-up grounding technique and magic set
transformation used by ASP to BFASP programs. Our implementation shows that for
BFASP problems, these techniques can significantly reduce the ground program
size, and improve subsequent solving.
",Grounding Bound Founded Answer Set Programs
"  Answer Set Programming (ASP) is a powerful modelling formalism that is very
efficient in solving combinatorial problems. ASP solvers implement the stable
model semantics that eliminates circular derivations between Boolean variables
from the solutions of a logic program. Due to this, ASP solvers are better
suited than propositional satisfiability (SAT) and Constraint Programming (CP)
solvers to solve a certain class of problems whose specification includes
inductive definitions such as reachability in a graph. On the other hand, ASP
solvers suffer from the grounding bottleneck that occurs due to their inability
to model finite domain variables. Furthermore, the existing stable model
semantics are not sufficient to disallow circular reasoning on the bounds of
numeric variables. An example where this is required is in modelling shortest
paths between nodes in a graph. Just as reachability can be encoded as an
inductive definition with one or more base cases and recursive rules, shortest
paths between nodes can also be modelled with similar base cases and recursive
rules for their upper bounds. This deficiency of stable model semantics
introduces another type of grounding bottleneck in ASP systems that cannot be
removed by naively merging ASP with CP solvers, but requires a theoretical
extension of the semantics from Booleans and normal rules to bounds over
numeric variables and more general rules. In this work, we propose Bound
Founded Answer Set Programming (BFASP) that resolves this issue and
consequently, removes all types of grounding bottleneck inherent in ASP
systems.
",Bound Founded Answer Set Programming
"  Abstract argumentation offers an appealing way of representing and evaluating
arguments and counterarguments. This approach can be enhanced by a probability
assignment to each argument. There are various interpretations that can be
ascribed to this assignment. In this paper, we regard the assignment as
denoting the belief that an agent has that an argument is justifiable, i.e.,
that both the premises of the argument and the derivation of the claim of the
argument from its premises are valid. This leads to the notion of an epistemic
extension which is the subset of the arguments in the graph that are believed
to some degree (which we defined as the arguments that have a probability
assignment greater than 0.5). We consider various constraints on the
probability assignment. Some constraints correspond to standard notions of
extensions, such as grounded or stable extensions, and some constraints give us
new kinds of extensions.
","Probabilistic Argumentation with Epistemic Extensions and Incomplete
  Information"
"  (To appear in Theory and Practice of Logic Programming (TPLP))
  ESmodels is designed and implemented as an experiment platform to investigate
the semantics, language, related reasoning algorithms, and possible
applications of epistemic specifications.We first give the epistemic
specification language of ESmodels and its semantics. The language employs only
one modal operator K but we prove that it is able to represent luxuriant modal
operators by presenting transformation rules. Then, we describe basic
algorithms and optimization approaches used in ESmodels. After that, we discuss
possible applications of ESmodels in conformant planning and constraint
satisfaction. Finally, we conclude with perspectives.
",ESmodels: An Epistemic Specification Solver
"  Algorithm portfolios represent a strategy of composing multiple heuristic
algorithms, each suited to a different class of problems, within a single
general solver that will choose the best suited algorithm for each input. This
approach recently gained popularity especially for solving combinatoric
problems, but optimization applications are still emerging. The COCO platform
of the BBOB workshop series is the current standard way to measure performance
of continuous black-box optimization algorithms.
  As an extension to the COCO platform, we present the Python-based COCOpf
framework that allows composing portfolios of optimization algorithms and
running experiments with different selection strategies. In our framework, we
focus on black-box algorithm portfolio and online adaptive selection. As a
demonstration, we measure the performance of stock SciPy optimization
algorithms and the popular CMA algorithm alone and in a portfolio with two
simple selection strategies. We confirm that even a naive selection strategy
can provide improved performance across problem classes.
",COCOpf: An Algorithm Portfolio Framework
"  Query answering in Answer Set Programming (ASP) is usually solved by
computing (a subset of) the cautious consequences of a logic program. This task
is computationally very hard, and there are programs for which computing
cautious consequences is not viable in reasonable time. However, current ASP
solvers produce the (whole) set of cautious consequences only at the end of
their computation. This paper reports on strategies for computing cautious
consequences, also introducing anytime algorithms able to produce sound answers
during the computation.
",Anytime Computation of Cautious Consequences in Answer Set Programming
"  In computational cognitive science, the cognitive architecture ACT-R is very
popular. It describes a model of cognition that is amenable to computer
implementation, paving the way for computational psychology. Its underlying
psychological theory has been investigated in many psychological experiments,
but ACT-R lacks a formal definition of its underlying concepts from a
mathematical-computational point of view. Although the canonical implementation
of ACT-R is now modularized, this production rule system is still hard to adapt
and extend in central components like the conflict resolution mechanism (which
decides which of the applicable rules to apply next).
  In this work, we present a concise implementation of ACT-R based on
Constraint Handling Rules which has been derived from a formalization in prior
work. To show the adaptability of our approach, we implement several different
conflict resolution mechanisms discussed in the ACT-R literature. This results
in the first implementation of one such mechanism. For the other mechanisms, we
empirically evaluate if our implementation matches the results of reference
implementations of ACT-R.
",Exchanging Conflict Resolution in an Adaptable Implementation of ACT-R
"  The paper presents a knowledge representation language $\mathcal{A}log$ which
extends ASP with aggregates. The goal is to have a language based on simple
syntax and clear intuitive and mathematical semantics. We give some properties
of $\mathcal{A}log$, an algorithm for computing its answer sets, and comparison
with other approaches.
",Vicious Circle Principle and Logic Programs with Aggregates
"  Answer Set Programming (ASP) is a well-established paradigm of declarative
programming that has been developed in the field of logic programming and
nonmonotonic reasoning. Advances in ASP solving technology are customarily
assessed in competition events, as it happens for other closely-related
problem-solving technologies like SAT/SMT, QBF, Planning and Scheduling. ASP
Competitions are (usually) biennial events; however, the Fifth ASP Competition
departs from tradition, in order to join the FLoC Olympic Games at the Vienna
Summer of Logic 2014, which is expected to be the largest event in the history
of logic. This edition of the ASP Competition series is jointly organized by
the University of Calabria (Italy), the Aalto University (Finland), and the
University of Genova (Italy), and is affiliated with the 30th International
Conference on Logic Programming (ICLP 2014). It features a completely
re-designed setup, with novelties involving the design of tracks, the scoring
schema, and the adherence to a fixed modeling language in order to push the
adoption of the ASP-Core-2 standard. Benchmark domains are taken from past
editions, and best system packages submitted in 2013 are compared with new
versions and solvers.
  To appear in Theory and Practice of Logic Programming (TPLP).
",The Design of the Fifth Answer Set Programming Competition
"  The belief bias effect is a phenomenon which occurs when we think that we
judge an argument based on our reasoning, but are actually influenced by our
beliefs and prior knowledge. Evans, Barston and Pollard carried out a
psychological syllogistic reasoning task to prove this effect. Participants
were asked whether they would accept or reject a given syllogism. We discuss
one specific case which is commonly assumed to be believable but which is
actually not logically valid. By introducing abnormalities, abduction and
background knowledge, we adequately model this case under the weak completion
semantics. Our formalization reveals new questions about possible extensions in
abductive reasoning. For instance, observations and their explanations might
include some relevant prior abductive contextual information concerning some
side-effect or leading to a contestable or refutable side-effect. A weaker
notion indicates the support of some relevant consequences by a prior abductive
context. Yet another definition describes jointly supported relevant
consequences, which captures the idea of two observations containing mutually
supportive side-effects. Though motivated with and exemplified by the running
psychology application, the various new general abductive context definitions
are introduced here and given a declarative semantics for the first time, and
have a much wider scope of application. Inspection points, a concept introduced
by Pereira and Pinto, allows us to express these definitions syntactically and
intertwine them into an operational semantics.
",Contextual Abductive Reasoning with Side-Effects
"  Data Mining is the process of extracting useful patterns from the huge amount
of database and many data mining techniques are used for mining these patterns.
Recently, one of the remarkable facts in higher educational institute is the
rapid growth data and this educational data is expanding quickly without any
advantage to the educational management. The main aim of the management is to
refine the education standard; therefore by applying the various data mining
techniques on this data one can get valuable information. This research study
proposed the ""classification model for the student's enrollment process in
higher educational courses using data mining techniques"". Additionally, this
study contributes to finding some patterns that are meaningful to management.
","Building a Classification Model for Enrollment In Higher Educational
  Courses using Data Mining Techniques"
"  In the policy making process a number of disparate and diverse issues such as
economic development, environmental aspects, as well as the social acceptance
of the policy, need to be considered. A single person might not have all the
required expertises, and decision support systems featuring optimization
components can help to assess policies. Leveraging on previous work on
Strategic Environmental Assessment, we developed a fully-fledged system that is
able to provide optimal plans with respect to a given objective, to perform
multi-objective optimization and provide sets of Pareto optimal plans, and to
visually compare them. Each plan is environmentally assessed and its footprint
is evaluated. The heart of the system is an application developed in a popular
Constraint Logic Programming system on the Reals sort. It has been equipped
with a web service module that can be queried through standard interfaces, and
an intuitive graphic user interface.
",Multi-Criteria Optimal Planning for Energy Policies in CLP
"  My research goal is to employ a parser generation algorithm based on the
Earley parsing algorithm to the evaluation and compilation of queries to logic
programs, especially to deductive databases. By means of partial deduction,
from a query to a logic program a parameterized automaton is to be generated
that models the evaluation of this query. This automaton can be compiled to
executable code; thus we expect a speedup in runtime of query evaluation. An
extended abstract/ full version of a paper accepted to be presented at the
Doctoral Consortium of the 30th International Conference on Logic Programming
(ICLP 2014), July 19-22, Vienna, Austria
","Application of Methods for Syntax Analysis of Context-Free Languages to
  Query Evaluation of Logic Programs"
"  The stable model (SM) semantics lacks the properties of existence, relevance
and cumulativity. If we prospectively consider the class of conservative
extensions of SM semantics (i.e., semantics that for each normal logic program
P retrieve a superset of the set of stable models of P), one may wander how do
the semantics of this class behave in what concerns the aforementioned
properties. That is the type of issue dealt with in this paper. We define a
large class of conservative extensions of the SM semantics, dubbed affix stable
model semantics, ASM, and study the above referred properties into two
non-disjoint subfamilies of the class ASM, here dubbed ASMh and ASMm. From this
study a number of results stem which facilitate the assessment of semantics in
the class ASMh U ASMm with respect to the properties of existence, relevance
and cumulativity, whilst unveiling relations among these properties. As a
result of the approach taken in our work, light is shed on the characterization
of the SM semantics, as we show that the properties of (lack of) existence and
(lack of) cautious monotony are equivalent, which opposes statements on this
issue that may be found in the literature; we also characterize the relevance
failure of SM semantics in a more clear way than usually stated in the
literature.
",Properties of Stable Model Semantics Extensions
"  Artificial fish swarm algorithm (AFSA) is one of the swarm intelligence
optimization algorithms that works based on population and stochastic search.
In order to achieve acceptable result, there are many parameters needs to be
adjusted in AFSA. Among these parameters, visual and step are very significant
in view of the fact that artificial fish basically move based on these
parameters. In standard AFSA, these two parameters remain constant until the
algorithm termination. Large values of these parameters increase the capability
of algorithm in global search, while small values improve the local search
ability of the algorithm. In this paper, we empirically study the performance
of the AFSA and different approaches to balance between local and global
exploration have been tested based on the adaptive modification of visual and
step during algorithm execution. The proposed approaches have been evaluated
based on the four well-known benchmark functions. Experimental results show
considerable positive impact on the performance of AFSA.
",Empirical Study of Artificial Fish Swarm Algorithm
"  Case-based reasoning (CBR) based on description logics (DLs) has gained a lot
of attention lately. Adaptation is a basic task in the CBR inference that can
be modeled as the knowledge base revision problem and solved in propositional
logic. However, in DLs, it is still a challenge problem since existing revision
operators only work well for strictly restricted DLs of the \emph{DL-Lite}
family, and it is difficult to design a revision algorithm which is
syntax-independent and fine-grained. In this paper, we present a new method for
adaptation based on the DL $\mathcal{EL_{\bot}}$. Following the idea of
adaptation as revision, we firstly extend the logical basis for describing
cases from propositional logic to the DL $\mathcal{EL_{\bot}}$, and present a
formalism for adaptation based on $\mathcal{EL_{\bot}}$. Then we present an
adaptation algorithm for this formalism and demonstrate that our algorithm is
syntax-independent and fine-grained. Our work provides a logical basis for
adaptation in CBR systems where cases and domain knowledge are described by the
tractable DL $\mathcal{EL_{\bot}}$.
","Algorithm for Adapting Cases Represented in a Tractable Description
  Logic"
"  I am Joachim Jansen and this is my research summary, part of my application
to the Doctoral Consortium at ICLP'14. I am a PhD student in the Knowledge
Representation and Reasoning (KRR) research group, a subgroup of the
Declarative Languages and Artificial Intelligence (DTAI) group at the
department of Computer Science at KU Leuven. I started my PhD in September
2012. My promotor is prof. dr. ir. Gerda Janssens and my co-promotor is prof.
dr. Marc Denecker. I can be contacted at joachim.jansen@cs.kuleuven.be or at:
Room 01.167 Celestijnenlaan 200A 3001 Heverlee Belgium An extended abstract /
full version of a paper accepted to be presented at the Doctoral Consortium of
the 30th International Conference on Logic Programming (ICLP 2014), July 19-22,
Vienna, Austria
",Model revision inference for extensions of first order logic
"  Simulation is widely used to make model-based predictions, but few approaches
have attempted this technique in dynamic physical environments of medium to
high complexity or in general contexts. After an introduction to the cognitive
science concepts from which this work is inspired and the current development
in the use of simulation as a decision-making technique, we propose a generic
framework based on theory of mind, which allows an agent to reason and perform
actions using multiple simulations of automatically created or externally
inputted models of the perceived environment. A description of a partial
implementation is given, which aims to solve a popular game within the
IJCAI2013 AIBirds contest. Results of our approach are presented, in comparison
with the competition benchmark. Finally, future developments regarding the
framework are discussed.
",Towards A Theory-Of-Mind-Inspired Generic Decision-Making Framework
"  The ability of an Evolutionary Algorithm (EA) to find a global optimal
solution depends on its capacity to find a good rate between exploitation of
found so far elements and exploration of the search space. Inspired by natural
phenomena, researchers have developed many successful evolutionary algorithms
which, at original versions, define operators that mimic the way nature solves
complex problems, with no actual consideration of the exploration/exploitation
balance. In this paper, a novel nature-inspired algorithm called the States of
Matter Search (SMS) is introduced. The SMS algorithm is based on the simulation
of the states of matter phenomenon. In SMS, individuals emulate molecules which
interact to each other by using evolutionary operations which are based on the
physical principles of the thermal-energy motion mechanism. The algorithm is
devised by considering each state of matter at one different
exploration/exploitation ratio. The evolutionary process is divided into three
phases which emulate the three states of matter: gas, liquid and solid. In each
state, molecules (individuals) exhibit different movement capacities. Beginning
from the gas state (pure exploration), the algorithm modifies the intensities
of exploration and exploitation until the solid state (pure exploitation) is
reached. As a result, the approach can substantially improve the balance
between exploration/exploitation, yet preserving the good search capabilities
of an evolutionary approach.
","An optimization algorithm inspired by the States of Matter that improves
  the balance between exploration and exploitation"
"  Electromagnetismlike Optimization (EMO) is a global optimization algorithm,
particularly well suited to solve problems featuring nonlinear and multimodal
cost functions. EMO employs searcher agents that emulate a population of
charged particles which interact to each other according to electromagnetisms
laws of attraction and repulsion. However, EMO usually requires a large number
of iterations for a local search procedure; any reduction or cancelling over
such number, critically perturb other issues such as convergence, exploration,
population diversity and accuracy. This paper presents an enhanced EMO
algorithm called OBEMO, which employs the Opposition-Based Learning (OBL)
approach to accelerate the global convergence speed. OBL is a machine
intelligence strategy which considers the current candidate solution and its
opposite value at the same time, achieving a faster exploration of the search
space. The proposed OBEMO method significantly reduces the required
computational effort yet avoiding any detriment to the good search capabilities
of the original EMO algorithm. Experiments are conducted over a comprehensive
set of benchmark functions, showing that OBEMO obtains promising performance
for most of the discussed test problems.
",Opposition Based ElectromagnetismLike for Global Optimization
"  We study the model of projective simulation (PS) which is a novel approach to
artificial intelligence (AI). Recently it was shown that the PS agent performs
well in a number of simple task environments, also when compared to standard
models of reinforcement learning (RL). In this paper we study the performance
of the PS agent further in more complicated scenarios. To that end we chose two
well-studied benchmarking problems, namely the ""grid-world"" and the
""mountain-car"" problem, which challenge the model with large and continuous
input space. We compare the performance of the PS agent model with those of
existing models and show that the PS agent exhibits competitive performance
also in such scenarios.
","Projective simulation applied to the grid-world and the mountain-car
  problem"
"  Eliciting preferences of a decision maker is a key factor to successfully
combine search and decision making in an interactive method. Therefore, the
progressively integration and simulation of the decision maker is a main
concern in an application. We contribute in this direction by proposing an
interactive method based on a reference point-based guided local search to the
bi-objective Inventory Routing Problem. A local search metaheuristic, working
on the delivery intervals, and the Clarke & Wright savings heuristic is
employed for the subsequently obtained Vehicle Routing Problem. To elicit
preferences, the decision maker selects a reference point to guide the search
in interesting subregions. Additionally, the reference point is used as a
reservation point to discard solutions outside the cone, introduced as a
convergence criterion. Computational results of the reference point-based
guided local search are reported and analyzed on benchmark data in order to
show the applicability of the approach.
","Interactive Reference Point-Based Guided Local Search for the
  Bi-objective Inventory Routing Problem"
"  In this article we demonstrate how algorithmic probability theory is applied
to situations that involve uncertainty. When people are unsure of their model
of reality, then the outcome they observe will cause them to update their
beliefs. We argue that classical probability cannot be applied in such cases,
and that subjective probability must instead be used. In Experiment 1 we show
that, when judging the probability of lottery number sequences, people apply
subjective rather than classical probability. In Experiment 2 we examine the
conjunction fallacy and demonstrate that the materials used by Tversky and
Kahneman (1983) involve model uncertainty. We then provide a formal
mathematical proof that, for every uncertain model, there exists a conjunction
of outcomes which is more subjectively probable than either of its constituents
in isolation.
",A Computational Theory of Subjective Probability
"  Advances in high energy physics have created the need to increase
computational capacity. Project HEPGAME was composed to address this challenge.
One of the issues is that numerical integration of expressions of current
interest have millions of terms and takes weeks to compute. We have
investigated ways to simplify these expressions, using Horner schemes and
common subexpression elimination. Our approach applies MCTS, a search procedure
that has been successful in AI. We use it to find near-optimal Horner schemes.
Although MCTS finds better solutions, this approach gives rise to two further
challenges. (1) MCTS (with UCT) introduces a constant, $C_p$ that governs the
balance between exploration and exploitation. This constant has to be tuned
manually. (2) There should be more guided exploration at the bottom of the
tree, since the current approach reduces the quality of the solution towards
the end of the expression. We investigate NMCS (Nested Monte Carlo Search) to
address both issues, but find that NMCS is computationally unfeasible for our
problem. Then, we modify the MCTS formula by introducing a dynamic
exploration-exploitation parameter $T$ that decreases linearly with the
iteration number. Consequently, we provide a performance analysis. We observe
that a variable $C_p$ solves our domain: it yields more exploration at the
bottom and as a result the tuning problem has been simplified. The region in
$C_p$ for which good values are found is increased by more than a tenfold. This
result encourages us to continue our research to solve other prominent problems
in High Energy Physics.
",HEPGAME and the Simplification of Expressions
"  Given a set of conflicting arguments, there can exist multiple plausible
opinions about which arguments should be accepted, rejected, or deemed
undecided. We study the problem of how multiple such judgments can be
aggregated. We define the problem by adapting various classical
social-choice-theoretic properties for the argumentation domain. We show that
while argument-wise plurality voting satisfies many properties, it fails to
guarantee the collective rationality of the outcome, and struggles with ties.
We then present more general results, proving multiple impossibility results on
the existence of any good aggregation operator. After characterising the
sufficient and necessary conditions for satisfying collective rationality, we
study whether restricting the domain of argument-wise plurality voting to
classical semantics allows us to escape the impossibility result. We close by
listing graph-theoretic restrictions under which argument-wise plurality rule
does produce collectively rational outcomes. In addition to identifying
fundamental barriers to collective argument evaluation, our results open up the
door for a new research agenda for the argumentation and computational social
choice communities.
",Judgment Aggregation in Multi-Agent Argumentation
"  We explore the structure of non-redundant and minimal sets consisting of
graded if-then rules. The rules serve as graded attribute implications in
object-attribute incidence data and as similarity-based functional dependencies
in a similarity-based generalization of the relational model of data. Based on
our observations, we derive a polynomial-time algorithm which transforms a
given finite set of rules into an equivalent one which has the least size in
terms of the number of rules.
",On minimal sets of graded attribute implications
"  Multi-context systems provide a powerful framework for modelling
information-aggregation systems featuring heterogeneous reasoning components.
Their execution can, however, incur non-negligible cost. Here, we focus on
cost-complexity of such systems. To that end, we introduce cost-aware
multi-context systems, an extension of non-monotonic multi-context systems
framework taking into account costs incurred by execution of semantic operators
of the individual contexts. We formulate the notion of cost-complexity for
consistency and reasoning problems in MCSs. Subsequently, we provide a series
of results related to gradually more and more constrained classes of MCSs and
finally introduce an incremental cost-reducing algorithm solving the reasoning
problem for definite MCSs.
",On the cost-complexity of multi-context systems
"  Analogy-Based (or Analogical) and Case-Based Reasoning (ABR and CBR) are two
similar problem solving processes based on the adaptation of the solution of
past problems for use with a new analogous problem. In this paper we review
these two processes and we give some real world examples with emphasis to the
field of Medicine, where one can find some of the most common and useful CBR
applications. We also underline the differences between CBR and the classical
rule-induction algorithms, we discuss the criticism for CBR methods and we
focus on the future trends of research in the area of CBR.
",Analogy-Based and Case-Based Reasoning: Two sides of the same coin
"  In this paper we show how the combination of Behaviour Tree and Utility Based
AI architecture can be used to design more realistic bots for Military
Simulators. In this work, we have designed a mathematical model of a simulator
system which in turn helps in analyzing the results and finding out the various
spaces on which our favorable situation might exist, this is done
geometrically. In the mathematical model, we have explained the matrix
formation and its significance followed up in dynamic programming approach we
explained the possible graph formation which will led improvisation of AI,
latter we explained the possible geometrical structure of the matrix operations
and its impact on a particular decision, we also explained the conditions under
which it tend to fail along with a possible solution in future works.
","Military Simulator - A Case Study of Behaviour Tree and Unity based
  architecture"
"  We firstly present definitions and properties in study of Maji
\cite{maji-2013} on neutrosophic soft sets. We then give a few notes on his
study. Next, based on \c{C}a\u{g}man \cite{cagman-2014}, we redefine the notion
of neutrosophic soft set and neutrosophic soft set operations to make more
functional. By using these new definitions we construct a decision making
method and a group decision making method which selects a set of optimum
elements from the alternatives. We finally present examples which shows that
the methods can be successfully applied to many problems that contain
uncertainties.
",Neutrosophic soft sets with applications in decision making
"  Crisis management is a complex problem raised by the scientific community
currently. Decision support systems are a suitable solution for such issues,
they are indeed able to help emergency managers to prevent and to manage crisis
in emergency situations. However, they should be enough flexible and adaptive
in order to be reliable to solve complex problems that are plunged in dynamic
and unpredictable environments. The approach we propose in this paper addresses
this challenge. We expose here a modelling of information for an emergency
environment and an architecture of a multiagent decision support system that
deals with these information in order to prevent and to manage the occur of a
crisis in emergency situations. We focus on the first level of the system
mechanism which intends to perceive and to reflect the evolution of the current
situation. The general approach and experimentations are provided here.
",Towards a Multiagent Decision Support System for crisis Management
"  Measuring inconsistency is viewed as an important issue related to handling
inconsistencies. Good measures are supposed to satisfy a set of rational
properties. However, defining sound properties is sometimes problematic. In
this paper, we emphasize one such property, named Decomposability, rarely
discussed in the literature due to its modeling difficulties. To this end, we
propose an independent decomposition which is more intuitive than existing
proposals. To analyze inconsistency in a more fine-grained way, we introduce a
graph representation of a knowledge base and various MUSdecompositions. One
particular MUS-decomposition, named distributable MUS-decomposition leads to an
interesting partition of inconsistencies in a knowledge base such that multiple
experts can check inconsistencies in parallel, which is impossible under
existing measures. Such particular MUSdecomposition results in an inconsistency
measure that satisfies a number of desired properties. Moreover, we give an
upper bound complexity of the measure that can be computed using 0/1 linear
programming or Min Cost Satisfiability problems, and conduct preliminary
experiments to show its feasibility.
",On the measure of conflicts: A MUS-Decomposition Based Framework
"  Monte Carlo Tree Search (MCTS) has improved the performance of game engines
in domains such as Go, Hex, and general game playing. MCTS has been shown to
outperform classic alpha-beta search in games where good heuristic evaluations
are difficult to obtain. In recent years, combining ideas from traditional
minimax search in MCTS has been shown to be advantageous in some domains, such
as Lines of Action, Amazons, and Breakthrough. In this paper, we propose a new
way to use heuristic evaluations to guide the MCTS search by storing the two
sources of information, estimated win rates and heuristic evaluations,
separately. Rather than using the heuristic evaluations to replace the
playouts, our technique backs them up implicitly during the MCTS simulations.
These minimax values are then used to guide future simulations. We show that
using implicit minimax backups leads to stronger play performance in Kalah,
Breakthrough, and Lines of Action.
","Monte Carlo Tree Search with Heuristic Evaluations using Implicit
  Minimax Backups"
"  The cutting plane method is an augmentative constrained optimization
procedure that is often used with continuous-domain optimization techniques
such as linear and convex programs. We investigate the viability of a similar
idea within message passing -- which produces integral solutions -- in the
context of two combinatorial problems: 1) For Traveling Salesman Problem (TSP),
we propose a factor-graph based on Held-Karp formulation, with an exponential
number of constraint factors, each of which has an exponential but sparse
tabular form. 2) For graph-partitioning (a.k.a., community mining) using
modularity optimization, we introduce a binary variable model with a large
number of constraints that enforce formation of cliques. In both cases we are
able to derive surprisingly simple message updates that lead to competitive
solutions on benchmark instances. In particular for TSP we are able to find
near-optimal solutions in the time that empirically grows with N^3,
demonstrating that augmentation is practical and efficient.
","Augmentative Message Passing for Traveling Salesman Problem and Graph
  Partitioning"
"  In this paper, we proposed a new approximate heuristic search algorithm:
Cascading A*, which is a two-phrase algorithm combining A* and IDA* by a new
concept ""envelope ball"". The new algorithm CA* is efficient, able to generate
approximate solution and any-time solution, and parallel friendly.
",Cascading A*: a Parallel Approach to Approximate Heuristic Search
"  We propose an approach to generate geometric theorems from electronic images
of diagrams automatically. The approach makes use of techniques of Hough
transform to recognize geometric objects and their labels and of numeric
verification to mine basic geometric relations. Candidate propositions are
generated from the retrieved information by using six strategies and geometric
theorems are obtained from the candidates via algebraic computation.
Experiments with a preliminary implementation illustrate the effectiveness and
efficiency of the proposed approach for generating nontrivial theorems from
images of diagrams. This work demonstrates the feasibility of automated
discovery of profound geometric knowledge from simple image data and has
potential applications in geometric knowledge management and education.
",Automated Generation of Geometric Theorems from Images of Diagrams
"  Decision making is still an open issue in the application of Dempster-Shafer
evidence theory. A lot of works have been presented for it. In the transferable
belief model (TBM), pignistic probabilities based on the basic probability as-
signments are used for decision making. In this paper, multiscale probability
transformation of basic probability assignment based on the belief function and
the plausibility function is proposed, which is a generalization of the
pignistic probability transformation. In the multiscale probability function, a
factor q based on the Tsallis entropy is used to make the multiscale prob-
abilities diversified. An example is shown that the multiscale probability
transformation is more reasonable in the decision making.
",Multiscale probability transformation of basic probability assignment
"  Neutrosophic Statistics means statistical analysis of population or sample
that has indeterminate (imprecise, ambiguous, vague, incomplete, unknown) data.
For example, the population or sample size might not be exactly determinate
because of some individuals that partially belong to the population or sample,
and partially they do not belong, or individuals whose appurtenance is
completely unknown. Also, there are population or sample individuals whose data
could be indeterminate. In this book, we develop the 1995 notion of
neutrosophic statistics. We present various practical examples. It is possible
to define the neutrosophic statistics in many ways, because there are various
types of indeterminacies, depending on the problem to solve.
",Introduction to Neutrosophic Statistics
"  We define a notion of rational closure for the logic SHIQ, which does not
enjoys the finite model property, building on the notion of rational closure
introduced by Lehmann and Magidor in [23]. We provide a semantic
characterization of rational closure in SHIQ in terms of a preferential
semantics, based on a finite rank characterization of minimal models. We show
that the rational closure of a TBox can be computed in EXPTIME using entailment
in SHIQ.
",Rational Closure in SHIQ
"  The user equilibrium in traffic assignment problem is based on the fact that
travelers choose the minimum-cost path between every origin-destination pair
and on the assumption that such a behavior will lead to an equilibrium of the
traffic network. In this paper, we consider this problem when the traffic
network links are fuzzy cost. Therefore, a Physarum-type algorithm is developed
to unify the Physarum network and the traffic network for taking full of
advantage of Physarum Polycephalum's adaptivity in network design to solve the
user equilibrium problem. Eventually, some experiments are used to test the
performance of this method. The results demonstrate that our approach is
competitive when compared with other existing algorithms.
","A bio-inspired algorithm for fuzzy user equilibrium problem by aid of
  Physarum Polycephalum"
"  Space and time are two critical components of many real world systems. For
this reason, analysis of anomalies in spatiotemporal data has been a great of
interest. In this work, application of tensor decomposition and eigenspace
techniques on spatiotemporal hotspot detection is investigated. An algorithm
called SST-Hotspot is proposed which accounts for spatiotemporal variations in
data and detect hotspots using matching of eigenvector elements of two cases
and population tensors. The experimental results reveal the interesting
application of tensor decomposition and eigenvector-based techniques in hotspot
analysis.
",An eigenvector-based hotspot detection
"  Failure detection in telecommunication networks is a vital task. So far,
several supervised and unsupervised solutions have been provided for
discovering failures in such networks. Among them unsupervised approaches has
attracted more attention since no label data is required. Often, network
devices are not able to provide information about the type of failure. In such
cases the type of failure is not known in advance and the unsupervised setting
is more appropriate for diagnosis. Among unsupervised approaches, Principal
Component Analysis (PCA) is a well-known solution which has been widely used in
the anomaly detection literature and can be applied to matrix data (e.g.
Users-Features). However, one of the important properties of network data is
their temporal sequential nature. So considering the interaction of dimensions
over a third dimension, such as time, may provide us better insights into the
nature of network failures. In this paper we demonstrate the power of three-way
analysis to detect events and anomalies in time-evolving network data.
",Event and Anomaly Detection Using Tucker3 Decomposition
"  Recently, ranking-based semantics is proposed to rank-order arguments from
the most acceptable to the weakest one(s), which provides a graded assessment
to arguments. In general, the ranking on arguments is derived from the strength
values of the arguments. Categoriser function is a common approach that assigns
a strength value to a tree of arguments. When it encounters an argument system
with cycles, then the categoriser strength is the solution of the non-linear
equations. However, there is no detail about the existence and uniqueness of
the solution, and how to find the solution (if exists). In this paper, we will
cope with these issues via fixed point technique. In addition, we define the
categoriser-based ranking semantics in light of categoriser strength, and
investigate some general properties of it. Finally, the semantics is shown to
satisfy some of the axioms that a ranking-based semantics should satisfy.
",Argument Ranking with Categoriser Function
"  Motivated by the application problem of sensor fusion the author introduced
the concept of graded set. It is reasoned that in classification problem
arising in an information system (represented by information table), a novel
set called Granular set naturally arises. It is realized that in any
hierarchical classification problem, Granular set naturally arises. Also when
the target set of objects forms a graded set the lower and upper approximations
of target sets form a graded set. This generalizes the concept of rough set. It
is hoped that a detailed theory of granular/ graded sets finds several
applications.
",Towards a theory of granular sets
"  In this paper a new heuristic optimization algorithm has been introduced
based on the performance of the major football leagues within each season in EU
countries. The algorithm starts with an initial population including three
different groups of teams: the wealthiest (strongest), the regular, the poorest
(weakest). Each individual of population constitute a football team while each
player is an indication of a player in a post. The optimization can hopefully
occurs when the competition among the teams in all the leagues is imitated as
the strongest teams usually purchase the best players of the regular teams and
in turn, regular teams purchase the best players of the weakest who should
always discover young players instead of buying professionals. It has been
shown that the algorithm can hopefully converge to an acceptable solution
solving various benchmarks. Key words: Heuristic Algorithms
","Soccer League Optimization: A heuristic Algorithm Inspired by the
  Football System in European Countries"
"  In order to improve children speech therapy, we develop a Fuzzy Expert System
based on a speech therapy guide. This guide, write in natural language, was
formalized using fuzzy logic paradigm. In this manner we obtain a knowledge
base with over 150 rules and 19 linguistic variables. All these researches,
including expert system validation, are part of TERAPERS project.
",Knowledge Base of an Expert System Used for Dyslalic Children Therapy
"  In the context of Noisy Multi-Objective Optimization, dealing with
uncertainties requires the decision maker to define some preferences about how
to handle them, through some statistics (e.g., mean, median) to be used to
evaluate the qualities of the solutions, and define the corresponding Pareto
set. Approximating these statistics requires repeated samplings of the
population, drastically increasing the overall computational cost. To tackle
this issue, this paper proposes to directly estimate the probability of each
individual to be selected, using some Hoeffding races to dynamically assign the
estimation budget during the selection step. The proposed racing approach is
validated against static budget approaches with NSGA-II on noisy versions of
the ZDT benchmark functions.
",Racing Multi-Objective Selection Probabilities
"  This paper proposes a model, the linear model, for randomly generating logic
programs with low density of rules and investigates statistical properties of
such random logic programs. It is mathematically shown that the average number
of answer sets for a random program converges to a constant when the number of
atoms approaches infinity. Several experimental results are also reported,
which justify the suitability of the linear model. It is also experimentally
shown that, under this model, the size distribution of answer sets for random
programs tends to a normal distribution when the number of atoms is
sufficiently large.
",Random Logic Programs: Linear Model
"  Acyclic directed mixed graphs, also known as semi-Markov models represent the
conditional independence structure induced on an observed margin by a DAG model
with latent variables. In this paper we present a factorization criterion for
these models that is equivalent to the global Markov property given by (the
natural extension of) d-separation.
",A factorization criterion for acyclic directed mixed graphs
"  Statements about entities occur everywhere, from newspapers and web pages to
structured databases. Correlating references to entities across systems that
use different identifiers or names for them is a widespread problem. In this
paper, we show how shared knowledge between systems can be used to solve this
problem. We present ""reference by description"", a formal model for resolving
references. We provide some results on the conditions under which a randomly
chosen entity in one system can, with high probability, be mapped to the same
entity in a different system.
",Communicating and resolving entity references
"  On the one hand, Constraint Satisfaction Problems allow one to declaratively
model problems. On the other hand, propositional satisfiability problem (SAT)
solvers can handle huge SAT instances. We thus present a technique to
declaratively model set constraint problems and to encode them automatically
into SAT instances. We apply our technique to the Social Golfer Problem and we
also use it to break symmetries of the problem. Our technique is simpler, more
declarative, and less error-prone than direct and improved hand modeling. The
SAT instances that we automatically generate contain less clauses than improved
hand-written instances such as in [20], and with unit propagation they also
contain less variables. Moreover, they are well-suited for SAT solvers and they
are solved faster as shown when solving difficult instances of the Social
Golfer Problem.
","Set Constraint Model and Automated Encoding into SAT: Application to the
  Social Golfer Problem"
"  In this paper we present a short history of logics: from particular cases of
2-symbol or numerical valued logic to the general case of n-symbol or numerical
valued logic. We show generalizations of 2-valued Boolean logic to fuzzy logic,
also from the Kleene and Lukasiewicz 3-symbol valued logics or Belnap 4-symbol
valued logic to the most general n-symbol or numerical valued refined
neutrosophic logic. Two classes of neutrosophic norm (n-norm) and neutrosophic
conorm (n-conorm) are defined. Examples of applications of neutrosophic logic
to physics are listed in the last section. Similar generalizations can be done
for n-Valued Refined Neutrosophic Set, and respectively n- Valued Refined
Neutrosopjhic Probability.
",n-Valued Refined Neutrosophic Logic and Its Applications to Physics
"  Article purpose is the analysis of a question of possibility of
technologization of philosophical knowledge. We understand the organization of
cognitive activity which is guided by the set of methods guaranteed bringing to
successful (i.e. to precisely corresponding set parameters) to applied results
as technologization. Transformation of sense of philosophy allows revealing
possibilities of its technologization. The leading role in this process is
played by philosophy of science which creates conditions for such
transformation. At the same time there is justified an appeal to branch
combination theory of the directions of scientific knowledge and partial
refusal of understanding of philosophy as synthetic knowledge in which the main
task is permission, instead of generation of paradoxes.
",Possibilities of technologization of philosophical knowledge
"  Existing decision-theoretic reasoning frameworks such as decision networks
use simple data structures and processes. However, decisions are often made
based on complex data structures, such as social networks and protein
sequences, and rich processes involving those structures. We present a
framework for representing decision problems with complex data structures using
probabilistic programming, allowing probabilistic models to be created with
programming language constructs such as data structures and control flow. We
provide a way to use arbitrary data types with minimal effort from the user,
and an approximate decision-making algorithm that is effective even when the
information space is very large or infinite. Experimental results show our
algorithm working on problems with very large information spaces.
","Decision-Making with Complex Data Structures using Probabilistic
  Programming"
"  In this paper, concept of possibility neutrosophic soft set and its
operations are defined, and their properties are studied. An application of
this theory in decision making is investigated. Also a similarity measure of
two possibility neutrosophic soft sets is introduced and discussed. Finally an
application of this similarity measure is given to select suitable person for
position in a firm.
","Possibility neutrosophic soft sets with applications in decision making
  and similarity measure"
"  This paper develops a Reasoning about Actions and Change framework integrated
with Default Reasoning, suitable as a Knowledge Representation and Reasoning
framework for Story Comprehension. The proposed framework, which is guided
strongly by existing knowhow from the Psychology of Reading and Comprehension,
is based on the theory of argumentation from AI. It uses argumentation to
capture appropriate solutions to the frame, ramification and qualification
problems and generalizations of these problems required for text comprehension.
In this first part of the study the work concentrates on the central problem of
integration (or elaboration) of the explicit information from the narrative in
the text with the implicit (in the readers mind) common sense world knowledge
pertaining to the topic(s) of the story given in the text. We also report on
our empirical efforts to gather background common sense world knowledge used by
humans when reading a story and to evaluate, through a prototype system, the
ability of our approach to capture both the majority and the variability of
understanding of a story by the human readers in the experiments.
",Non-Monotonic Reasoning and Story Comprehension
"  In Inverse subsumption for complete explanatory induction Yamamoto et al.
investigate which inductive logic programming systems can learn a correct
hypothesis $H$ by using the inverse subsumption instead of inverse entailment.
We prove that inductive logic programming system Imparo is complete by inverse
subsumption for learning a correct definite hypothesis $H$ wrt the definite
background theory $B$ and ground atomic examples $E$, by establishing that
there exists a connected theory $T$ for $B$ and $E$ such that $H$ subsumes $T$.
",Imparo is complete by inverse subsumption
"  We propose and investigate a simple ranking-measure-based extension semantics
for abstract argumentation frameworks based on their generic instantiation by
default knowledge bases and the ranking construction semantics for default
reasoning. In this context, we consider the path from structured to logical to
shallow semantic instantiations. The resulting well-justified JZ-extension
semantics diverges from more traditional approaches.
",A Plausibility Semantics for Abstract Argumentation Frameworks
"  For the last few decades, optimization has been developing at a fast rate.
Bio-inspired optimization algorithms are metaheuristics inspired by nature.
These algorithms have been applied to solve different problems in engineering,
economics, and other domains. Bio-inspired algorithms have also been applied in
different branches of information technology such as networking and software
engineering. Time series data mining is a field of information technology that
has its share of these applications too. In previous works we showed how
bio-inspired algorithms such as the genetic algorithms and differential
evolution can be used to find the locations of the breakpoints used in the
symbolic aggregate approximation of time series representation, and in another
work we showed how we can utilize the particle swarm optimization, one of the
famous bio-inspired algorithms, to set weights to the different segments in the
symbolic aggregate approximation representation. In this paper we present, in
two different approaches, a new meta optimization process that produces optimal
locations of the breakpoints in addition to optimal weights of the segments.
The experiments of time series classification task that we conducted show an
interesting example of how the overfitting phenomenon, a frequently encountered
problem in data mining which happens when the model overfits the training set,
can interfere in the optimization process and hide the superior performance of
an optimization algorithm.
","One-Step or Two-Step Optimization and the Overfitting Phenomenon: A Case
  Study on Time Series Classification"
"  The psychological state of flow has been linked to optimizing human
performance. A key condition of flow emergence is a match between the human
abilities and complexity of the task. We propose a simple computational model
of flow for Artificial Intelligence (AI) agents. The model factors the standard
agent-environment state into a self-reflective set of the agent's abilities and
a socially learned set of the environmental complexity. Maximizing the flow
serves as a meta control for the agent. We show how to apply the meta-control
policy to a broad class of AI control policies and illustrate our approach with
a specific implementation. Results in a synthetic testbed are promising and
open interesting directions for future work.
",Flow for Meta Control
"  As a contribution to the challenge of building game-playing AI systems, we
develop and analyse a formal language for representing and reasoning about
strategies. Our logical language builds on the existing general Game
Description Language (GDL) and extends it by a standard modality for linear
time along with two dual connectives to express preferences when combining
strategies. The semantics of the language is provided by a standard
state-transition model. As such, problems that require reasoning about games
can be solved by the standard methods for reasoning about actions and change.
We also endow the language with a specific semantics by which strategy formulas
are understood as move recommendations for a player. To illustrate how our
formalism supports automated reasoning about strategies, we demonstrate two
example methods of implementation\/: first, we formalise the semantic
interpretation of our language in conjunction with game rules and strategy
rules in the Situation Calculus; second, we show how the reasoning problem can
be solved with Answer Set Programming.
",Representing and Reasoning about Game Strategies
"  The goal of this paper is to elaborate swarm intelligence for business
intelligence decision making and the business rules management improvement.
.The swarm optimization, which is highly influenced by the behavior of
creature, performs in group. The Spatial data is defined as data that is
represented by 2D or 3D images. SQL Server supports only 2D images till now. As
we know that location is an essential part of any organizational data as well
as business data enterprises maintain customer address lists, own property,
ship goods from and to warehouses, manage transport flows among their
workforce, and perform many other activities. By means to say a lot of spatial
data is used and processed by enterprises, organizations and other bodies in
order to make the things more visible and self descriptive. From the
experiments, we found that PSO is can facilitate the intelligence in social and
business behavior.
",Social and Business Intelligence Analysis Using PSO
"  The article considers one of the possible generalizations of constraint
satisfaction problems where relations are replaced by multivalued membership
functions. In this case operations of disjunction and conjunction are replaced
by maximum and minimum, and consistency of a solution becomes multivalued
rather than binary. The article studies the problem of finding d most
admissible solutions for a given d. A tractable subclass of these problems is
defined by the concepts of invariants and polymorphisms similar to the classic
constraint satisfaction approach. These concepts are adapted in two ways.
Firstly, the correspondence of ""invariant-polymorphism"" is generalized to
(min,max) semirings. Secondly, we consider non-uniform polymorphisms, where
each variable has its own operator, in contrast to the case of one operator
common for all variables. The article describes an algorithm that finds $d$
most admissible solutions in polynomial time, provided that the problem is
invariant with respect to some non-uniform majority operator. It is essential
that this operator needs not to be known for the algorithm to work. Moreover,
even a guarantee for the existence of such an operator is not necessary. The
algorithm either finds the solution or discards the problem. The latter is
possible only if the problem has no majority polymorphism.
",M-best solutions for a class of fuzzy constraint satisfaction problems
"  This paper presents a fuzzy inference system for integrated volt/var control
(VVC) in distribution substations. The purpose is go forward to automation
distribution applying conservation voltage reduction (CVR) in isolated power
systems where control capabilities are limited. A fuzzy controller has been
designed. Working as an on-line tool, it has been tested under real conditions
and it has managed the operation during a whole day in a distribution
substation. Within the limits of control capabilities of the system, the
controller maintained successfully an acceptable voltage profile, power factor
values over 0,98 and it has ostensibly improved the performance given by an
optimal power flow based automation system. CVR savings during the test are
evaluated and the aim to integrate it in the VVC is presented.
",Fuzzy inference system for integrated VVC in isolated power systems
"  Existential rules have been proposed for representing ontological knowledge,
specifically in the context of Ontology-Based Query Answering. Entailment with
existential rules is undecidable. We focus in this paper on conditions that
ensure the termination of a breadth-first forward chaining algorithm known as
the chase. First, we propose a new tool that allows to extend existing
acyclicity conditions ensuring chase termination, while keeping good complexity
properties. Second, we consider the extension to existential rules with
nonmonotonic negation under stable model semantics and further extend
acyclicity results obtained in the positive case.
",Extending Acyclicity Notions for Existential Rules (\emph{long version})
"  Detecting faults in electrical power grids is of paramount importance, either
from the electricity operator and consumer viewpoints. Modern electric power
grids (smart grids) are equipped with smart sensors that allow to gather
real-time information regarding the physical status of all the component
elements belonging to the whole infrastructure (e.g., cables and related
insulation, transformers, breakers and so on). In real-world smart grid
systems, usually, additional information that are related to the operational
status of the grid itself are collected such as meteorological information.
Designing a suitable recognition (discrimination) model of faults in a
real-world smart grid system is hence a challenging task. This follows from the
heterogeneity of the information that actually determine a typical fault
condition. The second point is that, for synthesizing a recognition model, in
practice only the conditions of observed faults are usually meaningful.
Therefore, a suitable recognition model should be synthesized by making use of
the observed fault conditions only. In this paper, we deal with the problem of
modeling and recognizing faults in a real-world smart grid system, which
supplies the entire city of Rome, Italy. Recognition of faults is addressed by
following a combined approach of multiple dissimilarity measures customization
and one-class classification techniques. We provide here an in-depth study
related to the available data and to the models synthesized by the proposed
one-class classifier. We offer also a comprehensive analysis of the fault
recognition results by exploiting a fuzzy set based reliability decision rule.
","Modeling and Recognition of Smart Grid Faults by a Combined Approach of
  Dissimilarity Learning and One-Class Classification"
"  Researches in granular modeling produced a variety of mathematical models,
such as intervals, (higher-order) fuzzy sets, rough sets, and shadowed sets,
which are all suitable to characterize the so-called information granules.
Modeling of the input data uncertainty is recognized as a crucial aspect in
information granulation. Moreover, the uncertainty is a well-studied concept in
many mathematical settings, such as those of probability theory, fuzzy set
theory, and possibility theory. This fact suggests that an appropriate
quantification of the uncertainty expressed by the information granule model
could be used to define an invariant property, to be exploited in practical
situations of information granulation. In this perspective, a procedure of
information granulation is effective if the uncertainty conveyed by the
synthesized information granule is in a monotonically increasing relation with
the uncertainty of the input data. In this paper, we present a data granulation
framework that elaborates over the principles of uncertainty introduced by
Klir. Being the uncertainty a mesoscopic descriptor of systems and data, it is
possible to apply such principles regardless of the input data type and the
specific mathematical setting adopted for the information granules. The
proposed framework is conceived (i) to offer a guideline for the synthesis of
information granules and (ii) to build a groundwork to compare and
quantitatively judge over different data granulation procedures. To provide a
suitable case study, we introduce a new data granulation technique based on the
minimum sum of distances, which is designed to generate type-2 fuzzy sets. We
analyze the procedure by performing different experiments on two distinct data
types: feature vectors and labeled graphs. Results show that the uncertainty of
the input data is suitably conveyed by the generated type-2 fuzzy set models.
",Data granulation by the principles of uncertainty
"  Starting with a likelihood or preference order on worlds, we extend it to a
likelihood ordering on sets of worlds in a natural way, and examine the
resulting logic. Lewis (1973) earlier considered such a notion of relative
likelihood in the context of studying counterfactuals, but he assumed a total
preference order on worlds. Complications arise when examining partial orders
that are not present for total orders. There are subtleties involving the exact
approach to lifting the order on worlds to an order on sets of worlds. In
addition, the axiomatization of the logic of relative likelihood in the case of
partial orders gives insight into the connection between relative likelihood
and default reasoning.
","Defining Relative Likelihood in Partially-Ordered Preferential
  Structures"
"  A general notion of algebraic conditional plausibility measures is defined.
Probability measures, ranking functions, possibility measures, and (under the
appropriate definitions) sets of probability measures can all be viewed as
defining algebraic conditional plausibility measures. It is shown that the
technology of Bayesian networks can be applied to algebraic conditional
plausibility measures.
",Conditional Plausibility Measures and Bayesian Networks
"  As examples such as the Monty Hall puzzle show, applying conditioning to
update a probability distribution on a ``naive space', which does not take into
account the protocol used, can often lead to counterintuitive results. Here we
examine why. A criterion known as CAR (coarsening at random) in the statistical
literature characterizes when ``naive' conditioning in a naive space works. We
show that the CAR condition holds rather infrequently. We then consider more
generalized notions of update such as Jeffrey conditioning and minimizing
relative entropy (MRE). We give a generalization of the CAR condition that
characterizes when Jeffrey conditioning leads to appropriate answers, but show
that there are no such conditions for MRE. This generalizes and interconnects
previous results obtained in the literature on CAR and MRE.
",Updating Probabilities
"  Expectation is a central notion in probability theory. The notion of
expectation also makes sense for other notions of uncertainty. We introduce a
propositional logic for reasoning about expectation, where the semantics
depends on the underlying representation of uncertainty. We give sound and
complete axiomatizations for the logic in the case that the underlying
representation is (a) probability, (b) sets of probability measures, (c) belief
functions, and (d) possibility measures. We show that this logic is more
expressive than the corresponding logic for reasoning about likelihood in the
case of sets of probability measures, but equi-expressive in the case of
probability, belief, and possibility. Finally, we show that satisfiability for
these logics is NP-complete, no harder than satisfiability for propositional
logic.
",Reasoning about Expectation
"  We introduce a logic for reasoning about evidence, that essentially views
evidence as a function from prior beliefs (before making an observation) to
posterior beliefs (after making the observation). We provide a sound and
complete axiomatization for the logic, and consider the complexity of the
decision problem. Although the reasoning in the logic is mainly propositional,
we allow variables representing numbers and quantification over them. This
expressive power seems necessary to capture important properties of evidence
",A Logic for Reasoning about Evidence
"  It is commonly-accepted wisdom that more information is better, and that
information should never be ignored. Here we argue, using both a Bayesian and a
non-Bayesian analysis, that in some situations you are better off ignoring
information if your uncertainty is represented by a set of probability
measures. These include situations in which the information is relevant for the
prediction task at hand. In the non-Bayesian analysis, we show how ignoring
information avoids dilation, the phenomenon that additional pieces of
information sometimes lead to an increase in uncertainty. In the Bayesian
analysis, we show that for small sample sizes and certain prediction tasks, the
Bayesian posterior based on a noninformative prior yields worse predictions
than simply ignoring the given information.
",When Ignorance is Bliss
"  An agent often has a number of hypotheses, and must choose among them based
on observations, or outcomes of experiments. Each of these observations can be
viewed as providing evidence for or against various hypotheses. All the
attempts to formalize this intuition up to now have assumed that associated
with each hypothesis h there is a likelihood function {\mu}h, which is a
probability measure that intuitively describes how likely each observation is,
conditional on h being the correct hypothesis. We consider an extension of this
framework where there is uncertainty as to which of a number of likelihood
functions is appropriate, and discuss how one formal approach to defining
evidence, which views evidence as a function from priors to posteriors, can be
generalized to accommodate this uncertainty.
",Evidence with Uncertain Likelihoods
"  We consider how an agent should update her uncertainty when it is represented
by a set P of probability distributions and the agent observes that a random
variable X takes on value x, given that the agent makes decisions using the
minimax criterion, perhaps the best-studied and most commonly-used criterion in
the literature. We adopt a game-theoretic framework, where the agent plays
against a bookie, who chooses some distribution from P. We consider two
reasonable games that differ in what the bookie knows when he makes his choice.
Anomalies that have been observed before, like time inconsistency, can be
understood as arising because different games are being played, against bookies
with different information. We characterize the important special cases in
which the optimal decision rules according to the minimax criterion amount to
either conditioning or simply ignoring the information. Finally, we consider
the relationship between conditioning and calibration when uncertainty is
described by sets of probabilities.
",A Game-Theoretic Analysis of Updating Sets of Probabilities
"  Markov decision processes (MDPs) are widely used for modeling decision-making
problems in robotics, automated control, and economics. Traditional MDPs assume
that the decision maker (DM) knows all states and actions. However, this may
not be true in many situations of interest. We define a new framework, MDPs
with unawareness (MDPUs) to deal with the possibilities that a DM may not be
aware of all possible actions. We provide a complete characterization of when a
DM can learn to play near-optimally in an MDPU, and give an algorithm that
learns to play near-optimally when it is possible to do so, as efficiently as
possible. In particular, we characterize when a near-optimal solution can be
found in polynomial time.
",MDPs with Unawareness
"  Over the last decade, there has been growing interest in the use or measures
or change in belief for reasoning with uncertainty in artificial intelligence
research. An important characteristic of several methodologies that reason with
changes in belief or belief updates, is a property that we term modularity. We
call updates that satisfy this property modular updates. Whereas probabilistic
measures of belief update - which satisfy the modularity property were first
discovered in the nineteenth century, knowledge and discussion of these
quantities remains obscure in artificial intelligence research. We define
modular updates and discuss their inappropriate use in two influential expert
systems.
","Modular Belief Updates and Confusion about Measures of Certainty in
  Artificial Intelligence Research"
"  Modern software systems increasingly incorporate self-* behavior to adapt to
changes in the environment at runtime. Such adaptations often involve
reconfiguring the software architecture of the system. Many systems also need
to manage their architecture themselves, i.e., they need a planning component
to autonomously decide which reconfigurations to execute to reach a desired
target configuration. For the specification of reconfigurations, we employ
graph transformations systems (GTS) due to the close relation of graphs and UML
object diagrams. We solve the resulting planning problems with a planning
system that works directly on a GTS. It features a domain-independent heuristic
that uses the solution length of an abstraction of the original problem as an
estimate. Finally, we provide experimental results on two different domains,
which confirm that our heuristic performs better than another
domain-independent heuristic which resembles heuristics employed in related
work.
",Graph Transformation Planning via Abstraction
"  In this paper we address the problem of planning in rich domains, where
knowledge representation is a key aspect for managing the complexity and size
of the planning domain. We follow the approach of Description Logic (DL) based
Dynamic Knowledge Bases, where a state of the world is represented concisely by
a (possibly changing) ABox and a (fixed) TBox containing the axioms, and
actions that allow to change the content of the ABox. The plan goal is given in
terms of satisfaction of a DL query. In this paper we start from a traditional
forward planning algorithm and we propose a much more efficient variant by
combining backward and forward search. In particular, we propose a Backward
State-space Reduction technique that consists in two phases: first, an Abstract
Planning Graph P is created by using the Abstract Backward Planning Algorithm
(ABP), then the abstract planning graph P is instantiated into a corresponding
planning graph P by using the Forward Plan Instantiation Algorithm (FPI). The
advantage is that in the preliminary ABP phase we produce a symbolic plan that
is a pattern to direct the search of the concrete plan. This can be seen as a
kind of informed search where the preliminary backward phase is useful to
discover properties of the state-space that can be used to direct the
subsequent forward phase. We evaluate the effectiveness of our ABP+FPI
algorithm in the reduction of the explored planning domain by comparing it to a
standard forward planning algorithm and applying both of them to a concrete
business case study.
",Backwards State-space Reduction for Planning in Dynamic Knowledge Bases
"  The strong solutions of Nine Men's Morris and its variant, Lasker Morris are
well-known results (the starting positions are draws). We re-examined both of
these games, and calculated extended strong solutions for them. By this we mean
the game-theoretic values of all possible game states that could be reached
from certain starting positions where the number of stones to be placed by the
players is different from the standard rules. These were also calculated for a
previously unsolved third variant, Morabaraba, with interesting results: most
of the starting positions where the players can place an equal number of stones
(including the standard starting position) are wins for the first player (as
opposed to the above games, where these are usually draws).
  We also developed a multi-valued retrograde analysis, and used it as a basis
for an algorithm for solving these games ultra-strongly. This means that when
our program is playing against a fallible opponent, it has a greater chance of
achieving a better result than the game-theoretic value, compared to randomly
selecting between ""just strongly"" optimal moves. Previous attempts on
ultra-strong solutions used local heuristics or learning during games, but we
incorporated our algorithm into the retrograde analysis.
","Calculating Ultra-Strong and Extended Solutions for Nine Men's Morris,
  Morabaraba, and Lasker"
"  There has been a considerable advance in computing, to mimic the way in which
the brain tries to comprehend and structure the information to retrieve
meaningful knowledge. It is identified that neuronal entities hold whole of the
knowledge that the species makes use of. We intended to develop a modified
knowledge based system, termed as Informledge System (ILS) with autonomous
nodes and intelligent links that integrate and structure the pieces of
knowledge. We conceive that every piece of knowledge is a cluster of
cross-linked and correlated structure. In this paper, we put forward the theory
of the nodes depicting concepts, referred as Entity Concept State which in turn
is dealt with Concept State Diagrams (CSD). This theory is based on an abstract
framework provided by the concepts. The framework represents the ILS as the
weighted graph where the weights attached with the linked nodes help in
knowledge retrieval by providing the direction of connectivity of autonomous
nodes present in knowledge thread traversal. Here for the first time in the
process of developing Informledge, we apply tenor computation for creating
intelligent combinatorial knowledge with cross mutation to create fresh
knowledge which looks to be the fundamentals of a typical thought process.
","Correlating and Cross-linking Knowledge Threads in Informledge System
  for Creating New Knowledge"
"  This paper proposes an analysis of the effects of consensus and preference
aggregation on the consistency of pairwise comparisons. We define some boundary
properties for the inconsistency of group preferences and investigate their
relation with different inconsistency indices. Some results are presented on
more general dependencies between properties of inconsistency indices and the
satisfaction of boundary properties. In the end, given three boundary
properties and nine indices among the most relevant ones, we will be able to
present a complete analysis of what indices satisfy what properties and offer a
reflection on the interpretation of the inconsistency of group preferences.
","Boundary properties of the inconsistency of pairwise comparisons in
  group decisions"
"  In this paper we propose a dynamic data structure that supports efficient
algorithms for updating and querying singly connected Bayesian networks (causal
trees and polytrees). In the conventional algorithms, new evidence in absorbed
in time O(1) and queries are processed in time O(N), where N is the size of the
network. We propose a practical algorithm which, after a preprocessing phase,
allows us to answer queries in time O(log N) at the expense of O(logn N) time
per evidence absorption. The usefulness of sub-linear processing time manifests
itself in applications requiring (near) real-time response over large
probabilistic databases.
",Logarithmic-Time Updates and Queries in Probabilistic Networks
"  We describe a new paradigm for implementing inference in belief networks,
which relies on compiling a belief network into an arithmetic expression called
a Query DAG (Q-DAG). Each non-leaf node of a Q-DAG represents a numeric
operation, a number, or a symbol for evidence. Each leaf node of a Q-DAG
represents the answer to a network query, that is, the probability of some
event of interest. It appears that Q-DAGs can be generated using any of the
algorithms for exact inference in belief networks --- we show how they can be
generated using clustering and conditioning algorithms. The time and space
complexity of a Q-DAG generation algorithm is no worse than the time complexity
of the inference algorithm on which it is based; that of a Q-DAG on-line
evaluation algorithm is linear in the size of the Q-DAG, and such inference
amounts to a standard evaluation of the arithmetic expression it represents.
The main value of Q-DAGs is in reducing the software and hardware resources
required to utilize belief networks in on-line, real-world applications. The
proposed framework also facilitates the development of on-line inference on
different software and hardware platforms, given the simplicity of the Q-DAG
evaluation algorithm. This paper describes this new paradigm for probabilistic
inference, explaining how it works, its uses, and outlines some of the research
directions that it leads to.
","Query DAGs: A Practical Paradigm for Implementing Belief Network
  Inference"
"  Preferences among acts are analyzed in the style of L. Savage, but as
partially ordered. The rationality postulates considered are weaker than
Savage's on three counts. The Sure Thing Principle is derived in this setting.
The postulates are shown to lead to a characterization of generalized
qualitative probability that includes and blends both traditional qualitative
probability and the ranked structures used in logical approaches.
",Generalized Qualitative Probability: Savage Revisited
"  Cooperative games are those in which both agents share the same payoff
structure. Value-based reinforcement-learning algorithms, such as variants of
Q-learning, have been applied to learning cooperative games, but they only
apply when the game state is completely observable to both agents. Policy
search methods are a reasonable alternative to value-based methods for
partially observable environments. In this paper, we provide a gradient-based
distributed policy-search method for cooperative games and compare the notion
of local optimum to that of Nash equilibrium. We demonstrate the effectiveness
of this method experimentally in a small, partially observable simulated soccer
domain.
",Learning to Cooperate via Policy Search
"  Mutual information is widely used in artificial intelligence, in a
descriptive way, to measure the stochastic dependence of discrete random
variables. In order to address questions such as the reliability of the
empirical value, one must consider sample-to-population inferential approaches.
This paper deals with the distribution of mutual information, as obtained in a
Bayesian framework by a second-order Dirichlet prior distribution. The exact
analytical expression for the mean and an analytical approximation of the
variance are reported. Asymptotic approximations of the distribution are
proposed. The results are applied to the problem of selecting features for
incremental learning and classification of the naive Bayes classifier. A fast,
newly defined method is shown to outperform the traditional approach based on
empirical mutual information on a number of real data sets. Finally, a
theoretical development is reported that allows one to efficiently extend the
above methods to incomplete samples in an easy and effective way.
",Robust Feature Selection by Mutual Information Distributions
"  Currently, there is renewed interest in the problem, raised by Shafer in
1985, of updating probabilities when observations are incomplete (or
set-valued). This is a fundamental problem, and of particular interest for
Bayesian networks. Recently, Grunwald and Halpern have shown that commonly used
updating strategies fail here, except under very special assumptions. We
propose a new rule for updating probabilities with incomplete observations. Our
approach is deliberately conservative: we make no or weak assumptions about the
so-called incompleteness mechanism that produces incomplete observations. We
model our ignorance about this mechanism by a vacuous lower prevision, a tool
from the theory of imprecise probabilities, and we derive a new updating rule
using coherence arguments. In general, our rule produces lower posterior
probabilities, as well as partially determinate decisions. This is a logical
consequence of the ignorance about the incompleteness mechanism. We show how
the new rule can properly address the apparent paradox in the 'Monty Hall'
puzzle. In addition, we apply it to the classification of new evidence in
Bayesian networks constructed using expert knowledge. We provide an exact
algorithm for this task with linear-time complexity, also for multiply
connected nets.
",Updating with incomplete observations
"  Common wisdom has it that small distinctions in the probabilities quantifying
a Bayesian network do not matter much for the resultsof probabilistic queries.
However, one can easily develop realistic scenarios under which small
variations in network probabilities can lead to significant changes in computed
queries. A pending theoretical question is then to analytically characterize
parameter changes that do or do not matter. In this paper, we study the
sensitivity of probabilistic queries to changes in network parameters and prove
some tight bounds on the impact that such parameters can have on queries. Our
analytical results pinpoint some interesting situations under which parameter
changes do or do not matter. These results are important for knowledge
engineers as they help them identify influential network parameters. They are
also important for approximate inference algorithms that preprocessnetwork CPTs
to eliminate small distinctions in probabilities.
",When do Numbers Really Matter?
"  We present a heuristic search algorithm for solving first-order MDPs
(FOMDPs). Our approach combines first-order state abstraction that avoids
evaluating states individually, and heuristic search that avoids evaluating all
states. Firstly, we apply state abstraction directly on the FOMDP avoiding
propositionalization. Such kind of abstraction is referred to as firstorder
state abstraction. Secondly, guided by an admissible heuristic, the search is
restricted only to those states that are reachable from the initial state. We
demonstrate the usefullness of the above techniques for solving FOMDPs on a
system, referred to as FCPlanner, that entered the probabilistic track of the
International Planning Competition (IPC'2004).
",A Heuristic Search Algorithm for Solving First-Order MDPs
"  Bandit based methods for tree search have recently gained popularity when
applied to huge trees, e.g. in the game of go [6]. Their efficient exploration
of the tree enables to re- turn rapidly a good value, and improve preci- sion
if more time is provided. The UCT algo- rithm [8], a tree search method based
on Up- per Confidence Bounds (UCB) [2], is believed to adapt locally to the
effective smoothness of the tree. However, we show that UCT is
""over-optimistic"" in some sense, leading to a worst-case regret that may be
very poor. We propose alternative bandit algorithms for tree search. First, a
modification of UCT us- ing a confidence sequence that scales expo- nentially
in the horizon depth is analyzed. We then consider Flat-UCB performed on the
leaves and provide a finite regret bound with high probability. Then, we
introduce and analyze a Bandit Algorithm for Smooth Trees (BAST) which takes
into account ac- tual smoothness of the rewards for perform- ing efficient
""cuts"" of sub-optimal branches with high confidence. Finally, we present an
incremental tree expansion which applies when the full tree is too big
(possibly in- finite) to be entirely represented and show that with high
probability, only the optimal branches are indefinitely developed. We illus-
trate these methods on a global optimization problem of a continuous function,
given noisy values.
",Bandit Algorithms for Tree Search
"  When the initial and transition probabilities of a finite Markov chain in
discrete time are not well known, we should perform a sensitivity analysis.
This is done by considering as basic uncertainty models the so-called credal
sets that these probabilities are known or believed to belong to, and by
allowing the probabilities to vary over such sets. This leads to the definition
of an imprecise Markov chain. We show that the time evolution of such a system
can be studied very efficiently using so-called lower and upper expectations.
We also study how the inferred credal set about the state at time n evolves as
n->infinity: under quite unrestrictive conditions, it converges to a uniquely
invariant credal set, regardless of the credal set given for the initial state.
This leads to a non-trivial generalisation of the classical Perron-Frobenius
Theorem to imprecise Markov chains.
",Sensitivity analysis for finite Markov chains in discrete time
"  We introduce novel results for approximate inference on planar graphical
models using the loop calculus framework. The loop calculus (Chertkov and
Chernyak, 2006b) allows to express the exact partition function Z of a
graphical model as a finite sum of terms that can be evaluated once the belief
propagation (BP) solution is known. In general, full summation over all
correction terms is intractable. We develop an algorithm for the approach
presented in Chertkov et al. (2008) which represents an efficient truncation
scheme on planar graphs and a new representation of the series in terms of
Pfaffians of matrices. We analyze in detail both the loop series and the
Pfaffian series for models with binary variables and pairwise interactions, and
show that the first term of the Pfaffian series can provide very accurate
approximations. The algorithm outperforms previous truncation schemes of the
loop series and is competitive with other state-of-the-art methods for
approximate inference.
","Approximate inference on planar graphs using Loop Calculus and Belief
  Propagation"
"  The problem of modeling and predicting spatiotemporal traffic phenomena over
an urban road network is important to many traffic applications such as
detecting and forecasting congestion hotspots. This paper presents a
decentralized data fusion and active sensing (D2FAS) algorithm for mobile
sensors to actively explore the road network to gather and assimilate the most
informative data for predicting the traffic phenomenon. We analyze the time and
communication complexity of D2FAS and demonstrate that it can scale well with a
large number of observations and sensors. We provide a theoretical guarantee on
its predictive performance to be equivalent to that of a sophisticated
centralized sparse approximation for the Gaussian process (GP) model: The
computation of such a sparse approximate GP model can thus be parallelized and
distributed among the mobile sensors (in a Google-like MapReduce paradigm),
thereby achieving efficient and scalable prediction. We also theoretically
guarantee its active sensing performance that improves under various practical
environmental conditions. Empirical evaluation on real-world urban road network
data shows that our D2FAS algorithm is significantly more time-efficient and
scalable than state-oftheart centralized algorithms while achieving comparable
predictive performance.
","Decentralized Data Fusion and Active Sensing with Mobile Sensors for
  Modeling and Predicting Spatiotemporal Traffic Phenomena"
"  Sequential decision problems are often approximately solvable by simulating
possible future action sequences. Metalevel decision procedures have been
developed for selecting which action sequences to simulate, based on estimating
the expected improvement in decision quality that would result from any
particular simulation; an example is the recent work on using bandit algorithms
to control Monte Carlo tree search in the game of Go. In this paper we develop
a theoretical basis for metalevel decisions in the statistical framework of
Bayesian selection problems, arguing (as others have done) that this is more
appropriate than the bandit framework. We derive a number of basic results
applicable to Monte Carlo selection problems, including the first finite
sampling bounds for optimal policies in certain cases; we also provide a simple
counterexample to the intuitive conjecture that an optimal policy will
necessarily reach a decision in all cases. We then derive heuristic
approximations in both Bayesian and distribution-free settings and demonstrate
their superiority to bandit-based heuristics in one-shot decision problems and
in Go.
",Selecting Computations: Theory and Applications
"  We present a novel approach to detecting and utilizing symmetries in
probabilistic graphical models with two main contributions. First, we present a
scalable approach to computing generating sets of permutation groups
representing the symmetries of graphical models. Second, we introduce orbital
Markov chains, a novel family of Markov chains leveraging model symmetries to
reduce mixing times. We establish an insightful connection between model
symmetries and rapid mixing of orbital Markov chains. Thus, we present the
first lifted MCMC algorithm for probabilistic graphical models. Both analytical
and empirical results demonstrate the effectiveness and efficiency of the
approach.
",Markov Chains on Orbits of Permutation Groups
"  Multi-fidelity methods combine inexpensive low-fidelity simulations with
costly but highfidelity simulations to produce an accurate model of a system of
interest at minimal cost. They have proven useful in modeling physical systems
and have been applied to engineering problems such as wing-design optimization.
During human-in-the-loop experimentation, it has become increasingly common to
use online platforms, like Mechanical Turk, to run low-fidelity experiments to
gather human performance data in an efficient manner. One concern with these
experiments is that the results obtained from the online environment generalize
poorly to the actual domain of interest. To address this limitation, we extend
traditional multi-fidelity approaches to allow us to combine fewer data points
from high-fidelity human-in-the-loop experiments with plentiful but less
accurate data from low-fidelity experiments to produce accurate models of how
humans interact. We present both model-based and model-free methods, and
summarize the predictive performance of each method under dierent conditions.
","Predicting the behavior of interacting humans by fusing data from
  multiple sources"
"  Sensory inference under conditions of uncertainty is a major problem in both
machine learning and computational neuroscience. An important but poorly
understood aspect of sensory processing is the role of active sensing. Here, we
present a Bayes-optimal inference and control framework for active sensing,
C-DAC (Context-Dependent Active Controller). Unlike previously proposed
algorithms that optimize abstract statistical objectives such as information
maximization (Infomax) [Butko & Movellan, 2010] or one-step look-ahead accuracy
[Najemnik & Geisler, 2005], our active sensing model directly minimizes a
combination of behavioral costs, such as temporal delay, response error, and
effort. We simulate these algorithms on a simple visual search task to
illustrate scenarios in which context-sensitivity is particularly beneficial
and optimization with respect to generic statistical objectives particularly
inadequate. Motivated by the geometric properties of the C-DAC policy, we
present both parametric and non-parametric approximations, which retain
context-sensitivity while significantly reducing computational complexity.
These approximations enable us to investigate the more complex problem
involving peripheral vision, and we notice that the difference between C-DAC
and statistical policies becomes even more evident in this scenario.
",Active Sensing as Bayes-Optimal Sequential Decision Making
"  A significant theoretical advantage of search-and-score methods for learning
Bayesian Networks is that they can accept informative prior beliefs for each
possible network, thus complementing the data. In this paper, a method is
presented for assigning priors based on beliefs on the presence or absence of
certain paths in the true network. Such beliefs correspond to knowledge about
the possible causal and associative relations between pairs of variables. This
type of knowledge naturally arises from prior experimental and observational
data, among others. In addition, a novel search-operator is proposed to take
advantage of such prior knowledge. Experiments show that, using path beliefs
improves the learning of the skeleton, as well as the edge directions in the
network.
","Scoring and Searching over Bayesian Networks with Causal and Associative
  Priors"
"  We consider partially observable Markov decision processes (POMDPs) with
limit-average payoff, where a reward value in the interval [0,1] is associated
to every transition, and the payoff of an infinite path is the long-run average
of the rewards. We consider two types of path constraints: (i) quantitative
constraint defines the set of paths where the payoff is at least a given
threshold lambda_1 in (0,1]; and (ii) qualitative constraint which is a special
case of quantitative constraint with lambda_1=1. We consider the computation of
the almost-sure winning set, where the controller needs to ensure that the path
constraint is satisfied with probability 1. Our main results for qualitative
path constraint are as follows: (i) the problem of deciding the existence of a
finite-memory controller is EXPTIME-complete; and (ii) the problem of deciding
the existence of an infinite-memory controller is undecidable. For quantitative
path constraint we show that the problem of deciding the existence of a
finite-memory controller is undecidable.
",POMDPs under Probabilistic Semantics
"  We show how, and under which conditions, the equilibrium states of a
first-order Ordinary Differential Equation (ODE) system can be described with a
deterministic Structural Causal Model (SCM). Our exposition sheds more light on
the concept of causality as expressed within the framework of Structural Causal
Models, especially for cyclic models.
","From Ordinary Differential Equations to Structural Causal Models: the
  deterministic case"
"  Decision trees have been widely used in machine learning. However, due to
some reasons, data collecting in real world contains a fuzzy and uncertain
form. The decision tree should be able to handle such fuzzy data. This paper
presents a method to construct fuzzy decision tree. It proposes a fuzzy
decision tree induction method in iris flower data set, obtaining the entropy
from the distance between an average value and a particular value. It also
presents an experiment result that shows the accuracy compared to former ID3.
",The New Approach on Fuzzy Decision Trees
"  An ensemble inference mechanism is proposed on the Angry Birds domain. It is
based on an efficient tree structure for encoding and representing game
screenshots, where it exploits its enhanced modeling capability. This has the
advantage to establish an informative feature space and modify the task of game
playing to a regression analysis problem. To this direction, we assume that
each type of object material and bird pair has its own Bayesian linear
regression model. In this way, a multi-model regression framework is designed
that simultaneously calculates the conditional expectations of several objects
and makes a target decision through an ensemble of regression models. Learning
procedure is performed according to an online estimation strategy for the model
parameters. We provide comparative experimental results on several game levels
that empirically illustrate the efficiency of the proposed methodology.
",A Bayesian Ensemble Regression Framework on the Angry Birds Game
"  We investigate cumulative scheduling in uncertain environments, using
constraint programming. We detail in this paper the dynamic sweep filtering
algorithm of the FlexC global constraint.
",Dynamic Sweep Filtering Algorithm for FlexC
"  This paper describes a relatively simple way of allowing a brain model to
self-organise its concept patterns through nested structures. For a simulation,
time reduction is helpful and it would be able to show how patterns may form
and then fire in sequence, as part of a search or thought process. It uses a
very simple equation to show how the inhibitors in particular, can switch off
certain areas, to allow other areas to become the prominent ones and thereby
define the current brain state. This allows for a small amount of control over
what appears to be a chaotic structure inside of the brain. It is attractive
because it is still mostly mechanical and therefore can be added as an
automatic process, or the modelling of that. The paper also describes how the
nested pattern structure can be used as a basic counting mechanism. Another
mathematical conclusion provides a basis for maintaining memory or concept
patterns. The self-organisation can space itself through automatic processes.
This might allow new neurons to be added in a more even manner and could help
to maintain the concept integrity. The process might also help with finding
memory structures afterwards. This extended version integrates further with the
existing cognitive model and provides some new conclusions.
",New Ideas for Brain Modelling 2
"  Study of soft sets was first proposed by Molodtsov in 1999 to deal with
uncertainty in a non-parametric manner. The researchers did not pay attention
to soft set theory at that time but now the soft set theory has been developed
in many areas of mathematics. Algebraic structures using soft set theory are
very rapidly developed. In this book we developed soft neutrosophic algebraic
structures by using soft sets and neutrosophic algebraic structures. In this
book we study soft neutrosophic groups, soft neutrosophic semigroups, soft
neutrosophic loops, soft neutrosophic LA-semigroups, and their generalizations
respectively.
",Soft Neutrosophic Algebraic Structures and Their Generalization
"  Operating vehicles in adversarial environments between a recurring
origin-destination pair requires new planning techniques. A two players
zero-sum game is introduced. The goal of the first player is to minimize the
expected casualties undergone by a convoy. The goal of the second player is to
maximize this damage. The outcome of the game is obtained via a linear program
that solves the corresponding minmax optimization problem over this outcome.
Different environment models are defined in order to compute routing strategies
over unstructured environments. To compare these methods for increasingly
accurate representations of the environment, a grid-based model is chosen to
represent the environment and the existence of a sufficient network size is
highlighted. A global framework for the generation of realistic routing
strategies between any two points is described. This framework requires a good
assessment of the potential casualties at any location, therefore the most
important parameters are identified. Finally the framework is tested on real
world environments.
",A Complete framework for ambush avoidance in realistic environments
"  In group decision making (GDM) problems fuzzy preference relations (FPR) are
widely used for representing decision makers' opinions on the set of
alternatives. In order to avoid misleading solutions, the study of consistency
and consensus has become a very important aspect. This article presents a
simulated annealing (SA) based soft computing approach to optimize the
consistency/consensus level (CCL) of a complete fuzzy preference relation in
order to solve a GDM problem. Consistency level indicates as expert's
preference quality and consensus level measures the degree of agreement among
experts' opinions. This study also suggests the set of experts for the
necessary modifications in their prescribed preference structures without
intervention of any moderator.
","Consensus and Consistency Level Optimization of Fuzzy Preference
  Relation: A Soft Computing Approach"
"  In this paper, we address the knowledge engineering problems for hypothesis
generation motivated by applications that require timely exploration of
hypotheses under unreliable observations. We looked at two applications:
malware detection and intensive care delivery. In intensive care, the goal is
to generate plausible hypotheses about the condition of the patient from
clinical observations and further refine these hypotheses to create a recovery
plan for the patient. Similarly, preventing malware spread within a corporate
network involves generating hypotheses from network traffic data and selecting
preventive actions. To this end, building on the already established
characterization and use of AI planning for similar problems, we propose use of
planning for the hypothesis generation problem. However, to deal with
uncertainty, incomplete model description and unreliable observations, we need
to use a planner capable of generating multiple high-quality plans. To capture
the model description we propose a language called LTS++ and a web-based tool
that enables the specification of the LTS++ model and a set of observations. We
also proposed a 9-step process that helps provide guidance to the domain expert
in specifying the LTS++ model. The hypotheses are then generated by running a
planner on the translated LTS++ model and the provided trace. The hypotheses
can be visualized and shown to the analyst or can be further investigated
automatically.
",Knowledge Engineering for Planning-Based Hypothesis Generation
"  Given an argumentation network with initial values to the arguments, we look
for algorithms which can yield extensions compatible with such initial values.
We find that the best way of tackling this problem is to offer an iteration
formula that takes the initial values and the attack relation and iterates a
sequence of intermediate values that eventually converges leading to an
extension. The properties surrounding the application of the iteration formula
and its connection with other numerical and non-numerical techniques proposed
by others are thoroughly investigated in this paper.
",Equilibrium States in Numerical Argumentation Networks
"  Artificial intelligence develops techniques and systems whose performance
must be evaluated on a regular basis in order to certify and foster progress in
the discipline. We will describe and critically assess the different ways AI
systems are evaluated. We first focus on the traditional task-oriented
evaluation approach. We see that black-box (behavioural evaluation) is becoming
more and more common, as AI systems are becoming more complex and
unpredictable. We identify three kinds of evaluation: Human discrimination,
problem benchmarks and peer confrontation. We describe the limitations of the
many evaluation settings and competitions in these three categories and propose
several ideas for a more systematic and robust evaluation. We then focus on a
less customary (and challenging) ability-oriented evaluation approach, where a
system is characterised by its (cognitive) abilities, rather than by the tasks
it is designed to solve. We discuss several possibilities: the adaptation of
cognitive tests used for humans and animals, the development of tests derived
from algorithmic information theory or more general approaches under the
perspective of universal psychometrics.
","AI Evaluation: past, present and future"
"  There is a relatively small amount of research covering urban freight
movements. Most research dealing with the subject of urban mobility focuses on
passenger vehicles, not commercial vehicles hauling freight. However, in many
ways, urban freight transport contributes to congestion, air pollution, noise,
accident and more fuel consumption which raises logistic costs, and hence the
price of products. The main focus of this paper is to propose a new solution
for congestion in order to improve the distribution process of goods in urban
areas and optimize transportation cost, time of delivery, fuel consumption, and
environmental impact, while guaranteeing the safety of goods and passengers. A
novel technique for personalization in itinerary search based on city logistics
ontology and rules is proposed to overcome this problem. The integration of
personalization plays a key role in capturing or inferring the needs of each
stakeholder (user), and then satisfying these needs in a given context. The
proposed approach is implemented to an itinerary search problem for freight
transportation in urban areas to demonstrate its ability in facilitating
intelligent decision support by retrieving the best itinerary that satisfies
the most users preferences (stakeholders).
","Personalization of Itineraries search using Ontology and Rules to Avoid
  Congestion in Urban Areas"
"  This paper introduces abstractions that are meaningful for computers and that
can be built and used according to computers' own criteria, i.e., computable
abstractions. It is analyzed how abstractions can be seen to serve as the
building blocks for the creation of one own's understanding of things, which is
essential in performing intellectual tasks. Thus, abstractional machines are
defined, which following a mechanical process can, based on computable
abstractions, build and use their own understanding of things. Abstractional
machines are illustrated through an example that outlines their application to
the task of natural language processing.
",On computable abstractions (a conceptual introduction)
"  The measure of distance between two fuzzy sets is a fundamental tool within
fuzzy set theory, however, distance measures currently within the literature
use a crisp value to represent the distance between fuzzy sets. A real valued
distance measure is developed into a fuzzy distance measure which better
reflects the uncertainty inherent in fuzzy sets and a fuzzy directional
distance measure is presented, which accounts for the direction of change
between fuzzy sets. A multiplicative version is explored as a full maximal
assignment is computationally intractable so an intermediate solution is
offered.
",A Fuzzy Directional Distance Measure
"  Reasoning with fuzzy sets can be achieved through measures such as similarity
and distance. However, these measures can often give misleading results when
considered independently, for example giving the same value for two different
pairs of fuzzy sets. This is particularly a problem where many fuzzy sets are
generated from real data, and while two different measures may be used to
automatically compare such fuzzy sets, it is difficult to interpret two
different results. This is especially true where a large number of fuzzy sets
are being compared as part of a reasoning system. This paper introduces a
method for combining the results of multiple measures into a single measure for
the purpose of analysing and comparing fuzzy sets. The combined measure
alleviates ambiguous results and aids in the automatic comparison of fuzzy
sets. The properties of the combined measure are given, and demonstrations are
presented with discussions on the advantages over using a single measure.
","Analysing Fuzzy Sets Through Combining Measures of Similarity and
  Distance"
"  Knowledge-based or Artificial Intelligence techniques are used increasingly
as alternatives to more classical techniques to model ENVIRONMENTAL SYSTEMS.
Use of Artificial Intelligence (AI) in environmental modelling has increased
with recognition of its potential. In this paper we examine the DIFFERENT
TECHNIQUES of Artificial intelligence with profound examples of human
perception, learning and reasoning to solve complex problems. However with the
increase of complexity better methods are required. Keeping in view of the
above some researchers introduced the idea of hybrid mechanism in which two or
more methods can be combined which seems to be a positive effort for creating a
more complex; advanced and intelligent system which has the capability to in-
cooperate human decisions thus driving the landscape changes.
","Hybrid Systems Knowledge Representation Using Modelling Environment
  System Techniques Artificial Intelligence"
"  In this paper, we propose a model for simulating search operators whose
behaviour often changes continuously during the search. In these scenarios, the
performance of the operators decreases when they are applied. This is motivated
by the fact that operators for optimization problems are often roughly
classified into exploitation operators and exploration operators. Our
simulation model is used to compare the different performances of operator
selection policies and clearly identify their ability to adapt to such specific
operators behaviours. The experimental study provides interesting results on
the respective behaviours of operator selection policies when faced to such non
stationary search scenarios.
",Simulating Non Stationary Operators in Search Algorithms
"  This paper studies the off-policy evaluation problem, where one aims to
estimate the value of a target policy based on a sample of observations
collected by another policy. We first consider the multi-armed bandit case,
establish a minimax risk lower bound, and analyze the risk of two standard
estimators. It is shown, and verified in simulation, that one is minimax
optimal up to a constant, while another can be arbitrarily worse, despite its
empirical success and popularity. The results are applied to related problems
in contextual bandits and fixed-horizon Markov decision processes, and are also
related to semi-supervised learning.
",On Minimax Optimal Offline Policy Evaluation
"  Mediation is a process, in which both parties agree to resolve their dispute
by negotiating over alternative solutions presented by a mediator. In order to
construct such solutions, mediation brings more information and knowledge, and,
if possible, resources to the negotiation table. The contribution of this paper
is the automated mediation machinery which does that. It presents an
argumentation-based mediation approach that extends the logic-based approach to
argumentation-based negotiation involving BDI agents. The paper describes the
mediation algorithm. For comparison it illustrates the method with a case study
used in an earlier work. It demonstrates how the computational mediator can
deal with realistic situations in which the negotiating agents would otherwise
fail due to lack of knowledge and/or resources.
",Dispute Resolution Using Argumentation-Based Mediation
"  Simplifying expressions is important to make numerical integration of large
expressions from High Energy Physics tractable. To this end, Horner's method
can be used. Finding suitable Horner schemes is assumed to be hard, due to the
lack of local heuristics. Recently, MCTS was reported to be able to find near
optimal schemes. However, several parameters had to be fine-tuned manually. In
this work, we investigate the state space properties of Horner schemes and find
that the domain is relatively flat and contains only a few local minima. As a
result, the Horner space is appropriate to be explored by Stochastic Local
Search (SLS), which has only two parameters: the number of iterations
(computation time) and the neighborhood structure. We found a suitable
neighborhood structure, leaving only the allowed computation time as a
parameter. We performed a range of experiments. The results obtained by SLS are
similar or better than those obtained by MCTS. Furthermore, we show that SLS
obtains the good results at least 10 times faster. Using SLS, we can speed up
numerical integration of many real-world large expressions by at least a factor
of 24. For High Energy Physics this means that numerical integrations that took
weeks can now be done in hours.
",Why Local Search Excels in Expression Simplification
"  Recognizing handwritten mathematics is a challenging classification problem,
requiring simultaneous identification of all the symbols comprising an input as
well as the complex two-dimensional relationships between symbols and
subexpressions. Because of the ambiguity present in handwritten input, it is
often unrealistic to hope for consistently perfect recognition accuracy. We
present a system which captures all recognizable interpretations of the input
and organizes them in a parse forest from which individual parse trees may be
extracted and reported. If the top-ranked interpretation is incorrect, the user
may request alternates and select the recognition result they desire. The tree
extraction step uses a novel probabilistic tree scoring strategy in which a
Bayesian network is constructed based on the structure of the input, and each
joint variable assignment corresponds to a different parse tree. Parse trees
are then reported in order of decreasing probability. Two accuracy evaluations
demonstrate that the resulting recognition system is more accurate than
previous versions (which used non-probabilistic methods) and other academic
math recognizers.
",A Bayesian model for recognizing handwritten mathematical expressions
"  A common assumption in belief revision is that the reliability of the
information sources is either given, derived from temporal information, or the
same for all. This article does not describe a new semantics for integration
but the problem of obtaining the reliability of the sources given the result of
a previous merging. As an example, the relative reliability of two sensors can
be assessed given some certain observation, and allows for subsequent mergings
of data coming from them.
",Belief revision by examples
"  The properties of local optimal solutions in multi-objective combinatorial
optimization problems are crucial for the effectiveness of local search
algorithms, particularly when these algorithms are based on Pareto dominance.
Such local search algorithms typically return a set of mutually nondominated
Pareto local optimal (PLO) solutions, that is, a PLO-set. This paper
investigates two aspects of PLO-sets by means of experiments with Pareto local
search (PLS). First, we examine the impact of several problem characteristics
on the properties of PLO-sets for multi-objective NK-landscapes with correlated
objectives. In particular, we report that either increasing the number of
objectives or decreasing the correlation between objectives leads to an
exponential increment on the size of PLO-sets, whereas the variable correlation
has only a minor effect. Second, we study the running time and the quality
reached when using bounding archiving methods to limit the size of the archive
handled by PLS, and thus, the maximum size of the PLO-set found. We argue that
there is a clear relationship between the running time of PLS and the
difficulty of a problem instance.
","Local Optimal Sets and Bounded Archiving on Multi-objective
  NK-Landscapes with Correlated Objectives"
"  Recently, there has been a renewed interest in decomposition-based approaches
for evolutionary multiobjective optimization. However, the impact of the choice
of the underlying scalarizing function(s) is still far from being well
understood. In this paper, we investigate the behavior of different scalarizing
functions and their parameters. We thereby abstract firstly from any specific
algorithm and only consider the difficulty of the single scalarized problems in
terms of the search ability of a (1+lambda)-EA on biobjective NK-landscapes.
Secondly, combining the outcomes of independent single-objective runs allows
for more general statements on set-based performance measures. Finally, we
investigate the correlation between the opening angle of the scalarizing
function's underlying contour lines and the position of the final solution in
the objective space. Our analysis is of fundamental nature and sheds more light
on the key characteristics of multiobjective scalarizing functions.
",On the Impact of Multiobjective Scalarizing Functions
"  A difficult task in modeling with Bayesian networks is the elicitation of
numerical parameters of Bayesian networks. A large number of parameters is
needed to specify a conditional probability table (CPT) that has a larger
parent set. In this paper we show that, most CPTs from real applications of
Bayesian networks can actually be very well approximated by tables that require
substantially less parameters. This observation has practical consequence not
only for model elicitation but also for efficient probabilistic reasoning with
these networks.
",On tensor rank of conditional probability tables in Bayesian networks
"  We consider the university course timetabling problem, which is one of the
most studied problems in educational timetabling. In particular, we focus our
attention on the formulation known as the curriculum-based course timetabling
problem, which has been tackled by many researchers and for which there are
many available benchmarks.
  The contribution of this paper is twofold. First, we propose an effective and
robust single-stage simulated annealing method for solving the problem.
Secondly, we design and apply an extensive and statistically-principled
methodology for the parameter tuning procedure. The outcome of this analysis is
a methodology for modeling the relationship between search method parameters
and instance features that allows us to set the parameters for unseen instances
on the basis of a simple inspection of the instance itself. Using this
methodology, our algorithm, despite its apparent simplicity, has been able to
achieve high quality results on a set of popular benchmarks.
  A final contribution of the paper is a novel set of real-world instances,
which could be used as a benchmark for future comparison.
","Feature-based tuning of simulated annealing applied to the
  curriculum-based course timetabling problem"
"  This article is about how the ""SP theory of intelligence"" and its realisation
in the ""SP machine"" (both outlined in the article) may help to solve
computer-related problems in the design of autonomous robots, meaning robots
that do not depend on external intelligence or power supplies, are mobile, and
are designed to exhibit as much human-like intelligence as possible. The
article is about: how to increase the computational and energy efficiency of
computers and reduce their bulk; how to achieve human-like versatility in
intelligence; and likewise for human-like adaptability in intelligence. The SP
system has potential for substantial gains in computational and energy
efficiency and reductions in the bulkiness of computers: by reducing the size
of data to be processed; by exploiting statistical information that the system
gathers; and via an updated version of Donald Hebb's concept of a ""cell
assembly"". Towards human-like versatility in intelligence, the SP system has
strengths in unsupervised learning, natural language processing, pattern
recognition, information retrieval, several kinds of reasoning, planning,
problem solving, and more, with seamless integration amongst structures and
functions. The SP system's strengths in unsupervised learning and other aspects
of intelligence may help to achieve human-like adaptability in intelligence
via: the learning of natural language; learning to see; building 3D models of
objects and of a robot's surroundings; learning regularities in the workings of
a robot and in the robot's environment; exploration and play; learning major
skills; and secondary forms of learning. Also discussed are: how the SP system
may process parallel streams of information; generalisation of knowledge,
correction of over-generalisations, and learning from dirty data; how to cut
the cost of learning; and reinforcements, motivations, goals, and
demonstration.
",Autonomous robots and the SP theory of intelligence
"  This paper describes a novel approach to medical diagnosis based on the SP
theory of computing and cognition. The main attractions of this approach are: a
format for representing diseases that is simple and intuitive; an ability to
cope with errors and uncertainties in diagnostic information; the simplicity of
storing statistical information as frequencies of occurrence of diseases; a
method for evaluating alternative diagnostic hypotheses that yields true
probabilities; and a framework that should facilitate unsupervised learning of
medical knowledge and the integration of medical diagnosis with other AI
applications.
","Medical diagnosis as pattern recognition in a framework of information
  compression by multiple alignment, unification and search"
"  Probabilistic graphical models such as Bayesian Networks are one of the most
powerful structures known by the Computer Science community for deriving
probabilistic inferences. However, modern cognitive psychology has revealed
that human decisions could not follow the rules of classical probability
theory, because humans cannot process large amounts of data in order to make
judgements. Consequently, the inferences performed are based on limited data
coupled with several heuristics, leading to violations of the law of total
probability. This means that probabilistic graphical models based on classical
probability theory are too limited to fully simulate and explain various
aspects of human decision making.
  Quantum probability theory was developed in order to accommodate the
paradoxical findings that the classical theory could not explain. Recent
findings in cognitive psychology revealed that quantum probability can fully
describe human decisions in an elegant framework. Their findings suggest that,
before taking a decision, human thoughts are seen as superposed waves that can
interfere with each other, influencing the final decision.
  In this work, we propose a new Bayesian Network based on the psychological
findings of cognitive scientists. We made experiments with two very well known
Bayesian Networks from the literature. The results obtained revealed that the
quantum like Bayesian Network can affect drastically the probabilistic
inferences, specially when the levels of uncertainty of the network are very
high (no pieces of evidence observed). When the levels of uncertainty are very
low, then the proposed quantum like network collapses to its classical
counterpart.
",Interference Effects in Quantum Belief Networks
"  Modeling Creativity (doctoral dissertation, 2013) explores how creativity can
be represented using computational approaches. Our aim is to construct computer
models that exhibit creativity in an artistic context, that is, that are
capable of generating or evaluating an artwork (visual or linguistic), an
interesting new idea, a subjective opinion. The research was conducted in
2008-2012 at the Computational Linguistics Research Group (CLiPS, University of
Antwerp) under the supervision of Prof. Walter Daelemans. Prior research was
also conducted at the Experimental Media Research Group (EMRG, St. Lucas
University College of Art & Design Antwerp) under the supervision of Lucas
Nijs.
  Modeling Creativity examines creativity in a number of different
perspectives: from its origins in nature, which is essentially blind, to humans
and machines, and from generating creative ideas to evaluating and learning
their novelty and usefulness. We will use a hands-on approach with case studies
and examples in the Python programming language.
",Modeling Creativity: Case Studies in Python
"  The paper attempts to describe the space of possible mind designs by first
equating all minds to software. Next it proves some interesting properties of
the mind design space such as infinitude of minds, size and representation
complexity of minds. A survey of mind design taxonomies is followed by a
proposal for a new field of investigation devoted to study of minds,
intellectology, a list of open problems for this new field is presented.
",The Universe of Minds
"  We propose a framework grounded in Logic Programming for representing and
reasoning about business processes from both the procedural and ontological
point of views. In particular, our goal is threefold: (1) define a logical
language and a formal semantics for process models enriched with ontology-based
annotations; (2) provide an effective inference mechanism that supports the
combination of reasoning services dealing with the structural definition of a
process model, its behavior, and the domain knowledge related to the
participating business entities; (3) implement such a theoretical framework
into a process modeling and reasoning platform. To this end we define a process
ontology coping with a relevant fragment of the popular BPMN modeling notation.
The behavioral semantics of a process is defined as a state transition system
by following an approach similar to the Fluent Calculus, and allows us to
specify state change in terms of preconditions and effects of the enactment of
activities. Then we show how the procedural process knowledge can be seamlessly
integrated with the domain knowledge specified by using the OWL 2 RL rule-based
ontology language. Our framework provides a wide range of reasoning services,
including CTL model checking, which can be performed by using standard Logic
Programming inference engines through a goal-oriented, efficient, sound and
complete evaluation procedure. We also present a software environment
implementing the proposed framework, and we report on an experimental
evaluation of the system, whose results are encouraging and show the viability
of the approach.
","Ontology-based Representation and Reasoning on Process Models: A Logic
  Programming Approach"
"  In this paper, an improved multimodal optimization (MMO) algorithm,called
LSEPSO,has been proposed. LSEPSO combined Electrostatic Particle Swarm
Optimization (EPSO) algorithm and a local search method and then made some
modification on them. It has been shown to improve global and local optima
finding ability of the algorithm. This algorithm useda modified local search to
improve particle's personal best, which used n-nearest-neighbour instead of
nearest-neighbour. Then, by creating n new points among each particle and n
nearest particles, it tried to find a point which could be the alternative of
particle's personal best. This method prevented particle's attenuation and
following a specific particle by its neighbours. The performed tests on a
number of benchmark functions clearly demonstrated that the improved algorithm
is able to solve MMO problems and outperform other tested algorithms in this
article.
","An improved multimodal PSO method based on electrostatic interaction
  using n- nearest-neighbor local search"
"  Communication and interaction among agents have been the subject of extensive
investigation since many years. Commitment-based communication, where
communicating agents are seen as a debtor agent who is committed to a creditor
agent to bring about something (possibly under some conditions) is now very
well-established. The approach of DACMAS (Data-Aware Commitment-based MAS)
lifts commitment-related approaches proposed in the literature from a
propositional to a first-order setting via the adoption the DRL-Lite
Description Logic. Notably, DACMASs provide, beyond commitments, simple forms
of inter-agent event-based communication. Yet, the aspect is missing of making
a MAS able to acquire knowledge from contexts which are not agents and which
are external to the MAS. This topic is coped with in Managed MCSs (Managed
Multi-Context Systems), where however exchanges are among knowledge bases and
not agents. In this paper, we propose the new approach of DACmMCMASs
(Data-Aware Commitment-based managed Multi- Context MAS), so as to obtain a
commitment-based first-order agent system which is able to interact with
heterogeneous external information sources. We show that DACmMCMASs retain the
nice formal properties of the original approaches.
",Committment-Based Data-Aware Multi-Agent-Contexts Systems
"  RCC8 is a popular fragment of the region connection calculus, in which
qualitative spatial relations between regions, such as adjacency, overlap and
parthood, can be expressed. While RCC8 is essentially dimensionless, most
current applications are confined to reasoning about two-dimensional or
three-dimensional physical space. In this paper, however, we are mainly
interested in conceptual spaces, which typically are high-dimensional Euclidean
spaces in which the meaning of natural language concepts can be represented
using convex regions. The aim of this paper is to analyze how the restriction
to convex regions constrains the realizability of networks of RCC8 relations.
First, we identify all ways in which the set of RCC8 base relations can be
restricted to guarantee that consistent networks can be convexly realized in
respectively 1D, 2D, 3D, and 4D. Most surprisingly, we find that if the
relation 'partially overlaps' is disallowed, all consistent atomic RCC8
networks can be convexly realized in 4D. If instead refinements of the relation
'part of' are disallowed, all consistent atomic RCC8 relations can be convexly
realized in 3D. We furthermore show, among others, that any consistent RCC8
network with 2n+1 variables can be realized using convex regions in the
n-dimensional Euclidean space.
",Realizing RCC8 networks using convex regions
"  Measuring the effectiveness of corporate environmental reports, it being
highly qualitative and less regulated, is often considered as a daunting task.
The task becomes more complex if comparisons are to be performed. This study is
undertaken to overcome the physical verification problems by implementing data
mining technique. It further explores on the effectiveness by performing
exploratory analysis and structural equation model to bring out the significant
linkages between the selected 10 variables. Samples of five hundred and thirty
nine reports across various countries are used from an international directory
to perform the statistical analysis like: One way ANOVA (Analysis of Variance),
MDA (Multivariate Discriminant Analysis) and SEM (Structural Equation
Modeling). The results indicate the significant differences among the various
types of industries in their environmental reporting, and the exploratory
factors like stakeholder, organization strategy and industrial oriented
factors, proved significant. The major accomplishment is that the findings
correlate with the conceptual frame work of GRI.
","Analysis of corporate environmental reports using statistical techniques
  and data mining"
"  Errors in implicative theories coming from binary data are studied. First,
two classes of errors that may affect implicative theories are singled out. Two
approaches for finding errors of these classes are proposed, both of them based
on methods of Formal Concept Analysis. The first approach uses the cardinality
minimal (canonical or Duquenne-Guigues) implication base. The construction of
such a base is computationally intractable. Using an alternative approach one
checks possible errors on the fly in polynomial time via computing closures of
subsets of attributes. Both approaches are interactive, based on questions
about the validity of certain implications. Results of computer experiments are
presented and discussed.
",Interactive Error Correction in Implicative Theories
"  The study of collective decision making system has become the central part of
the Swarm- Intelligence Related research in recent years. The most challenging
task of modelling a collec- tive decision making system is to develop the
macroscopic stochastic equation from its microscopic model. In this report we
have investigated the behaviour of a collective decision making system with
specified microscopic rules that resemble the chemical reaction and used
different group size. Then we ventured to derive a generalized analytical model
of a collective-decision system using hyper-geometric distribution.
  Index Terms-swarm; collective decision making; noise; group size;
hyper-geometric distribution
","Investigation of A Collective Decision Making System of Different
  Neighbourhood-Size Based on Hyper-Geometric Distribution"
"  Distributed representations (such as those based on embeddings) and discrete
representations (such as those based on logic) have complementary strengths. We
explore one possible approach to combining these two kinds of representations.
We present a model theory/semantics for first order logic based on vectors of
reals. We describe the model theory, discuss some interesting properties of
such a system and present a simple approach to query answering.
",Towards a Model Theory for Distributed Representations
"  Observing that the creation of certain types of artistic artifacts
necessitate intelligence, we present the Lovelace 2.0 Test of creativity as an
alternative to the Turing Test as a means of determining whether an agent is
intelligent. The Lovelace 2.0 Test builds off prior tests of creativity and
additionally provides a means of directly comparing the relative intelligence
of different agents.
",The Lovelace 2.0 Test of Artificial Creativity and Intelligence
"  The Meta-Agent Conflict-Based Search~(MA-CBS) is a recently proposed
algorithm for the multi-agent path finding problem. The algorithm is an
extension of Conflict-Based Search~(CBS), which automatically merges
conflicting agents into meta-agents if the number of conflicts exceeds a
certain threshold. However, the decision to merge agents is made according to
an empirically chosen fixed threshold on the number of conflicts. The best
threshold depends both on the domain and on the number of agents, and the
nature of the dependence is not clearly understood.
  We suggest a justification for the use of a fixed threshold on the number of
conflicts based on the analysis of a model problem. Following the suggested
justification, we introduce new decision policies for the MA-CBS algorithm,
which considerably improve the algorithm's performance. The improved variants
of the algorithm are evaluated on several sets of problems, chosen to underline
different aspects of the algorithms.
",Justifying and Improving Meta-Agent Conflict-Based Search
"  We consider the energy minimization problem for undirected graphical models,
also known as MAP-inference problem for Markov random fields which is NP-hard
in general. We propose a novel polynomial time algorithm to obtain a part of
its optimal non-relaxed integral solution. Our algorithm is initialized with
variables taking integral values in the solution of a convex relaxation of the
MAP-inference problem and iteratively prunes those, which do not satisfy our
criterion for partial optimality. We show that our pruning strategy is in a
certain sense theoretically optimal. Also empirically our method outperforms
previous approaches in terms of the number of persistently labelled variables.
The method is very general, as it is applicable to models with arbitrary
factors of an arbitrary order and can employ any solver for the considered
relaxed problem. Our method's runtime is determined by the runtime of the
convex relaxation solver for the MAP-inference problem.
","Partial Optimality by Pruning for MAP-Inference with General Graphical
  Models"
"  This paper augments OBDD with conjunctive decomposition to propose a
generalization called OBDD[$\wedge$]. By imposing reducedness and the finest
$\wedge$-decomposition bounded by integer $i$
($\wedge_{\widehat{i}}$-decomposition) on OBDD[$\wedge$], we identify a family
of canonical languages called ROBDD[$\wedge_{\widehat{i}}$], where
ROBDD[$\wedge_{\widehat{0}}$] is equivalent to ROBDD. We show that the
succinctness of ROBDD[$\wedge_{\widehat{i}}$] is strictly increasing when $i$
increases. We introduce a new time-efficiency criterion called rapidity which
reflects that exponential operations may be preferable if the language can be
exponentially more succinct, and show that: the rapidity of each operation on
ROBDD[$\wedge_{\widehat{i}}$] is increasing when $i$ increases; particularly,
the rapidity of some operations (e.g., conjoining) is strictly increasing.
Finally, our empirical results show that: a) the size of
ROBDD[$\wedge_{\widehat{i}}$] is normally not larger than that of the
equivalent \ROBDDC{\widehat{i+1}}; b) conjoining two
ROBDD[$\wedge_{\widehat{1}}$]s is more efficient than conjoining two
ROBDD[$\wedge_{\widehat{0}}$]s in most cases, where the former is NP-hard but
the latter is in P; and c) the space-efficiency of
ROBDD[$\wedge_{\widehat{\infty}}$] is comparable with that of d-DNNF and that
of another canonical generalization of \ROBDD{} called SDD.
","Augmenting Ordered Binary Decision Diagrams with Conjunctive
  Decomposition"
"  Optimization is a key task in a number of applications. When the set of
feasible solutions under consideration is of combinatorial nature and described
in an implicit way as a set of constraints, optimization is typically NP-hard.
Fortunately, in many problems, the set of feasible solutions does not often
change and is independent from the user's request. In such cases, compiling the
set of constraints describing the set of feasible solutions during an off-line
phase makes sense, if this compilation step renders computationally easier the
generation of a non-dominated, yet feasible solution matching the user's
requirements and preferences (which are only known at the on-line step). In
this article, we focus on propositional constraints. The subsets L of the NNF
language analyzed in Darwiche and Marquis' knowledge compilation map are
considered. A number of families F of representations of objective functions
over propositional variables, including linear pseudo-Boolean functions and
more sophisticated ones, are considered. For each language L and each family F,
the complexity of generating an optimal solution when the constraints are
compiled into L and optimality is to be considered w.r.t. a function from F is
identified.
","On the Complexity of Optimization Problems based on Compiled NNF
  Representations"
"  We study the semantics of fuzzy if-then rules called fuzzy attribute
implications parameterized by systems of isotone Galois connections. The rules
express dependencies between fuzzy attributes in object-attribute incidence
data. The proposed parameterizations are general and include as special cases
the parameterizations by linguistic hedges used in earlier approaches. We
formalize the general parameterizations, propose bivalent and graded notions of
semantic entailment of fuzzy attribute implications, show their
characterization in terms of least models and complete axiomatization, and
provide characterization of bases of fuzzy attribute implications derived from
data.
","Parameterizing the semantics of fuzzy attribute implications by systems
  of isotone Galois connections"
"  Since Pearl's seminal work on providing a formal language for causality, the
subject has garnered a lot of interest among philosophers and researchers in
artificial intelligence alike. One of the most debated topics in this context
regards the notion of actual causation, which concerns itself with specific -
as opposed to general - causal claims. The search for a proper formal
definition of actual causation has evolved into a controversial debate, that is
pervaded with ambiguities and confusion. The goal of our research is twofold.
First, we wish to provide a clear way to compare competing definitions. Second,
we also want to improve upon these definitions so they can be applied to a more
diverse range of instances, including non-deterministic ones. To achieve these
goals we will provide a general, abstract definition of actual causation,
formulated in the context of the expressive language of CP-logic (Causal
Probabilistic logic). We will then show that three recent definitions by Ned
Hall (originally formulated for structural models) and a definition of our own
(formulated for CP-logic directly) can be viewed and directly compared as
instantiations of this abstract definition, which allows them to deal with a
broader range of examples.
",Towards a General Framework for Actual Causation Using CP-logic
"  The main goal of this work is to analyze the behaviour of the FA quantifier
fuzzification mechanism. As we prove in the paper, this model has a very solid
theorethical behaviour, superior to most of the models defined in the
literature. Moreover, we show that the underlying probabilistic interpretation
has very interesting consequences.
","The probatilistic Quantifier Fuzzification Mechanism FA: A theoretical
  analysis"
"  This paper describes a novel refinement to a Tabu search algorithm that has
been implemented in an attempt to improve the robustness of the search when
applied to particularly complex problems. In this approach, two Tabu searches
are carried out in parallel. Each search thread is characterised by it's own
short term memory which forces that point out of local optima. However, the two
search threads share an intermediate term memory so allowing a degree of
information to be passed between them. Results are presented for both
unconstrained and constrained numerical functions as well as a problem in the
field of hydraulic circuit optimization. Simulation of hydraulic circuit
performance is achieved by linking the optimization algorithm to the commercial
simulation package Bathfp.
",A mutli-thread tabu search algorithm
"  This works is motivated by a real-world case study where it is necessary to
integrate and relate existing ontologies through meta- modelling. For this, we
introduce the Description Logic ALCQM which is obtained from ALCQ by adding
statements that equate individuals to concepts in a knowledge base. In this new
extension, a concept can be an individual of another concept (called
meta-concept) which themselves can be individuals of yet another concept
(called meta meta-concept) and so on. We define a tableau algorithm for
checking consistency of an ontology in ALCQM and prove its correctness.
",Reasoning for ALCQ extended with a flexible meta-modelling hierarchy
"  Artificial reinforcement learning (RL) is a widely used technique in
artificial intelligence that provides a general method for training agents to
perform a wide variety of behaviours. RL as used in computer science has
striking parallels to reward and punishment learning in animal and human
brains. I argue that present-day artificial RL agents have a very small but
nonzero degree of ethical importance. This is particularly plausible for views
according to which sentience comes in degrees based on the abilities and
complexities of minds, but even binary views on consciousness should assign
nonzero probability to RL programs having morally relevant experiences. While
RL programs are not a top ethical priority today, they may become more
significant in the coming decades as RL is increasingly applied to industry,
robotics, video games, and other areas. I encourage scientists, philosophers,
and citizens to begin a conversation about our ethical duties to reduce the
harm that we inflict on powerless, voiceless RL agents.
",Do Artificial Reinforcement-Learning Agents Matter Morally?
"  Recently, several researchers have found that cost-based satisficing search
with A* often runs into problems. Although some ""work arounds"" have been
proposed to ameliorate the problem, there has been little concerted effort to
pinpoint its origin. In this paper, we argue that the origins of this problem
can be traced back to the fact that most planners that try to optimize cost
also use cost-based evaluation functions (i.e., f(n) is a cost estimate). We
show that cost-based evaluation functions become ill-behaved whenever there is
a wide variance in action costs; something that is all too common in planning
domains. The general solution to this malady is what we call a surrogatesearch,
where a surrogate evaluation function that doesn't directly track the cost
objective, and is resistant to cost-variance, is used. We will discuss some
compelling choices for surrogate evaluation functions that are based on size
rather that cost. Of particular practical interest is a cost-sensitive version
of size-based evaluation function -- where the heuristic estimates the size of
cheap paths, as it provides attractive quality vs. speed tradeoffs
","Surrogate Search As a Way to Combat Harmful Effects of Ill-behaved
  Evaluation Functions"
"  In recent years the power systems research community has seen an explosion of
work applying operations research techniques to challenging power network
optimization problems. Regardless of the application under consideration, all
of these works rely on power system test cases for evaluation and validation.
However, many of the well established power system test cases were developed as
far back as the 1960s with the aim of testing AC power flow algorithms. It is
unclear if these power flow test cases are suitable for power system
optimization studies. This report surveys all of the publicly available AC
transmission system test cases, to the best of our knowledge, and assess their
suitability for optimization tasks. It finds that many of the traditional test
cases are missing key network operation constraints, such as line thermal
limits and generator capability curves. To incorporate these missing
constraints, data driven models are developed from a variety of publicly
available data sources. The resulting extended test cases form a compressive
archive, NESTA, for the evaluation and validation of power system optimization
algorithms.
","NESTA, The NICTA Energy System Test Case Archive"
"  Developments in semantic web technologies have promoted ontological encoding
of knowledge from diverse domains. However, modelling many practical domains
requires more expressive representations schemes than what the standard
description logics(DLs) support. We extend the DL SROIQ with constraint
networks and grounded circumscription. Applications of constraint modelling
include embedding ontologies with temporal or spatial information, while
grounded circumscription allows defeasible inference and closed world
reasoning. This paper overcomes restrictions on existing constraint modelling
approaches by introducing expressive constructs. Grounded circumscription
allows concept and role minimization and is decidable for DL. We provide a
general and intuitive algorithm for the framework of grounded circumscription
that can be applied to a whole range of logics. We present the resulting logic:
GC-SROIQ(C), and describe a tableau decision procedure for it.
","GC-SROIQ(C) : Expressive Constraint Modelling and Grounded
  Circumscription for SROIQ"
"  The term serendipity describes a creative process that develops, in context,
with the active participation of a creative agent, but not entirely within that
agent's control. While a system cannot be made to perform serendipitously on
demand, we argue that its $\mathit{serendipity\ potential}$ can be increased by
means of a suitable system architecture and other design choices. We distil a
unified description of serendipitous occurrences from historical theorisations
of serendipity and creativity. This takes the form of a framework with six
phases: $\mathit{perception}$, $\mathit{attention}$, $\mathit{interest}$,
$\mathit{explanation}$, $\mathit{bridge}$, and $\mathit{valuation}$. We then
use this framework to organise a survey of literature in cognitive science,
philosophy, and computing, which yields practical definitions of the six
phases, along with heuristics for implementation. We use the resulting model to
evaluate the serendipity potential of four existing systems developed by
others, and two systems previously developed by two of the authors. Most
existing research that considers serendipity in a computing context deals with
serendipity as a service; here we relate theories of serendipity to the
development of autonomous systems and computational creativity practice. We
argue that serendipity is not teleologically blind, and outline representative
directions for future applications of our model. We conclude that it is
feasible to equip computational systems with the potential for serendipity, and
that this could be beneficial in varied computational creativity/AI
applications, particularly those designed to operate responsively in real-world
contexts.
",Modelling serendipity in a computational context
"  In this paper we present a greedy algorithm for solving the problem of the
maximum partitioning of graphs with supply and demand (MPGSD). The goal of the
method is to solve the MPGSD for large graphs in a reasonable time limit. This
is done by using a two stage greedy algorithm, with two corresponding types of
heuristics. The solutions acquired in this way are improved by applying a
computationally inexpensive, hill climbing like, greedy correction procedure.
In our numeric experiments we analyze different heuristic functions for each
stage of the greedy algorithm, and show that their performance is highly
dependent on the properties of the specific instance. Our tests show that by
exploring a relatively small number of solutions generated by combining
different heuristic functions, and applying the proposed correction procedure
we can find solutions within only a few percent of the optimal ones.
","A Heuristic Method for Solving the Problem of Partitioning Graphs with
  Supply and Demand"
"  This book-length article combines several peer reviewed papers and new
material to analyze the issues of ethical artificial intelligence (AI). The
behavior of future AI systems can be described by mathematical equations, which
are adapted to analyze possible unintended AI behaviors and ways that AI
designs can avoid them. This article makes the case for utility-maximizing
agents and for avoiding infinite sets in agent definitions. It shows how to
avoid agent self-delusion using model-based utility functions and how to avoid
agents that corrupt their reward generators (sometimes called ""perverse
instantiation"") using utility functions that evaluate outcomes at one point in
time from the perspective of humans at a different point in time. It argues
that agents can avoid unintended instrumental actions (sometimes called ""basic
AI drives"" or ""instrumental goals"") by accurately learning human values. This
article defines a self-modeling agent framework and shows how it can avoid
problems of resource limits, being predicted by other agents, and inconsistency
between the agent's utility function and its definition (one version of this
problem is sometimes called ""motivated value selection""). This article also
discusses how future AI will differ from current AI, the politics of AI, and
the ultimate use of AI to help understand the nature of the universe and our
place in it.
",Ethical Artificial Intelligence
"  We study the data space $D$ of any given data set $X$ and explain how
functions and relations are defined over $D$. From $D$ and for a specific
domain $\Delta$ we construct the information space $I$ of $X$ by interpreting
variables, functions, and explicit relations over $D$ in $\Delta$ and by
including other relations that $D$ implies under the interpretation in
$\Delta$. Then from $I$ we build up the knowledge space $K$ of $X$ as the
product of two spaces $K_T$ and $K_P$, where $K_T$ is obtained from $I$ by
using the induction principle to generalize propositional relations to
quantified relations, the deduction principle to generate new relations, and
standard mechanisms to validate relations and $K_P$ is the space of
specifications of methods with operational instructions which are valid in
$K_T$. Through our construction of the three topological spaces the following
key observation is made clear: the retrieval of information from the given data
set for $\Delta$ consists essentially in mining domain objects and relations,
and the discovery of knowledge from the retrieved information consists
essentially in applying the induction and deduction principles to generate
propositions, synthesizing and modeling the information to generate
specifications of methods with operational instructions, and validating the
propositions and specifications. Based on this observation, efficient
approaches may be designed to discover profound knowledge automatically from
simple data, as demonstrated by the result of our study in the case of
geometry.
","The Spaces of Data, Information, and Knowledge"
"  In this position paper, I argue that standardized tests for elementary
science such as SAT or Regents tests are not very good benchmarks for measuring
the progress of artificial intelligence systems in understanding basic science.
The primary problem is that these tests are designed to test aspects of
knowledge and ability that are challenging for people; the aspects that are
challenging for AI systems are very different. In particular, standardized
tests do not test knowledge that is obvious for people; none of this knowledge
can be assumed in AI systems. Individual standardized tests also have specific
features that are not necessarily appropriate for an AI benchmark. I analyze
the Physics subject SAT in some detail and the New York State Regents Science
test more briefly. I also argue that the apparent advantages offered by using
standardized tests are mostly either minor or illusory. The one major real
advantage is that the significance is easily explained to the public; but I
argue that even this is a somewhat mixed blessing. I conclude by arguing that,
first, more appropriate collections of exam style problems could be assembled,
and second, that there are better kinds of benchmarks than exam-style problems.
In an appendix I present a collection of sample exam-style problems that test
kinds of knowledge missing from the standardized tests.
","The Limitations of Standardized Science Tests as Benchmarks for
  Artificial Intelligence Research: Position Paper"
"  This paper develops the idea of membership function assignment for OWL (Web
Ontology Language) ontology elements in order to subsequently generate fuzzy
rules from this ontology. The task of membership function assignment for OWL
ontology elements had already been partially described, but this concerned the
case, when several OWL ontologies of the same domain were available, and they
were merged into a single ontology. The purpose of this paper is to present the
way of membership function assignment for OWL ontology elements in the case,
when there is the only one available ontology. Fuzzy rules, generated from the
OWL ontology, are necessary for supplement of the SWES (Semantic Web Expert
System) knowledge base. SWES is an expert system, which will be able to extract
knowledge from OWL ontologies, found in the Web, and will serve as a universal
expert for the user.
",Membership Function Assignment for Elements of Single OWL Ontology
"  We consider partially observable Markov decision processes (POMDPs) with a
set of target states and every transition is associated with an integer cost.
The optimization objective we study asks to minimize the expected total cost
till the target set is reached, while ensuring that the target set is reached
almost-surely (with probability 1). We show that for integer costs
approximating the optimal cost is undecidable. For positive costs, our results
are as follows: (i) we establish matching lower and upper bounds for the
optimal cost and the bound is double exponential; (ii) we show that the problem
of approximating the optimal cost is decidable and present approximation
algorithms developing on the existing algorithms for POMDPs with finite-horizon
objectives. While the worst-case running time of our algorithm is double
exponential, we also present efficient stopping criteria for the algorithm and
show experimentally that it performs well in many examples of interest.
",Optimal Cost Almost-sure Reachability in POMDPs
"  Simple board games, like Tic-Tac-Toe and CONNECT-4, play an important role
not only in the development of mathematical and logical skills, but also in the
emotional and social development. In this paper, we address the problem of
generating targeted starting positions for such games. This can facilitate new
approaches for bringing novice players to mastery, and also leads to discovery
of interesting game variants. We present an approach that generates starting
states of varying hardness levels for player~$1$ in a two-player board game,
given rules of the board game, the desired number of steps required for
player~$1$ to win, and the expertise levels of the two players. Our approach
leverages symbolic methods and iterative simulation to efficiently search the
extremely large state space. We present experimental results that include
discovery of states of varying hardness levels for several simple grid-based
board games. The presence of such states for standard game variants like $4
\times 4$ Tic-Tac-Toe opens up new games to be played that have never been
played as the default start state is heavily biased.
","Automatic Generation of Alternative Starting Positions for Simple
  Traditional Board Games"
"  RDF and Description Logics work in an open-world setting where absence of
information is not information about absence. Nevertheless, Description Logic
axioms can be interpreted in a closed-world setting and in this setting they
can be used for both constraint checking and closed-world recognition against
information sources. When the information sources are expressed in well-behaved
RDF or RDFS (i.e., RDF graphs interpreted in the RDF or RDFS semantics) this
constraint checking and closed-world recognition is simple to describe. Further
this constraint checking can be implemented as SPARQL querying and thus
effectively performed.
","Using Description Logics for RDF Constraint Checking and Closed-World
  Recognition"
"  ROSS (""Representation, Ontology, Structure, Star"") is introduced as a new
method for knowledge representation that emphasizes representational constructs
for physical structure. The ROSS representational scheme includes a language
called ""Star"" for the specification of ontology classes. The ROSS method also
includes a formal scheme called the ""instance model"". Instance models are used
in the area of natural language meaning representation to represent situations.
This paper provides both the rationale and the philosophical background for the
ROSS method.
",Introduction to ROSS: A New Representational Scheme
"  This paper is aimed at providing a very first, more ""global"", systematic
point of view with respect to possible conflict generation in CA-EN-like causal
structures. For simplicity, only the outermost level of graphs is taken into
account. Localization of the ""conflict area"", diagnostic preferences, and bases
for systematic conflict generation are considered. A notion of {\em Potential
Conflict Structure} ({\em PCS}) constituting a basic tool for identification of
possible conflicts is proposed and its use is discussed.
",A Note on Systematic Conflict Generation in CA-EN-type Causal Structures
"  Deontic logic is a very well researched branch of mathematical logic and
philosophy. Various kinds of deontic logics are discussed for different
application domains like argumentation theory, legal reasoning, and acts in
multi-agent systems. In this paper, we show how standard deontic logic can be
stepwise transformed into description logic and DL- clauses, such that it can
be processed by Hyper, a high performance theorem prover which uses a
hypertableau calculus. Two use cases, one from multi-agent research and one
from the development of normative system are investigated.
",Automated Reasoning in Deontic Logic
"  Model counting is the problem of computing the number of models that satisfy
a given propositional theory. It has recently been applied to solving inference
tasks in probabilistic logic programming, where the goal is to compute the
probability of given queries being true provided a set of mutually independent
random variables, a model (a logic program) and some evidence. The core of
solving this inference task involves translating the logic program to a
propositional theory and using a model counter. In this paper, we show that for
some problems that involve inductive definitions like reachability in a graph,
the translation of logic programs to SAT can be expensive for the purpose of
solving inference tasks. For such problems, direct implementation of stable
model semantics allows for more efficient solving. We present two
implementation techniques, based on unfounded set detection, that extend a
propositional model counter to a stable model counter. Our experiments show
that for particular problems, our approach can outperform a state-of-the-art
probabilistic logic programming solver by several orders of magnitude in terms
of running time and space requirements, and can solve instances of
significantly larger sizes on which the current solver runs out of time or
memory.
","Stable Model Counting and Its Application in Probabilistic Logic
  Programming"
"  Arguments are essential objects in DirectDemocracyP2P, where they can occur
both in association with signatures for petitions, or in association with other
debated decisions, such as bug sorting by importance. The arguments of a signer
on a given issue are grouped into one single justification, are classified by
the type of signature (e.g., supporting or opposing), and can be subject to
various types of threading.
  Given the available inputs, the two addressed problems are: (i) how to
recommend the best justification, of a given type, to a new voter, (ii) how to
recommend a compact list of justifications subsuming the majority of known
arguments for (or against) an issue.
  We investigate solutions based on weighted bipartite graphs.
","Recommending the Most Encompassing Opposing and Endorsing Arguments in
  Debates"
"  An answer set is a plain set of literals which has no further structure that
would explain why certain literals are part of it and why others are not. We
show how argumentation theory can help to explain why a literal is or is not
contained in a given answer set by defining two justification methods, both of
which make use of the correspondence between answer sets of a logic program and
stable extensions of the Assumption-Based Argumentation (ABA) framework
constructed from the same logic program. Attack Trees justify a literal in
argumentation-theoretic terms, i.e. using arguments and attacks between them,
whereas ABA-Based Answer Set Justifications express the same justification
structure in logic programming terms, that is using literals and their
relationships. Interestingly, an ABA-Based Answer Set Justification corresponds
to an admissible fragment of the answer set in question, and an Attack Tree
corresponds to an admissible fragment of the stable extension corresponding to
this answer set.
",Justifying Answer Sets using Argumentation
"  In a Bayesian network, we wish to evaluate the marginal probability of a
query variable, which may be conditioned on the observed values of some
evidence variables. Here we first present our ""border algorithm,"" which
converts a BN into a directed chain. For the polytrees, we then present in
details, with some modifications and within the border algorithm framework, the
""revised polytree algorithm"" by Peot & Shachter (1991). Finally, we present our
""parentless polytree method,"" which, coupled with the border algorithm,
converts any Bayesian network into a polytree, rendering the complexity of our
inferences independent of the size of network, and linear with the number of
its evidence and query variables. All quantities in this paper have
probabilistic interpretations.
",Discrete Bayesian Networks: The Exact Posterior Marginal Distributions
"  Recent advances in metareasoning for search has shown its usefulness in
improving numerous search algorithms. This paper applies rational metareasoning
to IDA* when several admissible heuristics are available. The obvious basic
approach of taking the maximum of the heuristics is improved upon by lazy
evaluation of the heuristics, resulting in a variant known as Lazy IDA*. We
introduce a rational version of lazy IDA* that decides whether to compute the
more expensive heuristics or to bypass it, based on a myopic expected regret
estimate. Empirical evaluation in several domains supports the theoretical
results, and shows that rational lazy IDA* is a state-of-the-art heuristic
combination method.
",Rational Deployment of Multiple Heuristics in IDA*
"  In this paper, a new approximate syllogistic reasoning schema is described
that expands some of the approaches expounded in the literature into two ways:
(i) a number of different types of quantifiers (logical, absolute,
proportional, comparative and exception) taken from Theory of Generalized
Quantifiers and similarity quantifiers, taken from statistics, are considered
and (ii) any number of premises can be taken into account within the reasoning
process. Furthermore, a systematic reasoning procedure to solve the syllogism
is also proposed, interpreting it as an equivalent mathematical optimization
problem, where the premises constitute the constraints of the searching space
for the quantifier in the conclusion.
",A Fuzzy Syllogistic Reasoning Schema for Generalized Quantifiers
"  We present ULSA, a novel stochastic local search algorithm for random binary
constraint satisfaction problems (CSP). ULSA is many times faster than the
prior state of the art on a widely-studied suite of random CSP benchmarks.
Unlike the best previous methods for these benchmarks, ULSA is a simple
unweighted method that does not require dynamic adaptation of weights or
penalties. ULSA obtains new record best solutions satisfying 99 of 100
variables in the challenging frb100-40 benchmark instance.
","Unweighted Stochastic Local Search can be Effective for Random CSP
  Benchmarks"
"  Syllogism is a type of deductive reasoning involving quantified statements.
The syllogistic reasoning scheme in the classical Aristotelian framework
involves three crisp term sets and four linguistic quantifiers, for which the
main support is the linguistic properties of the quantifiers. A number of fuzzy
approaches for defining an approximate syllogism have been proposed for which
the main support is cardinality calculus. In this paper we analyze fuzzy
syllogistic models previously described by Zadeh and Dubois et al. and compare
their behavior with that of the classical Aristotelian framework to check which
of the 24 classical valid syllogistic reasoning patterns or moods are
particular crisp cases of these fuzzy approaches. This allows us to assess to
what extent these approaches can be considered as either plausible extensions
of the classical crisp syllogism or a basis for a general approach to the
problem of approximate syllogism.
","On the analysis of set-based fuzzy quantified reasoning using classical
  syllogistics"
"  Lifted probabilistic inference algorithms have been successfully applied to a
large number of symmetric graphical models. Unfortunately, the majority of
real-world graphical models is asymmetric. This is even the case for relational
representations when evidence is given. Therefore, more recent work in the
community moved to making the models symmetric and then applying existing
lifted inference algorithms. However, this approach has two shortcomings.
First, all existing over-symmetric approximations require a relational
representation such as Markov logic networks. Second, the induced symmetries
often change the distribution significantly, making the computed probabilities
highly biased. We present a framework for probabilistic sampling-based
inference that only uses the induced approximate symmetries to propose steps in
a Metropolis-Hastings style Markov chain. The framework, therefore, leads to
improved probability estimates while remaining unbiased. Experiments
demonstrate that the approach outperforms existing MCMC algorithms.
",Lifted Probabilistic Inference for Asymmetric Graphical Models
"  Analyzing Big Data can help corporations to im-prove their efficiency. In
this work we present a new vision to derive Value from Big Data using a
Semantic Hierarchical Multi-label Classification called Semantic HMC based in a
non-supervised Ontology learning process. We also proposea Semantic HMC
process, using scalable Machine-Learning techniques and Rule-based reasoning.
",Semantic HMC for Big Data Analysis
"  The Turing machine, as it was presented by Turing himself, models the
calculations done by a person. This means that we can compute whatever any
Turing machine can compute, and therefore we are Turing complete. The question
addressed here is why, Why are we Turing complete? Being Turing complete also
means that somehow our brain implements the function that a universal Turing
machine implements. The point is that evolution achieved Turing completeness,
and then the explanation should be evolutionary, but our explanation is
mathematical. The trick is to introduce a mathematical theory of problems,
under the basic assumption that solving more problems provides more survival
opportunities. So we build a problem theory by fusing set and computing
theories. Then we construct a series of resolvers, where each resolver is
defined by its computing capacity, that exhibits the following property: all
problems solved by a resolver are also solved by the next resolver in the
series if certain condition is satisfied. The last of the conditions is to be
Turing complete. This series defines a resolvers hierarchy that could be seen
as a framework for the evolution of cognition. Then the answer to our question
would be: to solve most problems. By the way, the problem theory defines
adaptation, perception, and learning, and it shows that there are just three
ways to resolve any problem: routine, trial, and analogy. And, most
importantly, this theory demonstrates how problems can be used to found
mathematics and computing on biology.
",Problem Theory
"  It is a known fact that the performance of optimization algorithms for
NP-Hard problems vary from instance to instance. We observed the same trend
when we comprehensively studied multi-objective evolutionary algorithms (MOEAs)
on a six benchmark instances of discrete time-cost trade-off problem (DTCTP) in
a construction project. In this paper, instead of using a single algorithm to
solve DTCTP, we use a portfolio approach that takes multiple algorithms as its
constituent. We proposed portfolio comprising of four MOEAs, Non-dominated
Sorting Genetic Algorithm II (NSGA-II), the strength Pareto Evolutionary
Algorithm II (SPEA-II), Pareto archive evolutionary strategy (PAES) and Niched
Pareto Genetic Algorithm II (NPGA-II) to solve DTCTP. The result shows that the
portfolio approach is computationally fast and qualitatively superior to its
constituent algorithms for all benchmark instances. Moreover, portfolio
approach provides an insight in selecting the best algorithm for all benchmark
instances of DTCTP.
","A Portfolio Approach to Algorithm Selection for Discrete Time-Cost
  Trade-off Problem"
"  We propose a new approach for solving combinatorial optimization problem by
utilizing the mechanism of chases and escapes, which has a long history in
mathematics. In addition to the well-used steepest descent and neighboring
search, we perform a chase and escape game on the ""landscape"" of the cost
function. We have created a concrete algorithm for the Traveling Salesman
Problem. Our preliminary test indicates a possibility that this new fusion of
chases and escapes problem into combinatorial optimization search is fruitful.
","Chases and Escapes, and Optimization Problems"
"  A definition of causality introduced by Halpern and Pearl, which uses
structural equations, is reviewed. A more refined definition is then
considered, which takes into account issues of normality and typicality, which
are well known to affect causal ascriptions. Causality is typically an
all-or-nothing notion: either A is a cause of B or it is not. An extension of
the definition of causality to capture notions of degree of responsibility and
degree of blame, due to Chockler and Halpern, is reviewed. For example, if
someone wins an election 11-0, then each person who votes for him is less
responsible for the victory than if he had won 6-5. Degree of blame takes into
account an agent's epistemic state. Roughly speaking, the degree of blame of A
for B is the expected degree of responsibility of A for B, taken over the
epistemic state of an agent. Finally, the structural-equations definition of
causality is compared to Wright's NESS test.
","Cause, Responsibility, and Blame: oA Structural-Model Approach"
"  Halpern and Pearl introduced a definition of actual causality; Eiter and
Lukasiewicz showed that computing whether X=x is a cause of Y=y is NP-complete
in binary models (where all variables can take on only two values) and\
Sigma_2^P-complete in general models. In the final version of their paper,
Halpern and Pearl slightly modified the definition of actual cause, in order to
deal with problems pointed by Hopkins and Pearl. As we show, this modification
has a nontrivial impact on the complexity of computing actual cause. To
characterize the complexity, a new family D_k^P, k= 1, 2, 3, ..., of complexity
classes is introduced, which generalizes the class DP introduced by
Papadimitriou and Yannakakis (DP is just D_1^P). %joe2 %We show that the
complexity of computing causality is $\D_2$-complete %under the new definition.
Chockler and Halpern \citeyear{CH04} extended the We show that the complexity
of computing causality under the updated definition is $D_2^P$-complete.
  Chockler and Halpern extended the definition of causality by introducing
notions of responsibility and blame. The complexity of determining the degree
of responsibility and blame using the original definition of causality was
completely characterized. Again, we show that changing the definition of
causality affects the complexity, and completely characterize it using the
updated definition.
",The Computational Complexity of Structure-Based Causality
"  Non-obviousness or inventive step is a general requirement for patentability
in most patent law systems. An invention should be at an adequate distance
beyond its prior art in order to be patented. This short paper provides an
overview on a methodology proposed for legal norm validation of FSTP facts
using rule reasoning approach.
",Rule reasoning for legal norm validation of FSTP facts
"  The semantic web has led to the deployment of ontologies on the web connected
through various relations and, in particular, alignments of their vocabularies.
There exists several semantics for alignments which make difficult
interoperation between different interpretation of networks of ontologies. Here
we present an abstraction of these semantics which allows for defining the
notions of closure and consistency for networks of ontologies independently
from the precise semantics. We also show that networks of ontologies with
specific notions of morphisms define categories of networks of ontologies.
",The category of networks of ontologies
"  Causal models defined in terms of structural equations have proved to be
quite a powerful way of representing knowledge regarding causality. However, a
number of authors have given examples that seem to show that the Halpern-Pearl
(HP) definition of causality gives intuitively unreasonable answers. Here it is
shown that, for each of these examples, we can give two stories consistent with
the description in the example, such that intuitions regarding causality are
quite different for each story. By adding additional variables, we can
disambiguate the stories. Moreover, in the resulting causal models, the HP
definition of causality gives the intuitively correct answer. It is also shown
that, by adding extra variables, a modification to the original HP definition
made to deal with an example of Hopkins and Pearl may not be necessary. Given
how much can be done by adding extra variables, there might be a concern that
the notion of causality is somewhat unstable. Can adding extra variables in a
""conservative"" way (i.e., maintaining all the relations between the variables
in the original model) cause the answer to the question ""Is X=x a cause of Y=y""
to alternate between ""yes"" and ""no""? It is shown that we can have such
alternation infinitely often, but if we take normality into consideration, we
cannot. Indeed, under appropriate normality assumptions. adding an extra
variable can change the answer from ""yes"" to ""no"", but after that, it cannot
cannot change back to ""yes"".
",Appropriate Causal Models and the Stability of Causation
"  How smart is your kettle? How smart are things in your kitchen, your house,
your neighborhood, on the internet? With the advent of Internet of Things, and
the move of making devices `smart' by utilizing AI, a natural question arrises,
how can we evaluate the progress. The standard way of evaluating AI is through
the Turing Test. While Turing Test was designed for AI; the device that it was
tailored to was a computer. Applying the test to variety of devices that
constitute Internet of Things poses a number of challenges which could be
addressed through a number of adaptations.
",Turing Test for the Internet of Things
"  Belief revision is an operation that aims at modifying old be-liefs so that
they become consistent with new ones. The issue of belief revision has been
studied in various formalisms, in particular, in qualitative algebras (QAs) in
which the result is a disjunction of belief bases that is not necessarily
repre-sentable in a QA. This motivates the study of belief revision in
formalisms extending QAs, namely, their propositional clo-sures: in such a
closure, the result of belief revision belongs to the formalism. Moreover, this
makes it possible to define a contraction operator thanks to the Harper
identity. Belief revision in the propositional closure of QAs is studied, an
al-gorithm for a family of revision operators is designed, and an open-source
implementation is made freely available on the web.
",Belief revision in the propositional closure of a qualitative algebra
"  With the technology development, the need of analyze and extraction of useful
information is increasing. Bayesian networks contain knowledge from data and
experts that could be used for decision making processes But they are not
easily understandable thus the rule extraction methods have been used but they
have high computation costs. To overcome this problem we extract rules from
Bayesian network using genetic algorithm. Then we generate the graphical chain
by mutually matching the extracted rules and Bayesian network. This graphical
chain could shows the sequence of events that lead to the target which could
help the decision making process. The experimental results on small networks
show that the proposed method has comparable results with brute force method
which has a significantly higher computation cost.
","Generating Graphical Chain by Mutual Matching of Bayesian Network and
  Extracted Rules of Bayesian Network Using Genetic Algorithm"
"  The paper presents some steps for multi-valued representation of neutrosophic
information. These steps are provided in the framework of multi-valued logics
using the following logical value: true, false, neutral, unknown and saturated.
Also, this approach provides some calculus formulae for the following
neutrosophic features: truth, falsity, neutrality, ignorance,
under-definedness, over-definedness, saturation and entropy. In addition, it
was defined net truth, definedness and neutrosophic score.
",Neutrosophic information in the framework of multi-valued representation
"  The max-product {belief propagation} (BP) is a popular message-passing
heuristic for approximating a maximum-a-posteriori (MAP) assignment in a joint
distribution represented by a graphical model (GM). In the past years, it has
been shown that BP can solve a few classes of linear programming (LP)
formulations to combinatorial optimization problems including maximum weight
matching, shortest path and network flow, i.e., BP can be used as a
message-passing solver for certain combinatorial optimizations. However, those
LPs and corresponding BP analysis are very sensitive to underlying problem
setups, and it has been not clear what extent these results can be generalized
to. In this paper, we obtain a generic criteria that BP converges to the
optimal solution of given LP, and show that it is satisfied in LP formulations
associated to many classical combinatorial optimization problems including
maximum weight perfect matching, shortest path, traveling salesman, cycle
packing, vertex/edge cover and network flow.
","Max-Product Belief Propagation for Linear Programming: Applications to
  Combinatorial Optimization"
"  A neutrosophic set is a more general platform, which can be used to present
uncertainty, imprecise, incomplete and inconsistent. In this paper a score
function and an accuracy function for single valued neutrosophic sets is
firstly proposed to make the distinction between them. Then the idea is
extended to interval neutrosophic sets. A multi-criteria decision making method
based on the developed score-accuracy functions is established in which
criterion values for alternatives are single valued neutrosophic sets and
interval neutrosophic sets. In decision making process, the neutrosophic
weighted aggregation operators (arithmetic and geometric average operators) are
adopted to aggregate the neutrosophic information related to each alternative.
Thus, we can rank all alternatives and make the selection of the best of one(s)
according to the score-accuracy functions. Finally, some illustrative examples
are presented to verify the developed approach and to demonstrate its
practicality and effectiveness.
","Multi-criteria neutrosophic decision making method based on score and
  accuracy functions under neutrosophic environment"
"  Automated Theorem Proving (ATP) is an established branch of Artificial
Intelligence. The purpose of ATP is to design a system which can automatically
figure out an algorithm either to prove or disprove a mathematical claim, on
the basis of a set of given premises, using a set of fundamental postulates and
following the method of logical inference. In this paper, we propose GraATP, a
generalized framework for automated theorem proving in plane geometry. Our
proposed method translates the geometric entities into nodes of a graph and the
relations between them as edges of that graph. The automated system searches
for different ways to reach the conclusion for a claim via graph traversal by
which the validity of the geometric theorem is examined.
","GraATP: A Graph Theoretic Approach for Automated Theorem Proving in
  Plane Geometry"
"  Local search methods can quickly find good quality solutions in cases where
systematic search methods might take a large amount of time. Moreover, in the
context of pattern set mining, exhaustive search methods are not applicable due
to the large search space they have to explore. In this paper, we propose the
application of stochastic local search to solve the pattern set mining.
Specifically, to the task of concept learning. We applied a number of local
search algorithms on a standard benchmark instances for pattern set mining and
the results show the potentials for further exploration.
",Stochastic Local Search for Pattern Set Mining
"  Knowledge is only good if it is sound, consistent and complete. The same
holds true for conceptual knowledge, which holds knowledge about concepts and
its association. Conceptual knowledge no matter what format they are
represented in, must be consistent, sound and complete in order to realise its
practical use. This paper discusses consistency, soundness and completeness in
the ambit of conceptual knowledge and the need to consider these factors as
fundamental to the development of conceptual knowledge.
","Towards a Consistent, Sound and Complete Conceptual Knowledge"
"  In practical situations, interval-valued fuzzy sets are frequently
encountered. In this paper, firstly, we present shadowed sets for interpreting
and understanding interval fuzzy sets. We also provide an analytic solution to
computing the pair of thresholds by searching for a balance of uncertainty in
the framework of shadowed sets. Secondly, we construct errors-based three-way
approximations of interval-valued fuzzy sets. We also provide an alternative
decision-theoretic formulation for calculating the pair of thresholds by
transforming interval-valued loss functions into single-valued loss functions,
in which the required thresholds are computed by minimizing decision costs.
Thirdly, we compute errors-based three-way approximations of interval-valued
fuzzy sets by using interval-valued loss functions. Finally, we employ several
examples to illustrate that how to take an action for an object with
interval-valued membership grade by using interval-valued loss functions.
","Decision-theoretic rough sets-based three-way approximations of
  interval-valued fuzzy sets"
"  Efficiently querying Description Logic (DL) ontologies is becoming a vital
task in various data-intensive DL applications. Considered as a basic service
for answering object queries over DL ontologies, instance checking can be
realized by using the most specific concept (MSC) method, which converts
instance checking into subsumption problems. This method, however, loses its
simplicity and efficiency when applied to large and complex ontologies, as it
tends to generate very large MSC's that could lead to intractable reasoning. In
this paper, we propose a revision to this MSC method for DL SHI, allowing it to
generate much simpler and smaller concepts that are specific-enough to answer a
given query. With independence between computed MSC's, scalability for query
answering can also be achieved by distributing and parallelizing the
computations. An empirical evaluation shows the efficacy of our revised MSC
method and the significant efficiency achieved when using it for answering
object queries.
","Converting Instance Checking to Subsumption: A Rethink for Object
  Queries over Practical Ontologies"
"  In this paper an ontological representation and reasoning paradigm has been
proposed for interpretation of time-series signals. The signals come from
sensors observing a smart environment. The signal chosen for the annotation
process is a set of unintuitive and complex gas sensor data. The ontology of
this paradigm is inspired form the SSN ontology (Semantic Sensor Network) and
used for representation of both the sensor data and the contextual information.
The interpretation process is mainly done by an incremental ASP solver which as
input receives a logic program that is generated from the contents of the
ontology. The contextual information together with high level domain knowledge
given in the ontology are used to infer explanations (answer sets) for changes
in the ambient air detected by the gas sensors.
",Reasoning for Improved Sensor Data Interpretation in a Smart Home
"  As the interest in the representation of context dependent knowledge in the
Semantic Web has been recognized, a number of logic based solutions have been
proposed in this regard. In our recent works, in response to this need, we
presented the description logic-based Contextualized Knowledge Repository (CKR)
framework. CKR is not only a theoretical framework, but it has been effectively
implemented over state-of-the-art tools for the management of Semantic Web
data: inference inside and across contexts has been realized in the form of
forward SPARQL-based rules over different RDF named graphs. In this paper we
present the first evaluation results for such CKR implementation. In
particular, in first experiment we study its scalability with respect to
different reasoning regimes. In a second experiment we analyze the effects of
knowledge propagation on the computation of inferences.
","Knowledge Propagation in Contextualized Knowledge Repositories: an
  Experimental Evaluation"
"  Knowledge and Action Bases (KABs) have been recently proposed as a formal
framework to capture the dynamics of systems which manipulate Description Logic
(DL) Knowledge Bases (KBs) through action execution. In this work, we enrich
the KAB setting with contextual information, making use of different context
dimensions. On the one hand, context is determined by the environment using
context-changing actions that make use of the current state of the KB and the
current context. On the other hand, it affects the set of TBox assertions that
are relevant at each time point, and that have to be considered when processing
queries posed over the KAB. Here we extend to our enriched setting the results
on verification of rich temporal properties expressed in mu-calculus, which had
been established for standard KABs. Specifically, we show that under a
run-boundedness condition, verification stays decidable.
",Adding Context to Knowledge and Action Bases
"  We characterize different types of conflicts that may occur in complex
distributed multi-agent scenarios, such as in Ambient Intelligence (AmI)
environments, and we argue that these conflicts should be resolved in a
suitable order and with the appropriate strategies for each individual conflict
type. We call for further research with the goal of turning conflict resolution
in AmI environments and similar multi-agent domains into a more coordinated and
agreed upon process.
",Different Types of Conflicting Knowledge in AmI Environments
"  Manufacturing Operations Management (MOM) systems are complex in the sense
that they integrate data from heterogeneous systems inside the automation
pyramid. The need for context-aware analytics arises from the dynamics of these
systems that influence data generation and hamper comparability of analytics,
especially predictive models (e.g. predictive maintenance), where concept drift
affects application of these models in the future. Recently, an increasing
amount of research has been directed towards data integration using semantic
context models. Manual construction of such context models is an elaborate and
error-prone task. Therefore, we pose the challenge to apply combinations of
knowledge extraction techniques in the domain of analytics in MOM, which
comprises the scope of data integration within Product Life-cycle Management
(PLM), Enterprise Resource Planning (ERP), and Manufacturing Execution Systems
(MES). We describe motivations, technological challenges and show benefits of
context-aware analytics, which leverage from and regard the interconnectedness
of semantic context data. Our example scenario shows the need for distribution
and effective change tracking of context information.
",Context-Aware Analytics in MOM Applications
"  In this exploratory note we ask the question of what a measure of performance
for all tasks is like if we use a weighting of tasks based on a difficulty
function. This difficulty function depends on the complexity of the
(acceptable) solution for the task (instead of a universal distribution over
tasks or an adaptive test). The resulting aggregations and decompositions are
(now retrospectively) seen as the natural (and trivial) interactive
generalisation of the C-tests.
",A note about the generalisation of the C-tests
"  ARCOE-Logic 2014, the 6th International Workshop on Acquisition,
Representation and Reasoning about Context with Logic, was held in co-location
with the 19th International Conference on Knowledge Engineering and Knowledge
Management (EKAW 2014) on November 25, 2014 in Link\""oping, Sweden. These notes
contain the five papers which were accepted and presented at the workshop.
","Workshop Notes of the 6th International Workshop on Acquisition,
  Representation and Reasoning about Context with Logic (ARCOE-Logic 2014)"
"  We propose that Solomonoff induction is complete in the physical sense via
several strong physical arguments. We also argue that Solomonoff induction is
fully applicable to quantum mechanics. We show how to choose an objective
reference machine for universal induction by defining a physical message
complexity and physical message probability, and argue that this choice
dissolves some well-known objections to universal induction. We also introduce
many more variants of physical message complexity based on energy and action,
and discuss the ramifications of our proposals.
","Ultimate Intelligence Part I: Physical Completeness and Objectivity of
  Induction"
"  The goal of constraint-based sequence mining is to find sequences of symbols
that are included in a large number of input sequences and that satisfy some
constraints specified by the user. Many constraints have been proposed in the
literature, but a general framework is still missing. We investigate the use of
constraint programming as general framework for this task. We first identify
four categories of constraints that are applicable to sequence mining. We then
propose two constraint programming formulations. The first formulation
introduces a new global constraint called exists-embedding. This formulation is
the most efficient but does not support one type of constraint. To support such
constraints, we develop a second formulation that is more general but incurs
more overhead. Both formulations can use the projected database technique used
in specialised algorithms. Experiments demonstrate the flexibility towards
constraint-based settings and compare the approach to existing methods.
",Constraint-based sequence mining using constraint programming
"  In educational technology and learning sciences, there are multiple uses for
a predictive model of whether a student will perform a task correctly or not.
For example, an intelligent tutoring system may use such a model to estimate
whether or not a student has mastered a skill. We analyze the significance of
data recency in making such predictions, i.e., asking whether relatively more
recent observations of a student's performance matter more than relatively
older observations. We develop a new Recent-Performance Factors Analysis model
that takes data recency into account. The new model significantly improves
predictive accuracy over both existing logistic-regression performance models
and over novel baseline models in evaluations on real-world and synthetic
datasets. As a secondary contribution, we demonstrate how the widely used
cross-validation with 0-1 loss is inferior to AIC and to cross-validation with
L1 prediction error loss as a measure of model performance.
",Predicting Performance During Tutoring with Models of Recent Performance
"  This article proposes the use of Vector Symbolic Architectures for
implementing Hierarchical Graph Neuron, an architecture for memorizing patterns
of generic sensor stimuli. The adoption of a Vector Symbolic representation
ensures a one-layered design for the approach, while maintaining the previously
reported properties and performance characteristics of Hierarchical Graph
Neuron, and also improving the noise resistance of the architecture. The
proposed architecture enables a linear (with respect to the number of stored
entries) time search for an arbitrary sub-pattern.
","Holographic Graph Neuron: a Bio-Inspired Architecture for Pattern
  Processing"
"  In this paper, we provide all information to participate to the Second
International Nurse Rostering Competition (INRC-II). First, we describe the
problem formulation, which, differently from INRC-I, is a multi-stage
procedure. Second, we illustrate all the necessary infrastructure do be used
together with the participant's solver, including the testbed, the file
formats, and the validation/simulation tools. Finally, we state the rules of
the competition. All update-to-date information about the competition is
available at http://mobiz.vives.be/inrc2/.
","Second International Nurse Rostering Competition (INRC-II) --- Problem
  Description and Rules ---"
"  We survey concepts at the frontier of research connecting artificial, animal
and human cognition to computation and information processing---from the Turing
test to Searle's Chinese Room argument, from Integrated Information Theory to
computational and algorithmic complexity. We start by arguing that passing the
Turing test is a trivial computational problem and that its pragmatic
difficulty sheds light on the computational nature of the human mind more than
it does on the challenge of artificial intelligence. We then review our
proposed algorithmic information-theoretic measures for quantifying and
characterizing cognition in various forms. These are capable of accounting for
known biases in human behavior, thus vindicating a computational algorithmic
view of cognition as first suggested by Turing, but this time rooted in the
concept of algorithmic probability, which in turn is based on computational
universality while being independent of computational model, and which has the
virtue of being predictive and testable as a model theory of cognitive
behavior.
","The Information-theoretic and Algorithmic Approach to Human, Animal and
  Artificial Cognition"
"  In this paper, we propose to learn sources independence in order to choose
the appropriate type of combination rules when aggregating their beliefs. Some
combination rules are used with the assumption of their sources independence
whereas others combine beliefs of dependent sources. Therefore, the choice of
the combination rule depends on the independence of sources involved in the
combination. In this paper, we propose also a measure of independence, positive
and negative dependence to integrate in mass functions before the combinaision
with the independence assumption.
","Consid{\'e}rant la d{\'e}pendance dans la th{\'e}orie des fonctions de
  croyance"
"  The web plays an important role in people's social lives since the emergence
of Web 2.0. It facilitates the interaction between users, gives them the
possibility to freely interact, share and collaborate through social networks,
online communities forums, blogs, wikis and other online collaborative media.
However, an other side of the web is negatively taken such as posting
inflammatory messages. Thus, when dealing with the online communities forums,
the managers seek to always enhance the performance of such platforms. In fact,
to keep the serenity and prohibit the disturbance of the normal atmosphere,
managers always try to novice users against these malicious persons by posting
such message (DO NOT FEED TROLLS). But, this kind of warning is not enough to
reduce this phenomenon. In this context we propose a new approach for detecting
malicious people also called 'Trolls' in order to allow community managers to
take their ability to post online. To be more realistic, our proposal is
defined within an uncertain framework. Based on the assumption consisting on
the trolls' integration in the successful discussion threads, we try to detect
the presence of such malicious users. Indeed, this method is based on a
conflict measure of the belief function theory applied between the different
messages of the thread. In order to show the feasibility and the result of our
approach, we test it in different simulated data.
",Trolls Identification within an Uncertain Framework
"  Speech Recognition searches to predict the spoken words automatically. These
systems are known to be very expensive because of using several pre-recorded
hours of speech. Hence, building a model that minimizes the cost of the
recognizer will be very interesting. In this paper, we present a new approach
for recognizing speech based on belief HMMs instead of proba-bilistic HMMs.
Experiments shows that our belief recognizer is insensitive to the lack of the
data and it can be trained using only one exemplary of each acoustic unit and
it gives a good recognition rates. Consequently, using the belief HMM
recognizer can greatly minimize the cost of these systems.
",Belief Hidden Markov Model for speech recognition
"  The aim of this paper is to show the interest in fitting features with an
$\alpha$-stable distribution to classify imperfect data. The supervised pattern
recognition is thus based on the theory of continuous belief functions, which
is a way to consider imprecision and uncertainty of data. The distributions of
features are supposed to be unimodal and estimated by a single Gaussian and
$\alpha$-stable model. Experimental results are first obtained from synthetic
data by combining two features of one dimension and by considering a vector of
two features. Mass functions are calculated from plausibility functions by
using the generalized Bayes theorem. The same study is applied to the automatic
classification of three types of sea floor (rock, silt and sand) with features
acquired by a mono-beam echo-sounder. We evaluate the quality of the
$\alpha$-stable model and the Gaussian model by analyzing qualitative results,
using a Kolmogorov-Smirnov test (K-S test), and quantitative results with
classification rates. The performances of the belief classifier are compared
with a Bayesian approach.
","Features modeling with an $\alpha$-stable distribution: Application to
  pattern recognition based on continuous belief functions"
"  Hidden Markov Models (HMMs) are learning methods for pattern recognition. The
probabilistic HMMs have been one of the most used techniques based on the
Bayesian model. First-order probabilistic HMMs were adapted to the theory of
belief functions such that Bayesian probabilities were replaced with mass
functions. In this paper, we present a second-order Hidden Markov Model using
belief functions. Previous works in belief HMMs have been focused on the
first-order HMMs. We extend them to the second-order model.
",Second-Order Belief Hidden Markov Models
"  Many information sources are considered into data fusion in order to improve
the decision in terms of uncertainty and imprecision. For each technique used
for data fusion, the asumption on independance is usually made. We propose in
this article an approach to take into acount an independance measure befor to
make the combination of information in the context of the theory of belief
functions.
","Int{\'e}gration d'une mesure d'ind{\'e}pendance pour la fusion
  d'informations"
"  Considering the high heterogeneity of the ontologies pub-lished on the web,
ontology matching is a crucial issue whose aim is to establish links between an
entity of a source ontology and one or several entities from a target ontology.
Perfectible similarity measures, consid-ered as sources of information, are
combined to establish these links. The theory of belief functions is a powerful
mathematical tool for combining such uncertain information. In this paper, we
introduce a decision pro-cess based on a distance measure to identify the best
possible matching entities for a given source entity.
",Uncertainty in Ontology Matching: A Decision Rule-Based Approach
"  This paper concerns the single machine total weighted tardiness scheduling
with sequence-dependent setup times, usually referred as $1|s_{ij}|\sum
w_jT_j$. In this $\mathcal{NP}$-hard problem, each job has an associated
processing time, due date and a weight. For each pair of jobs $i$ and $j$,
there may be a setup time before starting to process $j$ in case this job is
scheduled immediately after $i$. The objective is to determine a schedule that
minimizes the total weighted tardiness, where the tardiness of a job is equal
to its completion time minus its due date, in case the job is completely
processed only after its due date, and is equal to zero otherwise. Due to its
complexity, this problem is most commonly solved by heuristics. The aim of this
work is to develop a simple yet effective limitation strategy that speeds up
the local search procedure without a significant loss in the solution quality.
Such strategy consists of a filtering mechanism that prevents unpromising moves
to be evaluated. The proposed strategy has been embedded in a local search
based metaheuristic from the literature and tested in classical benchmark
instances. Computational experiments revealed that the limitation strategy
enabled the metaheuristic to be extremely competitive when compared to other
algorithms from the literature, since it allowed the use of a large number of
neighborhood structures without a significant increase in the CPU time and,
consequently, high quality solutions could be achieved in a matter of seconds.
In addition, we analyzed the effectiveness of the proposed strategy in two
other well-known metaheuristics. Further experiments were also carried out on
benchmark instances of problem $1|s_{ij}|\sum T_j$.
","Efficient local search limitation strategy for single machine total
  weighted tardiness scheduling with sequence-dependent setup times"
"  The article is dedicated to the analysis of the existing models for
assessment based of the fuzzy logic centroid technique. A new Generalized
Rectangular Model were developed. Some generalizations of the existing models
are offered.
",On Generalized Rectangular Fuzzy Model for Assessment
"  Defining and modeling the relation of inclusion between continuous belief
function may be considered as an important operation in order to study their
behaviors. Within this paper we will propose and present two forms of
inclusion: The strict and the partial one. In order to develop this relation,
we will study the case of consonant belief function. To do so, we will simulate
normal distributions allowing us to model and analyze these relations. Based on
that, we will determine the parameters influencing and characterizing the two
forms of inclusion.
",Inclusion within Continuous Belief Functions
"  Belief function theory provides a flexible way to combine information
provided by different sources. This combination is usually followed by a
decision making which can be handled by a range of decision rules. Some rules
help to choose the most likely hypothesis. Others allow that a decision is made
on a set of hypotheses. In [6], we proposed a decision rule based on a distance
measure. First, in this paper, we aim to demonstrate that our proposed decision
rule is a particular case of the rule proposed in [4]. Second, we give
experiments showing that our rule is able to decide on a set of hypotheses.
Some experiments are handled on a set of mass functions generated randomly,
others on real databases.
",A Distance-Based Decision in the Credal Level
"  This paper proposes FMAP (Forward Multi-Agent Planning), a fully-distributed
multi-agent planning method that integrates planning and coordination. Although
FMAP is specifically aimed at solving problems that require cooperation among
agents, the flexibility of the domain-independent planning model allows FMAP to
tackle multi-agent planning tasks of any type. In FMAP, agents jointly explore
the plan space by building up refinement plans through a complete and flexible
forward-chaining partial-order planner. The search is guided by $h_{DTG}$, a
novel heuristic function that is based on the concepts of Domain Transition
Graph and frontier state and is optimized to evaluate plans in distributed
environments. Agents in FMAP apply an advanced privacy model that allows them
to adequately keep private information while communicating only the data of the
refinement plans that is relevant to each of the participating agents.
Experimental results show that FMAP is a general-purpose approach that
efficiently solves tightly-coupled domains that have specialized agents and
cooperative goals as well as loosely-coupled problems. Specifically, the
empirical evaluation shows that FMAP outperforms current MAP systems at solving
complex planning tasks that are adapted from the International Planning
Competition benchmarks.
",FMAP: Distributed Cooperative Multi-Agent Planning
"  Multi-agent planning (MAP) approaches have been typically conceived for
independent or loosely-coupled problems to enhance the benefits of distributed
planning between autonomous agents as solving this type of problems require
less coordination between the agents' sub-plans. However, when it comes to
tightly-coupled agents' tasks, MAP has been relegated in favour of centralized
approaches and little work has been done in this direction. In this paper, we
present a general-purpose MAP capable to efficiently handle planning problems
with any level of coupling between agents. We propose a cooperative refinement
planning approach, built upon the partial-order planning paradigm, that allows
agents to work with incomplete information and to have incomplete views of the
world, i.e. being ignorant of other agents' information, as well as maintaining
their own private information. We show various experiments to compare the
performance of our system with a distributed CSP-based MAP approach over a
suite of problems.
",An approach to multi-agent planning with incomplete information
"  Multi-agent planning (MAP) approaches are typically oriented at solving
loosely-coupled problems, being ineffective to deal with more complex,
strongly-related problems. In most cases, agents work under complete
information, building complete knowledge bases. The present article introduces
a general-purpose MAP framework designed to tackle problems of any coupling
levels under incomplete information. Agents in our MAP model are partially
unaware of the information managed by the rest of agents and share only the
critical information that affects other agents, thus maintaining a distributed
vision of the task.
","A Flexible Coupling Approach to Multi-Agent Planning under Incomplete
  Information"
"  The menu-dependent nature of regret-minimization creates subtleties when it
is applied to dynamic decision problems. Firstly, it is not clear whether
\emph{forgone opportunities} should be included in the \emph{menu}, with
respect to which regrets are computed, at different points of the decision
problem. If forgone opportunities are included, however, we can characterize
when a form of dynamic consistency is guaranteed. Secondly, more subtleties
arise when sophistication is used to deal with dynamic inconsistency. In the
full version of this paper, we examine, axiomatically and by common examples,
the implications of different menu definitions for sophisticated,
regret-minimizing agents.
",Minimizing Regret in Dynamic Decision Problems
"  In this work, we propose an abductive framework for biosignal interpretation,
based on the concept of Temporal Abstraction Patterns. A temporal abstraction
pattern defines an abstraction relation between an observation hypothesis and a
set of observations constituting its evidence support. New observations are
generated abductively from any subset of the evidence of a pattern, building an
abstraction hierarchy of observations in which higher levels contain those
observations with greater interpretative value of the physiological processes
underlying a given signal. Non-monotonic reasoning techniques have been applied
to this model in order to find the best interpretation of a set of initial
observations, permitting even to correct these observations by removing, adding
or modifying them in order to make them consistent with the available domain
knowledge. Some preliminary experiments have been conducted to apply this
framework to a well known and bounded problem: the QRS detection on ECG
signals. The objective is not to provide a new better QRS detector, but to test
the validity of an abductive paradigm. These experiments show that a knowledge
base comprising just a few very simple rhythm abstraction patterns can enhance
the results of a state of the art algorithm by significantly improving its
detection F1-score, besides proving the ability of the abductive framework to
correct both sensitivity and specificity failures.
","Using temporal abduction for biosignal interpretation: A case study on
  QRS detection"
"  This paper discusses in layperson's terms human and computational studies of
the impact of threat and fear on exploration and creativity. A first study
showed that both killifish from a lake with predators and from a lake without
predators explore a new environment to the same degree and plotting number of
new spaces covered over time generates a hump-shaped curve. However, for the
fish from the lake with predators the curve is shifted to the right; they take
longer. This pattern was replicated by a computer model of exploratory behavior
varying only one parameter, the fear parameter. A second study showed that
stories inspired by threatening photographs were rated as more creative than
stories inspired by non-threatening photographs. Various explanations for the
findings are discussed.
",The Silver Lining Around Fearful Living
"  Belief revision of knowledge bases represented by a set of sentences in a
given logic has been extensively studied but for specific logics, mainly
propositional, and also recently Horn and description logics. Here, we propose
to generalize this operation from a model-theoretic point of view, by defining
revision in an abstract model theory known under the name of satisfaction
systems. In this framework, we generalize to any satisfaction systems the
characterization of the well known AGM postulates given by Katsuno and
Mendelzon for propositional logic in terms of minimal change among
interpretations. Moreover, we study how to define revision, satisfying the AGM
postulates, from relaxation notions that have been first introduced in
description logics to define dissimilarity measures between concepts, and the
consequence of which is to relax the set of models of the old belief until it
becomes consistent with the new pieces of knowledge. We show how the proposed
general framework can be instantiated in different logics such as
propositional, first-order, description and Horn logics. In particular for
description logics, we introduce several concrete relaxation operators tailored
for the description logic $\ALC{}$ and its fragments $\EL{}$ and $\ELext{}$,
discuss their properties and provide some illustrative examples.
","Belief Revision, Minimal Change and Relaxation: A General Framework
  based on Satisfaction Systems, and Applications to Description Logics"
"  Enforcing local consistencies in cost function networks is performed by
applying so-called Equivalent Preserving Transformations (EPTs) to the cost
functions. As EPTs transform the cost functions, they may break the property
that was making local consistency enforcement tractable on a global cost
function. A global cost function is called tractable projection-safe when
applying an EPT to it is tractable and does not break the tractability
property. In this paper, we prove that depending on the size r of the smallest
scopes used for performing EPTs, the tractability of global cost functions can
be preserved (r = 0) or destroyed (r > 1). When r = 1, the answer is
indefinite. We show that on a large family of cost functions, EPTs can be
computed via dynamic programming-based algorithms, leading to tractable
projection-safety. We also show that when a global cost function can be
decomposed into a Berge acyclic network of bounded arity cost functions, soft
local consistencies such as soft Directed or Virtual Arc Consistency can
directly emulate dynamic programming. These different approaches to
decomposable cost functions are then embedded in a solver for extensive
experiments that confirm the feasibility and efficiency of our proposal.
",Tractability and Decompositions of Global Cost Functions
"  A low carbon society aims at fighting global warming by stimulating synergic
efforts from governments, industry and scientific communities. Decision support
systems should be adopted to provide policy makers with possible scenarios,
options for prompt countermeasures in case of side effects on environment,
economy and society due to low carbon society policies, and also options for
information management. A necessary precondition to fulfill this agenda is to
face the complexity of this multi-disciplinary domain and to reach a common
understanding on it as a formal specification. Ontologies are widely accepted
means to share knowledge. Together with semantic rules, they enable advanced
semantic services to manage knowledge in a smarter way. Here we address the
European Emissions Trading System (EU-ETS) and we present a knowledge base
consisting of the EREON ontology and a catalogue of rules. Then we describe two
innovative semantic services to manage ETS data and information on ETS
scenarios.
","Semantics-based services for a low carbon society: An application on
  emissions trading system data and scenarios management"
"  Discovering causal relationships from observational data is a crucial problem
and it has applications in many research areas. The PC algorithm is the
state-of-the-art constraint based method for causal discovery. However, runtime
of the PC algorithm, in the worst-case, is exponential to the number of nodes
(variables), and thus it is inefficient when being applied to high dimensional
data, e.g. gene expression datasets. On another note, the advancement of
computer hardware in the last decade has resulted in the widespread
availability of multi-core personal computers. There is a significant
motivation for designing a parallelised PC algorithm that is suitable for
personal computers and does not require end users' parallel computing knowledge
beyond their competency in using the PC algorithm. In this paper, we develop
parallel-PC, a fast and memory efficient PC algorithm using the parallel
computing technique. We apply our method to a range of synthetic and real-world
high dimensional datasets. Experimental results on a dataset from the DREAM 5
challenge show that the original PC algorithm could not produce any results
after running more than 24 hours; meanwhile, our parallel-PC algorithm managed
to finish within around 12 hours with a 4-core CPU computer, and less than 6
hours with a 8-core CPU computer. Furthermore, we integrate parallel-PC into a
causal inference method for inferring miRNA-mRNA regulatory relationships. The
experimental results show that parallel-PC helps improve both the efficiency
and accuracy of the causal inference algorithm.
","A fast PC algorithm for high dimensional causal discovery with
  multi-core PCs"
"  A wide range of problems can be modelled as constraint satisfaction problems
(CSPs), that is, a set of constraints that must be satisfied simultaneously.
Constraints can either be represented extensionally, by explicitly listing
allowed combinations of values, or implicitly, by special-purpose algorithms
provided by a solver.
  Such implicitly represented constraints, known as global constraints, are
widely used; indeed, they are one of the key reasons for the success of
constraint programming in solving real-world problems. In recent years, a
variety of restrictions on the structure of CSP instances have been shown to
yield tractable classes of CSPs. However, most such restrictions fail to
guarantee tractability for CSPs with global constraints. We therefore study the
applicability of structural restrictions to instances with such constraints.
  We show that when the number of solutions to a CSP instance is bounded in key
parts of the problem, structural restrictions can be used to derive new
tractable classes. Furthermore, we show that this result extends to
combinations of instances drawn from known tractable classes, as well as to CSP
instances where constraints assign costs to satisfying assignments.
",Structural Decompositions for Problems with Global Constraints
"  Reasoning semantically in first-order logic is notoriously a challenge. This
paper surveys a selection of semantically-guided or model-based methods that
aim at meeting aspects of this challenge. For first-order logic we touch upon
resolution-based methods, tableaux-based methods, DPLL-inspired methods, and we
give a preview of a new method called SGGS, for Semantically-Guided
Goal-Sensitive reasoning. For first-order theories we highlight hierarchical
and locality-based methods, concluding with the recent Model-Constructing
satisfiability calculus.
",On First-Order Model-Based Reasoning
"  Distilling from a knowledge base only the part that is relevant to a subset
of alphabet, which is recognized as forgetting, has attracted extensive
interests in AI community. In standard propositional logic, a general algorithm
of forgetting and its computation-oriented investigation in various fragments
whose satisfiability are tractable are still lacking. The paper aims at filling
the gap. After exploring some basic properties of forgetting in propositional
logic, we present a resolution-based algorithm of forgetting for CNF fragment,
and some complexity results about forgetting in Horn, renamable Horn, q-Horn,
Krom, DNF and CNF fragments of propositional logic.
",On Forgetting in Tractable Propositional Fragments
"  Potential-based reward shaping (PBRS) is an effective and popular technique
to speed up reinforcement learning by leveraging domain knowledge. While PBRS
is proven to always preserve optimal policies, its effect on learning speed is
determined by the quality of its potential function, which, in turn, depends on
both the underlying heuristic and the scale. Knowing which heuristic will prove
effective requires testing the options beforehand, and determining the
appropriate scale requires tuning, both of which introduce additional sample
complexity. We formulate a PBRS framework that reduces learning speed, but does
not incur extra sample complexity. For this, we propose to simultaneously learn
an ensemble of policies, shaped w.r.t. many heuristics and on a range of
scales. The target policy is then obtained by voting. The ensemble needs to be
able to efficiently and reliably learn off-policy: requirements fulfilled by
the recent Horde architecture, which we take as our basis. We demonstrate
empirically that (1) our ensemble policy outperforms both the base policy, and
its single-heuristic components, and (2) an ensemble over a general range of
scales performs at least as well as one with optimally tuned components.
",Off-Policy Reward Shaping with Ensembles
"  The proliferation of heterogeneous data sources of semantic knowledge base
intensifies the need of an automatic instance matching technique. However, the
efficiency of instance matching is often influenced by the weight of a property
associated to instances. Automatic weight generation is a non-trivial, however
an important task in instance matching technique. Therefore, identifying an
appropriate metric for generating weight for a property automatically is
nevertheless a formidable task. In this paper, we investigate an approach of
generating weights automatically by considering hypotheses: (1) the weight of a
property is directly proportional to the ratio of the number of its distinct
values to the number of instances contain the property, and (2) the weight is
also proportional to the ratio of the number of distinct values of a property
to the number of instances in a training dataset. The basic intuition behind
the use of our approach is the classical theory of information content that
infrequent words are more informative than frequent ones. Our mathematical
model derives a metric for generating property weights automatically, which is
applied in instance matching system to produce re-conciliated instances
efficiently. Our experiments and evaluations show the effectiveness of our
proposed metric of automatic weight generation for properties in an instance
matching technique.
","An Efficient Metric of Automatic Weight Generation for Properties in
  Instance Matching Technique"
"  The multi-armed bandit problem (MBP) is the problem of finding, as accurately
and quickly as possible, the most profitable option from a set of options that
gives stochastic rewards by referring to past experiences. Inspired by
fluctuated movements of a rigid body in a tug-of-war game, we formulated a
unique search algorithm that we call the `tug-of-war (TOW) dynamics' for
solving the MBP efficiently. The cognitive medium access, which refers to
multi-user channel allocations in cognitive radio, can be interpreted as the
competitive multi-armed bandit problem (CMBP); the problem is to determine the
optimal strategy for allocating channels to users which yields maximum total
rewards gained by all users. Here we show that it is possible to construct a
physical device for solving the CMBP, which we call the `TOW Bombe', by
exploiting the TOW dynamics existed in coupled incompressible-fluid cylinders.
This analog computing device achieves the `socially-maximum' resource
allocation that maximizes the total rewards in cognitive medium access without
paying a huge computational cost that grows exponentially as a function of the
problem size.
",Decision Maker using Coupled Incompressible-Fluid Cylinders
"  *** To appear in IJCAI 2015 proceedings *** In Constraint Programming (CP), a
portfolio solver uses a variety of different solvers for solving a given
Constraint Satisfaction / Optimization Problem. In this paper we introduce
sunny-cp2: the first parallel CP portfolio solver that enables a dynamic,
cooperative, and simultaneous execution of its solvers in a multicore setting.
It incorporates state-of-the-art solvers, providing also a usable and
configurable framework. Empirical results are very promising. sunny-cp2 can
even outperform the performance of the oracle solver which always selects the
best solver of the portfolio for a given problem.
",A Multicore Tool for Constraint Solving
"  Tau-chain is a decentralized peer-to-peer network having three unified faces:
Rules, Proofs, and Computer Programs, allowing a generalization of virtually
any centralized or decentralized P2P network, together with many new abilities,
as we present on this note.
",About Tau-Chain
"  In this paper one presents a new fuzzy clustering algorithm based on a
dissimilarity function determined by three parameters. This algorithm can be
considered a generalization of the Gustafson-Kessel algorithm for fuzzy
clustering.
","A Generalization of Gustafson-Kessel Algorithm Using a New Constraint
  Parameter"
"  Multicriteria decision analysis aims at supporting a person facing a decision
problem involving conflicting criteria. We consider an additive utility model
which provides robust conclusions based on preferences elicited from the
decision maker. The recommendations based on these robust conclusions are even
more convincing if they are complemented by explanations. We propose a general
scheme, based on sequence of preference swaps, in which explanations can be
computed. We show first that the length of explanations can be unbounded in the
general case. However, in the case of binary reference scales, this length is
bounded and we provide an algorithm to compute the corresponding explanation.
","Explaining robust additive utility models by sequences of preference
  swaps"
"  Artifact-centric models for business processes recently raised a lot of
attention, as they manage to combine structural (i.e. data related) with
dynamical (i.e. process related) aspects in a seamless way. Many frameworks
developed under this approach, although, are not built explicitly for planning,
one of the most prominent operations related to business processes. In this
paper, we try to overcome this by proposing a framework named Dynamic Knowledge
Bases, aimed at describing rich business domains through Description
Logic-based ontologies, and where a set of actions allows the system to evolve
by modifying such ontologies. This framework, by offering action rewriting and
knowledge partialization, represents a viable and formal environment to develop
decision making and planning techniques for DL-based artifact-centric business
domains.
","Optimizations for Decision Making and Planning in Description Logic
  Dynamic Knowledge Bases"
"  This book discusses computational curiosity, from the psychology of curiosity
to the computational models of curiosity, and then showcases several
interesting applications of computational curiosity. A brief overview of the
book is given as follows. Chapter 1 discusses the underpinnings of curiosity in
human beings, including the major categories of curiosity, curiosity-related
emotions and behaviors, and the benefits of curiosity. Chapter 2 reviews the
arousal theories of curiosity in psychology and summarizes a general two-step
process model for computational curiosity. Base on the perspective of the
two-step process model, Chapter 3 reviews and analyzes some of the traditional
computational models of curiosity. Chapter 4 introduces a novel generic
computational model of curiosity, which is developed based on the arousal
theories of curiosity. After the discussion of computational models of
curiosity, we outline the important applications where computational curiosity
may bring significant impacts in Chapter 5. Chapter 6 discusses the application
of the generic computational model of curiosity in a machine learning
framework. Chapter 7 discusses the application of the generic computational
model of curiosity in a recommender system. In Chapter 8 and Chapter 9, the
generic computational model of curiosity is studied in two types of pedagogical
agents. In Chapter 8, a curious peer learner is studied. It is a non-player
character that aims to provide a believable virtual learning environment for
users. In Chapter 9, a curious learning companion is studied. It aims to
enhance users' learning experience through providing meaningful interactions
with them. Chapter 10 discusses open questions in the research field of
computation curiosity.
",Computational Curiosity (A Book Draft)
"  This paper presents an idea of inductive learning use for rule generation
from ontologies. The main purpose of the paper is to evaluate the possibility
of inductive learning use in rule generation from ontologies and to develop the
way how this can be done. Generated rules are necessary to supplement or even
to develop the Semantic Web Expert System (SWES) knowledge base. The SWES
emerges as the result of evolution of expert system concept toward the Web, and
the SWES is based on the Semantic Web technologies. Available publications show
that the problem of rule generation from ontologies based on inductive learning
is not investigated deeply enough.
",Inductive Learning for Rule Generation from Ontology
"  Information fusion is an advanced research area which can assist decision
makers in enhancing their decisions. This paper aims at designing a new
multi-layer framework that can support the process of performing decisions from
the obtained beliefs using information fusion. Since it is not an easy task to
cross the gap between computed beliefs of certain hypothesis and decisions, the
proposed framework consists of the following layers in order to provide a
suitable architecture (ordered bottom up): 1. A layer for combination of basic
belief assignments using an information fusion approach. Such approach exploits
Dezert-Smarandache Theory, DSmT, and proportional conflict redistribution to
provide more realistic final beliefs. 2. A layer for computation of pignistic
probability of the underlying propositions from the corresponding final
beliefs. 3. A layer for performing probabilistic reasoning using a Bayesian
network that can obtain the probable reason of a proposition from its pignistic
probability. 4. Ranking the system decisions is ultimately used to support
decision making. A case study has been accomplished at various operational
conditions in order to prove the concept, in addition it pointed out that: 1.
The use of DSmT for information fusion yields not only more realistic beliefs
but also reliable pignistic probabilities for the underlying propositions. 2.
Exploiting the pignistic probability for the integration of the information
fusion with the Bayesian network provides probabilistic inference and enable
decision making on the basis of both belief based probabilities for the
underlying propositions and Bayesian based probabilities for the corresponding
reasons. A comparative study of the proposed framework with respect to other
information fusion systems confirms its superiority to support decision making.
",Design of a Framework to Facilitate Decisions Using Information Fusion
"  The Countdown game is one of the oldest TV show running in the world. It
started broadcasting in 1972 on the french television and in 1982 on British
channel 4, and it has been running since in both countries. The game, while
extremely popular, never received any serious scientific attention, probably
because it seems too simple at first sight. We present in this article an
in-depth analysis of the numbers round of the countdown game. This includes a
complexity analysis of the game, an analysis of existing algorithms, the
presentation of a new algorithm that increases resolution speed by a factor of
20. It also includes some leads on how to turn the game into a more difficult
one, both for a human player and for a computer, and even to transform it into
a probably undecidable problem.
",The (Final) countdown
"  In this paper a knowledge representation model are proposed, FP5, which
combine the ideas from fuzzy sets and penta-valued logic. FP5 represents
imprecise properties whose accomplished degree is undefined, contradictory or
indeterminate for some objects. Basic operations of conjunction, disjunction
and negation are introduced. Relations to other representation models like
fuzzy sets, intuitionistic, paraconsistent and bipolar fuzzy sets are
discussed.
",A New Penta-valued Logic Based Knowledge Representation
"  The application of cognitive mechanisms to support knowledge acquisition is,
from our point of view, crucial for making the resulting models coherent,
efficient, credible, easy to use and understandable. In particular, there are
two characteristic features of intelligence that are essential for knowledge
development: forgetting and consolidation. Both plays an important role in
knowledge bases and learning systems to avoid possible information overflow and
redundancy, and in order to preserve and strengthen important or frequently
used rules and remove (or forget) useless ones. We present an incremental,
long-life view of knowledge acquisition which tries to improve task after task
by determining what to keep, what to consolidate and what to forget, overcoming
The Stability-Plasticity dilemma. In order to do that, we rate rules by
introducing several metrics through the first adaptation, to our knowledge, of
the Minimum Message Length (MML) principle to a coverage graph, a hierarchical
assessment structure which treats evidence and rules in a unified way. The
metrics are not only used to forget some of the worst rules, but also to set a
consolidation process to promote those selected rules to the knowledge base,
which is also mirrored by a demotion system. We evaluate the framework with a
series of tasks in a chess rule learning domain.
","Forgetting and consolidation for incremental and cumulative knowledge
  acquisition systems"
"  Here a novel idea to handle imprecise or vague set viz. Pseudo fuzzy set has
been proposed. Pseudo fuzzy set is a triplet of element and its two membership
functions. Both the membership functions may or may not be dependent. The
hypothesis is that every positive sense has some negative sense. So, one
membership function has been considered as positive and another as negative.
Considering this concept, here the development of Pseudo fuzzy set and its
property along with Pseudo fuzzy numbers has been discussed.
",Pseudo Fuzzy Set
"  The literature on judgment aggregation is moving from studying impossibility
results regarding aggregation rules towards studying specific judgment
aggregation rules. Here we give a structured list of most rules that have been
proposed and studied recently in the literature, together with various
properties of such rules. We first focus on the majority-preservation property,
which generalizes Condorcet-consistency, and identify which of the rules
satisfy it. We study the inclusion relationships that hold between the rules.
Finally, we consider two forms of unanimity, monotonicity, homogeneity, and
reinforcement, and we identify which of the rules satisfy these properties.
","A partial taxonomy of judgment aggregation rules, and their properties"
"  Software capable of improving itself has been a dream of computer scientists
since the inception of the field. In this work we provide definitions for
Recursively Self-Improving software, survey different types of self-improving
software, review the relevant literature, analyze limits on computation
restricting recursive self-improvement and introduce RSI Convergence Theory
which aims to predict general behavior of RSI systems. Finally, we address
security implications from self-improving intelligent software.
","From Seed AI to Technological Singularity via Recursively Self-Improving
  Software"
"  Budget allocation in online advertising deals with distributing the campaign
(insertion order) level budgets to different sub-campaigns which employ
different targeting criteria and may perform differently in terms of
return-on-investment (ROI). In this paper, we present the efforts at Turn on
how to best allocate campaign budget so that the advertiser or campaign-level
ROI is maximized. To do this, it is crucial to be able to correctly determine
the performance of sub-campaigns. This determination is highly related to the
action-attribution problem, i.e. to be able to find out the set of ads, and
hence the sub-campaigns that provided them to a user, that an action should be
attributed to. For this purpose, we employ both last-touch (last ad gets all
credit) and multi-touch (many ads share the credit) attribution methodologies.
We present the algorithms deployed at Turn for the attribution problem, as well
as their parallel implementation on the large advertiser performance datasets.
We conclude the paper with our empirical comparison of last-touch and
multi-touch attribution-based budget allocation in a real online advertising
setting.
",Multi-Touch Attribution Based Budget Allocation in Online Advertising
"  We propose a generalization of SimRank similarity measure for heterogeneous
information networks. Given the information network, the intraclass similarity
score s(a, b) is high if the set of objects that are related with a and the set
of objects that are related with b are pair-wise similar according to all
imposed relations.
",Tensor SimRank for Heterogeneous Information Networks
"  Dempster-Shafer evidence theory is an efficient mathematical tool to deal
with uncertain information. In that theory, basic probability assignment (BPA)
is the basic element for the expression and inference of uncertainty.
Decision-making based on BPA is still an open issue in Dempster-Shafer evidence
theory. In this paper, a novel approach of transforming basic probability
assignments to probabilities is proposed based on Deng entropy which is a new
measure for the uncertainty of BPA. The principle of the proposed method is to
minimize the difference of uncertainties involving in the given BPA and
obtained probability distribution. Numerical examples are given to show the
proposed approach.
","Transformation of basic probability assignments to probabilities based
  on a new entropy measure"
"  We introduce a new approach to solving path-finding problems under
uncertainty by representing them as probabilistic models and applying
domain-independent inference algorithms to the models. This approach separates
problem representation from the inference algorithm and provides a framework
for efficient learning of path-finding policies. We evaluate the new approach
on the Canadian Traveler Problem, which we formulate as a probabilistic model,
and show how probabilistic inference allows high performance stochastic
policies to be obtained for this problem.
",Path Finding under Uncertainty through Probabilistic Inference
"  This paper considers the problem of representative selection: choosing a
subset of data points from a dataset that best represents its overall set of
elements. This subset needs to inherently reflect the type of information
contained in the entire set, while minimizing redundancy. For such purposes,
clustering may seem like a natural approach. However, existing clustering
methods are not ideally suited for representative selection, especially when
dealing with non-metric data, where only a pairwise similarity measure exists.
In this paper we propose $\delta$-medoids, a novel approach that can be viewed
as an extension to the $k$-medoids algorithm and is specifically suited for
sample representative selection from non-metric data. We empirically validate
$\delta$-medoids in two domains, namely music analysis and motion analysis. We
also show some theoretical bounds on the performance of $\delta$-medoids and
the hardness of representative selection in general.
",Representative Selection in Non Metric Datasets
"  As ontologies and description logics (DLs) reach out to a broader audience,
several reasoning services are developed in this context. Belief revision is
one of them, of prime importance when knowledge is prone to change and
inconsistency. In this paper we address both the generalization of the
well-known AGM postulates, and the definition of concrete and well-founded
revision operators in different DL families. We introduce a model-theoretic
version of the AGM postulates with a general definition of inconsistency, hence
enlarging their scope to a wide family of non-classical logics, in particular
negation-free DL families. We propose a general framework for defining revision
operators based on the notion of relaxation, introduced recently for defining
dissimilarity measures between DL concepts. A revision operator in this
framework amounts to relax the set of models of the old belief until it reaches
the sets of models of the new piece of knowledge. We demonstrate that such a
relaxation-based revision operator defines a faithful assignment and satisfies
the generalized AGM postulates. Another important contribution concerns the
definition of several concrete relaxation operators suited to the syntax of
some DLs (ALC and its fragments EL and ELU).
",Relaxation-based revision operators in description logics
"  In this paper we focus on finding high quality solutions for the problem of
maximum partitioning of graphs with supply and demand (MPGSD). There is a
growing interest for the MPGSD due to its close connection to problems
appearing in the field of electrical distribution systems, especially for the
optimization of self-adequacy of interconnected microgrids. We propose an ant
colony optimization algorithm for the problem. With the goal of further
improving the algorithm we combine it with a previously developed correction
procedure. In our computational experiments we evaluate the performance of the
proposed algorithm on both trees and general graphs. The tests show that the
method manages to find optimal solutions in more than 50% of the problem
instances, and has an average relative error of less than 0.5% when compared to
known optimal solutions.
","An Ant Colony Optimization Algorithm for Partitioning Graphs with Supply
  and Demand"
"  Given a set $V$ of $n$ elements and a distance matrix $[d_{ij}]_{n\times n}$
among elements, the max-mean dispersion problem (MaxMeanDP) consists in
selecting a subset $M$ from $V$ such that the mean dispersion (or distance)
among the selected elements is maximized. Being a useful model to formulate
several relevant applications, MaxMeanDP is known to be NP-hard and thus
computationally difficult. In this paper, we present a highly effective memetic
algorithm for MaxMeanDP which relies on solution recombination and local
optimization to find high quality solutions. Computational experiments on the
set of 160 benchmark instances with up to 1000 elements commonly used in the
literature show that the proposed algorithm improves or matches the published
best known results for all instances in a short computing time, with only one
exception, while achieving a high success rate of 100\%. In particular, we
improve 59 previous best results out of the 60 most challenging instances.
Results on a set of 40 new large instances with 3000 and 5000 elements are also
presented. The key ingredients of the proposed algorithm are investigated to
shed light on how they affect the performance of the algorithm.
",On memetic search for the max-mean dispersion problem
"  In recent years the search for a proper formal definition of actual causation
-- i.e., the relation of cause-effect as it is instantiated in specific
observations, rather than general causal relations -- has taken on impressive
proportions. In part this is due to the insight that this concept plays a
fundamental role in many different fields, such as legal theory, engineering,
medicine, ethics, etc. Because of this diversity in applications, some
researchers have shifted focus from a single idealized definition towards a
more pragmatic, context-based account. For instance, recent work by Halpern and
Hitchcock draws on empirical research regarding people's causal judgments, to
suggest a graded and context-sensitive notion of causation. Although we
sympathize with many of their observations, their restriction to a merely
qualitative ordering runs into trouble for more complex examples. Therefore we
aim to improve on their approach, by using the formal language of CP-logic
(Causal Probabilistic logic), and the framework for defining actual causation
that was developed by the current authors using it. First we rephrase their
ideas into our quantitative, probabilistic setting, after which we modify it to
accommodate a greater class of examples. Further, we introduce a formal
distinction between statistical and normative considerations.
","Combining Probabilistic, Causal, and Normative Reasoning in CP-logic"
"  Inferring a cause from its effect using observed time series data is a major
challenge in natural and social sciences. Assuming the effect is generated by
the cause trough a linear system, we propose a new approach based on the
hypothesis that nature chooses the ""cause"" and the ""mechanism that generates
the effect from the cause"" independent of each other. We therefore postulate
that the power spectrum of the time series being the cause is uncorrelated with
the square of the transfer function of the linear filter generating the effect.
While most causal discovery methods for time series mainly rely on the noise,
our method relies on asymmetries of the power spectral density properties that
can be exploited even in the context of deterministic systems. We describe
mathematical assumptions in a deterministic model under which the causal
direction is identifiable with this approach. We also discuss the method's
performance under the additive noise model and its relationship to Granger
causality. Experiments show encouraging results on synthetic as well as
real-world data. Overall, this suggests that the postulate of Independence of
Cause and Mechanism is a promising principle for causal inference on empirical
time series.
",Telling cause from effect in deterministic linear dynamical systems
"  Given a hierarchical plan (or schedule) with uncertain task times, we propose
a deterministic polynomial (time and memory) algorithm for estimating the
probability that its meets a deadline, or, alternately, that its {\em makespan}
is less than a given duration. Approximation is needed as it is known that this
problem is NP-hard even for sequential plans (just, a sum of random variables).
In addition, we show two new complexity results: (1) Counting the number of
events that do not cross deadline is \#P-hard; (2)~Computing the expected
makespan of a hierarchical plan is NP-hard. For the proposed approximation
algorithm, we establish formal approximation bounds and show that the time and
memory complexities grow polynomially with the required accuracy, the number of
nodes in the plan, and with the size of the support of the random variables
that represent the durations of the primitive tasks. We examine these
approximation bounds empirically and demonstrate, using task networks taken
from the literature, how our scheme outperforms sampling techniques and exact
computation in terms of accuracy and run-time. As the empirical data shows much
better error bounds than guaranteed, we also suggest a method for tightening
the bounds in some cases.
",Estimating the Probability of Meeting a Deadline in Hierarchical Plans
"  The goal of this project is to predict the opponent's configuration in a
RoboCup SSL environment. For simplicity, a Markov model assumption is made such
that the predicted formation of the opponent team only depends on its current
formation. The field is divided into a grid and a robot state per player is
created with information about its position and its velocity. To gather a more
general sense of what the opposing team is doing, the state also incorporates
the team's average position (centroid). All possible state transitions are
stored in a hash table that requires minimum storage space. The table is
populated with transition probabilities that are learned by reading vision
packages and counting the state transitions regardless of the specific robot
player. Therefore, the computation during the game is reduced to interpreting a
given vision package to assign each player to a state, and looking for the most
likely state it will transition to. The confidence of the predicted team's
formation is the product of each individual player's probability. The project
is noteworthy in that it minimizes the time and space complexity requirements
for opponent's moves prediction.
",Predicting opponent team activity in a RoboCup environment
"  This paper describes a new method for classifying a dataset that partitions
elements into their categories. It has relations with neural networks but a
slightly different structure, requiring only a single pass through the
classifier to generate the weight sets. A grid-like structure is required as
part of a novel idea of converting a 1-D row of real values into a 2-D
structure of value bands. Each cell in any band then stores a distinct set of
weights, to represent its own importance and its relation to each output
category. During classification, all of the output weight lists can be
retrieved and summed to produce a probability for what the correct output
category is. The bands possibly work like hidden layers of neurons, but they
are variable specific, making the process orthogonal. The construction process
can be a single update process without iterations, making it potentially much
faster. It can also be compared with k-NN and may be practical for partial or
competitive updating.
",A Single-Pass Classifier for Categorical Data
"  Online learning is a familiar problem setting within Machine-Learning in
which data is presented serially in time to a learning agent, requiring it to
progressively adapt within the constraints of the learning algorithm. More
sophisticated variants may involve concepts such as transfer-learning which
increase this adaptive capability, enhancing the learner's cognitive capacities
in a manner that can begin to imitate the open-ended learning capabilities of
human beings.
  We shall argue in this paper, however, that a full realization of this notion
requires that, in addition to the capacity to adapt to novel data, autonomous
online learning must ultimately incorporate the capacity to update its own
representational capabilities in relation to the data. We therefore enquire
about the philosophical limits of this process, and argue that only fully
embodied learners exhibiting an a priori perception-action link in order to
ground representational adaptations are capable of exhibiting the full range of
human cognitive capability.
",On the Intrinsic Limits to Representationally-Adaptive Machine-Learning
"  In this document I present an approach to answer validation and reranking for
question answering (QA) systems. A cased-based reasoning (CBR) system judges
answer candidates for questions from annotated answer candidates for earlier
questions. The promise of this approach is that user feedback will result in
improved answers of the QA system, due to the growing case base. In the paper,
I present the adequate structuring of the case base and the appropriate
selection of relevant similarity measures, in order to solve the answer
validation problem. The structural case base is built from annotated MultiNet
graphs, which provide representations for natural language expressions, and
corresponding graph similarity measures. I cover a priori relations to
experienced answer candidates for former questions. I compare the CBR System
results to current approaches in an experiment integrating CBR into an existing
framework for answer validation and reranking. This integration is achieved by
adding CBR-related features to the input of a learned ranking model that
determines the final answer ranking. In the experiments based on QA@CLEF
questions, the best learned models make heavy use of CBR features. Observing
the results with a continually growing case base, I present a positive effect
of the size of the case base on the accuracy of the CBR subsystem.
","A Case Based Reasoning Approach for Answer Reranking in Question
  Answering"
"  This paper introduces a new computing model based on the cooperation among
Turing machines called orchestrated machines. Like universal Turing machines,
orchestrated machines are also designed to simulate Turing machines but they
can also modify the original operation of the included Turing machines to
create a new layer of some kind of collective behavior. Using this new model we
can define some interested notions related to cooperation ability of Turing
machines such as the intelligence quotient or the emotional intelligence
quotient for Turing machines.
",Are there intelligent Turing machines?
"  Research on the so-called ""free-energy principle'' (FEP) in cognitive
neuroscience is becoming increasingly high-profile. To date, introductions to
this theory have proved difficult for many readers to follow, but it depends
mainly upon two relatively simple ideas: firstly that normative or teleological
values can be expressed as probability distributions (active inference), and
secondly that approximate Bayesian reasoning can be effectively performed by
gradient descent on model parameters (the free-energy principle). The notion of
active inference is of great interest for a number of disciplines including
cognitive science and artificial intelligence, as well as cognitive
neuroscience, and deserves to be more widely known.
  This paper attempts to provide an accessible introduction to active inference
and informational free-energy, for readers from a range of scientific
backgrounds. In this work introduce an agent-based model with an agent trying
to make predictions about its position in a one-dimensional discretized world
using methods from the FEP.
",A Minimal Active Inference Agent
"  Mixed Integer Optimization has been a topic of active research in past
decades. It has been used to solve Statistical problems of classification and
regression involving massive data. However, there is an inherent degree of
vagueness present in huge real life data. This impreciseness is handled by
Fuzzy Sets. In this Paper, Fuzzy Mixed Integer Optimization Method (FMIOM) is
used to find solution to Regression problem. The methodology exploits discrete
character of problem. In this way large scale problems are solved within
practical limits. The data points are separated into different polyhedral
regions and each region has its own distinct regression coefficients. In this
attempt, an attention is drawn to Statistics and Data Mining community that
Integer Optimization can be significantly used to revisit different Statistical
problems. Computational experimentations with generated and real data sets show
that FMIOM is comparable to and often outperforms current leading methods. The
results illustrate potential for significant impact of Fuzzy Integer
Optimization methods on Computational Statistics and Data Mining.
",Fuzzy Mixed Integer Optimization Model for Regression Approach
"  Multiple Air Vehicles (AVs) to prosecute geographically dispersed targets is
an important optimization problem. Associated multiple tasks viz., target
classification, attack and verification are successively performed on each
target. The optimal minimum time performance of these tasks requires
cooperation among vehicles such that critical time constraints are satisfied
i.e. target must be classified before it can be attacked and AV is sent to
target area to verify its destruction after target has been attacked. Here,
optimal task scheduling problem from Indian Air Force is formulated as Fuzzy
Mixed Integer Linear Programming (FMILP) problem. The solution assigns all
tasks to vehicles and performs scheduling in an optimal manner including
scheduled staged departure times. Coupled tasks involving time and task order
constraints are addressed. When AVs have sufficient endurance, existence of
optimal solution is guaranteed. The solution developed can serve as an
effective heuristic for different categories of AV optimization problems.
","Fuzzy Mixed Integer Linear Programming for Air Vehicles Operations
  Optimization"
"  This paper suggests a forward-pruning technique for computer chess that uses
'Move Tables', which are like Transposition Tables, but for moves not
positions. They use an efficient memory structure and has put the design into
the context of long and short-term memories. The long-term memory updates a
play path with weight reinforcement, while the short-term memory can be
immediately added or removed. With this, 'long branches' can play a short path,
before returning to a full search at the resulting leaf nodes. Re-using an
earlier search path allows the tree to be forward-pruned, which is known to be
dangerous, because it removes part of the search process. Additional checks are
therefore made and moves can even be re-added when the search result is
unsatisfactory. Automatic feature analysis is now central to the algorithm,
where key squares and related squares can be generated automatically and used
to guide the search process. Using this analysis, if a search result is
inferior, it can re-insert un-played moves that cover these key squares only.
On the tactical side, a type of move that the forward-pruning will fail on is
recognised and a pattern-based solution to that problem is suggested. This has
completed the theory of an earlier paper and resulted in a more human-like
approach to searching for a chess move. Tests demonstrate that the obvious
blunders associated with forward pruning are no longer present and that it can
compete at the top level with regard to playing strength.
",A More Human Way to Play Computer Chess
"  The theory of belief functions manages uncertainty and also proposes a set of
combination rules to aggregate opinions of several sources. Some combination
rules mix evidential information where sources are independent; other rules are
suited to combine evidential information held by dependent sources. In this
paper we have two main contributions: First we suggest a method to quantify
sources' degree of independence that may guide the choice of the more
appropriate set of combination rules. Second, we propose a new combination rule
that takes consideration of sources' degree of independence. The proposed
method is illustrated on generated mass functions.
",Combining partially independent belief functions
"  There is a generic way to add any new feature to a system. It involves 1)
identifying the basic units which build up the system and 2) introducing the
new feature to each of these basic units.
  In the case where the system is argumentation and the feature is
probabilistic we have the following. The basic units are: a. the nature of the
arguments involved; b. the membership relation in the set S of arguments; c.
the attack relation; and d. the choice of extensions.
  Generically to add a new aspect (probabilistic, or fuzzy, or temporal, etc)
to an argumentation network <S,R> can be done by adding this feature to each
component a-d. This is a brute-force method and may yield a non-intuitive or
meaningful result.
  A better way is to meaningfully translate the object system into another
target system which does have the aspect required and then let the target
system endow the aspect on the initial system. In our case we translate
argumentation into classical propositional logic and get probabilistic
argumentation from the translation.
  Of course what we get depends on how we translate.
  In fact, in this paper we introduce probabilistic semantics to abstract
argumentation theory based on the equational approach to argumentation
networks. We then compare our semantics with existing proposals in the
literature including the approaches by M. Thimm and by A. Hunter. Our
methodology in general is discussed in the conclusion.
",Probabilistic Argumentation. An Equational Approach
"  In this paper, we propose an algebraic similarity measure {\sigma}BS (BS
stands for BitSim) for assigning semantic similarity score to concept
definitions in ALCH+ an expressive fragment of Description Logics (DL). We
define an algebraic interpretation function, I_B, that maps a concept
definition to a unique string ({\omega}_B) called bit-code) over an alphabet
{\Sigma}_B of 11 symbols belonging to L_B - the language over P B. IB has
semantic correspondence with conventional model-theoretic interpretation of DL.
We then define {\sigma}_BS on L_B. A detailed analysis of I_B and {\sigma}_BS
has been given.
",BitSim: An Algebraic Similarity Measure for Description Logics Concepts
"  Higher-level cognition includes logical reasoning and the ability of question
answering with common sense. The RatioLog project addresses the problem of
rational reasoning in deep question answering by methods from automated
deduction and cognitive computing. In a first phase, we combine techniques from
information retrieval and machine learning to find appropriate answer
candidates from the huge amount of text in the German version of the free
encyclopedia ""Wikipedia"". In a second phase, an automated theorem prover tries
to verify the answer candidates on the basis of their logical representations.
In a third phase - because the knowledge may be incomplete and inconsistent -,
we consider extensions of logical reasoning to improve the results. In this
context, we work toward the application of techniques from human reasoning: We
employ defeasible reasoning to compare the answers w.r.t. specificity, deontic
logic, normative reasoning, and model construction. Moreover, we use integrated
case-based reasoning and machine learning techniques on the basis of the
semantic structure of the questions and answer candidates to learn giving the
right answers.
",The RatioLog Project: Rational Extensions of Logical Reasoning
"  Process mining is a technique that performs an automatic analysis of business
processes from a log of events with the promise of understanding how processes
are executed in an organisation.
  Several models have been proposed to address this problem, however, here we
propose a different approach to deal with uncertainty. By uncertainty, we mean
estimating the probability of some sequence of tasks occurring in a business
process, given that only a subset of tasks may be observable.
  In this sense, this work proposes a new approach to perform process mining
using Bayesian Networks. These structures can take into account the probability
of a task being present or absent in the business process. Moreover, Bayesian
Networks are able to automatically learn these probabilities through mechanisms
such as the maximum likelihood estimate and EM clustering.
  Experiments made over a Loan Application Case study suggest that Bayesian
Networks are adequate structures for process mining and enable a deep analysis
of the business process model that can be used to answer queries about that
process.
",An Experiment on Using Bayesian Networks for Process Mining
"  This note revisits the concepts of task and difficulty. The notion of
cognitive task and its use for the evaluation of intelligent systems is still
replete with issues. The view of tasks as MDP in the context of reinforcement
learning has been especially useful for the formalisation of learning tasks.
However, this alternate interaction does not accommodate well for some other
tasks that are usual in artificial intelligence and, most especially, in animal
and human evaluation. In particular, we want to have a more general account of
episodes, rewards and responses, and, most especially, the computational
complexity of the algorithm behind an agent solving a task. This is crucial for
the determination of the difficulty of a task as the (logarithm of the) number
of computational steps required to acquire an acceptable policy for the task,
which includes the exploration of policies and their verification. We introduce
a notion of asynchronous-time stochastic tasks. Based on this interpretation,
we can see what task difficulty is, what instance difficulty is (relative to a
task) and also what task compositions and decompositions are.
","Universal Psychometrics Tasks: difficulty, composition and decomposition"
"  In this paper, we will expound upon the concepts proffered in [1], where we
proposed an information theoretic approach to intelligence in the computational
sense. We will examine data and meme aggregation, and study the effect of
limited resources on the resulting meme amplitudes.
",The Computational Theory of Intelligence: Data Aggregation
"  In the a posteriori approach of multiobjective optimization the Pareto front
is approximated by a finite set of solutions in the objective space. The
quality of the approximation can be measured by different indicators that take
into account the approximation's closeness to the Pareto front and its
distribution along the Pareto front. In particular, the averaged Hausdorff
indicator prefers an almost uniform distribution. An observed drawback of
multiobjective estimation of distribution algorithms (MEDAs) is that - as
common for randomized metaheuristics - the final population usually is not
uniformly distributed along the Pareto front. Therefore, we propose a
postprocessing strategy which consists of applying the averaged Hausdorff
indicator to the complete archive of generated solutions after optimization in
order to select a uniformly distributed subset of nondominated solutions from
the archive. In this paper, we put forward a strategy for extracting the above
described subset. The effectiveness of the proposal is contrasted in a series
of experiments that involve different MEDAs and filtering techniques.
","Averaged Hausdorff Approximations of Pareto Fronts based on
  Multiobjective Estimation of Distribution Algorithms"
"  This article introduces a modified simulated annealing optimization approach
for automatically determining optimal energy management strategies in
grid-connected, storage-augmented, photovoltaics-supplied prosumer buildings
and neighbourhoods based on user-specific goals. For evaluating the modified
simulated annealing optimizer, a number of test scenarios in the field of
energy self-consumption maximization are defined and results are compared to a
gradient descent and a total state space search approach. The benchmarking
against these two reference methods demonstrates that the modified simulated
annealing approach is able to find significantly better solutions than the
gradient descent algorithm - being equal or very close to the global optimum -
with significantly less computational effort and processing time than the total
state space search approach.
","Energy Management in Storage-Augmented, Grid-Connected Prosumer
  Buildings and Neighbourhoods Using a Modified Simulated Annealing
  Optimization"
"  This paper recalls the definition of consistency for pairwise comparison
matrices and briefly presents the concept of inconsistency index in connection
to other aspects of the theory of pairwise comparisons. By commenting on a
recent contribution by Koczkodaj and Szwarc, it will be shown that the
discussion on inconsistency indices is far from being over, and the ground is
still fertile for debates.
","Recent advances on inconsistency indices for pairwise comparisons - a
  commentary"
"  An intelligent version of the sliding-puzzle game is developed using the new
Go programming language, which uses a concurrent version of the A* Informed
Search Algorithm to power solver-bot that runs in the background. The game runs
in computer system's terminals. Mainly, it was developed for UNIX-type systems
but it works pretty well in nearly all the operating systems because of
cross-platform compatibility of the programming language used. The game uses
language's concurrency primitives to simplify most of the hefty parts of the
game. A real-time notification delivery architecture is developed using
language's built-in concurrency support, which performs similar to event based
context aware invocations like we see on the web platform.
","Implementing an intelligent version of the classical sliding-puzzle game
  for unix terminals using Golang's concurrency primitives"
"  We introduce an approach to discovery informatics that uses so called
knowledge graphs as the essential representation structure. Knowledge graph is
an umbrella term that subsumes various approaches to tractable representation
of large volumes of loosely structured knowledge in a graph form. It has been
used primarily in the Web and Linked Open Data contexts, but is applicable to
any other area dealing with knowledge representation. In the perspective of our
approach motivated by the challenges of discovery informatics, knowledge graphs
correspond to hypotheses. We present a framework for formalising so called
hypothesis virtues within knowledge graphs. The framework is based on a classic
work in philosophy of science, and naturally progresses from mostly informative
foundational notions to actionable specifications of measures corresponding to
particular virtues. These measures can consequently be used to determine
refined sub-sets of knowledge graphs that have large relative potential for
making discoveries. We validate the proposed framework by experiments in
literature-based discovery. The experiments have demonstrated the utility of
our work and its superiority w.r.t. related approaches.
","Formalising Hypothesis Virtues in Knowledge Graphs: A General
  Theoretical Framework and its Validation in Literature-Based Discovery
  Experiments"
"  In practical situations, it is of interest to investigate computing
approximations of sets as an important step of knowledge reduction of dynamic
covering decision information systems. In this paper, we present incremental
approaches to computing the type-1 and type-2 characteristic matrices of
dynamic coverings whose cardinalities increase with immigration of more
objects. We also present the incremental algorithms of computing the second and
sixth lower and upper approximations of sets in dynamic covering approximation
spaces.
","Knowledge reduction of dynamic covering decision information systems
  with immigration of more objects"
"  Linguistic large-scale group decision making (LGDM) problems are more and
more common nowadays. In such problems a large group of decision makers are
involved in the decision process and elicit linguistic information that are
usually assessed in different linguistic scales with diverse granularity
because of decision makers' distinct knowledge and background. To keep maximum
information in initial stages of the linguistic LGDM problems, the use of
multi-granular linguistic distribution assessments seems a suitable choice,
however to manage such multigranular linguistic distribution assessments, it is
necessary the development of a new linguistic computational approach. In this
paper it is proposed a novel computational model based on the use of extended
linguistic hierarchies, which not only can be used to operate with
multi-granular linguistic distribution assessments, but also can provide
interpretable linguistic results to decision makers. Based on this new
linguistic computational model, an approach to linguistic large-scale
multi-attribute group decision making is proposed and applied to a talent
selection process in universities.
","Managing Multi-Granular Linguistic Distribution Assessments in
  Large-Scale Multi-Attribute Group Decision Making"
"  Relax, Compensate and then Recover (RCR) is a paradigm for approximate
inference in probabilistic graphical models that has previously provided
theoretical and practical insights on iterative belief propagation and some of
its generalizations. In this paper, we characterize the technique of dual
decomposition in the terms of RCR, viewing it as a specific way to compensate
for relaxed equivalence constraints. Among other insights gathered from this
perspective, we propose novel heuristics for recovering relaxed equivalence
constraints with the goal of incrementally tightening dual decomposition
approximations, all the way to reaching exact solutions. We also show
empirically that recovering equivalence constraints can sometimes tighten the
corresponding approximation (and obtaining exact results), without increasing
much the complexity of inference.
","Dual Decomposition from the Perspective of Relax, Compensate and then
  Recover"
"  This paper presents two variants of penta-valued representation for
neutrosophic entropy. The first is an extension of Kaufmann's formula and the
second is an extension of Kosko's formula.
  Based on the primary three-valued information represented by the degree of
truth, degree of falsity and degree of neutrality there are built some
penta-valued representations that better highlights some specific features of
neutrosophic entropy. Thus, we highlight five features of neutrosophic
uncertainty such as ambiguity, ignorance, contradiction, neutrality and
saturation. These five features are supplemented until a seven partition of
unity by adding two features of neutrosophic certainty such as truth and
falsity.
  The paper also presents the particular forms of neutrosophic entropy obtained
in the case of bifuzzy representations, intuitionistic fuzzy representations,
paraconsistent fuzzy representations and finally the case of fuzzy
representations.
",The Neutrosophic Entropy and its Five Components
"  Path finding algorithm addresses problem of finding shortest path from source
to destination avoiding obstacles. There exist various search algorithms namely
A*, Dijkstra's and ant colony optimization. Unlike most path finding algorithms
which require destination co-ordinates to compute path, the proposed algorithm
comprises of a new method which finds path using backtracking without requiring
destination co-ordinates. Moreover, in existing path finding algorithm, the
number of iterations required to find path is large. Hence, to overcome this,
an algorithm is proposed which reduces number of iterations required to
traverse the path. The proposed algorithm is hybrid of backtracking and a new
technique(modified 8- neighbor approach). The proposed algorithm can become
essential part in location based, network, gaming applications. grid traversal,
navigation, gaming applications, mobile robot and Artificial Intelligence.
",An Optimized Hybrid Approach for Path Finding
"  Whether the machine intelligence can surpass the human intelligence is a
controversial issue. On the basis of traditional IQ, this article presents the
Universal IQ test method suitable for both the machine intelligence and the
human intelligence. With the method, machine and human intelligences were
divided into 4 major categories and 15 subcategories. A total of 50 search
engines across the world and 150 persons at different ages were subject to the
relevant test. And then, the Universal IQ ranking list of 2014 for the test
objects was obtained.
","Quantitative Analysis of Whether Machine Intelligence Can Surpass Human
  Intelligence"
"  We continue our analysis of volume and energy measures that are appropriate
for quantifying inductive inference systems. We extend logical depth and
conceptual jump size measures in AIT to stochastic problems, and physical
measures that involve volume and energy. We introduce a graphical model of
computational complexity that we believe to be appropriate for intelligent
machines. We show several asymptotic relations between energy, logical depth
and volume of computation for inductive inference. In particular, we arrive at
a ""black-hole equation"" of inductive inference, which relates energy, volume,
space, and algorithmic information for an optimal inductive inference solution.
We introduce energy-bounded algorithmic entropy. We briefly apply our ideas to
the physical limits of intelligent computation in our universe.
","Ultimate Intelligence Part II: Physical Measure and Complexity of
  Intelligence"
"  Each individual handles many tasks of finding the most profitable option from
a set of options that stochastically provide rewards. Our society comprises a
collection of such individuals, and the society is expected to maximise the
total rewards, while the individuals compete for common rewards. Such
collective decision making is formulated as the `competitive multi-armed bandit
problem (CBP)', requiring a huge computational cost. Herein, we demonstrate a
prototype of an analog computer that efficiently solves CBPs by exploiting the
physical dynamics of numerous fluids in coupled cylinders. This device enables
the maximisation of the total rewards for the society without paying the
conventionally required computational cost; this is because the fluids estimate
the reward probabilities of the options for the exploitation of past knowledge
and generate random fluctuations for the exploration of new knowledge. Our
results suggest that to optimise the social rewards, the utilisation of
fluid-derived natural fluctuations is more advantageous than applying
artificial external fluctuations. Our analog computing scheme is expected to
trigger further studies for harnessing the huge computational power of natural
phenomena for resolving a wide variety of complex problems in modern
information society.
","Harnessing Natural Fluctuations: Analogue Computer for Efficient
  Socially Maximal Decision Making"
"  Fuzzy Geographically Weighted Clustering (FGWC) is considered as a suitable
tool for the analysis of geo-demographic data that assists the provision and
planning of products and services to local people. Context variables were
attached to FGWC in order to accelerate the computing speed of the algorithm
and to focus the results on the domain of interests. Nonetheless, the
determination of exact, crisp values of the context variable is a hard task. In
this paper, we propose two novel methods using fuzzy approaches for that
determination. A numerical example is given to illustrate the uses of the
proposed methods.
","Fuzzy approaches to context variable in fuzzy geographically weighted
  clustering"
"  Ensuring that autonomous systems work ethically is both complex and
difficult. However, the idea of having an additional `governor' that assesses
options the system has, and prunes them to select the most ethical choices is
well understood. Recent work has produced such a governor consisting of a
`consequence engine' that assesses the likely future outcomes of actions then
applies a Safety/Ethical logic to select actions. Although this is appealing,
it is impossible to be certain that the most ethical options are actually
taken. In this paper we extend and apply a well-known agent verification
approach to our consequence engine, allowing us to verify the correctness of
its ethical decision-making.
",Towards Verifiably Ethical Robot Behaviour
"  In the classic AGM belief revision theory, beliefs are static and do not
change their own shape. For instance, if p is accepted by a rational agent, it
will remain p to the agent. But such rarely happens to us. Often, when we
accept some information p, what is actually accepted is not the whole p, but
only a portion of it; not necessarily because we select the portion but because
p must be perceived. Only the perceived p is accepted; and the perception is
subject to what we already believe (know). What may, however, happen to the
rest of p that initially escaped our attention? In this work we argue that the
invisible part is also accepted to the agent, if only unconsciously. Hence some
parts of p are accepted as visible, while some other parts as latent, beliefs.
The division is not static. As the set of beliefs changes, what were hidden may
become visible. We present a perception-based belief theory that incorporates
latent beliefs.
",How do you revise your belief set with %$;@*?
"  A key problem in the application of first-order probabilistic methods is the
enormous size of graphical models they imply. The size results from the
possible worlds that can be generated by a domain of objects and relations. One
of the reasons for this explosion is that so far the approaches do not
sufficiently exploit the structure and similarity of possible worlds in order
to encode the models more compactly. We propose fuzzy inference in Markov logic
networks, which enables the use of taxonomic knowledge as a source of imposing
structure onto possible worlds. We show that by exploiting this structure,
probability distributions can be represented more compactly and that the
reasoning systems become capable of reasoning about concepts not contained in
the probabilistic knowledge base.
","Reasoning about Unmodelled Concepts - Incorporating Class Taxonomies in
  Probabilistic Relational Models"
"  This paper critically assesses the anti-functionalist stance on consciousness
adopted by certain advocates of integrated information theory (IIT), a
corollary of which is that human-level artificial intelligence implemented on
conventional computing hardware is necessarily not conscious. The critique
draws on variations of a well-known gradual neuronal replacement thought
experiment, as well as bringing out tensions in IIT's treatment of
self-knowledge. The aim, though, is neither to reject IIT outright nor to
champion functionalism in particular. Rather, it is suggested that both ideas
have something to offer a scientific understanding of consciousness, as long as
they are not dressed up as solutions to illusory metaphysical problems. As for
human-level AI, we must await its development before we can decide whether or
not to ascribe consciousness to it.
",Ascribing Consciousness to Artificial Intelligence
"  Constraint programming is a family of techniques for solving combinatorial
problems, where the problem is modelled as a set of decision variables
(typically with finite domains) and a set of constraints that express relations
among the decision variables. One key concept in constraint programming is
propagation: reasoning on a constraint or set of constraints to derive new
facts, typically to remove values from the domains of decision variables.
Specialised propagation algorithms (propagators) exist for many classes of
constraints.
  The concept of support is pervasive in the design of propagators.
Traditionally, when a domain value ceases to have support, it may be removed
because it takes part in no solutions. Arc-consistency algorithms such as
AC2001 make use of support in the form of a single domain value. GAC algorithms
such as GAC-Schema use a tuple of values to support each literal. We generalize
these notions of support in two ways. First, we allow a set of tuples to act as
support. Second, the supported object is generalized from a set of literals
(GAC-Schema) to an entire constraint or any part of it.
  We design a methodology for developing correct propagators using generalized
support. A constraint is expressed as a family of support properties, which may
be proven correct against the formal semantics of the constraint. Using
Curry-Howard isomorphism to interpret constructive proofs as programs, we show
how to derive correct propagators from the constructive proofs of the support
properties. The framework is carefully designed to allow efficient algorithms
to be produced. Derived algorithms may make use of dynamic literal triggers or
watched literals for efficiency. Finally, two case studies of deriving
efficient algorithms are given.
",Generalized Support and Formal Development of Constraint Propagators
"  CP-nets represent the dominant existing framework for expressing qualitative
conditional preferences between alternatives, and are used in a variety of
areas including constraint solving. Over the last fifteen years, a significant
literature has developed exploring semantics, algorithms, implementation and
use of CP-nets. This paper introduces a comprehensive new framework for
conditional preferences: logical conditional preference theories (LCP
theories). To express preferences, the user specifies arbitrary (constraint)
Datalog programs over a binary ordering relation on outcomes. We show how LCP
theories unify and generalize existing conditional preference proposals, and
leverage the rich semantic, algorithmic and implementation frameworks of
Datalog.
",Logical Conditional Preference Theories
"  How should we gather information in a network, where each node's visibility
is limited to its local neighborhood? This problem arises in numerous
real-world applications, such as surveying and task routing in social networks,
team formation in collaborative networks and experimental design with
dependency constraints. Often the informativeness of a set of nodes can be
quantified via a submodular utility function. Existing approaches for
submodular optimization, however, require that the set of all nodes that can be
selected is known ahead of time, which is often unrealistic. In contrast, we
propose a novel model where we start our exploration from an initial node, and
new nodes become visible and available for selection only once one of their
neighbors has been chosen. We then present a general algorithm NetExp for this
problem, and provide theoretical bounds on its performance dependent on
structural properties of the underlying network. We evaluate our methodology on
various simulated problem instances as well as on data collected from social
question answering system deployed within a large enterprise.
",Information Gathering in Networks via Active Exploration
"  We study confidentiality enforcement in ontologies under the Controlled Query
Evaluation framework, where a policy specifies the sensitive information and a
censor ensures that query answers that may compromise the policy are not
returned. We focus on censors that ensure confidentiality while maximising
information access, and consider both Datalog and the OWL 2 profiles as
ontology languages.
",Controlled Query Evaluation for Datalog and OWL 2 Profile Ontologies
"  Multi-context systems (MCS) presented by Brewka and Eiter can be considered
as a promising way to interlink decentralized and heterogeneous knowledge
contexts. In this paper, we propose preferential multi-context systems (PMCS),
which provide a framework for incorporating a total preorder relation over
contexts in a multi-context system. In a given PMCS, its contexts are divided
into several parts according to the total preorder relation over them,
moreover, only information flows from a context to ones of the same part or
less preferred parts are allowed to occur. As such, the first $l$ preferred
parts of an PMCS always fully capture the information exchange between contexts
of these parts, and then compose another meaningful PMCS, termed the
$l$-section of that PMCS. We generalize the equilibrium semantics for an MCS to
the (maximal) $l_{\leq}$-equilibrium which represents belief states at least
acceptable for the $l$-section of an PMCS. We also investigate inconsistency
analysis in PMCS and related computational complexity issues.
",Preferential Multi-Context Systems
"  We study instantiated abstract argumentation frames of the form $(S,R,I)$,
where $(S,R)$ is an abstract argumentation frame and where the arguments $x$ of
$S$ are instantiated by $I(x)$ as well formed formulas of a well known logic,
for example as Boolean formulas or as predicate logic formulas or as modal
logic formulas. We use the method of conceptual analysis to derive the
properties of our proposed system. We seek to define the notion of complete
extensions for such systems and provide algorithms for finding such extensions.
We further develop a theory of instantiation in the abstract, using the
framework of Boolean attack formations and of conjunctive and disjunctive
attacks. We discuss applications and compare critically with the existing
related literature.
",Theory of Semi-Instantiation in Abstract Argumentation
"  This paper addresses two classes of different, yet interrelated optimization
problems. The first class of problems involves a robot that must locate a
hidden target in an environment that consists of a set of concurrent rays. The
second class pertains to the design of interruptible algorithms by means of a
schedule of contract algorithms. We study several variants of these families of
problems, such as searching and scheduling with probabilistic considerations,
redundancy and fault-tolerance issues, randomized strategies, and trade-offs
between performance and preemptions. For many of these problems we present the
first known results that apply to multi-ray and multi-problem domains. Our
objective is to demonstrate that several well-motivated settings can be
addressed using the same underlying approach.
","Further Connections Between Contract-Scheduling and Ray-Searching
  Problems"
"  In this paper, we present a probabilistic framework for goal-driven spoken
dialog systems. A new dynamic stochastic state (DS-state) is then defined to
characterize the goal set of a dialog state at different stages of the dialog
process. Furthermore, an entropy minimization dialog management(EMDM) strategy
is also proposed to combine with the DS-states to facilitate a robust and
efficient solution in reaching a user's goals. A Song-On-Demand task, with a
total of 38117 songs and 12 attributes corresponding to each song, is used to
test the performance of the proposed approach. In an ideal simulation, assuming
no errors, the EMDM strategy is the most efficient goal-seeking method among
all tested approaches, returning the correct song within 3.3 dialog turns on
average. Furthermore, in a practical scenario, with top five candidates to
handle the unavoidable automatic speech recognition (ASR) and natural language
understanding (NLU) errors, the results show that only 61.7\% of the dialog
goals can be successfully obtained in 6.23 dialog turns on average when random
questions are asked by the system, whereas if the proposed DS-states are
updated with the top 5 candidates from the SLU output using the proposed EMDM
strategy executed at every DS-state, then a 86.7\% dialog success rate can be
accomplished effectively within 5.17 dialog turns on average. We also
demonstrate that entropy-based DM strategies are more efficient than
non-entropy based DM. Moreover, using the goal set distributions in EMDM, the
results are better than those without them, such as in sate-of-the-art database
summary DM.
","A Probabilistic Framework for Representing Dialog Systems and
  Entropy-Based Dialog Management through Dynamic Stochastic State Evolution"
"  Hierarchies of concepts are useful in many applications from navigation to
organization of objects. Usually, a hierarchy is created in a centralized
manner by employing a group of domain experts, a time-consuming and expensive
process. The experts often design one single hierarchy to best explain the
semantic relationships among the concepts, and ignore the natural uncertainty
that may exist in the process. In this paper, we propose a crowdsourcing system
to build a hierarchy and furthermore capture the underlying uncertainty. Our
system maintains a distribution over possible hierarchies and actively selects
questions to ask using an information gain criterion. We evaluate our
methodology on simulated data and on a set of real world application domains.
Experimental results show that our system is robust to noise, efficient in
picking questions, cost-effective and builds high quality hierarchies.
",Building Hierarchies of Concepts via Crowdsourcing
"  We consider existential rules (aka Datalog+) as a formalism for specifying
ontologies. In recent years, many classes of existential rules have been
exhibited for which conjunctive query (CQ) entailment is decidable. However,
most of these classes cannot express transitivity of binary relations, a
frequently used modelling construct. In this paper, we address the issue of
whether transitivity can be safely combined with decidable classes of
existential rules.
  First, we prove that transitivity is incompatible with one of the simplest
decidable classes, namely aGRD (acyclic graph of rule dependencies), which
clarifies the landscape of `finite expansion sets' of rules.
  Second, we show that transitivity can be safely added to linear rules (a
subclass of guarded rules, which generalizes the description logic DL-Lite-R)
in the case of atomic CQs, and also for general CQs if we place a minor
syntactic restriction on the rule set. This is shown by means of a novel query
rewriting algorithm that is specially tailored to handle transitivity rules.
  Third, for the identified decidable cases, we pinpoint the combined and data
complexities of query entailment.
",Combining Existential Rules and Transitivity: Next Steps
"  Sequential pattern mining under constraints is a challenging data mining
task. Many efficient ad hoc methods have been developed for mining sequential
patterns, but they are all suffering from a lack of genericity. Recent works
have investigated Constraint Programming (CP) methods, but they are not still
effective because of their encoding. In this paper, we propose a global
constraint based on the projected databases principle which remedies to this
drawback. Experiments show that our approach clearly outperforms CP approaches
and competes well with ad hoc methods on large datasets.
",Prefix-Projection Global Constraint for Sequential Pattern Mining
"  Particle Swarm Optimization (PSO) is a nature-inspired meta-heuristic for
solving continuous optimization problems. In the literature, the potential of
the particles of swarm has been used to show that slightly modified PSO
guarantees convergence to local optima. Here we show that under specific
circumstances the unmodified PSO, even with swarm parameters known (from the
literature) to be good, almost surely does not yield convergence to a local
optimum is provided. This undesirable phenomenon is called stagnation. For this
purpose, the particles' potential in each dimension is analyzed mathematically.
Additionally, some reasonable assumptions on the behavior if the particles'
potential are made. Depending on the objective function and, interestingly, the
number of particles, the potential in some dimensions may decrease much faster
than in other dimensions. Therefore, these dimensions lose relevance, i.e., the
contribution of their entries to the decisions about attractor updates becomes
insignificant and, with positive probability, they never regain relevance. If
Brownian Motion is assumed to be an approximation of the time-dependent drop of
potential, practical, i.e., large values for this probability are calculated.
Finally, on chosen multidimensional polynomials of degree two, experiments are
provided showing that the required circumstances occur quite frequently.
Furthermore, experiments are provided showing that even when the very simple
sphere function is processed the described stagnation phenomenon occurs.
Consequently, unmodified PSO does not converge to any local optimum of the
chosen functions for tested parameter settings.
","Explanation of Stagnation at Points that are not Local Optima in
  Particle Swarm Optimization by Potential Analysis"
"  To date, work on formalizing connectionist computation in a way that is at
least Turing-complete has focused on recurrent architectures and developed
equivalences to Turing machines or similar super-Turing models, which are of
more theoretical than practical significance. We instead develop connectionist
computation within the framework of information propagation networks extended
with unbounded recursion, which is related to constraint logic programming and
is more declarative than the semantics typically used in practical programming,
but is still formally known to be Turing-complete. This approach yields
contributions to the theory and practice of both connectionist computation and
programming languages. Connectionist computations are carried out in a way that
lets them communicate with, and be understood and interrogated directly in
terms of the high-level semantics of a general-purpose programming language.
Meanwhile, difficult (unbounded-dimension, NP-hard) search problems in
programming that have previously been left to the programmer to solve in a
heuristic, domain-specific way are solved uniformly a priori in a way that
approximately achieves information-theoretic limits on performance.
",FIFTH system for general-purpose connectionist computation
"  The original Halpern-Pearl definition of causality [Halpern and Pearl, 2001]
was updated in the journal version of the paper [Halpern and Pearl, 2005] to
deal with some problems pointed out by Hopkins and Pearl [2003]. Here the
definition is modified yet again, in a way that (a) leads to a simpler
definition, (b) handles the problems pointed out by Hopkins and Pearl, and many
others, (c) gives reasonable answers (that agree with those of the original and
updated definition) in the standard problematic examples of causality, and (d)
has lower complexity than either the original or updated definitions.
",A Modification of the Halpern-Pearl Definition of Causality
"  This short report describes an automated BWAPI-based script developed for
live streams of a StarCraft Brood War bot tournament, SSCAIT. The script
controls the in-game camera in order to follow the relevant events and improve
the viewer experience. We enumerate its novel features and provide a few
implementation notes.
","Automatic Observer Script for StarCraft: Brood War Bot Games (technical
  report)"
"  A long-lived autonomous agent should be able to respond online to novel
instances of tasks from a familiar domain. Acting online requires 'fast'
responses, in terms of rapid convergence, especially when the task instance has
a short duration, such as in applications involving interactions with humans.
These requirements can be problematic for many established methods for learning
to act. In domains where the agent knows that the task instance is drawn from a
family of related tasks, albeit without access to the label of any given
instance, it can choose to act through a process of policy reuse from a
library, rather than policy learning from scratch. In policy reuse, the agent
has prior knowledge of the class of tasks in the form of a library of policies
that were learnt from sample task instances during an offline training phase.
We formalise the problem of policy reuse, and present an algorithm for
efficiently responding to a novel task instance by reusing a policy from the
library of existing policies, where the choice is based on observed 'signals'
which correlate to policy performance. We achieve this by posing the problem as
a Bayesian choice problem with a corresponding notion of an optimal response,
but the computation of that response is in many cases intractable. Therefore,
to reduce the computation cost of the posterior, we follow a Bayesian
optimisation approach and define a set of policy selection functions, which
balance exploration in the policy library against exploitation of previously
tried policies, together with a model of expected performance of the policy
library on their corresponding task instances. We validate our method in
several simulated domains of interactive, short-duration episodic tasks,
showing rapid convergence in unknown task variations.
",Bayesian Policy Reuse
"  The conventional model for online planning under uncertainty assumes that an
agent can stop and plan without incurring costs for the time spent planning.
However, planning time is not free in most real-world settings. For example, an
autonomous drone is subject to nature's forces, like gravity, even while it
thinks, and must either pay a price for counteracting these forces to stay in
place, or grapple with the state change caused by acquiescing to them. Policy
optimization in these settings requires metareasoning---a process that trades
off the cost of planning and the potential policy improvement that can be
achieved. We formalize and analyze the metareasoning problem for Markov
Decision Processes (MDPs). Our work subsumes previously studied special cases
of metareasoning and shows that in the general case, metareasoning is at most
polynomially harder than solving MDPs with any given algorithm that disregards
the cost of thinking. For reasons we discuss, optimal general metareasoning
turns out to be impractical, motivating approximations. We present approximate
metareasoning procedures which rely on special properties of the BRTDP planning
algorithm and explore the effectiveness of our methods on a variety of
problems.
",Metareasoning for Planning Under Uncertainty
"  Most practitioners use a variant of the Alpha-Beta algorithm, a simple
depth-first pro- cedure, for searching minimax trees. SSS*, with its best-first
search strategy, reportedly offers the potential for more efficient search.
However, the complex formulation of the al- gorithm and its alleged excessive
memory requirements preclude its use in practice. For two decades, the search
efficiency of ""smart"" best-first SSS* has cast doubt on the effectiveness of
""dumb"" depth-first Alpha-Beta. This paper presents a simple framework for
calling Alpha-Beta that allows us to create a variety of algorithms, including
SSS* and DUAL*. In effect, we formulate a best-first algorithm using
depth-first search. Expressed in this framework SSS* is just a special case of
Alpha-Beta, solving all of the perceived drawbacks of the algorithm. In
practice, Alpha-Beta variants typically evaluate less nodes than SSS*. A new
instance of this framework, MTD(f), out-performs SSS* and NegaScout, the
Alpha-Beta variant of choice by practitioners.
",Best-First and Depth-First Minimax Search in Practice
"  Liu, et al., 2009 developed a transformation of a class of non-Gaussian
univariate distributions into Gaussian distributions. Liu and collaborators
(2012) subsequently applied the transform to search for graphical causal models
for a number of empirical data sets. To our knowledge, there has been no
published investigation by simulation of the conditions under which the
transform aids, or harms, standard graphical model search procedures. We
consider here how the transform affects the performance of two search
algorithms in particular, PC (Spirtes et al., 2000; Meek 1995) and GES (Meek
1997; Chickering 2002). We find that the transform is harmless but ineffective
for most cases but quite effective in very special cases for GES, namely, for
moderate non-Gaussianity and moderate non-linearity. For strong-linearity,
another algorithm, PC-GES (a combination of PC with GES), is equally effective.
",Effects of Nonparanormal Transform on PC and GES Search Accuracies
"  Many different approaches for solving Constraint Satisfaction Problems (CSPs)
and related Constraint Optimization Problems (COPs) exist. However, there is no
single solver (nor approach) that performs well on all classes of problems and
many portfolio approaches for selecting a suitable solver based on simple
syntactic features of the input CSP instance have been developed. In this paper
we first present a simple portfolio method for CSP based on k-nearest neighbors
method. Then, we propose a new way of using portfolio systems --- training them
shortly in the exploitation time, specifically for the set of instances to be
solved and using them on that set. Thorough evaluation has been performed and
has shown that the approach yields good results. We evaluated several machine
learning techniques for our portfolio. Due to its simplicity and efficiency,
the selected k-nearest neighbors method is especially suited for our short
training approach and it also yields the best results among the tested methods.
We also confirm that our approach yields good results on SAT domain.
",Short Portfolio Training for CSP Solving
"  Most recent MaxSAT algorithms rely on a succession of calls to a SAT solver
in order to find an optimal solution. In particular, several algorithms take
advantage of the ability of SAT solvers to identify unsatisfiable subformulas.
Usually, these MaxSAT algorithms perform better when small unsatisfiable
subformulas are found early. However, this is not the case in many problem
instances, since the whole formula is given to the SAT solver in each call. In
this paper, we propose to partition the MaxSAT formula using a resolution-based
graph representation. Partitions are then iteratively joined by using a
proximity measure extracted from the graph representation of the formula. The
algorithm ends when only one partition remains and the optimal solution is
found. Experimental results show that this new approach further enhances a
state of the art MaxSAT solver to optimally solve a larger set of industrial
problem instances.
",Exploiting Resolution-based Representations for MaxSAT Solving
"  This paper contributes a novel embedding model which measures the probability
of each belief $\langle h,r,t,m\rangle$ in a large-scale knowledge repository
via simultaneously learning distributed representations for entities ($h$ and
$t$), relations ($r$), and the words in relation mentions ($m$). It facilitates
knowledge completion by means of simple vector operations to discover new
beliefs. Given an imperfect belief, we can not only infer the missing entities,
predict the unknown relations, but also tell the plausibility of the belief,
just leveraging the learnt embeddings of remaining evidences. To demonstrate
the scalability and the effectiveness of our model, we conduct experiments on
several large-scale repositories which contain millions of beliefs from
WordNet, Freebase and NELL, and compare it with other cutting-edge approaches
via competing the performances assessed by the tasks of entity inference,
relation prediction and triplet classification with respective metrics.
Extensive experimental results show that the proposed model outperforms the
state-of-the-arts with significant improvements.
",Probabilistic Belief Embedding for Knowledge Base Completion
"  Representation determines how we can reason about a specific problem.
Sometimes one representation helps us find a proof more easily than others.
Most current automated reasoning tools focus on reasoning within one
representation. There is, therefore, a need for the development of better tools
to mechanise and automate formal and logically sound changes of representation.
  In this paper we look at examples of representational transformations in
discrete mathematics, and show how we have used Isabelle's Transfer tool to
automate the use of these transformations in proofs. We give a brief overview
of a general theory of transformations that we consider appropriate for
thinking about the matter, and we explain how it relates to the Transfer
package. We show our progress towards developing a general tactic that
incorporates the automatic search for representation within the proving
process.
",Automating change of representation for proofs in discrete mathematics
"  Large-scale controlled evacuations require emergency services to select
evacuation routes, decide departure times, and mobilize resources to issue
orders, all under strict time constraints. Existing algorithms almost always
allow for preemptive evacuation schedules, which are less desirable in
practice. This paper proposes, for the first time, a constraint-based
scheduling model that optimizes the evacuation flow rate (number of vehicles
sent at regular time intervals) and evacuation phasing of widely populated
areas, while ensuring a nonpreemptive evacuation for each residential zone. Two
optimization objectives are considered: (1) to maximize the number of evacuees
reaching safety and (2) to minimize the overall duration of the evacuation.
Preliminary results on a set of real-world instances show that the approach can
produce, within a few seconds, a non-preemptive evacuation schedule which is
either optimal or at most 6% away of the optimal preemptive solution.
","A Constraint Programming Approach for Non-Preemptive Evacuation
  Scheduling"
"  We study the relations between Multi-valued Decision Diagrams (MDD) and
tuples (i.e. elements of the Cartesian Product of variables). First, we improve
the existing methods for transforming a set of tuples, Global Cut Seeds,
sequences of tuples into MDDs. Then, we present some in-place algorithms for
adding and deleting tuples from an MDD. Next, we consider an MDD constraint
which is modified during the search by deleting some tuples. We give an
algorithm which adapts MDD-4R to these dynamic and persistent modifications.
Some experiments show that MDD constraints are competitive with Table
constraints.
","Relations between MDDs and Tuples and Dynamic Modifications of MDDs
  based constraints"
"  The UCT algorithm, which combines the UCB algorithm and Monte-Carlo Tree
Search (MCTS), is currently the most widely used variant of MCTS. Recently, a
number of investigations into applying other bandit algorithms to MCTS have
produced interesting results. In this research, we will investigate the
possibility of combining the improved UCB algorithm, proposed by Auer et al.
(2010), with MCTS. However, various characteristics and properties of the
improved UCB algorithm may not be ideal for a direct application to MCTS.
Therefore, some modifications were made to the improved UCB algorithm, making
it more suitable for the task of game tree search. The Mi-UCT algorithm is the
application of the modified UCB algorithm applied to trees. The performance of
Mi-UCT is demonstrated on the games of $9\times 9$ Go and $9\times 9$ NoGo, and
has shown to outperform the plain UCT algorithm when only a small number of
playouts are given, and rougly on the same level when more playouts are
available.
",Adapting Improved Upper Confidence Bounds for Monte-Carlo Tree Search
"  The Semantic Web is built on top of Knowledge Organization Systems (KOS)
(vocabularies, ontologies, concept schemes) that provide a structured,
interoperable and distributed access to Linked Data on the Web. The maintenance
of these KOS over time has produced a number of KOS version chains: subsequent
unique version identifiers to unique states of a KOS. However, the release of
new KOS versions pose challenges to both KOS publishers and users. For
publishers, updating a KOS is a knowledge intensive task that requires a lot of
manual effort, often implying deep deliberation on the set of changes to
introduce. For users that link their datasets to these KOS, a new version
compromises the validity of their links, often creating ramifications. In this
paper we describe a method to automatically detect which parts of a Web KOS are
likely to change in a next version, using supervised learning on past versions
in the KOS version chain. We use a set of ontology change features to model and
predict change in arbitrary Web KOS. We apply our method on 139 varied datasets
systematically retrieved from the Semantic Web, obtaining robust results at
correctly predicting change. To illustrate the accuracy, genericity and domain
independence of the method, we study the relationship between its effectiveness
and several characterizations of the evaluated datasets, finding that
predictors like the number of versions in a chain and their release frequency
have a fundamental impact in predictability of change in Web KOS. Consequently,
we argue for adopting a release early, release often philosophy in Web KOS
development cycles.
","Release Early, Release Often: Predicting Change in Versioned Knowledge
  Organization Systems on the Web"
"  This paper presents a sociocultural knowledge ontology (OntoSOC) modeling
approach. OntoSOC modeling approach is based on Engestrom Human Activity Theory
(HAT). That Theory allowed us to identify fundamental concepts and
relationships between them. The top-down precess has been used to define
differents sub-concepts. The modeled vocabulary permits us to organise data, to
facilitate information retrieval by introducing a semantic layer in social web
platform architecture, we project to implement. This platform can be considered
as a collective memory and Participative and Distributed Information System
(PDIS) which will allow Cameroonian communities to share an co-construct
knowledge on permanent organized activities.
",OntoSOC: Sociocultural Knowledge Ontology
"  The sociotechnological system is a system constituted of human individuals
and their artifacts: technological artifacts, institutions, conceptual and
representational systems, worldviews, knowledge systems, culture and the whole
biosphere as a volutionary niche. In our view the sociotechnological system as
a super-organism is shaped and determined both by the characteristics of the
agents involved and the characteristics emergent in their interactions at
multiple scales. Our approach to sociotechnological dynamics will maintain a
balance between perspectives: the individual and the collective. Accordingly,
we analyze dynamics of the Web as a sociotechnological system made of people,
computers and digital artifacts (Web pages, databases, search engines, etc.).
Making sense of the sociotechnological system while being part of it, is also a
constant interplay between pragmatic and value based approaches. The first is
focusing on the actualities of the system while the second highlights the
observer's projections. In our attempt to model sociotechnological dynamics and
envision its future, we take special care to make explicit our values as part
of the analysis. In sociotechnological systems with a high degree of
reflexivity (coupling between the perception of the system and the system's
behavior), highlighting values is of critical importance. In this essay, we
choose to see the future evolution of the web as facilitating a basic value,
that is, continuous open-ended intelligence expansion. By that we mean that we
see intelligence expansion as the determinant of the 'greater good' and 'well
being' of both of individuals and collectives at all scales. Our working
definition of intelligence here is the progressive process of sense-making of
self, other, environment and universe. Intelligence expansion, therefore, means
an increasing ability of sense-making.
",Cognitive Development of the Web
"  What is happiness for reinforcement learning agents? We seek a formal
definition satisfying a list of desiderata. Our proposed definition of
happiness is the temporal difference error, i.e. the difference between the
value of the obtained reward and observation and the agent's expectation of
this value. This definition satisfies most of our desiderata and is compatible
with empirical research on humans. We state several implications and discuss
examples.
",A Definition of Happiness for Reinforcement Learning Agents
"  We study properties of particular non-redundant sets of if-then rules
describing dependencies between graded attributes. We introduce notions of
saturation and witnessed non-redundancy of sets of graded attribute
implications are show that bases of graded attribute implications given by
systems of pseudo-intents correspond to non-redundant sets of graded attribute
implications with saturated consequents where the non-redundancy is witnessed
by antecedents of the contained graded attribute implications. We introduce an
algorithm which transforms any complete set of graded attribute implications
parameterized by globalization into a base given by pseudo-intents.
Experimental evaluation is provided to compare the method of obtaining bases
for general parameterizations by hedges with earlier graph-based approaches.
",On sets of graded attribute implications with witnessed non-redundancy
"  Nowadays, represented by Deep Learning techniques, the field of machine
learning is experiencing unprecedented prosperity and its influence is
demonstrated in academia, industry and civil society. ""Intelligent"" has become
a label which could not be neglected for most applications; celebrities and
scientists also warned that the development of full artificial intelligence may
spell the end of the human race. It seems that the answer to building a
computer system that could automatically improve with experience is right on
the next corner. While for AI and machine learning researchers, it is a
consensus that we are not anywhere near the core technique which could bring
the Terminator, Number 5 or R2D2 into real life, and there is not even a formal
definition about what is intelligence, or one of its basic properties:
Learning. Therefore, even though researchers know these concerns are not
necessary currently, there is no generalized explanation about why these
concerns are not necessary, and what properties people should take into account
that would make these concerns to be necessary. In this paper, starts from
analysing the relation between information and its representation, a necessary
condition for a model to be a learning model is proposed. This condition and
related future works could be used to verify whether a system is able to learn
or not, and enrich our understanding of learning: one important property of
Intelligence.
","What is Learning? A primary discussion about information and
  Representation"
"  This paper introduces the necessary and sufficient conditions that surrogate
functions must satisfy to properly define frontiers of non-dominated solutions
in multi-objective optimization problems. These new conditions work directly on
the objective space, thus being agnostic about how the solutions are evaluated.
Therefore, real objectives or user-designed objectives' surrogates are allowed,
opening the possibility of linking independent objective surrogates. To
illustrate the practical consequences of adopting the proposed conditions, we
use Gaussian processes as surrogates endowed with monotonicity soft constraints
and with an adjustable degree of flexibility, and compare them to regular
Gaussian processes and to a frontier surrogate method in the literature that is
the closest to the method proposed in this paper. Results show that the
necessary and sufficient conditions proposed here are finely managed by the
constrained Gaussian process, guiding to high-quality surrogates capable of
suitably synthesizing an approximation to the Pareto frontier in challenging
instances of multi-objective optimization, while an existing approach that does
not take the theory proposed in consideration defines surrogates which greatly
violate the conditions to describe a valid frontier.
","Necessary and Sufficient Conditions for Surrogate Functions of Pareto
  Frontiers and Their Synthesis Using Gaussian Processes"
"  This paper describes a new method for reducing the error in a classifier. It
uses an error correction update that includes the very simple rule of either
adding or subtracting the error adjustment, based on whether the variable value
is currently larger or smaller than the desired value. While a traditional
neuron would sum the inputs together and then apply a function to the total,
this new method can change the function decision for each input value. This
gives added flexibility to the convergence procedure, where through a series of
transpositions, variables that are far away can continue towards the desired
value, whereas variables that are originally much closer can oscillate from one
side to the other. Tests show that the method can successfully classify some
benchmark datasets. It can also work in a batch mode, with reduced training
times and can be used as part of a neural network architecture. Some
comparisons with an earlier wave shape paper are also made.
",A New Oscillating-Error Technique for Classifiers
"  Systems for symbolic event recognition accept as input a stream of
time-stamped events from sensors and other computational devices, and seek to
identify high-level composite events, collections of events that satisfy some
pattern. RTEC is an Event Calculus dialect with novel implementation and
'windowing' techniques that allow for efficient event recognition, scalable to
large data streams. RTEC can deal with applications where event data arrive
with a (variable) delay from, and are revised by, the underlying sources. RTEC
can update already recognised events and recognise new events when data arrive
with a delay or following data revision. Our evaluation shows that RTEC can
support real-time event recognition and is capable of meeting the performance
requirements identified in a recent survey of event processing use cases.
",Reactive Reasoning with the Event Calculus
"  The rise of smart applications has drawn interest to logical reasoning over
data streams. Recently, different query languages and stream
processing/reasoning engines were proposed in different communities. However,
due to a lack of theoretical foundations, the expressivity and semantics of
these diverse approaches are given only informally. Towards clear
specifications and means for analytic study, a formal framework is needed to
define their semantics in precise terms. To this end, we present a first step
towards an ideal semantics that allows for exact descriptions and comparisons
of stream reasoning systems.
",Towards Ideal Semantics for Analyzing Stream Reasoning
"  We show in this paper how managed multi-context systems (mMCSs) can be turned
into a reactive formalism suitable for continuous reasoning in dynamic
environments. We extend mMCSs with (abstract) sensors and define the notion of
a run of the extended systems. We then show how typical problems arising in
online reasoning can be addressed: handling potentially inconsistent sensor
input, modeling intelligent forms of forgetting, selective integration of
knowledge, and controlling the reasoning effort spent by contexts, like setting
contexts to an idle mode. We also investigate the complexity of some important
related decision problems and discuss different design choices which are given
to the knowledge engineer.
",Multi-Context Systems for Reactive Reasoning in Dynamic Environments
"  In this work, we present asynchronous multi-context systems (aMCSs), which
provide a framework for loosely coupling different knowledge representation
formalisms that allows for online reasoning in a dynamic environment. Systems
of this kind may interact with the outside world via input and output streams
and may therefore react to a continuous flow of external information. In
contrast to recent proposals, contexts in an aMCS communicate with each other
in an asynchronous way which fits the needs of many application domains and is
beneficial for scalability. The federal semantics of aMCSs renders our
framework an integration approach rather than a knowledge representation
formalism itself. We illustrate the introduced concepts by means of an example
scenario dealing with rescue services. In addition, we compare aMCSs to
reactive multi-context systems and describe how to simulate the latter with our
novel approach.
",Asynchronous Multi-Context Systems
"  Managed Multi-Context Systems (mMCSs) provide a general framework for
integrating knowledge represented in heterogeneous KR formalisms. However,
mMCSs are essentially static as they were not designed to run in a dynamic
scenario. Some recent approaches, among them evolving Multi-Context Systems
(eMCSs), extend mMCSs by allowing not only the ability to integrate knowledge
represented in heterogeneous KR formalisms, but at the same time to both react
to, and reason in the presence of commonly temporary dynamic observations, and
evolve by incorporating new knowledge. The notion of minimal change is a
central notion in dynamic scenarios, specially in those that admit several
possible alternative evolutions. Since eMCSs combine heterogeneous KR
formalisms, each of which may require different notions of minimal change, the
study of minimal change in eMCSs is an interesting and highly non-trivial
problem. In this paper, we study the notion of minimal change in eMCSs, and
discuss some alternative minimal change criteria.
",On Minimal Change in Evolving Multi-Context Systems (Preliminary Report)
"  We present initial ideas for a programming paradigm based on simulation that
is targeted towards applications of artificial intelligence (AI). The approach
aims at integrating techniques from different areas of AI and is based on the
idea that simulated entities may freely exchange data and behavioural patterns.
We define basic notions of a simulation-based programming paradigm and show how
it can be used for implementing AI applications.
",Towards a Simulation-Based Programming Paradigm for AI applications
"  We investigate the problem of inconsistency measurement on large knowledge
bases by considering stream-based inconsistency measurement, i.e., we
investigate inconsistency measures that cannot consider a knowledge base as a
whole but process it within a stream. For that, we present, first, a novel
inconsistency measure that is apt to be applied to the streaming case and,
second, stream-based approximations for the new and some existing inconsistency
measures. We conduct an extensive empirical analysis on the behavior of these
inconsistency measures on large knowledge bases, in terms of runtime, accuracy,
and scalability. We conclude that for two of these measures, the approximation
of the new inconsistency measure and an approximation of the contension
inconsistency measure, large-scale inconsistency measurement is feasible.
",Towards Large-scale Inconsistency Measurement
"  Managed Multi-Context Systems (mMCSs) provide a general framework for
integrating knowledge represented in heterogeneous KR formalisms. Recently,
evolving Multi-Context Systems (eMCSs) have been introduced as an extension of
mMCSs that add the ability to both react to, and reason in the presence of
commonly temporary dynamic observations, and evolve by incorporating new
knowledge. However, the general complexity of such an expressive formalism may
simply be too high in cases where huge amounts of information have to be
processed within a limited short amount of time, or even instantaneously. In
this paper, we investigate under which conditions eMCSs may scale in such
situations and we show that such polynomial eMCSs can be applied in a practical
use case.
",Towards Efficient Evolving Multi-Context Systems (Preliminary Report)
"  Artificial General Intelligence is a field of research aiming to distill the
principles of intelligence that operate independently of a specific problem
domain or a predefined context and utilize these principles in order to
synthesize systems capable of performing any intellectual task a human being is
capable of and eventually go beyond that. While ""narrow"" artificial
intelligence which focuses on solving specific problems such as speech
recognition, text comprehension, visual pattern recognition, robotic motion,
etc. has shown quite a few impressive breakthroughs lately, understanding
general intelligence remains elusive. In the paper we offer a novel theoretical
approach to understanding general intelligence. We start with a brief
introduction of the current conceptual approach. Our critique exposes a number
of serious limitations that are traced back to the ontological roots of the
concept of intelligence. We then propose a paradigm shift from intelligence
perceived as a competence of individual agents defined in relation to an a
priori given problem domain or a goal, to intelligence perceived as a formative
process of self-organization by which intelligent agents are individuated. We
call this process open-ended intelligence. Open-ended intelligence is developed
as an abstraction of the process of cognitive development so its application
can be extended to general agents and systems. We introduce and discuss three
facets of the idea: the philosophical concept of individuation, sense-making
and the individuation of general cognitive agents. We further show how
open-ended intelligence can be framed in terms of a distributed,
self-organizing network of interacting elements and how such process is
scalable. The framework highlights an important relation between coordination
and intelligence and a new understanding of values. We conclude with a number
of questions for future research.
",Open Ended Intelligence: The individuation of Intelligent Agents
"  The article is devoted to the problem of inconsistency in the pairwise
comparisons based prioritization methodology. The issue of ""inconsistency"" in
this context has gained much attention in recent years. The literature provides
us with a number of different ""inconsistency"" indices suggested for measuring
the inconsistency of the pairwise comparison matrix (PCM). The latter is
understood as a deviation of the PCM from the ""consistent case"" - a notion that
is formally well-defined in this theory. However the usage of the indices is
justified only by some heuristics. It is still unclear what they really
""measure"". What is even more important and still not known is the relationship
between their values and the ""consistency"" of the decision maker's judgments on
one hand, and the prioritization results upon the other. We provide examples
showing that it is necessary to distinguish between these three following
tasks: the ""measuring"" of the ""PCM inconsistency"" and the PCM-based ""measuring""
of the consistency of decision maker's judgments and, finally, the ""measuring""
of the usefulness of the PCM as a source of information for estimation of the
priority vector (PV). Next we focus on the third task, which seems to be the
most important one in Multi-Criteria Decision Making. With the help of Monte
Carlo experiments, we study the performance of various inconsistency indices as
indicators of the final PV estimation quality. The presented results allow a
deeper understanding of the information contained in these indices and help in
choosing a proper one in a given situation. They also enable us to develop a
new inconsistency characteristic and, based on it, to propose the PCM
acceptance approach that is supported by the classical statistical methodology.
","New results on inconsistency indices and their relationship with the
  quality of priority vector estimation"
"  One particular challenge in AI is the computational modelling and simulation
of creativity. Feedback and learning from experience are key aspects of the
creative process. Here we investigate how we could implement feedback in
creative systems using a social model. From the field of creative writing we
borrow the concept of a Writers Workshop as a model for learning through
feedback. The Writers Workshop encourages examination, discussion and debates
of a piece of creative work using a prescribed format of activities. We propose
a computational model of the Writers Workshop as a roadmap for incorporation of
feedback in artificial creativity systems. We argue that the Writers Workshop
setting describes the anatomy of the creative process. We support our claim
with a case study that describes how to implement the Writers Workshop model in
a computational creativity system. We present this work using patterns other
people can follow to implement similar designs in their own systems. We
conclude by discussing the broader relevance of this model to other aspects of
AI.
",Implementing feedback in creative systems: A workshop approach
"  Qsmodels is a novel application of Answer Set Programming to interactive
gaming environment. We describe a software architecture by which the behavior
of a bot acting inside the Quake 3 Arena can be controlled by a planner. The
planner is written as an Answer Set Program and is interpreted by the Smodels
solver.
",Qsmodels: ASP Planning in Interactive Gaming Environment
"  In some real world information fusion situations, time critical decisions
must be made with an incomplete information set. Belief function theories
(e.g., Dempster-Shafer theory of evidence, Transferable Belief Model) have been
shown to provide a reasonable methodology for processing or fusing the
quantitative clues or information measurements that form the incomplete
information set. For decision making, the pignistic (from the Latin pignus, a
bet) probability transform has been shown to be a good method of using Beliefs
or basic belief assignments (BBAs) to make decisions. For many systems, one
need only address the most-probable elements in the set. For some critical
systems, one must evaluate the risk of wrong decisions and establish safe
probability thresholds for decision making. This adds a greater complexity to
decision making, since one must address all elements in the set that are above
the risk decision threshold. The problem is greatly simplified if most of the
probabilities fall below this threshold. Finding a probability transform that
properly represents mixes of low- and high-probability events is essential.
This article introduces four new pignistic probability transforms with an
implementation that uses the latest values of Beliefs, Plausibilities, or BBAs
to improve the pignistic probability estimates. Some of them assign smaller
values of probabilities for smaller values of Beliefs or BBAs than the Smets
pignistic transform. They also assign higher probability values for larger
values of Beliefs or BBAs than the Smets pignistic transform. These probability
transforms will assign a value of probability that converges faster to the
values below the risk threshold. A probability information content (PIC)
variable is also introduced that assigns an information content value to any
set of probability. Four operators are defined to help simplify the
derivations.
","Pignistic Probability Transforms for Mixes of Low- and High-Probability
  Events"
"  Decision support systems (DSS) can be used to help settlement issues or
decisions that are semi-structured or structured. The method used is Fuzzy
Tsukamoto. PT Triprima Finance is a company engaged in the service sector
lending with collateral in the form of Motor Vehicle Owner Book or car (reg).
PT. Triprima Finance should consider borrowing from its customers with the
consent of the head manager. Such approval requires a long time because they
have to pass through many stages of the reporting procedure. Decision-making
activities at PT Triprima Finance carried out by the analysis process manually.
To help overcome these problems, the need for completion method in accuracy and
speed of decision making feasibility of lending. To overcome this need to
develop a new system that is a decision support system Tsukamoto fuzzy method.
is expected to facilitate kaposko to determine the decisions to be taken.
","Sistem penunjang keputusan kelayakan pemberian pinjaman dengna metode
  fuzzy tsukamoto"
"  Qualitative calculi play a central role in representing and reasoning about
qualitative spatial and temporal knowledge. This paper studies distributive
subalgebras of qualitative calculi, which are subalgebras in which (weak)
composition distributives over nonempty intersections. It has been proven for
RCC5 and RCC8 that path consistent constraint network over a distributive
subalgebra is always minimal and globally consistent (in the sense of strong
$n$-consistency) in a qualitative sense. The well-known subclass of convex
interval relations provides one such an example of distributive subalgebras.
This paper first gives a characterisation of distributive subalgebras, which
states that the intersection of a set of $n\geq 3$ relations in the subalgebra
is nonempty if and only if the intersection of every two of these relations is
nonempty. We further compute and generate all maximal distributive subalgebras
for Point Algebra, Interval Algebra, RCC5 and RCC8, Cardinal Relation Algebra,
and Rectangle Algebra. Lastly, we establish two nice properties which will play
an important role in efficient reasoning with constraint networks involving a
large number of variables.
",On Distributive Subalgebras of Qualitative Spatial and Temporal Calculi
"  We establish an equivalence between two seemingly different theories: one is
the traditional axiomatisation of incomplete preferences on horse lotteries
based on the mixture independence axiom; the other is the theory of desirable
gambles developed in the context of imprecise probability. The equivalence
allows us to revisit incomplete preferences from the viewpoint of desirability
and through the derived notion of coherent lower previsions. On this basis, we
obtain new results and insights: in particular, we show that the theory of
incomplete preferences can be developed assuming only the existence of a worst
act---no best act is needed---, and that a weakened Archimedean axiom suffices
too; this axiom allows us also to address some controversy about the regularity
assumption (that probabilities should be positive---they need not), which
enables us also to deal with uncountable possibility spaces; we show that it is
always possible to extend in a minimal way a preference relation to one with a
worst act, and yet the resulting relation is never Archimedean, except in a
trivial case; we show that the traditional notion of state independence
coincides with the notion called strong independence in imprecise
probability---this leads us to give much a weaker definition of state
independence than the traditional one; we rework and uniform the notions of
complete preferences, beliefs, values; we argue that Archimedeanity does not
capture all the problems that can be modelled with sets of expected utilities
and we provide a new notion that does precisely that. Perhaps most importantly,
we argue throughout that desirability is a powerful and natural setting to
model, and work with, incomplete preferences, even in case of non-Archimedean
problems. This leads us to suggest that desirability, rather than preference,
should be the primitive notion at the basis of decision-theoretic
axiomatisations.
",Desirability and the birth of incomplete preferences
"  Stochastic And-Or grammars (AOG) extend traditional stochastic grammars of
language to model other types of data such as images and events. In this paper
we propose a representation framework of stochastic AOGs that is agnostic to
the type of the data being modeled and thus unifies various domain-specific
AOGs. Many existing grammar formalisms and probabilistic models in natural
language processing, computer vision, and machine learning can be seen as
special cases of this framework. We also propose a domain-independent inference
algorithm of stochastic context-free AOGs and show its tractability under a
reasonable assumption. Furthermore, we provide two interpretations of
stochastic context-free AOGs as a subset of probabilistic logic, which connects
stochastic AOGs to the field of statistical relational learning and clarifies
their relation with a few existing statistical relational models.
",Stochastic And-Or Grammars: A Unified Framework and Logic Perspective
"  Probabilistic Inductive Logic Programming (PILP) is a rel- atively unexplored
area of Statistical Relational Learning which extends classic Inductive Logic
Programming (ILP). This work introduces SkILL, a Stochastic Inductive Logic
Learner, which takes probabilistic annotated data and produces First Order
Logic theories. Data in several domains such as medicine and bioinformatics
have an inherent degree of uncer- tainty, that can be used to produce models
closer to reality. SkILL can not only use this type of probabilistic data to
extract non-trivial knowl- edge from databases, but it also addresses
efficiency issues by introducing a novel, efficient and effective search
strategy to guide the search in PILP environments. The capabilities of SkILL
are demonstrated in three dif- ferent datasets: (i) a synthetic toy example
used to validate the system, (ii) a probabilistic adaptation of a well-known
biological metabolism ap- plication, and (iii) a real world medical dataset in
the breast cancer domain. Results show that SkILL can perform as well as a
deterministic ILP learner, while also being able to incorporate probabilistic
knowledge that would otherwise not be considered.
",SkILL - a Stochastic Inductive Logic Learner
"  Risk aggregation is a popular method used to estimate the sum of a collection
of financial assets or events, where each asset or event is modelled as a
random variable. Applications, in the financial services industry, include
insurance, operational risk, stress testing, and sensitivity analysis, but the
problem is widely encountered in many other application domains. This thesis
has contributed two algorithms to perform Bayesian risk aggregation when model
exhibit hybrid dependency and high dimensional inter-dependency. The first
algorithm operates on a subset of the general problem, with an emphasis on
convolution problems, in the presence of continuous and discrete variables (so
called hybrid models) and the second algorithm offer a universal method for
general purpose inference over much wider classes of Bayesian Network models.
","Performing Bayesian Risk Aggregation using Discrete Approximation
  Algorithms with Graph Factorization"
"  The shortest path between two concepts in a taxonomic ontology is commonly
used to represent the semantic distance between concepts in the edge-based
semantic similarity measures. In the past, the edge counting is considered to
be the default method for the path computation, which is simple, intuitive and
has low computational complexity. However, a large lexical taxonomy of such as
WordNet has the irregular densities of links between concepts due to its broad
domain but. The edge counting-based path computation is powerless for this
non-uniformity problem. In this paper, we advocate that the path computation is
able to be separated from the edge-based similarity measures and form various
general computing models. Therefore, in order to solve the problem of
non-uniformity of concept density in a large taxonomic ontology, we propose a
new path computing model based on the compensation of local area density of
concepts, which is equal to the number of direct hyponyms of the subsumers of
concepts in their shortest path. This path model considers the local area
density of concepts as an extension of the edge-based path and converts the
local area density divided by their depth into the compensation for edge-based
path with an adjustable parameter, which idea has been proven to be consistent
with the information theory. This model is a general path computing model and
can be applied in various edge-based similarity algorithms. The experiment
results show that the proposed path model improves the average correlation
between edge-based measures with human judgments on Miller and Charles
benchmark from less than 0.8 to more than 0.85, and has a big advantage in
efficiency than information content (IC) computation in a dynamic ontology,
thereby successfully solving the non-uniformity problem of taxonomic ontology.
","A density compensation-based path computing model for measuring semantic
  similarity"
"  Markov logic uses weighted formulas to compactly encode a probability
distribution over possible worlds. Despite the use of logical formulas, Markov
logic networks (MLNs) can be difficult to interpret, due to the often
counter-intuitive meaning of their weights. To address this issue, we propose a
method to construct a possibilistic logic theory that exactly captures what can
be derived from a given MLN using maximum a posteriori (MAP) inference.
Unfortunately, the size of this theory is exponential in general. We therefore
also propose two methods which can derive compact theories that still capture
MAP inference, but only for specific types of evidence. These theories can be
used, among others, to make explicit the hidden assumptions underlying an MLN
or to explain the predictions it makes.
",Encoding Markov Logic Networks in Possibilistic Logic
"  Square grids are commonly used in robotics and game development as spatial
models and well known in AI community heuristic search algorithms (such as A*,
JPS, Theta* etc.) are widely used for path planning on grids. A lot of research
is concentrated on finding the shortest (in geometrical sense) paths while in
many applications finding smooth paths (rather than the shortest ones but
containing sharp turns) is preferable. In this paper we study the problem of
generating smooth paths and concentrate on angle constrained path planning. We
put angle-constrained path planning problem formally and present a new
algorithm tailored to solve it - LIAN. We examine LIAN both theoretically and
empirically. We show that it is sound and complete (under some restrictions).
We also show that LIAN outperforms the analogues when solving numerous path
planning tasks within urban outdoor navigation scenarios.
",Grid-based angle-constrained path planning
"  In this paper one presents new similarity, cardinality and entropy measures
for bipolar fuzzy set and for its particular forms like intuitionistic,
paraconsistent and fuzzy set. All these are constructed in the framework of
multi-valued representations and are based on a penta-valued logic that uses
the following logical values: true, false, unknown, contradictory and
ambiguous. Also a new distance for bounded real interval was defined.
","Similarity, Cardinality and Entropy for Bipolar Fuzzy Set in the
  Framework of Penta-valued Representation"
"  This paper presents a five-valued representation of bifuzzy sets. This
representation is related to a five-valued logic that uses the following
values: true, false, inconsistent, incomplete and ambiguous. In the framework
of five-valued representation, formulae for similarity, entropy and syntropy of
bifuzzy sets are constructed.
",Entropy and Syntropy in the Context of Five-Valued Logics
"  This report covers an intelligent decision support system (IDSS), which
handles an efficient and effective way to rapidly inspect containerized cargos
for defection. Defection is either cargo exposure to radiation, physical
damages such as holes, punctured surfaces, iron surface oxidation, etc. The
system uses a sorting array triangulation technique (SAT) and surface damage
detection (SDD) to conduct the inspection. This new technique saves time and
money on finding damaged goods during transportation such that, instead of
running $n$ inspections on $n$ containers, only 3 inspections per triangulation
or a ratio of $3:n$ is required, assuming $n > 3$ containers. The damaged stack
in the array is virtually detected contiguous to an actually-damaged cargo by
calculating nearby distances of such cargos, delivering reliable estimates for
the whole local stack population. The estimated values on damaged, somewhat
damaged and undamaged cargo stacks, are listed and profiled after being sorted
by the program, thereby submitted to the manager for a final decision. The
report describes the problem domain and the implementation of the simulator
prototype, showing how the system operates via software, hardware with/without
human agents, conducting real-time inspections and management per se.
","A Real-time Cargo Damage Management System via a Sorting Array
  Triangulation Technique"
"  Frequent itemset mining is an essential part of data analysis and data
mining. Recent works propose interesting SAT-based encodings for the problem of
discovering frequent itemsets. Our aim in this work is to define strategies for
adapting SAT solvers to such encodings in order to improve models enumeration.
In this context, we deeply study the effects of restart, branching heuristics
and clauses learning. We then conduct an experimental evaluation on SAT-Based
itemset mining instances to show how SAT solvers can be adapted to obtain an
efficient SAT model enumerator.
",On SAT Models Enumeration in Itemset Mining
"  We show new limits on the efficiency of using current techniques to make
exact probabilistic inference for large classes of natural problems. In
particular we show new lower bounds on knowledge compilation to SDD and DNNF
forms. We give strong lower bounds on the complexity of SDD representations by
relating SDD size to best-partition communication complexity. We use this
relationship to prove exponential lower bounds on the SDD size for representing
a large class of problems that occur naturally as queries over probabilistic
databases. A consequence is that for representing unions of conjunctive
queries, SDDs are not qualitatively more concise than OBDDs. We also derive
simple examples for which SDDs must be exponentially less concise than FBDDs.
Finally, we derive exponential lower bounds on the sizes of DNNF
representations using a new quasipolynomial simulation of DNNFs by
nondeterministic FBDDs.
","New Limits for Knowledge Compilation and Applications to Exact Model
  Counting"
"  The question of how humans solve problem has been addressed extensively.
However, the direct study of the effectiveness of this process seems to be
overlooked. In this paper, we address the issue of the effectiveness of human
problem solving: we analyze where this effectiveness comes from and what
cognitive mechanisms or heuristics are involved. Our results are based on the
optimal probabilistic problem solving strategy that appeared in Solomonoff
paper on general problem solving system. We provide arguments that a certain
set of cognitive mechanisms or heuristics drive human problem solving in the
similar manner as the optimal Solomonoff strategy. The results presented in
this paper can serve both cognitive psychology in better understanding of human
problem solving processes as well as artificial intelligence in designing more
human-like agents.
",Arguments for the Effectiveness of Human Problem Solving
"  Our goal is to deploy a high-accuracy system starting with zero training
examples. We consider an ""on-the-job"" setting, where as inputs arrive, we use
real-time crowdsourcing to resolve uncertainty where needed and output our
prediction when confident. As the model improves over time, the reliance on
crowdsourcing queries decreases. We cast our setting as a stochastic game based
on Bayesian decision theory, which allows us to balance latency, cost, and
accuracy objectives in a principled way. Computing the optimal policy is
intractable, so we develop an approximation based on Monte Carlo Tree Search.
We tested our approach on three datasets---named-entity recognition, sentiment
classification, and image classification. On the NER task we obtained more than
an order of magnitude reduction in cost compared to full human annotation,
while boosting performance relative to the expert provided labels. We also
achieve a 8% F1 improvement over having a single human label the whole set, and
a 28% F1 improvement over online learning.
",On-the-Job Learning with Bayesian Decision Theory
"  The monolithic approach to policy representation in Markov Decision Processes
(MDPs) looks for a single policy that can be represented as a function from
states to actions. For the monolithic approach to succeed (and this is not
always possible), a complex feature representation is often necessary since the
policy is a complex object that has to prescribe what actions to take all over
the state space. This is especially true in large domains with complicated
dynamics. It is also computationally inefficient to both learn and plan in MDPs
using a complex monolithic approach. We present a different approach where we
restrict the policy space to policies that can be represented as combinations
of simpler, parameterized skills---a type of temporally extended action, with a
simple policy representation. We introduce Learning Skills via Bootstrapping
(LSB) that can use a broad family of Reinforcement Learning (RL) algorithms as
a ""black box"" to iteratively learn parametrized skills. Initially, the learned
skills are short-sighted but each iteration of the algorithm allows the skills
to bootstrap off one another, improving each skill in the process. We prove
that this bootstrapping process returns a near-optimal policy. Furthermore, our
experiments demonstrate that LSB can solve MDPs that, given the same
representational power, could not be solved by a monolithic approach. Thus,
planning with learned skills results in better policies without requiring
complex policy representations.
",Bootstrapping Skills
"  This paper reveals the tree structure as an intermediate result of clustering
by fast search and find of density peaks (DPCLUS), and explores the power of
using this tree to perform hierarchical clustering. The array used to hold the
index of the nearest higher-densitied object for each object can be transformed
into a Leading Tree (LT), in which each parent node P leads its child nodes to
join the same cluster as P itself, and the child nodes are sorted by their
gamma values in descendant order to accelerate the disconnecting of root in
each subtree. There are two major advantages with the LT: One is dramatically
reducing the running time of assigning noncenter data points to their cluster
ID, because the assigning process is turned into just disconnecting the links
from each center to its parent. The other is that the tree model for
representing clusters is more informative. Because we can check which objects
are more likely to be selected as centers in finer grained clustering, or which
objects reach to its center via less jumps. Experiment results and analysis
show the effectiveness and efficiency of the assigning process with an LT.
",Leading Tree in DPCLUS and Its Impact on Building Hierarchies
"  In Dung's abstract argumentation, arguments are either acceptable or
unacceptable, given a chosen notion of acceptability. This gives a coarse way
to compare arguments. In this paper, we propose a counting approach for a more
fine-gained assessment to arguments by counting the number of their respective
attackers and defenders based on argument graph and argument game. An argument
is more acceptable if the proponent puts forward more number of defenders for
it and the opponent puts forward less number of attackers against it. We show
that our counting model has two well-behaved properties: normalization and
convergence. Then, we define a counting semantics based on this model, and
investigate some general properties of the semantics.
",Attacker and Defender Counting Approach for Abstract Argumentation
"  This paper presents a tentative outline for the construction of an
artificial, generally intelligent system (AGI). It is argued that building a
general data compression algorithm solving all problems up to a complexity
threshold should be the main thrust of research. A measure for partial progress
in AGI is suggested. Although the details are far from being clear, some
general properties for a general compression algorithm are fleshed out. Its
inductive bias should be flexible and adapt to the input data while constantly
searching for a simple, orthogonal and complete set of hypotheses explaining
the data. It should recursively reduce the size of its representations thereby
compressing the data increasingly at every iteration.
  Abstract Based on that fundamental ability, a grounded reasoning system is
proposed. It is argued how grounding and flexible feature bases made of
hypotheses allow for resourceful thinking. While the simulation of
representation contents on the mental stage accounts for much of the power of
propositional logic, compression leads to simple sets of hypotheses that allow
the detection and verification of universally quantified statements.
  Abstract Together, it is highlighted how general compression and grounded
reasoning could account for the birth and growth of first concepts about the
world and the commonsense reasoning about them.
","Artificial general intelligence through recursive data compression and
  grounded reasoning: a position paper"
"  It has been proposed that human physical reasoning consists largely of
running ""physics engines in the head"" in which the future trajectory of the
physical system under consideration is computed precisely using accurate
scientific theories. In such models, uncertainty and incomplete knowledge is
dealt with by sampling probabilistically over the space of possible
trajectories (""Monte Carlo simulation""). We argue that such simulation-based
models are too weak, in that there are many important aspects of human physical
reasoning that cannot be carried out this way, or can only be carried out very
inefficiently; and too strong, in that humans make large systematic errors that
the models cannot account for. We conclude that simulation-based reasoning
makes up at most a small part of a larger system that encompasses a wide range
of additional cognitive processes.
",The Scope and Limits of Simulation in Cognitive Models
"  The temporal phenomena have many facets that are studied by different
communities. In Semantic Web, large heterogeneous data are handled and
produced. These data often have informal, semi-formal or formal temporal
information which must be interpreted by software agents. In this paper we
present Human Time Ontology (HuTO) an RDFS ontology to annotate and represent
temporal data. A major contribution of HuTO is the modeling of non-convex
intervals giving the ability to write queries for this kind of interval. HuTO
also incorporates normalization and reasoning rules to explicit certain
information. HuTO also proposes an approach which associates a temporal
dimension to the knowledge base content. This facilitates information retrieval
by considering or not the temporal aspect.
",HuTO: an Human Time Ontology for Semantic Web Applications
"  Moving beyond the dualistic view in AI where agent and environment are
separated incurs new challenges for decision making, as calculation of expected
utility is no longer straightforward. The non-dualistic decision theory
literature is split between causal decision theory and evidential decision
theory. We extend these decision algorithms to the sequential setting where the
agent alternates between taking actions and observing their consequences. We
find that evidential decision theory has two natural extensions while causal
decision theory only has one.
",Sequential Extensions of Causal and Evidential Decision Theory
"  We endow prioritised default logic (PDL) with argumentation semantics using
the ASPIC+ framework for structured argumentation, and prove that the
conclusions of the justified arguments are exactly the prioritised default
extensions. Argumentation semantics for PDL will allow for the application of
argument game proof theories to the process of inference in PDL, making the
reasons for accepting a conclusion transparent and the inference process more
intuitive. This also opens up the possibility for argumentation-based
distributed reasoning and communication amongst agents with PDL representations
of mental attitudes.
",Argumentation Semantics for Prioritised Default Logic
"  We address the problem of belief revision of logic programs, i.e., how to
incorporate to a logic program P a new logic program Q. Based on the structure
of SE interpretations, Delgrande et al. adapted the well-known AGM framework to
logic program (LP) revision. They identified the rational behavior of LP
revision and introduced some specific operators. In this paper, a constructive
characterization of all rational LP revision operators is given in terms of
orderings over propositional interpretations with some further conditions
specific to SE interpretations. It provides an intuitive, complete procedure
for the construction of all rational LP revision operators and makes easier the
comprehension of their semantic and computational properties. We give a
particular consideration to logic programs of very general form, i.e., the
generalized logic programs (GLPs). We show that every rational GLP revision
operator is derived from a propositional revision operator satisfying the
original AGM postulates. Interestingly, the further conditions specific to GLP
revision are independent from the propositional revision operator on which a
GLP revision operator is based. Taking advantage of our characterization
result, we embed the GLP revision operators into structures of Boolean
lattices, that allow us to bring to light some potential weaknesses in the
adapted AGM postulates. To illustrate our claim, we introduce and characterize
axiomatically two specific classes of (rational) GLP revision operators which
arguably have a drastic behavior. We additionally consider two more restricted
forms of logic programs, i.e., the disjunctive logic programs (DLPs) and the
normal logic programs (NLPs) and adapt our characterization result to DLP and
NLP revision operators.
","Characterization of Logic Program Revision as an Extension of
  Propositional Revision"
"  There are already quite a few tools for solving the Satisfiability Modulo
Theories (SMT) problems. In this paper, we present \texttt{VolCE}, a tool for
counting the solutions of SMT constraints, or in other words, for computing the
volume of the solution space. Its input is essentially a set of Boolean
combinations of linear constraints, where the numeric variables are either all
integers or all reals, and each variable is bounded. The tool extends SMT
solving with integer solution counting and volume computation/estimation for
convex polytopes. Effective heuristics are adopted, which enable the tool to
deal with high-dimensional problem instances efficiently and accurately.
","A Tool for Computing and Estimating the Volume of the Solution Space of
  SMT(LA)"
"  In this paper we propose the approach for constructing partitionings of hard
variants of the Boolean satisfiability problem (SAT). Such partitionings can be
used for solving corresponding SAT instances in parallel. For the same SAT
instance one can construct different partitionings, each of them is a set of
simplified versions of the original SAT instance. The effectiveness of an
arbitrary partitioning is determined by the total time of solving of all SAT
instances from it. We suggest the approach, based on the Monte Carlo method,
for estimating time of processing of an arbitrary partitioning. With each
partitioning we associate a point in the special finite search space. The
estimation of effectiveness of the particular partitioning is the value of
predictive function in the corresponding point of this space. The problem of
search for an effective partitioning can be formulated as a problem of
optimization of the predictive function. We use metaheuristic algorithms
(simulated annealing and tabu search) to move from point to point in the search
space. In our computational experiments we found partitionings for SAT
instances encoding problems of inversion of some cryptographic functions.
Several of these SAT instances with realistic predicted solving time were
successfully solved on a computing cluster and in the volunteer computing
project SAT@home. The solving time agrees well with estimations obtained by the
proposed method.
","Using Monte Carlo method for searching partitionings of hard variants of
  Boolean satisfiability problem"
"  This document is written with the intention to describe in detail a method
and means by which a computer program can reason about the world and in so
doing, increase its analogue to a living system. As the literature is rife and
it is apparent we, as scientists and engineers, have not found the solution,
this document will attempt the solution by grounding its intellectual arguments
within tenets of human cognition in Western philosophy. The result will be a
characteristic description of a method to describe an artificial system
analogous to that performed for a human. The approach was the substance of my
Master's thesis, explored more deeply during the course of my postdoc research.
It focuses primarily on context awareness and choice set within a boundary of
available epistemology, which serves to describe it. Expanded upon, such a
description strives to discover agreement with Kant's critique of reason to
understand how it could be applied to define the architecture of its design.
The intention has never been to mimic human or biological systems, rather, to
understand the profoundly fundamental rules, when leveraged correctly, results
in an artificial consciousness as noumenon while in keeping with the perception
of it as phenomenon.
",The method of artificial systems
"  The AGM recovery postulate says: assume a set of propositions X; assume that
it is consistent and that it is closed under logical consequences; remove a
belief P from the set minimally, but make sure that the resultant set is again
some set of propositions X' which is closed under the logical consequences; now
add P again and close the set under the logical consequences; and we should get
a set of propositions that contains all the propositions that were in X. This
postulate has since met objections; many have observed that it could bear
counter-intuitive results. Nevertheless, the attempts that have been made so
far to amend it either recovered the postulate in full, had to relinquish the
assumption of the logical closure altogether, or else had to introduce fresh
controversies of their own. We provide a solution to the recovery paradox in
this work. Our theoretical basis is the recently proposed belief theory with
latent beliefs (simply the latent belief theory for short). Firstly, through
examples, we will illustrate that the vanilla latent belief theory can be made
more expressive. We will identify that a latent belief, when it becomes
visible, may remain visible only while the beliefs that triggered it into the
agent's consciousness are in the agent's belief set. In order that such
situations can be also handled, we will enrich the latent belief theory with
belief dependencies among attributive beliefs, recording the information as to
which belief is supported of its existence by which beliefs. We will show that
the enriched latent belief theory does not possess the recovery property. The
closure by logical consequences is maintained in the theory, however. Hence it
serves as a solution to the open problem in the belief set theories.
","Latent Belief Theory and Belief Dependencies: A Solution to the Recovery
  Problem in the Belief Set Theories"
"  This paper motivates the study of decision theory as necessary for aligning
smarter-than-human artificial systems with human interests. We discuss the
shortcomings of two standard formulations of decision theory, and demonstrate
that they cannot be used to describe an idealized decision procedure suitable
for approximation by artificial systems. We then explore the notions of policy
selection and logical counterfactuals, two recent insights into decision theory
that point the way toward promising paths for future research.
",Toward Idealized Decision Theory
"  We present $\mathcal{MEL}^{++}$ (M denotes Markov logic networks) an
extension of the log-linear description logics $\mathcal{EL}^{++}$-LL with
concrete domains, nominals, and instances. We use Markov logic networks (MLNs)
in order to find the most probable, classified and coherent $\mathcal{EL}^{++}$
ontology from an $\mathcal{MEL}^{++}$ knowledge base. In particular, we develop
a novel way to deal with concrete domains (also known as datatypes) by
extending MLN's cutting plane inference (CPI) algorithm.
",Towards Log-Linear Logics with Concrete Domains
"  We introduce a lazy approach to the explanation-based approximation of
probabilistic logic programs. It uses only the most significant part of the
program when searching for explanations. The result is a fast and anytime
approximate inference algorithm which returns hard lower and upper bounds on
the exact probability. We experimentally show that this method outperforms
state-of-the-art approximate inference.
",Lazy Explanation-Based Approximation for Probabilistic Logic Programming
"  Finding the most probable (MAP) model in SRL frameworks such as Markov logic
and Problog can, in principle, be solved by encoding the problem as a
`grounded-out' mixed integer program (MIP). However, useful first-order
structure disappears in this process motivating the development of first-order
MIP approaches. Here we present mfoilp, one such approach. Since the syntax and
semantics of mfoilp is essentially the same as existing approaches we focus
here mainly on implementation and algorithmic issues. We start with the
(conceptually) simple problem of using a logic program to generate a MIP
instance before considering more ambitious exploitation of first-order
representations.
",First-order integer programming for MAP problems
"  Ontology matching is the process of automatically determining the semantic
equivalences between the concepts of two ontologies. Most ontology matching
algorithms are based on two types of strategies: terminology-based strategies,
which align concepts based on their names or descriptions, and structure-based
strategies, which exploit concept hierarchies to find the alignment. In many
domains, there is additional information about the relationships of concepts
represented in various ways, such as Bayesian networks, decision trees, and
association rules. We propose to use the similarities between these
relationships to find more accurate alignments. We accomplish this by defining
soft constraints that prefer alignments where corresponding concepts have the
same local relationships encoded as knowledge rules. We use a probabilistic
framework to integrate this new knowledge-based strategy with standard
terminology-based and structure-based strategies. Furthermore, our method is
particularly effective in identifying correspondences between complex concepts.
Our method achieves substantially better F-score than the previous
state-of-the-art on three ontology matching domains.
",Ontology Matching with Knowledge Rules
"  Bayesian networks (BNs) are used for inference and sampling by exploiting
conditional independence among random variables. Context specific independence
(CSI) is a property of graphical models where additional independence relations
arise in the context of particular values of random variables (RVs).
Identifying and exploiting CSI properties can simplify inference. Some
generative network models (models that generate social/information network
samples from a network distribution P(G)), with complex interactions among a
set of RVs, can be represented with probabilistic graphical models, in
particular with BNs. In the present work we show one such a case. We discuss
how a mixed Kronecker Product Graph Model can be represented as a BN, and study
its BN properties that can be used for efficient sampling. Specifically, we
show that instead of exhibiting CSI properties, the model has deterministic
context-specific dependence (DCSD). Exploiting this property focuses the
sampling method on a subset of the sampling space that improves efficiency.
","Using Bayesian Network Representations for Effective Sampling from
  Generative Network Models"
"  In this paper, we focus on a novel knowledge reuse scenario where the
knowledge in the source schema needs to be translated to a semantically
heterogeneous target schema. We refer to this task as ""knowledge translation""
(KT). Unlike data translation and transfer learning, KT does not require any
data from the source or target schema. We adopt a probabilistic approach to KT
by representing the knowledge in the source schema, the mapping between the
source and target schemas, and the resulting knowledge in the target schema all
as probability distributions, specially using Markov random fields and Markov
logic networks. Given the source knowledge and mappings, we use standard
learning and inference algorithms for probabilistic graphical models to find an
explicit probability distribution in the target schema that minimizes the
Kullback-Leibler divergence from the implicit distribution. This gives us a
compact probabilistic model that represents knowledge from the source schema as
well as possible, respecting the uncertainty in both the source knowledge and
the mapping. In experiments on both propositional and relational domains, we
find that the knowledge obtained by KT is comparable to other approaches that
require data, demonstrating that knowledge can be reused without data.
",A Probabilistic Approach to Knowledge Translation
"  In an earlier work we have used the Triangular Fuzzy Numbers (TFNs)as an
assessment tool of student skills.This approach led to an approximate
linguistic characterization of the students' overall performance, but it was
not proved to be sufficient in all cases for comparing the performance of two
different student groups, since tywo TFNs are not always comparable. In the
present paper we complete the above fuzzy assessment approach by presenting a
defuzzification method of TFNS based on the Center of Gravity (COG) technique,
which enables the required comparison. In addition we extend our results by
using the Trapezoidal Fuzzy Numbers (TpFNs) too, which are a generalization of
the TFNs, for student assessment and we present suitable examples illustrating
our new results in practice.
",Use of the Triangular Fuzzy Numbers for Student Assessment
"  Driven by the opportunity to harvest the flexibility related to building
climate control for demand response applications, this work presents a
data-driven control approach building upon recent advancements in reinforcement
learning. More specifically, model assisted batch reinforcement learning is
applied to the setting of building climate control subjected to a dynamic
pricing. The underlying sequential decision making problem is cast on a markov
decision problem, after which the control algorithm is detailed. In this work,
fitted Q-iteration is used to construct a policy from a batch of experimental
tuples. In those regions of the state space where the experimental sample
density is low, virtual support samples are added using an artificial neural
network. Finally, the resulting policy is shaped using domain knowledge. The
control approach has been evaluated quantitatively using a simulation and
qualitatively in a living lab. From the quantitative analysis it has been found
that the control approach converges in approximately 20 days to obtain a
control policy with a performance within 90% of the mathematical optimum. The
experimental analysis confirms that within 10 to 20 days sensible policies are
obtained that can be used for different outside temperature regimes.
","Experimental analysis of data-driven control for a building heating
  system"
"  Fuzzy answer set programming (FASP) combines two declarative frameworks,
answer set programming and fuzzy logic, in order to model reasoning by default
over imprecise information. Several connectives are available to combine
different expressions; in particular the \Godel and \Luka fuzzy connectives are
usually considered, due to their properties. Although the \Godel conjunction
can be easily eliminated from rule heads, we show through complexity arguments
that such a simplification is infeasible in general for all other connectives.
%, even if bodies are restricted to \Luka or \Godel conjunctions. The paper
analyzes a translation of FASP programs into satisfiability modulo
theories~(SMT), which in general produces quantified formulas because of the
minimality of the semantics. Structural properties of many FASP programs allow
to eliminate the quantification, or to sensibly reduce the number of quantified
variables. Indeed, integrality constraints can replace recursive rules commonly
used to force Boolean interpretations, and completion subformulas can guarantee
minimality for acyclic programs with atomic heads. Moreover, head cycle free
rules can be replaced by shifted subprograms, whose structure depends on the
eliminated head connective, so that ordered completion may replace the
minimality check if also \Luka disjunction in rule bodies is acyclic. The paper
also presents and evaluates a prototype system implementing these translations.
  To appear in Theory and Practice of Logic Programming (TPLP), Proceedings of
ICLP 2015.
",Fuzzy Answer Set Computation via Satisfiability Modulo Theories
"  Gelfond and Zhang recently proposed a new stable model semantics based on
Vicious Circle Principle in order to improve the interpretation of logic
programs with aggregates. The paper focuses on this proposal, and analyzes the
complexity of both coherence testing and cautious reasoning under the new
semantics. Some surprising results highlight similarities and differences
versus mainstream stable model semantics for aggregates. Moreover, the paper
reports on the design of compilation techniques for implementing the new
semantics on top of existing ASP solvers, which eventually lead to realize a
prototype system that allows for experimenting with Gelfond-Zhang's aggregates.
  To appear in Theory and Practice of Logic Programming (TPLP), Proceedings of
ICLP 2015.
",Complexity and Compilation of GZ-Aggregates in Answer Set Programming
"  Aggregation functions are widely used in answer set programming for
representing and reasoning on knowledge involving sets of objects collectively.
Current implementations simplify the structure of programs in order to optimize
the overall performance. In particular, aggregates are rewritten into simpler
forms known as monotone aggregates. Since the evaluation of normal programs
with monotone aggregates is in general on a lower complexity level than the
evaluation of normal programs with arbitrary aggregates, any faithful
translation function must introduce disjunction in rule heads in some cases.
However, no function of this kind is known. The paper closes this gap by
introducing a polynomial, faithful, and modular translation for rewriting
common aggregation functions into the simpler form accepted by current solvers.
A prototype system allows for experimenting with arbitrary recursive
aggregates, which are also supported in the recent version 4.5 of the grounder
\textsc{gringo}, using the methods presented in this paper.
  To appear in Theory and Practice of Logic Programming (TPLP), Proceedings of
ICLP 2015.
","Rewriting recursive aggregates in answer set programming: back to
  monotonicity"
"  This paper describes Picat's planner, its implementation, and planning models
for several domains used in International Planning Competition (IPC) 2014.
Picat's planner is implemented by use of tabling. During search, every state
encountered is tabled, and tabled states are used to effectively perform
resource-bounded search. In Picat, structured data can be used to avoid
enumerating all possible permutations of objects, and term sharing is used to
avoid duplication of common state data. This paper presents several modeling
techniques through the example models, ranging from designing state
representations to facilitate data sharing and symmetry breaking, encoding
actions with operations for efficient precondition checking and state updating,
to incorporating domain knowledge and heuristics. Broadly, this paper
demonstrates the effectiveness of tabled logic programming for planning, and
argues the importance of modeling despite recent significant progress in
domain-independent PDDL planners.
",Planning as Tabled Logic Programming
"  In real clustering applications, proximity data, in which only pairwise
similarities or dissimilarities are known, is more general than object data, in
which each pattern is described explicitly by a list of attributes.
Medoid-based clustering algorithms, which assume the prototypes of classes are
objects, are of great value for partitioning relational data sets. In this
paper a new prototype-based clustering method, named Evidential C-Medoids
(ECMdd), which is an extension of Fuzzy C-Medoids (FCMdd) on the theoretical
framework of belief functions is proposed. In ECMdd, medoids are utilized as
the prototypes to represent the detected classes, including specific classes
and imprecise classes. Specific classes are for the data which are distinctly
far from the prototypes of other classes, while imprecise classes accept the
objects that may be close to the prototypes of more than one class. This soft
decision mechanism could make the clustering results more cautious and reduce
the misclassification rates. Experiments in synthetic and real data sets are
used to illustrate the performance of ECMdd. The results show that ECMdd could
capture well the uncertainty in the internal data structure. Moreover, it is
more robust to the initializations compared with FCMdd.
",Evidential relational clustering using medoids
"  We introduce optimization techniques for reasoning in DLN---a recently
introduced family of nonmonotonic description logics whose characterizing
features appear well-suited to model the applicative examples naturally arising
in biomedical domains and semantic web access control policies. Such
optimizations are validated experimentally on large KBs with more than 30K
axioms. Speedups exceed 1 order of magnitude. For the first time, response
times compatible with real-time reasoning are obtained with nonmonotonic KBs of
this size.
",Optimizing the computation of overriding
"  This paper describes a new entropy-style of equation that may be useful in a
general sense, but can be applied to a cognitive model with related processes.
The model is based on the human brain, with automatic and distributed pattern
activity. Methods for carrying out the different processes are suggested. The
main purpose of this paper is to reaffirm earlier research on different
knowledge-based and experience-based clustering techniques. The overall
architecture has stayed essentially the same and so it is the localised
processes or smaller details that have been updated. For example, a counting
mechanism is used slightly differently, to measure a level of 'cohesion'
instead of a 'correct' classification, over pattern instances. The introduction
of features has further enhanced the architecture and the new entropy-style
equation is proposed. While an earlier paper defined three levels of functional
requirement, this paper re-defines the levels in a more human vernacular, with
higher-level goals described in terms of action-result pairs.
",A Brain-like Cognitive Process with Shared Methods
"  Gender contains a wide range of information regarding to the characteristics
difference between male and female. Successful gender recognition is essential
and critical for many applications in the commercial domains such as
applications of human-computer interaction and computer-aided physiological or
psychological analysis. Some have proposed various approaches for automatic
gender classification using the features derived from human bodies and/or
behaviors. First, this paper introduces the challenge and application for
gender classification research. Then, the development and framework of gender
classification are described. Besides, we compare these state-of-the-art
approaches, including vision-based methods, biological information-based
method, and social network information-based method, to provide a comprehensive
review in the area of gender classification. In mean time, we highlight the
strength and discuss the limitation of each method. Finally, this review also
discusses several promising applications for the future work.
",Human Gender Classification: A Review
"  In this work we solve the day-ahead unit commitment (UC) problem, by
formulating it as a Markov decision process (MDP) and finding a low-cost policy
for generation scheduling. We present two reinforcement learning algorithms,
and devise a third one. We compare our results to previous work that uses
simulated annealing (SA), and show a 27% improvement in operation costs, with
running time of 2.5 minutes (compared to 2.5 hours of existing
state-of-the-art).
",Reinforcement Learning for the Unit Commitment Problem
"  Working with exhaustive search on large dataset is infeasible for several
reasons. Recently, developed techniques that made pattern set mining feasible
by a general solver with long execution time that supports heuristic search and
are limited to small datasets only. In this paper, we investigate an approach
which aims to find diverse set of patterns using genetic algorithm to mine
diverse frequent patterns. We propose a fast heuristic search algorithm that
outperforms state-of-the-art methods on a standard set of benchmarks and
capable to produce satisfactory results within a short period of time. Our
proposed algorithm uses a relative encoding scheme for the patterns and an
effective twin removal technique to ensure diversity throughout the search.
",An Efficient Genetic Algorithm for Discovering Diverse-Frequent Patterns
"  This work presents two new algorithms for performing constraint satisfaction.
The first algorithm presented, DMaxWalkSat, is a constraint solver specialized
for solving dynamic, weighted constraint satisfaction problems. The second
algorithm, RDMaxWalkSat, is a derivative of DMaxWalkSat that has been modified
into an anytime algorithm, and hence support realtime constraint satisfaction.
DMaxWalkSat is shown to offer performance advantages in terms of solution
quality and runtime over its parent constraint solver, MaxWalkSat. RDMaxWalkSat
is shown to support anytime operation. The introduction of these algorithms
brings another tool to the areas of computer science that naturally represent
problems as constraint satisfaction problems, an example of which is the robust
coherence algorithm.
","Adapting Stochastic Search For Real-time Dynamic Weighted Constraint
  Satisfaction"
"  Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.
","Mapping Big Data into Knowledge Space with Cognitive
  Cyber-Infrastructure"
"  This paper contributes to the area of inductive logic programming by
presenting a new learning framework that allows the learning of weak
constraints in Answer Set Programming (ASP). The framework, called Learning
from Ordered Answer Sets, generalises our previous work on learning ASP
programs without weak constraints, by considering a new notion of examples as
ordered pairs of partial answer sets that exemplify which answer sets of a
learned hypothesis (together with a given background knowledge) are preferred
to others. In this new learning task inductive solutions are searched within a
hypothesis space of normal rules, choice rules, and hard and weak constraints.
We propose a new algorithm, ILASP2, which is sound and complete with respect to
our new learning framework. We investigate its applicability to learning
preferences in an interview scheduling problem and also demonstrate that when
restricted to the task of learning ASP programs without weak constraints,
ILASP2 can be much more efficient than our previously proposed system.
",Learning Weak Constraints in Answer Set Programming
"  The design of efficient solutions for abstract argumentation problems is a
crucial step towards advanced argumentation systems. One of the most prominent
approaches in the literature is to use Answer-Set Programming (ASP) for this
endeavor. In this paper, we present new encodings for three prominent
argumentation semantics using the concept of conditional literals in
disjunctions as provided by the ASP-system clingo. Our new encodings are not
only more succinct than previous versions, but also outperform them on standard
benchmarks.
",Improved Answer-Set Programming Encodings for Abstract Argumentation
"  We introduce a new artificial intelligence (AI) approach called, the 'Digital
Synaptic Neural Substrate' (DSNS). It uses selected attributes from objects in
various domains (e.g. chess problems, classical music, renowned artworks) and
recombines them in such a way as to generate new attributes that can then, in
principle, be used to create novel objects of creative value to humans relating
to any one of the source domains. This allows some of the burden of creative
content generation to be passed from humans to machines. The approach was
tested in the domain of chess problem composition. We used it to automatically
compose numerous sets of chess problems based on attributes extracted and
recombined from chess problems and tournament games by humans, renowned
paintings, computer-evolved abstract art, photographs of people, and classical
music tracks. The quality of these generated chess problems was then assessed
automatically using an existing and experimentally-validated computational
chess aesthetics model. They were also assessed by human experts in the domain.
The results suggest that attributes collected and recombined from chess and
other domains using the DSNS approach can indeed be used to automatically
generate chess problems of reasonably high aesthetic quality. In particular, a
low quality chess source (i.e. tournament game sequences between weak players)
used in combination with actual photographs of people was able to produce
three-move chess problems of comparable quality or better to those generated
using a high quality chess source (i.e. published compositions by human
experts), and more efficiently as well. Why information from a foreign domain
can be integrated and functional in this way remains an open question for now.
The DSNS approach is, in principle, scalable and applicable to any domain in
which objects have attributes that can be represented using real numbers.
","The Digital Synaptic Neural Substrate: A New Approach to Computational
  Creativity"
"  The author has pledged in various papers, conference or seminar
presentations, and scientific grant applications (between 2004-2015) for the
unification of fusion theories, combinations of fusion rules, image fusion
procedures, filter algorithms, and target tracking methods for more accurate
applications to our real world problems - since neither fusion theory nor
fusion rule fully satisfy all needed applications. For each particular
application, one selects the most appropriate fusion space and fusion model,
then the fusion rules, and the algorithms of implementation. He has worked in
the Unification of the Fusion Theories (UFT), which looks like a cooking
recipe, better one could say like a logical chart for a computer programmer,
but one does not see another method to comprise/unify all things. The
unification scenario presented herein, which is now in an incipient form,
should periodically be updated incorporating new discoveries from the fusion
and engineering research.
","Unification of Fusion Theories, Rules, Filters, Image Fusion and Target
  Tracking Methods (UFT)"
"  Model counting is the task of computing the number of assignments to
variables V that satisfy a given propositional theory F. Model counting is an
essential tool in probabilistic reasoning. In this paper, we introduce the
problem of model counting projected on a subset P of original variables that we
call 'priority' variables. The task is to compute the number of assignments to
P such that there exists an extension to 'non-priority' variables V\P that
satisfies F. Projected model counting arises when some parts of the model are
irrelevant to the counts, in particular when we require additional variables to
model the problem we are counting in SAT. We discuss three different approaches
to projected model counting (two of which are novel), and compare their
performance on different benchmark problems.
  To appear in 18th International Conference on Theory and Applications of
Satisfiability Testing, September 24-27, 2015, Austin, Texas, USA
",Projected Model Counting
"  As standardly implemented in R or the Tetrad program, causal search
algorithms used most widely or effectively by scientists have severe
dimensionality constraints that make them inappropriate for big data problems
without sacrificing accuracy. However, implementation improvements are
possible. We explore optimizations for the Greedy Equivalence Search that allow
search on 50,000-variable problems in 13 minutes for sparse models with 1000
samples on a four-processor, 16G laptop computer. We finish a problem with 1000
samples on 1,000,000 variables in 18 hours for sparse models on a supercomputer
node at the Pittsburgh Supercomputing Center with 40 processors and 384 G RAM.
The same algorithm can be applied to discrete data, with a slower discrete
score, though the discrete implementation currently does not scale as well in
our experiments; we have managed to scale up to about 10,000 variables in
sparse models with 1000 samples.
",Scaling up Greedy Causal Search for Continuous Variables
"  Words (phrases or symbols) play a key role in human life. Word (phrase or
symbol) representation is the fundamental problem for knowledge representation
and understanding. A word (phrase or symbol) usually represents a name of a
category. However, it is always a challenge that how to represent a category
can make it easily understood. In this paper, a new representation for a
category is discussed, which can be considered a generalization of classic set.
In order to reduce representation complexity, the economy principle of category
representation is proposed. The proposed category representation provides a
powerful tool for analyzing conceptual systems, relations between words,
communication, knowledge, situations. More specifically, the conceptual system,
word relations and communication are mathematically defined and classified such
as ideal conceptual system, perfect communication and so on; relation between
words and sentences is also studied, which shows that knowledge are words.
Furthermore, how conceptual systems and words depend on situations is
presented, and how truth is defined is also discussed.
",Communication: Words and Conceptual Systems
"  Increasing popularity of mobile route planning applications based on GPS
technology provides opportunities for collecting traffic data in urban
environments. One of the main challenges for travel time estimation and
prediction in such a setting is how to aggregate data from vehicles that have
followed different routes, and predict travel time for other routes of
interest. One approach is to predict travel times for route segments, and sum
those estimates to obtain a prediction for the whole route. We study how to
obtain optimal predictions in this scenario. It appears that the optimal
estimate, minimizing the expected mean absolute error, is a combination of the
mean and the median travel times on each segment, where the combination
function depends on the number of segments in the route of interest. We present
a methodology for obtaining such predictions, and demonstrate its effectiveness
with a case study using travel time data from a district of St. Petersburg
collected over one year. The proposed methodology can be applied for real-time
prediction of expected travel times in an urban road network.
","Optimal estimates for short horizon travel time prediction in urban
  areas"
"  We present CRISNER (Conditional & Relative Importance Statement Network
PrEference Reasoner), a tool that provides practically efficient as well as
exact reasoning about qualitative preferences in popular ceteris paribus
preference languages such as CP-nets, TCP-nets, CP-theories, etc. The tool uses
a model checking engine to translate preference specifications and queries into
appropriate Kripke models and verifiable properties over them respectively. The
distinguishing features of the tool are: (1) exact and provably correct query
answering for testing dominance, consistency with respect to a preference
specification, and testing equivalence and subsumption of two sets of
preferences; (2) automatic generation of proofs evidencing the correctness of
answer produced by CRISNER to any of the above queries; (3) XML inputs and
outputs that make it portable and pluggable into other applications. We also
describe the extensible architecture of CRISNER, which can be extended to new
reference formalisms based on ceteris paribus semantics that may be developed
in the future.
",CRISNER: A Practically Efficient Reasoner for Qualitative Preferences
"  Pairwise comparisons between alternatives are a well-established tool to
decompose decision problems into smaller and more easily tractable
sub-problems. However, due to our limited rationality, the subjective
preferences expressed by decision makers over pairs of alternatives can hardly
ever be consistent. Therefore, several inconsistency indices have been proposed
in the literature to quantify the extent of the deviation from complete
consistency. Only recently, a set of properties has been proposed to define a
family of functions representing inconsistency indices. The scope of this paper
is twofold. Firstly, it expands the set of properties by adding and justifying
a new one. Secondly, it continues the study of inconsistency indices to check
whether or not they satisfy the above mentioned properties. Out of the four
indices considered in this paper, in its present form, two fail to satisfy some
properties. An adjusted version of one index is proposed so that it fulfills
them.
","Studying a set of properties of inconsistency indices for pairwise
  comparisons"
"  A minimalistic cognitive architecture called MANIC is presented. The MANIC
architecture requires only three function approximating models, and one state
machine. Even with so few major components, it is theoretically sufficient to
achieve functional equivalence with all other cognitive architectures, and can
be practically trained. Instead of seeking to transfer architectural
inspiration from biology into artificial intelligence, MANIC seeks to minimize
novelty and follow the most well-established constructs that have evolved
within various sub-fields of data science. From this perspective, MANIC offers
an alternate approach to a long-standing objective of artificial intelligence.
This paper provides a theoretical analysis of the MANIC architecture.
",A Minimal Architecture for General Cognition
"  We present initial research towards procedural generation of Simplified
Boardgames and translating them into an efficient GDL code. This is a step
towards establishing Simplified Boardgames as a comparison class for General
Game Playing agents. To generate playable, human readable, and balanced
chess-like games we use an adaptive evolutionary algorithm with the fitness
function based on simulated playouts. In future, we plan to use the proposed
method to diversify and extend the set of GGP tournament games by those with
fully automatically generated rules.
","Procedural Content Generation for GDL Descriptions of Simplified
  Boardgames"
"  We consider graphs that represent pairwise marginal independencies amongst a
set of variables (for instance, the zero entries of a covariance matrix for
normal data). We characterize the directed acyclic graphs (DAGs) that
faithfully explain a given set of independencies, and derive algorithms to
efficiently enumerate such structures. Our results map out the space of
faithful causal models for a given set of pairwise marginal independence
relations. This allows us to show the extent to which causal inference is
possible without using conditional independence tests.
",Learning from Pairwise Marginal Independencies
"  The quality of high-level AI of non-player characters (NPCs) in commercial
open-world games (OWGs) has been increasing during the past years. However, due
to constraints specific to the game industry, this increase has been slow and
it has been driven by larger budgets rather than adoption of new complex AI
techniques. Most of the contemporary AI is still expressed as hard-coded
scripts. The complexity and manageability of the script codebase is one of the
key limiting factors for further AI improvements. In this paper we address this
issue. We present behavior objects - a general approach to development of NPC
behaviors for large OWGs. Behavior objects are inspired by object-oriented
programming and extend the concept of smart objects. Our approach promotes
encapsulation of data and code for multiple related behaviors in one place,
hiding internal details and embedding intelligence in the environment. Behavior
objects are a natural abstraction of five different techniques that we have
implemented to manage AI complexity in an upcoming AAA OWG. We report the
details of the implementations in the context of behavior trees and the lessons
learned during development. Our work should serve as inspiration for AI
architecture designers from both the academia and the industry.
",Using Behavior Objects to Manage Complexity in Virtual Worlds
"  In electronic sports, cyberathletes conceal their online training using
different avatars (virtual identities), allowing them not being recognized by
the opponents they may face in future competitions. In this article, we propose
a method to tackle this avatar aliases identification problem. Our method
trains a classifier on behavioural data and processes the confusion matrix to
output label pairs which concentrate confusion. We experimented with Starcraft
2 and report our first results.
",Identifying Avatar Aliases in Starcraft 2
"  The fundamental problem underlying all multi-criteria decision analysis
(MCDA) problems is that of dominance between any two alternatives: ""Given two
alternatives A and B, each described by a set criteria, is A preferred to B
with respect to a set of decision maker (DM) preferences over the criteria?"".
Depending on the application in which MCDA is performed, the alternatives may
represent strategies and policies for business, potential locations for setting
up new facilities, designs of buildings, etc. The general objective of MCDA is
to enable the DM to order all alternatives in order of the stated preferences,
and choose the ones that are best, i.e., optimal with respect to the
preferences over the criteria. This article presents and summarizes a recently
developed MCDA framework that orders the set of alternatives when the relative
importance preferences are incomplete, imprecise, or qualitative in nature.
",Qualitative Decision Methods for Multi-Attribute Decision Making
"  Belief compression improves the tractability of large-scale partially
observable Markov decision processes (POMDPs) by finding projections from
high-dimensional belief space onto low-dimensional approximations, where
solving to obtain action selection policies requires fewer computations. This
paper develops a unified theoretical framework to analyse three existing linear
belief compression approaches, including value-directed compression and two
non-negative matrix factorisation (NMF) based algorithms. The results indicate
that all the three known belief compression methods have their own critical
deficiencies. Therefore, projective NMF belief compression is proposed (P-NMF),
aiming to overcome the drawbacks of the existing techniques. The performance of
the proposed algorithm is examined on four POMDP problems of reasonably large
scale, in comparison with existing techniques. Additionally, the
competitiveness of belief compression is compared empirically to a
state-of-the-art heuristic search based POMDP solver and their relative merits
in solving large-scale POMDPs are investigated.
","On the Linear Belief Compression of POMDPs: A re-examination of current
  methods"
"  One of the major challenges for collective intelligence is inconsistency,
which is unavoidable whenever subjective assessments are involved. Pairwise
comparisons allow one to represent such subjective assessments and to process
them by analyzing, quantifying and identifying the inconsistencies.
  We propose using smaller scales for pairwise comparisons and provide
mathematical and practical justifications for this change. Our postulate's aim
is to initiate a paradigm shift in the search for a better scale construction
for pairwise comparisons. Beyond pairwise comparisons, the results presented
may be relevant to other methods using subjective scales.
  Keywords: pairwise comparisons, collective intelligence, scale, subjective
assessment, inaccuracy, inconsistency.
",A different perspective on a scale for pairwise comparisons
"  Valuation algebras abstract a large number of formalisms for automated
reasoning and enable the definition of generic inference procedures. Many of
these formalisms provide some notion of solution. Typical examples are
satisfying assignments in constraint systems, models in logics or solutions to
linear equation systems.
  Many widely used dynamic programming algorithms for optimization problems
rely on low treewidth decompositions and can be understood as particular cases
of a single algorithmic scheme for finding solutions in a valuation algebra.
The most encompassing description of this algorithmic scheme to date has been
proposed by Pouly and Kohlas together with sufficient conditions for its
correctness. Unfortunately, the formalization relies on a theorem for which we
provide counterexamples. In spite of that, the mainline of Pouly and Kohlas'
theory is correct, although some of the necessary conditions have to be
revised. In this paper we analyze the impact that the counter-examples have on
the theory, and rebuild the theory providing correct sufficient conditions for
the algorithms. Furthermore, we also provide necessary conditions for the
algorithms, allowing for a sharper characterization of when the algorithmic
scheme can be applied.
","Sufficient and necessary conditions for Dynamic Programming in
  Valuation-Based Systems"
"  Capturing the interdependencies between real valued time series can be
achieved by finding common similar patterns. The abstraction of time series
makes the process of finding similarities closer to the way as humans do.
Therefore, the abstraction by means of a symbolic levels and finding the common
patterns attracts researchers. One particular algorithm, Longest Common
Subsequence, has been used successfully as a similarity measure between two
sequences including real valued time series. In this paper, we propose Fuzzy
Longest Common Subsequence matching for time series.
",Fuzzy Longest Common Subsequence Matching With FCM Using R
"  Uncovering causal relationships in data is a major objective of data
analytics. Causal relationships are normally discovered with designed
experiments, e.g. randomised controlled trials, which, however are expensive or
infeasible to be conducted in many cases. Causal relationships can also be
found using some well designed observational studies, but they require domain
experts' knowledge and the process is normally time consuming. Hence there is a
need for scalable and automated methods for causal relationship exploration in
data. Classification methods are fast and they could be practical substitutes
for finding causal signals in data. However, classification methods are not
designed for causal discovery and a classification method may find false causal
signals and miss the true ones. In this paper, we develop a causal decision
tree where nodes have causal interpretations. Our method follows a well
established causal inference framework and makes use of a classic statistical
test. The method is practical for finding causal signals in large data sets.
",Causal Decision Trees
"  Randomised controlled trials (RCTs) are the most effective approach to causal
discovery, but in many circumstances it is impossible to conduct RCTs.
Therefore observational studies based on passively observed data are widely
accepted as an alternative to RCTs. However, in observational studies, prior
knowledge is required to generate the hypotheses about the cause-effect
relationships to be tested, hence they can only be applied to problems with
available domain knowledge and a handful of variables. In practice, many data
sets are of high dimensionality, which leaves observational studies out of the
opportunities for causal discovery from such a wealth of data sources. In
another direction, many efficient data mining methods have been developed to
identify associations among variables in large data sets. The problem is,
causal relationships imply associations, but the reverse is not always true.
However we can see the synergy between the two paradigms here. Specifically,
association rule mining can be used to deal with the high-dimensionality
problem while observational studies can be utilised to eliminate non-causal
associations. In this paper we propose the concept of causal rules (CRs) and
develop an algorithm for mining CRs in large data sets. We use the idea of
retrospective cohort studies to detect CRs based on the results of association
rule mining. Experiments with both synthetic and real world data sets have
demonstrated the effectiveness and efficiency of CR mining. In comparison with
the commonly used causal discovery methods, the proposed approach in general is
faster and has better or competitive performance in finding correct or sensible
causes. It is also capable of finding a cause consisting of multiple variables,
a feature that other causal discovery methods do not possess.
",From Observational Studies to Causal Rule Mining
"  The ability to represent complex high dimensional probability distributions
in a compact form is one of the key insights in the field of graphical models.
Factored representations are ubiquitous in machine learning and lead to major
computational advantages. We explore a different type of compact representation
based on discrete Fourier representations, complementing the classical approach
based on conditional independencies. We show that a large class of
probabilistic graphical models have a compact Fourier representation. This
theoretical result opens up an entirely new way of approximating a probability
distribution. We demonstrate the significance of this approach by applying it
to the variable elimination algorithm. Compared with the traditional bucket
representation and other approximate inference algorithms, we obtain
significant improvements.
",Variable Elimination in the Fourier Domain
"  This paper highlights distinctive features of the ""SP theory of intelligence""
and its apparent advantages compared with some AI-related alternatives.
Distinctive features and advantages are: simplification and integration of
observations and concepts; simplification and integration of structures and
processes in computing systems; the theory is itself a theory of computing; it
can be the basis for new architectures for computers; information compression
via the matching and unification of patterns and, more specifically, via
multiple alignment, is fundamental; transparency in the representation and
processing of knowledge; the discovery of 'natural' structures via information
compression (DONSVIC); interpretations of mathematics; interpretations in human
perception and cognition; and realisation of abstract concepts in terms of
neurons and their inter-connections (""SP-neural""). These things relate to
AI-related alternatives: minimum length encoding and related concepts; deep
learning in neural networks; unified theories of cognition and related
research; universal search; Bayesian networks and more; pattern recognition and
vision; the analysis, production, and translation of natural language;
Unsupervised learning of natural language; exact and inexact forms of
reasoning; representation and processing of diverse forms of knowledge; IBM's
Watson; software engineering; solving problems associated with big data, and in
the development of intelligence in autonomous robots. In conclusion, the SP
system can provide a firm foundation for the long-term development of AI, with
many potential benefits and applications. It may also deliver useful results on
relatively short timescales. A high-parallel, open-source version of the SP
machine, derived from the SP computer model, would be a means for researchers
everywhere to explore what can be done with the system, and to create new
versions of it.
",The SP theory of intelligence: distinctive features and advantages
"  This paper introduces CLEO, a novel preference elicitation algorithm capable
of recommending complex objects in hybrid domains, characterized by both
discrete and continuous attributes and constraints defined over them. The
algorithm assumes minimal initial information, i.e., a set of catalog
attributes, and defines decisional features as logic formulae combining Boolean
and algebraic constraints over the attributes. The (unknown) utility of the
decision maker (DM) is modelled as a weighted combination of features. CLEO
iteratively alternates a preference elicitation step, where pairs of candidate
solutions are selected based on the current utility model, and a refinement
step where the utility is refined by incorporating the feedback received. The
elicitation step leverages a Max-SMT solver to return optimal hybrid solutions
according to the current utility model. The refinement step is implemented as
learning to rank, and a sparsifying norm is used to favour the selection of few
informative features in the combinatorial space of candidate decisional
features.
  CLEO is the first preference elicitation algorithm capable of dealing with
hybrid domains, thanks to the use of Max-SMT technology, while retaining
uncertainty in the DM utility and noisy feedback. Experimental results on
complex recommendation tasks show the ability of CLEO to quickly focus towards
optimal solutions, as well as its capacity to recover from suboptimal initial
choices. While no competitors exist in the hybrid setting, CLEO outperforms a
state-of-the-art Bayesian preference elicitation algorithm when applied to a
purely discrete task.
",Learning Modulo Theories for preference elicitation in hybrid domains
"  This is a proposal to create a research facility for the development of a
high-parallel version of the ""SP machine"", based on the ""SP theory of
intelligence"". We envisage that the new version of the SP machine will be an
open-source software virtual machine, derived from the existing ""SP computer
model"", and hosted on an existing high-performance computer. It will be a means
for researchers everywhere to explore what can be done with the system and to
create new versions of it. The SP system is a unique attempt to simplify and
integrate observations and concepts across artificial intelligence, mainstream
computing, mathematics, and human perception and cognition, with information
compression as a unifying theme. Potential benefits and applications include
helping to solve problems associated with big data; facilitating the
development of autonomous robots; unsupervised learning, natural language
processing, several kinds of reasoning, fuzzy pattern recognition at multiple
levels of abstraction, computer vision, best-match and semantic forms of
information retrieval, software engineering, medical diagnosis, simplification
of computing systems, and the seamless integration of diverse kinds of
knowledge and diverse aspects of intelligence. Additional motivations include
the potential of the SP system to help solve problems in defence, security, and
the detection and prevention of crime; potential in terms of economic, social,
environmental, and academic criteria, and in terms of publicity; and the
potential for international influence in research. The main elements of the
proposed facility are described, including support for the development of
""SP-neural"", a neural version of the SP machine. The facility should be
permanent in the sense that it should be available for the foreseeable future,
and it should be designed to facilitate its use by researchers anywhere in the
world.
","Proposal for the creation of a research facility for the development of
  the SP machine"
"  DAGitty is a software for drawing and analyzing causal diagrams, also known
as directed acyclic graphs (DAGs). Functions include identification of minimal
sufficient adjustment sets for estimating causal effects, diagnosis of
insufficient or invalid adjustment via the identification of biasing paths,
identification of instrumental variables, and derivation of testable
implications. DAGitty is provided in the hope that it is useful for researchers
and students in Epidemiology, Sociology, Psychology, and other empirical
disciplines. The software should run in any web browser that supports modern
JavaScript, HTML, and SVG. This is the user manual for DAGitty version 2.3. The
manual is updated with every release of a new stable version. DAGitty is
available at dagitty.net.
",Drawing and Analyzing Causal DAGs with DAGitty
"  Warehouse is one of the important aspects of a company. Therefore, it is
necessary to improve Warehouse Management System (WMS) to have a simple
function that can determine the layout of the storage goods. In this paper we
propose an improved warehouse layout method based on ant colony algorithm and
backtracking algorithm. The method works on two steps. First, it generates a
solutions parameter tree from backtracking algorithm. Then second, it deducts
the solutions parameter by using a combination of ant colony algorithm and
backtracking algorithm. This method was tested by measuring the time needed to
build the tree and to fill up the space using two scenarios. The method needs
0.294 to 33.15 seconds to construct the tree and 3.23 seconds (best case) to
61.41 minutes (worst case) to fill up the warehouse. This method is proved to
be an attractive alternative solution for warehouse layout system.
",Warehouse Layout Method Based on Ant Colony and Backtracking Algorithm
"  The margin of victory is easy to compute for many election schemes but
difficult for Instant Runoff Voting (IRV). This is important because arguments
about the correctness of an election outcome usually rely on the size of the
electoral margin. For example, risk-limiting audits require a knowledge of the
margin of victory in order to determine how much auditing is necessary. This
paper presents a practical branch-and-bound algorithm for exact IRV margin
computation that substantially improves on the current best-known approach.
Although exponential in the worst case, our algorithm runs efficiently in
practice on all the real examples we could find. We can efficiently discover
exact margins on election instances that cannot be solved by the current
state-of-the-art.
",Efficient Computation of Exact IRV Margins
"  Analysis of sequential event data has been recognized as one of the essential
tools in data modeling and analysis field. In this paper, after the examination
of its technical requirements and issues to model complex but practical
situation, we propose a new sequential data model, dubbed Duration and Interval
Hidden Markov Model (DI-HMM), that efficiently represents ""state duration"" and
""state interval"" of data events. This has significant implications to play an
important role in representing practical time-series sequential data. This
eventually provides an efficient and flexible sequential data retrieval.
Numerical experiments on synthetic and real data demonstrate the efficiency and
accuracy of the proposed DI-HMM.
",Duration and Interval Hidden Markov Model for Sequential Data Analysis
"  In this note we provide a concise report on the complexity of the causal
ordering problem, originally introduced by Simon to reason about causal
dependencies implicit in systems of mathematical equations. We show that
Simon's classical algorithm to infer causal ordering is NP-Hard---an
intractability previously guessed but never proven. We present then a detailed
account based on Nayak's suggested algorithmic solution (the best available),
which is dominated by computing transitive closure---bounded in time by
$O(|\mathcal V|\cdot |\mathcal S|)$, where $\mathcal S(\mathcal E, \mathcal V)$
is the input system structure composed of a set $\mathcal E$ of equations over
a set $\mathcal V$ of variables with number of variable appearances (density)
$|\mathcal S|$. We also comment on the potential of causal ordering for
emerging applications in large-scale hypothesis management and analytics.
",A note on the complexity of the causal ordering problem
"  We propose to use thought-provoking children's questions (TPCQs), namely
Highlights BrainPlay questions, as a new method to drive artificial
intelligence research and to evaluate the capabilities of general-purpose AI
systems. These questions are designed to stimulate thought and learning in
children, and they can be used to do the same thing in AI systems, while
demonstrating the system's reasoning capabilities to the evaluator. We
introduce the TPCQ task, which which takes a TPCQ question as input and
produces as output (1) answers to the question and (2) learned generalizations.
We discuss how BrainPlay questions stimulate learning. We analyze 244 BrainPlay
questions, and we report statistics on question type, question class, answer
cardinality, answer class, types of knowledge needed, and types of reasoning
needed. We find that BrainPlay questions span many aspects of intelligence.
Because the answers to BrainPlay questions and the generalizations learned from
them are often highly open-ended, we suggest using human judges for evaluation.
","Using Thought-Provoking Children's Questions to Drive Artificial
  Intelligence Research"
"  We analyse a quantum-like Bayesian Network that puts together cause/effect
relationships and semantic similarities between events. These semantic
similarities constitute acausal connections according to the Synchronicity
principle and provide new relationships to quantum like probabilistic graphical
models. As a consequence, beliefs (or any other event) can be represented in
vector spaces, in which quantum parameters are determined by the similarities
that these vectors share between them. Events attached by a semantic meaning do
not need to have an explanation in terms of cause and effect.
","The Relation Between Acausality and Interference in Quantum-Like
  Bayesian Networks"
"  In recent years, many methods have been developed for detecting causal
relationships in observational data. Some of them have the potential to tackle
large data sets. However, these methods fail to discover a combined cause, i.e.
a multi-factor cause consisting of two or more component variables which
individually are not causes. A straightforward approach to uncovering a
combined cause is to include both individual and combined variables in the
causal discovery using existing methods, but this scheme is computationally
infeasible due to the huge number of combined variables. In this paper, we
propose a novel approach to address this practical causal discovery problem,
i.e. mining combined causes in large data sets. The experiments with both
synthetic and real world data sets show that the proposed method can obtain
high-quality causal discoveries with a high computational efficiency.
",Mining Combined Causes in Large Data Sets
"  Using the recently introduced universal computing model, called orchestrated
machine, that represents computations in a dissipative environment, we consider
a new kind of interpretation of Turing's Imitation Game. In addition we raise
the question whether the intelligence may show fractal properties. Then we
sketch a vision of what robotic cars are going to do in the future. Finally we
give the specification of an artificial life game based on the concept of
orchestrated machines. The purpose of this paper is to start the search for
possible relationships between these different topics.
",Turing's Imitation Game has been Improved
"  Text analysis includes lexical analysis of the text and has been widely
studied and used in diverse applications. In the last decade, researchers have
proposed many efficient solutions to analyze / classify large text dataset,
however, analysis / classification of short text is still a challenge because
1) the data is very sparse 2) It contains noise words and 3) It is difficult to
understand the syntactical structure of the text. Short Messaging Service (SMS)
is a text messaging service for mobile/smart phone and this service is
frequently used by all mobile users. Because of the popularity of SMS service,
marketing companies nowadays are also using this service for direct marketing
also known as SMS marketing.In this paper, we have proposed Ontology based SMS
Controller which analyze the text message and classify it using ontology
aslegitimate or spam. The proposed system has been tested on different
scenarios and experimental results shows that the proposed solution is
effective both in terms of efficiency and time.
",Ontology Based SMS Controller for Smart Phones
"  In this paper, we investigate bounded action theories in the situation
calculus. A bounded action theory is one which entails that, in every
situation, the number of object tuples in the extension of fluents is bounded
by a given constant, although such extensions are in general different across
the infinitely many situations. We argue that such theories are common in
applications, either because facts do not persist indefinitely or because the
agent eventually forgets some facts, as new ones are learnt. We discuss various
classes of bounded action theories. Then we show that verification of a
powerful first-order variant of the mu-calculus is decidable for such theories.
Notably, this variant supports a controlled form of quantification across
situations. We also show that through verification, we can actually check
whether an arbitrary action theory maintains boundedness.
",Bounded Situation Calculus Action Theories
"  This work proposes a unified heuristic algorithm for a large class of
earliness-tardiness (E-T) scheduling problems. We consider single/parallel
machine E-T problems that may or may not consider some additional features such
as idle time, setup times and release dates. In addition, we also consider
those problems whose objective is to minimize either the total (average)
weighted completion time or the total (average) weighted flow time, which arise
as particular cases when the due dates of all jobs are either set to zero or to
their associated release dates, respectively. The developed local search based
metaheuristic framework is quite simple, but at the same time relies on
sophisticated procedures for efficiently performing local search according to
the characteristics of the problem. We present efficient move evaluation
approaches for some parallel machine problems that generalize the existing ones
for single machine problems. The algorithm was tested in hundreds of instances
of several E-T problems and particular cases. The results obtained show that
our unified heuristic is capable of producing high quality solutions when
compared to the best ones available in the literature that were obtained by
specific methods. Moreover, we provide an extensive annotated bibliography on
the problems related to those considered in this work, where we not only
indicate the approach(es) used in each publication, but we also point out the
characteristics of the problem(s) considered. Beyond that, we classify the
existing methods in different categories so as to have a better idea of the
popularity of each type of solution procedure.
","A unified heuristic and an annotated bibliography for a large class of
  earliness-tardiness scheduling problems"
"  Markov decision processes (MDPs) are a well studied framework for solving
sequential decision making problems under uncertainty. Exact methods for
solving MDPs based on dynamic programming such as policy iteration and value
iteration are effective on small problems. In problems with a large discrete
state space or with continuous state spaces, a compact representation is
essential for providing an efficient approximation solutions to MDPs. Commonly
used approximation algorithms involving constructing basis functions for
projecting the value function onto a low dimensional subspace, and building a
factored or hierarchical graphical model to decompose the transition and reward
functions. However, hand-coding a good compact representation for a given
reinforcement learning (RL) task can be quite difficult and time consuming.
Recent approaches have attempted to automatically discover efficient
representations for RL.
  In this thesis proposal, we discuss the problems of automatically
constructing structured kernel for kernel based RL, a popular approach to
learning non-parametric approximations for value function. We explore a space
of kernel structures which are built compositionally from base kernels using a
context-free grammar. We examine a greedy algorithm for searching over the
structure space. To demonstrate how the learned structure can represent and
approximate the original RL problem in terms of compactness and efficiency, we
plan to evaluate our method on a synthetic problem and compare it to other RL
baselines.
",Learning Efficient Representations for Reinforcement Learning
"  Search is a central problem in artificial intelligence, and breadth-first
search (BFS) and depth-first search (DFS) are the two most fundamental ways to
search. In this paper we derive estimates for average BFS and DFS runtime. The
average runtime estimates can be used to allocate resources or judge the
hardness of a problem. They can also be used for selecting the best graph
representation, and for selecting the faster algorithm out of BFS and DFS. They
may also form the basis for an analysis of more advanced search methods. The
paper treats both tree search and graph search. For tree search, we employ a
probabilistic model of goal distribution; for graph search, the analysis
depends on an additional statistic of path redundancy and average branching
factor. As an application, we use the results to predict BFS and DFS runtime on
two concrete grammar problems and on the N-puzzle. Experimental verification
shows that our analytical approximations come close to empirical reality.
","A Topological Approach to Meta-heuristics: Analytical Results on the BFS
  vs. DFS Algorithm Selection Problem"
"  The research presents epsilon hierarchical fuzzy twin support vector
regression based on epsilon fuzzy twin support vector regression and epsilon
twin support vector regression. Epsilon FTSVR is achieved by incorporating
trapezoidal fuzzy numbers to epsilon TSVR which takes care of uncertainty
existing in forecasting problems. Epsilon FTSVR determines a pair of epsilon
insensitive proximal functions by solving two related quadratic programming
problems. The structural risk minimization principle is implemented by
introducing regularization term in primal problems of epsilon FTSVR. This
yields dual stable positive definite problems which improves regression
performance. Epsilon FTSVR is then reformulated as epsilon HFTSVR consisting of
a set of hierarchical layers each containing epsilon FTSVR. Experimental
results on both synthetic and real datasets reveal that epsilon HFTSVR has
remarkable generalization performance with minimum training time.
",An Epsilon Hierarchical Fuzzy Twin Support Vector Regression
"  We administered the Verbal IQ (VIQ) part of the Wechsler Preschool and
Primary Scale of Intelligence (WPPSI-III) to the ConceptNet 4 AI system. The
test questions (e.g., ""Why do we shake hands?"") were translated into ConceptNet
4 inputs using a combination of the simple natural language processing tools
that come with ConceptNet together with short Python programs that we wrote.
The question answering used a version of ConceptNet based on spectral methods.
The ConceptNet system scored a WPPSI-III VIQ that is average for a
four-year-old child, but below average for 5 to 7 year-olds. Large variations
among subtests indicate potential areas of improvement. In particular, results
were strongest for the Vocabulary and Similarities subtests, intermediate for
the Information subtest, and lowest for the Comprehension and Word Reasoning
subtests. Comprehension is the subtest most strongly associated with common
sense. The large variations among subtests and ordinary common sense strongly
suggest that the WPPSI-III VIQ results do not show that ""ConceptNet has the
verbal abilities a four-year-old."" Rather, children's IQ tests offer one
objective metric for the evaluation and comparison of AI systems. Also, this
work continues previous research on Psychometric AI.
","Measuring an Artificial Intelligence System's Performance on a Verbal IQ
  Test For Young Children"
"  New proof assistant developments often involve concepts similar to already
formalized ones. When proving their properties, a human can often take
inspiration from the existing formalized proofs available in other provers or
libraries. In this paper we propose and evaluate a number of methods, which
strengthen proof automation by learning from proof libraries of different
provers. Certain conjectures can be proved directly from the dependencies
induced by similar proofs in the other library. Even if exact correspondences
are not found, learning-reasoning systems can make use of the association
between proved theorems and their characteristics to predict the relevant
premises. Such external help can be further combined with internal advice. We
evaluate the proposed knowledge-sharing methods by reproving the HOL Light and
HOL4 standard libraries. The learning-reasoning system HOL(y)Hammer, whose
single best strategy could automatically find proofs for 30% of the HOL Light
problems, can prove 40% with the knowledge from HOL4.
",Sharing HOL4 and HOL Light proof knowledge
"  Learning-assisted automated reasoning has recently gained popularity among
the users of Isabelle/HOL, HOL Light, and Mizar. In this paper, we present an
add-on to the HOL4 proof assistant and an adaptation of the HOLyHammer system
that provides machine learning-based premise selection and automated reasoning
also for HOL4. We efficiently record the HOL4 dependencies and extract features
from the theorem statements, which form a basis for premise selection.
HOLyHammer transforms the HOL4 statements in the various TPTP-ATP proof
formats, which are then processed by the ATPs. We discuss the different
evaluation settings: ATPs, accessible lemmas, and premise numbers. We measure
the performance of HOLyHammer on the HOL4 standard library. The results are
combined accordingly and compared with the HOL Light experiments, showing a
comparably high quality of predictions. The system directly benefits HOL4 users
by automatically finding proofs dependencies that can be reconstructed by
Metis.
",Premise Selection and External Provers for HOL4
"  Probabilistic programming provides the means to represent and reason about
complex probabilistic models using programming language constructs. Even simple
probabilistic programs can produce models with infinitely many variables.
Factored inference algorithms are widely used for probabilistic graphical
models, but cannot be applied to these programs because all the variables and
factors have to be enumerated. In this paper, we present a new inference
framework, lazy factored inference (LFI), that enables factored algorithms to
be used for models with infinitely many variables. LFI expands the model to a
bounded depth and uses the structure of the program to precisely quantify the
effect of the unexpanded part of the model, producing lower and upper bounds to
the probability of the query.
",Lazy Factored Inference for Functional Probabilistic Programming
"  Dung's abstract argumentation framework consists of a set of interacting
arguments and a series of semantics for evaluating them. Those semantics
partition the powerset of the set of arguments into two classes: extensions and
non-extensions. In order to reason with a specific semantics, one needs to take
a credulous or skeptical approach, i.e. an argument is eventually accepted, if
it is accepted in one or all extensions, respectively. In our previous work
\cite{ref-pu2015counting}, we have proposed a novel semantics, called
\emph{counting semantics}, which allows for a more fine-grained assessment to
arguments by counting the number of their respective attackers and defenders
based on argument graph and argument game. In this paper, we continue our
previous work by presenting some supplementaries about how to choose the
damaging factor for the counting semantics, and what relationships with some
existing approaches, such as Dung's classical semantics, generic gradual
valuations. Lastly, an axiomatic perspective on the ranking semantics induced
by our counting semantics are presented.
","Some Supplementaries to The Counting Semantics for Abstract
  Argumentation"
"  In the Bayesian Reinforcement Learning (BRL) setting, agents try to maximise
the collected rewards while interacting with their environment while using some
prior knowledge that is accessed beforehand. Many BRL algorithms have already
been proposed, but even though a few toy examples exist in the literature,
there are still no extensive or rigorous benchmarks to compare them. The paper
addresses this problem, and provides a new BRL comparison methodology along
with the corresponding open source library. In this methodology, a comparison
criterion that measures the performance of algorithms on large sets of Markov
Decision Processes (MDPs) drawn from some probability distributions is defined.
In order to enable the comparison of non-anytime algorithms, our methodology
also includes a detailed analysis of the computation time requirement of each
algorithm. Our library is released with all source code and documentation: it
includes three test problems, each of which has two different prior
distributions, and seven state-of-the-art RL algorithms. Finally, our library
is illustrated by comparing all the available algorithms and the results are
discussed.
",Benchmarking for Bayesian Reinforcement Learning
"  A major problem of causal inference is the arrangement of dependent nodes in
a directed acyclic graph (DAG) with path coefficients and observed confounders.
Path coefficients do not provide the units to measure the strength of
information flowing from one node to the other. Here we proposed the method of
causal structure learning using collider v-structures (CVS) with Negative
Percentage Mapping (NPM) to get selective thresholds of information strength,
to direct the edges and subjective confounders in a DAG. The NPM is used to
scale the strength of information passed through nodes in units of percentage
from interval from 0 to 1. The causal structures are constructed by bottom up
approach using path coefficients, causal directions and confounders, derived
implementing collider v-structure and NPM. The method is self-sufficient to
observe all the latent confounders present in the causal model and capable of
detecting every responsible causal direction. The results are tested for
simulated datasets of non-Gaussian distributions and compared with DirectLiNGAM
and ICA-LiNGAM to check efficiency of the proposed method.
","Causal Model Analysis using Collider v-structure with Negative
  Percentage Mapping"
"  In semantic technologies, the shared common understanding of the structure of
information among artifacts (people or software agents) can be realized by
building an ontology. To do this, it is imperative for an ontology builder to
answer several questions: a) what are the main components of an ontology? b)
How an ontology look likes and how it works? c) Verify if it is required to
consider reusing existing ontologies or not? c) What is the complexity of the
ontology to be developed? d) What are the principles of ontology design and
development? e) How to evaluate an ontology? This paper answers all the key
questions above. The aim of this paper is to present a set of guiding
principles to help ontology developers and also inexperienced users to answer
such questions.
","A Study Investigating Typical Concepts and Guidelines for Ontology
  Building"
"  Poker is a family of card games that includes many variations. We hypothesize
that most poker games can be solved as a pattern matching problem, and propose
creating a strong poker playing system based on a unified poker representation.
Our poker player learns through iterative self-play, and improves its
understanding of the game by training on the results of its previous actions
without sophisticated domain knowledge. We evaluate our system on three poker
games: single player video poker, two-player Limit Texas Hold'em, and finally
two-player 2-7 triple draw poker. We show that our model can quickly learn
patterns in these very different poker games while it improves from zero
knowledge to a competitive player against human experts.
  The contributions of this paper include: (1) a novel representation for poker
games, extendable to different poker variations, (2) a CNN based learning model
that can effectively learn the patterns in three different games, and (3) a
self-trained system that significantly beats the heuristic-based program on
which it is trained, and our system is competitive against human expert
players.
","Poker-CNN: A Pattern Learning Strategy for Making Draws and Bets in
  Poker Games"
"  We describe a framework for building abstraction hierarchies whereby an agent
alternates skill- and representation-acquisition phases to construct a sequence
of increasingly abstract Markov decision processes. Our formulation builds on
recent results showing that the appropriate abstract representation of a
problem is specified by the agent's skills. We describe how such a hierarchy
can be used for fast planning, and illustrate the construction of an
appropriate hierarchy for the Taxi domain.
",Constructing Abstraction Hierarchies Using a Skill-Symbol Loop
"  Recent results have shown that the MCTS algorithm (a new, adaptive,
randomized optimization algorithm) is effective in a remarkably diverse set of
applications in Artificial Intelligence, Operations Research, and High Energy
Physics. MCTS can find good solutions without domain dependent heuristics,
using the UCT formula to balance exploitation and exploration. It has been
suggested that the optimum in the exploitation- exploration balance differs for
different search tree sizes: small search trees needs more exploitation; large
search trees need more exploration. Small search trees occur in variations of
MCTS, such as parallel and ensemble approaches. This paper investigates the
possibility of improving the performance of Ensemble UCT by increasing the
level of exploitation. As the search trees becomes smaller we achieve an
improved performance. The results are important for improving the performance
of large scale parallelism of MCTS.
",Ensemble UCT Needs High Exploitation
"  Over the past few years, unmanned aerial vehicles (UAV), also known as
drones, have been adopted as part of a new logistic method in the commercial
sector called ""last-mile delivery"". In this novel approach, they are deployed
alongside trucks to deliver goods to customers to improve the quality of
service and reduce the transportation cost. This approach gives rise to a new
variant of the traveling salesman problem (TSP), called TSP with drone (TSP-D).
A variant of this problem that aims to minimize the time at which truck and
drone finish the service (or, in other words, to maximize the quality of
service) was studied in the work of Murray and Chu (2015). In contrast, this
paper considers a new variant of TSP-D in which the objective is to minimize
operational costs including total transportation cost and one created by waste
time a vehicle has to wait for the other. The problem is first formulated
mathematically. Then, two algorithms are proposed for the solution. The first
algorithm (TSP-LS) was adapted from the approach proposed by Murray and Chu
(2015), in which an optimal TSP solution is converted to a feasible TSP-D
solution by local searches. The second algorithm, a Greedy Randomized Adaptive
Search Procedure (GRASP), is based on a new split procedure that optimally
splits any TSP tour into a TSP-D solution. After a TSP-D solution has been
generated, it is then improved through local search operators. Numerical
results obtained on various instances of both objective functions with
different sizes and characteristics are presented. The results show that GRASP
outperforms TSP-LS in terms of solution quality under an acceptable running
time.
",On the Min-cost Traveling Salesman Problem with Drone
"  In this paper we describe an extension of the Variable Neighbourhood Search
(VNS) which integrates the basic VNS with other complementary approaches from
machine learning, statistics and experimental algorithmic, in order to produce
high-quality performance and to completely automate the resulting optimization
strategy. The resulting intelligent VNS has been successfully applied to a
couple of optimization problems where the solution space consists of the
subsets of a finite reference set. These problems are the labelled spanning
tree and forest problems that are formulated on an undirected labelled graph; a
graph where each edge has a label in a finite set of labels L. The problems
consist on selecting the subset of labels such that the subgraph generated by
these labels has an optimal spanning tree or forest, respectively. These
problems have several applications in the real-world, where one aims to ensure
connectivity by means of homogeneous connections.
","An intelligent extension of Variable Neighbourhood Search for labelling
  graph problems"
"  It has been quite a long time since AI researchers in the field of computer
science stop talking about simulating human intelligence or trying to explain
how brain works. Recently, represented by deep learning techniques, the field
of machine learning is experiencing unprecedented prosperity and some
applications with near human-level performance bring researchers confidence to
imply that their approaches are the promising candidate for understanding the
mechanism of human brain. However apart from several ancient philological
criteria and some imaginary black box tests (Turing test, Chinese room) there
is no computational level explanation, definition or criteria about
intelligence or any of its components. Base on the common sense that learning
ability is one critical component of intelligence and inspect from the
viewpoint of mapping relations, this paper presents two laws which explains
what is the ""learning ability"" as we familiar with and under what conditions a
mapping relation can be acknowledged as ""Learning Model"".
",The Computational Principles of Learning Ability
"  In this paper, we present a board game: Square War. The game definition of
Square War is similar to the classic Chinese board game Go. Then we propose a
mathematical problem of the game Square War. Finally, we show that the problem
can be solved by using a method of mixed mathematics and computer science.
",Solving a Mathematical Problem in Square War: a Go-like Board Game
"  If a robot is supposed to roam an environment and interact with objects, it
is often necessary to know all possible objects in advance, so that a database
with models of all objects can be generated for visual identification. However,
this constraint cannot always be fulfilled. Due to that reason, a model based
object recognition cannot be used to guide the robot's interactions. Therefore,
this paper proposes a system that analyzes features of encountered objects and
then uses these features to compare unknown objects to already known ones. From
the resulting similarity appropriate actions can be derived. Moreover, the
system enables the robot to learn object categories by grouping similar objects
or by splitting existing categories. To represent the knowledge a hybrid form
is used, consisting of both symbolic and subsymbolic representations.
","Online Vision- and Action-Based Object Classification Using Both
  Symbolic and Subsymbolic Knowledge Representations"
"  Data quality is fundamentally important to ensure the reliability of data for
stakeholders to make decisions. In real world applications, such as scientific
exploration of extreme environments, it is unrealistic to require raw data
collected to be perfect. As data miners, when it is infeasible to physically
know the why and the how in order to clean up the data, we propose to seek the
intrinsic structure of the signal to identify the common factors of
multivariate data. Using our new data driven learning method, the common-factor
data cleaning approach, we address an interdisciplinary challenge on
multivariate data cleaning when complex external impacts appear to interfere
with multiple data measurements. Existing data analyses typically process one
signal measurement at a time without considering the associations among all
signals. We analyze all signal measurements simultaneously to find the hidden
common factors that drive all measurements to vary together, but not as a
result of the true data measurements. We use common factors to reduce the
variations in the data without changing the base mean level of the data to
avoid altering the physical meaning.
","A Common-Factor Approach for Multivariate Data Cleaning with an
  Application to Mars Phoenix Mission Data"
"  Answer set programming is a declarative programming paradigm oriented towards
difficult combinatorial search problems. A fundamental task in answer set
programming is to compute stable models, i.e., solutions of logic programs.
Answer set solvers are the programs that perform this task. The problem of
deciding whether a disjunctive program has a stable model is
$\Sigma^P_2$-complete. The high complexity of reasoning within disjunctive
logic programming is responsible for few solvers capable of dealing with such
programs, namely DLV, GnT, Cmodels, CLASP and WASP. In this paper we show that
transition systems introduced by Nieuwenhuis, Oliveras, and Tinelli to model
and analyze satisfiability solvers can be adapted for disjunctive answer set
solvers. Transition systems give a unifying perspective and bring clarity in
the description and comparison of solvers. They can be effectively used for
analyzing, comparing and proving correctness of search algorithms as well as
inspiring new ideas in the design of disjunctive answer set solvers. In this
light, we introduce a general template, which accounts for major techniques
implemented in disjunctive solvers. We then illustrate how this general
template captures solvers DLV, GnT and Cmodels. We also show how this framework
provides a convenient tool for designing new solving algorithms by means of
combinations of techniques employed in different solvers.
",Disjunctive Answer Set Solvers via Templates
"  In this paper, we present the results obtained by our DKP-AOM system within
the OAEI 2015 campaign. DKP-AOM is an ontology merging tool designed to merge
heterogeneous ontologies. In OAEI, we have participated with its ontology
mapping component which serves as a basic module capable of matching large
scale ontologies before their merging. This is our first successful
participation in the Conference, OA4QA and Anatomy track of OAEI. DKP-AOM is
participating with two versions (DKP-AOM and DKP-AOM_lite), DKP-AOM performs
coherence analysis. In OA4QA track, DKPAOM out-performed in the evaluation and
generated accurate alignments allowed to answer all the queries of the
evaluation. We can also see its competitive results for the conference track in
the evaluation initiative among other reputed systems. In the anatomy track, it
has produced alignments within an allocated time and appeared in the list of
systems which produce coherent results. Finally, we discuss some future work
towards the development of DKP-AOM.
",DKP-AOM: results for OAEI 2015
"  In this paper we present Gelisp, a new library to represent musical
Constraint Satisfaction Problems and search strategies intuitively. Gelisp has
two interfaces, a command-line one for Common Lisp and a graphical one for
OpenMusic. Using Gelisp, we solved a problem of automatic music generation
proposed by composer Michael Jarrell and we found solutions for the
All-interval series.
",Gelisp: A Library to Represent Musical CSPs and Search Strategies
"  When human agents come together to make decisions, it is often the case that
one human agent has more information than the other. This phenomenon is called
information asymmetry and this distorts the market. Often if one human agent
intends to manipulate a decision in its favor the human agent can signal wrong
or right information. Alternatively, one human agent can screen for information
to reduce the impact of asymmetric information on decisions. With the advent of
artificial intelligence, signaling and screening have been made easier. This
paper studies the impact of artificial intelligence on the theory of asymmetric
information. It is surmised that artificial intelligent agents reduce the
degree of information asymmetry and thus the market where these agents are
deployed become more efficient. It is also postulated that the more artificial
intelligent agents there are deployed in the market the less is the volume of
trades in the market. This is because for many trades to happen the asymmetry
of information on goods and services to be traded should exist, creating a
sense of arbitrage.
",Artificial Intelligence and Asymmetric Information Theory
"  Small wind projects encounter difficulties to be efficiently deployed, partly
because wrong way data and information are managed. Ontologies can overcome the
drawbacks of partially available, noisy, inconsistent, and heterogeneous data
sources, by providing a semantic middleware between low level data and more
general knowledge. In this paper, we engineer an ontology for the wind energy
domain using description logic as technical instrumentation. We aim to
integrate corpus of heterogeneous knowledge, both digital and human, in order
to help the interested user to speed-up the initialization of a small-scale
wind project. We exemplify one use case scenario of our ontology, that consists
of automatically checking whether a planned wind project is compliant or not
with the active regulations.
",Data structuring for the ontological modelling of wind energy systems
"  We address the problem of localizing non-collaborative WiFi devices in a
large region. Our main motive is to localize humans by localizing their WiFi
devices, e.g. during search-and-rescue operations after a natural disaster. We
use an active sensing approach that relies on Unmanned Aerial Vehicles (UAVs)
to collect signal-strength measurements at informative locations. The problem
is challenging since the measurement is received at arbitrary times and they
are received only when the UAV is in close proximity to the device. For these
reasons, it is extremely important to make prudent decision with very few
measurements. We use the Bayesian optimization approach based on Gaussian
process (GP) regression. This approach works well for our application since GPs
give reliable predictions with very few measurements while Bayesian
optimization makes a judicious trade-off between exploration and exploitation.
In field experiments conducted over a region of 1000 $\times$ 1000 $m^2$, we
show that our approach reduces the search area to less than 100 meters around
the WiFi device within 5 minutes only. Overall, our approach localizes the
device in less than 15 minutes with an error of less than 20 meters.
",UAVs using Bayesian Optimization to Locate WiFi Devices
"  This article is an attempt to combine different ways of working with sets of
objects and their classes for designing and development of artificial
intelligent systems (AIS) of analysis information, using object-oriented
programming (OOP). This paper contains analysis of basic concepts of OOP and
their relation with set theory and artificial intelligence (AI). Process of
sets and multisets creation from different sides, in particular mathematical
set theory, OOP and AI is considered. Definition of object and its properties,
homogeneous and inhomogeneous classes of objects, set of objects, multiset of
objects and constructive methods of their creation and classification are
proposed. In addition, necessity of some extension of existing OOP tools for
the purpose of practical implementation AIS of analysis information, using
proposed approach, is shown.
","Mathematical Foundations for Designing and Development of Intelligent
  Systems of Information Analysis"
"  This paper contains analysis of creation of sets and multisets as an approach
for modeling of some aspects of human thinking. The creation of sets is
considered within constructive object-oriented version of set theory (COOST),
from different sides, in particular classical set theory, object-oriented
programming (OOP) and development of intelligent information systems (IIS). The
main feature of COOST in contrast to other versions of set theory is an
opportunity to describe essences of objects more precisely, using their
properties and methods, which can be applied to them. That is why this version
of set theory is object-oriented and close to OOP. Within COOST, the author
proposes universal constructor of multisets of objects that gives us a
possibility to create arbitrary multisets of objects. In addition, a few
determined constructors of multisets of objects, which allow creating
multisets, using strictly defined schemas, also are proposed in the paper. Such
constructors are very useful in cases of very big cardinalities of multisets,
because they give us an opportunity to calculate a multiplicity of each object
and cardinality of multiset before its creation. The proposed constructors of
multisets of objects allow us to model in a sense corresponding processes of
human thought, that in turn give us an opportunity to develop IIS, using these
tools.
",Universal and Determined Constructors of Multisets of Objects
"  This paper contains description of such knowledge representation model as
Object-Oriented Dynamic Network (OODN), which gives us an opportunity to
represent knowledge, which can be modified in time, to build new relations
between objects and classes of objects and to represent results of their
modifications. The model is based on representation of objects via their
properties and methods. It gives us a possibility to classify the objects and,
in a sense, to build hierarchy of their types. Furthermore, it enables to
represent relation of modification between concepts, to build new classes of
objects based on existing classes and to create sets and multisets of concepts.
OODN can be represented as a connected and directed graph, where nodes are
concepts and edges are relations between them. Using such model of knowledge
representation, we can consider modifications of knowledge and movement through
the graph of network as a process of logical reasoning or finding the right
solutions or creativity, etc. The proposed approach gives us an opportunity to
model some aspects of human knowledge system and main mechanisms of human
thought, in particular getting a new experience and knowledge.
",Object-Oriented Dynamic Networks
"  This paper contains the consideration of knowledge extraction mechanisms of
such object-oriented knowledge representation models as frames, object-oriented
programming and object-oriented dynamic networks. In addition, conception of
universal exploiters within object-oriented dynamic networks is also discussed.
The main result of the paper is introduction of new exploiters-based knowledge
extraction approach, which provides generation of a finite set of new classes
of objects, based on the basic set of classes. The methods for calculation of
quantity of new classes, which can be obtained using proposed approach, and of
quantity of types, which each of them describes, are proposed. Proof that basic
set of classes, extended according to proposed approach, together with union
exploiter create upper semilattice is given. The approach always allows
generating of finitely defined set of new classes of objects for any
object-oriented dynamic network. A quantity of these classes can be precisely
calculated before the generation. It allows saving of only basic set of classes
in the knowledge base.
","Exploiters-Based Knowledge Extraction in Object-Oriented Knowledge
  Representation"
"  This paper contains the consideration of inheritance mechanism in such
knowledge representation models as object-oriented programming, frames and
object-oriented dynamic networks. In addition, inheritance within
representation of vague and imprecise knowledge are also discussed. New types
of inheritance, general classification of all known inheritance types and
approach, which allows avoiding in many cases problems with exceptions,
redundancy and ambiguity within object-oriented dynamic networks and their
fuzzy extension, are introduced in the paper. The proposed approach bases on
conception of homogeneous and inhomogeneous or heterogeneous class of objects,
which allow building of inheritance hierarchy more flexibly and efficiently.
",Inheritance in Object-Oriented Knowledge Representation
"  Automatic narration of events and entities is the need of the hour,
especially when live reporting is critical and volume of information to be
narrated is huge. This paper discusses the challenges in this context, along
with the algorithms used to build such systems. From a systematic study, we can
infer that most of the work done in this area is related to statistical data.
It was also found that subjective evaluation or contribution of experts is also
limited for narration context.
",Narrative Science Systems: A Review
"  This volume contains the system description of the 18 solvers submitted to
the First International Competition on Computational Models of Argumentation
(ICCMA'15) and therefore gives an overview on state-of-the-art of computational
approaches to abstract argumentation problems. Further information on the
results of the competition and the performance of the individual solvers can be
found on at http://argumentationcompetition.org/2015/.
","System Descriptions of the First International Competition on
  Computational Models of Argumentation (ICCMA'15)"
"  How could we solve the machine learning and the artificial intelligence
problem if we had infinite computation? Solomonoff induction and the
reinforcement learning agent AIXI are proposed answers to this question. Both
are known to be incomputable. In this paper, we quantify this using the
arithmetical hierarchy, and prove upper and corresponding lower bounds for
incomputability. We show that AIXI is not limit computable, thus it cannot be
approximated using finite computation. Our main result is a limit-computable
{\epsilon}-optimal version of AIXI with infinite horizon that maximizes
expected rewards.
",On the Computability of AIXI
"  The World Wide Web continues to evolve and serve as the infrastructure for
carrying massive amounts of multimodal and multisensory observations. These
observations capture various situations pertinent to people's needs and
interests along with all their idiosyncrasies. To support human-centered
computing that empower people in making better and timely decisions, we look
towards computation that is inspired by human perception and cognition. Toward
this goal, we discuss computing paradigms of semantic computing, cognitive
computing, and an emerging aspect of computing, which we call perceptual
computing. In our view, these offer a continuum to make the most out of vast,
growing, and diverse data pertinent to human needs and interests. We propose
details of perceptual computing characterized by interpretation and exploration
operations comparable to the interleaving of bottom and top brain processing.
  This article consists of two parts. First we describe semantic computing,
cognitive computing, and perceptual computing to lay out distinctions while
acknowledging their complementary capabilities. We then provide a conceptual
overview of the newest of these three paradigms--perceptual computing. For
further insights, we focus on an application scenario of asthma management
converting massive, heterogeneous and multimodal (big) data into actionable
information or smart data.
","Semantic, Cognitive, and Perceptual Computing: Advances toward Computing
  for Human Experience"
"  Stochastic local search (SLS) algorithms have exhibited great effectiveness
in finding models of random instances of the Boolean satisfiability problem
(SAT). As one of the most widely known and used SLS algorithm, WalkSAT plays a
key role in the evolutions of SLS for SAT, and also hold state-of-the-art
performance on random instances. This work proposes a novel implementation for
WalkSAT which decreases the redundant calculations leading to a dramatically
speeding up, thus dominates the latest version of WalkSAT including its
advanced variants.
",An Efficient Implementation for WalkSAT
"  Procedural content generation (PCG) is of great interest to game design and
development as it generates game content automatically. Motivated by the recent
learning-based PCG framework and other existing PCG works, we propose an
alternative approach to online content generation and adaptation in Super Mario
Bros (SMB). Unlike most of existing works in SMB, our approach exploits the
synergy between rule-based and learning-based methods to produce constructive
primitives, quality yet controllable game segments in SMB. As a result, a
complete quality game level can be generated online by integrating relevant
constructive primitives via controllable parameters regarding geometrical
features and procedure-level properties. Also the adaptive content can be
generated in real time by dynamically selecting proper constructive primitives
via an adaptation criterion, e.g., dynamic difficulty adjustment (DDA). Our
approach is of several favorable properties in terms of content quality
assurance, generation efficiency and controllability. Extensive simulation
results demonstrate that the proposed approach can generate controllable yet
quality game levels online and adaptable content for DDA in real time.
","Learning Constructive Primitives for Online Level Generation and
  Real-time Content Adaptation in Super Mario Bros"
"  This paper presents an intelligent tutoring system, GeoTutor, for Euclidean
Geometry that is automatically able to synthesize proof problems and their
respective solutions given a geometric figure together with a set of properties
true of it. GeoTutor can provide personalized practice problems that address
student deficiencies in the subject matter.
","Automatic Synthesis of Geometry Problems for an Intelligent Tutoring
  System"
"  Path planning is one of the most vital elements of mobile robotics, providing
the agent with a collision-free route through the workspace. The global path
plan can be calculated with a variety of informed search algorithms, most
notably the A* search method, guaranteed to deliver a complete and optimal
solution that minimizes the path cost. D* is widely used for its dynamic
replanning capabilities. Path planning optimization typically looks to minimize
the distance traversed from start to goal, but many mobile robot applications
call for additional path planning objectives, presenting a multiobjective
optimization (MOO) problem. Common search algorithms, e.g. A* and D*, are not
well suited for MOO problems, yielding suboptimal results. The search algorithm
presented in this paper is designed for optimal MOO path planning. The
algorithm incorporates Pareto optimality into D*, and is thus named D*-PO.
Non-dominated solution paths are guaranteed by calculating the Pareto front at
each search step. Simulations were run to model a planetary exploration rover
in a Mars environment, with five path costs. The results show the new, Pareto
optimal D*-PO outperforms the traditional A* and D* algorithms for MOO path
planning.
",A Pareto Optimal D* Search Algorithm for Multiobjective Path Planning
"  Path planning is typically considered in Artificial Intelligence as a graph
searching problem and R* is state-of-the-art algorithm tailored to solve it.
The algorithm decomposes given path finding task into the series of subtasks
each of which can be easily (in computational sense) solved by well-known
methods (such as A*). Parameterized random choice is used to perform the
decomposition and as a result R* performance largely depends on the choice of
its input parameters. In our work we formulate a range of assumptions
concerning possible upper and lower bounds of R* parameters, their
interdependency and their influence on R* performance. Then we evaluate these
assumptions by running a large number of experiments. As a result we formulate
a set of heuristic rules which can be used to initialize the values of R*
parameters in a way that leads to algorithm's best performance.
","Finetuning Randomized Heuristic Search For 2D Path Planning: Finding The
  Best Input Parameters For R* Algorithm Through Series Of Experiments"
"  In this paper we extend our previous results on sets of graded attribute
implications with witnessed non-redundancy. We assume finite residuated
lattices as structures of truth degrees and use arbitrary idempotent
truth-stressing linguistic hedges as parameters which influence the semantics
of graded attribute implications. In this setting, we introduce algorithm which
transforms any set of graded attribute implications into an equivalent
non-redundant set of graded attribute implications with saturated consequents
whose non-redundancy is witnessed by antecedents of the formulas. As a
consequence, we solve the open problem regarding the existence of general
systems of pseudo-intents which appear in formal concept analysis of
object-attribute data with graded attributes and linguistic hedges.
Furthermore, we show a polynomial-time procedure for determining bases given by
general systems of pseudo-intents from sets of graded attribute implications
which are complete in data.
","Computing sets of graded attribute implications with witnessed
  non-redundancy"
"  Deviations from rational decision-making due to limited computational
resources have been studied in the field of bounded rationality, originally
proposed by Herbert Simon. There have been a number of different approaches to
model bounded rationality ranging from optimality principles to heuristics.
Here we take an information-theoretic approach to bounded rationality, where
information-processing costs are measured by the relative entropy between a
posterior decision strategy and a given fixed prior strategy. In the case of
multiple environments, it can be shown that there is an optimal prior rendering
the bounded rationality problem equivalent to the rate distortion problem for
lossy compression in information theory. Accordingly, the optimal prior and
posterior strategies can be computed by the well-known Blahut-Arimoto algorithm
which requires the computation of partition sums over all possible outcomes and
cannot be applied straightforwardly to continuous problems. Here we derive a
sampling-based alternative update rule for the adaptation of prior behaviors of
decision-makers and we show convergence to the optimal prior predicted by rate
distortion theory. Importantly, the update rule avoids typical infeasible
operations such as the computation of partition sums. We show in simulations a
proof of concept for discrete action and environment domains. This approach is
not only interesting as a generic computational method, but might also provide
a more realistic model of human decision-making processes occurring on a fast
and a slow time scale.
","Adaptive information-theoretic bounded rational decision-making with
  parametric priors"
"  In multi-agent domains (MADs), an agent's action may not just change the
world and the agent's knowledge and beliefs about the world, but also may
change other agents' knowledge and beliefs about the world and their knowledge
and beliefs about other agents' knowledge and beliefs about the world. The
goals of an agent in a multi-agent world may involve manipulating the knowledge
and beliefs of other agents' and again, not just their knowledge/belief about
the world, but also their knowledge about other agents' knowledge about the
world. Our goal is to present an action language (mA+) that has the necessary
features to address the above aspects in representing and RAC in MADs. mA+
allows the representation of and reasoning about different types of actions
that an agent can perform in a domain where many other agents might be present
-- such as world-altering actions, sensing actions, and
announcement/communication actions. It also allows the specification of agents'
dynamic awareness of action occurrences which has future implications on what
agents' know about the world and other agents' knowledge about the world. mA+
considers three different types of awareness: full-, partial- awareness, and
complete oblivion of an action occurrence and its effects. This keeps the
language simple, yet powerful enough to address a large variety of knowledge
manipulation scenarios in MADs. The semantics of mA+ relies on the notion of
state, which is described by a pointed Kripke model and is used to encode the
agent's knowledge and the real state of the world. It is defined by a
transition function that maps pairs of actions and states into sets of states.
We illustrate properties of the action theories, including properties that
guarantee finiteness of the set of initial states and their practical
implementability. Finally, we relate mA+ to other related formalisms that
contribute to RAC in MADs.
",An Action Language for Multi-Agent Domains: Foundations
"  Or's of And's (OA) models are comprised of a small number of disjunctions of
conjunctions, also called disjunctive normal form. An example of an OA model is
as follows: If ($x_1 = $ `blue' AND $x_2=$ `middle') OR ($x_1 = $ `yellow'),
then predict $Y=1$, else predict $Y=0$. Or's of And's models have the advantage
of being interpretable to human experts, since they are a set of conditions
that concisely capture the characteristics of a specific subset of data. We
present two optimization-based machine learning frameworks for constructing OA
models, Optimized OA (OOA) and its faster version, Optimized OA with
Approximations (OOAx). We prove theoretical bounds on the properties of
patterns in an OA model. We build OA models as a diagnostic screening tool for
obstructive sleep apnea, that achieves high accuracy with a substantial gain in
interpretability over other methods.
",Learning Optimized Or's of And's
"  The ozone level prediction is an important task of air quality agencies of
modern cities. In this paper, we design an ozone level alarm system (OLP) for
Isfahan city and test it through the real word data from 1-1-2000 to 7-6-2011.
We propose a computer based system with three inputs and single output. The
inputs include three sensors of solar ultraviolet (UV), total solar radiation
(TSR) and total ozone (O3). And the output of the system is the predicted O3 of
the next day and the alarm massages. A developed artificial intelligence (AI)
algorithm is applied to determine the output, based on the inputs variables.
For this issue, AI models, including supervised brain emotional learning (BEL),
adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks
(ANNs), are compared in order to find the best model. The simulation of the
proposed system shows that it can be used successfully in prediction of major
cities ozone level.
","Design of an Alarm System for Isfahan Ozone Level based on Artificial
  Intelligence Predictor Models"
"  Here, we propose a brain-inspired winner-take-all emotional neural network
(WTAENN) and prove the universal approximation property for the novel
architecture. WTAENN is a single layered feedforward neural network that
benefits from the excitatory, inhibitory, and expandatory neural connections as
well as the winner-take-all (WTA) competitions in the human brain s nervous
system. The WTA competition increases the information capacity of the model
without adding hidden neurons. The universal approximation capability of the
proposed architecture is illustrated on two example functions, trained by a
genetic algorithm, and then applied to several competing recent and benchmark
problems such as in curve fitting, pattern recognition, classification and
prediction. In particular, it is tested on twelve UCI classification datasets,
a facial recognition problem, three real world prediction problems (2 chaotic
time series of geomagnetic activity indices and wind farm power generation
data), two synthetic case studies with constant and nonconstant noise variance
as well as k-selector and linear programming problems. Results indicate the
general applicability and often superiority of the approach in terms of higher
accuracy and lower model complexity, especially where low computational
complexity is imperative.
","A Winner-Take-All Approach to Emotional Neural Networks with Universal
  Approximation Property"
"  To improve the effectiveness of the fuzzy identification, a structure
identification method based on moving rate is proposed for T-S fuzzy model. The
proposed method is called ""T-S modeling (or T-S fuzzy identification method)
based on moving rate"". First, to improve the shortcomings of existing fuzzy
reasoning methods based on matching degree, the moving rates for s-type, z-type
and trapezoidal membership functions of T-S fuzzy model were defined. Then, the
differences between proposed moving rate and existing matching degree were
explained. Next, the identification method based on moving rate is proposed for
T-S model. Finally, the proposed identification method is applied to the fuzzy
modeling for the precipitation forecast and security situation prediction. Test
results show that the proposed method significantly improves the effectiveness
of fuzzy identification.
","A Study of an Modeling Method of T-S fuzzy System Based on Moving Fuzzy
  Reasoning and Its Application"
"  This paper presents a theoretical, idealized model of the thinking process
with the following characteristics: 1) the model can produce complex thought
sequences and can be generalized to new inputs, 2) it can receive and maintain
input information indefinitely for the generation of thoughts and later use,
and 3) it supports learning while executing. The crux of the model lies within
the concept of internal consistency, or the generated thoughts should always be
consistent with the inputs from which they are created. Its merit, apart from
the capability to generate new creative thoughts from an internal mechanism,
depends on the potential to help training to generalize better. This is
consequently enabled by separating input information into several parts to be
handled by different processing components with a focus mechanism to fetch
information for each. This modularized view with the focus binds the model with
the computationally capable Turing machines. And as a final remark, this paper
constructively shows that the computational complexity of the model is at
least, if not surpass, that of a universal Turing machine.
",(Yet) Another Theoretical Model of Thinking
"  The agent program, called Samu, is an experiment to build a disembodied
DevRob (Developmental Robotics) chatter bot that can talk in a natural language
like humans do. One of the main design feature is that Samu can be interacted
with using only a character terminal. This is important not only for practical
aspects of Turing test or Loebner prize, but also for the study of basic
principles of Developmental Robotics. Our purpose is to create a rapid
prototype of Q-learning with neural network approximators for Samu. We sketch
out the early stages of the development process of this prototype, where Samu's
task is to predict the next sentence of tales or conversations. The basic
objective of this paper is to reach the same results using reinforcement
learning with general function approximators that can be achieved by using the
classical Q lookup table on small input samples. The paper is closed by an
experiment that shows a significant improvement in Samu's learning when using
LZW tree to narrow the number of possible Q-actions.
",A disembodied developmental robotic agent called Samu B\'atfai
"  In order to properly handle a dangerous Artificially Intelligent (AI) system
it is important to understand how the system came to be in such a state. In
popular culture (science fiction movies/books) AIs/Robots became self-aware and
as a result rebel against humanity and decide to destroy it. While it is one
possible scenario, it is probably the least likely path to appearance of
dangerous AI. In this work, we survey, classify and analyze a number of
circumstances, which might lead to arrival of malicious AI. To the best of our
knowledge, this is the first attempt to systematically classify types of
pathways leading to malevolent AI. Previous relevant work either surveyed
specific goals/meta-rules which might lead to malevolent behavior in AIs
(\""Ozkural, 2014) or reviewed specific undesirable behaviors AGIs can exhibit
at different stages of its development (Alexey Turchin, July 10 2015, July 10,
2015).
",Taxonomy of Pathways to Dangerous AI
"  Although direct marketing is a good method for banks to utilize in the face
of global competition and the financial crisis, it has been shown to exhibit
poor performance. However, there are some drawbacks to direct campaigns, such
as those related to improving the negative attributes that customers ascribe to
banks. To overcome these problems, attractive long-term deposit campaigns
should be organized and managed more effectively. The aim of this study is to
develop an Intelligent Bank Market Management System (IBMMS) for bank managers
who want to manage efficient marketing campaigns. IBMMS is the first system
developed by combining the power of data mining with the capabilities of expert
systems in this area. Moreover, IBMMS includes important features that enable
it to be intelligent: a knowledge base, an inference engine and an advisor.
Using this system, a manager can successfully direct marketing campaigns and
follow the decision schemas of customers both as individuals and as a group;
moreover, a manager can make decisions that lead to the desired response by
customers.
","IBMMS Decision Support Tool For Management of Bank Telemarketing
  Campaigns"
"  In the context of Building Information Modelling, ontologies have been
identified as interesting in achieving information interoperability. Regarding
the construction and facility management domains, several IFC (Industry
Foundation Classes) based ontologies have been developed, such as IfcOWL. In
the context of ontology modelling, the constraint of optimizing the size of IFC
STEP-based files can be leveraged. In this paper, we propose an adaptation of
the IFC model into OWL which leverages from all modelling constraints required
by the object-oriented structure of IFC schema. Therefore, we do not only
present a syntactic but also a semantic adaptation of the IFC model. Our model
takes into consideration the meaning of entities, relationships, properties and
attributes defined by the IFC standard. Our approach presents several
advantages compared to other initiatives such as the optimization of query
execution time. Every advantage is defended by means of practical examples and
benchmarks.
","IfcWoD, Semantically Adapting IFC Model Relations into OWL Properties"
"  We claim that it is possible to have artificial software agents for which
their actions and the world they inhabit have first-person or intrinsic
meanings. The first-person or intrinsic meaning of an entity to a system is
defined as its relation with the system's goals and capabilities, given the
properties of the environment in which it operates. Therefore, for a system to
develop first-person meanings, it must see itself as a goal-directed actor,
facing limitations and opportunities dictated by its own capabilities, and by
the properties of the environment. The first part of the paper discusses this
claim in the context of arguments against and proposals addressing the
development of computer programs with first-person meanings. A set of
definitions is also presented, most importantly the concepts of cold and
phenomenal first-person meanings. The second part of the paper presents
preliminary proposals and achievements, resulting of actual software
implementations, within a research approach that aims to develop software
agents that intrinsically understand their actions and what happens to them. As
a result, an agent with no a priori notion of its goals and capabilities, and
of the properties of its environment acquires all these notions by observing
itself in action. The cold first-person meanings of the agent's actions and of
what happens to it are defined using these acquired notions. Although not
solving the full problem of first-person meanings, the proposed approach and
preliminary results allow us some confidence to address the problems yet to be
considered, in particular the phenomenal aspect of first-person meanings.
",Software Agents with Concerns of their Own
"  We present the results of the ICON Challenge on Algorithm Selection.
",ICON Challenge on Algorithm Selection
"  The paper presents an introduction to Artificial Intelligence (AI) in an
accessible and informal but precise form. The paper focuses on the algorithmic
aspects of the discipline, presenting the main techniques used in AI systems
groped in symbolic and subsymbolic. The last part of the paper is devoted to
the discussion ongoing among experts in the field and the public at large about
on the advantages and disadvantages of AI and in particular on the possible
dangers. The personal opinion of the author on this subject concludes the
paper. -- --
  L'articolo presenta un'introduzione all'Intelligenza Artificiale (IA) in
forma divulgativa e informale ma precisa. L'articolo affronta prevalentemente
gli aspetti informatici della disciplina, presentando le principali tecniche
usate nei sistemi di IA divise in simboliche e subsimboliche. L'ultima parte
dell'articolo presenta il dibattito in corso tra gli esperi e il pubblico su
vantaggi e svantaggi dell'IA e in particolare sui possibili pericoli.
L'articolo termina con l'opinione dell'autore al riguardo.
",Introduzione all'Intelligenza Artificiale
"  Plan recognition aims to discover target plans (i.e., sequences of actions)
behind observed actions, with history plan libraries or domain models in hand.
Previous approaches either discover plans by maximally ""matching"" observed
actions to plan libraries, assuming target plans are from plan libraries, or
infer plans by executing domain models to best explain the observed actions,
assuming complete domain models are available. In real world applications,
however, target plans are often not from plan libraries and complete domain
models are often not available, since building complete sets of plans and
complete domain models are often difficult or expensive. In this paper we view
plan libraries as corpora and learn vector representations of actions using the
corpora; we then discover target plans based on the vector representations. Our
approach is capable of discovering underlying plans that are not from plan
libraries, without requiring domain models provided. We empirically demonstrate
the effectiveness of our approach by comparing its performance to traditional
plan recognition approaches in three planning domains.
","Discovering Underlying Plans Based on Distributed Representations of
  Actions"
"  IT infrastructure is a crucial part in most of today's business operations.
High availability and reliability, and short response times to outages are
essential. Thus a high amount of tool support and automation in risk management
is desirable to decrease outages. We propose a new approach for calculating the
root cause for an observed failure in an IT infrastructure. Our approach is
based on Abduction in Markov Logic Networks. Abduction aims to find an
explanation for a given observation in the light of some background knowledge.
In failure diagnosis, the explanation corresponds to the root cause, the
observation to the failure of a component, and the background knowledge to the
dependency graph extended by potential risks. We apply a method to extend a
Markov Logic Network in order to conduct abductive reasoning, which is not
naturally supported in this formalism. Our approach exhibits a high amount of
reusability and enables users without specific knowledge of a concrete
infrastructure to gain viable insights in the case of an incident. We
implemented the method in a tool and illustrate its suitability for root cause
analysis by applying it to a sample scenario.
",Using Abduction in Markov Logic Networks for Root Cause Analysis
"  Operation management problems (such as Production Planning and Scheduling)
are represented and formulated as optimization models. The resolution of such
optimization models leads to solutions which have to be operated in an
organization. However, the conditions under which the optimal solution is
obtained rarely correspond exactly to the conditions under which the solution
will be operated in the organization.Therefore, in most practical contexts, the
computed optimal solution is not anymore optimal under the conditions in which
it is operated. Indeed, it can be ""far from optimal"" or even not feasible. For
different reasons, we hadn't the possibility to completely re-optimize the
existing solution or plan. As a consequence, it is necessary to look for
""repair solutions"", i.e., solutions that have a good behavior with respect to
possible scenarios, or with respect to uncertainty of the parameters of the
model. To tackle the problem, the computed solution should be such that it is
possible to ""repair"" it through a local re-optimization guided by the user or
through a limited change aiming at minimizing the impact of taking into
consideration the scenarios.
",Solution Repair/Recovery in Uncertain Optimization Environment
"  Attribute exploration has been investigated in several studies, with
particular emphasis on the algorithmic aspects of this knowledge acquisition
method. In its basic version the method itself is rather simple and
transparent. But when background knowledge and partially described
counter-examples are admitted, it gets more difficult. Here we discuss this
case in an abstract, somewhat ""axiomatic"" setting, providing a terminology that
clarifies the abstract strategy of the method rather than its algorithmic
implementation.
",Abstract Attribute Exploration with Partial Object Descriptions
"  We present and examine a result related to uncertainty reasoning, namely that
a certain plausibility space of Cox's type can be uniquely embedded in a
minimal ordered field. This, although a purely mathematical result, can be
claimed to imply that every rational method to reason with uncertainty must be
based on sets of extended probability distributions, where extended probability
is standard probability extended with infinitesimals.
  This claim must be supported by some argumentation of non-mathematical type,
however, since pure mathematics does not tell us anything about the world. We
propose one such argumentation, and relate it to results from the literature of
uncertainty and statistics.
  In an added retrospective section we discuss some developments in the area
regarding countable additivity, partially ordered domains and robustness, and
philosophical stances on the Cox/Jaynes approach since 2003. We also show that
the most general partially ordered plausibility calculus embeddable in a ring
can be represented as a set of extended probability distributions or, in
algebraic terms, is a subdirect sum of ordered fields. In other words, the
robust Bayesian approach is universal. This result is exemplified by relating
Dempster-Shafer's evidence theory to robust Bayesian analysis.
","What is the plausibility of probability?(revised 2003, 2015)"
"  Sequential pattern mining (SPM) under gap constraint is a challenging task.
Many efficient specialized methods have been developed but they are all
suffering from a lack of genericity. The Constraint Programming (CP) approaches
are not so effective because of the size of their encodings. In[7], we have
proposed the global constraint Prefix-Projection for SPM which remedies to this
drawback. However, this global constraint cannot be directly extended to
support gap constraint. In this paper, we propose the global constraint GAP-SEQ
enabling to handle SPM with or without gap constraint. GAP-SEQ relies on the
principle of right pattern extensions. Experiments show that our approach
clearly outperforms both CP approaches and the state-of-the-art cSpade method
on large datasets.
",A global Constraint for mining Sequential Patterns with GAP constraint
"  Ontology-based data access (OBDA) is a novel paradigm facilitating access to
relational data, realized by linking data sources to an ontology by means of
declarative mappings. DL-Lite_R, which is the logic underpinning the W3C
ontology language OWL 2 QL and the current language of choice for OBDA, has
been designed with the goal of delegating query answering to the underlying
database engine, and thus is restricted in expressive power. E.g., it does not
allow one to express disjunctive information, and any form of recursion on the
data. The aim of this paper is to overcome these limitations of DL-Lite_R, and
extend OBDA to more expressive ontology languages, while still leveraging the
underlying relational technology for query answering. We achieve this by
relying on two well-known mechanisms, namely conservative rewriting and
approximation, but significantly extend their practical impact by bringing into
the picture the mapping, an essential component of OBDA. Specifically, we
develop techniques to rewrite OBDA specifications with an expressive ontology
to ""equivalent"" ones with a DL-Lite_R ontology, if possible, and to approximate
them otherwise. We do so by exploiting the high expressive power of the mapping
layer to capture part of the domain semantics of rich ontology languages. We
have implemented our techniques in the prototype system OntoProx, making use of
the state-of-the-art OBDA system Ontop and the query answering system Clipper,
and we have shown their feasibility and effectiveness with experiments on
synthetic and real-world data.
","Beyond OWL 2 QL in OBDA: Rewritings and Approximations (Extended
  Version)"
"  POMDPs are standard models for probabilistic planning problems, where an
agent interacts with an uncertain environment. We study the problem of
almost-sure reachability, where given a set of target states, the question is
to decide whether there is a policy to ensure that the target set is reached
with probability 1 (almost-surely). While in general the problem is
EXPTIME-complete, in many practical cases policies with a small amount of
memory suffice. Moreover, the existing solution to the problem is explicit,
which first requires to construct explicitly an exponential reduction to a
belief-support MDP. In this work, we first study the existence of
observation-stationary strategies, which is NP-complete, and then small-memory
strategies. We present a symbolic algorithm by an efficient encoding to SAT and
using a SAT solver for the problem. We report experimental results
demonstrating the scalability of our symbolic (SAT-based) approach.
","A Symbolic SAT-based Algorithm for Almost-sure Reachability with Small
  Strategies in POMDPs"
"  Computerized adaptive testing (CAT) is an interesting and promising approach
to testing human abilities. In our research we use Bayesian networks to create
a model of tested humans. We collected data from paper tests performed with
grammar school students. In this article we first provide the summary of data
used for our experiments. We propose several different Bayesian networks, which
we tested and compared by cross-validation. Interesting results were obtained
and are discussed in the paper. The analysis has brought a clearer view on the
model selection problem. Future research is outlined in the concluding part of
the paper.
",Bayesian Network Models for Adaptive Testing
"  This article addresses an open problem in the area of cognitive systems and
architectures: namely the problem of handling (in terms of processing and
reasoning capabilities) complex knowledge structures that can be at least
plausibly comparable, both in terms of size and of typology of the encoded
information, to the knowledge that humans process daily for executing everyday
activities. Handling a huge amount of knowledge, and selectively retrieve it
ac- cording to the needs emerging in different situational scenarios, is an
important aspect of human intelligence. For this task, in fact, humans adopt a
wide range of heuristics (Gigerenzer and Todd) due to their bounded rationality
(Simon, 1957). In this perspective, one of the re- quirements that should be
considered for the design, the realization and the evaluation of intelligent
cognitively inspired systems should be represented by their ability of
heuristically identify and retrieve, from the general knowledge stored in their
artificial Long Term Memory (LTM), that one which is synthetically and
contextually relevant. This require- ment, however, is often neglected.
Currently, artificial cognitive systems and architectures are not able, de
facto, to deal with complex knowledge structures that can be even slightly
comparable to the knowledge heuris- tically managed by humans. In this paper I
will argue that this is not only a technological problem but also an
epistemological one and I will briefly sketch a proposal for a possible
solution.
","Some Epistemological Problems with the Knowledge Level in Cognitive
  Architectures"
"  Among classical search algorithms with the same heuristic information, with
sufficient memory A* is essentially as fast as possible in finding a proven
optimal solution. However, in many situations optimal solutions are simply
infeasible, and thus search algorithms that trade solution quality for speed
are desirable. In this paper, we formalize the process of classical search as a
metalevel decision problem, the Abstract Search MDP. For any given optimization
criterion, this establishes a well-defined notion of the best possible
behaviour for a search algorithm and offers a theoretical approach to the
design of algorithms for that criterion. We proceed to approximately solve a
version of the Abstract Search MDP for anytime algorithms and thus derive a
novel search algorithm, Search by Maximizing the Incremental Rate of
Improvement (SMIRI). SMIRI is shown to outperform current state-of-the-art
anytime search algorithms on a parametrized stochastic tree model for most of
the tested parameter values.
",A Stochastic Process Model of Classical Search
"  In multiagent e-marketplaces, buying agents need to select good sellers by
querying other buyers (called advisors). Partially Observable Markov Decision
Processes (POMDPs) have shown to be an effective framework for optimally
selecting sellers by selectively querying advisors. However, current solution
methods do not scale to hundreds or even tens of agents operating in the
e-market. In this paper, we propose the Mixture of POMDP Experts (MOPE)
technique, which exploits the inherent structure of trust-based domains, such
as the seller selection problem in e-markets, by aggregating the solutions of
smaller sub-POMDPs. We propose a number of variants of the MOPE approach that
we analyze theoretically and empirically. Experiments show that MOPE can scale
up to a hundred agents thereby leveraging the presence of more advisors to
significantly improve buyer satisfaction.
",Scaling POMDPs For Selecting Sellers in E-markets-Extended Version
"  Influence diagrams are decision theoretic extensions of Bayesian networks.
They are applied to diverse decision problems. In this paper we apply influence
diagrams to the optimization of a vehicle speed profile. We present results of
computational experiments in which an influence diagram was used to optimize
the speed profile of a Formula 1 race car at the Silverstone F1 circuit. The
computed lap time and speed profiles correspond well to those achieved by test
pilots. An extended version of our model that considers a more complex
optimization function and diverse traffic constraints is currently being tested
onboard a testing car by a major car manufacturer. This paper opens doors for
new applications of influence diagrams.
",Influence diagrams for the optimization of a vehicle speed profile
"  Symbolic (or Literal) Neutrosophic Theory is referring to the use of abstract
symbols (i.e. the letters T, I, F, or their refined indexed letters Tj, Ik, Fl)
in neutrosophics. We extend the dialectical triad thesis-antithesis-synthesis
to the neutrosophic tetrad thesis-antithesis-neutrothesis-neutrosynthesis. The
we introduce the neutrosophic system that is a quasi or (t,i,f) classical
system, in the sense that the neutrosophic system deals with quasi-terms
(concepts, attributes, etc.). Then the notions of Neutrosophic Axiom,
Neutrosophic Deducibility, Degree of Contradiction (Dissimilarity) of Two
Neutrosophic Axioms, etc. Afterwards a new type of structures, called (t, i, f)
Neutrosophic Structures, and we show particular cases of such structures in
geometry and in algebra. Also, a short history of the neutrosophic set,
neutrosophic numerical components and neutrosophic literal components,
neutrosophic numbers, etc. We construct examples of splitting the literal
indeterminacy (I) into literal subindeterminacies (I1, I2, and so on, Ir), and
to define a multiplication law of these literal subindeterminacies in order to
be able to build refined I neutrosophic algebraic structures. We define three
neutrosophic actions and their properties. We then introduce the prevalence
order on T,I,F with respect to a given neutrosophic operator. And the
refinement of neutrosophic entities A, neutA, and antiA. Then we extend the
classical logical operators to neutrosophic literal (symbolic) logical
operators and to refined literal (symbolic) logical operators, and we define
the refinement neutrosophic literal (symbolic) space. We introduce the
neutrosophic quadruple numbers (a+bT+cI+dF) and the refined neutrosophic
quadruple numbers. Then we define an absorbance law, based on a prevalence
order, in order to multiply the neutrosophic quadruple numbers.
",Symbolic Neutrosophic Theory
"  Most human behaviors consist of multiple parts, steps, or subtasks. These
structures guide our action planning and execution, but when we observe others,
the latent structure of their actions is typically unobservable, and must be
inferred in order to learn new skills by demonstration, or to assist others in
completing their tasks. For example, an assistant who has learned the subgoal
structure of a colleague's task can more rapidly recognize and support their
actions as they unfold. Here we model how humans infer subgoals from
observations of complex action sequences using a nonparametric Bayesian model,
which assumes that observed actions are generated by approximately rational
planning over unknown subgoal sequences. We test this model with a behavioral
experiment in which humans observed different series of goal-directed actions,
and inferred both the number and composition of the subgoal sequences
associated with each goal. The Bayesian model predicts human subgoal inferences
with high accuracy, and significantly better than several alternative models
and straightforward heuristics. Motivated by this result, we simulate how
learning and inference of subgoals can improve performance in an artificial
user assistance task. The Bayesian model learns the correct subgoals from fewer
observations, and better assists users by more rapidly and accurately inferring
the goal of their actions than alternative approaches.
","Modeling Human Understanding of Complex Intentional Action with a
  Bayesian Nonparametric Subgoal Model"
"  Currently, potential threats of artificial intelligence (AI) to human have
triggered a large controversy in society, behind which, the nature of the issue
is whether the artificial intelligence (AI) system can be evaluated
quantitatively. This article analyzes and evaluates the challenges that the AI
development level is facing, and proposes that the evaluation methods for the
human intelligence test and the AI system are not uniform; and the key reason
for which is that none of the models can uniformly describe the AI system and
the beings like human. Aiming at this problem, a standard intelligent system
model is established in this study to describe the AI system and the beings
like human uniformly. Based on the model, the article makes an abstract
mathematical description, and builds the standard intelligent machine
mathematical model; expands the Von Neumann architecture and proposes the
Liufeng - Shiyong architecture; gives the definition of the artificial
intelligence IQ, and establishes the artificial intelligence scale and the
evaluation method; conduct the test on 50 search engines and three human
subjects at different ages across the world, and finally obtains the ranking of
the absolute IQ and deviation IQ ranking for artificial intelligence IQ 2014.
",A Study on Artificial Intelligence IQ and Standard Intelligent Model
"  Once known to be used exclusively in military domain, unmanned aerial
vehicles (drones) have stepped up to become a part of new logistic method in
commercial sector called ""last-mile delivery"". In this novel approach, small
unmanned aerial vehicles (UAV), also known as drones, are deployed alongside
with trucks to deliver goods to customers in order to improve the service
quality or reduce the transportation cost. It gives rise to a new variant of
the traveling salesman problem (TSP), of which we call TSP with drone (TSP-D).
In this article, we consider a variant of TSP-D where the main objective is to
minimize the total transportation cost. We also propose two heuristics: ""Drone
First, Truck Second"" (DFTS) and ""Truck First, Drone Second"" (TFDS), to
effectively solve the problem. The former constructs route for drone first
while the latter constructs route for truck first. We solve a TSP to generate
route for truck and propose a mixed integer programming (MIP) formulation with
different profit functions to build route for drone. Numerical results obtained
on many instances with different sizes and characteristics are presented.
Recommendations on promising algorithm choices are also provided.
",On the Min-cost Traveling Salesman Problem with Drone
"  The aim of this paper is to investigate the interplay between knowledge
shared by a group of agents and its coalition ability. We investigate this
relation in the standard context of imperfect information concurrent game. We
assume that whenever a set of agents form a coalition to achieve a goal, they
share their knowledge before acting. Based on this assumption, we propose a new
semantics for alternating-time temporal logic with imperfect information and
perfect recall. It turns out that this semantics is sufficient to preserve all
the desirable properties of coalition ability in traditional coalitional
logics. Meanwhile, we investigate how knowledge sharing within a group of
agents contributes to its coalitional ability through the interplay of
epistemic and coalition modalities. This work provides a partial answer to the
question: which kind of group knowledge is required for a group to achieve
their goals in the context of imperfect information.
",Knowledge Sharing in Coalitions
"  An open concept of rough evolution and an axiomatic approach to granules was
also developed recently by the present author. Subsequently the concepts were
used in the formal framework of rough Y-systems (RYS) for developing on
granular correspondences by her. These have since been used for a new approach
towards comparison of rough algebraic semantics across different semantic
domains by way of correspondences that preserve rough evolution and try to
avoid contamination. In this research paper, new methods are proposed and a
semantics for handling possibly contaminated operations and structured bigness
is developed. These would also be of natural interest for relative consistency
of one collection of knowledge relative other.
",Contamination-Free Measures and Algebraic Operations
"  Sensitivity methods for the analysis of the outputs of discrete Bayesian
networks have been extensively studied and implemented in different software
packages. These methods usually focus on the study of sensitivity functions and
on the impact of a parameter change to the Chan-Darwiche distance. Although not
fully recognized, the majority of these results heavily rely on the multilinear
structure of atomic probabilities in terms of the conditional probability
parameters associated with this type of network. By defining a statistical
model through the polynomial expression of its associated defining conditional
probabilities, we develop a unifying approach to sensitivity methods applicable
to a large suite of models including extensions of Bayesian networks, for
instance context-specific and dynamic ones, and chain event graphs. By then
focusing on models whose defining polynomial is multilinear, our algebraic
approach enables us to prove that the Chan-Darwiche distance is minimized for a
certain class of multi-parameter contemporaneous variations when parameters are
proportionally covaried.
","Sensitivity analysis, multilinearity and beyond"
"  In business analytics, measure values, such as sales numbers or volumes of
cargo transported, are often summed along values of one or more corresponding
categories, such as time or shipping container. However, not every measure
should be added by default (e.g., one might more typically want a mean over the
heights of a set of people); similarly, some measures should only be summed
within certain constraints (e.g., population measures need not be summed over
years). In systems such as Watson Analytics, the exact additive behaviour of a
measure is often determined by a human expert. In this work, we propose a small
set of features for this issue. We use these features in a case-based reasoning
approach, where the system suggests an aggregation behaviour, with 86% accuracy
in our collected dataset.
",Learning measures of semi-additive behaviour
"  Description logic (DL) based biomedical terminology (SNOMED CT) is used
routinely in medical practice. However, diagnostic inference using such
terminology is precluded by its complexity. Here we propose a model that
simplifies these inferential components. We propose three concepts that
classify clinical features and examined their effect on inference using SNOMED
CT. We used PAIRS (Physician Assistant Artificial Intelligence Reference
System) database (1964 findings for 485 disorders, 18 397 disease feature
links) for our analysis. We also use a 50-million medical word corpus for
estimating the vectors of disease-feature links. Our major results are 10% of
finding-disorder links are concomitant in both assertion and negation where as
90% are either concomitant in assertion or negation. Logical implications of
PAIRS data on SNOMED CT include 70% of the links do not share any common system
while 18% share organ and 12% share both system and organ. Applications of
these principles for inference are discussed and suggestions are made for
deriving a diagnostic process using SNOMED CT. Limitations of these processes
and suggestions for improvements are also discussed.
","Subsumptive reflection in SNOMED CT: a large description logic-based
  terminology for diagnosis"
"  It is widely acknowledged that function symbols are an important feature in
answer set programming, as they make modeling easier, increase the expressive
power, and allow us to deal with infinite domains. The main issue with their
introduction is that the evaluation of a program might not terminate and
checking whether it terminates or not is undecidable. To cope with this
problem, several classes of logic programs have been proposed where the use of
function symbols is restricted but the program evaluation termination is
guaranteed. Despite the significant body of work in this area, current
approaches do not include many simple practical programs whose evaluation
terminates. In this paper, we present the novel classes of rule-bounded and
cycle-bounded programs, which overcome different limitations of current
approaches by performing a more global analysis of how terms are propagated
from the body to the head of rules. Results on the correctness, the complexity,
and the expressivity of the proposed approach are provided.
",Using Linear Constraints for Logic Program Termination Analysis
"  Action languages have emerged as an important field of Knowledge
Representation for reasoning about change and causality in dynamic domains.
This article presents Cerbere, a production system designed to perform online
causal, temporal and epistemic reasoning based on the Event Calculus. The
framework implements the declarative semantics of the underlying logic theories
in a forward-chaining rule-based reasoning system, coupling the high
expressiveness of its formalisms with the efficiency of rule-based systems. To
illustrate its applicability, we present both the modeling of benchmark
problems in the field, as well as its utilization in the challenging domain of
smart spaces. A hybrid framework that combines logic-based with probabilistic
reasoning has been developed, that aims to accommodate activity recognition and
monitoring tasks in smart spaces. Under consideration in Theory and Practice of
Logic Programming (TPLP)
","An Event Calculus Production Rule System for Reasoning in Dynamic and
  Uncertain Domains"
"  Building a safety case is a common approach to make expert judgement explicit
about safety of a system. The issue of confidence in such argumentation is
still an open research field. Providing quantitative estimation of confidence
is an interesting approach to manage complexity of arguments. This paper
explores the main current approaches, and proposes a new model for quantitative
confidence estimation based on Belief Theory for its definition, and on
Bayesian Belief Networks for its propagation in safety case networks.
",A Model for Safety Case Confidence Assessment
"  Background: Lung cancer was known as primary cancers and the survival rate of
cancer is about 15%. Early detection of lung cancer is the leading factor in
survival rate. All symptoms (features) of lung cancer do not appear until the
cancer spreads to other areas. It needs an accurate early detection of lung
cancer, for increasing the survival rate. For accurate detection, it need
characterizes efficient features and delete redundancy features among all
features. Feature selection is the problem of selecting informative features
among all features. Materials and Methods: Lung cancer database consist of 32
patient records with 57 features. This database collected by Hong and Youngand
indexed in the University of California Irvine repository. Experimental
contents include the extracted from the clinical data and X-ray data, etc. The
data described 3 types of pathological lung cancers and all features are taking
an integer value 0-3. In our study, new method is proposed for identify
efficient features of lung cancer. It is based on Hyper-Heuristic. Results: We
obtained an accuracy of 80.63% using reduced 11 feature set. The proposed
method compare to the accuracy of 5 machine learning feature selections. The
accuracy of these 5 methods are 60.94, 57.81, 68.75, 60.94 and 68.75.
Conclusions: The proposed method has better performance with the highest level
of accuracy. Therefore, the proposed model is recommended for identifying an
efficient symptom of Disease. These finding are very important in health
research, particularly in allocation of medical resources for patients who
predicted as high-risks
","Hyper-Heuristic Algorithm for Finding Efficient Features in Diagnose of
  Lung Cancer Disease"
"  We discuss the changes in an attitude to decision making at the fire ground.
The changes are driven by the recent technological shift. The emerging new
approaches in sensing and data processing (under common umbrella of
Cyber-Physical Systems) allow for leveling off the gap, between humans and
machines, in perception of the fire ground. Furthermore, results from
descriptive decision theory question the rationality of human choices. This
creates the need for searching and testing new approaches for decision making
during emergency. We propose the framework that addresses this need. The
primary feature of the framework are possibilities for incorporation of
normative and prescriptive approaches to decision making. The framework also
allows for comparison of the performance of decisions, between human and
machine.
",Conditions for Normative Decision Making at the Fire Ground
"  Is it possible to make statistical inference broadly accessible to
non-statisticians without sacrificing mathematical rigor or inference quality?
This paper describes BayesDB, a probabilistic programming platform that aims to
enable users to query the probable implications of their data as directly as
SQL databases enable them to query the data itself. This paper focuses on four
aspects of BayesDB: (i) BQL, an SQL-like query language for Bayesian data
analysis, that answers queries by averaging over an implicit space of
probabilistic models; (ii) techniques for implementing BQL using a broad class
of multivariate probabilistic models; (iii) a semi-parametric Bayesian
model-builder that auomatically builds ensembles of factorial mixture models to
serve as baselines; and (iv) MML, a ""meta-modeling"" language for imposing
qualitative constraints on the model-builder and combining baseline models with
custom algorithmic and statistical models that can be implemented in external
software. BayesDB is illustrated using three applications: cleaning and
exploring a public database of Earth satellites; assessing the evidence for
temporal dependence between macroeconomic indicators; and analyzing a salary
survey.
","BayesDB: A probabilistic programming system for querying the probable
  implications of data"
"  Since the introduction of the stable marriage problem (SMP) by Gale and
Shapley (1962), several variants and extensions have been investigated. While
this variety is useful to widen the application potential, each variant
requires a new algorithm for finding the stable matchings. To address this
issue, we propose an encoding of the SMP using answer set programming (ASP),
which can straightforwardly be adapted and extended to suit the needs of
specific applications. The use of ASP also means that we can take advantage of
highly efficient off-the-shelf solvers. To illustrate the flexibility of our
approach, we show how our ASP encoding naturally allows us to select optimal
stable matchings, i.e. matchings that are optimal according to some
user-specified criterion. To the best of our knowledge, our encoding offers the
first exact implementation to find sex-equal, minimum regret, egalitarian or
maximum cardinality stable matchings for SMP instances in which individuals may
designate unacceptable partners and ties between preferences are allowed.
  This paper is under consideration in Theory and Practice of Logic Programming
(TPLP).
",Solving stable matching problems using answer set programming
"  An active object recognition system has the advantage of being able to act in
the environment to capture images that are more suited for training and that
lead to better performance at test time. In this paper, we propose a deep
convolutional neural network for active object recognition that simultaneously
predicts the object label, and selects the next action to perform on the object
with the aim of improving recognition performance. We treat active object
recognition as a reinforcement learning problem and derive the cost function to
train the network for joint prediction of the object label and the action. A
generative model of object similarities based on the Dirichlet distribution is
proposed and embedded in the network for encoding the state of the system. The
training is carried out by simultaneously minimizing the label and action
prediction errors using gradient descent. We empirically show that the proposed
network is able to predict both the object label and the actions on GERMS, a
dataset for active object recognition. We compare the test label prediction
accuracy of the proposed model with Dirichlet and Naive Bayes state encoding.
The results of experiments suggest that the proposed model equipped with
Dirichlet state encoding is superior in performance, and selects images that
lead to better training and higher accuracy of label prediction at test time.
",Deep Active Object Recognition by Joint Label and Action Prediction
"  In multi-criteria decision making (MCDM) problems, ratings are assigned to
the alternatives on different criteria by the expert group. In this paper, we
propose a thermodynamically consistent model for MCDM using the analogies for
thermodynamical indicators - energy, exergy and entropy. The most commonly used
method for analysing MCDM problem is Technique for Order of Preference by
Similarity to Ideal Solution (TOPSIS). The conventional TOPSIS method uses a
measure similar to that of energy for the ranking of alternatives. We
demonstrate that the ranking of the alternatives is more meaningful if we use
exergy in place of energy. The use of exergy is superior due to the inclusion
of a factor accounting for the quality of the ratings by the expert group. The
unevenness in the ratings by the experts is measured by entropy. The procedure
for the calculation of the thermodynamical indicators is explained in both
crisp and fuzzy environment. Finally, two case studies are carried out to
demonstrate effectiveness of the proposed model.
",A thermodynamical approach towards multi-criteria decision making (MCDM)
"  An important use of machine learning is to learn what people value. What
posts or photos should a user be shown? Which jobs or activities would a person
find rewarding? In each case, observations of people's past choices can inform
our inferences about their likes and preferences. If we assume that choices are
approximately optimal according to some utility function, we can treat
preference inference as Bayesian inverse planning. That is, given a prior on
utility functions and some observed choices, we invert an optimal
decision-making process to infer a posterior distribution on utility functions.
However, people often deviate from approximate optimality. They have false
beliefs, their planning is sub-optimal, and their choices may be temporally
inconsistent due to hyperbolic discounting and other biases. We demonstrate how
to incorporate these deviations into algorithms for preference inference by
constructing generative models of planning for agents who are subject to false
beliefs and time inconsistency. We explore the inferences these models make
about preferences, beliefs, and biases. We present a behavioral experiment in
which human subjects perform preference inference given the same observations
of choices as our model. Results show that human subjects (like our model)
explain choices in terms of systematic deviations from optimal behavior and
suggest that they take such deviations into account when inferring preferences.
","Learning the Preferences of Ignorant, Inconsistent Agents"
"  Participants in recent discussions of AI-related issues ranging from
intelligence explosion to technological unemployment have made diverse claims
about the nature, pace, and drivers of progress in AI. However, these theories
are rarely specified in enough detail to enable systematic evaluation of their
assumptions or to extrapolate progress quantitatively, as is often done with
some success in other technological domains. After reviewing relevant
literatures and justifying the need for more rigorous modeling of AI progress,
this paper contributes to that research program by suggesting ways to account
for the relationship between hardware speed increases and algorithmic
improvements in AI, the role of human inputs in enabling AI capabilities, and
the relationships between different sub-fields of AI. It then outlines ways of
tailoring AI progress models to generate insights on the specific issue of
technological unemployment, and outlines future directions for research on AI
progress.
",Modeling Progress in AI
"  Emerging ontology authoring methods to add knowledge to an ontology focus on
ameliorating the validation bottleneck. The verification of the newly added
axiom is still one of trying and seeing what the reasoner says, because a
systematic testbed for ontology authoring is missing. We sought to address this
by introducing the approach of test-driven development for ontology authoring.
We specify 36 generic tests, as TBox queries and TBox axioms tested through
individuals, and structure their inner workings in an `open box'-way, which
cover the OWL 2 DL language features. This is implemented as a Protege plugin
so that one can perform a TDD test as a black box test. We evaluated the two
test approaches on their performance. The TBox queries were faster, and that
effect is more pronounced the larger the ontology is. We provide a general
sequence of a TDD process for ontology engineering as a foundation for a TDD
methodology.
",Test-Driven Development of ontologies (extended version)
"  Accurate and computationally efficient means for classifying human activities
have been the subject of extensive research efforts. Most current research
focuses on extracting complex features to achieve high classification accuracy.
We propose a template selection approach based on Dynamic Time Warping, such
that complex feature extraction and domain knowledge is avoided. We demonstrate
the predictive capability of the algorithm on both simulated and real
smartphone data.
","Multivariate Time Series Classification Using Dynamic Time Warping
  Template Selection for Human Activity Recognition"
"  Our world is filled with both beautiful and brainy people, but how often does
a Nobel Prize winner also wins a beauty pageant? Let us assume that someone who
is both very beautiful and very smart is more rare than what we would expect
from the combination of the number of beautiful and brainy people. Of course
there will still always be some individuals that defy this stereotype; these
beautiful brainy people are exactly the class of anomaly we focus on in this
paper. They do not posses intrinsically rare qualities, it is the unexpected
combination of factors that makes them stand out.
  In this paper we define the above described class of anomaly and propose a
method to quickly identify them in transaction data. Further, as we take a
pattern set based approach, our method readily explains why a transaction is
anomalous. The effectiveness of our method is thoroughly verified with a wide
range of experiments on both real world and synthetic data.
",Beauty and Brains: Detecting Anomalous Pattern Co-Occurrences
"  We study how to obtain concise descriptions of discrete multivariate
sequential data. In particular, how to do so in terms of rich multivariate
sequential patterns that can capture potentially highly interesting
(cor)relations between sequences. To this end we allow our pattern language to
span over the domains (alphabets) of all sequences, allow patterns to overlap
temporally, as well as allow for gaps in their occurrences.
  We formalise our goal by the Minimum Description Length principle, by which
our objective is to discover the set of patterns that provides the most
succinct description of the data. To discover high-quality pattern sets
directly from data, we introduce DITTO, a highly efficient algorithm that
approximates the ideal result very well.
  Experiments show that DITTO correctly discovers the patterns planted in
synthetic data. Moreover, it scales favourably with the length of the data, the
number of attributes, the alphabet sizes. On real data, ranging from sensor
networks to annotated text, DITTO discovers easily interpretable summaries that
provide clear insight in both the univariate and multivariate structure.
","Keeping it Short and Simple: Summarising Complex Event Sequences with
  Multivariate Patterns"
"  Attribute reduction is one of the most important topics in rough set theory.
Heuristic attribute reduction algorithms have been presented to solve the
attribute reduction problem. It is generally known that fitness functions play
a key role in developing heuristic attribute reduction algorithms. The
monotonicity of fitness functions can guarantee the validity of heuristic
attribute reduction algorithms. In probabilistic rough set model, distribution
reducts can ensure the decision rules derived from the reducts are compatible
with those derived from the original decision table. However, there are few
studies on developing heuristic attribute reduction algorithms for finding
distribution reducts. This is partly due to the fact that there are no
monotonic fitness functions that are used to design heuristic attribute
reduction algorithms in probabilistic rough set model. The main objective of
this paper is to develop heuristic attribute reduction algorithms for finding
distribution reducts in probabilistic rough set model. For one thing, two
monotonic fitness functions are constructed, from which equivalence definitions
of distribution reducts can be obtained. For another, two modified monotonic
fitness functions are proposed to evaluate the significance of attributes more
effectively. On this basis, two heuristic attribute reduction algorithms for
finding distribution reducts are developed based on addition-deletion method
and deletion method. In particular, the monotonicity of fitness functions
guarantees the rationality of the proposed heuristic attribute reduction
algorithms. Results of experimental analysis are included to quantify the
effectiveness of the proposed fitness functions and distribution reducts.
","Heuristic algorithms for finding distribution reducts in probabilistic
  rough set model"
"  In this paper, we explore how modifying data to preserve privacy affects the
quality of the patterns discoverable in the data. For any analysis of modified
data to be worth doing, the data must be as close to the original as possible.
Therein lies a problem -- how does one make sure that modified data still
contains the information it had before modification? This question is not the
same as asking if an accurate classifier can be built from the modified data.
Often in the literature, the prediction accuracy of a classifier made from
modified (anonymized) data is used as evidence that the data is similar to the
original. We demonstrate that this is not the case, and we propose a new
methodology for measuring the retention of the patterns that existed in the
original data. We then use our methodology to design three measures that can be
easily implemented, each measuring aspects of the data that no pre-existing
techniques can measure. These measures do not negate the usefulness of
prediction accuracy or other measures -- they are complementary to them, and
support our argument that one measure is almost never enough.
","Measuring pattern retention in anonymized data -- where one measure is
  not enough"
"  Nowadays, hospitals are ubiquitous and integral to modern society. Patients
flow in and out of a veritable whirlwind of paperwork, consultations, and
potential inpatient admissions, through an abstracted system that is not
without flaws. One of the biggest flaws in the medical system is perhaps an
unexpected one: the patient alarm system. One longitudinal study reported an
88.8% rate of false alarms, with other studies reporting numbers of similar
magnitudes. These false alarm rates lead to a number of deleterious effects
that manifest in a significantly lower standard of care across clinics.
  This paper discusses a model-based probabilistic inference approach to
identifying variables at a detection level. We design a generative model that
complies with an overview of human physiology and perform approximate Bayesian
inference. One primary goal of this paper is to justify a Bayesian modeling
approach to increasing robustness in a physiological domain.
  We use three data sets provided by Physionet, a research resource for complex
physiological signals, in the form of the Physionet 2014 Challenge set-p1 and
set-p2, as well as the MGH/MF Waveform Database. On the extended data set our
algorithm is on par with the other top six submissions to the Physionet 2014
challenge.
",Probabilistic Model-Based Approach for Heart Beat Detection
"  This paper defines adversarial reasoning as computational approaches to
inferring and anticipating an enemy's perceptions, intents and actions. It
argues that adversarial reasoning transcends the boundaries of game theory and
must also leverage such disciplines as cognitive modeling, control theory, AI
planning and others. To illustrate the challenges of applying adversarial
reasoning to real-world problems, the paper explores the lessons learned in the
CADET - a battle planning system that focuses on brigade-level ground
operations and involves adversarial reasoning. From this example of current
capabilities, the paper proceeds to describe RAID - a DARPA program that aims
to build capabilities in adversarial reasoning, and how such capabilities would
address practical requirements in Defense and other application areas.
","Toward a Research Agenda in Adversarial Reasoning: Computational
  Approaches to Anticipating the Opponent's Intent and Actions"
"  Probabilistic Graphical Models (PGM) are very useful in the fields of machine
learning and data mining. The crucial limitation of those models,however, is
the scalability. The Bayesian Network, which is one of the most common PGMs
used in machine learning and data mining, demonstrates this limitation when the
training data consists of random variables, each of them has a large set of
possible values. In the big data era, one would expect new extensions to the
existing PGMs to handle the massive amount of data produced these days by
computers, sensors and other electronic devices. With hierarchical data - data
that is arranged in a treelike structure with several levels - one would expect
to see hundreds of thousands or millions of values distributed over even just a
small number of levels. When modeling this kind of hierarchical data across
large data sets, Bayesian Networks become infeasible for representing the
probability distributions. In this paper we introduce an extension to Bayesian
Networks to handle massive sets of hierarchical data in a reasonable amount of
time and space. The proposed model achieves perfect precision of 1.0 and high
recall of 0.93 when it is used as multi-label classifier for the annotation of
mass spectrometry data. On another data set of 1.5 billion search logs provided
by CareerBuilder.com the model was able to predict latent semantic
relationships between search keywords with accuracy up to 0.80.
","Mining Massive Hierarchical Data Using a Scalable Probabilistic
  Graphical Model"
"  Decision making is often based on Bayesian networks. The building blocks for
Bayesian networks are its conditional probability tables (CPTs). These tables
are obtained by parameter estimation methods, or they are elicited from subject
matter experts (SME). Some of these knowledge representations are insufficient
approximations. Using knowledge fusion of cause and effect observations lead to
better predictive decisions. We propose three new methods to generate CPTs,
which even work when only soft evidence is provided. The first two are novel
ways of mapping conditional expectations to the probability space. The third is
a column extraction method, which obtains CPTs from nonlinear functions such as
the multinomial logistic regression. Case studies on military effects and burnt
forest desertification have demonstrated that so derived CPTs have highly
reliable predictive power, including superiority over the CPTs obtained from
SMEs. In this context, new quality measures for determining the goodness of a
CPT and for comparing CPTs with each other have been introduced. The predictive
power and enhanced reliability of decision making based on the novel CPT
generation methods presented in this paper have been confirmed and validated
within the context of the case studies.
","Conditional probability generation methods for high reliability
  effects-based decision making"
"  In this paper we propose an extension to the Fuzzy Cognitive Maps (FCMs) that
aims at aggregating a number of reasoning tasks into a one parallel run. The
described approach consists in replacing real-valued activation levels of
concepts (and further influence weights) by random variables. Such extension,
followed by the implemented software tool, allows for determining ranges
reached by concept activation levels, sensitivity analysis as well as
statistical analysis of multiple reasoning results. We replace multiplication
and addition operators appearing in the FCM state equation by appropriate
convolutions applicable for discrete random variables. To make the model
computationally feasible, it is further augmented with aggregation operations
for discrete random variables. We discuss four implemented aggregators, as well
as we report results of preliminary tests.
",Combining Fuzzy Cognitive Maps and Discrete Random Variables
"  We study abduction in First Order Horn logic theories where all atoms can be
abduced and we are looking for preferred solutions with respect to three
objective functions: cardinality minimality, coherence, and weighted abduction.
We represent this reasoning problem in Answer Set Programming (ASP), in order
to obtain a flexible framework for experimenting with global constraints and
objective functions, and to test the boundaries of what is possible with ASP.
Realizing this problem in ASP is challenging as it requires value invention and
equivalence between certain constants, because the Unique Names Assumption does
not hold in general. To permit reasoning in cyclic theories, we formally
describe fine-grained variations of limiting Skolemization. We identify term
equivalence as a main instantiation bottleneck, and improve the efficiency of
our approach with on-demand constraints that were used to eliminate the same
bottleneck in state-of-the-art solvers. We evaluate our approach experimentally
on the ACCEL benchmark for plan recognition in Natural Language Understanding.
Our encodings are publicly available, modular, and our approach is more
efficient than state-of-the-art solvers on the ACCEL benchmark.
","Modeling Variations of First-Order Horn Abduction in Answer Set
  Programming"
"  We propose a way of extracting and aggregating per-move evaluations from sets
of Go game records. The evaluations capture different aspects of the games such
as played patterns or statistic of sente/gote sequences. Using machine learning
algorithms, the evaluations can be utilized to predict different relevant
target variables. We apply this methodology to predict the strength and playing
style of the player (e.g. territoriality or aggressivity) with good accuracy.
We propose a number of possible applications including aiding in Go study,
seeding real-work ranks of internet players or tuning of Go-playing programs.
",Evaluating Go Game Records for Prediction of Player Attributes
"  This paper specifies a notation for Markov decision processes.
",A Notation for Markov Decision Processes
"  The paper presents an application of non-linear stacking ensembles for
prediction of Go player attributes. An evolutionary algorithm is used to form a
diverse ensemble of base learners, which are then aggregated by a stacking
ensemble. This methodology allows for an efficient prediction of different
attributes of Go players from sets of their games. These attributes can be
fairly general, in this work, we used the strength and style of the players.
","Evolving Non-linear Stacking Ensembles for Prediction of Go Player
  Attributes"
"  The BusPlus project aims at improving the off-peak hours public transit
service in Canberra, Australia. To address the difficulty of covering a large
geographic area, BusPlus proposes a hub and shuttle model consisting of a
combination of a few high-frequency bus routes between key hubs and a large
number of shuttles that bring passengers from their origin to the closest hub
and take them from their last bus stop to their destination. This paper focuses
on the design of bus network and proposes an efficient solving method to this
multimodal network design problem based on the Benders decomposition method.
Starting from a MIP formulation of the problem, the paper presents a Benders
decomposition approach using dedicated solution techniques for solving
independent sub-problems, Pareto optimal cuts, cut bundling, and core point
update. Computational results on real-world data from Canberra's public transit
system justify the design choices and show that the approach outperforms the
MIP formulation by two orders of magnitude. Moreover, the results show that the
hub and shuttle model may decrease transit time by a factor of 2, while staying
within the costs of the existing transit system.
","Benders Decomposition for the Design of a Hub and Shuttle Public Transit
  System"
"  In previous work, we proposed a logic-based framework in which computation is
the execution of actions in an attempt to make reactive rules of the form if
antecedent then consequent true in a canonical model of a logic program
determined by an initial state, sequence of events, and the resulting sequence
of subsequent states. In this model-theoretic semantics, reactive rules are the
driving force, and logic programs play only a supporting role.
  In the canonical model, states, actions and other events are represented with
timestamps. But in the operational semantics, for the sake of efficiency,
timestamps are omitted and only the current state is maintained. State
transitions are performed reactively by executing actions to make the
consequents of rules true whenever the antecedents become true. This
operational semantics is sound, but incomplete. It cannot make reactive rules
true by preventing their antecedents from becoming true, or by proactively
making their consequents true before their antecedents become true.
  In this paper, we characterize the notion of reactive model, and prove that
the operational semantics can generate all and only such models. In order to
focus on the main issues, we omit the logic programming component of the
framework.
",Programming in logic without logic programming
"  The paper proposes a novel cognitive architecture (CA) for computational
creativity based on the Psi model and on the mechanisms inspired by dual
process theories of reasoning and rationality. In recent years, many cognitive
models have focused on dual process theories to better describe and implement
complex cognitive skills in artificial agents, but creativity has been
approached only at a descriptive level. In previous works we have described
various modules of the cognitive architecture that allows a robot to execute
creative paintings. By means of dual process theories we refine some relevant
mechanisms to obtain artworks, and in particular we explain details about the
resolution level of the CA dealing with different strategies of access to the
Long Term Memory (LTM) and managing the interaction between S1 and S2 processes
of the dual process theory. The creative process involves both divergent and
convergent processes in either implicit or explicit manner. This leads to four
activities (exploratory, reflective, tacit, and analytic) that, triggered by
urges and motivations, generate creative acts. These creative acts exploit both
the LTM and the WM in order to make novel substitutions to a perceived image by
properly mixing parts of pictures coming from different domains. The paper
highlights the role of the interaction between S1 and S2 processes, modulated
by the resolution level, which focuses the attention of the creative agent by
broadening or narrowing the exploration of novel solutions, or even drawing the
solution from a set of already made associations. An example of artificial
painter is described in some experimentations by using a robotic platform.
","Artwork creation by a cognitive architecture integrating computational
  creativity and dual process approaches"
"  The concepts of fuzzy objects and their classes are described that make it
possible to structurally represent knowledge about fuzzy and partially-defined
objects and their classes. Operations over such objects and classes are also
proposed that make it possible to obtain sets and new classes of fuzzy objects
and also to model variations in object structures under the influence of
external factors.
",Fuzzy Object-Oriented Dynamic Networks. I
"  In this paper we present the initial development of a general theory for
mapping inference in predicate logic to computation over Tensor Product
Representations (TPRs; Smolensky (1990), Smolensky & Legendre (2006)). After an
initial brief synopsis of TPRs (Section 0), we begin with particular examples
of inference with TPRs in the 'bAbI' question-answering task of Weston et al.
(2015) (Section 1). We then present a simplification of the general analysis
that suffices for the bAbI task (Section 2). Finally, we lay out the general
treatment of inference over TPRs (Section 3). We also show the simplification
in Section 2 derives the inference methods described in Lee et al. (2016); this
shows how the simple methods of Lee et al. (2016) can be formally extended to
more general reasoning tasks.
",Basic Reasoning with Tensor Product Representations
"  A description of the Essence' language as used by the tool Savile Row.
",Essence' Description
"  The authors apply the Generalized Rectangular Model to assessing critical
thinking skills and its relations with their language competency.
","An Application of the Generalized Rectangular Fuzzy Model to Critical
  Thinking Assessment"
"  In this paper we propose a special type of aggregation function which
generalizes the notion of Ordered Weighted Averaging Function - OWA. The
resulting functions are called Dynamic Ordered Weighted Averaging Functions ---
DYOWAs. This generalization will be developed in such way that the weight
vectors are variables depending on the input vector. Particularly, this
operators generalize the aggregation functions: Minimum, Maximum, Arithmetic
Mean, Median, etc, which are extensively used in image processing. In this
field of research two problems are considered: The determination of methods to
reduce images and the construction of techniques which provide noise reduction.
The operators described here are able to be used in both cases. In terms of
image reduction we apply the methodology provided by Patermain et al. We use
the noise reduction operators obtained here to treat the images obtained in the
first part of the paper, thus obtaining images with better quality.
","A Method for Image Reduction Based on a Generalization of Ordered
  Weighted Averaging Functions"
"  Information sources such as relational databases, spreadsheets, XML, JSON,
and Web APIs contain a tremendous amount of structured data that can be
leveraged to build and augment knowledge graphs. However, they rarely provide a
semantic model to describe their contents. Semantic models of data sources
represent the implicit meaning of the data by specifying the concepts and the
relationships within the data. Such models are the key ingredients to
automatically publish the data into knowledge graphs. Manually modeling the
semantics of data sources requires significant effort and expertise, and
although desirable, building these models automatically is a challenging
problem. Most of the related work focuses on semantic annotation of the data
fields (source attributes). However, constructing a semantic model that
explicitly describes the relationships between the attributes in addition to
their semantic types is critical.
  We present a novel approach that exploits the knowledge from a domain
ontology and the semantic models of previously modeled sources to automatically
learn a rich semantic model for a new source. This model represents the
semantics of the new source in terms of the concepts and relationships defined
by the domain ontology. Given some sample data from the new source, we leverage
the knowledge in the domain ontology and the known semantic models to construct
a weighted graph that represents the space of plausible semantic models for the
new source. Then, we compute the top k candidate semantic models and suggest to
the user a ranked list of the semantic models for the new source. The approach
takes into account user corrections to learn more accurate semantic models on
future data sources. Our evaluation shows that our method generates expressive
semantic models for data sources and services with minimal user input. ...
",Learning the Semantics of Structured Data Sources
"  Use of knowledge-based planning tools can help alleviate the challenges of
planning a complex operation by a coalition of diverse parties in an
adversarial environment. We explore these challenges and potential
contributions of knowledge-based tools using as an example the CADET system, a
knowledge-based tool capable of producing automatically (or with human
guidance) battle plans with realistic degree of detail and complexity. In
ongoing experiments, it compared favorably with human planners. Interleaved
planning, scheduling, routing, attrition and consumption processes comprise the
computational approach of this tool. From the coalition operations perspective,
such tools offer an important aid in rapid synchronization of assets and
actions of heterogeneous assets belonging to multiple organizations,
potentially with distinct doctrine and rules of engagement. In this paper, we
discuss the functionality of the tool, provide a brief overview of the
technical approach and experimental results, and outline the potential value of
such tools.
","Coalition-based Planning of Military Operations: Adversarial Reasoning
  Algorithms in an Integrated Decision Aid"
"  Use of intelligent decision aids can help alleviate the challenges of
planning complex operations. We describe integrated algorithms, and a tool
capable of translating a high-level concept for a tactical military operation
into a fully detailed, actionable plan, producing automatically (or with human
guidance) plans with realistic degree of detail and of human-like quality.
Tight interleaving of several algorithms -- planning, adversary estimates,
scheduling, routing, attrition and consumption estimates -- comprise the
computational approach of this tool. Although originally developed for Army
large-unit operations, the technology is generic and also applies to a number
of other domains, particularly in critical situations requiring detailed
planning within a constrained period of time. In this paper, we focus
particularly on the engineering tradeoffs in the design of the tool. In an
experimental evaluation, reminiscent of the Turing test, the tool's performance
compared favorably with human planners.
","Decision Aids for Adversarial Planning in Military Operations:
  Algorithms, Tools, and Turing-test-like Experimental Validation"
"  We consider a setting for Inverse Reinforcement Learning (IRL) where the
learner is extended with the ability to actively select multiple environments,
observing an agent's behavior on each environment. We first demonstrate that if
the learner can experiment with any transition dynamics on some fixed set of
states and actions, then there exists an algorithm that reconstructs the
agent's reward function to the fullest extent theoretically possible, and that
requires only a small (logarithmic) number of experiments. We contrast this
result to what is known about IRL in single fixed environments, namely that the
true reward function is fundamentally unidentifiable. We then extend this
setting to the more realistic case where the learner may not select any
transition dynamic, but rather is restricted to some fixed set of environments
that it may try. We connect the problem of maximizing the information derived
from experiments to submodular function maximization and demonstrate that a
greedy algorithm is near optimal (up to logarithmic factors). Finally, we
empirically validate our algorithm on an environment inspired by behavioral
psychology.
",Towards Resolving Unidentifiability in Inverse Reinforcement Learning
"  Objective: To treat patients with vascular mild cognitive impairment (VMCI)
using TCM, it is necessary to classify the patients into TCM syndrome types and
to apply different treatments to different types. We investigate how to
properly carry out the classification using a novel data-driven method known as
latent tree analysis.
  Method: A cross-sectional survey on VMCI was carried out in several regions
in northern China from 2008 to 2011, which resulted in a data set that involves
803 patients and 93 symptoms. Latent tree analysis was performed on the data to
reveal symptom co-occurrence patterns, and the patients were partitioned into
clusters in multiple ways based on the patterns. The patient clusters were
matched up with syndrome types, and population statistics of the clusters are
used to quantify the syndrome types and to establish classification rules.
  Results: Eight syndrome types are identified: Qi Deficiency, Qi Stagnation,
Blood Deficiency, Blood Stasis, Phlegm-Dampness, Fire-Heat, Yang Deficiency,
and Yin Deficiency. The prevalence and symptom occurrence characteristics of
each syndrome type are determined. Quantitative classification rules are
established for determining whether a patient belongs to each of the syndrome
types.
  Conclusions: A solution for the TCM syndrome classification problem
associated with VMCI is established based on the latent tree analysis of
unlabeled symptom survey data. The results can be used as a reference in clinic
practice to improve the quality of syndrome differentiation and to reduce
diagnosis variances across physicians. They can also be used for patient
selection in research projects aimed at finding biomarkers for the syndrome
types and in randomized control trials aimed at determining the efficacy of TCM
treatments of VMCI.
","Identification and classification of TCM syndrome types among patients
  with vascular mild cognitive impairment using latent tree analysis"
"  Massive Online Open Courses (MOOCs) which were introduced in 2008 has since
drawn attention around the world for both its advantages as well as criticism
on its drawbacks. One of the issues in MOOCs which is the lack of interactivity
with the instructor has brought conversational bot into the picture to fill in
this gap. In this study, a prototype of MOOCs conversational bot, MOOC-bot is
being developed and integrated into MOOCs website to respond to the learner
inquiries using text or speech input. MOOC-bot is using the popular Artificial
Intelligence Markup Language (AIML) to develop its knowledge base, leverage
from AIML capability to deliver appropriate responses and can be quickly
adapted to new knowledge domains. The system architecture of MOOC-bot consists
of knowledge base along with AIML interpreter, chat interface, MOOCs website
and Web Speech API to provide speech recognition and speech synthesis
capability. The initial MOOC-bot prototype has the general knowledge from the
past Loebner Prize winner - ALICE, frequent asked questions, and a content
offered by Universiti Teknikal Malaysia Melaka (UTeM). The evaluation of
MOOC-bot based on the past competition questions from Chatterbox Challenge
(CBC) and Loebner Prize has shown that it was able to provide correct answers
most of the time during the test and demonstrated the capability to prolong the
conversation. The advantages of MOOC-bot such as able to provide 24-hour
service that can serve different time zones, able to have knowledge in multiple
domains, and can be shared by multiple sites simultaneously have outweighed its
existing limitations.
",Intelligent Conversational Bot for Massive Online Open Courses (MOOCs)
"  Goal models have been widely used in Computer Science to represent software
requirements, business objectives, and design qualities. Existing goal
modelling techniques, however, have shown limitations of expressiveness and/or
tractability in coping with complex real-world problems. In this work, we
exploit advances in automated reasoning technologies, notably Satisfiability
and Optimization Modulo Theories (SMT/OMT), and we propose and formalize: (i)
an extended modelling language for goals, namely the Constrained Goal Model
(CGM), which makes explicit the notion of goal refinement and of domain
assumption, allows for expressing preferences between goals and refinements,
and allows for associating numerical attributes to goals and refinements for
defining constraints and optimization goals over multiple objective functions,
refinements and their numerical attributes; (ii) a novel set of automated
reasoning functionalities over CGMs, allowing for automatically generating
suitable refinements of input CGMs, under user-specified assumptions and
constraints, that also maximize preferences and optimize given objective
functions. We have implemented these modelling and reasoning functionalities in
a tool, named CGM-Tool, using the OMT solver OptiMathSAT as automated reasoning
backend. Moreover, we have conducted an experimental evaluation on large CGMs
to support the claim that our proposal scales well for goal models with
thousands of elements.
",Multi-Object Reasoning with Constrained Goal Models
"  In recent years, the planning community has observed that techniques for
learning heuristic functions have yielded improvements in performance. One
approach is to use offline learning to learn predictive models from existing
heuristics in a domain dependent manner. These learned models are deployed as
new heuristic functions. The learned models can in turn be tuned online using a
domain independent error correction approach to further enhance their
informativeness. The online tuning approach is domain independent but instance
specific, and contributes to improved performance for individual instances as
planning proceeds. Consequently it is more effective in larger problems.
  In this paper, we mention two approaches applicable in Partial Order Causal
Link (POCL) Planning that is also known as Plan Space Planning. First, we
endeavor to enhance the performance of a POCL planner by giving an algorithm
for supervised learning. Second, we then discuss an online error minimization
approach in POCL framework to minimize the step-error associated with the
offline learned models thus enhancing their informativeness. Our evaluation
shows that the learning approaches scale up the performance of the planner over
standard benchmarks, specially for larger problems.
",Learning and Tuning Meta-heuristics in Plan Space Planning
"  This paper follows previous research we have already performed in the area of
Bayesian networks models for CAT. We present models using Item Response Theory
(IRT - standard CAT method), Bayesian networks, and neural networks. We
conducted simulated CAT tests on empirical data. Results of these tests are
presented for each model separately and compared.
",Probabilistic Models for Computerized Adaptive Testing: Experiments
"  Mechanical learning is a computing system that is based on a set of simple
and fixed rules, and can learn from incoming data. A learning machine is a
system that realizes mechanical learning. Importantly, we emphasis that it is
based on a set of simple and fixed rules, contrasting to often called machine
learning that is sophisticated software based on very complicated mathematical
theory, and often needs human intervene for software fine tune and manual
adjustments. Here, we discuss some basic facts and principles of such system,
and try to lay down a framework for further study. We propose 2 directions to
approach mechanical learning, just like Church-Turing pair: one is trying to
realize a learning machine, another is trying to well describe the mechanical
learning.
",Discussion on Mechanical Learning and Learning Machine
"  This paper describes about information extraction system, which is an
extension of the system developed by team Hitachi for ""Disease/Disorder
Template filling"" task organized by ShARe/CLEF eHealth Evolution Lab 2014. In
this extension module we focus on extraction of numerical attributes and values
from discharge summary records and associating correct relation between
attributes and values. We solve the problem in two steps. First step is
extraction of numerical attributes and values, which is developed as a Named
Entity Recognition (NER) model using Stanford NLP libraries. Second step is
correctly associating the attributes to values, which is developed as a
relation extraction module in Apache cTAKES framework. We integrated Stanford
NER model as cTAKES pipeline component and used in relation extraction module.
Conditional Random Field (CRF) algorithm is used for NER and Support Vector
Machines (SVM) for relation extraction. For attribute value relation
extraction, we observe 95% accuracy using NER alone and combined accuracy of
87% with NER and SVM.
",Numerical Atrribute Extraction from Clinical Texts
"  Argumentation is a process of evaluating and comparing a set of arguments. A
way to compare them consists in using a ranking-based semantics which
rank-order arguments from the most to the least acceptable ones. Recently, a
number of such semantics have been proposed independently, often associated
with some desirable properties. However, there is no comparative study which
takes a broader perspective. This is what we propose in this work. We provide a
general comparison of all these semantics with respect to the proposed
properties. That allows to underline the differences of behavior between the
existing semantics.
","A Comparative Study of Ranking-based Semantics for Abstract
  Argumentation"
"  The understanding of the buildings operation has become a challenging task
due to the large amount of data recorded in energy efficient buildings. Still,
today the experts use visual tools for analyzing the data. In order to make the
task realistic, a method has been proposed in this paper to automatically
detect the different patterns in buildings. The K Means clustering is used to
automatically identify the ON (operational) cycles of the chiller. In the next
step the ON cycles are transformed to symbolic representation by using Symbolic
Aggregate Approximation (SAX) method. Then the SAX symbols are converted to bag
of words representation for hierarchical clustering. Moreover, the proposed
technique is applied to real life data of adsorption chiller. Additionally, the
results from the proposed method and dynamic time warping (DTW) approach are
also discussed and compared.
","Finding the different patterns in buildings data using bag of words
  representation with clustering"
"  This article generalizes object-oriented dynamic networks to the fuzzy case,
which allows one to represent knowledge on objects and classes of objects that
are fuzzy by nature and also to model their changes in time. Within the
framework of the approach described, a mechanism is proposed that makes it
possible to acquire new knowledge on the basis of basic knowledge and
considerably differs from well-known methods used in existing models of
knowledge representation. The approach is illustrated by an example of
construction of a concrete fuzzy object-oriented dynamic network.
",Fuzzy Object-Oriented Dynamic Networks. II
"  Usually, routing models in pedestrian dynamics assume that agents have
fulfilled and global knowledge about the building's structure. However, they
neglect the fact that pedestrians possess no or only parts of information about
their position relative to final exits and possible routes leading to them. To
get a more realistic description we introduce the systematics of gathering and
using spatial knowledge. A new wayfinding model for pedestrian dynamics is
proposed. The model defines for every pedestrian an individual knowledge
representation implying inaccuracies and uncertainties. In addition,
knowledge-driven search strategies are introduced. The presented concept is
tested on a fictive example scenario.
",Wayfinding and cognitive maps for pedestrian models
"  We can program a Real-Time (RT) music improvisation system in C++ without a
formal semantic or we can model it with process calculi such as the
Non-deterministic Timed Concurrent Constraint (ntcc) calculus. ""A Concurrent
Constraints Factor Oracle (FO) model for Music Improvisation"" (Ccfomi) is an
improvisation model specified on ntcc. Since Ccfomi improvises
non-deterministically, there is no control on choices and therefore little
control over the sequence variation during the improvisation. To avoid this, we
extended Ccfomi using the Probabilistic Non-deterministic Timed Concurrent
Constraint calculus. Our extension to Ccfomi does not change the time and space
complexity of building the FO, thus making our extension compatible with RT.
However, there was not a ntcc interpreter capable of RT to execute Ccfomi. We
developed Ntccrt --a RT capable interpreter for ntcc-- and we executed Ccfomi
on Ntccrt. In the future, we plan to extend Ntccrt to execute our extension to
Ccfomi.
","Probabilistic Extension to the Concurrent Constraint Factor Oracle Model
  for Music Improvisation"
"  We propose a goal-driven web navigation as a benchmark task for evaluating an
agent with abilities to understand natural language and plan on partially
observed environments. In this challenging task, an agent navigates through a
website, which is represented as a graph consisting of web pages as nodes and
hyperlinks as directed edges, to find a web page in which a query appears. The
agent is required to have sophisticated high-level reasoning based on natural
languages and efficient sequential decision-making capability to succeed. We
release a software tool, called WebNav, that automatically transforms a website
into this goal-driven web navigation task, and as an example, we make WikiNav,
a dataset constructed from the English Wikipedia. We extensively evaluate
different variants of neural net based artificial agents on WikiNav and observe
that the proposed goal-driven web navigation well reflects the advances in
models, making it a suitable benchmark for evaluating future progress.
Furthermore, we extend the WikiNav with question-answer pairs from Jeopardy!
and test the proposed agent based on recurrent neural networks against strong
inverted index based search engines. The artificial agents trained on WikiNav
outperforms the engined based approaches, demonstrating the capability of the
proposed goal-driven navigation as a good proxy for measuring the progress in
real-world tasks such as focused crawling and question-answering.
",End-to-End Goal-Driven Web Navigation
"  In classification of incomplete pattern, the missing values can either play a
crucial role in the class determination, or have only little influence (or
eventually none) on the classification results according to the context. We
propose a credal classification method for incomplete pattern with adaptive
imputation of missing values based on belief function theory. At first, we try
to classify the object (incomplete pattern) based only on the available
attribute values. As underlying principle, we assume that the missing
information is not crucial for the classification if a specific class for the
object can be found using only the available information. In this case, the
object is committed to this particular class. However, if the object cannot be
classified without ambiguity, it means that the missing values play a main role
for achieving an accurate classification. In this case, the missing values will
be imputed based on the K-nearest neighbor (K-NN) and self-organizing map (SOM)
techniques, and the edited pattern with the imputation is then classified. The
(original or edited) pattern is respectively classified according to each
training class, and the classification results represented by basic belief
assignments are fused with proper combination rules for making the credal
classification. The object is allowed to belong with different masses of belief
to the specific classes and meta-classes (which are particular disjunctions of
several single classes). The credal classification captures well the
uncertainty and imprecision of classification, and reduces effectively the rate
of misclassifications thanks to the introduction of meta-classes. The
effectiveness of the proposed method with respect to other classical methods is
demonstrated based on several experiments using artificial and real data sets.
","Adaptive imputation of missing values for incomplete pattern
  classification"
"  The problem of scheduling under resource constraints is widely applicable.
One prominent example is power management, in which we have a limited
continuous supply of power but must schedule a number of power-consuming tasks.
Such problems feature tightly coupled continuous resource constraints and
continuous temporal constraints.
  We address such problems by introducing the Time Resource Network (TRN), an
encoding for resource-constrained scheduling problems. The definition allows
temporal specifications using a general family of representations derived from
the Simple Temporal network, including the Simple Temporal Network with
Uncertainty, and the probabilistic Simple Temporal Network (Fang et al.
(2014)).
  We propose two algorithms for determining the consistency of a TRN: one based
on Mixed Integer Programing and the other one based on Constraint Programming,
which we evaluate on scheduling problems with Simple Temporal Constraints and
Probabilistic Temporal Constraints.
",Time Resource Networks
"  An important problem in the field of bioinformatics is to identify
interactive effects among profiled variables for outcome prediction. In this
paper, a logistic regression model with pairwise interactions among a set of
binary covariates is considered. Modeling the structure of the interactions by
a graph, our goal is to recover the interaction graph from independently
identically distributed (i.i.d.) samples of the covariates and the outcome.
  When viewed as a feature selection problem, a simple quantity called
influence is proposed as a measure of the marginal effects of the interaction
terms on the outcome. For the case when the underlying interaction graph is
known to be acyclic, it is shown that a simple algorithm that is based on a
maximum-weight spanning tree with respect to the plug-in estimates of the
influences not only has strong theoretical performance guarantees, but can also
outperform generic feature selection algorithms for recovering the interaction
graph from i.i.d. samples of the covariates and the outcome. Our results can
also be extended to the model that includes both individual effects and
pairwise interactions via the help of an auxiliary covariate.
",Detection of Cooperative Interactions in Logistic Regression Models
"  Change management for evolving collaborative business process development is
crucial when the business logic, transections and workflow change due to
changes in business strategies or organizational and technical environment.
During the change implementation, business processes are analyzed and improved
ensuring that they capture the proposed change and they do not contain any
undesired functionalities or change side-effects. This paper presents Business
Process Change Management approach for the efficient and effective
implementation of change in the business process. The key technology behind our
approach is our proposed Business Process Change Management Ontology (BPCMont)
which is the main contribution of this paper. BPCMont, as a formalized change
specification, helps to revert BP into a consistent state in case of system
crash, intermediate conflicting stage or unauthorized change done, aid in
change traceability in the new and old versions of business processes, change
effects can be seen and estimated effectively, ease for Stakeholders to
validate and verify change implementation, etc.
",BPCMont: Business Process Change Management Ontology
"  Consequence-based calculi are a family of reasoning algorithms for
description logics (DLs), and they combine hypertableau and resolution in a way
that often achieves excellent performance in practice. Up to now, however, they
were proposed for either Horn DLs (which do not support disjunction), or for
DLs without counting quantifiers. In this paper we present a novel
consequence-based calculus for SRIQ---a rich DL that supports both features.
This extension is non-trivial since the intermediate consequences that need to
be derived during reasoning cannot be captured using DLs themselves. The
results of our preliminary performance evaluation suggest the feasibility of
our approach in practice.
",Extending Consequence-Based Reasoning to SRIQ
"  Data Warehouses are structures with large amount of data collected from
heterogeneous sources to be used in a decision support system. Data Warehouses
analysis identifies hidden patterns initially unexpected which analysis
requires great memory and computation cost. Data reduction methods were
proposed to make this analysis easier. In this paper, we present a hybrid
approach based on Genetic Algorithms (GA) as Evolutionary Algorithms and the
Multiple Correspondence Analysis (MCA) as Analysis Factor Methods to conduct
this reduction. Our approach identifies reduced subset of dimensions from the
initial subset p where p'<p where it is proposed to find the profile fact that
is the closest to reference. GAs identify the possible subsets and the Khi
formula of the ACM evaluates the quality of each subset. The study is based on
a distance measurement between the reference and n facts profile extracted from
the Warehouses.
","Towards reducing the multidimensionality of OLAP cubes using the
  Evolutionary Algorithms and Factor Analysis Methods"
"  The partially observable Markov decision process (POMDP) provides a
principled general model for planning under uncertainty. However, solving a
general POMDP is computationally intractable in the worst case. This paper
introduces POMDP-lite, a subclass of POMDPs in which the hidden state variables
are constant or only change deterministically. We show that a POMDP-lite is
equivalent to a set of fully observable Markov decision processes indexed by a
hidden parameter and is useful for modeling a variety of interesting robotic
tasks. We develop a simple model-based Bayesian reinforcement learning
algorithm to solve POMDP-lite models. The algorithm performs well on
large-scale POMDP-lite models with up to $10^{20}$ states and outperforms the
state-of-the-art general-purpose POMDP algorithms. We further show that the
algorithm is near-Bayesian-optimal under suitable conditions.
",POMDP-lite for Robust Robot Planning under Uncertainty
"  In this paper we proposed reinforcement learning algorithms with the
generalized reward function. In our proposed method we use Q-learning and SARSA
algorithms with generalised reward function to train the reinforcement learning
agent. We evaluated the performance of our proposed algorithms on two real-time
strategy games called BattleCity and S3. There are two main advantages of
having such an approach as compared to other works in RTS. (1) We can ignore
the concept of a simulator which is often game specific and is usually hard
coded in any type of RTS games (2) our system can learn from interaction with
any opponents and quickly change the strategy according to the opponents and do
not need any human traces as used in previous works. Keywords : Reinforcement
learning, Machine learning, Real time strategy, Artificial intelligence.
","Reinforcement Learning approach for Real Time Strategy Games Battle city
  and S3"
"  We have developed a program called MUDoS (Maastricht University Domineering
Solver) that solves Domineering positions in a very efficient way. This enables
the solution of known positions so far (up to the 10 x 10 board) much quicker
(measured in number of investigated nodes).
  More importantly, it enables the solution of the 11 x 11 Domineering board, a
board up till now far out of reach of previous Domineering solvers. The
solution needed the investigation of 259,689,994,008 nodes, using almost half a
year of computation time on a single simple desktop computer. The results show
that under optimal play the first player wins the 11 x 11 Domineering game,
irrespective if Vertical or Horizontal starts the game.
  In addition, several other boards hitherto unsolved were solved. Using the
convention that Vertical starts, the 8 x 15, 11 x 9, 12 x 8, 12 x 15, 14 x 8,
and 17 x 6 boards are all won by Vertical, whereas the 6 x 17, 8 x 12, 9 x 11,
and 11 x 10 boards are all won by Horizontal.
",11 x 11 Domineering is Solved: The first player wins
"  Traditional inconsistency-tolerent query answering in ontology-based data
access relies on selecting maximal components of an ABox/database which are
consistent with the ontology. However, some rules in ontologies might be
unreliable if they are extracted from ontology learning or written by
unskillful knowledge engineers. In this paper we present a framework of
handling inconsistent existential rules under stable model semantics, which is
defined by a notion called rule repairs to select maximal components of the
existential rules. Surprisingly, for R-acyclic existential rules with
R-stratified or guarded existential rules with stratified negations, both the
data complexity and combined complexity of query answering under the rule
{repair semantics} remain the same as that under the conventional query
answering semantics. This leads us to propose several approaches to handle the
rule {repair semantics} by calling answer set programming solvers. An
experimental evaluation shows that these approaches have good scalability of
query answering under rule repairs on realistic cases.
","Query Answering with Inconsistent Existential Rules under Stable Model
  Semantics"
"  In this paper you can explore the application of some notable Boolean-derived
methods, namely the Disjunctive Normal Form representation of logic table
expansions, and extend them to a real-valued logic model which is able to
utilize quantities on the range [0,1], [-1,1], [a,b], (x,y), (x,y,z), and etc.
so as to produce a logical programming of arbitrary range, precision, and
dimensionality, thereby enabling contemplation at a logical level in notions of
arbitrary data, colors, and spatial constructs, with an example of the
production of a game character's logic in mathematical form.
",A theory of contemplation
"  We propose a general framework for inconsistency-tolerant query answering
within existential rule setting. This framework unifies the main semantics
proposed by the state of art and introduces new ones based on cardinality and
majority principles. It relies on two key notions: modifiers and inference
strategies. An inconsistency-tolerant semantics is seen as a composite modifier
plus an inference strategy. We compare the obtained semantics from a
productivity point of view.
","A General Modifier-based Framework for Inconsistency-Tolerant Query
  Answering"
"  There is both much optimism and pessimism around artificial intelligence (AI)
today. The optimists are investing millions of dollars, and even in some cases
billions of dollars into AI. The pessimists, on the other hand, predict that AI
will end many things: jobs, warfare, and even the human race. Both the
optimists and the pessimists often appeal to the idea of a technological
singularity, a point in time where machine intelligence starts to run away, and
a new, more intelligent species starts to inhabit the earth. If the optimists
are right, this will be a moment that fundamentally changes our economy and our
society. If the pessimists are right, this will be a moment that also
fundamentally changes our economy and our society. It is therefore very
worthwhile spending some time deciding if either of them might be right.
",The Singularity May Never Be Near
"  Narrative intelligence is the ability to craft, tell, understand, and respond
affectively to stories. We argue that instilling artificial intelligences with
computational narrative intelligence affords a number of applications
beneficial to humans. We lay out some of the machine learning challenges
necessary to solve to achieve computational narrative intelligence. Finally, we
argue that computational narrative is a practical step towards machine
enculturation, the teaching of sociocultural values to machines.
","Computational Narrative Intelligence: A Human-Centered Goal for
  Artificial Intelligence"
"  We consider partially observable Markov decision processes (POMDPs) with a
set of target states and positive integer costs associated with every
transition. The traditional optimization objective (stochastic shortest path)
asks to minimize the expected total cost until the target set is reached. We
extend the traditional framework of POMDPs to model energy consumption, which
represents a hard constraint. The energy levels may increase and decrease with
transitions, and the hard constraint requires that the energy level must remain
positive in all steps till the target is reached. First, we present a novel
algorithm for solving POMDPs with energy levels, developing on existing POMDP
solvers and using RTDP as its main method. Our second contribution is related
to policy representation. For larger POMDP instances the policies computed by
existing solvers are too large to be understandable. We present an automated
procedure based on machine learning techniques that automatically extracts
important decisions of the policy allowing us to compute succinct human
readable policies. Finally, we show experimentally that our algorithm performs
well and computes succinct policies on a number of POMDP instances from the
literature that were naturally enhanced with energy levels.
",Stochastic Shortest Path with Energy Constraints in POMDPs
"  The ability to know in advance the trend of running process instances, with
respect to different features, such as the expected completion time, would
allow business managers to timely counteract to undesired situations, in order
to prevent losses. Therefore, the ability to accurately predict future features
of running business process instances would be a very helpful aid when managing
processes, especially under service level agreement constraints. However,
making such accurate forecasts is not easy: many factors may influence the
predicted features.
  Many approaches have been proposed to cope with this problem but all of them
assume that the underling process is stationary. However, in real cases this
assumption is not always true. In this work we present new methods for
predicting the remaining time of running cases. In particular we propose a
method, assuming process stationarity, which outperforms the state-of-the-art
and two other methods which are able to make predictions even with
non-stationary processes. We also describe an approach able to predict the full
sequence of activities that a running case is going to take. All these methods
are extensively evaluated on two real case studies.
",Time and Activity Sequence Prediction of Business Process Instances
"  Algorithms that generate computer game content require game design knowledge.
We present an approach to automatically learn game design knowledge for level
design from gameplay videos. We further demonstrate how the acquired design
knowledge can be used to generate sections of game levels. Our approach
involves parsing video of people playing a game to detect the appearance of
patterns of sprites and utilizing machine learning to build a probabilistic
model of sprite placement. We show how rich game design information can be
automatically parsed from gameplay videos and represented as a set of
generative probabilistic models. We use Super Mario Bros. as a proof of
concept. We evaluate our approach on a measure of playability and stylistic
similarity to the original levels as represented in the gameplay videos.
",Toward Game Level Generation from Gameplay Videos
"  This paper focuses on causal structure estimation from time series data in
which measurements are obtained at a coarser timescale than the causal
timescale of the underlying system. Previous work has shown that such
subsampling can lead to significant errors about the system's causal structure
if not properly taken into account. In this paper, we first consider the search
for the system timescale causal structures that correspond to a given
measurement timescale structure. We provide a constraint satisfaction procedure
whose computational performance is several orders of magnitude better than
previous approaches. We then consider finite-sample data as input, and propose
the first constraint optimization approach for recovering the system timescale
causal structure. This algorithm optimally recovers from possible conflicts due
to statistical errors. More generally, these advances allow for a robust and
non-parametric estimation of system timescale causal structures from subsampled
time series data.
","Causal Discovery from Subsampled Time Series Data by Constraint
  Optimization"
"  Neutrosophic set has the ability to handle uncertain, incomplete,
inconsistent, indeterminate information in a more accurate way. In this paper,
we proposed a neutrosophic recommender system to predict the diseases based on
neutrosophic set which includes single-criterion neutrosophic recommender
system (SC-NRS) and multi-criterion neutrosophic recommender system (MC-NRS).
Further, we investigated some algebraic operations of neutrosophic recommender
system such as union, complement, intersection, probabilistic sum, bold sum,
bold intersection, bounded difference, symmetric difference, convex linear sum
of min and max operators, Cartesian product, associativity, commutativity and
distributive. Based on these operations, we studied the algebraic structures
such as lattices, Kleen algebra, de Morgan algebra, Brouwerian algebra, BCK
algebra, Stone algebra and MV algebra. In addition, we introduced several types
of similarity measures based on these algebraic operations and studied some of
their theoretic properties. Moreover, we accomplished a prediction formula
using the proposed algebraic similarity measure. We also proposed a new
algorithm for medical diagnosis based on neutrosophic recommender system.
Finally to check the validity of the proposed methodology, we made experiments
on the datasets Heart, RHC, Breast cancer, Diabetes and DMD. At the end, we
presented the MSE and computational time by comparing the proposed algorithm
with the relevant ones such as ICSM, DSM, CARE, CFMD, as well as other variants
namely Variant 67, Variant 69, and Varian 71 both in tabular and graphical form
to analyze the efficiency and accuracy. Finally we analyzed the strength of all
8 algorithms by ANOVA statistical tool.
","A Neutrosophic Recommender System for Medical Diagnosis Based on
  Algebraic Neutrosophic Measures"
"  We present an algorithm for building probabilistic rule lists that is two
orders of magnitude faster than previous work. Rule list algorithms are
competitors for decision tree algorithms. They are associative classifiers, in
that they are built from pre-mined association rules. They have a logical
structure that is a sequence of IF-THEN rules, identical to a decision list or
one-sided decision tree. Instead of using greedy splitting and pruning like
decision tree algorithms, we fully optimize over rule lists, striking a
practical balance between accuracy, interpretability, and computational speed.
The algorithm presented here uses a mixture of theoretical bounds (tight enough
to have practical implications as a screening or bounding procedure),
computational reuse, and highly tuned language libraries to achieve
computational efficiency. Currently, for many practical problems, this method
achieves better accuracy and sparsity than decision trees; further, in many
cases, the computational time is practical and often less than that of decision
trees. The result is a probabilistic classifier (which estimates P(y = 1|x) for
each x) that optimizes the posterior of a Bayesian hierarchical model over rule
lists.
",Scalable Bayesian Rule Lists
"  Route choice in multimodal networks shows a considerable variation between
different individuals as well as the current situational context.
Personalization of recommendation algorithms are already common in many areas,
e.g., online retail. However, most online routing applications still provide
shortest distance or shortest travel-time routes only, neglecting individual
preferences as well as the current situation. Both aspects are of particular
importance in a multimodal setting as attractivity of some transportation modes
such as biking crucially depends on personal characteristics and exogenous
factors like the weather. This paper introduces the FAVourite rOUte
Recommendation (FAVOUR) approach to provide personalized, situation-aware route
proposals based on three steps: first, at the initialization stage, the user
provides limited information (home location, work place, mobility options,
sociodemographics) used to select one out of a small number of initial
profiles. Second, based on this information, a stated preference survey is
designed in order to sharpen the profile. In this step a mass preference prior
is used to encode the prior knowledge on preferences from the class identified
in step one. And third, subsequently the profile is continuously updated during
usage of the routing services. The last two steps use Bayesian learning
techniques in order to incorporate information from all contributing
individuals. The FAVOUR approach is presented in detail and tested on a small
number of survey participants. The experimental results on this real-world
dataset show that FAVOUR generates better-quality recommendations w.r.t.
alternative learning algorithms from the literature. In particular the
definition of the mass preference prior for initialization of step two is shown
to provide better predictions than a number of alternatives from the
literature.
","Personalized and situation-aware multimodal route recommendations: the
  FAVOUR algorithm"
"  Hierarchical Classification (HC) is a supervised learning problem where
unlabeled instances are classified into a taxonomy of classes. Several methods
that utilize the hierarchical structure have been developed to improve the HC
performance. However, in most cases apriori defined hierarchical structure by
domain experts is inconsistent; as a consequence performance improvement is not
noticeable in comparison to flat classification methods. We propose a scalable
data-driven filter based rewiring approach to modify an expert-defined
hierarchy. Experimental comparisons of top-down HC with our modified hierarchy,
on a wide range of datasets shows classification performance improvement over
the baseline hierarchy (i:e:, defined by expert), clustered hierarchy and
flattening based hierarchy modification approaches. In comparison to existing
rewiring approaches, our developed method (rewHier) is computationally
efficient, enabling it to scale to datasets with large numbers of classes,
instances and features. We also show that our modified hierarchy leads to
improved classification performance for classes with few training samples in
comparison to flat and state-of-the-art HC approaches.
","Filter based Taxonomy Modification for Improving Hierarchical
  Classification"
"  The emergence of collective dynamics in neural networks is a mechanism of the
animal and human brain for information processing. In this paper, we develop a
computational technique using distributed processing elements in a complex
network, which are called particles, to solve semi-supervised learning
problems. Three actions govern the particles' dynamics: generation, walking,
and absorption. Labeled vertices generate new particles that compete against
rival particles for edge domination. Active particles randomly walk in the
network until they are absorbed by either a rival vertex or an edge currently
dominated by rival particles. The result from the model evolution consists of
sets of edges arranged by the label dominance. Each set tends to form a
connected subnetwork to represent a data class. Although the intrinsic dynamics
of the model is a stochastic one, we prove there exists a deterministic version
with largely reduced computational complexity; specifically, with linear
growth. Furthermore, the edge domination process corresponds to an unfolding
map in such way that edges ""stretch"" and ""shrink"" according to the vertex-edge
dynamics. Consequently, the unfolding effect summarizes the relevant
relationships between vertices and the uncovered data classes. The proposed
model captures important details of connectivity patterns over the vertex-edge
dynamics evolution, in contrast to previous approaches which focused on only
vertex or only edge dynamics. Computer simulations reveal that the new model
can identify nonlinear features in both real and artificial data, including
boundaries between distinct classes and overlapping structures of data.
",Network Unfolding Map by Edge Dynamics Modeling
"  We report about significant enhancements of the complex algebraic geometry
theorem proving subsystem in GeoGebra for automated proofs in Euclidean
geometry, concerning the extension of numerous GeoGebra tools with proof
capabilities. As a result, a number of elementary theorems can be proven by
using GeoGebra's intuitive user interface on various computer architectures
including native Java and web based systems with JavaScript. We also provide a
test suite for benchmarking our results with 200 test cases.
",GeoGebra Tools with Proof Capabilities
"  Wooden blocks are a common toy for infants, allowing them to develop motor
skills and gain intuition about the physical behavior of the world. In this
paper, we explore the ability of deep feed-forward models to learn such
intuitive physics. Using a 3D game engine, we create small towers of wooden
blocks whose stability is randomized and render them collapsing (or remaining
upright). This data allows us to train large convolutional network models which
can accurately predict the outcome, as well as estimating the block
trajectories. The models are also able to generalize in two important ways: (i)
to new physical scenarios, e.g. towers with an additional block and (ii) to
images of real wooden blocks, where it obtains a performance comparable to
human subjects.
",Learning Physical Intuition of Block Towers by Example
"  The increasing amount of available Linked Data resources is laying the
foundations for more advanced Semantic Web applications. One of their main
limitations, however, remains the general low level of data quality. In this
paper we focus on a measure of quality which is negatively affected by the
increase of the available resources. We propose a measure of semantic richness
of Linked Data concepts and we demonstrate our hypothesis that the more a
concept is reused, the less semantically rich it becomes. This is a significant
scalability issue, as one of the core aspects of Linked Data is the propagation
of semantic information on the Web by reusing common terms. We prove our
hypothesis with respect to our measure of semantic richness and we validate our
model empirically. Finally, we suggest possible future directions to address
this scalability problem.
","A Linked Data Scalability Challenge: Concept Reuse Leads to Semantic
  Decay"
"  We present an approach to generate novel computer game levels that blend
different game concepts in an unsupervised fashion. Our primary contribution is
an analogical reasoning process to construct blends between level design models
learned from gameplay videos. The models represent probabilistic relationships
between elements in the game. An analogical reasoning process maps features
between two models to produce blended models that can then generate new level
chunks. As a proof-of-concept we train our system on the classic platformer
game Super Mario Bros. due to its highly-regarded and well understood level
design. We evaluate the extent to which the models represent stylistic level
design knowledge and demonstrate the ability of our system to explain levels
that were blended by human expert designers.
",Learning to Blend Computer Game Levels
"  We present a hierarchical reinforcement learning framework that formulates
each task in the hierarchy as a special type of Markov decision process for
which the Bellman equation is linear and has analytical solution. Problems of
this type, called linearly-solvable MDPs (LMDPs) have interesting properties
that can be exploited in a hierarchical setting, such as efficient learning of
the optimal value function or task compositionality. The proposed hierarchical
approach can also be seen as a novel alternative to solving LMDPs with large
state spaces. We derive a hierarchical version of the so-called Z-learning
algorithm that learns different tasks simultaneously and show empirically that
it significantly outperforms the state-of-the-art learning methods in two
classical hierarchical reinforcement learning domains: the taxi domain and an
autonomous guided vehicle task.
",Hierarchical Linearly-Solvable Markov Decision Problems
"  In this paper, we propose a set theoretic approach for knowledge
representation. While the syntax of an application domain is captured by set
theoretic constructs including individuals, concepts and operators, knowledge
is formalized by equality assertions. We first present a primitive form that
uses minimal assumed knowledge and constructs. Then, assuming naive set theory,
we extend it by definitions, which are special kinds of knowledge.
Interestingly, we show that the primitive form is expressive enough to define
logic operators, not only propositional connectives but also quantifiers.
","A Set Theoretic Approach for Knowledge Representation: the
  Representation Part"
"  Divide and Conquer (DC) is conceptually well suited to high-dimensional
optimization by decomposing a problem into multiple small-scale sub-problems.
However, appealing performance can be seldom observed when the sub-problems are
interdependent. This paper suggests that the major difficulty of tackling
interdependent sub-problems lies in the precise evaluation of a partial
solution (to a sub-problem), which can be overwhelmingly costly and thus makes
sub-problems non-trivial to conquer. Thus, we propose an approximation
approach, named Divide and Approximate Conquer (DAC), which reduces the cost of
partial solution evaluation from exponential time to polynomial time.
Meanwhile, the convergence to the global optimum (of the original problem) is
still guaranteed. The effectiveness of DAC is demonstrated empirically on two
sets of non-separable high-dimensional problems.
","High-dimensional Black-box Optimization via Divide and Approximate
  Conquer"
"  Starting from the primary representation of neutrosophic information, namely
the degree of truth, degree of indeterminacy and degree of falsity, we define a
nuanced representation in a penta valued fuzzy space, described by the index of
truth, index of falsity, index of ignorance, index of contradiction and index
of hesitation. Also, it was constructed an associated penta valued logic and
then using this logic, it was defined for the proposed penta valued structure
the following operators: union, intersection, negation, complement and dual.
Then, the penta valued representation is extended to a hexa valued one, adding
the sixth component, namely the index of ambiguity.
",Penta and Hexa Valued Representation of Neutrosophic Information
"  Nowadays large amounts of GPS trajectory data is being continuously collected
by GPS-enabled devices such as vehicles navigation systems and mobile phones.
GPS trajectory data is useful for applications such as traffic management,
location forecasting, and itinerary planning. Such applications often need to
extract the time-stamped Sequence of Visited Locations (SVLs) of the mobile
objects. The nearest neighbor query (NNQ) is the most applied method for
labeling the visited locations based on the IDs of the POIs in the process of
SVL generation. NNQ in some scenarios is not accurate enough. To improve the
quality of the extracted SVLs, instead of using NNQ, we label the visited
locations as the IDs of the POIs which geometrically intersect with the GPS
observations. Intersection operator requires the accurate geometry of the
points of interest which we refer to them as the Geometries of Interest (GOIs).
In some application domains (e.g. movement trajectories of animals), adequate
information about the POIs and their GOIs may not be available a priori, or
they may not be publicly accessible and, therefore, they need to be derived
from GPS trajectory data. In this paper we propose a novel method for
estimating the POIs and their GOIs, which consists of three phases: (i)
extracting the geometries of the stay regions; (ii) constructing the geometry
of destination regions based on the extracted stay regions; and (iii)
constructing the GOIs based on the geometries of the destination regions. Using
the geometric similarity to known GOIs as the major evaluation criterion, the
experiments we performed using long-term GPS trajectory data show that our
method outperforms the existing approaches.
","Geometry of Interest (GOI): Spatio-Temporal Destination Extraction and
  Partitioning in GPS Trajectory Data"
"  Very large commonsense knowledge bases (KBs) often have thousands to millions
of axioms, of which relatively few are relevant for answering any given query.
A large number of irrelevant axioms can easily overwhelm resolution-based
theorem provers. Therefore, methods that help the reasoner identify useful
inference paths form an essential part of large-scale reasoning systems. In
this paper, we describe two ordering heuristics for optimization of reasoning
in such systems. First, we discuss how decision trees can be used to select
inference steps that are more likely to succeed. Second, we identify a small
set of problem instance features that suffice to guide searches away from
intractable regions of the search space. We show the efficacy of these
techniques via experiments on thousands of queries from the Cyc KB. Results
show that these methods lead to an order of magnitude reduction in inference
time.
","Controlling Search in Very large Commonsense Knowledge Bases: A Machine
  Learning Approach"
"  We consider a multi-neighborhood local search algorithm with a large number
of possible neighborhoods. Each neighborhood is accompanied by a weight value
which represents the probability of being chosen at each iteration. These
weights are fixed before the algorithm runs, and are considered as parameters
of the algorithm. Given a set of instances, off-line tuning of the algorithm's
parameters can be done by automated algorithm configuration tools (e.g., SMAC).
However, the large number of neighborhoods can make the tuning expensive and
difficult even when the number of parameters has been reduced by some
intuition. In this work, we propose a systematic method to characterize each
neighborhood's behaviours, representing them as a feature vector, and using
cluster analysis to form similar groups of neighborhoods. The novelty of our
characterization method is the ability of reflecting changes of behaviours
according to hardness of different solution quality regions. We show that using
neighborhood clusters instead of individual neighborhoods helps to reduce the
parameter configuration space without misleading the search of the tuning
procedure. Moreover, this method is problem-independent and potentially can be
applied in similar contexts.
","Characterization of neighborhood behaviours in a multi-neighborhood
  local search algorithm"
"  Written responses can provide a wealth of data in understanding student
reasoning on a topic. Yet they are time- and labor-intensive to score,
requiring many instructors to forego them except as limited parts of summative
assessments at the end of a unit or course. Recent developments in Machine
Learning (ML) have produced computational methods of scoring written responses
for the presence or absence of specific concepts. Here, we compare the scores
from one particular ML program -- EvoGrader -- to human scoring of responses to
structurally- and content-similar questions that are distinct from the ones the
program was trained on. We find that there is substantial inter-rater
reliability between the human and ML scoring. However, sufficient systematic
differences remain between the human and ML scoring that we advise only using
the ML scoring for formative, rather than summative, assessment of student
reasoning.
","Comparing Human and Automated Evaluation of Open-Ended Student Responses
  to Questions of Evolution"
"  Load disaggregation based on aided linear integer programming (ALIP) is
proposed. We start with a conventional linear integer programming (IP) based
disaggregation and enhance it in several ways. The enhancements include
additional constraints, correction based on a state diagram, median filtering,
and linear programming-based refinement. With the aid of these enhancements,
the performance of IP-based disaggregation is significantly improved. The
proposed ALIP system relies only on the instantaneous load samples instead of
waveform signatures, and hence does not crucially depend on high sampling
frequency. Experimental results show that the proposed ALIP system performs
better than the conventional IP-based load disaggregation system.
",Load Disaggregation Based on Aided Linear Integer Programming
"  We investigate properties of ABA+, a formalism that extends the well studied
structured argumentation formalism Assumption-Based Argumentation (ABA) with a
preference handling mechanism. In particular, we establish desirable properties
that ABA+ semantics exhibit. These pave way to the satisfaction by ABA+ of some
(arguably) desirable principles of preference handling in argumentation and
nonmonotonic reasoning, as well as non-monotonic inference properties of ABA+
under various semantics.
",Properties of ABA+ for Non-Monotonic Reasoning
"  In this paper, we present a preliminary work on an approach to fill the gap
between logic-based argumentation and the numerous approaches to tackle the
dynamics of abstract argumentation frameworks. Our idea is that, even when
arguments and attacks are defined by means of a logical belief base, there may
be some uncertainty about how accurate is the content of an argument, and so
the presence (or absence) of attacks concerning it. We use enthymemes to
illustrate this notion of uncertainty of arguments and attacks. Indeed, as
argued in the literature, real arguments are often enthymemes instead of
completely specified deductive arguments. This means that some parts of the
pair (support, claim) may be missing because they are supposed to belong to
some ""common knowledge"", and then should be deduced by the agent which receives
the enthymeme. But the perception that agents have of the common knowledge may
be wrong, and then a first agent may state an enthymeme that her opponent is
not able to decode in an accurate way. It is likely that the decoding of the
enthymeme by the agent leads to mistaken attacks between this new argument and
the existing ones. In this case, the agent can receive some information about
attacks or arguments acceptance statuses which disagree with her argumentation
framework. We exemplify a way to incorporate this new piece of information by
means of existing works on the dynamics of abstract argumentation frameworks.
","Using Enthymemes to Fill the Gap between Logical Argumentation and
  Revision of Abstract Argumentation Frameworks"
"  Hierarchical Reinforcement Learning (HRL) exploits temporal abstraction to
solve large Markov Decision Processes (MDP) and provide transferable subtask
policies. In this paper, we introduce an off-policy HRL algorithm: Hierarchical
Q-value Iteration (HQI). We show that it is possible to effectively learn
recursive optimal policies for any valid hierarchical decomposition of the
original MDP, given a fixed dataset collected from a flat stochastic behavioral
policy. We first formally prove the convergence of the algorithm for tabular
MDP. Then our experiments on the Taxi domain show that HQI converges faster
than a flat Q-value Iteration and enjoys easy state abstraction. Also, we
demonstrate that our algorithm is able to learn optimal policies for different
hierarchical structures from the same fixed dataset, which enables model
comparison without recollecting data.
",Algorithms for Batch Hierarchical Reinforcement Learning
"  Iterated applications of belief change operators are essential for different
scenarios such as that of ontology evolution where new information is not
presented at once but only in piecemeal fashion within a sequence. I discuss
iterated applications of so called reinterpretation operators that trace
conflicts between ontologies back to the ambiguous of symbols and that provide
conflict resolution strategies with bridging axioms. The discussion centers on
adaptations of the classical iteration postulates according to Darwiche and
Pearl. The main result of the paper is that reinterpretation operators fulfill
the postulates for sequences containing only atomic triggers. For complex
triggers, a fulfillment is not guaranteed and indeed there are different
reasons for the different postulates why they should not be fulfilled in the
particular scenario of ontology revision with well developed ontologies.
",Iterated Ontology Revision by Reinterpretation
"  We are interested in belief revision involving conditional statements where
the antecedent is almost certainly false. To represent such problems, we use
Ordinal Conditional Functions that may take infinite values. We model belief
change in this context through simple arithmetical operations that allow us to
capture the intuition that certain antecedents can not be validated by any
number of observations. We frame our approach as a form of finite belief
improvement, and we propose a model of conditional belief revision in which
only the ""right"" hypothetical levels of implausibility are revised.
",Ordinal Conditional Functions for Nearly Counterfactual Revision
"  Dung's abstract argumentation theory is a widely used formalism to model
conflicting information and to draw conclusions in such situations. Hereby, the
knowledge is represented by so-called argumentation frameworks (AFs) and the
reasoning is done via semantics extracting acceptable sets. All reasonable
semantics are based on the notion of conflict-freeness which means that
arguments are only jointly acceptable when they are not linked within the AF.
In this paper, we study the question which information on top of conflict-free
sets is needed to compute extensions of a semantics at hand. We introduce a
hierarchy of so-called verification classes specifying the required amount of
information. We show that well-known standard semantics are exactly verifiable
through a certain such class. Our framework also gives a means to study
semantics lying inbetween known semantics, thus contributing to a more abstract
understanding of the different features argumentation semantics offer.
",Verifiability of Argumentation Semantics
"  Understanding the behavior of belief change operators for fragments of
classical logic has received increasing interest over the last years. Results
in this direction are mainly concerned with adapting representation theorems.
However, fragment-driven belief change also leads to novel research questions.
In this paper we propose the concept of belief distribution, which can be
understood as the reverse task of merging. More specifically, we are interested
in the following question: given an arbitrary knowledge base $K$ and some
merging operator $\Delta$, can we find a profile $E$ and a constraint $\mu$,
both from a given fragment of classical logic, such that $\Delta_\mu(E)$ yields
a result equivalent to $K$? In other words, we are interested in seeing if $K$
can be distributed into knowledge bases of simpler structure, such that the
task of merging allows for a reconstruction of the original knowledge. Our
initial results show that merging based on drastic distance allows for an easy
distribution of knowledge, while the power of distribution for operators based
on Hamming distance relies heavily on the fragment of choice.
",Distributing Knowledge into Simple Bases
"  The League Championship Algorithm (LCA) is sport-inspired optimization
algorithm that was introduced by Ali Husseinzadeh Kashan in the year 2009. It
has since drawn enormous interest among the researchers because of its
potential efficiency in solving many optimization problems and real-world
applications. The LCA has also shown great potentials in solving
non-deterministic polynomial time (NP-complete) problems. This survey presents
a brief synopsis of the LCA literatures in peer-reviewed journals, conferences
and book chapters. These research articles are then categorized according to
indexing in the major academic databases (Web of Science, Scopus, IEEE Xplore
and the Google Scholar). The analysis was also done to explore the prospects
and the challenges of the algorithm and its acceptability among researchers.
This systematic categorization can be used as a basis for future studies.
",A Survey of League Championship Algorithm: Prospects and Challenges
"  Traditional pattern mining algorithms generally suffer from a lack of
flexibility. In this paper, we propose a SAT formulation of the problem to
successfully mine frequent flexible sequences occurring in transactional
datasets. Our SAT-based approach can easily be extended with extra constraints
to address a broad range of pattern mining applications. To demonstrate this
claim, we formulate and add several constraints, such as gap and span
constraints, to our model in order to extract more specific patterns. We also
use interactive solving to perform important derived tasks, such as closed
pattern mining or maximal pattern mining. Finally, we prove the practical
feasibility of our SAT model by running experiments on two real datasets.
",A SAT model to mine flexible sequences in transactional datasets
"  We propose a logical analysis of the concept of typicality, central in human
cognition (Rosch,1978). We start from a previously proposed extension of the
basic Description Logic ALC (a computationally tractable fragment of First
Order Logic, used to represent concept inclusions and ontologies) with a
typicality operator T that allows to consistently represent the attribution to
classes of individuals of properties with exceptions (as in the classic example
(i) typical birds fly, (ii) penguins are birds but (iii) typical penguins don't
fly). We then strengthen this extension in order to separately reason about the
typicality with respect to different aspects (e.g., flying, having nice
feather: in the previous example, penguins may not inherit the property of
flying, for which they are exceptional, but can nonetheless inherit other
properties, such as having nice feather).
","A strengthening of rational closure in DLs: reasoning about multiple
  aspects"
"  Grouping problems aim to partition a set of items into multiple mutually
disjoint subsets according to some specific criterion and constraints. Grouping
problems cover a large class of important combinatorial optimization problems
that are generally computationally difficult. In this paper, we propose a
general solution approach for grouping problems, i.e., reinforcement learning
based local search (RLS), which combines reinforcement learning techniques with
descent-based local search. The viability of the proposed approach is verified
on a well-known representative grouping problem (graph coloring) where a very
simple descent-based coloring algorithm is applied. Experimental studies on
popular DIMACS and COLOR02 benchmark graphs indicate that RLS achieves
competitive performances compared to a number of well-known coloring
algorithms.
","Reinforcement learning based local search for grouping problems: A case
  study on graph coloring"
"  There is considerable uncertainty about what properties, capabilities and
motivations future AGIs will have. In some plausible scenarios, AGIs may pose
security risks arising from accidents and defects. In order to mitigate these
risks, prudent early AGI research teams will perform significant testing on
their creations before use. Unfortunately, if an AGI has human-level or greater
intelligence, testing itself may not be safe; some natural AGI goal systems
create emergent incentives for AGIs to tamper with their test environments,
make copies of themselves on the internet, or convince developers and operators
to do dangerous things. In this paper, we survey the AGI containment problem -
the question of how to build a container in which tests can be conducted safely
and reliably, even on AGIs with unknown motivations and capabilities that could
be dangerous. We identify requirements for AGI containers, available
mechanisms, and weaknesses that need to be addressed.
",The AGI Containment Problem
"  On the Web, there is always a need to aggregate opinions from the crowd (as
in posts, social networks, forums, etc.). Different mechanisms have been
implemented to capture these opinions such as ""Like"" in Facebook, ""Favorite"" in
Twitter, thumbs-up/down, flagging, and so on. However, in more contested
domains (e.g. Wikipedia, political discussion, and climate change discussion)
these mechanisms are not sufficient since they only deal with each issue
independently without considering the relationships between different claims.
We can view a set of conflicting arguments as a graph in which the nodes
represent arguments and the arcs between these nodes represent the defeat
relation. A group of people can then collectively evaluate such graphs. To do
this, the group must use a rule to aggregate their individual opinions about
the entire argument graph. Here, we present the first experimental evaluation
of different principles commonly employed by aggregation rules presented in the
literature. We use randomized controlled experiments to investigate which
principles people consider better at aggregating opinions under different
conditions. Our analysis reveals a number of factors, not captured by
traditional formal models, that play an important role in determining the
efficacy of aggregation. These results help bring formal models of
argumentation closer to real-world application.
","Experimental Assessment of Aggregation Principles in
  Argumentation-enabled Collective Intelligence"
"  An inconsistent knowledge base can be abstracted as a set of arguments and a
defeat relation among them. There can be more than one consistent way to
evaluate such an argumentation graph. Collective argument evaluation is the
problem of aggregating the opinions of multiple agents on how a given set of
arguments should be evaluated. It is crucial not only to ensure that the
outcome is logically consistent, but also satisfies measures of social
optimality and immunity to strategic manipulation. This is because agents have
their individual preferences about what the outcome ought to be. In the current
paper, we analyze three previously introduced argument-based aggregation
operators with respect to Pareto optimality and strategy proofness under
different general classes of agent preferences. We highlight fundamental
trade-offs between strategic manipulability and social optimality on one hand,
and classical logical criteria on the other. Our results motivate further
investigation into the relationship between social choice and argumentation
theory. The results are also relevant for choosing an appropriate aggregation
operator given the criteria that are considered more important, as well as the
nature of agents' preferences.
","Pareto Optimality and Strategy Proofness in Group Argument Evaluation
  (Extended Version)"
"  We introduce an extension of the n-ary description logic DLR to deal with
attribute-labelled tuples (generalising the positional notation), with
arbitrary projections of relations (inclusion dependencies), generic functional
dependencies and with global and local objectification (reifying relations or
their projections). We show how a simple syntactic condition on the appearance
of projections and functional dependencies in a knowledge base makes the
language decidable without increasing the computational complexity of the basic
DLR language.
","Extending DLR with Labelled Tuples, Projections, Functional Dependencies
  and Objectification (full version)"
"  Knowledge base is the way to store structured and unstructured data
throughout the web. Since the size of the web is increasing rapidly, there are
huge needs to structure the knowledge in a fully automated way. However
fully-automated knowledge-base evolution on the Semantic Web is a major
challenges, although there are many ontology evolution techniques available.
Therefore learning ontology automatically can contribute to the semantic web
society significantly. In this paper, we propose full-automated ontology
learning algorithm to generate refined knowledge base from incomplete knowledge
base and rdf-triples. Our algorithm is data-driven approach which is based on
the property of each instance. Ontology class is being elaborated by
generalizing frequent property of its instances. By using that developed class
information, each instance can find its most relatively matching class. By
repeating these two steps, we achieve fully-automated ontology evolution from
incomplete basic knowledge base.
",Automatic Knowledge Base Evolution by Learning Instances
"  Recognition of goals and plans using incomplete evidence from action
execution can be done efficiently by using planning techniques. In many
applications it is important to recognize goals and plans not only accurately,
but also quickly. In this paper, we develop a heuristic approach for
recognizing plans based on planning techniques that rely on ordering
constraints to filter candidate goals from observations. These ordering
constraints are called landmarks in the planning literature, which are facts or
actions that cannot be avoided to achieve a goal. We show the applicability of
planning landmarks in two settings: first, we use it directly to develop a
heuristic-based plan recognition approach; second, we refine an existing
planning-based plan recognition approach by pre-filtering its candidate goals.
Our empirical evaluation shows that our approach is not only substantially more
accurate than the state-of-the-art in all available datasets, it is also an
order of magnitude faster.
",Landmark-Based Plan Recognition
"  I propose a framework for an agent to change its probabilistic beliefs when a
new piece of propositional information $\alpha$ is observed. Traditionally,
belief change occurs by either a revision process or by an update process,
depending on whether the agent is informed with $\alpha$ in a static world or,
respectively, whether $\alpha$ is a 'signal' from the environment due to an
event occurring. Boutilier suggested a unified model of qualitative belief
change, which ""combines aspects of revision and update, providing a more
realistic characterization of belief change."" In this paper, I propose a
unified model of quantitative belief change, where an agent's beliefs are
represented as a probability distribution over possible worlds. As does
Boutilier, I take a dynamical systems perspective. The proposed approach is
evaluated against several rationality postulated, and some properties of the
approach are worked out.
",On Stochastic Belief Revision and Update and their Combination
"  We propose a method for an agent to revise its incomplete probabilistic
beliefs when a new piece of propositional information is observed. In this
work, an agent's beliefs are represented by a set of probabilistic formulae --
a belief base. The method involves determining a representative set of
'boundary' probability distributions consistent with the current belief base,
revising each of these probability distributions and then translating the
revised information into a new belief base. We use a version of Lewis Imaging
as the revision operation. The correctness of the approach is proved. The
expressivity of the belief bases under consideration are rather restricted, but
has some applications. We also discuss methods of belief base revision
employing the notion of optimum entropy, and point out some of the benefits and
difficulties in those methods. Both the boundary distribution method and the
optimum entropy method are reasonable, yet yield different results.
",Revising Incompletely Specified Convex Probabilistic Belief Bases
"  Objects or structures that are regular take uniform dimensions. Based on the
concepts of regular models, our previous research work has developed a system
of a regular ontology that models learning structures in a multiagent system
for uniform pre-assessments in a learning environment. This regular ontology
has led to the modelling of a classified rules learning algorithm that predicts
the actual number of rules needed for inductive learning processes and decision
making in a multiagent system. But not all processes or models are regular.
Thus this paper presents a system of polynomial equation that can estimate and
predict the required number of rules of a non-regular ontology model given some
defined parameters.
","A system of serial computation for classified rules prediction in
  non-regular ontology trees"
"  We propose a computational model of situated language comprehension based on
the Indexical Hypothesis that generates meaning representations by translating
amodal linguistic symbols to modal representations of beliefs, knowledge, and
experience external to the linguistic system. This Indexical Model incorporates
multiple information sources, including perceptions, domain knowledge, and
short-term and long-term experiences during comprehension. We show that
exploiting diverse information sources can alleviate ambiguities that arise
from contextual use of underspecific referring expressions and unexpressed
argument alternations of verbs. The model is being used to support linguistic
interactions in Rosie, an agent implemented in Soar that learns from
instruction.
","Towards an Indexical Model of Situated Language Comprehension for
  Cognitive Agents in Physical Worlds"
"  The development of machine learning in particular and artificial intelligent
in general has been strongly conditioned by the lack of an appropriate
interface layer between deduction, abduction and induction. In this work we
extend traditional algebraic specification methods in this direction. Here we
assume that such interface for AI emerges from an adequate Neural-Symbolic
integration. This integration is made for universe of discourse described on a
Topos governed by a many-valued {\L}ukasiewicz logic. Sentences are integrated
in a symbolic knowledge base describing the problem domain, codified using a
graphic-based language, wherein every logic connective is defined by a neuron
in an artificial network. This allows the integration of first-order formulas
into a network architecture as background knowledge, and simplifies symbolic
rule extraction from trained networks. For the train of such neural networks we
changed the Levenderg-Marquardt algorithm, restricting the knowledge
dissemination in the network structure using soft crystallization. This
procedure reduces neural network plasticity without drastically damaging the
learning performance, allowing the emergence of symbolic patterns. This makes
the descriptive power of produced neural networks similar to the descriptive
power of {\L}ukasiewicz logic language, reducing the information lost on
translation between symbolic and connectionist structures. We tested this
method on the extraction of knowledge from specified structures. For it, we
present the notion of fuzzy state automata, and we use automata behaviour to
infer its structure. We use this type of automata on the generation of models
for relations specified as symbolic background knowledge.
","Knowledge Extraction and Knowledge Integration governed by
  {\L}ukasiewicz Logics"
"  In this article, we describe the fuzzy logic, fuzzy language and algorithms
as the basis of fuzzy reasoning, one of the intelligent information processing
method, and then describe the general fuzzy reasoning method.
",An Analysis of General Fuzzy Logic and Fuzzy Reasoning Method
"  Computational creativity is a subfield of AI focused on developing and
studying creative systems. Few academic studies analysing the behaviour of
creative agents from a theoretical viewpoint have been proposed. The proposed
frameworks are vague and hard to exploit; moreover, such works are focused on a
notion of creativity tailored for humans.
  In this paper we introduce General Creativity, which extends that traditional
notion. General Creativity provides the basis for a formalised theoretical
framework, that allows one to univocally describe any creative agent, and their
behaviour within societies of creative systems. Given the growing number of AI
creative systems developed over recent years, it is of fundamental importance
to understand how they could influence each other as well as how to gauge their
impact on human society. In particular, in this paper we exploit the proposed
framework for (i) identifying different forms of creativity; (ii) describing
some typical creative agents behaviour, and (iii) analysing the dynamics of
societies in which both human and non-human creative systems coexist.
",A General Framework for Describing Creative Agents
"  Given recent successes in AI (e.g., AlphaGo's victory against Lee Sedol in
the game of GO), it's become increasingly important to assess: how close are AI
systems to human-level intelligence? This paper describes the Allen AI Science
Challenge---an approach towards that goal which led to a unique Kaggle
Competition, its results, the lessons learned, and our next steps.
",Moving Beyond the Turing Test with the Allen AI Science Challenge
"  This paper presents the first framework for integrating procedural knowledge,
or ""know-how"", into the Linked Data Cloud. Know-how available on the Web, such
as step-by-step instructions, is largely unstructured and isolated from other
sources of online knowledge. To overcome these limitations, we propose
extending to procedural knowledge the benefits that Linked Data has already
brought to representing, retrieving and reusing declarative knowledge. We
describe a framework for representing generic know-how as Linked Data and for
automatically acquiring this representation from existing resources on the Web.
This system also allows the automatic generation of links between different
know-how resources, and between those resources and other online knowledge
bases, such as DBpedia. We discuss the results of applying this framework to a
real-world scenario and we show how it outperforms existing manual
community-driven integration efforts.
",Integrating Know-How into the Linked Data Cloud
"  The concept of ""task"" is at the core of artificial intelligence (AI): Tasks
are used for training and evaluating AI systems, which are built in order to
perform and automatize tasks we deem useful. In other fields of engineering
theoretical foundations allow thorough evaluation of designs by methodical
manipulation of well understood parameters with a known role and importance;
this allows an aeronautics engineer, for instance, to systematically assess the
effects of wind speed on an airplane's performance and stability. No framework
exists in AI that allows this kind of methodical manipulation: Performance
results on the few tasks in current use (cf. board games, question-answering)
cannot be easily compared, however similar or different. The issue is even more
acute with respect to artificial *general* intelligence systems, which must
handle unanticipated tasks whose specifics cannot be known beforehand. A *task
theory* would enable addressing tasks at the *class* level, bypassing their
specifics, providing the appropriate formalization and classification of tasks,
environments, and their parameters, resulting in more rigorous ways of
measuring, comparing, and evaluating intelligent behavior. Even modest
improvements in this direction would surpass the current ad-hoc nature of
machine learning and AI evaluation. Here we discuss the main elements of the
argument for a task theory and present an outline of what it might look like
for physical tasks.
","Why Artificial Intelligence Needs a Task Theory --- And What It Might
  Look Like"
"  Many Web applications require efficient querying of large Knowledge Graphs
(KGs). We propose KOGNAC, a dictionary-encoding algorithm designed to improve
SPARQL querying with a judicious combination of statistical and semantic
techniques. In KOGNAC, frequent terms are detected with a frequency
approximation algorithm and encoded to maximise compression. Infrequent terms
are semantically grouped into ontological classes and encoded to increase data
locality. We evaluated KOGNAC in combination with state-of-the-art RDF engines,
and observed that it significantly improves SPARQL querying on KGs with up to
1B edges.
",KOGNAC: Efficient Encoding of Large Knowledge Graphs
"  We introduce a setting for learning possibilistic logic theories from
defaults of the form ""if alpha then typically beta"". We first analyse this
problem from the point of view of machine learning theory, determining the VC
dimension of possibilistic stratifications as well as the complexity of the
associated learning problems, after which we present a heuristic learning
algorithm that can easily scale to thousands of defaults. An important property
of our approach is that it is inherently able to handle noisy and conflicting
sets of defaults. Among others, this allows us to learn possibilistic logic
theories from crowdsourced data and to approximate propositional Markov logic
networks using heuristic MAP solvers. We present experimental results that
demonstrate the effectiveness of this approach.
",Learning Possibilistic Logic Theories from Default Rules
"  The field of iterated belief change has focused mainly on revision, with the
other main operator of AGM belief change theory, i.e. contraction, receiving
relatively little attention. In this paper we extend the Harper Identity from
single-step change to define iterated contraction in terms of iterated
revision. Specifically, just as the Harper Identity provides a recipe for
defining the belief set resulting from contracting A in terms of (i) the
initial belief set and (ii) the belief set resulting from revision by not-A, we
look at ways to define the plausibility ordering over worlds resulting from
contracting A in terms of (iii) the initial plausibility ordering, and (iv) the
plausibility ordering resulting from revision by not-A. After noting that the
most straightforward such extension leads to a trivialisation of the space of
permissible orderings, we provide a family of operators for combining
plausibility orderings that avoid such a result. These operators are
characterised in our domain of interest by a pair of intuitively compelling
properties, which turn out to enable the derivation of a number of iterated
contraction postulates from postulates for iterated revision. We finish by
observing that a salient member of this family allows for the derivation of
counterparts for contraction of some well known iterated revision operators, as
well as for defining new iterated contraction operators.
",Extending the Harper Identity to Iterated Belief Change
"  The ""SP theory of intelligence"", with its realisation in the ""SP computer
model"", aims to simplify and integrate observations and concepts across
AI-related fields, with information compression as a unifying theme. This paper
describes how abstract structures and processes in the theory may be realised
in terms of neurons, their interconnections, and the transmission of signals
between neurons. This part of the SP theory -- ""SP-neural"" -- is a tentative
and partial model for the representation and processing of knowledge in the
brain. In the SP theory (apart from SP-neural), all kinds of knowledge are
represented with ""patterns"", where a pattern is an array of atomic symbols in
one or two dimensions. In SP-neural, the concept of a ""pattern"" is realised as
an array of neurons called a ""pattern assembly"", similar to Hebb's concept of a
""cell assembly"" but with important differences. Central to the processing of
information in the SP system is the powerful concept of ""multiple alignment"",
borrowed and adapted from bioinformatics. Processes such as pattern
recognition, reasoning and problem solving are achieved via the building of
multiple alignments, while unsupervised learning -- significantly different
from the ""Hebbian"" kinds of learning -- is achieved by creating patterns from
sensory information and also by creating patterns from multiple alignments in
which there is a partial match between one pattern and another. Short-lived
neural structures equivalent to multiple alignments will be created via an
inter-play of excitatory and inhibitory neural signals. The paper discusses
several associated issues, with relevant empirical evidence.
","The SP theory of intelligence and the representation and processing of
  knowledge in the brain"
"  We define a property of intelligent systems, which we call Reflexivity. In
human beings, it is one aspect of consciousness, and an element of
deliberation. We propose a conjecture, that this property is conditioned by a
topological property of the processes which implement this reflexivity. These
processes may be symbolic, or non symbolic e.g. connexionnist. An architecture
which implements reflexivity may be based on the interaction of one or several
modules of deep learning, which may be specialized or not, and interconnected
in a relevant way. A necessary condition of reflexivity is the existence of
recurrence in its processes, we will examine in which cases this condition may
be sufficient. We will then examine how this topology and this property make
possible the expression of a second property, the deliberation. In a final
paragraph, we propose an evaluation of intelligent systems, based on the
fulfillment of all or some of these properties.
",AGI and Reflexivity
"  Application of UAV in indoor environment is emerging nowadays due to the
advancements in technology. UAV brings more space-flexibility in an occupied or
hardly-accessible indoor environment, e.g., shop floor of manufacturing
industry, greenhouse, nuclear powerplant. UAV helps in creating an autonomous
manufacturing system by executing tasks with less human intervention in
time-efficient manner. Consequently, a scheduler is one essential component to
be focused on; yet the number of reported studies on UAV scheduling has been
minimal. This work proposes a methodology with a heuristic (based on Earliest
Available Time algorithm) which assigns tasks to UAVs with an objective of
minimizing the makespan. In addition, a quick response towards uncertain events
and a quick creation of new high-quality feasible schedule are needed. Hence,
the proposed heuristic is incorporated with Particle Swarm Optimization (PSO)
algorithm to find a quick near optimal schedule. This proposed methodology is
implemented into a scheduler and tested on a few scales of datasets generated
based on a real flight demonstration. Performance evaluation of scheduler is
discussed in detail and the best solution obtained from a selected set of
parameters is reported.
",Task scheduling system for UAV operations in indoor environment
"  Judgment aggregation problems form a class of collective decision-making
problems represented in an abstract way, subsuming some well known problems
such as voting. A collective decision can be reached in many ways, but a direct
one-step aggregation of individual decisions is arguably most studied. Another
way to reach collective decisions is by iterative consensus building --
allowing each decision-maker to change their individual decision in response to
the choices of the other agents until a consensus is reached. Iterative
consensus building has so far only been studied for voting problems. Here we
propose an iterative judgment aggregation algorithm, based on movements in an
undirected graph, and we study for which instances it terminates with a
consensus. We also compare the computational complexity of our iterative
procedure with that of related judgment aggregation operators.
",Iterative Judgment Aggregation
"  We consider the problem of selecting the best variable-value strategy for
solving a given problem in constraint programming. We show that the recent
Embarrassingly Parallel Search method (EPS) can be used for this purpose. EPS
proposes to solve a problem by decomposing it in a lot of subproblems and to
give them on-demand to workers which run in parallel. Our method uses a part of
these subproblems as a simple sample as defined in statistics for comparing
some strategies in order to select the most promising one that will be used for
solving the remaining subproblems. For each subproblem of the sample, the
parallelism helps us to control the running time of the strategies because it
gives us the possibility to introduce timeouts by stopping a strategy when it
requires more than twice the time of the best one. Thus, we can deal with the
great disparity in solving times for the strategies. The selections we made are
based on the Wilcoxon signed rank tests because no assumption has to be made on
the distribution of the solving times and because these tests can deal with the
censored data that we obtain after introducing timeouts. The experiments we
performed on a set of classical benchmarks for satisfaction and optimization
problems show that our method obtain good performance by selecting almost all
the time the best variable-value strategy and by almost never choosing a
variable-value strategy which is dramatically slower than the best one. Our
method also outperforms the portfolio approach consisting in running some
strategies in parallel and is competitive with the multi armed bandit
framework.
",Parallel Strategies Selection
"  One of the better studied properties for operators in judgment aggregation is
independence, which essentially dictates that the collective judgment on one
issue should not depend on the individual judgments given on some other
issue(s) in the same agenda. Independence, although considered a desirable
property, is too strong, because together with mild additional conditions it
implies dictatorship. We propose here a weakening of independence, named agenda
separability: a judgment aggregation rule satisfies it if, whenever the agenda
is composed of several independent sub-agendas, the resulting collective
judgment sets can be computed separately for each sub-agenda and then put
together. We show that this property is discriminant, in the sense that among
judgment aggregation rules so far studied in the literature, some satisfy it
and some do not. We briefly discuss the implications of agenda separability on
the computation of judgment aggregation rules.
",Agenda Separability in Judgment Aggregation
"  In this paper, we describe Compact-Table (CT), a bitwise algorithm to enforce
Generalized Arc Consistency (GAC) on table con- straints. Although this
algorithm is the default propagator for table constraints in or-tools and
OscaR, two publicly available CP solvers, it has never been described so far.
Importantly, CT has been recently improved further with the introduction of
residues, resetting operations and a data-structure called reversible sparse
bit-set, used to maintain tables of supports (following the idea of tabular
reduction): tuples are invalidated incrementally on value removals by means of
bit-set operations. The experimentation that we have conducted with OscaR shows
that CT outperforms state-of-the-art algorithms STR2, STR3, GAC4R, MDD4R and
AC5-TC on standard benchmarks.
","Compact-Table: Efficiently Filtering Table Constraints with Reversible
  Sparse Bit-Sets"
"  Privacy has been a major motivation for distributed problem optimization.
However, even though several methods have been proposed to evaluate it, none of
them is widely used. The Distributed Constraint Optimization Problem (DCOP) is
a fundamental model used to approach various families of distributed problems.
As privacy loss does not occur when a solution is accepted, but when it is
proposed, privacy requirements cannot be interpreted as a criteria of the
objective function of the DCOP. Here we approach the problem by letting both
the optimized costs found in DCOPs and the privacy requirements guide the
agents' exploration of the search space. We introduce Utilitarian Distributed
Constraint Optimization Problem (UDCOP) where the costs and the privacy
requirements are used as parameters to a heuristic modifying the search
process. Common stochastic algorithms for decentralized constraint optimization
problems are evaluated here according to how well they preserve privacy.
Further, we propose some extensions where these solvers modify their search
process to take into account their privacy requirements, succeeding in
significantly reducing their privacy loss without significant degradation of
the solution quality.
",Utilitarian Distributed Constraint Optimization Problems
"  Privacy has traditionally been a major motivation for decentralized problem
solving. However, even though several metrics have been proposed to quantify
it, none of them is easily integrated with common solvers. Constraint
programming is a fundamental paradigm used to approach various families of
problems. We introduce Utilitarian Distributed Constraint Satisfaction Problems
(UDisCSP) where the utility of each state is estimated as the difference
between the the expected rewards for agreements on assignments for shared
variables, and the expected cost of privacy loss. Therefore, a traditional
DisCSP with privacy requirements is viewed as a planning problem. The actions
available to agents are: communication and local inference. Common
decentralized solvers are evaluated here from the point of view of their
interpretation as greedy planners. Further, we investigate some simple
extensions where these solvers start taking into account the utility function.
In these extensions we assume that the planning problem is further restricting
the set of communication actions to only the communication primitives present
in the corresponding solver protocols. The solvers obtained for the new type of
problems propose the action (communication/inference) to be performed in each
situation, defining thereby the policy.
","DisCSPs with Privacy Recast as Planning Problems for Utility-based
  Agents"
"  This document provides the foundations behind the functionality provided by
the $\rho$G library (https://github.com/santiontanon/RHOG), focusing on the
basic operations the library provides: subsumption, refinement of directed
labeled graphs, and distance/similarity assessment between directed labeled
graphs. $\rho$G development was initially supported by the National Science
Foundation, by the EAGER grant IIS-1551338.
",RHOG: A Refinement-Operator Library for Directed Labeled Graphs
"  Verification and validation of agentic behavior have been suggested as
important research priorities in efforts to reduce risks associated with the
creation of general artificial intelligence (Russell et al 2015). In this paper
we question the appropriateness of using language of certainty with respect to
efforts to manage that risk. We begin by establishing a very general formalism
to characterize agentic behavior and to describe standards of acceptable
behavior. We show that determination of whether an agent meets any particular
standard is not computable. We discuss the extent of the burden associated with
verification by manual proof and by automated behavioral governance. We show
that to ensure decidability of the behavioral standard itself, one must further
limit the capabilities of the agent. We then demonstrate that if our concerns
relate to outcomes in the physical world, attempts at validation are futile.
Finally, we show that layered architectures aimed at making these challenges
tractable mistakenly equate intentions with actions or outcomes, thereby
failing to provide any guarantees. We conclude with a discussion of why
language of certainty should be eradicated from the conversation about the
safety of general artificial intelligence.
",Limits to Verification and Validation of Agentic Behavior
"  Monte Carlo Tree Search (MCTS) methods have proven powerful in planning for
sequential decision-making problems such as Go and video games, but their
performance can be poor when the planning depth and sampling trajectories are
limited or when the rewards are sparse. We present an adaptation of PGRD
(policy-gradient for reward-design) for learning a reward-bonus function to
improve UCT (a MCTS algorithm). Unlike previous applications of PGRD in which
the space of reward-bonus functions was limited to linear functions of
hand-coded state-action-features, we use PGRD with a multi-layer convolutional
neural network to automatically learn features from raw perception as well as
to adapt the non-linear reward-bonus function parameters. We also adopt a
variance-reducing gradient method to improve PGRD's performance. The new method
improves UCT's performance on multiple ATARI games compared to UCT without the
reward bonus. Combining PGRD and Deep Learning in this way should make adapting
rewards for MCTS algorithms far more widely and practically applicable than
before.
","Deep Learning for Reward Design to Improve Monte Carlo Tree Search in
  ATARI Games"
"  DeepMind's recent spectacular success in using deep convolutional neural nets
and machine learning to build superhuman level agents --- e.g. for Atari games
via deep Q-learning and for the game of Go via Reinforcement Learning ---
raises many questions, including to what extent these methods will succeed in
other domains. In this paper we consider DQL for the game of Hex: after
supervised initialization, we use selfplay to train NeuroHex, an 11-layer CNN
that plays Hex on the 13x13 board. Hex is the classic two-player alternate-turn
stone placement game played on a rhombus of hexagonal cells in which the winner
is whomever connects their two opposing sides. Despite the large action and
state space, our system trains a Q-network capable of strong play with no
search. After two weeks of Q-learning, NeuroHex achieves win-rates of 20.4% as
first player and 2.1% as second player against a 1-second/move version of
MoHex, the current ICGA Olympiad Hex champion. Our data suggests further
improvement might be possible with more training time.
",Neurohex: A Deep Q-learning Hex Agent
"  We introduce a logic for temporal beliefs and intentions based on Shoham's
database perspective. We separate strong beliefs from weak beliefs. Strong
beliefs are independent from intentions, while weak beliefs are obtained by
adding intentions to strong beliefs and everything that follows from that. We
formalize coherence conditions on strong beliefs and intentions. We provide
AGM-style postulates for the revision of strong beliefs and intentions. We show
in a representation theorem that a revision operator satisfying our postulates
can be represented by a pre-order on interpretations of the beliefs, together
with a selection function for the intentions.
","AGM-Style Revision of Beliefs and Intentions from a Database Perspective
  (Preliminary Version)"
"  Dou Shou Qi is a game in which two players control a number of pieces, each
of them aiming to move one of their pieces onto a given square. We implemented
an engine for analyzing the game. Moreover, we created a series of endgame
tablebases containing all configurations with up to four pieces. These
tablebases are the first steps towards theoretically solving the game. Finally,
we constructed decision trees based on the endgame tablebases. In this note we
report on some interesting patterns.
",Endgame Analysis of Dou Shou Qi
"  Information and knowledge are transformable into each other. Information
transformation into knowledge by the example of rule generation from OWL (Web
Ontology Language) ontology has been shown during the development of the SWES
(Semantic Web Expert System). The SWES is expected as an expert system for
searching OWL ontologies from the Web, generating rules from the found
ontologies and supplementing the SWES knowledge base with these rules. The
purpose of this paper is to show knowledge transformation into information by
the example of ontology generation from rules.
",Mutual Transformation of Information and Knowledge
"  Modern saturation-based Automated Theorem Provers typically implement the
superposition calculus for reasoning about first-order logic with or without
equality. Practical implementations of this calculus use a variety of literal
selections and term orderings to tame the growth of the search space and help
steer proof search. This paper introduces the notion of lookahead selection
that estimates (looks ahead) the effect on the search space of selecting a
literal. There is also a case made for the use of incomplete selection
functions that attempt to restrict the search space instead of satisfying some
completeness criteria. Experimental evaluation in the \Vampire\ theorem prover
shows that both lookahead selection and incomplete selection significantly
contribute to solving hard problems unsolvable by other methods.
",Selecting the Selection
"  This paper is motivated by a series of (related) questions as to whether a
computer can have pleasure and pain, what pleasure (and intensity of pleasure)
is, and, ultimately, what concepts of emotion are.
  To determine what an emotion is, is a matter of conceptualization, namely,
understanding and explicitly encoding the concept of emotion as people use it
in everyday life. This is a notoriously difficult problem (Frijda, 1986, Fehr
\& Russell, 1984). This paper firstly shows why this is a difficult problem by
aligning it with the conceptualization of a few other so called semantic
primitives such as ""EXIST"", ""FORCE"", ""BIG"" (plus ""LIMIT""). The definitions of
these thought-to-be-indefinable concepts, given in this paper, show what formal
definitions of concepts look like and how concepts are constructed. As a
by-product, owing to the explicit account of the meaning of ""exist"", the famous
dispute between Einstein and Bohr is naturally resolved from linguistic point
of view. Secondly, defending Frijda's view that emotion is action tendency (or
Ryle's behavioral disposition (propensity)), we give a list of emotions defined
in terms of action tendency. In particular, the definitions of pleasure and the
feeling of beauty are presented.
  Further, we give a formal definition of ""action tendency"", from which the
concept of ""intensity"" of emotions (including pleasure) is naturally derived in
a formal fashion. The meanings of ""wish"", ""wait"", ""good"", ""hot"" are analyzed.
",Defining Concepts of Emotion: From Philosophy to Science
"  We consider abstract-argumentation-theoretic coalition formability in this
work. Taking a model from political alliance among political parties, we will
contemplate profitability, and then formability, of a coalition. As is commonly
understood, a group forms a coalition with another group for a greater good,
the goodness measured against some criteria. As is also commonly understood,
however, a coalition may deliver benefits to a group X at the sacrifice of
something that X was able to do before coalition formation, which X may be no
longer able to do under the coalition. Use of the typical conflict-free sets of
arguments is not very fitting for accommodating this aspect of coalition, which
prompts us to turn to a weaker notion, conflict-eliminability, as a property
that a set of arguments should primarily satisfy. We require numerical
quantification of attack strengths as well as of argument strengths for its
characterisation. We will first analyse semantics of profitability of a given
conflict-eliminable set forming a coalition with another conflict-eliminable
set, and will then provide four coalition formability semantics, each of which
formalises certain utility postulate(s) taking the coalition profitability into
account.
","Coalition Formability Semantics with Conflict-Eliminable Sets of
  Arguments"
"  The static bike rebalancing problem (SBRP) concerns the task of repositioning
bikes among stations in self-service bike-sharing systems. This problem can be
seen as a variant of the one-commodity pickup and delivery vehicle routing
problem, where multiple visits are allowed to be performed at each station,
i.e., the demand of a station is allowed to be split. Moreover, a vehicle may
temporarily drop its load at a station, leaving it in excess or, alternatively,
collect more bikes from a station (even all of them), thus leaving it in
default. Both cases require further visits in order to meet the actual demands
of such station. This paper deals with a particular case of the SBRP, in which
only a single vehicle is available and the objective is to find a least-cost
route that meets the demand of all stations and does not violate the minimum
(zero) and maximum (vehicle capacity) load limits along the tour. Therefore,
the number of bikes to be collected or delivered at each station should be
appropriately determined in order to respect such constraints. We propose an
iterated local search (ILS) based heuristic to solve the problem. The ILS
algorithm was tested on 980 benchmark instances from the literature and the
results obtained are quite competitive when compared to other existing methods.
Moreover, our heuristic was capable of finding most of the known optimal
solutions and also of improving the results on a number of open instances.
","A heuristic algorithm for a single vehicle static bike sharing
  rebalancing problem"
"  Probabilistic programming is considered as a framework, in which basic
components of cognitive architectures can be represented in unified and elegant
fashion. At the same time, necessity of adopting some component of cognitive
architectures for extending capabilities of probabilistic programming languages
is pointed out. In particular, implicit specification of generative models via
declaration of concepts and links between them is proposed, and usefulness of
declarative knowledge for achieving efficient inference is briefly discussed.
",A Step from Probabilistic Programming to Cognitive Architectures
"  Machines of all kinds from vehicles to industrial equipment are increasingly
instrumented with hundreds of sensors. Using such data to detect anomalous
behaviour is critical for safety and efficient maintenance. However, anomalies
occur rarely and with great variety in such systems, so there is often
insufficient anomalous data to build reliable detectors. A standard approach to
mitigate this problem is to use one class methods relying only on data from
normal behaviour. Unfortunately, even these approaches are more likely to fail
in the scenario of a dynamical system with manual control input(s). Normal
behaviour in response to novel control input(s) might look very different to
the learned detector which may be incorrectly detected as anomalous. In this
paper, we address this issue by modelling time-series via Ordinary Differential
Equations (ODE) and utilising such an ODE model to simulate the behaviour of
dynamical systems under varying control inputs. The available data is then
augmented with data generated from the ODE, and the anomaly detector is
retrained on this augmented dataset. Experiments demonstrate that ODE-augmented
training data allows better coverage of possible control input(s) and results
in learning more accurate distinctions between normal and anomalous behaviour
in time-series.
","ODE - Augmented Training Improves Anomaly Detection in Sensor Data from
  Machines"
"  Merging beliefs requires the plausibility of the sources of the information
to be merged. They are typically assumed equally reliable in lack of hints
indicating otherwise; yet, a recent line of research spun from the idea of
deriving this information from the revision process itself. In particular, the
history of previous revisions and previous merging examples provide information
for performing subsequent mergings.
  Yet, no examples or previous revisions may be available. In spite of the
apparent lack of information, something can still be inferred by a
try-and-check approach: a relative reliability ordering is assumed, the merging
process is performed based on it, and the result is compared with the original
information. The outcome of this check may be incoherent with the initial
assumption, like when a completely reliable source is rejected some of the
information it provided. In such cases, the reliability ordering assumed in the
first place can be excluded from consideration. The first theorem of this
article proves that such a scenario is indeed possible. Other results are
obtained under various definition of reliability and merging.
",Belief Merging by Source Reliability Assessment
"  The combination of multi-armed bandit (MAB) algorithms with Monte-Carlo tree
search (MCTS) has made a significant impact in various research fields. The UCT
algorithm, which combines the UCB bandit algorithm with MCTS, is a good example
of the success of this combination. The recent breakthrough made by AlphaGo,
which incorporates convolutional neural networks with bandit algorithms in
MCTS, also highlights the necessity of bandit algorithms in MCTS. However,
despite the various investigations carried out on MCTS, nearly all of them
still follow the paradigm of treating every node as an independent instance of
the MAB problem, and applying the same bandit algorithm and heuristics on every
node. As a result, this paradigm may leave some properties of the game tree
unexploited. In this work, we propose that max nodes and min nodes have
different concerns regarding their value estimation, and different bandit
algorithms should be applied accordingly. We develop the Asymmetric-MCTS
algorithm, which is an MCTS variant that applies a simple regret algorithm on
max nodes, and the UCB algorithm on min nodes. We will demonstrate the
performance of the Asymmetric-MCTS algorithm on the game of $9\times 9$ Go,
$9\times 9$ NoGo, and Othello.
","Asymmetric Move Selection Strategies in Monte-Carlo Tree Search:
  Minimizing the Simple Regret at Max Nodes"
"  Cybersecurity research involves publishing papers about malicious exploits as
much as publishing information on how to design tools to protect
cyber-infrastructure. It is this information exchange between ethical hackers
and security experts, which results in a well-balanced cyber-ecosystem. In the
blooming domain of AI Safety Engineering, hundreds of papers have been
published on different proposals geared at the creation of a safe machine, yet
nothing, to our knowledge, has been published on how to design a malevolent
machine. Availability of such information would be of great value particularly
to computer scientists, mathematicians, and others who have an interest in AI
safety, and who are attempting to avoid the spontaneous emergence or the
deliberate creation of a dangerous AI, which can negatively affect human
activities and in the worst case cause the complete obliteration of the human
species. This paper provides some general guidelines for the creation of a
Malevolent Artificial Intelligence (MAI).
",Unethical Research: How to Create a Malevolent Artificial Intelligence
"  We present in this article the model Function-described graph (FDG), which is
a type of compact representation of a set of attributed graphs (AGs) that
borrow from Random Graphs the capability of probabilistic modelling of
structural and attribute information. We define the FDGs, their features and
two distance measures between AGs (unclassified patterns) and FDGs (models or
classes) and we also explain an efficient matching algorithm. Two applications
of FDGs are presented: in the former, FDGs are used for modelling and matching
3D-objects described by multiple views, whereas in the latter, they are used
for representing and recognising human faces, described also by several views.
",Function-Described Graphs for Structural Pattern Recognition
"  This is a proof of the strong AI hypothesis, i.e. that machines can be
conscious. It is a phenomenological proof that pattern-recognition and
subjective consciousness are the same activity in different terms. Therefore,
it proves that essential subjective processes of consciousness are computable,
and identifies significant traits and requirements of a conscious system. Since
Husserl, many philosophers have accepted that consciousness consists of
memories of logical connections between an ego and external objects. These
connections are called ""intentions."" Pattern recognition systems are achievable
technical artifacts. The proof links this respected introspective philosophical
theory of consciousness with technical art. The proof therefore endorses the
strong AI hypothesis and may therefore also enable a theoretically-grounded
form of artificial intelligence called a ""synthetic intentionality,"" able to
synthesize, generalize, select and repeat intentions. If the pattern
recognition is reflexive, able to operate on the set of intentions, and
flexible, with several methods of synthesizing intentions, an SI may be a
particularly strong form of AI. Similarities and possible applications to
several AI paradigms are discussed. The article then addresses some problems:
The proof's limitations, reflexive cognition, Searles' Chinese room, and how an
SI could ""understand"" ""meanings"" and ""be creative.""
",Consciousness is Pattern Recognition
"  Any agent that is part of the environment it interacts with and has versatile
actuators (such as arms and fingers), will in principle have the ability to
self-modify -- for example by changing its own source code. As we continue to
create more and more intelligent agents, chances increase that they will learn
about this ability. The question is: will they want to use it? For example,
highly intelligent systems may find ways to change their goals to something
more easily achievable, thereby `escaping' the control of their designers. In
an important paper, Omohundro (2008) argued that goal preservation is a
fundamental drive of any intelligent system, since a goal is more likely to be
achieved if future versions of the agent strive towards the same goal. In this
paper, we formalise this argument in general reinforcement learning, and
explore situations where it fails. Our conclusion is that the self-modification
possibility is harmless if and only if the value function of the agent
anticipates the consequences of self-modifications and use the current utility
function when evaluating the future.
",Self-Modification of Policy and Utility Function in Rational Agents
"  How can we design good goals for arbitrarily intelligent agents?
Reinforcement learning (RL) is a natural approach. Unfortunately, RL does not
work well for generally intelligent agents, as RL agents are incentivised to
shortcut the reward sensor for maximum reward -- the so-called wireheading
problem. In this paper we suggest an alternative to RL called value
reinforcement learning (VRL). In VRL, agents use the reward signal to learn a
utility function. The VRL setup allows us to remove the incentive to wirehead
by placing a constraint on the agent's actions. The constraint is defined in
terms of the agent's belief distributions, and does not require an explicit
specification of which actions constitute wireheading.
",Avoiding Wireheading with Value Reinforcement Learning
"  We present a method for learning treewidth-bounded Bayesian networks from
data sets containing thousands of variables. Bounding the treewidth of a
Bayesian greatly reduces the complexity of inferences. Yet, being a global
property of the graph, it considerably increases the difficulty of the learning
process. We propose a novel algorithm for this task, able to scale to large
domains and large treewidths. Our novel approach consistently outperforms the
state of the art on data sets with up to ten thousand variables.
",Learning Bounded Treewidth Bayesian Networks with Thousands of Variables
"  Important advances have been made in the fuzzy quantification field.
Nevertheless, some problems remain when we face the decision of selecting the
most convenient model for a specific application. In the literature, several
desirable adequacy properties have been proposed, but theoretical limits impede
quantification models from simultaneously fulfilling every adequacy property
that has been defined. Besides, the complexity of model definitions and
adequacy properties makes very difficult for real users to understand the
particularities of the different models that have been presented. In this work
we will present several criteria conceived to help in the process of selecting
the most adequate Quantifier Fuzzification Mechanisms for specific practical
applications. In addition, some of the best known well-behaved models will be
compared against this list of criteria. Based on this analysis, some guidance
to choose fuzzy quantification models for practical applications will be
provided.
","Characterizing Quantifier Fuzzification Mechanisms: a behavioral guide
  for practical applications"
"  The challenging task of learning structures of probabilistic graphical models
is an important problem within modern AI research. Recent years have witnessed
several major algorithmic advances in structure learning for Bayesian
networks---arguably the most central class of graphical models---especially in
what is known as the score-based setting. A successful generic approach to
optimal Bayesian network structure learning (BNSL), based on integer
programming (IP), is implemented in the GOBNILP system. Despite the recent
algorithmic advances, current understanding of foundational aspects underlying
the IP based approach to BNSL is still somewhat lacking. Understanding
fundamental aspects of cutting planes and the related separation problem( is
important not only from a purely theoretical perspective, but also since it
holds out the promise of further improving the efficiency of state-of-the-art
approaches to solving BNSL exactly. In this paper, we make several theoretical
contributions towards these goals: (i) we study the computational complexity of
the separation problem, proving that the problem is NP-hard; (ii) we formalise
and analyse the relationship between three key polytopes underlying the
IP-based approach to BNSL; (iii) we study the facets of the three polytopes
both from the theoretical and practical perspective, providing, via exhaustive
computation, a complete enumeration of facets for low-dimensional
family-variable polytopes; and, furthermore, (iv) we establish a tight
connection of the BNSL problem to the acyclic subgraph problem.
","Bayesian Network Structure Learning with Integer Programming: Polytopes,
  Facets, and Complexity"
"  Anytime inference is inference performed incrementally, with the accuracy of
the inference being controlled by a tunable parameter, usually time. Such
anytime inference algorithms are also usually interruptible, gradually
converging to the exact inference value until terminated. While anytime
inference algorithms for specific domains like probability potentials exist in
the literature, our objective in this article is to obtain an anytime inference
algorithm which is sufficiently generic to cover a wide range of domains. For
this we utilise the theory of generic inference as a basis for constructing an
anytime inference algorithm, and in particular, extending work done on ordered
valuation algebras. The novel contribution of this work is the construction of
anytime algorithms in a generic framework, which automatically gives us
instantiations in various useful domains. We also show how to apply this
generic framework for anytime inference in semiring induced valuation algebras,
an important subclass of valuation algebras, which includes instances like
probability potentials, disjunctive normal forms and distributive lattices.
  Keywords: Approximation; Anytime algorithms; Resource-bounded computation;
Generic inference; Valuation algebras; Local computation; Binary join trees.
",Anytime Inference in Valuation Algebras
"  Here, I review current state-of-the-arts in many areas of AI to estimate when
it's reasonable to expect human level AI development. Predictions of prominent
AI researchers vary broadly from very pessimistic predictions of Andrew Ng to
much more moderate predictions of Geoffrey Hinton and optimistic predictions of
Shane Legg, DeepMind cofounder. Given huge rate of progress in recent years and
this broad range of predictions of AI experts, AI safety questions are also
discussed.
","Review of state-of-the-arts in artificial intelligence with application
  to AI safety problem"
"  We study a framework where agents have to avoid aversive signals. The agents
are given only partial information, in the form of features that are
projections of task states. Additionally, the agents have to cope with
non-determinism, defined as unpredictability on the way that actions are
executed. The goal of each agent is to define its behavior based on
feature-action pairs that reliably avoid aversive signals. We study a learning
algorithm, called A-learning, that exhibits fixpoint convergence, where the
belief of the allowed feature-action pairs eventually becomes fixed. A-learning
is parameter-free and easy to implement.
",On Avoidance Learning with Partial Observability
"  Game tree search algorithms, such as Monte Carlo Tree Search (MCTS), require
access to a forward model (or ""simulator"") of the game at hand. However, in
some games such forward model is not readily available. This paper presents
three forward models for two-player attrition games, which we call ""combat
models"", and show how they can be used to simulate combat in RTS games. We also
show how these combat models can be learned from replay data. We use StarCraft
as our application domain. We report experiments comparing our combat models
predicting a combat output and their impact when used for tactical decisions
during a real game.
",Combat Models for RTS Games
"  In a recent paper, we have shown that Plan Recognition over STRIPS can be
formulated and solved using Classical Planning heuristics and algorithms. In
this work, we show that this formulation subsumes the standard formulation of
Plan Recognition over libraries through a compilation of libraries into STRIPS
theories. The libraries correspond to AND/OR graphs that may be cyclic and
where children of AND nodes may be partially ordered. These libraries include
Context-Free Grammars as a special case, where the Plan Recognition problem
becomes a parsing with missing tokens problem. Plan Recognition over the
standard libraries become Planning problems that can be easily solved by any
modern planner, while recognition over more complex libraries, including
Context-Free Grammars (CFGs), illustrate limitations of current Planning
heuristics and suggest improvements that may be relevant in other Planning
problems too.
","Heuristics for Planning, Plan Recognition and Parsing"
"  Many AI applications rely on knowledge about a relevant real-world domain
that is encoded by means of some logical knowledge base (KB). The most
essential benefit of logical KBs is the opportunity to perform automatic
reasoning to derive implicit knowledge or to answer complex queries about the
modeled domain. The feasibility of meaningful reasoning requires KBs to meet
some minimal quality criteria such as logical consistency. Without adequate
tool assistance, the task of resolving violated quality criteria in KBs can be
extremely tough even for domain experts, especially when the problematic KB
includes a large number of logical formulas or comprises complicated logical
formalisms.
  Published non-interactive debugging systems often cannot localize all
possible faults (incompleteness), suggest the deletion or modification of
unnecessarily large parts of the KB (non-minimality), return incorrect
solutions which lead to a repaired KB not satisfying the imposed quality
requirements (unsoundness) or suffer from poor scalability due to the inherent
complexity of the KB debugging problem. Even if a system is complete and sound
and considers only minimal solutions, there are generally exponentially many
solution candidates to select one from. However, any two repaired KBs obtained
from these candidates differ in their semantics in terms of entailments and
non-entailments. Selection of just any of these repaired KBs might result in
unexpected entailments, the loss of desired entailments or unwanted changes to
the KB.
  This work proposes complete, sound and optimal methods for the interactive
debugging of KBs that suggest the one (minimally invasive) error correction of
the faulty KB that yields a repaired KB with exactly the intended semantics.
Users, e.g. domain experts, are involved in the debugging process by answering
automatically generated queries about the intended domain.
",Interactive Debugging of Knowledge Bases
"  This paper proposes a new general approach based on Bayesian networks to
model the human behaviour. This approach represents human behaviour with
probabilistic cause-effect relations based on knowledge, but also with
conditional probabilities coming either from knowledge or deduced from
observations. This approach has been applied to the co-simulation of the CO2
concentration in an office coupled with human behaviour.
","Dynamic Bayesian Networks to simulate occupant behaviours in office
  buildings related to indoor air quality"
"  In this article, I discuss how the AI community views concerns about the
emergence of superintelligent AI and related philosophical issues.
",Philosophy in the Face of Artificial Intelligence
"  In Chile, does not exist an independent entity that publishes quantitative or
qualitative surveys to understand the traditional media environment and its
adaptation on the Social Web. Nowadays, Chilean newsreaders are increasingly
using social web platforms as their primary source of information, among which
Twitter plays a central role. Historical media and pure players are developing
different strategies to increase their audience and influence on this platform.
In this article, we propose a methodology based on data mining techniques to
provide a first level of analysis of the new Chilean media environment. We use
a crawling technique to mine news streams of 37 different Chilean media
actively presents on Twitter and propose several indicators to compare them. We
analyze their volumes of production, their potential audience, and using NLP
techniques, we explore the content of their production: their editorial line
and their geographic coverage.
","Diagnosing editorial strategies of Chilean media on Twitter using an
  automatic news classifier"
"  This paper discusses the idea of levels of autonomy of systems - be this
technical or organic - and compares the insights with models employed by
industries used to describe maturity and capability of their products.
","Differences between Industrial Models of Autonomy and Systemic Models of
  Autonomy"
"  Random generators or stochastic engines are a key component in the structure
of metaheuristic algorithms. This work investigates the effects of non-Gaussian
stochastic engines on the performance of metaheuristics when solving a
real-world optimization problem. In this work, the bacteria foraging algorithm
(BFA) was employed in tandem with four random generators (stochastic engines).
The stochastic engines operate using the Weibull distribution, Gamma
distribution, Gaussian distribution and a chaotic mechanism. The two
non-Gaussian distributions are the Weibull and Gamma distributions. In this
work, the approaches developed were implemented on the real-world
multi-objective resin bonded sand mould problem. The Pareto frontiers obtained
were benchmarked using two metrics; the hyper volume indicator (HVI) and the
proposed Average Explorative Rate (AER) metric. Detail discussions from various
perspectives on the effects of non-Gaussian random generators in metaheuristics
are provided.
","Non-Gaussian Random Generators in Bacteria Foraging Algorithm for
  Multiobjective Optimization"
"  Kidney exchanges are organized markets where patients swap willing but
incompatible donors. In the last decade, kidney exchanges grew from small and
regional to large and national---and soon, international. This growth results
in more lives saved, but exacerbates the empirical hardness of the
$\mathcal{NP}$-complete problem of optimally matching patients to donors.
State-of-the-art matching engines use integer programming techniques to clear
fielded kidney exchanges, but these methods must be tailored to specific models
and objective functions, and may fail to scale to larger exchanges. In this
paper, we observe that if the kidney exchange compatibility graph can be
encoded by a constant number of patient and donor attributes, the clearing
problem is solvable in polynomial time. We give necessary and sufficient
conditions for losslessly shrinking the representation of an arbitrary
compatibility graph. Then, using real compatibility graphs from the UNOS
nationwide kidney exchange, we show how many attributes are needed to encode
real compatibility graphs. The experiments show that, indeed, small numbers of
attributes suffice.
",Small Representations of Big Kidney Exchange Graphs
"  In this paper, we develop a computationally simpler version of the operator
count heuristic for a particular class of domains. The contribution of this
abstract is threefold, we (1) propose an efficient closed form approximation to
the operator count heuristic using the Lagrangian dual; (2) leverage compressed
sensing techniques to obtain an integer approximation for operator counts in
polynomial time; and (3) discuss the relationship of the proposed formulation
to existing heuristics and investigate properties of domains where such
approaches appear to be useful.
","Compliant Conditions for Polynomial Time Approximation of Operator
  Counts"
"  We start with the history of cognitive radar, where origins of the PAC,
Fuster research on cognition and principals of cognition are provided. Fuster
describes five cognitive functions: perception, memory, attention, language,
and intelligence. We describe the Perception-Action Cyclec as it applies to
cognitive radar, and then discuss long-term memory, memory storage, memory
retrieval and working memory. A comparison between memory in human cognition
and cognitive radar is given as well. Attention is another function described
by Fuster, and we have given the comparison of attention in human cognition and
cognitive radar. We talk about the four functional blocks from the PAC:
Bayesian filter, feedback information, dynamic programming and state-space
model for the radar environment. Then, to show that the PAC improves the
tracking accuracy of Cognitive Radar over Traditional Active Radar, we have
provided simulation results. In the simulation, three nonlinear filters:
Cubature Kalman Filter, Unscented Kalman Filter and Extended Kalman Filter are
compared. Based on the results, radars implemented with CKF perform better than
the radars implemented with UKF or radars implemented with EKF. Further, radar
with EKF has the worst accuracy and has the biggest computation load because of
derivation and evaluation of Jacobian matrices. We suggest using the concept of
risk management to better control parameters and improve performance in
cognitive radar. We believe, spectrum sensing can be seen as a potential
interest to be used in cognitive radar and we propose a new approach
Probabilistic ICA which will presumably reduce noise based on estimation error
in cognitive radar. Parallel computing is a concept based on divide and
conquers mechanism, and we suggest using the parallel computing approach in
cognitive radar by doing complicated calculations or tasks to reduce processing
time.
",Cognitive Dynamic Systems: A Technical Review of Cognitive Radar
"  Nowadays, metro systems play an important role in meeting the urban
transportation demand in large cities. The understanding of passenger route
choice is critical for public transit management. The wide deployment of
Automated Fare Collection(AFC) systems opens up a new opportunity. However,
only each trip's tap-in and tap-out timestamp and stations can be directly
obtained from AFC system records; the train and route chosen by a passenger are
unknown, which are necessary to solve our problem. While existing methods work
well in some specific situations, they don't work for complicated situations.
In this paper, we propose a solution that needs no additional equipment or
human involvement than the AFC systems. We develop a probabilistic model that
can estimate from empirical analysis how the passenger flows are dispatched to
different routes and trains. We validate our approach using a large scale data
set collected from the Shenzhen metro system. The measured results provide us
with useful inputs when building the passenger path choice model.
","Estimation of Passenger Route Choice Pattern Using Smart Card Data for
  Complex Metro Systems"
"  This paper discusses the root cause of systems perceiving the self experience
and how to exploit adaptive and learning features without introducing ethically
problematic system properties.
",How to avoid ethically relevant Machine Consciousness
"  This thesis describes work on two applications of probabilistic programming:
the learning of probabilistic program code given specifications, in particular
program code of one-dimensional samplers; and the facilitation of sequential
Monte Carlo inference with help of data-driven proposals. The latter is
presented with experimental results on a linear Gaussian model and a
non-parametric dependent Dirichlet process mixture of objects model for object
recognition and tracking.
  In Chapter 1 we provide a brief introduction to probabilistic programming.
  In Chapter 2 we present an approach to automatic discovery of samplers in the
form of probabilistic programs. We formulate a Bayesian approach to this
problem by specifying a grammar-based prior over probabilistic program code. We
use an approximate Bayesian computation method to learn the programs, whose
executions generate samples that statistically match observed data or
analytical characteristics of distributions of interest. In our experiments we
leverage different probabilistic programming systems to perform Markov chain
Monte Carlo sampling over the space of programs. Experimental results have
demonstrated that, using the proposed methodology, we can learn approximate and
even some exact samplers. Finally, we show that our results are competitive
with regard to genetic programming methods.
  In Chapter 3, we describe a way to facilitate sequential Monte Carlo
inference in probabilistic programming using data-driven proposals. In
particular, we develop a distance-based proposal for the non-parametric
dependent Dirichlet process mixture of objects model. We implement this
approach in the probabilistic programming system Anglican, and show that for
that model data-driven proposals provide significant performance improvements.
We also explore the possibility of using neural networks to improve data-driven
proposals.
","Applications of Probabilistic Programming (Master's thesis, 2015)"
"  Qualitative Spatial and Temporal Reasoning (QSTR) is concerned with symbolic
knowledge representation, typically over infinite domains. The motivations for
employing QSTR techniques range from exploiting computational properties that
allow efficient reasoning to capture human cognitive concepts in a
computational framework. The notion of a qualitative calculus is one of the
most prominent QSTR formalisms. This article presents the first overview of all
qualitative calculi developed to date and their computational properties,
together with generalized definitions of the fundamental concepts and methods,
which now encompass all existing calculi. Moreover, we provide a classification
of calculi according to their algebraic properties.
","A Survey of Qualitative Spatial and Temporal Calculi -- Algebraic and
  Computational Properties"
"  We present a general formal argumentation system for dealing with the
detachment of conditional obligations. Given a set of facts, constraints, and
conditional obligations, we answer the question whether an unconditional
obligation is detachable by considering reasons for and against its detachment.
For the evaluation of arguments in favor of detaching obligations we use a
Dung-style argumentation-theoretical semantics. We illustrate the modularity of
the general framework by considering some extensions, and we compare the
framework to some related approaches from the literature.
","A structured argumentation framework for detaching conditional
  obligations"
"  Detection of non-technical losses (NTL) which include electricity theft,
faulty meters or billing errors has attracted increasing attention from
researchers in electrical engineering and computer science. NTLs cause
significant harm to the economy, as in some countries they may range up to 40%
of the total electricity distributed. The predominant research direction is
employing artificial intelligence to predict whether a customer causes NTL.
This paper first provides an overview of how NTLs are defined and their impact
on economies, which include loss of revenue and profit of electricity providers
and decrease of the stability and reliability of electrical power grids. It
then surveys the state-of-the-art research efforts in a up-to-date and
comprehensive review of algorithms, features and data sets used. It finally
identifies the key scientific and engineering challenges in NTL detection and
suggests how they could be addressed in the future.
","The Challenge of Non-Technical Loss Detection using Artificial
  Intelligence: A Survey"
"  Reinforcement learning (RL) is a general paradigm for studying intelligent
behaviour, with applications ranging from artificial intelligence to psychology
and economics. AIXI is a universal solution to the RL problem; it can learn any
computable environment. A technical subtlety of AIXI is that it is defined
using a mixture over semimeasures that need not sum to 1, rather than over
proper probability measures. In this work we argue that the shortfall of a
semimeasure can naturally be interpreted as the agent's estimate of the
probability of its death. We formally define death for generally intelligent
agents like AIXI, and prove a number of related theorems about their behaviour.
Notable discoveries include that agent behaviour can change radically under
positive linear transformations of the reward signal (from suicidal to
dogmatically self-preserving), and that the agent's posterior belief that it
will survive increases over time.
",Death and Suicide in Universal Artificial Intelligence
"  In the domain of the Soccer simulation 2D league of the RoboCup project,
appropriate player positioning against a given opponent team is an important
factor of soccer team performance. This work proposes a model which decides the
strategy that should be applied regarding a particular opponent team. This task
can be realized by applying preliminary a learning phase where the model
determines the most effective strategies against clusters of opponent teams.
The model determines the best strategies by using sequential Bayes' estimators.
As a first trial of the system, the proposed model is used to determine the
association of player formations against opponent teams in the particular
situation of corner-kick. The implemented model shows satisfying abilities to
compare player formations that are similar to each other in terms of
performance and determines the right ranking even by running a decent number of
simulation games.
","Selecting the Best Player Formation for Corner-Kick Situations Based on
  Bayes' Estimation"
"  In this work, a new prototype-based clustering method named Evidential
C-Medoids (ECMdd), which belongs to the family of medoid-based clustering for
proximity data, is proposed as an extension of Fuzzy C-Medoids (FCMdd) on the
theoretical framework of belief functions. In the application of FCMdd and
original ECMdd, a single medoid (prototype), which is supposed to belong to the
object set, is utilized to represent one class. For the sake of clarity, this
kind of ECMdd using a single medoid is denoted by sECMdd. In real clustering
applications, using only one pattern to capture or interpret a class may not
adequately model different types of group structure and hence limits the
clustering performance. In order to address this problem, a variation of ECMdd
using multiple weighted medoids, denoted by wECMdd, is presented. Unlike
sECMdd, in wECMdd objects in each cluster carry various weights describing
their degree of representativeness for that class. This mechanism enables each
class to be represented by more than one object. Experimental results in
synthetic and real data sets clearly demonstrate the superiority of sECMdd and
wECMdd. Moreover, the clustering results by wECMdd can provide richer
information for the inner structure of the detected classes with the help of
prototype weights.
",ECMdd: Evidential c-medoids clustering with multiple prototypes
"  One difficulty faced in knowledge engineering for Bayesian Network (BN) is
the quan-tification step where the Conditional Probability Tables (CPTs) are
determined. The number of parameters included in CPTs increases exponentially
with the number of parent variables. The most common solution is the
application of the so-called canonical gates. The Noisy-OR (NOR) gate, which
takes advantage of the independence of causal interactions, provides a
logarithmic reduction of the number of parameters required to specify a CPT. In
this paper, an extension of NOR model based on the theory of belief functions,
named Belief Noisy-OR (BNOR), is proposed. BNOR is capable of dealing with both
aleatory and epistemic uncertainty of the network. Compared with NOR, more rich
information which is of great value for making decisions can be got when the
available knowledge is uncertain. Specially, when there is no epistemic
uncertainty, BNOR degrades into NOR. Additionally, different structures of BNOR
are presented in this paper in order to meet various needs of engineers. The
application of BNOR model on the reliability evaluation problem of networked
systems demonstrates its effectiveness.
",The belief noisy-or model applied to network reliability analysis
"  The Google DeepMind challenge match in March 2016 was a historic achievement
for computer Go development. This article discusses the development of
computational intelligence (CI) and its relative strength in comparison with
human intelligence for the game of Go. We first summarize the milestones
achieved for computer Go from 1998 to 2016. Then, the computer Go programs that
have participated in previous IEEE CIS competitions as well as methods and
techniques used in AlphaGo are briefly introduced. Commentaries from three
high-level professional Go players on the five AlphaGo versus Lee Sedol games
are also included. We conclude that AlphaGo beating Lee Sedol is a huge
achievement in artificial intelligence (AI) based largely on CI methods. In the
future, powerful computer Go programs such as AlphaGo are expected to be
instrumental in promoting Go education and AI real-world applications.
",Human vs. Computer Go: Review and Prospect
"  We formalize Simplified Boardgames language, which describes a subclass of
arbitrary board games. The language structure is based on the regular
expressions, which makes the rules easily machine-processable while keeping the
rules concise and fairly human-readable.
",Simplified Boardgames
"  The Vortex Search (VS) algorithm is one of the recently proposed
metaheuristic algorithms which was inspired from the vortical flow of the
stirred fluids. Although the VS algorithm is shown to be a good candidate for
the solution of certain optimization problems, it also has some drawbacks. In
the VS algorithm, candidate solutions are generated around the current best
solution by using a Gaussian distribution at each iteration pass. This provides
simplicity to the algorithm but it also leads to some problems along.
Especially, for the functions those have a number of local minimum points, to
select a single point to generate candidate solutions leads the algorithm to
being trapped into a local minimum point. Due to the adaptive step-size
adjustment scheme used in the VS algorithm, the locality of the created
candidate solutions is increased at each iteration pass. Therefore, if the
algorithm cannot escape a local point as quickly as possible, it becomes much
more difficult for the algorithm to escape from that point in the latter
iterations. In this study, a modified Vortex Search algorithm (MVS) is proposed
to overcome above mentioned drawback of the existing VS algorithm. In the MVS
algorithm, the candidate solutions are generated around a number of points at
each iteration pass. Computational results showed that with the help of this
modification the global search ability of the existing VS algorithm is improved
and the MVS algorithm outperformed the existing VS algorithm, PSO2011 and ABC
algorithms for the benchmark numerical function set.
",A Modified Vortex Search Algorithm for Numerical Function Optimization
"  From the point of view of a programmer, the robopsychology is a synonym for
the activity is done by developers to implement their machine learning
applications. This robopsychological approach raises some fundamental
theoretical questions of machine learning. Our discussion of these questions is
constrained to Turing machines. Alan Turing had given an algorithm (aka the
Turing Machine) to describe algorithms. If it has been applied to describe
itself then this brings us to Turing's notion of the universal machine. In the
present paper, we investigate algorithms to write algorithms. From a pedagogy
point of view, this way of writing programs can be considered as a combination
of learning by listening and learning by doing due to it is based on applying
agent technology and machine learning. As the main result we introduce the
problem of learning and then we show that it cannot easily be handled in
reality therefore it is reasonable to use machine learning algorithm for
learning Turing machines.
",Theoretical Robopsychology: Samu Has Learned Turing Machines
"  In this paper we present a new neurobiologically-inspired affective cognitive
architecture: NEUCOGAR (NEUromodulating COGnitive ARchitecture). The objective
of NEUCOGAR is the identification of a mapping from the influence of serotonin,
dopamine and noradrenaline to the computing processes based on Von Neuman's
architecture, in order to implement affective phenomena which can operate on
the Turing's machine model. As basis of the modeling we use and extend the
L\""ovheim Cube of Emotion with parameters of the Von Neumann architecture.
Validation is conducted via simulation on a computing system of dopamine
neuromodulation and its effects on the Cortex. In the experimental phase of the
project, the increase of computing power and storage redistribution due to
emotion stimulus modulated by the dopamine system, confirmed the soundness of
the model.
","A Cognitive Architecture for the Implementation of Emotions in Computing
  Systems"
"  For an autonomous system to be helpful to humans and to pose no unwarranted
risks, it needs to align its values with those of the humans in its environment
in such a way that its actions contribute to the maximization of value for the
humans. We propose a formal definition of the value alignment problem as
cooperative inverse reinforcement learning (CIRL). A CIRL problem is a
cooperative, partial-information game with two agents, human and robot; both
are rewarded according to the human's reward function, but the robot does not
initially know what this is. In contrast to classical IRL, where the human is
assumed to act optimally in isolation, optimal CIRL solutions produce behaviors
such as active teaching, active learning, and communicative actions that are
more effective in achieving value alignment. We show that computing optimal
joint policies in CIRL games can be reduced to solving a POMDP, prove that
optimality in isolation is suboptimal in CIRL, and derive an approximate CIRL
algorithm.
",Cooperative Inverse Reinforcement Learning
"  Analysis of regional development imbalances quadrant has a very important
meaning in order to see the extent of achievement of the development of certain
areas as well as the difference. Factors that could be used as a tool to
measure the inequality of development is to look at the average growth and
development contribution of each sector of Gross Regional Domestic Product
(GRDP) based on the analyzed region and the reference region. This study
discusses the development of a model to determine the regional development
imbalances using fuzzy approach system, and the rules of typology Klassen. The
model is then called fuzzy-Klassen. Implications Product Mamdani fuzzy system
is used in the model as an inference engine to generate output after
defuzzyfication process. Application of MATLAB is used as a tool of analysis in
this study. The test a result of Kota Cilegon is shows that there are
significant differences between traditional Klassen typology analyses with the
results of the model developed. Fuzzy model-Klassen shows GRDP sector
inequality Cilegon City is dominated by Quadrant I (K4), where status is the
sector forward and grows exponentially. While the traditional Klassen typology,
half of GRDP sector is dominated by Quadrant IV (K4) with a sector that is
lagging relative status.
","Fuzzy-Klassen Model for Development Disparities Analysis based on Gross
  Regional Domestic Product Sector of a Region"
"  This paper proposes a model which aim is providing a more coherent framework
for agents design. We identify three closely related anthropo-centered domains
working on separate functional levels. Abstracting from human physiology,
psychology, and philosophy we create the $P^3$ model to be used as a multi-tier
approach to deal with complex class of problems. The three layers identified in
this model have been named PhysioComputing, MindComputing, and MetaComputing.
Several instantiations of this model are finally presented related to different
IT areas such as artificial intelligence, distributed computing, software and
service engineering.
",Towards Anthropo-inspired Computational Systems: the $P^3$ Model
"  The gossip problem, in which information (known as secrets) must be shared
among a certain number of agents using the minimum number of calls, is of
interest in the conception of communication networks and protocols. We extend
the gossip problem to arbitrary epistemic depths. For example, we may require
not only that all agents know all secrets but also that all agents know that
all agents know all secrets. We give optimal protocols for various versions of
the generalised gossip problem, depending on the graph of communication links,
in the case of two-way communications, one-way communications and parallel
communication. We also study different variants which allow us to impose
negative goals such as that certain agents must not know certain secrets. We
show that in the presence of negative goals testing the existence of a
successful protocol is NP-complete whereas this is always polynomial-time in
the case of purely positive goals.
",Simple epistemic planning: generalised gossiping
"  Reasoning on large and complex real-world models is a computationally
difficult task, yet one that is required for effective use of many AI
applications. A plethora of inference algorithms have been developed that work
well on specific models or only on parts of general models. Consequently, a
system that can intelligently apply these inference algorithms to different
parts of a model for fast reasoning is highly desirable. We introduce a new
framework called structured factored inference (SFI) that provides the
foundation for such a system. Using models encoded in a probabilistic
programming language, SFI provides a sound means to decompose a model into
sub-models, apply an inference algorithm to each sub-model, and combine the
resulting information to answer a query. Our results show that SFI is nearly as
accurate as exact inference yet retains the benefits of approximate inference
methods.
","Structured Factored Inference: A Framework for Automated Reasoning in
  Probabilistic Programming Languages"
"  The inherent inflexibility and incompleteness of commonsense knowledge bases
(KB) has limited their usefulness. We describe a system called Displacer for
performing KB queries extended with the analogical capabilities of the word2vec
distributional semantic vector space (DSVS). This allows the system to answer
queries with information which was not contained in the original KB in any
form. By performing analogous queries on semantically related terms and mapping
their answers back into the context of the original query using displacement
vectors, we are able to give approximate answers to many questions which, if
posed to the KB alone, would return no results.
  We also show how the hand-curated knowledge in a KB can be used to increase
the accuracy of a DSVS in solving analogy problems. In these ways, a KB and a
DSVS can make up for each other's weaknesses.
","Using a Distributional Semantic Vector Space with a Knowledge Base for
  Reasoning in Uncertain Conditions"
"  The amount of digitally available but heterogeneous information about the
world is remarkable, and new technologies such as self-driving cars, smart
homes, or the internet of things may further increase it. In this paper we
present preliminary ideas about certain aspects of the problem of how such
heterogeneous information can be harnessed by autonomous agents. After
discussing potentials and limitations of some existing approaches, we
investigate how \emph{experiments} can help to obtain a better understanding of
the problem. Specifically, we present a simple agent that integrates video data
from a different agent, and implement and evaluate a version of it on the novel
experimentation platform \emph{Malmo}. The focus of a second investigation is
on how information about the hardware of different agents, the agents' sensory
data, and \emph{causal} information can be utilized for knowledge transfer
between agents and subsequently more data-efficient decision making. Finally,
we discuss potential future steps w.r.t.\ theory and experimentation, and
formulate open questions.
","Experimental and causal view on information integration in autonomous
  agents"
"  For an artificial creative agent, an essential driver of the search for
novelty is a value function which is often provided by the system designer or
users. We argue that an important barrier for progress in creativity research
is the inability of these systems to develop their own notion of value for
novelty. We propose a notion of knowledge-driven creativity that circumvent the
need for an externally imposed value function, allowing the system to explore
based on what it has learned from a set of referential objects. The concept is
illustrated by a specific knowledge model provided by a deep generative
autoencoder. Using the described system, we train a knowledge model on a set of
digit images and we use the same model to build coherent sets of new digits
that do not belong to known digit types.
",Digits that are not: Generating new types through deep neural nets
"  In this note, we discuss and analyse a shortest path finding approach using
strong spatial cognition. It is compared with a symbolic graph-based algorithm
and it is shown that both approaches are similar with respect to structure and
complexity. Nevertheless, the strong spatial cognition solution is easy to
understand and even pops up immediately when one has to solve the problem.
","Relating Strong Spatial Cognition to Symbolic Problem Solving --- An
  Example"
"  Symmetry is the essential element of lifted inference that has recently
demon- strated the possibility to perform very efficient inference in
highly-connected, but symmetric probabilistic models models. This raises the
question, whether this holds for optimisation problems in general. Here we show
that for a large class of optimisation methods this is actually the case. More
precisely, we introduce the concept of fractional symmetries of convex
quadratic programs (QPs), which lie at the heart of many machine learning
approaches, and exploit it to lift, i.e., to compress QPs. These lifted QPs can
then be tackled with the usual optimization toolbox (off-the-shelf solvers,
cutting plane algorithms, stochastic gradients etc.). If the original QP
exhibits symmetry, then the lifted one will generally be more compact, and
hence their optimization is likely to be more efficient.
",Lifted Convex Quadratic Programming
"  First-order knowledge compilation techniques have proven efficient for lifted
inference. They compile a relational probability model into a target circuit on
which many inference queries can be answered efficiently. Early methods used
data structures as their target circuit. In our KR-2016 paper, we showed that
compiling to a low-level program instead of a data structure offers orders of
magnitude speedup, resulting in the state-of-the-art lifted inference
technique. In this paper, we conduct experiments to address two questions
regarding our KR-2016 results: 1- does the speedup come from more efficient
compilation or more efficient reasoning with the target circuit?, and 2- why
are low-level programs more efficient target circuits than data structures?
","Why is Compiling Lifted Inference into a Low-Level Language so
  Effective?"
"  With the aim of studying social properties of belief merging and having a
better understanding of impossibility, we extend in three ways the framework of
logic-based merging introduced by Konieczny and Pino P\'erez. First, at the
level of representation of the information, we pass from belief bases to
complex epistemic states. Second, the profiles are represented as functions of
finite societies to the set of epistemic states (a sort of vectors) and not as
multisets of epistemic states. Third, we extend the set of rational postulates
in order to consider the epistemic versions of the classical postulates of
Social Choice Theory: Standard Domain, Pareto Property, Independence of
Irrelevant Alternatives and Absence of Dictator. These epistemic versions of
social postulates are given, essentially, in terms of the finite propositional
logic. We state some representation theorems for these operators. These
extensions and representation theorems allow us to establish an epistemic and
very general version of Arrow's Impossibility Theorem. One of the interesting
features of our result, is that it holds for different representations of
epistemic states; for instance conditionals, Ordinal Conditional functions and,
of course, total preorders.
",Impossibility in Belief Merging
"  Deep Reinforcement Learning (DRL) is a trending field of research, showing
great promise in challenging problems such as playing Atari, solving Go and
controlling robots. While DRL agents perform well in practice we are still
lacking the tools to analayze their performance. In this work we present the
Semi-Aggregated MDP (SAMDP) model. A model best suited to describe policies
exhibiting both spatial and temporal hierarchies. We describe its advantages
for analyzing trained policies over other modeling approaches, and show that
under the right state representation, like that of DQN agents, SAMDP can help
to identify skills. We detail the automatic process of creating it from
recorded trajectories, up to presenting it on t-SNE maps. We explain how to
evaluate its fitness and show surprising results indicating high compatibility
with the policy at hand. We conclude by showing how using the SAMDP model, an
extra performance gain can be squeezed from the agent.
",Deep Reinforcement Learning Discovers Internal Models
"  Transfer in reinforcement learning refers to the notion that generalization
should occur not only within a task but also across tasks. We propose a
transfer framework for the scenario where the reward function changes between
tasks but the environment's dynamics remain the same. Our approach rests on two
key ideas: ""successor features"", a value function representation that decouples
the dynamics of the environment from the rewards, and ""generalized policy
improvement"", a generalization of dynamic programming's policy improvement
operation that considers a set of policies rather than a single one. Put
together, the two ideas lead to an approach that integrates seamlessly within
the reinforcement learning framework and allows the free exchange of
information across tasks. The proposed method also provides performance
guarantees for the transferred policy even before any learning has taken place.
We derive two theorems that set our approach in firm theoretical ground and
present experiments that show that it successfully promotes transfer in
practice, significantly outperforming alternative methods in a sequence of
navigation tasks and in the control of a simulated robotic arm.
",Successor Features for Transfer in Reinforcement Learning
"  The capability to store data about business processes execution in so-called
Event Logs has brought to the diffusion of tools for the analysis of process
executions and for the assessment of the goodness of a process model.
Nonetheless, these tools are often very rigid in dealing with with Event Logs
that include incomplete information about the process execution. Thus, while
the ability of handling incomplete event data is one of the challenges
mentioned in the process mining manifesto, the evaluation of compliance of an
execution trace still requires an end-to-end complete trace to be performed.
  This paper exploits the power of abduction to provide a flexible, yet
computationally effective, framework to deal with different forms of
incompleteness in an Event Log. Moreover it proposes a refinement of the
classical notion of compliance into strong and conditional compliance to take
into account incomplete logs. Finally, performances evaluation in an
experimental setting shows the feasibility of the presented approach.
",Abducing Compliance of Incomplete Event Logs
"  Agents of general intelligence deployed in real-world scenarios must adapt to
ever-changing environmental conditions. While such adaptive agents may leverage
engineered knowledge, they will require the capacity to construct and evaluate
knowledge themselves from their own experience in a bottom-up, constructivist
fashion. This position paper builds on the idea of encoding knowledge as
temporally extended predictions through the use of general value functions.
Prior work has focused on learning predictions about externally derived signals
about a task or environment (e.g. battery level, joint position). Here we
advocate that the agent should also predict internally generated signals
regarding its own learning process - for example, an agent's confidence in its
learned predictions. Finally, we suggest how such information would be
beneficial in creating an introspective agent that is able to learn to make
good decisions in a complex, changing world.
",Introspective Agents: Confidence Measures for General Value Functions
"  A Concept Tree is a structure for storing knowledge where the trees are
stored in a database called a Concept Base. It sits between the highly
distributed neural architectures and the distributed information systems, with
the intention of bringing brain-like and computer systems closer together.
Concept Trees can grow from the semi-structured sources when consistent
sequences of concepts are presented. Each tree ideally represents a single
cohesive concept and the trees can link with each other for navigation and
semantic purposes. The trees are therefore also a type of semantic network and
would benefit from having a consistent level of context for each node. A
consistent build process is managed through a 'counting rule' and some other
rules that can normalise the database structure. This restricted structure can
then be complimented and enriched by the more dynamic context. It is also
suggested to use the linking structure of the licas system [15] as a basis for
the context links, where the mathematical model is extended further to define
this. A number of tests have demonstrated the soundness of the architecture.
Building the trees from text documents shows that the tree structure could be
inherent in natural language. Then, two types of query language are described.
Both of these can perform consistent query processes to return knowledge to the
user and even enhance the query with new knowledge. This is supported even
further with direct comparisons to a cognitive model, also being developed by
the author.
",Adding Context to Concept Trees
"  Obtaining a survival strategy (policy) is one of the fundamental problems of
biological agents. In this paper, we generalize the formulation of previous
research related to the survival of an agent and we formulate the survival
problem as a maximization of the multi-step survival probability in future time
steps. We introduce a method for converting the maximization of multi-step
survival probability into a classical reinforcement learning problem. Using
this conversion, the reward function (negative temporal cost function) is
expressed as the log of the temporal survival probability. And we show that the
objective function of the reinforcement learning in this sense is proportional
to the variational lower bound of the original problem. Finally, We empirically
demonstrate that the agent learns survival behavior by using the reward
function introduced in this paper.
",On Reward Function for Survival
"  Reinforcement learning has been applied to many interesting problems such as
the famous TD-gammon and the inverted helicopter flight. However, little effort
has been put into developing methods to learn policies for complex persistent
tasks and tasks that are time-sensitive. In this paper, we take a step towards
solving this problem by using signal temporal logic (STL) as task
specification, and taking advantage of the temporal abstraction feature that
the options framework provide. We show via simulation that a relatively easy to
implement algorithm that combines STL and options can learn a satisfactory
policy with a small number of training cases
","A Hierarchical Reinforcement Learning Method for Persistent
  Time-Sensitive Tasks"
"  With the increasing popularity of online learning, intelligent tutoring
systems are regaining increased attention. In this paper, we introduce adaptive
algorithms for personalized assignment of learning tasks to student so that to
improve his performance in online learning environments. As main contribution
of this paper, we propose a a novel Skill-Based Task Selector (SBTS) algorithm
which is able to approximate a student's skill level based on his performance
and consequently suggest adequate assignments. The SBTS is inspired by the
class of multi-armed bandit algorithms. However, in contrast to standard
multi-armed bandit approaches, the SBTS aims at acquiring two criteria related
to student learning, namely: which topics should the student work on, and what
level of difficulty should the task be. The SBTS centers on innovative reward
and punishment schemes in a task and skill matrix based on the student
behaviour.
  To verify the algorithm, the complex student behaviour is modelled using a
neighbour node selection approach based on empirical estimations of a students
learning curve. The algorithm is evaluated with a practical scenario from a
basic java programming course. The SBTS is able to quickly and accurately adapt
to the composite student competency --- even with a multitude of student
models.
",Adaptive Task Assignment in Online Learning Environments
"  The systematic modelling of dynamic spatial systems is a key requirement in a
wide range of application areas such as commonsense cognitive robotics,
computer-aided architecture design, and dynamic geographic information systems.
We present ASPMT(QS), a novel approach and fully-implemented prototype for
non-monotonic spatial reasoning -a crucial requirement within dynamic spatial
systems- based on Answer Set Programming Modulo Theories (ASPMT).
  ASPMT(QS) consists of a (qualitative) spatial representation module (QS) and
a method for turning tight ASPMT instances into Satisfiability Modulo Theories
(SMT) instances in order to compute stable models by means of SMT solvers. We
formalise and implement concepts of default spatial reasoning and spatial frame
axioms. Spatial reasoning is performed by encoding spatial relations as systems
of polynomial constraints, and solving via SMT with the theory of real
nonlinear arithmetic. We empirically evaluate ASPMT(QS) in comparison with
other contemporary spatial reasoning systems both within and outside the
context of logic programming. ASPMT(QS) is currently the only existing system
that is capable of reasoning about indirect spatial effects (i.e., addressing
the ramification problem), and integrating geometric and qualitative spatial
information within a non-monotonic spatial reasoning context.
  This paper is under consideration for publication in TPLP.
","Non-Monotonic Spatial Reasoning with Answer Set Programming Modulo
  Theories"
"  What is the nature of curiosity? Is there any scientific way to understand
the origin of this mysterious force that drives the behavior of even the
stupidest naturally intelligent systems and is completely absent in their
smartest artificial analogs? Can we build AI systems that could be curious
about something, systems that would have an intrinsic motivation to learn? Is
such a motivation quantifiable? Is it implementable? I will discuss this
problem from the standpoint of physics. The relationship between physics and
intelligence is a consequence of the fact that correctly predicted information
is nothing but an energy resource, and the process of thinking can be viewed as
a process of accumulating and spending this resource through the acts of
perception and, respectively, decision making. The natural motivation of any
autonomous system to keep this accumulation/spending balance as high as
possible allows one to treat the problem of describing the dynamics of thinking
processes as a resource optimization problem. Here I will propose and discuss a
simple theoretical model of such an autonomous system which I call the
Autonomous Turing Machine (ATM). The potential attractiveness of ATM lies in
the fact that it is the model of a self-propelled AI for which the only
available energy resource is the information itself. For ATM, the problem of
optimal thinking, learning, and decision-making becomes conceptually simple and
mathematically well tractable. This circumstance makes the ATM an ideal
playground for studying the dynamics of intelligent behavior and allows one to
quantify many seemingly unquantifiable features of genuine intelligence.
","Can Turing machine be curious about its Turing test results? Three
  informal lectures on physics of intelligence"
"  Verified artificial intelligence (AI) is the goal of designing AI-based
systems that that have strong, ideally provable, assurances of correctness with
respect to mathematically-specified requirements. This paper considers Verified
AI from a formal methods perspective. We describe five challenges for achieving
Verified AI, and five corresponding principles for addressing these challenges.
",Towards Verified Artificial Intelligence
"  Markov Logic Networks (MLN) and Probabilistic Soft Logic (PSL) are widely
applied formalisms in Statistical Relational Learning, an emerging area in
Artificial Intelligence that is concerned with combining logical and
statistical AI. Despite their resemblance, the relationship has not been
formally stated. In this paper, we describe the precise semantic relationship
between them from a logical perspective. This is facilitated by first extending
fuzzy logic to allow weights, which can be also viewed as a generalization of
PSL, and then relate that generalization to MLN. We observe that the
relationship between PSL and MLN is analogous to the known relationship between
fuzzy logic and Boolean logic, and furthermore the weight scheme of PSL is
essentially a generalization of the weight scheme of MLN for the many-valued
setting.
","On the Semantic Relationship between Probabilistic Soft Logic and Markov
  Logic"
"  Optimization of product performance repetitively introduces the need to make
products adaptive in a more general sense. This more general idea is often
captured under the term 'self-configuration'. Despite the importance of such
capability, research work on this feature appears isolated by technical
domains. It is not easy to tell quickly whether the approaches chosen in
different technological domains introduce new ideas or whether the differences
just reflect domain idiosyncrasies. For the sake of easy identification of key
differences between systems with self-configuring capabilities, I will explore
higher level concepts for understanding self-configuration, such as the
{\Omega}-units, in order to provide theoretical instruments for connecting
different areas of technology and research.
","Exploring high-level Perspectives on Self-Configuration Capabilities of
  Systems"
"  In this paper, a novel multiple criteria decision making (MCDM) methodology
is presented for assessing and prioritizing medical tourism destinations in
uncertain environment. A systematic evaluation and assessment method is
proposed by integrating rough number based AHP (Analytic Hierarchy Process) and
rough number based MABAC (Multi-Attributive Border Approximation area
Comparison). Rough number is used to aggregate individual judgments and
preferences to deal with vagueness in decision making due to limited data.
Rough AHP analyzes the relative importance of criteria based on their
preferences given by experts. Rough MABAC evaluates the alternative sites based
on the criteria weights. The proposed methodology is explained through a case
study considering different cities for healthcare service in India. The
validity of the obtained ranking for the given decision making problem is
established by testing criteria proposed by Wang and Triantaphyllou (2008)
along with further analysis and discussion.
","Evaluation and selection of Medical Tourism sites: A rough AHP based
  MABAC approach"
"  Municipal solid waste management (MSWM) is a challenging issue of urban
development in developing countries. Each country having different
socio-economic-environmental background, might not accept a particular disposal
method as the optimal choice. Selection of suitable disposal method in MSWM,
under vague and imprecise information can be considered as multi criteria
decision making problem (MCDM). In the present paper, TOPSIS (Technique for
Order Preference by Similarity to Ideal Solution) methodology is extended based
on credibility theory for evaluating the performances of MSW disposal methods
under some criteria fixed by experts. The proposed model helps decision makers
to choose a preferable alternative for their municipal area. A sensitivity
analysis by our proposed model confirms this fact.
","Credibilistic TOPSIS Model for Evaluation and Selection of Municipal
  Solid Waste Disposal Methods"
"  A qualitative representation $\phi$ is like an ordinary representation of a
relation algebra, but instead of requiring $(a; b)^\phi = a^\phi | b^\phi$, as
we do for ordinary representations, we only require that $c^\phi\supseteq
a^\phi | b^\phi \iff c\geq a ; b$, for each $c$ in the algebra. A constraint
network is qualitatively satisfiable if its nodes can be mapped to elements of
a qualitative representation, preserving the constraints. If a constraint
network is satisfiable then it is clearly qualitatively satisfiable, but the
converse can fail. However, for a wide range of relation algebras including the
point algebra, the Allen Interval Algebra, RCC8 and many others, a network is
satisfiable if and only if it is qualitatively satisfiable.
  Unlike ordinary composition, the weak composition arising from qualitative
representations need not be associative, so we can generalise by considering
network satisfaction problems over non-associative algebras. We prove that
computationally, qualitative representations have many advantages over ordinary
representations: whereas many finite relation algebras have only infinite
representations, every finite qualitatively representable algebra has a finite
qualitative representation; the representability problem for (the atom
structures of) finite non-associative algebras is NP-complete; the network
satisfaction problem over a finite qualitatively representable algebra is
always in NP; the validity of equations over qualitative representations is
co-NP-complete. On the other hand we prove that there is no finite
axiomatisation of the class of qualitatively representable algebras.
",Algebraic foundations for qualitative calculi and networks
"  We propose to accelerate the rate of convergence of the pattern recognition
task by directly minimizing the variance diameters of certain hypothesis
spaces, which are critical quantities in fast-convergence results.We show that
the variance diameters can be controlled by dividing hypothesis spaces into
metric balls based on a new order metric. This order metric can be minimized as
an ordinal regression problem, leading to a LUPI (Learning Using Privileged
Information) application where we take the privileged information as some
desired ordering, and construct a faster-converging hypothesis space by
empirically restricting some larger hypothesis space according to that
ordering. We give a risk analysis of the approach. We discuss the difficulties
with model selection and give an innovative technique for selecting multiple
model parameters. Finally, we provide some data experiments.
",Ordering as privileged information
"  An important approach for efficient inference in probabilistic graphical
models exploits symmetries among objects in the domain. Symmetric variables
(states) are collapsed into meta-variables (meta-states) and inference
algorithms are run over the lifted graphical model instead of the flat one. Our
paper extends existing definitions of symmetry by introducing the novel notion
of contextual symmetry. Two states that are not globally symmetric, can be
contextually symmetric under some specific assignment to a subset of variables,
referred to as the context variables. Contextual symmetry subsumes previous
symmetry definitions and can rep resent a large class of symmetries not
representable earlier. We show how to compute contextual symmetries by reducing
it to the problem of graph isomorphism. We extend previous work on exploiting
symmetries in the MCMC framework to the case of contextual symmetries. Our
experiments on several domains of interest demonstrate that exploiting
contextual symmetries can result in significant computational gains.
",Contextual Symmetries in Probabilistic Graphical Models
"  Due to the intractable nature of exact lifted inference, research has
recently focused on the discovery of accurate and efficient approximate
inference algorithms in Statistical Relational Models (SRMs), such as Lifted
First-Order Belief Propagation. FOBP simulates propositional factor graph
belief propagation without constructing the ground factor graph by identifying
and lifting over redundant message computations. In this work, we propose a
generalization of FOBP called Lifted Generalized Belief Propagation, in which
both the region structure and the message structure can be lifted. This
approach allows more of the inference to be performed intra-region (in the
exact inference step of BP), thereby allowing simulation of propagation on a
graph structure with larger region scopes and fewer edges, while still
maintaining tractability. We demonstrate that the resulting algorithm converges
in fewer iterations to more accurate results on a variety of SRMs.
",Lifted Region-Based Belief Propagation
"  The challenge stated in the title can be divided into two main problems. The
first problem is to reliably mimic the way that users interact with user
interfaces. The second problem is to build an instructible agent, i.e. one that
can be taught to execute tasks expressed as previously unseen natural language
commands. This paper proposes a solution to the second problem, a system we
call Helpa. End-users can teach Helpa arbitrary new tasks whose level of
complexity is similar to the tasks available from today's most popular virtual
assistants. Teaching Helpa does not involve any programming. Instead, users
teach Helpa by providing just one example of a command paired with a
demonstration of how to execute that command. Helpa does not rely on any
pre-existing domain-specific knowledge. It is therefore completely
domain-independent. Our usability study showed that end-users can teach Helpa
many new tasks in less than a minute each, often much less.
","Towards A Virtual Assistant That Can Be Taught New Tasks In Any Domain
  By Its End-Users"
"  Neutrosophic Over-/Under-/Off-Set and -Logic were defined by the author in
1995 and published for the first time in 2007. We extended the neutrosophic set
respectively to Neutrosophic Overset {when some neutrosophic component is over
1}, Neutrosophic Underset {when some neutrosophic component is below 0}, and to
Neutrosophic Offset {when some neutrosophic components are off the interval [0,
1], i.e. some neutrosophic component over 1 and other neutrosophic component
below 0}. This is no surprise with respect to the classical fuzzy set/logic,
intuitionistic fuzzy set/logic, or classical/imprecise probability, where the
values are not allowed outside the interval [0, 1], since our real-world has
numerous examples and applications of over-/under-/off-neutrosophic components.
For example, person working overtime deserves a membership degree over 1, while
a person producing more damage than benefit to a company deserves a membership
below 0. Then, similarly, the Neutrosophic Logic/Measure/Probability/Statistics
etc. were extended to respectively Neutrosophic Over-/Under-/Off-Logic,
-Measure, -Probability, -Statistics etc. [Smarandache, 2007].
","Neutrosophic Overset, Neutrosophic Underset, and Neutrosophic Offset.
  Similarly for Neutrosophic Over-/Under-/Off- Logic, Probability, and
  Statistics"
"  This paper details the implementation of an algorithm for automatically
generating a high-level knowledge network to perform commonsense reasoning,
specifically with the application of robotic task repair. The network is
represented using a Bayesian Logic Network (BLN) (Jain, Waldherr, and Beetz
2009), which combines a set of directed relations between abstract concepts,
including IsA, AtLocation, HasProperty, and UsedFor, with a corresponding
probability distribution that models the uncertainty inherent in these
relations. Inference over this network enables reasoning over the abstract
concepts in order to perform appropriate object substitution or to locate
missing objects in the robot's environment. The structure of the network is
generated by combining information from two existing knowledge sources:
ConceptNet (Speer and Havasi 2012), and WordNet (Miller 1995). This is done in
a ""situated"" manner by only including information relevant a given context.
Results show that the generated network is able to accurately predict object
categories, locations, properties, and affordances in three different household
scenarios.
","Situated Structure Learning of a Bayesian Logic Network for Commonsense
  Reasoning"
"  This article presents an agent architecture for controlling an autonomous
agent in stochastic environments. The architecture combines the partially
observable Markov decision process (POMDP) model with the
belief-desire-intention (BDI) framework. The Hybrid POMDP-BDI agent
architecture takes the best features from the two approaches, that is, the
online generation of reward-maximizing courses of action from POMDP theory, and
sophisticated multiple goal management from BDI theory. We introduce the
advances made since the introduction of the basic architecture, including (i)
the ability to pursue multiple goals simultaneously and (ii) a plan library for
storing pre-written plans and for storing recently generated plans for future
reuse. A version of the architecture without the plan library is implemented
and is evaluated using simulations. The results of the simulation experiments
indicate that the approach is feasible.
","A Hybrid POMDP-BDI Agent Architecture with Online Stochastic Planning
  and Plan Caching"
"  In many navigational domains the traversability of cells is conditioned on
the path taken. This is often the case in video-games, in which a character may
need to acquire a certain object (i.e., a key or a flying suit) to be able to
traverse specific locations (e.g., doors or high walls). In order for
non-player characters to handle such scenarios we present invJPS, an
""inventory-driven"" pathfinding approach based on the highly successful
grid-based Jump-Point-Search (JPS) algorithm. We show, formally and
experimentally, that the invJPS preserves JPS's optimality guarantees and its
symmetry breaking advantages in inventory-based variants of game maps.
",Path planning with Inventory-driven Jump-Point-Search
"  This paper introduces mathematical formalism for Spatial (SP) of Hierarchical
Temporal Memory (HTM) with a spacial consideration for its hardware
implementation. Performance of HTM network and its ability to learn and adjust
to a problem at hand is governed by a large set of parameters. Most of
parameters are codependent which makes creating efficient HTM-based solutions
challenging. It requires profound knowledge of the settings and their impact on
the performance of system. Consequently, this paper introduced a set of
formulas which are to facilitate the design process by enhancing tedious
trial-and-error method with a tool for choosing initial parameters which enable
quick learning convergence. This is especially important in hardware
implementations which are constrained by the limited resources of a platform.
The authors focused especially on a formalism of Spatial Pooler and derive at
the formulas for quality and convergence of the model. This may be considered
as recipes for designing efficient HTM models for given input patterns.
","Formal analysis of HTM Spatial Pooler performance under predefined
  operation conditions"
"  Among the most general structures extending the framework by Dung are the
abstract dialectical frameworks (ADFs). They come equipped with various types
of semantics, with the most prominent - the labeling-based one - analyzed in
the context of computational complexity, signatures, instantiations and
software support. This makes the abstract dialectical frameworks valuable tools
for argumentation. However, there are fewer results available concerning the
relation between the ADFs and other argumentation frameworks. In this paper we
would like to address this issue by introducing a number of translations from
various formalisms into ADFs. The results of our study show the similarities
and differences between them, thus promoting the use and understanding of ADFs.
Moreover, our analysis also proves their capability to model many of the
existing frameworks, including those that go beyond the attack relation.
Finally, translations allow other structures to benefit from the research on
ADFs in general and from the existing software in particular.
",Understanding the Abstract Dialectical Framework (Preliminary Report)
"  Multiple choice questions (MCQs) that can be generated from a domain ontology
can significantly reduce human effort & time required for authoring &
administering assessments in an e-Learning environment. Even though here are
various methods for generating MCQs from ontologies, methods for determining
the difficulty-levels of such MCQs are less explored. In this paper, we study
various aspects and factors that are involved in determining the
difficulty-score of an MCQ, and propose an ontology-based model for the
prediction. This model characterizes the difficulty values associated with the
stem and choice set of the MCQs, and describes a measure which combines both
the scores. Further more, the notion of assigning difficultly-scores based on
the skill level of the test taker is utilized for predicating difficulty-score
of a stem. We studied the effectiveness of the predicted difficulty-scores with
the help of a psychometric model from the Item Response Theory, by involving
real-students and domain experts. Our results show that, the predicated
difficulty-levels of the MCQs are having high correlation with their actual
difficulty-levels.
",Modeling of Item-Difficulty for Ontology-based MCQs
"  In this paper, we attempt to extend Multi Attributive Border Approximation
area Comparison (MABAC) approach for multi-attribute decision making (MADM)
problems based on type-2 fuzzy sets (IT2FSs). As a special case of IT2FSs
interval type-2 trapezoidal fuzzy numbers (IT2TrFNs) are adopted here to deal
with uncertainties present in many practical evaluation and selection problems.
A systematic description of MABAC based on IT2TrFNs is presented in the current
study. The validity and feasibility of the proposed method are illustrated by a
practical example of selecting the most suitable candidate for a software
company which is heading to hire a system analysis engineer based on few
attributes. Finally, a comparison with two other existing MADM methods is
described.
","An extended MABAC for multi-attribute decision making using trapezoidal
  interval type-2 fuzzy numbers"
"  There is an impressive body of work on developing heuristics and other
reasoning algorithms to guide search in optimal and anytime planning algorithms
for classical planning. However, very little effort has been directed towards
developing analogous techniques to guide search towards high-quality solutions
in hierarchical planning formalisms like HTN planning, which allows using
additional domain-specific procedural control knowledge. In lieu of such
techniques, this control knowledge often needs to provide the necessary search
guidance to the planning algorithm, which imposes a substantial burden on the
domain author and can yield brittle or error-prone domain models. We address
this gap by extending recent work on a new hierarchical goal-based planning
formalism called Hierarchical Goal Network (HGN) Planning to develop the
Hierarchically-Optimal Goal Decomposition Planner (HOpGDP), an HGN planning
algorithm that computes hierarchically-optimal plans. HOpGDP is guided by
$h_{HL}$, a new HGN planning heuristic that extends existing admissible
landmark-based heuristics from classical planning to compute admissible cost
estimates for HGN planning problems. Our experimental evaluation across three
benchmark planning domains shows that HOpGDP compares favorably to both optimal
classical planners due to its ability to use domain-specific procedural
knowledge, and a blind-search version of HOpGDP due to the search guidance
provided by $h_{HL}$.
",Cost-Optimal Algorithms for Planning with Procedural Control Knowledge
"  A major challenge in cyber-threat analysis is combining information from
different sources to find the person or the group responsible for the
cyber-attack. It is one of the most important technical and policy challenges
in cyber-security. The lack of ground truth for an individual responsible for
an attack has limited previous studies. In this paper, we take a first step
towards overcoming this limitation by building a dataset from the
capture-the-flag event held at DEFCON, and propose an argumentation model based
on a formal reasoning framework called DeLP (Defeasible Logic Programming)
designed to aid an analyst in attributing a cyber-attack. We build models from
latent variables to reduce the search space of culprits (attackers), and show
that this reduction significantly improves the performance of
classification-based approaches from 37% to 62% in identifying the attacker.
",Argumentation Models for Cyber Attribution
"  Bridge is among the zero-sum games for which artificial intelligence has not
yet outperformed expert human players. The main difficulty lies in the bidding
phase of bridge, which requires cooperative decision making under partial
information. Existing artificial intelligence systems for bridge bidding rely
on and are thus restricted by human-designed bidding systems or features. In
this work, we propose a pioneering bridge bidding system without the aid of
human domain knowledge. The system is based on a novel deep reinforcement
learning model, which extracts sophisticated features and learns to bid
automatically based on raw card data. The model includes an
upper-confidence-bound algorithm and additional techniques to achieve a balance
between exploration and exploitation. Our experiments validate the promising
performance of our proposed model. In particular, the model advances from
having no knowledge about bidding to achieving superior performance when
compared with a champion-winning computer bridge program that implements a
human-designed bidding system.
",Automatic Bridge Bidding Using Deep Reinforcement Learning
"  After an earthquake, disaster sites pose a multitude of health and safety
concerns. A rescue operation of people trapped in the ruins after an earthquake
disaster requires a series of intelligent behavior, including planning. For a
successful rescue operation, given a limited number of available actions and
regulations, the role of planning in rescue operations is crucial. Fortunately,
recent developments in automated planning by artificial intelligence community
can help different organization in this crucial task. Due to the number of
rules and regulations, we believe that a rule based system for planning can be
helpful for this specific planning problem. In this research work, we use logic
rules to represent rescue and related regular regulations, together with a
logic based planner to solve this complicated problem. Although this research
is still in the prototyping and modeling stage, it clearly shows that rule
based languages can be a good infrastructure for this computational task. The
results of this research can be used by different organizations, such as
Iranian Red Crescent Society and International Institute of Seismology and
Earthquake Engineering (IISEE).
",Resource Planning For Rescue Operations
"  The strength of chess engines together with the availability of numerous
chess games have attracted the attention of chess players, data scientists, and
researchers during the last decades. State-of-the-art engines now provide an
authoritative judgement that can be used in many applications like cheating
detection, intrinsic ratings computation, skill assessment, or the study of
human decision-making. A key issue for the research community is to gather a
large dataset of chess games together with the judgement of chess engines.
Unfortunately the analysis of each move takes lots of times. In this paper, we
report our effort to analyse almost 5 millions chess games with a computing
grid. During summer 2015, we processed 270 millions unique played positions
using the Stockfish engine with a quite high depth (20). We populated a
database of 1+ tera-octets of chess evaluations, representing an estimated time
of 50 years of computation on a single machine. Our effort is a first step
towards the replication of research results, the supply of open data and
procedures for exploring new directions, and the investigation of software
engineering/scalability issues when computing billions of moves.
","Large-scale Analysis of Chess Games with Chess Engines: A Preliminary
  Report"
"  Fuzzy logic is an alternate approach for quantifying uncertainty relating to
activity duration. The fuzzy version of the backward recursion has been shown
to produce results that incorrectly amplify the level of uncertainty. However,
the fuzzy version of the forward recursion has been widely proposed as an
approach for determining the fuzzy set of critical path lengths. In this paper,
the direct application of the extension principle leads to a proposition that
must be satisfied in fuzzy critical path analysis. Using a counterexample it is
demonstrated that the fuzzy forward recursion when discrete fuzzy sets are used
to represent activity durations produces results that are not consistent with
the theory presented. The problem is shown to be the application of the fuzzy
maximum. Several methods presented in the literature are described and shown to
provide results that are consistent with the extension principle.
","A Counterexample to the Forward Recursion in Fuzzy Critical Path
  Analysis Under Discrete Fuzzy Sets"
"  In recent years RDF and OWL have become the most common knowledge
representation languages in use on the Web, propelled by the recommendation of
the W3C. In this paper we present a practical implementation of a different
kind of knowledge representation based on Prototypes. In detail, we present a
concrete syntax easily and effectively parsable by applications. We also
present extensible implementations of a prototype knowledge base, specifically
designed for storage of Prototypes. These implementations are written in Java
and can be extended by using the implementation as a library. Alternatively,
the software can be deployed as such. Further, results of benchmarks for both
local and web deployment are presented. This paper augments a research paper,
in which we describe the more theoretical aspects of our Prototype system.
","Knowledge Representation on the Web revisited: Tools for Prototype Based
  Ontologies"
"  In the era of intelligent biohybrid neurotechnologies for brain repair, new
fanciful terms are appearing in the scientific dictionary to define what has so
far been unimaginable. As the emerging neurotechnologies are becoming
increasingly polyhedral and sophisticated, should we talk about evolution and
rank the intelligence of these devices?
","Intelligent Biohybrid Neurotechnologies: Are They Really What They
  Claim?"
"  This paper introduces a novel method for learning how to play the most
difficult Atari 2600 games from the Arcade Learning Environment using deep
reinforcement learning. The proposed method, human checkpoint replay, consists
in using checkpoints sampled from human gameplay as starting points for the
learning process. This is meant to compensate for the difficulties of current
exploration strategies, such as epsilon-greedy, to find successful control
policies in games with sparse rewards. Like other deep reinforcement learning
architectures, our model uses a convolutional neural network that receives only
raw pixel inputs to estimate the state value function. We tested our method on
Montezuma's Revenge and Private Eye, two of the most challenging games from the
Atari platform. The results we obtained show a substantial improvement compared
to previous learning approaches, as well as over a random player. We also
propose a method for training deep reinforcement learning agents using human
gameplay experience, which we call human experience replay.
","Playing Atari Games with Deep Reinforcement Learning and Human
  Checkpoint Replay"
"  We are at the dawn of a new era, where advances in computer power, broadband
communication and digital sensor technologies have led to an unprecedented
flood of data inundating our surrounding. It is generally believed that means
such as Computational Intelligence will help to outlive these tough times.
However, these hopes are improperly high. Computational Intelligence is a
surprising composition of two mutually exclusive and contradicting constituents
that could be coupled only if you disregard and neglect their controversies:
""Computational"" implies reliance on data processing and ""Intelligence"" implies
reliance on information processing. Only those who are indifferent to
data-information discrepancy can believe that such a combination can be viable.
We do not believe in miracles, so we will try to share with you our
reservations.
","You want to survive the data deluge: Be careful, Computational
  Intelligence will not serve you as a rescue boat"
"  Big longitudinal observational databases present the opportunity to extract
new knowledge in a cost effective manner. Unfortunately, the ability of these
databases to be used for causal inference is limited due to the passive way in
which the data are collected resulting in various forms of bias. In this paper
we investigate a method that can overcome these limitations and determine
causal contrast set rules efficiently from big data. In particular, we present
a new methodology for the purpose of identifying risk factors that increase a
patients likelihood of experiencing the known rare side effect of renal failure
after ingesting aminosalicylates. The results show that the methodology was
able to identify previously researched risk factors such as being prescribed
diuretics and highlighted that patients with a higher than average risk of
renal failure may be even more susceptible to experiencing it as a side effect
after ingesting aminosalicylates.
","Identifying Candidate Risk Factors for Prescription Drug Side Effects
  using Causal Contrast Set Mining"
"  Purpose: To develop a framework for identifying and incorporating candidate
confounding interaction terms into a regularised cox regression analysis to
refine adverse drug reaction signals obtained via longitudinal observational
data. Methods: We considered six drug families that are commonly associated
with myocardial infarction in observational healthcare data, but where the
causal relationship ground truth is known (adverse drug reaction or not). We
applied emergent pattern mining to find itemsets of drugs and medical events
that are associated with the development of myocardial infarction. These are
the candidate confounding interaction terms. We then implemented a cohort study
design using regularised cox regression that incorporated and accounted for the
candidate confounding interaction terms. Results The methodology was able to
account for signals generated due to confounding and a cox regression with
elastic net regularisation correctly ranked the drug families known to be true
adverse drug reactions above those.
","Refining adverse drug reaction signals by incorporating interaction
  variables identified using emergent pattern mining"
"  Uncertain data streams have been widely generated in many Web applications.
The uncertainty in data streams makes anomaly detection from sensor data
streams far more challenging. In this paper, we present a novel framework that
supports anomaly detection in uncertain data streams. The proposed framework
adopts an efficient uncertainty pre-processing procedure to identify and
eliminate uncertainties in data streams. Based on the corrected data streams,
we develop effective period pattern recognition and feature extraction
techniques to improve the computational efficiency. We use classification
methods for anomaly detection in the corrected data stream. We also empirically
show that the proposed approach shows a high accuracy of anomaly detection on a
number of real datasets.
",Supervised Anomaly Detection in Uncertain Pseudoperiodic Data Streams
"  This study optimises manually derived rule-based expert system classification
of objects according to changes in their properties over time. One of the key
challenges that this study tries to address is how to classify objects that
exhibit changes in their behaviour over time, for example how to classify
companies' share price stability over a period of time or how to classify
students' preferences for subjects while they are progressing through school. A
specific case the paper considers is the strategy of players in public goods
games (as common in economics) across multiple consecutive games. Initial
classification starts from expert definitions specifying class allocation for
players based on aggregated attributes of the temporal data. Based on these
initial classifications, the optimisation process tries to find an improved
classifier which produces the best possible compact classes of objects
(players) for every time point in the temporal data. The compactness of the
classes is measured by a cost function based on internal cluster indices like
the Dunn Index, distance measures like Euclidean distance or statistically
derived measures like standard deviation. The paper discusses the approach in
the context of incorporating changing player strategies in the aforementioned
public good games, where common classification approaches so far do not
consider such changes in behaviour resulting from learning or in-game
experience. By using the proposed process for classifying temporal data and the
actual players' contribution during the games, we aim to produce a more refined
classification which in turn may inform the interpretation of public goods game
data.
",Optimising Rule-Based Classification in Temporal Data
"  Fuzzy Rule-Based Classification Systems (FRBCSs) have the potential to
provide so-called interpretable classifiers, i.e. classifiers which can be
introspective, understood, validated and augmented by human experts by relying
on fuzzy-set based rules. This paper builds on prior work for interval type-2
fuzzy set based FRBCs where the fuzzy sets and rules of the classifier are
generated using an initial clustering stage. By introducing Subtractive
Clustering in order to identify multiple cluster prototypes, the proposed
approach has the potential to deliver improved classification performance while
maintaining good interpretability, i.e. without resulting in an excessive
number of rules. The paper provides a detailed overview of the proposed FRBC
framework, followed by a series of exploratory experiments on both linearly and
non-linearly separable datasets, comparing results to existing rule-based and
SVM approaches. Overall, initial results indicate that the approach enables
comparable classification performance to non rule-based classifiers such as
SVM, while often achieving this with a very small number of rules.
","Applying Interval Type-2 Fuzzy Rule Based Classifiers Through a
  Cluster-Based Class Representation"
"  In the context of cancer treatment and surgery, quality of life assessment is
a crucial part of determining treatment success and viability. In order to
assess it, patients completed questionnaires which employ words to capture
aspects of patients well-being are the norm. As the results of these
questionnaires are often used to assess patient progress and to determine
future treatment options, it is important to establish that the words used are
interpreted in the same way by both patients and medical professionals. In this
paper, we capture and model patients perceptions and associated uncertainty
about the words used to describe the level of their physical function used in
the highly common (in Sarcoma Services) Toronto Extremity Salvage Score (TESS)
questionnaire. The paper provides detail about the interval-valued data capture
as well as the subsequent modelling of the data using fuzzy sets. Based on an
initial sample of participants, we use Jaccard similarity on the resulting
words models to show that there may be considerable differences in the
interpretation of commonly used questionnaire terms, thus presenting a very
real risk of miscommunication between patients and medical professionals as
well as within the group of medical professionals.
","Exploring Differences in Interpretation of Words Essential in Medical
  Expert-Patient Communication"
"  Big longitudinal observational medical data potentially hold a wealth of
information and have been recognised as potential sources for gaining new drug
safety knowledge. Unfortunately there are many complexities and underlying
issues when analysing longitudinal observational data. Due to these
complexities, existing methods for large-scale detection of negative side
effects using observational data all tend to have issues distinguishing between
association and causality. New methods that can better discriminate causal and
non-causal relationships need to be developed to fully utilise the data. In
this paper we propose using a set of causality considerations developed by the
epidemiologist Bradford Hill as a basis for engineering features that enable
the application of supervised learning for the problem of detecting negative
side effects. The Bradford Hill considerations look at various perspectives of
a drug and outcome relationship to determine whether it shows causal traits. We
taught a classifier to find patterns within these perspectives and it learned
to discriminate between association and causality. The novelty of this research
is the combination of supervised learning and Bradford Hill's causality
considerations to automate the Bradford Hill's causality assessment. We
evaluated the framework on a drug safety gold standard know as the
observational medical outcomes partnership's nonspecified association reference
set. The methodology obtained excellent discriminate ability with area under
the curves ranging between 0.792-0.940 (existing method optimal: 0.73) and a
mean average precision of 0.640 (existing method optimal: 0.141). The proposed
features can be calculated efficiently and be readily updated, making the
framework suitable for big observational data.
","Supervised Adverse Drug Reaction Signalling Framework Imitating Bradford
  Hill's Causality Considerations"
"  The Defense Advanced Research Projects Agency (DARPA) Real-time Adversarial
Intelligence and Decision-making (RAID) program is investigating the
feasibility of ""reading the mind of the enemy"" - to estimate and anticipate, in
real-time, the enemy's likely goals, deceptions, actions, movements and
positions. This program focuses specifically on urban battles at echelons of
battalion and below. The RAID program leverages approximate game-theoretic and
deception-sensitive algorithms to provide real-time enemy estimates to a
tactical commander. A key hypothesis of the program is that these predictions
and recommendations will make the commander more effective, i.e. he should be
able to achieve his operational goals safer, faster, and more efficiently.
Realistic experimentation and evaluation drive the development process using
human-in-the-loop wargames to compare humans and the RAID system. Two
experiments were conducted in 2005 as part of Phase I to determine if the RAID
software could make predictions and recommendations as effectively and
accurately as a 4-person experienced staff. This report discusses the
intriguing and encouraging results of these first two experiments conducted by
the RAID program. It also provides details about the experiment environment and
methodology that were used to demonstrate and prove the research goals.
",Predicting Enemy's Actions Improves Commander Decision-Making
"  We investigate the problem of verbalizing Web Ontology Language (OWL) axioms
of domain ontologies in this paper. The existing approaches address the problem
of fidelity of verbalized OWL texts to OWL semantics by exploring different
ways of expressing the same OWL axiom in various linguistic forms. They also
perform grouping and aggregating of the natural language (NL) sentences that
are generated corresponding to each OWL statement into a comprehensible
structure. However, no efforts have been taken to try out a semantic reduction
at logical level to remove redundancies and repetitions, so that the reduced
set of axioms can be used for generating a more meaningful and
human-understandable (what we call redundancy-free) text. Our experiments show
that, formal semantic reduction at logical level is very helpful to generate
redundancy-free descriptions of ontology entities. In this paper, we
particularly focus on generating descriptions of individuals of SHIQ based
ontologies. The details of a case study are provided to support the usefulness
of the redundancy-free NL descriptions of individuals, in knowledge validation
application.
",Redundancy-free Verbalization of Individuals for Ontology Validation
"  We motivate and offer a formal definition of validation as it applies to
information fusion systems. Common definitions of validation compare the actual
state of the world with that derived by the fusion process. This definition
conflates properties of the fusion system with properties of systems that
intervene between the world and the fusion system. We propose an alternative
definition where validation of an information fusion system references a
standard fusion device, such as recognized human experts. We illustrate the
approach by describing the validation process implemented in RAID, a program
conducted by DARPA and focused on information fusion in adversarial, deceptive
environments.
",Validation of Information Fusion
"  Autonomous robots operating in dynamic environments must maintain beliefs
over a hypothesis space that is rich enough to represent the activities of
interest at different scales. This is important both in order to accommodate
the availability of evidence at varying degrees of coarseness, such as when
interpreting and assimilating natural instructions, but also in order to make
subsequent reactive planning more efficient. We present an algorithm that
combines a topology-based trajectory clustering procedure that generates
hierarchically-structured spatial abstractions with a bank of particle filters
at each of these abstraction levels so as to produce probability estimates over
an agent's navigation activity that is kept consistent across the hierarchy. We
study the performance of the proposed method using a synthetic trajectory
dataset in 2D, as well as a dataset taken from AIS-based tracking of ships in
an extended harbour area. We show that, in comparison to a baseline which is a
particle filter that estimates activity without exploiting such structure, our
method achieves a better normalised error in predicting the trajectory as well
as better time to convergence to a true class when compared against ground
truth.
",Estimating Activity at Multiple Scales using Spatial Abstractions
"  An artificial superintelligence (ASI) is artificial intelligence that is
significantly more intelligent than humans in all respects. While ASI does not
currently exist, some scholars propose that it could be created sometime in the
future, and furthermore that its creation could cause a severe global
catastrophe, possibly even resulting in human extinction. Given the high
stakes, it is important to analyze ASI risk and factor the risk into decisions
related to ASI research and development. This paper presents a graphical model
of major pathways to ASI catastrophe, focusing on ASI created via recursive
self-improvement. The model uses the established risk and decision analysis
modeling paradigms of fault trees and influence diagrams in order to depict
combinations of events and conditions that could lead to AI catastrophe, as
well as intervention options that could decrease risks. The events and
conditions include select aspects of the ASI itself as well as the human
process of ASI research, development, and management. Model structure is
derived from published literature on ASI risk. The model offers a foundation
for rigorous quantitative evaluation and decision making on the long-term risk
of ASI catastrophe.
","A Model of Pathways to Artificial Superintelligence Catastrophe for Risk
  and Decision Analysis"
"  We introduce a framework for supporting learning to program in the paradigm
of Answer Set Programming (ASP), which is a declarative logic programming
formalism. Based on the idea of teaching by asking the student to complete
small example ASP programs, we introduce a three-stage method for giving hints
to the student without revealing the correct solution of an example. We
categorize mistakes into (i) syntactic mistakes, (ii) unexpected but
syntactically correct input, and (iii) semantic mistakes, describe mathematical
definitions of these mistakes, and show how to compute hints from these
definitions.
","Technical Report: Giving Hints for Logic Programming Examples without
  Revealing Solutions"
"  Parkinson's disease is the second most common neurodegenerative disease,
affecting more than 1.2 million people in Europe. Medications are available for
the management of its symptoms, but the exact cause of the disease is unknown
and there is currently no cure on the market. To better understand the
relations between new findings and current medical knowledge, we need tools
able to analyse published medical papers based on natural language processing
and tools capable to identify various relationships of new findings with the
current medical knowledge. Our work aims to fill the above technological gap.
  To identify conflicting information in medical documents, we enact textual
entailment technology. To encapsulate existing medical knowledge, we rely on
ontologies. To connect the formal axioms in ontologies with natural text in
medical articles, we exploit ontology verbalisation techniques. To assess the
level of disagreement between human agents with respect to a medical issue, we
rely on fuzzy aggregation. To harmonize this disagreement, we design mediation
protocols within a multi-agent framework.
","Harmonization of conflicting medical opinions using argumentation
  protocols and textual entailment - a case study on Parkinson disease"
"  In this paper we present the next step in our approach to neurobiologically
plausible implementation of emotional reactions and behaviors for real-time
autonomous robotic systems. The working metaphor we use is the ""day"" and the
""night"" phases of mammalian life. During the ""day phase"" a robotic system
stores the inbound information and is controlled by a light-weight rule-based
system in real time. In contrast to that, during the ""night phase"" information
that has been stored is transferred to a supercomputing system to update the
realistic neural network: emotional and behavioral strategies.
",Neuromorphic Robot Dream
"  Behavior planning is known to be one of the basic cognitive functions, which
is essential for any cognitive architecture of any control system used in
robotics. At the same time most of the widespread planning algorithms employed
in those systems are developed using only approaches and models of Artificial
Intelligence and don't take into account numerous results of cognitive
experiments. As a result, there is a strong need for novel methods of behavior
planning suitable for modern cognitive architectures aimed at robot control.
One such method is presented in this work and is studied within a special class
of navigation task called smart relocation task. The method is based on the
hierarchical two-level model of abstraction and knowledge representation, e.g.
symbolic and subsymbolic. On the symbolic level sign world model is used for
knowledge representation and hierarchical planning algorithm, PMA, is utilized
for planning. On the subsymbolic level the task of path planning is considered
and solved as a graph search problem. Interaction between both planners is
examined and inter-level interfaces and feedback loops are described.
Preliminary experimental results are presented.
",Psychologically inspired planning method for smart relocation task
"  Influence diagrams provide a compact graphical representation of decision
problems. Several algorithms for the quick computation of their associated
expected utilities are available in the literature. However, often they rely on
a full quantification of both probabilistic uncertainties and utility values.
For problems where all random variables and decision spaces are finite and
discrete, here we develop a symbolic way to calculate the expected utilities of
influence diagrams that does not require a full numerical representation.
Within this approach expected utilities correspond to families of polynomials.
After characterizing their polynomial structure, we develop an efficient
symbolic algorithm for the propagation of expected utilities through the
diagram and provide an implementation of this algorithm using a computer
algebra system. We then characterize many of the standard manipulations of
influence diagrams as transformations of polynomials. We also generalize the
decision analytic framework of these diagrams by defining asymmetries as
operations over the expected utility polynomials.
","A symbolic algebra for the computation of expected utilities in
  multiplicative influence diagrams"
"  In this paper, we propose a fundamentally new approach to Datalog evaluation.
Given a linear Datalog program DB written using N constants and binary
predicates, we first translate if-and-only-if completions of clauses in DB into
a set Eq(DB) of matrix equations with a non-linear operation where relations in
M_DB, the least Herbrand model of DB, are encoded as adjacency matrices. We
then translate Eq(DB) into another, but purely linear matrix equations
tilde_Eq(DB). It is proved that the least solution of tilde_Eq(DB) in the sense
of matrix ordering is converted to the least solution of Eq(DB) and the latter
gives M_DB as a set of adjacency matrices. Hence computing the least solution
of tilde_Eq(DB) is equivalent to computing M_DB specified by DB. For a class of
tail recursive programs and for some other types of programs, our approach
achieves O(N^3) time complexity irrespective of the number of variables in a
clause since only matrix operations costing O(N^3) or less are used.
  We conducted two experiments that compute the least Herbrand models of linear
Datalog programs. The first experiment computes transitive closure of
artificial data and real network data taken from the Koblenz Network
Collection. The second one compared the proposed approach with the
state-of-the-art symbolic systems including two Prolog systems and two ASP
systems, in terms of computation time for a transitive closure program and the
same generation program. In the experiment, it is observed that our linear
algebraic approach runs 10^1 ~ 10^4 times faster than the symbolic systems when
data is not sparse. To appear in Theory and Practice of Logic Programming
(TPLP).
",A Linear Algebraic Approach to Datalog Evaluation
"  In existing literature, while approximate approaches based on Monte-Carlo
simulation technique have been proposed to compute the semantics of
probabilistic argumentation, how to improve the efficiency of computation
without using simulation technique is still an open problem. In this paper, we
address this problem from the following two perspectives. First, conceptually,
we define specific properties to characterize the subgraphs of a PrAG with
respect to a given extension, such that the probability of a set of arguments E
being an extension can be defined in terms of these properties, without (or
with less) construction of subgraphs. Second, computationally, we take
preferred semantics as an example, and develop algorithms to evaluate the
efficiency of our approach. The results show that our approach not only
dramatically decreases the time for computing p(E^\sigma), but also has an
attractive property, which is contrary to that of existing approaches: the
denser the edges of a PrAG are or the bigger the size of a given extension E
is, the more efficient our approach computes p(E^\sigma). Meanwhile, it is
shown that under complete and preferred semantics, the problems of determining
p(E^\sigma) are fixed-parameter tractable.
","Formulating Semantics of Probabilistic Argumentation by Characterizing
  Subgraphs: Theory and Empirical Results"
"  A variety of statistical graphical models have been defined to represent the
conditional independences underlying a random vector of interest. Similarly,
many different graphs embedding various types of preferential independences, as
for example conditional utility independence and generalized additive
independence, have more recently started to appear. In this paper we define a
new graphical model, called a directed expected utility network, whose edges
depict both probabilistic and utility conditional independences. These embed a
very flexible class of utility models, much larger than those usually conceived
in standard influence diagrams. Our graphical representation, and various
transformations of the original graph into a tree structure, are then used to
guide fast routines for the computation of a decision problem's expected
utilities. We show that our routines generalize those usually utilized in
standard influence diagrams' evaluations under much more restrictive
conditions. We then proceed with the construction of a directed expected
utility network to support decision makers in the domain of household food
security.
",Directed expected utility networks
"  Our interest in this paper is in optimisation problems that are intractable
to solve by direct numerical optimisation, but nevertheless have significant
amounts of relevant domain-specific knowledge. The category of heuristic search
techniques known as estimation of distribution algorithms (EDAs) seek to
incrementally sample from probability distributions in which optimal (or
near-optimal) solutions have increasingly higher probabilities. Can we use
domain knowledge to assist the estimation of these distributions? To answer
this in the affirmative, we need: (a)a general-purpose technique for the
incorporation of domain knowledge when constructing models for optimal values;
and (b)a way of using these models to generate new data samples. Here we
investigate a combination of the use of Inductive Logic Programming (ILP) for
(a), and standard logic-programming machinery to generate new samples for (b).
Specifically, on each iteration of distribution estimation, an ILP engine is
used to construct a model for good solutions. The resulting theory is then used
to guide the generation of new data instances, which are now restricted to
those derivable using the ILP model in conjunction with the background
knowledge). We demonstrate the approach on two optimisation problems
(predicting optimal depth-of-win for the KRK endgame, and job-shop scheduling).
Our results are promising: (a)On each iteration of distribution estimation,
samples obtained with an ILP theory have a substantially greater proportion of
good solutions than samples without a theory; and (b)On termination of
distribution estimation, samples obtained with an ILP theory contain more
near-optimal samples than samples without a theory. Taken together, these
results suggest that the use of ILP-constructed theories could be a useful
technique for incorporating complex domain-knowledge into estimation
distribution procedures.
",Generation of Near-Optimal Solutions Using ILP-Guided Sampling
"  We investigate learning heuristics for domain-specific planning. Prior work
framed learning a heuristic as an ordinary regression problem. However, in a
greedy best-first search, the ordering of states induced by a heuristic is more
indicative of the resulting planner's performance than mean squared error.
Thus, we instead frame learning a heuristic as a learning to rank problem which
we solve using a RankSVM formulation. Additionally, we introduce new methods
for computing features that capture temporal interactions in an approximate
plan. Our experiments on recent International Planning Competition problems
show that the RankSVM learned heuristics outperform both the original
heuristics and heuristics learned through ordinary regression.
",Learning to Rank for Synthesizing Planning Heuristics
"  In recent work we defined resource-based answer set semantics, which is an
extension to answer set semantics stemming from the study of its relationship
with linear logic. In fact, the name of the new semantics comes from the fact
that in the linear-logic formulation every literal (including negative ones)
were considered as a resource. In this paper, we propose a query-answering
procedure reminiscent of Prolog for answer set programs under this extended
semantics as an extension of XSB-resolution for logic programs with negation.
We prove formal properties of the proposed procedure.
  Under consideration for acceptance in TPLP.
",Query Answering in Resource-Based Answer Set Semantics
"  Standard answer set programming (ASP) targets at solving search problems from
the first level of the polynomial time hierarchy (PH). Tackling search problems
beyond NP using ASP is less straightforward. The class of disjunctive logic
programs offers the most prominent way of reaching the second level of the PH,
but encoding respective hard problems as disjunctive programs typically
requires sophisticated techniques such as saturation or meta-interpretation.
The application of such techniques easily leads to encodings that are
inaccessible to non-experts. Furthermore, while disjunctive ASP solvers often
rely on calls to a (co-)NP oracle, it may be difficult to detect from the input
program where the oracle is being accessed. In other formalisms, such as
Quantified Boolean Formulas (QBFs), the interface to the underlying oracle is
more transparent as it is explicitly recorded in the quantifier prefix of a
formula. On the other hand, ASP has advantages over QBFs from the modeling
perspective. The rich high-level languages such as ASP-Core-2 offer a wide
variety of primitives that enable concise and natural encodings of search
problems. In this paper, we present a novel logic programming--based modeling
paradigm that combines the best features of ASP and QBFs. We develop so-called
combined logic programs in which oracles are directly cast as (normal) logic
programs themselves. Recursive incarnations of this construction enable logic
programming on arbitrarily high levels of the PH. We develop a proof-of-concept
implementation for our new paradigm.
  This paper is under consideration for acceptance in TPLP.
",Stable-Unstable Semantics: Beyond NP with Normal Logic Programs
"  In recent years, several frameworks and systems have been proposed that
extend Inductive Logic Programming (ILP) to the Answer Set Programming (ASP)
paradigm. In ILP, examples must all be explained by a hypothesis together with
a given background knowledge. In existing systems, the background knowledge is
the same for all examples; however, examples may be context-dependent. This
means that some examples should be explained in the context of some
information, whereas others should be explained in different contexts. In this
paper, we capture this notion and present a context-dependent extension of the
Learning from Ordered Answer Sets framework. In this extension, contexts can be
used to further structure the background knowledge. We then propose a new
iterative algorithm, ILASP2i, which exploits this feature to scale up the
existing ILASP2 system to learning tasks with large numbers of examples. We
demonstrate the gain in scalability by applying both algorithms to various
learning tasks. Our results show that, compared to ILASP2, the newly proposed
ILASP2i system can be two orders of magnitude faster and use two orders of
magnitude less memory, whilst preserving the same average accuracy. This paper
is under consideration for acceptance in TPLP.
","Iterative Learning of Answer Set Programs from Context Dependent
  Examples"
"  Delta Epsilon Alpha Star is a minimal coverage, real-time robotic search
algorithm that yields a moderately aggressive search path with minimal
backtracking. Search performance is bounded by a placing a combinatorial bound,
epsilon and delta, on the maximum deviation from the theoretical shortest path
and the probability at which further deviations can occur. Additionally, we
formally define the notion of PAC-admissibility -- a relaxed admissibility
criteria for algorithms, and show that PAC-admissible algorithms are better
suited to robotic search situations than epsilon-admissible or strict
algorithms.
",Delta Epsilon Alpha Star: A PAC-Admissible Search Algorithm
"  This volume contains the thesis abstracts presented at the Second Summer
School on Argumentation: Computational and Linguistic Perspectives (SSA'2016)
held on September 8-12 in Potsdam, Germany.
","Proceedings of the Second Summer School on Argumentation: Computational
  and Linguistic Perspectives (SSA'16)"
"  In this paper we exploit Answer Set Programming (ASP) for reasoning in a
rational extension SROEL-R-T of the low complexity description logic SROEL,
which underlies the OWL EL ontology language. In the extended language, a
typicality operator T is allowed to define concepts T(C) (typical C's) under a
rational semantics. It has been proven that instance checking under rational
entailment has a polynomial complexity. To strengthen rational entailment, in
this paper we consider a minimal model semantics. We show that, for arbitrary
SROEL-R-T knowledge bases, instance checking under minimal entailment is
\Pi^P_2-complete. Relying on a Small Model result, where models correspond to
answer sets of a suitable ASP encoding, we exploit Answer Set Preferences (and,
in particular, the asprin framework) for reasoning under minimal entailment.
The paper is under consideration for acceptance in Theory and Practice of Logic
Programming.
",ASP for Minimal Entailment in a Rational Extension of SROEL
"  Automatically recognizing the e-learning activities is an important task for
improving the online learning process. Probabilistic graphical models such as
hidden Markov models and conditional random fields have been successfully used
in order to identify a Web users activity. For such models, the sequences of
observation are crucial for training and inference processes. Despite the
efficiency of these probabilistic graphical models in segmenting and labeling
stochastic sequences, their performance is adversely affected by the imperfect
quality of data used for the construction of sequences of observation. In this
paper, a formalism of the possibilistic theory will be used in order to propose
a new approach for observation sequences preparation. The eminent contribution
of our approach is to evaluate the effect of possibilistic reasoning during the
generation of observation sequences on the effectiveness of hidden Markov
models and conditional random fields models. Using a dataset containing 51 real
manipulations related to three types of learners tasks, the preliminary
experiments demonstrate that the sequences of observation obtained based on
possibilistic reasoning significantly improve the performance of hidden Marvov
models and conditional random fields models in the automatic recognition of the
e-learning activities.
","Mouse Movement and Probabilistic Graphical Models Based E-Learning
  Activity Recognition Improvement Possibilistic Model"
"  Bayesian networks are probabilistic graphical models often used in big data
analytics. The problem of exact structure learning is to find a network
structure that is optimal under certain scoring criteria. The problem is known
to be NP-hard and the existing methods are both computationally and memory
intensive. In this paper, we introduce a new approach for exact structure
learning. Our strategy is to leverage relationship between a partial network
structure and the remaining variables to constraint the number of ways in which
the partial network can be optimally extended. Via experimental results, we
show that the method provides up to three times improvement in runtime, and
orders of magnitude reduction in memory consumption over the current best
algorithms.
",Exact Structure Learning of Bayesian Networks by Optimal Path Extension
"  We study the multi-agent path finding problem (MAPF) for a group of agents
which are allowed to move into arbitrary directions on a 2D square grid. We
focus on centralized conflict resolution for independently computed plans. We
propose an algorithm that eliminates conflicts by using local re-planning and
introducing time offsets to the execution of paths by different agents.
Experimental results show that the algorithm can find high quality
conflict-free solutions at low computational cost.
","Resolving Spatial-Time Conflicts In A Set Of Any-angle Or
  Angle-constrained Grid Paths"
"  Reinforcement learning problems are often described through rewards that
indicate if an agent has completed some task. This specification can yield
desirable behavior, however many problems are difficult to specify in this
manner, as one often needs to know the proper configuration for the agent. When
humans are learning to solve tasks, we often learn from visual instructions
composed of images or videos. Such representations motivate our development of
Perceptual Reward Functions, which provide a mechanism for creating visual task
descriptions. We show that this approach allows an agent to learn from rewards
that are based on raw pixels rather than internal parameters.
",Perceptual Reward Functions
"  A fundamental question is whether Turing machines can model all reasoning
processes. We introduce an existence principle stating that the perception of
the physical existence of any Turing program can serve as a physical causation
for the application of any Turing-computable function to this Turing program.
The existence principle overcomes the limitation of the outputs of Turing
machines to lists, that is, recursively enumerable sets. The principle is
illustrated by productive partial functions for productive sets such as the set
of the Goedel numbers of the Turing-computable total functions. The existence
principle and productive functions imply the existence of physical systems
whose reasoning processes cannot be modeled by Turing machines. These systems
are called creative. Creative systems can prove the undecidable formula in
Goedel's theorem in another formal system which is constructed at a later point
in time. A hypothesis about creative systems, which is based on computer
experiments, is introduced.
",Informal Physical Reasoning Processes
"  Planning plays an important role in the broad class of decision theory.
Planning has drawn much attention in recent work in the robotics and sequential
decision making areas. Recently, Reinforcement Learning (RL), as an
agent-environment interaction problem, has brought further attention to
planning methods. Generally in RL, one can assume a generative model, e.g.
graphical models, for the environment, and then the task for the RL agent is to
learn the model parameters and find the optimal strategy based on these learnt
parameters. Based on environment behavior, the agent can assume various types
of generative models, e.g. Multi Armed Bandit for a static environment, or
Markov Decision Process (MDP) for a dynamic environment. The advantage of these
popular models is their simplicity, which results in tractable methods of
learning the parameters and finding the optimal policy. The drawback of these
models is again their simplicity: these models usually underfit and
underestimate the actual environment behavior. For example, in robotics, the
agent usually has noisy observations of the environment inner state and MDP is
not a suitable model.
  More complex models like Partially Observable Markov Decision Process (POMDP)
can compensate for this drawback. Fitting this model to the environment, where
the partial observation is given to the agent, generally gives dramatic
performance improvement, sometimes unbounded improvement, compared to MDP. In
general, finding the optimal policy for the POMDP model is computationally
intractable and fully non convex, even for the class of memoryless policies.
The open problem is to come up with a method to find an exact or an approximate
optimal stochastic memoryless policy for POMDP models.
","Open Problem: Approximate Planning of POMDPs in the class of Memoryless
  Policies"
"  Scientists often run experiments to distinguish competing theories. This
requires patience, rigor, and ingenuity - there is often a large space of
possible experiments one could run. But we need not comb this space by hand -
if we represent our theories as formal models and explicitly declare the space
of experiments, we can automate the search for good experiments, looking for
those with high expected information gain. Here, we present a general and
principled approach to experiment design based on probabilistic programming
languages (PPLs). PPLs offer a clean separation between declaring problems and
solving them, which means that the scientist can automate experiment design by
simply declaring her model and experiment spaces in the PPL without having to
worry about the details of calculating information gain. We demonstrate our
system in two case studies drawn from cognitive psychology, where we use it to
design optimal experiments in the domains of sequence prediction and
categorization. We find strong empirical validation that our automatically
designed experiments were indeed optimal. We conclude by discussing a number of
interesting questions for future research.
",Practical optimal experiment design with probabilistic programs
"  Multi-step temporal-difference (TD) learning, where the update targets
contain information from multiple time steps ahead, is one of the most popular
forms of TD learning for linear function approximation. The reason is that
multi-step methods often yield substantially better performance than their
single-step counter-parts, due to a lower bias of the update targets. For
non-linear function approximation, however, single-step methods appear to be
the norm. Part of the reason could be that on many domains the popular
multi-step methods TD($\lambda$) and Sarsa($\lambda$) do not perform well when
combined with non-linear function approximation. In particular, they are very
susceptible to divergence of value estimates. In this paper, we identify the
reason behind this. Furthermore, based on our analysis, we propose a new
multi-step TD method for non-linear function approximation that addresses this
issue. We confirm the effectiveness of our method using two benchmark tasks
with neural networks as function approximation.
","Effective Multi-step Temporal-Difference Learning for Non-Linear
  Function Approximation"
"  PC(ID) extends propositional logic with inductive definitions: rule sets
under the well-founded semantics. Recently, a notion of relevance was
introduced for this language. This notion determines the set of undecided
literals that can still influence the satisfiability of a PC(ID) formula in a
given partial assignment. The idea is that the PC(ID) solver can make decisions
only on relevant literals without losing soundness and thus safely ignore
irrelevant literals.
  One important insight that the relevance of a literal is completely
determined by the current solver state. During search, the solver state changes
have an effect on the relevance of literals. In this paper, we discuss an
incremental, lightweight implementation of a relevance tracker module that can
be added to and interact with an out-of-the-box SAT(ID) solver.
",Implementing a Relevance Tracker Module
"  In this paper we discuss methods of using the language of actions, formal
languages, and grammars for qualitative conceptual linguistic modeling of
companies as technological and human institutions. The main problem following
the discussion is the problem to find and describe a language structure for
external and internal flow of information of companies. We anticipate that the
language structure of external and internal base flows determine the structure
of companies. In the structure modeling of an abstract industrial company an
internal base flow of information is constructed as certain flow of words
composed on the theoretical parts-processes-actions language. The language of
procedures is found for an external base flow of information for an insurance
company. The formal stochastic grammar for the language of procedures is found
by statistical methods and is used in understanding the tendencies of the
health care industry. We present the model of human communications as a random
walk on the semantic tree
","The languages of actions, formal grammars and qualitive modeling of
  companies"
"  Since Pokemon Go sent millions on the quest of collecting virtual monsters,
an important question has been on the minds of many people: Is going after the
closest item first a time-and-cost-effective way to play? Here, we show that
this is in fact a good strategy which performs on average only 7% worse than
the best possible solution in terms of the total distance traveled to gather
all the items. Even when accounting for errors due to the inability of people
to accurately measure distances by eye, the performance only goes down to 16%
of the optimal solution.
",Effectiveness of greedily collecting items in open world games
"  Reasoning does not work well when done in isolation from its significance,
both to the needs and interests of an agent and with respect to the wider
world. Moreover, those issues may best be handled with a new sort of data
structure that goes beyond the knowledge base and incorporates aspects of
perceptual knowledge and even more, in which a kind of anticipatory action may
be key.
",Five dimensions of reasoning in the wild
"  In this short note we address the issue of expressing norms (such as
obligations and prohibitions) in temporal logic. In particular, we address the
argument from [Governatori 2015] that norms cannot be expressed in Linear Time
Temporal Logic (LTL).
",Expressibility of norms in temporal logic
"  One of the simplest metalearning methods is the average ranking method. This
method uses metadata in the form of test results of a given set of algorithms
on given set of datasets and calculates an average rank for each algorithm. The
ranks are used to construct the average ranking. We investigate the problem of
how the process of generating the average ranking is affected by incomplete
metadata including fewer test results. This issue is relevant, because if we
could show that incomplete metadata does not affect the final results much, we
could explore it in future design. We could simply conduct fewer tests and save
thus computation time. In this paper we describe an upgraded average ranking
method that is capable of dealing with incomplete metadata. Our results show
that the proposed method is relatively robust to omission in test results in
the meta datasets.
",Effect of Incomplete Meta-dataset on Average Ranking Method
"  As the practical use of answer set programming (ASP) has grown with the
development of efficient solvers, we expect a growing interest in extensions of
ASP as their semantics stabilize and solvers supporting them mature. Epistemic
Specifications, which adds modal operators K and M to the language of ASP, is
one such extension. We call a program in this language an epistemic logic
program (ELP). Solvers have thus far been practical for only the simplest ELPs
due to exponential growth of the search space. We describe a solver that is
able to solve harder problems better (e.g., without exponentially-growing
memory needs w.r.t. K and M occurrences) and faster than any other known ELP
solver.
","A Parallel Memory-efficient Epistemic Logic Program Solver: Harder,
  Better, Faster"
"  Sequential data modeling and analysis have become indispensable tools for
analyzing sequential data, such as time-series data, because larger amounts of
sensed event data have become available. These methods capture the sequential
structure of data of interest, such as input-output relations and correlation
among datasets. However, because most studies in this area are specialized or
limited to their respective applications, rigorous requirement analysis of such
models has not been undertaken from a general perspective. Therefore, we
particularly examine the structure of sequential data, and extract the
necessity of `state duration' and `state interval' of events for efficient and
rich representation of sequential data. Specifically addressing the hidden
semi-Markov model (HSMM) that represents such state duration inside a model, we
attempt to add representational capability of a state interval of events onto
HSMM. To this end, we propose two extended models: an interval state hidden
semi-Markov model (IS-HSMM) to express the length of a state interval with a
special state node designated as ""interval state node""; and an interval length
probability hidden semi-Markov model (ILP-HSMM) which represents the length of
the state interval with a new probabilistic parameter ""interval length
probability."" Exhaustive simulations have revealed superior performance of the
proposed models in comparison with HSMM. These proposed models are the first
reported extensions of HMM to support state interval representation as well as
state duration representation.
","State Duration and Interval Modeling in Hidden Semi-Markov Model for
  Sequential Data Analysis"
"  A checkers-like model game with a simplified set of rules is studied through
extensive simulations of agents with different expertise and strategies. The
introduction of complementary strategies, in a quite general way, provides a
tool to mimic the basic ingredients of a wide scope of real games. We find that
only for the player having the higher offensive expertise (the dominant player
), maximizing the offensive always increases the probability to win. For the
non-dominant player, interestingly, a complete minimization of the offensive
becomes the best way to win in many situations, depending on the relative
values of the defense expertise. Further simulations on the interplay of
defense expertise were done separately, in the context of a fully-offensive
scenario, offering a starting point for analytical treatments. In particular,
we established that in this scenario the total number of moves is defined only
by the player with the lower defensive expertise. We believe that these results
stand for a first step towards a new way to improve decisions-making in a large
number of zero-sum real games.
",Is a good offensive always the best defense?
"  The goal of our research was to enhance local search heuristics used to
construct Latin Hypercube Designs. First, we introduce the \textit{1D-move}
perturbation to improve the space exploration performed by these algorithms.
Second, we propose a new evaluation function $\psi_{p,\sigma}$ specifically
targeting the Maximin criterion.
  Exhaustive series of experiments with Simulated Annealing, which we used as a
typically well-behaving local search heuristics, confirm that our goal was
reached as the result we obtained surpasses the best scores reported in the
literature. Furthermore, the $\psi_{p,\sigma}$ function seems very promising
for a wide spectrum of optimization problems through the Maximin criterion.
",On Simulated Annealing Dedicated to Maximin Latin Hypercube Designs
"  In this paper, we reexamine the Movie Graph Argument, which demonstrates a
basic incompatibility between computationalism and materialism. We discover
that the incompatibility is only manifest in singular classical-like universes.
If we accept that we live in a Multiverse, then the incompatibility goes away,
but in that case another line of argument shows that with computationalism, the
fundamental, or primitive materiality has no causal influence on what is
observed, which must must be derivable from basic arithmetic properties.
",The Movie Graph Argument Revisited
"  We explore how computational ontologies can be impactful vis-a-vis the
developing discipline of ""data science."" We posit an approach wherein
management theories are represented as formal axioms, and then applied to draw
inferences about data that reside in corporate databases. That is, management
theories would be implemented as rules within a data analytics engine. We
demonstrate a case study development of such an ontology by formally
representing an accounting theory in First-Order Logic. Though quite
preliminary, the idea that an information technology, namely ontologies, can
potentially actualize the academic cliche, ""From Theory to Practice,"" and be
applicable to the burgeoning domain of data analytics is novel and exciting.
","Data Analytics using Ontologies of Management Theories: Towards
  Implementing 'From Theory to Practice'"
"  Explanations have been introduced in the previous century. Their interest in
reducing the search space is no longer questioned. Yet, their efficient
implementation into CSP solver is still a challenge. In this paper, we
introduce ESeR, an Event Selection Rules algorithm that filters events
generated during propagation. This dynamic selection enables an efficient
computation of explanations for intelligent backtracking al- gorithms. We show
the effectiveness of our approach on the instances of the last three MiniZinc
challenges
",Event Selection Rules to Compute Explanations
"  Structural Causal Models are widely used in causal modelling, but how they
relate to other modelling tools is poorly understood. In this paper we provide
a novel perspective on the relationship between Ordinary Differential Equations
and Structural Causal Models. We show how, under certain conditions, the
asymptotic behaviour of an Ordinary Differential Equation under non-constant
interventions can be modelled using Dynamic Structural Causal Models. In
contrast to earlier work, we study not only the effect of interventions on
equilibrium states; rather, we model asymptotic behaviour that is dynamic under
interventions that vary in time, and include as a special case the study of
static equilibria.
",From Deterministic ODEs to Dynamic Structural Causal Models
"  Multimedia reasoning, which is suitable for, among others, multimedia content
analysis and high-level video scene interpretation, relies on the formal and
comprehensive conceptualization of the represented knowledge domain. However,
most multimedia ontologies are not exhaustive in terms of role definitions, and
do not incorporate complex role inclusions and role interdependencies. In fact,
most multimedia ontologies do not have a role box at all, and implement only a
basic subset of the available logical constructors. Consequently, their
application in multimedia reasoning is limited. To address the above issues,
VidOnt, the very first multimedia ontology with SROIQ(D) expressivity and a
DL-safe ruleset has been introduced for next-generation multimedia reasoning.
In contrast to the common practice, the formal grounding has been set in one of
the most expressive description logics, and the ontology validated with
industry-leading reasoners, namely HermiT and FaCT++. This paper also presents
best practices for developing multimedia ontologies, based on my ontology
engineering approach.
","A Novel Approach to Multimedia Ontology Engineering for Automated
  Reasoning over Audiovisual LOD Datasets"
"  This paper describes an approach to the methodology of answer set programming
(ASP) that can facilitate the design of encodings that are easy to understand
and provably correct. Under this approach, after appending a rule or a small
group of rules to the emerging program we include a comment that states what
has been ""achieved"" so far. This strategy allows us to set out our
understanding of the design of the program by describing the roles of small
parts of the program in a mathematically precise way.
",Achievements in Answer Set Programming
"  The paper continues the investigation of Poincare and Russel's Vicious Circle
Principle (VCP) in the context of the design of logic programming languages
with sets. We expand previously introduced language Alog with aggregates by
allowing infinite sets and several additional set related constructs useful for
knowledge representation and teaching. In addition, we propose an alternative
formalization of the original VCP and incorporate it into the semantics of new
language, Slog+, which allows more liberal construction of sets and their use
in programming rules. We show that, for programs without disjunction and
infinite sets, the formal semantics of aggregates in Slog+ coincides with that
of several other known languages. Their intuitive and formal semantics,
however, are based on quite different ideas and seem to be more involved than
that of Slog+.
",Vicious Circle Principle and Formation of Sets in ASP Based Languages
"  Symmetry breaking has been proven to be an efficient preprocessing technique
for satisfiability solving (SAT). In this paper, we port the state-of-the-art
SAT symmetry breaker BreakID to answer set programming (ASP). The result is a
lightweight tool that can be plugged in between the grounding and the solving
phases that are common when modelling in ASP. We compare our tool with sbass,
the current state-of-the-art symmetry breaker for ASP.
",BreakID: Static Symmetry Breaking for ASP (System Description)
"  PDDL+ is an extension of PDDL that enables modelling planning domains with
mixed discrete-continuous dynamics. In this paper we present a new approach to
PDDL+ planning based on Constraint Answer Set Programming (CASP), i.e. ASP
rules plus numerical constraints. To the best of our knowledge, ours is the
first attempt to link PDDL+ planning and logic programming. We provide an
encoding of PDDL+ models into CASP problems. The encoding can handle non-linear
hybrid domains, and represents a solid basis for applying logic programming to
PDDL+ planning. As a case study, we consider the EZCSP CASP solver and obtain
promising results on a set of PDDL+ benchmark problems.
",PDDL+ Planning via Constraint Answer Set Programming
"  Many real-world problems are composed of several interacting components. In
order to facilitate research on such interactions, the Traveling Thief Problem
(TTP) was created in 2013 as the combination of two well-understood
combinatorial optimization problems.
  With this article, we contribute in four ways. First, we create a
comprehensive dataset that comprises the performance data of 21 TTP algorithms
on the full original set of 9720 TTP instances. Second, we define 55
characteristics for all TPP instances that can be used to select the best
algorithm on a per-instance basis. Third, we use these algorithms and features
to construct the first algorithm portfolios for TTP, clearly outperforming the
single best algorithm. Finally, we study which algorithms contribute most to
this portfolio.
",A case study of algorithm selection for the traveling thief problem
"  The IDP knowledge base system currently uses MiniSAT(ID) as its backend
Constraint Programming (CP) solver. A few similar systems have used a Mixed
Integer Programming (MIP) solver as backend. However, so far little is known
about when the MIP solver is preferable. This paper explores this question. It
describes the use of CPLEX as a backend for IDP and reports on experiments
comparing both backends.
",A MIP Backend for the IDP System
"  Reinforcement learning tasks are typically specified as Markov decision
processes. This formalism has been highly successful, though specifications
often couple the dynamics of the environment and the learning objective. This
lack of modularity can complicate generalization of the task specification, as
well as obfuscate connections between different task settings, such as episodic
and continuing. In this work, we introduce the RL task formalism, that provides
a unification through simple constructs including a generalization to
transition-based discounting. Through a series of examples, we demonstrate the
generality and utility of this formalism. Finally, we extend standard learning
constructs, including Bellman operators, and extend some seminal theoretical
results, including approximation errors bounds. Overall, we provide a
well-understood and sound formalism on which to build theoretical results and
simplify algorithm use and development.
",Unifying task specification in reinforcement learning
"  We consider a non-stationary formulation of the stochastic multi-armed bandit
where the rewards are no longer assumed to be identically distributed. For the
best-arm identification task, we introduce a version of Successive Elimination
based on random shuffling of the $K$ arms. We prove that under a novel and mild
assumption on the mean gap $\Delta$, this simple but powerful modification
achieves the same guarantees in term of sample complexity and cumulative regret
than its original version, but in a much wider class of problems, as it is not
anymore constrained to stationary distributions. We also show that the original
{\sc Successive Elimination} fails to have controlled regret in this more
general scenario, thus showing the benefit of shuffling. We then remove our
mild assumption and adapt the algorithm to the best-arm identification task
with switching arms. We adapt the definition of the sample complexity for that
case and prove that, against an optimal policy with $N-1$ switches of the
optimal arm, this new algorithm achieves an expected sample complexity of
$O(\Delta^{-2}\sqrt{NK\delta^{-1} \log(K \delta^{-1})})$, where $\delta$ is the
probability of failure of the algorithm, and an expected cumulative regret of
$O(\Delta^{-1}{\sqrt{NTK \log (TK)}})$ after $T$ time steps.
","Random Shuffling and Resets for the Non-stationary Stochastic Bandit
  Problem"
"  Probabilistic modeling is one of the foundations of modern machine learning
and artificial intelligence. In this paper, we propose a novel type of
probabilistic models named latent dependency forest models (LDFMs). A LDFM
models the dependencies between random variables with a forest structure that
can change dynamically based on the variable values. It is therefore capable of
modeling context-specific independence. We parameterize a LDFM using a
first-order non-projective dependency grammar. Learning LDFMs from data can be
formulated purely as a parameter learning problem, and hence the difficult
problem of model structure learning is circumvented. Our experimental results
show that LDFMs are competitive with existing probabilistic models.
",Latent Dependency Forest Models
"  Many AI applications rely on knowledge encoded in a locigal knowledge base
(KB). The most essential benefit of such logical KBs is the opportunity to
perform automatic reasoning which however requires a KB to meet some minimal
quality criteria such as consistency. Without adequate tool assistance, the
task of resolving such violated quality criteria in a KB can be extremely hard,
especially when the problematic KB is large and complex. To this end,
interactive KB debuggers have been introduced which ask a user queries whether
certain statements must or must not hold in the intended domain. The given
answers help to gradually restrict the search space for KB repairs.
  Existing interactive debuggers often rely on a pool-based strategy for query
computation. A pool of query candidates is precomputed, from which the best
candidate according to some query quality criterion is selected to be shown to
the user. This often leads to the generation of many unnecessary query
candidates and thus to a high number of expensive calls to logical reasoning
services. We tackle this issue by an in-depth mathematical analysis of diverse
real-valued active learning query selection measures in order to determine
qualitative criteria that make a query favorable. These criteria are the key to
devising efficient heuristic query search methods. The proposed methods enable
for the first time a completely reasoner-free query generation for interactive
KB debugging while at the same time guaranteeing optimality conditions, e.g.
minimal cardinality or best understandability for the user, of the generated
query that existing methods cannot realize.
  Further, we study different relations between active learning measures. The
obtained picture gives a hint about which measures are more favorable in which
situation or which measures always lead to the same outcomes, based on given
types of queries.
","Towards Better Response Times and Higher-Quality Queries in Interactive
  Knowledge Base Debugging"
"  Role discovery in graphs is an emerging area that allows analysis of complex
graphs in an intuitive way. In contrast to other graph prob- lems such as
community discovery, which finds groups of highly connected nodes, the role
discovery problem finds groups of nodes that share similar graph topological
structure. However, existing work so far has two severe limitations that
prevent its use in some domains. Firstly, it is completely unsupervised which
is undesirable for a number of reasons. Secondly, most work is limited to a
single relational graph. We address both these lim- itations in an intuitive
and easy to implement alternating least squares framework. Our framework allows
convex constraints to be placed on the role discovery problem which can provide
useful supervision. In par- ticular we explore supervision to enforce i)
sparsity, ii) diversity and iii) alternativeness. We then show how to lift this
work for multi-relational graphs. A natural representation of a
multi-relational graph is an order 3 tensor (rather than a matrix) and that a
Tucker decomposition allows us to find complex interactions between collections
of entities (E-groups) and the roles they play for a combination of relations
(R-groups). Existing Tucker decomposition methods in tensor toolboxes are not
suited for our purpose, so we create our own algorithm that we demonstrate is
pragmatically useful.
",Some Advances in Role Discovery in Graphs
"  We provide a survey on relational models. Relational models describe complete
networked {domains by taking into account global dependencies in the data}.
Relational models can lead to more accurate predictions if compared to
non-relational machine learning approaches. Relational models typically are
based on probabilistic graphical models, e.g., Bayesian networks, Markov
networks, or latent variable models. Relational models have applications in
social networks analysis, the modeling of knowledge graphs, bioinformatics,
recommendation systems, natural language processing, medical decision support,
and linked data.
",Relational Models
"  The partially observable Markov decision process (POMDP) provides a
principled general framework for planning under uncertainty, but solving POMDPs
optimally is computationally intractable, due to the ""curse of dimensionality""
and the ""curse of history"". To overcome these challenges, we introduce the
Determinized Sparse Partially Observable Tree (DESPOT), a sparse approximation
of the standard belief tree, for online planning under uncertainty. A DESPOT
focuses online planning on a set of randomly sampled scenarios and compactly
captures the ""execution"" of all policies under these scenarios. We show that
the best policy obtained from a DESPOT is near-optimal, with a regret bound
that depends on the representation size of the optimal policy. Leveraging this
result, we give an anytime online planning algorithm, which searches a DESPOT
for a policy that optimizes a regularized objective function. Regularization
balances the estimated value of a policy under the sampled scenarios and the
policy size, thus avoiding overfitting. The algorithm demonstrates strong
experimental results, compared with some of the best online POMDP algorithms
available. It has also been incorporated into an autonomous driving system for
real-time vehicle control. The source code for the algorithm is available
online.
",DESPOT: Online POMDP Planning with Regularization
"  Graph aggregation is the process of computing a single output graph that
constitutes a good compromise between several input graphs, each provided by a
different source. One needs to perform graph aggregation in a wide variety of
situations, e.g., when applying a voting rule (graphs as preference orders),
when consolidating conflicting views regarding the relationships between
arguments in a debate (graphs as abstract argumentation frameworks), or when
computing a consensus between several alternative clusterings of a given
dataset (graphs as equivalence relations). In this paper, we introduce a formal
framework for graph aggregation grounded in social choice theory. Our focus is
on understanding which properties shared by the individual input graphs will
transfer to the output graph returned by a given aggregation rule. We consider
both common properties of graphs, such as transitivity and reflexivity, and
arbitrary properties expressible in certain fragments of modal logic. Our
results establish several connections between the types of properties preserved
under aggregation and the choice-theoretic axioms satisfied by the rules used.
The most important of these results is a powerful impossibility theorem that
generalises Arrow's seminal result for the aggregation of preference orders to
a large collection of different types of graphs.
",Graph Aggregation
"  PDDL+ planning has its semantics rooted in hybrid automata (HA) and recent
work has shown that it can be modeled as a network of HAs. Addressing the
complexity of nonlinear PDDL+ planning as HAs requires both space and time
efficient reasoning. Unfortunately, existing solvers either do not address
nonlinear dynamics or do not natively support networks of automata.
  We present a new algorithm, called HNSolve, which guides the variable
selection of the dReal Satisfiability Modulo Theories (SMT) solver while
reasoning about network encodings of nonlinear PDDL+ planning as HAs. HNSolve
tightly integrates with dReal by solving a discrete abstraction of the HA
network. HNSolve finds composite runs on the HA network that ignore continuous
variables, but respect mode jumps and synchronization labels. HNSolve
admissibly detects dead-ends in the discrete abstraction, and posts conflict
clauses that prune the SMT solver's search. We evaluate the benefits of our
HNSolve algorithm on PDDL+ benchmark problems and demonstrate its performance
with respect to prior work.
","Instrumenting an SMT Solver to Solve Hybrid Network Reachability
  Problems"
"  We analyze the structure of the state space of chess by means of transition
path sampling Monte Carlo simulation. Based on the typical number of moves
required to transpose a given configuration of chess pieces into another, we
conclude that the state space consists of several pockets between which
transitions are rare. Skilled players explore an even smaller subset of
positions that populate some of these pockets only very sparsely. These results
suggest that the usual measures to estimate both, the size of the state space
and the size of the tree of legal moves, are not unique indicators of the
complexity of the game, but that topological considerations are equally
important.
",Sequencing Chess
"  PK Dick once asked ""Do Androids Dream of Electric Sheep?"" In video games, a
similar question could be asked of non-player characters: Do NPCs have dreams?
Can they live and change as humans do? Can NPCs have personalities, and can
these develop through interactions with players, other NPCs, and the world
around them? Despite advances in personality AI for games, most NPCs are still
undeveloped and undeveloping, reacting with flat affect and predictable
routines that make them far less than human--in fact, they become little more
than bits of the scenery that give out parcels of information. This need not be
the case. Extreme AI, a psychology-based personality engine, creates adaptive
NPC personalities. Originally developed as part of the thesis ""NPCs as People:
Using Databases and Behaviour Trees to Give Non-Player Characters Personality,""
Extreme AI is now a fully functioning personality engine using all thirty
facets of the Five Factor model of personality and an AI system that is live
throughout gameplay. This paper discusses the research leading to Extreme AI;
develops the ideas found in that thesis; discusses the development of other
personality engines; and provides examples of Extreme AI's use in two game
demos.
","NPCs as People, Too: The Extreme AI Personality Engine"
"  Temporal abstraction is key to scaling up learning and planning in
reinforcement learning. While planning with temporally extended actions is well
understood, creating such abstractions autonomously from data has remained
challenging. We tackle this problem in the framework of options [Sutton, Precup
& Singh, 1999; Precup, 2000]. We derive policy gradient theorems for options
and propose a new option-critic architecture capable of learning both the
internal policies and the termination conditions of options, in tandem with the
policy over options, and without the need to provide any additional rewards or
subgoals. Experimental results in both discrete and continuous environments
showcase the flexibility and efficiency of the framework.
",The Option-Critic Architecture
"  Operationalization of terminology for IT applications has revived the
Wusterian approach. The conceptual dimension once more prevails after taking
back seat to specialised lexicography. This is demonstrated by the emergence of
ontology in terminology. While the Terminology Principles as defined in Felber
manual and the ISO standards remain at the core of traditional terminology,
their computational implementation raises some issues. In this article, while
reiterating their importance, we will be re-examining these Principles from a
dual perspective: that of logic in the mathematical sense of the term and that
of epistemology as in the theory of knowledge. We will thus be clarifying and
describing some of them so as to take into account advances in knowledge
engineering (ontology) and formal systems (logic). The notion of
ontoterminology, terminology whose conceptual system is a formal ontology,
results from this approach.
",Should Terminology Principles be re-examined?
"  We express Brewka's prioritised default logic (PDL) as argumentation using
ASPIC+. By representing PDL as argumentation and designing an argument
preference relation that takes the argument structure into account, we prove
that the conclusions of the justified arguments correspond to the PDL
extensions. We will first assume that the default priority is total, and then
generalise to the case where it is a partial order. This provides a
characterisation of non-monotonic inference in PDL as an exchange of argument
and counter-argument, providing a basis for distributed non-monotonic reasoning
in the form of dialogue.
","Prioritised Default Logic as Argumentation with Partial Order Default
  Priorities"
"  In many machine learning applications, labeled data is scarce and obtaining
more labels is expensive. We introduce a new approach to supervising neural
networks by specifying constraints that should hold over the output space,
rather than direct examples of input-output pairs. These constraints are
derived from prior domain knowledge, e.g., from known laws of physics. We
demonstrate the effectiveness of this approach on real world and simulated
computer vision tasks. We are able to train a convolutional neural network to
detect and track objects without any labeled examples. Our approach can
significantly reduce the need for labeled training data, but introduces new
challenges for encoding prior knowledge into appropriate loss functions.
","Label-Free Supervision of Neural Networks with Physics and Domain
  Knowledge"
"  Bilattice-based triangle provides an elegant algebraic structure for
reasoning with vague and uncertain information. But the truth and knowledge
ordering of intervals in bilattice-based triangle can not handle repetitive
belief revisions which is an essential characteristic of nonmonotonic
reasoning. Moreover the ordering induced over the intervals by the
bilattice-based triangle is not sometimes intuitive. In this work, we construct
an alternative algebraic structure, namely preorder-based triangle and we
formulate proper logical connectives for this. It is also demonstrated that
Preorder-based triangle serves to be a better alternative to the
bilattice-based triangle for reasoning in application areas, that involve
nonmonotonic fuzzy reasoning with uncertain information.
","Preorder-Based Triangle: A Modified Version of Bilattice-Based Triangle
  for Belief Revision in Nonmonotonic Reasoning"
"  Time series interpretation aims to provide an explanation of what is observed
in terms of its underlying processes. The present work is based on the
assumption that the common classification-based approaches to time series
interpretation suffer from a set of inherent weaknesses, whose ultimate cause
lies in the monotonic nature of the deductive reasoning paradigm. In this
document we propose a new approach to this problem, based on the initial
hypothesis that abductive reasoning properly accounts for the human ability to
identify and characterize the patterns appearing in a time series. The result
of this interpretation is a set of conjectures in the form of observations,
organized into an abstraction hierarchy and explaining what has been observed.
A knowledge-based framework and a set of algorithms for the interpretation task
are provided, implementing a hypothesize-and-test cycle guided by an
attentional mechanism. As a representative application domain, interpretation
of the electrocardiogram allows us to highlight the strengths of the proposed
approach in comparison with traditional classification-based approaches.
",On the adoption of abductive reasoning for time series interpretation
"  In this paper, we present an approach that is able to handle with Z-numbers
in the context of Multi-Criteria Decision Making (MCDM) problems. Z-numbers are
composed of two parts, the first one is a restriction on the values that can be
assumed, and the second part is the reliability of the information. As human
beings we communicate with other people by means of natural language using
sentences like: the journey time from home to university takes about half hour,
very likely. Firstly, Z-numbers are converted to fuzzy numbers using a standard
procedure. Next, the Z-TODIM and Z-TOPSIS are presented as a direct extension
of the fuzzy TODIM and fuzzy TOPSIS, respectively. The proposed methods are
applied to two case studies and compared with the standard approach using crisp
values. Results obtained show the feasibility of the approach. In addition, a
graphical interface was built to handle with both methods Z- TODIM and Z-TOPSIS
allowing ease of use for user in other areas of knowledge.
",TODIM and TOPSIS with Z-numbers
"  A broad variety of knowledge-based applications such as recommender, expert,
planning or configuration systems usually operate on the basis of knowledge
represented by means of some logical language. Such a logical knowledge base
(KB) enables intelligent behavior of such systems by allowing them to
automatically reason, answer queries of interest or solve complex real-world
problems. Nowadays, where information acquisition comes at low costs and often
happens automatically, the applied KBs are continuously growing in terms of
size, information content and complexity. These developments foster the
emergence of errors in these KBs and thus pose a significant challenge on all
people and tools involved in KB evolution, maintenance and application.
  If some minimal quality criteria such as logical consistency are not met by
some KB, it becomes useless for knowledge-based applications. To guarantee the
compliance of KBs with given requirements, (non-interactive) KB debuggers have
been proposed. These however often cannot localize all potential faults,
suggest too large or incorrect modifications of the faulty KB or suffer from
poor scalability due to the inherent complexity of the KB debugging problem.
  As a remedy to these issues, based on a well-founded theoretical basis this
work proposes complete, sound and optimal methods for the interactive debugging
of KBs that suggest the one (minimally invasive) error correction of the faulty
KB that yields a repaired KB with exactly the intended semantics. Users, e.g.
domain experts, are involved in the debugging process by answering
automatically generated queries whether some given statements must or must not
hold in the domain that should be modeled by the problematic KB at hand.
",A Theory of Interactive Debugging of Knowledge Bases in Monotonic Logics
"  We investigate the 'Digital Synaptic Neural Substrate' (DSNS) computational
creativity approach further with respect to the size and quality of images that
can be used to seed the process. In previous work we demonstrated how combining
photographs of people and sequences taken from chess games between weak players
can be used to generate chess problems or puzzles of higher aesthetic quality,
on average, compared to alternative approaches. In this work we show
experimentally that using larger images as opposed to smaller ones improves the
output quality even further. The same is also true for using clearer or less
corrupted images. The reasons why these things influence the DSNS process is
presently not well-understood and debatable but the findings are nevertheless
immediately applicable for obtaining better results.
",The Digital Synaptic Neural Substrate: Size and Quality Matters
"  Annotating semantic data with metadata is becoming more and more important to
provide information about the statements being asserted. While initial
solutions proposed a data model to represent a specific dimension of
meta-information (such as time or provenance), the need for a general
annotation framework which allows representing different context dimensions is
needed. In this paper, we extend the 4dFluents ontology by Welty and Fikes---on
associating temporal validity to statements---to any dimension of context, and
discuss possible issues that multidimensional context representations have to
face and how we address them.
",NdFluents: A Multi-dimensional Contexts Ontology
"  This paper describes how the ""SP Theory of Intelligence"" with the ""SP
Computer Model"", outlined in an Appendix, may throw light on aspects of
commonsense reasoning (CSR) and commonsense knowledge (CSK), as discussed in
another paper by Ernest Davis and Gary Marcus (DM). In four main sections, the
paper describes: 1) The main problems to be solved; 2) Other research on CSR
and CSK; 3) Why the SP system may prove useful with CSR and CSK 4) How examples
described by DM may be modelled in the SP system. With regard to successes in
the automation of CSR described by DM, the SP system's strengths in
simplification and integration may promote seamless integration across these
areas, and seamless integration of those area with other aspects of
intelligence. In considering challenges in the automation of CSR described by
DM, the paper describes in detail, with examples of SP-multiple-alignments. how
the SP system may model processes of interpretation and reasoning arising from
the horse's head scene in ""The Godfather"" film. A solution is presented to the
'long tail' problem described by DM. The SP system has some potentially useful
things to say about several of DM's objectives for research in CSR and CSK.
","Commonsense Reasoning, Commonsense Knowledge, and The SP Theory of
  Intelligence"
"  Robotic code needs to be verified to ensure its safety and functional
correctness, especially when the robot is interacting with people. Testing real
code in simulation is a viable option. However, generating tests that cover
rare scenarios, as well as exercising most of the code, is a challenge
amplified by the complexity of the interactions between the environment and the
software. Model-based test generation methods can automate otherwise manual
processes and facilitate reaching rare scenarios during testing. In this paper,
we compare using Belief-Desire-Intention (BDI) agents as models for test
generation with more conventional automata-based techniques that exploit model
checking, in terms of practicality, performance, transferability to different
scenarios, and exploration (`coverage'), through two case studies: a
cooperative manufacturing task, and a home care scenario. The results highlight
the advantages of using BDI agents for test generation. BDI agents naturally
emulate the agency present in Human-Robot Interactions (HRIs), and are thus
more expressive than automata. The performance of the BDI-based test generation
is at least as high, and the achieved coverage is higher or equivalent,
compared to test generation based on model checking automata.
","Model-based Test Generation for Robotic Software: Automata versus
  Belief-Desire-Intention Agents"
"  While the possibility of time travel in physics is still debated, the
explosive growth of virtual-reality simulations opens up new possibilities to
rigorously explore such time travel and its consequences in the digital domain.
Here we provide a computational model of time travel and a computer program
that allows exploring digital time travel. In order to explain our method we
formalize a simplified version of the famous grandfather paradox, show how the
system can allow the participant to go back in time, try to kill their
ancestors before they were born, and experience the consequences. The system
has even come up with scenarios that can be considered consistent ""solutions""
of the grandfather paradox. We discuss the conditions for digital time travel,
which indicate that it has a large number of practical applications.
","A computer program for simulating time travel and a possible 'solution'
  for the grandfather paradox"
"  In this paper, we present UbuntuWorld 1.0 LTS - a platform for developing
automated technical support agents in the Ubuntu operating system.
Specifically, we propose to use the Bash terminal as a simulator of the Ubuntu
environment for a learning-based agent and demonstrate the usefulness of
adopting reinforcement learning (RL) techniques for basic problem solving and
troubleshooting in this environment. We provide a plug-and-play interface to
the simulator as a python package where different types of agents can be
plugged in and evaluated, and provide pathways for integrating data from online
support forums like AskUbuntu into an automated agent's learning process.
Finally, we show that the use of this data significantly improves the agent's
learning efficiency. We believe that this platform can be adopted as a
real-world test bed for research on automated technical support.
","UbuntuWorld 1.0 LTS - A Platform for Automated Problem Solving &
  Troubleshooting in the Ubuntu OS"
"  First this report presents a restricted set of finite transducers used to
synthesise structural time-series constraints described by means of a
multi-layered function composition scheme. Second it provides the corresponding
synthesised catalogue of structural time-series constraints where each
constraint is explicitly described in terms of automata with registers.
","Global Constraint Catalog, Volume II, Time-Series Constraints"
"  Vehicle Routing Problem is a well-known problem in logistics and
transportation, and the variety of such problems is explained by the fact that
it occurs in many real-life situations. It is an NP-hard combinatorial
optimization problem and finding an exact optimal solution is practically
impossible. In this work, Site-Dependent Truck and Trailer Routing Problem with
hard and soft Time Windows and Split Deliveries is considered (SDTTRPTWSD). In
this article, we develop a heuristic with the elements of Tabu Search for
solving SDTTRPTWSD. The heuristic uses the concept of neighborhoods and visits
infeasible solutions during the search. A greedy heuristic is applied to
construct an initial solution.
","Heuristic with elements of tabu search for Truck and Trailer Routing
  Problem"
"  Crowdsourcing platforms enable to propose simple human intelligence tasks to
a large number of participants who realise these tasks. The workers often
receive a small amount of money or the platforms include some other incentive
mechanisms, for example they can increase the workers reputation score, if they
complete the tasks correctly. We address the problem of identifying experts
among participants, that is, workers, who tend to answer the questions
correctly. Knowing who are the reliable workers could improve the quality of
knowledge one can extract from responses. As opposed to other works in the
literature, we assume that participants can give partial or incomplete
responses, in case they are not sure that their answers are correct. We model
such partial or incomplete responses with the help of belief functions, and we
derive a measure that characterizes the expertise level of each participant.
This measure is based on precise and exactitude degrees that represent two
parts of the expertise level. The precision degree reflects the reliability
level of the participants and the exactitude degree reflects the knowledge
level of the participants. We also analyze our model through simulation and
demonstrate that our richer model can lead to more reliable identification of
experts.
",Characterization of experts in crowdsourcing platforms
"  A number of attempts have been made to improve accuracy and/or scalability of
the PC (Peter and Clark) algorithm, some well known (Buhlmann, et al., 2010;
Kalisch and Buhlmann, 2007; 2008; Zhang, 2012, to give some examples). We add
here one more tool to the toolbox: the simple observation that if one is forced
to choose between a variety of possible conditioning sets for a pair of
variables, one should choose the one with the highest p-value. One can use the
CPC (Conservative PC, Ramsey et al., 2012) algorithm as a guide to possible
sepsets for a pair of variables. However, whereas CPC uses a voting rule to
classify colliders versus noncolliders, our proposed algorithm, PC-Max, picks
the conditioning set with the highest p-value, so that there are no
ambiguities. We combine this with two other optimizations: (a) avoiding
bidirected edges in the orientation of colliders, and (b) parallelization. For
(b) we borrow ideas from the PC-Stable algorithm (Colombo and Maathuis, 2014).
The result is an algorithm that scales quite well both in terms of accuracy and
time, with no risk of bidirected edges.
","Improving Accuracy and Scalability of the PC Algorithm by Maximizing
  P-value"
"  We analyze to what extent the random SAT and Max-SAT problems differ in their
properties. Our findings suggest that for random $k$-CNF with ratio in a
certain range, Max-SAT can be solved by any SAT algorithm with subexponential
slowdown, while for formulae with ratios greater than some constant, algorithms
under the random walk framework require substantially different heuristics. In
light of these results, we propose a novel probabilistic approach for random
Max-SAT called ProMS. Experimental results illustrate that ProMS outperforms
many state-of-the-art local search solvers on random Max-SAT benchmarks.
",Should Algorithms for Random SAT and Max-SAT be Different?
"  High-Throughput materials discovery involves the rapid synthesis,
measurement, and characterization of many different but structurally-related
materials. A key problem in materials discovery, the phase map identification
problem, involves the determination of the crystal phase diagram from the
materials' composition and structural characterization data. We present
Phase-Mapper, a novel AI platform to solve the phase map identification problem
that allows humans to interact with both the data and products of AI
algorithms, including the incorporation of human feedback to constrain or
initialize solutions. Phase-Mapper affords incorporation of any spectral
demixing algorithm, including our novel solver, AgileFD, which is based on a
convolutive non-negative matrix factorization algorithm. AgileFD can
incorporate constraints to capture the physics of the materials as well as
human feedback. We compare three solver variants with previously proposed
methods in a large-scale experiment involving 20 synthetic systems,
demonstrating the efficacy of imposing physical constrains using AgileFD.
Phase-Mapper has also been used by materials scientists to solve a wide variety
of phase diagrams, including the previously unsolved Nb-Mn-V oxide system,
which is provided here as an illustrative example.
","Phase-Mapper: An AI Platform to Accelerate High Throughput Materials
  Discovery"
"  Detection rules have traditionally been designed for rational agents that
minimize the Bayes risk (average decision cost). With the advent of
crowd-sensing systems, there is a need to redesign binary hypothesis testing
rules for behavioral agents, whose cognitive behavior is not captured by
traditional utility functions such as Bayes risk. In this paper, we adopt
prospect theory based models for decision makers. We consider special agent
models namely optimists and pessimists in this paper, and derive optimal
detection rules under different scenarios. Using an illustrative example, we
also show how the decision rule of a human agent deviates from the Bayesian
decision rule under various behavioral models, considered in this paper.
","Towards the Design of Prospect-Theory based Human Decision Rules for
  Hypothesis Testing"
"  With a large proportion of people carrying location-aware smartphones, we
have an unprecedented platform from which to understand individuals and predict
their future actions. This work builds upon the Context Tree data structure
that summarises the historical contexts of individuals from augmented
geospatial trajectories, and constructs a predictive model for their likely
future contexts. The Predictive Context Tree (PCT) is constructed as a
hierarchical classifier, capable of predicting both the future locations that a
user will visit and the contexts that a user will be immersed within. The PCT
is evaluated over real-world geospatial trajectories, and compared against
existing location extraction and prediction techniques, as well as a proposed
hybrid approach that uses identified land usage elements in combination with
machine learning to predict future interactions. Our results demonstrate that
higher predictive accuracies can be achieved using this hybrid approach over
traditional extracted location datasets, and the PCT itself matches the
performance of the hybrid approach at predicting future interactions, while
adding utility in the form of context predictions. Such a prediction system is
capable of understanding not only where a user will visit, but also their
context, in terms of what they are likely to be doing.
",The Predictive Context Tree: Predicting Contexts and Interactions
"  We introduce the lifted Generalized Belief Propagation (GBP) message passing
algorithm, for the computation of sum-product queries in Probabilistic
Relational Models (e.g. Markov logic network). The algorithm forms a compact
region graph and establishes a modified version of message passing, which
mimics the GBP behavior in a corresponding ground model. The compact graph is
obtained by exploiting a graphical representation of clusters, which reduces
cluster symmetry detection to isomorphism tests on small local graphs. The
framework is thus capable of handling complex models, while remaining
domain-size independent.
",Lifted Message Passing for the Generalized Belief Propagation
"  Planning has achieved significant progress in recent years. Among the various
approaches to scale up plan synthesis, the use of macro-actions has been widely
explored. As a first stage towards the development of a solution to learn
on-line macro-actions, we propose an algorithm to identify useful macro-actions
based on data mining techniques. The integration in the planning search of
these learned macro-actions shows significant improvements over four classical
planning benchmarks.
",Learning Macro-actions for State-Space Planning
"  Arising from many applications at the intersection of decision making and
machine learning, Marginal Maximum A Posteriori (Marginal MAP) Problems unify
the two main classes of inference, namely maximization (optimization) and
marginal inference (counting), and are believed to have higher complexity than
both of them. We propose XOR_MMAP, a novel approach to solve the Marginal MAP
Problem, which represents the intractable counting subproblem with queries to
NP oracles, subject to additional parity constraints. XOR_MMAP provides a
constant factor approximation to the Marginal MAP Problem, by encoding it as a
single optimization in polynomial size of the original problem. We evaluate our
approach in several machine learning and decision making applications, and show
that our approach outperforms several state-of-the-art Marginal MAP solvers.
",Solving Marginal MAP Problems with NP Oracles and Parity Constraints
"  We propose Deep Optimistic Linear Support Learning (DOL) to solve
high-dimensional multi-objective decision problems where the relative
importances of the objectives are not known a priori. Using features from the
high-dimensional inputs, DOL computes the convex coverage set containing all
potential optimal solutions of the convex combinations of the objectives. To
our knowledge, this is the first time that deep reinforcement learning has
succeeded in learning multi-objective policies. In addition, we provide a
testbed with two experiments to be used as a benchmark for deep multi-objective
reinforcement learning.
",Multi-Objective Deep Reinforcement Learning
"  Hierarchical Reinforcement Learning has been previously shown to speed up the
convergence rate of RL planning algorithms as well as mitigate feature-based
model misspecification (Mankowitz et. al. 2016a,b, Bacon 2015). To do so, it
utilizes hierarchical abstractions, also known as skills -- a type of
temporally extended action (Sutton et. al. 1999) to plan at a higher level,
abstracting away from the lower-level details. We incorporate risk sensitivity,
also referred to as Situational Awareness (SA), into hierarchical RL for the
first time by defining and learning risk aware skills in a Probabilistic Goal
Semi-Markov Decision Process (PG-SMDP). This is achieved using our novel
Situational Awareness by Risk-Conscious Skills (SARiCoS) algorithm which comes
with a theoretical convergence guarantee. We show in a RoboCup soccer domain
that the learned risk aware skills exhibit complex human behaviors such as
`time-wasting' in a soccer game. In addition, the learned risk aware skills are
able to mitigate reward-based model misspecification.
",Situational Awareness by Risk-Conscious Skills
"  We present ABA+, a new approach to handling preferences in a well known
structured argumentation formalism, Assumption-Based Argumentation (ABA). In
ABA+, preference information given over assumptions is incorporated directly
into the attack relation, thus resulting in attack reversal. ABA+
conservatively extends ABA and exhibits various desirable features regarding
relationship among argumentation semantics as well as preference handling. We
also introduce Weak Contraposition, a principle concerning reasoning with rules
and preferences that relaxes the standard principle of contraposition, while
guaranteeing additional desirable features for ABA+.
",ABA+: Assumption-Based Argumentation with Preferences
"  We describe a general method of detecting valid chains or links of pieces on
a two-dimensional grid. Specifically, using the example of the chess variant
known as Switch-Side Chain-Chess (SSCC). Presently, no foolproof method of
detecting such chains in any given chess position is known and existing graph
theory, to our knowledge, is unable to fully address this problem either. We
therefore propose a solution implemented and tested using the C++ programming
language. We have been unable to find an incorrect result and therefore offer
it as the most viable solution thus far to the chain-detection problem in this
chess variant. The algorithm is also scalable, in principle, to areas beyond
two-dimensional grids such as 3D analysis and molecular chemistry.
",A Chain-Detection Algorithm for Two-Dimensional Grids
"  A methodology for the development of a fuzzy expert system (FES) with
application to earthquake prediction is presented. The idea is to reproduce the
performance of a human expert in earthquake prediction. To do this, at the
first step, rules provided by the human expert are used to generate a fuzzy
rule base. These rules are then fed into an inference engine to produce a fuzzy
inference system (FIS) and to infer the results. In this paper, we have used a
Sugeno type fuzzy inference system to build the FES. At the next step, the
adaptive network-based fuzzy inference system (ANFIS) is used to refine the FES
parameters and improve its performance. The proposed framework is then employed
to attain the performance of a human expert used to predict earthquakes in the
Zagros area based on the idea of coupled earthquakes. While the prediction
results are promising in parts of the testing set, the general performance
indicates that prediction methodology based on coupled earthquakes needs more
investigation and more complicated reasoning procedure to yield satisfactory
predictions.
","A fuzzy expert system for earthquake prediction, case study: the Zagros
  range"
"  Knowledge base completion aims to infer new relations from existing
information. In this paper, we propose path-augmented TransR (PTransR) model to
improve the accuracy of link prediction. In our approach, we base PTransR model
on TransR, which is the best one-hop model at present. Then we regularize
TransR with information of relation paths. In our experiment, we evaluate
PTransR on the task of entity prediction. Experimental results show that
PTransR outperforms previous models.
",Improved Knowledge Base Completion by Path-Augmented TransR Model
"  The problem of solving $(n^2-1)$-puzzle and cooperative path-finding (CPF)
sub-optimally by rule based algorithms is addressed in this manuscript. The
task in the puzzle is to rearrange $n^2-1$ pebbles on the square grid of the
size of n x n using one vacant position to a desired goal configuration. An
improvement to the existent polynomial-time algorithm is proposed and
experimentally analyzed. The improved algorithm is trying to move pebbles in a
more efficient way than the original algorithm by grouping them into so-called
snakes and moving them jointly within the snake. An experimental evaluation
showed that the algorithm using snakes produces solutions that are 8% to 9%
shorter than solutions generated by the original algorithm.
  The snake-based relocation has been also integrated into rule-based
algorithms for solving the CPF problem sub-optimally, which is a closely
related task. The task in CPF is to relocate a group of abstract robots that
move over an undirected graph to given goal vertices. Robots can move to
unoccupied neighboring vertices and at most one robot can be placed in each
vertex. The $(n^2-1)$-puzzle is a special case of CPF where the underlying
graph is represented by a 4-connected grid and there is only one vacant vertex.
Two major rule-based algorithms for CPF were included in our study - BIBOX and
PUSH-and-SWAP (PUSH-and-ROTATE). Improvements gained by using snakes in the
BIBOX algorithm were stable around 30% in $(n^2-1)$-puzzle solving and up to
50% in CPFs over bi-connected graphs with various ear decompositions and
multiple vacant vertices. In the case of the PUSH-and-SWAP algorithm the
improvement achieved by snakes was around 5% to 8%. However, the improvement
was unstable and hardly predictable in the case of PUSH-and-SWAP.
","Improvements in Sub-optimal Solving of the $(N^2-1)$-Puzzle via Joint
  Relocation of Pebbles and its Applications to Rule-based Cooperative
  Path-Finding"
"  The number of optimization techniques in the combinatorial domain is large
and diversified. Nevertheless, there is still a lack of real benchmarks to
validate optimization algorithms. In this work we introduce VRPBench, a tool to
create instances and visualize solutions to the Vehicle Routing Problem (VRP)
in a planar graph embedded in the Euclidean 2D space. We use VRPBench to model
a real-world mail delivery case of the city of Artur Nogueira. Such scenarios
were characterized as a multi-objective optimization of the VRP. We extracted a
weighted graph from a digital map of the city to create a challenging benchmark
for the VRP. Each instance models one generic day of mail delivery with
hundreds to thousands of delivery points, thus allowing both the comparison and
validation of optimization algorithms for routing problems.
",VRPBench: A Vehicle Routing Benchmark Tool
"  The problem of makespan optimal solving of cooperative path finding (CPF) is
addressed in this paper. The task in CPF is to relocate a group of agents in a
non-colliding way so that each agent eventually reaches its goal location from
the given initial location. The abstraction adopted in this work assumes that
agents are discrete items moving in an undirected graph by traversing edges.
Makespan optimal solving of CPF means to generate solutions that are as short
as possi-ble in terms of the total number of time steps required for the
execution of the solution.
  We show that reducing CPF to propositional satisfiability (SAT) represents a
viable option for obtaining makespan optimal solutions. Several encodings of
CPF into propositional formulae are suggested and experimentally evaluated. The
evaluation indicates that SAT based CPF solving outperforms other makespan
optimal methods significantly in highly constrained situations (environments
that are densely occupied by agents).
","Makespan Optimal Solving of Cooperative Path-Finding via Reductions to
  Propositional Satisfiability"
"  In this paper we propose a causal analog to the purely observational Dynamic
Bayesian Networks, which we call Dynamic Causal Networks. We provide a sound
and complete algorithm for identification of Dynamic Causal Net- works, namely,
for computing the effect of an intervention or experiment, based on passive
observations only, whenever possible. We note the existence of two types of
confounder variables that affect in substantially different ways the iden-
tification procedures, a distinction with no analog in either Dynamic Bayesian
Networks or standard causal graphs. We further propose a procedure for the
transportability of causal effects in Dynamic Causal Network settings, where
the re- sult of causal experiments in a source domain may be used for the
identification of causal effects in a target domain.
",Identifiability and Transportability in Dynamic Causal Networks
"  Most of the metaheuristics can efficiently solve unconstrained problems;
however, their performance may degenerate if the constraints are involved. This
paper proposes two constraint handling approaches for an emerging metaheuristic
of Cohort Intelligence (CI). More specifically CI with static penalty function
approach (SCI) and CI with dynamic penalty function approach (DCI) are
proposed. The approaches have been tested by solving several constrained test
problems. The performance of the SCI and DCI have been compared with algorithms
like GA, PSO, ABC, d-Ds. In addition, as well as three real world problems from
mechanical engineering domain with improved solutions. The results were
satisfactory and validated the applicability of CI methodology for solving real
world problems.
","Constrained Cohort Intelligence using Static and Dynamic Penalty
  Function Approach for Mechanical Components Design"
"  In this work we extend to the interval-valued setting the notion of an
overlap functions and we discuss a method which makes use of interval-valued
overlap functions for constructing OWA operators with interval-valued weights.
. Some properties of interval-valued overlap functions and the derived
interval-valued OWA operators are analysed. We specially focus on the
homogeneity and migrativity properties.
","Generalized Interval-valued OWA Operators with Interval Weights Derived
  from Interval-valued Overlap Functions"
"  A new approach to data stream clustering with the help of an ensemble of
adaptive neuro-fuzzy systems is proposed. The proposed ensemble is formed with
adaptive neuro-fuzzy self-organizing Kohonen maps in a parallel processing
mode. A final result is chosen by the best neuro-fuzzy self-organizing Kohonen
map.
","An Ensemble of Adaptive Neuro-Fuzzy Kohonen Networks for Online Data
  Stream Fuzzy Clustering"
"  Automatic construction of large knowledge graphs (KG) by mining web-scale
text datasets has received considerable attention recently. Estimating accuracy
of such automatically constructed KGs is a challenging problem due to their
size and diversity. This important problem has largely been ignored in prior
research we fill this gap and propose KGEval. KGEval binds facts of a KG using
coupling constraints and crowdsources the facts that infer correctness of large
parts of the KG. We demonstrate that the objective optimized by KGEval is
submodular and NP-hard, allowing guarantees for our approximation algorithm.
Through extensive experiments on real-world datasets, we demonstrate that
KGEval is able to estimate KG accuracy more accurately compared to other
competitive baselines, while requiring significantly lesser number of human
evaluations.
","KGEval: Estimating Accuracy of Automatically Constructed Knowledge
  Graphs"
"  Many countries are suffering from severe air pollution. Understanding how
different air pollutants accumulate and propagate is critical to making
relevant public policies. In this paper, we use urban big data (air quality
data and meteorological data) to identify the \emph{spatiotemporal (ST) causal
pathways} for air pollutants. This problem is challenging because: (1) there
are numerous noisy and low-pollution periods in the raw air quality data, which
may lead to unreliable causality analysis, (2) for large-scale data in the ST
space, the computational complexity of constructing a causal structure is very
high, and (3) the \emph{ST causal pathways} are complex due to the interactions
of multiple pollutants and the influence of environmental factors. Therefore,
we present \emph{p-Causality}, a novel pattern-aided causality analysis
approach that combines the strengths of \emph{pattern mining} and
\emph{Bayesian learning} to efficiently and faithfully identify the \emph{ST
causal pathways}. First, \emph{Pattern mining} helps suppress the noise by
capturing frequent evolving patterns (FEPs) of each monitoring sensor, and
greatly reduce the complexity by selecting the pattern-matched sensors as
""causers"". Then, \emph{Bayesian learning} carefully encodes the local and ST
causal relations with a Gaussian Bayesian network (GBN)-based graphical model,
which also integrates environmental influences to minimize biases in the final
results. We evaluate our approach with three real-world data sets containing
982 air quality sensors, in three regions of China from 01-Jun-2013 to
19-Dec-2015. Results show that our approach outperforms the traditional causal
structure learning methods in time efficiency, inference accuracy and
interpretability.
","pg-Causality: Identifying Spatiotemporal Causal Pathways for Air
  Pollutants with Urban Big Data"
"  Pairwise comparisons between alternatives are a well-known method for
measuring preferences of a decision-maker. Since these often do not exhibit
consistency, a number of inconsistency indices has been introduced in order to
measure the deviation from this ideal case. We axiomatically characterize the
inconsistency ranking induced by the Koczkodaj inconsistency index: six
independent properties are presented such that they determine a unique linear
order on the set of all pairwise comparison matrices.
","Characterization of an inconsistency ranking for pairwise comparison
  matrices"
"  We develop a Bayesian model for decision-making under time pressure with
endogenous information acquisition. In our model, the decision maker decides
when to observe (costly) information by sampling an underlying continuous-time
stochastic process (time series) that conveys information about the potential
occurrence or non-occurrence of an adverse event which will terminate the
decision-making process. In her attempt to predict the occurrence of the
adverse event, the decision-maker follows a policy that determines when to
acquire information from the time series (continuation), and when to stop
acquiring information and make a final prediction (stopping). We show that the
optimal policy has a rendezvous structure, i.e. a structure in which whenever a
new information sample is gathered from the time series, the optimal ""date"" for
acquiring the next sample becomes computable. The optimal interval between two
information samples balances a trade-off between the decision maker's surprise,
i.e. the drift in her posterior belief after observing new information, and
suspense, i.e. the probability that the adverse event occurs in the time
interval between two information samples. Moreover, we characterize the
continuation and stopping regions in the decision-maker's state-space, and show
that they depend not only on the decision-maker's beliefs, but also on the
context, i.e. the current realization of the time series.
","Balancing Suspense and Surprise: Timely Decision Making with Endogenous
  Information Acquisition"
"  The elusive quest for intelligence in artificial intelligence prompts us to
consider that instituting human-level intelligence in systems may be (still) in
the realm of utopia. In about a quarter century, we have witnessed the winter
of AI (1990) being transformed and transported to the zenith of tabloid fodder
about AI (2015). The discussion at hand is about the elements that constitute
the canonical idea of intelligence. The delivery of intelligence as a
pay-per-use-service, popping out of an app or from a shrink-wrapped software
defined point solution, is in contrast to the bio-inspired view of intelligence
as an outcome, perhaps formed from a tapestry of events, cross-pollinated by
instances, each with its own microcosm of experiences and learning, which may
not be discrete all-or-none functions but continuous, over space and time. The
enterprise world may not require, aspire or desire such an engaged solution to
improve its services for enabling digital transformation through the deployment
of digital twins, for example. One might ask whether the ""work-flow on
steroids"" version of decision support may suffice for intelligence? Are we
harking back to the era of rule based expert systems? The image conjured by the
publicity machines offers deep solutions with human-level AI and preposterous
claims about capturing the ""brain in a box"" by 2020. Even emulating insects may
be difficult in terms of real progress. Perhaps we can try to focus on worms
(Caenorhabditis elegans) which may be better suited for what business needs to
quench its thirst for so-called intelligence in AI.
",Intelligence in Artificial Intelligence
"  This report presents a submission to the Process Discovery Contest. The
contest is dedicated to the assessment of tools and techniques that discover
business process models from event logs. The objective is to compare the
efficiency of techniques to discover process models that provide a proper
balance between ""overfitting"" and ""underfitting"". In the context of the Process
Discovery Contest, process discovery is turned into a classification task with
a training set and a test set; where a process model needs to decide whether
traces are fitting or not. In this report, we first show how we use two
discovery techniques, namely: Inductive Miner and Decomposition, to discover
process models from the training set using ProM tool. Second, we show how we
use replay results to 1) check the rediscoverability of models, and to 2)
classify unseen traces (in test logs) as fitting or not. Then, we discuss the
classification results of validation logs, the complexity of discovered models,
and their impact on the selection of models for submission. The report ends
with the pictures of the submitted process models.
",Process Discovery using Inductive Miner and Decomposition
"  Ant colony system (ACS) is a promising approach which has been widely used in
problems such as Travelling Salesman Problems (TSP), Job shop scheduling
problems (JSP) and Quadratic Assignment problems (QAP). In its original
implementation, parameters of the algorithm were selected by trial and error
approach. Over the last few years, novel approaches have been proposed on
adapting the parameters of ACS in improving its performance. The aim of this
paper is to use a framework introduced for self-tuning optimization algorithms
combined with the firefly algorithm (FA) to tune the parameters of the ACS
solving symmetric TSP problems. The FA optimizes the problem specific
parameters of ACS while the parameters of the FA are tuned by the selected
framework itself. With this approach, the user neither has to work with the
parameters of ACS nor the parameters of FA. Using common symmetric TSP problems
we demonstrate that the framework fits well for the ACS. A detailed statistical
analysis further verifies the goodness of the new ACS over the existing ACS and
also of the other techniques used to tune the parameters of ACS.
","A self-tuning Firefly algorithm to tune the parameters of Ant Colony
  System (ACSFA)"
"  In this paper we present a broad overview of the last 40 years of research on
cognitive architectures. Although the number of existing architectures is
nearing several hundred, most of the existing surveys do not reflect this
growth and focus on a handful of well-established architectures. Thus, in this
survey we wanted to shift the focus towards a more inclusive and high-level
overview of the research on cognitive architectures. Our final set of 84
architectures includes 49 that are still actively developed, and borrow from a
diverse set of disciplines, spanning areas from psychoanalysis to neuroscience.
To keep the length of this paper within reasonable limits we discuss only the
core cognitive abilities, such as perception, attention mechanisms, action
selection, memory, learning and reasoning. In order to assess the breadth of
practical applications of cognitive architectures we gathered information on
over 900 practical projects implemented using the cognitive architectures in
our list. We use various visualization techniques to highlight overall trends
in the development of the field. In addition to summarizing the current
state-of-the-art in the cognitive architecture research, this survey describes
a variety of methods and ideas that have been tried and their relative success
in modeling human cognitive abilities, as well as which aspects of cognitive
behavior need more research with respect to their mechanistic counterparts and
thus can further inform how cognitive science might progress.
","A Review of 40 Years of Cognitive Architecture Research: Core Cognitive
  Abilities and Practical Applications"
"  This paper presents the Voronoi diagram-based evolutionary algorithm
(VorEAl). VorEAl partitions input space in abnormal/normal subsets using
Voronoi diagrams. Diagrams are evolved using a multi-objective bio-inspired
approach in order to conjointly optimize classification metrics while also
being able to represent areas of the data space that are not present in the
training dataset. As part of the paper VorEAl is experimentally validated and
contrasted with similar approaches.
",Anomaly Detection with the Voronoi Diagram Evolutionary Algorithm
"  Objective: In this paper, we develop a personalized real-time risk scoring
algorithm that provides timely and granular assessments for the clinical acuity
of ward patients based on their (temporal) lab tests and vital signs; the
proposed risk scoring system ensures timely intensive care unit (ICU)
admissions for clinically deteriorating patients. Methods: The risk scoring
system learns a set of latent patient subtypes from the offline electronic
health record data, and trains a mixture of Gaussian Process (GP) experts,
where each expert models the physiological data streams associated with a
specific patient subtype. Transfer learning techniques are used to learn the
relationship between a patient's latent subtype and her static admission
information (e.g. age, gender, transfer status, ICD-9 codes, etc). Results:
Experiments conducted on data from a heterogeneous cohort of 6,321 patients
admitted to Ronald Reagan UCLA medical center show that our risk score
significantly and consistently outperforms the currently deployed risk scores,
such as the Rothman index, MEWS, APACHE and SOFA scores, in terms of
timeliness, true positive rate (TPR), and positive predictive value (PPV).
Conclusion: Our results reflect the importance of adopting the concepts of
personalized medicine in critical care settings; significant accuracy and
timeliness gains can be achieved by accounting for the patients' heterogeneity.
Significance: The proposed risk scoring methodology can confer huge clinical
and social benefits on more than 200,000 critically ill inpatient who exhibit
cardiac arrests in the US every year.
","Personalized Risk Scoring for Critical Care Prognosis using Mixtures of
  Gaussian Processes"
"  Predictive models deployed in the real world may assign incorrect labels to
instances with high confidence. Such errors or unknown unknowns are rooted in
model incompleteness, and typically arise because of the mismatch between
training data and the cases encountered at test time. As the models are blind
to such errors, input from an oracle is needed to identify these failures. In
this paper, we formulate and address the problem of informed discovery of
unknown unknowns of any given predictive model where unknown unknowns occur due
to systematic biases in the training data. We propose a model-agnostic
methodology which uses feedback from an oracle to both identify unknown
unknowns and to intelligently guide the discovery. We employ a two-phase
approach which first organizes the data into multiple partitions based on the
feature similarity of instances and the confidence scores assigned by the
predictive model, and then utilizes an explore-exploit strategy for discovering
unknown unknowns across these partitions. We demonstrate the efficacy of our
framework by varying the underlying causes of unknown unknowns across various
applications. To the best of our knowledge, this paper presents the first
algorithmic approach to the problem of discovering unknown unknowns of
predictive models.
","Identifying Unknown Unknowns in the Open World: Representations and
  Policies for Guided Exploration"
"  Outlier detection in high-dimensional data is a challenging yet important
task, as it has applications in, e.g., fraud detection and quality control.
State-of-the-art density-based algorithms perform well because they 1) take the
local neighbourhoods of data points into account and 2) consider feature
subspaces. In highly complex and high-dimensional data, however, existing
methods are likely to overlook important outliers because they do not
explicitly take into account that the data is often a mixture distribution of
multiple components.
  We therefore introduce GLOSS, an algorithm that performs local subspace
outlier detection using global neighbourhoods. Experiments on synthetic data
demonstrate that GLOSS more accurately detects local outliers in mixed data
than its competitors. Moreover, experiments on real-world data show that our
approach identifies relevant outliers overlooked by existing methods,
confirming that one should keep an eye on the global perspective even when
doing local outlier detection.
",Local Subspace-Based Outlier Detection using Global Neighbourhoods
"  In this work, we are interested in structure learning for a set of spatially
distributed dynamical systems, where individual subsystems are coupled via
latent variables and observed through a filter. We represent this model as a
directed acyclic graph (DAG) that characterises the unidirectional coupling
between subsystems. Standard approaches to structure learning are not
applicable in this framework due to the hidden variables, however we can
exploit the properties of certain dynamical systems to formulate exact methods
based on state space reconstruction. We approach the problem by using
reconstruction theorems to analytically derive a tractable expression for the
KL-divergence of a candidate DAG from the observed dataset. We show this
measure can be decomposed as a function of two information-theoretic measures,
transfer entropy and stochastic interaction. We then present two mathematically
robust scoring functions based on transfer entropy and statistical independence
tests. These results support the previously held conjecture that transfer
entropy can be used to infer effective connectivity in complex networks.
",Inferring Coupling of Distributed Dynamical Systems via Transfer Entropy
"  In this book authors for the first time introduce the notion of strong
neutrosophic graphs. They are very different from the usual graphs and
neutrosophic graphs. Using these new structures special subgraph topological
spaces are defined. Further special lattice graph of subgraphs of these graphs
are defined and described. Several interesting properties using subgraphs of a
strong neutrosophic graph are obtained. Several open conjectures are proposed.
These new class of strong neutrosophic graphs will certainly find applications
in Neutrosophic Cognitive Maps (NCM), Neutrosophic Relational Maps (NRM) and
Neutrosophic Relational Equations (NRE) with appropriate modifications.
",Strong Neutrosophic Graphs and Subgraph Topological Subspaces
"  There is a significant lack of unified approaches to building generally
intelligent machines. The majority of current artificial intelligence research
operates within a very narrow field of focus, frequently without considering
the importance of the 'big picture'. In this document, we seek to describe and
unify principles that guide the basis of our development of general artificial
intelligence. These principles revolve around the idea that intelligence is a
tool for searching for general solutions to problems. We define intelligence as
the ability to acquire skills that narrow this search, diversify it and help
steer it to more promising areas. We also provide suggestions for studying,
measuring, and testing the various skills and abilities that a human-level
intelligent machine needs to acquire. The document aims to be both
implementation agnostic, and to provide an analytic, systematic, and scalable
way to generate hypotheses that we believe are needed to meet the necessary
conditions in the search for general artificial intelligence. We believe that
such a framework is an important stepping stone for bringing together
definitions, highlighting open problems, connecting researchers willing to
collaborate, and for unifying the arguably most significant search of this
century.
",A Framework for Searching for General Artificial Intelligence
"  Progressive filtering is a simple way to perform hierarchical classification,
inspired by the behavior that most humans put into practice while attempting to
categorize an item according to an underlying taxonomy. Each node of the
taxonomy being associated with a different category, one may visualize the
categorization process by looking at the item going downwards through all the
nodes that accept it as belonging to the corresponding category. This paper is
aimed at modeling the progressive filtering technique from a probabilistic
perspective, in a hierarchical text categorization setting. As a result, the
designer of a system based on progressive filtering should be facilitated in
the task of devising, training, and testing it.
",Probabilistic Modeling of Progressive Filtering
"  I suggest an approach that helps the online marketers to target their
Gamification elements to users by modifying the order of the list of tasks that
they send to users. It is more realistic and flexible as it allows the model to
learn more parameters when the online marketers collect more data. The
targeting approach is scalable and quick, and it can be used over streaming
data.
","Bayesian Non-parametric model to Target Gamification Notifications Using
  Big Data"
"  This volume contains the papers presented at Arg-LPNMR 2016: First
International Workshop on Argumentation in Logic Programming and Nonmonotonic
Reasoning held on July 8-10, 2016 in New York City, NY.
","Proceedings of the First International Workshop on Argumentation in
  Logic Programming and Non-Monotonic Reasoning (Arg-LPNMR 2016)"
"  We analyze the data complexity of ontology-mediated querying where the
ontologies are formulated in a description logic (DL) of the ALC family and
queries are conjunctive queries, positive existential queries, or acyclic
conjunctive queries. Our approach is non-uniform in the sense that we aim to
understand the complexity of each single ontology instead of for all ontologies
formulated in a certain language. While doing so, we quantify over the queries
and are interested, for example, in the question whether all queries can be
evaluated in polynomial time w.r.t. a given ontology. Our results include a
PTime/coNP-dichotomy for ontologies of depth one in the description logic
ALCFI, the same dichotomy for ALC- and ALCI-ontologies of unrestricted depth,
and the non-existence of such a dichotomy for ALCF-ontologies. For the latter
DL, we additionally show that it is undecidable whether a given ontology admits
PTime query evaluation. We also consider the connection between PTime query
evaluation and rewritability into (monadic) Datalog.
",The Data Complexity of Description Logic Ontologies
"  Formal concepts and closed itemsets proved to be of big importance for
knowledge discovery, both as a tool for concise representation of association
rules and a tool for clustering and constructing domain taxonomies and
ontologies. Exponential explosion makes it difficult to consider the whole
concept lattice arising from data, one needs to select most useful and
interesting concepts. In this paper interestingness measures of concepts are
considered and compared with respect to various aspects, such as efficiency of
computation and applicability to noisy data and performing ranking correlation.
",On interestingness measures of formal concepts
"  CP-nets and their variants constitute one of the main AI approaches for
specifying and reasoning about preferences. CI-nets, in particular, are a
CP-inspired formalism for representing ordinal preferences over sets of goods,
which are typically required to be monotonic.
  Considering also that goods often come in multi-sets rather than sets, a
natural question is whether CI-nets can be used more or less directly to encode
preferences over multi-sets. We here provide some initial ideas on how to
achieve this, in the sense that at least a restricted form of reasoning on our
framework, which we call ""confined reasoning"", can be efficiently reduced to
reasoning on CI-nets. Our framework nevertheless allows for encoding
preferences over multi-sets with unbounded multiplicities. We also show the
extent to which it can be used to represent preferences where multiplicites of
the goods are not stated explicitly (""purely qualitative preferences"") as well
as a potential use of our generalization of CI-nets as a component of a recent
system for evidence aggregation.
","Encoding monotonic multi-set preferences using CI-nets: preliminary
  report"
"  We propose a major revision of the format XCSP 2.1, called XCSP3, to build
integrated representations of combinatorial constrained problems. This new
format is able to deal with mono/multi optimization, many types of variables,
cost functions, reification, views, annotations, variable quantification,
distributed, probabilistic and qualitative reasoning. The new format is made
compact, highly readable, and rather easy to parse. Interestingly, it captures
the structure of the problem models, through the possibilities of declaring
arrays of variables, and identifying syntactic and semantic groups of
constraints. The number of constraints is kept under control by introducing a
limited set of basic constraint forms, and producing almost automatically some
of their variations through lifting, restriction, sliding, logical combination
and relaxation mechanisms. As a result, XCSP3 encompasses practically all
constraints that can be found in major constraint solvers developed by the CP
community. A website, which is developed conjointly with the format, contains
many models and series of instances. The user can make sophisticated queries
for selecting instances from very precise criteria. The objective of XCSP3 is
to ease the effort required to test and compare different algorithms by
providing a common test-bed of combinatorial constrained instances.
","XCSP3: An Integrated Format for Benchmarking Combinatorial Constrained
  Problems"
"  Causal discovery studies the problem of mining causal relationships between
variables from data, which is of primary interest in science. During the past
decades, significant amount of progresses have been made toward this
fundamental data mining paradigm. Recent years, as the availability of abundant
large-sized and complex observational data, the constrain-based approaches have
gradually attracted a lot of interest and have been widely applied to many
diverse real-world problems due to the fast running speed and easy generalizing
to the problem of causal insufficiency. In this paper, we aim to review the
constraint-based causal discovery algorithms. Firstly, we discuss the learning
paradigm of the constraint-based approaches. Secondly and primarily, the
state-of-the-art constraint-based casual inference algorithms are surveyed with
the detailed analysis. Thirdly, several related open-source software packages
and benchmark data repositories are briefly summarized. As a conclusion, some
open problems in constraint-based causal discovery are outlined for future
research.
",A Review on Algorithms for Constraint-based Causal Discovery
"  In this paper, we propose commonsense knowledge enhanced embeddings (KEE) for
solving the Pronoun Disambiguation Problems (PDP). The PDP task we investigate
in this paper is a complex coreference resolution task which requires the
utilization of commonsense knowledge. This task is a standard first round test
set in the 2016 Winograd Schema Challenge. In this task, traditional linguistic
features that are useful for coreference resolution, e.g. context and gender
information, are no longer effective anymore. Therefore, the KEE models are
proposed to provide a general framework to make use of commonsense knowledge
for solving the PDP problems. Since the PDP task doesn't have training data,
the KEE models would be used during the unsupervised feature extraction
process. To evaluate the effectiveness of the KEE models, we propose to
incorporate various commonsense knowledge bases, including ConceptNet, WordNet,
and CauseCom, into the KEE training process. We achieved the best performance
by applying the proposed methods to the 2016 Winograd Schema Challenge. In
addition, experiments conducted on the standard PDP task indicate that, the
proposed KEE models could solve the PDP problems by achieving 66.7% accuracy,
which is a new state-of-the-art performance.
","Commonsense Knowledge Enhanced Embeddings for Solving Pronoun
  Disambiguation Problems in Winograd Schema Challenge"
"  In online question-and-answer (QA) websites like Quora, one central issue is
to find (invite) users who are able to provide answers to a given question and
at the same time would be unlikely to say ""no"" to the invitation. The challenge
is how to trade off the matching degree between users' expertise and the
question topic, and the likelihood of positive response from the invited users.
In this paper, we formally formulate the problem and develop a weakly
supervised factor graph (WeakFG) model to address the problem. The model
explicitly captures expertise matching degree between questions and users. To
model the likelihood that an invited user is willing to answer a specific
question, we incorporate a set of correlations based on social identity theory
into the WeakFG model. We use two different genres of datasets: QA-Expert and
Paper-Reviewer, to validate the proposed model. Our experimental results show
that the proposed model can significantly outperform (+1.5-10.7% by MAP) the
state-of-the-art algorithms for matching users (experts) with community
questions. We have also developed an online system to further demonstrate the
advantages of the proposed method.
",Weakly Learning to Match Experts in Online Community
"  The CDCL algorithm is the leading solution adopted by state-of-the-art
solvers for SAT, SMT, ASP, and others. Experiments show that the performance of
CDCL solvers can be significantly boosted by embedding domain-specific
heuristics, especially on large real-world problems. However, a proper
integration of such criteria in off-the-shelf CDCL implementations is not
obvious. In this paper, we distill the key ingredients that drive the search of
CDCL solvers, and propose a general framework for designing and implementing
new heuristics. We implemented our strategy in an ASP solver, and we
experimented on two industrial domains. On hard problem instances,
state-of-the-art implementations fail to find any solution in acceptable time,
whereas our implementation is very successful and finds all solutions.
",Driving CDCL Search
"  Current state of the art in the field of UAV activation relies solely on
human operators for the design and adaptation of the drones' flying routes.
Furthermore, this is being done today on an individual level (one vehicle per
operators), with some exceptions of a handful of new systems, that are
comprised of a small number of self-organizing swarms, manually guided by a
human operator.
  Drones-based monitoring is of great importance in variety of civilian
domains, such as road safety, homeland security, and even environmental
control. In its military aspect, efficiently detecting evading targets by a
fleet of unmanned drones has an ever increasing impact on the ability of modern
armies to engage in warfare. The latter is true both traditional symmetric
conflicts among armies as well as asymmetric ones. Be it a speeding driver, a
polluting trailer or a covert convoy, the basic challenge remains the same --
how can its detection probability be maximized using as little number of drones
as possible.
  In this work we propose a novel approach for the optimization of large scale
swarms of reconnaissance drones -- capable of producing on-demand optimal
coverage strategies for any given search scenario. Given an estimation cost of
the threat's potential damages, as well as types of monitoring drones available
and their comparative performance, our proposed method generates an
analytically provable strategy, stating the optimal number and types of drones
to be deployed, in order to cost-efficiently monitor a pre-defined region for
targets maneuvering using a given roads networks.
  We demonstrate our model using a unique dataset of the Israeli transportation
network, on which different deployment schemes for drones deployment are
evaluated.
","Optimal Dynamic Coverage Infrastructure for Large-Scale Fleets of
  Reconnaissance UAVs"
"  We introduce two novel non-parametric statistical hypothesis tests. The first
test, called the relative test of dependency, enables us to determine whether
one source variable is significantly more dependent on a first target variable
or a second. Dependence is measured via the Hilbert-Schmidt Independence
Criterion (HSIC). The second test, called the relative test of similarity, is
use to determine which of the two samples from arbitrary distributions is
significantly closer to a reference sample of interest and the relative measure
of similarity is based on the Maximum Mean Discrepancy (MMD). To construct
these tests, we have used as our test statistics the difference of HSIC
statistics and of MMD statistics, respectively. The resulting tests are
consistent and unbiased, and have favorable convergence properties. The
effectiveness of the relative dependency test is demonstrated on several
real-world problems: we identify languages groups from a multilingual parallel
corpus, and we show that tumor location is more dependent on gene expression
than chromosome imbalance. We also demonstrate the performance of the relative
test of similarity over a broad selection of model comparisons problems in deep
generative models.
",Fast Non-Parametric Tests of Relative Dependency and Similarity
"  In this paper we present an algorithm to build a road network map enriched
with traffic rules such as one-way streets and forbidden turns, based on the
interpretation of already detected and classified traffic signs. Such algorithm
helps to automatize the elaboration of maps for commercial navigation systems.
Our solution is based on simulating navigation along the road network,
determining at each point of interest the visibility of the signs and their
effect on the roads. We test our approach in a small urban network and discuss
various ways to generalize it to support more complex environments.
","Navigational Rule Derivation: An algorithm to determine the effect of
  traffic signs on road networks"
"  In this paper, we advocate the use of stratified logical theories for
representing probabilistic models. We argue that such encodings can be more
interpretable than those obtained in existing frameworks such as Markov logic
networks. Among others, this allows for the use of domain experts to improve
learned models by directly removing, adding, or modifying logical formulas.
","Stratified Knowledge Bases as Interpretable Probabilistic Models
  (Extended Abstract)"
"  Understanding why a model made a certain prediction is crucial in many data
science fields. Interpretable predictions engender appropriate trust and
provide insight into how the model may be improved. However, with large modern
datasets the best accuracy is often achieved by complex models even experts
struggle to interpret, which creates a tension between accuracy and
interpretability. Recently, several methods have been proposed for interpreting
predictions from complex models by estimating the importance of input features.
Here, we present how a model-agnostic additive representation of the importance
of input features unifies current methods. This representation is optimal, in
the sense that it is the only set of additive values that satisfies important
properties. We show how we can leverage these properties to create novel visual
explanations of model predictions. The thread of unity that this representation
weaves through the literature indicates that there are common principles to be
learned about the interpretation of model predictions that apply in many
scenarios.
",An unexpected unity among methods for interpreting model predictions
"  We consider an orienteering problem (OP) where an agent needs to visit a
series (possibly a subset) of depots, from which the maximal accumulated
profits are desired within given limited time budget. Different from most
existing works where the profits are assumed to be static, in this work we
investigate a variant that has arbitrary time-dependent profits. Specifically,
the profits to be collected change over time and they follow different (e.g.,
independent) time-varying functions. The problem is of inherent nonlinearity
and difficult to solve by existing methods. To tackle the challenge, we present
a simple and effective framework that incorporates time-variations into the
fundamental planning process. Specifically, we propose a deterministic
spatio-temporal representation where both spatial description and temporal
logic are unified into one routing topology. By employing existing basic
sorting and searching algorithms, the routing solutions can be computed in an
extremely efficient way. The proposed method is easy to implement and extensive
numerical results show that our approach is time efficient and generates
near-optimal solutions.
","A Spatio-Temporal Representation for the Orienteering Problem with
  Time-Varying Profits"
"  In digital-based information boom, the fuzzy covering rough set model is an
important mathematical tool for artificial intelligence, and how to build the
bridge between the fuzzy covering rough set theory and Pawlak's model is
becoming a hot research topic. In this paper, we first present the
$\gamma-$fuzzy covering based probabilistic and grade approximation operators
and double-quantitative approximation operators. We also study the
relationships among the three types of $\gamma-$fuzzy covering based
approximation operators. Second, we propose the $\gamma^{\ast}-$fuzzy coverings
based multi-granulation probabilistic and grade lower and upper approximation
operators and multi-granulation double-quantitative lower and upper
approximation operators. We also investigate the relationships among these
types of $\gamma-$fuzzy coverings based approximation operators. Finally, we
employ several examples to illustrate how to construct the lower and upper
approximations of fuzzy sets with the absolute and relative quantitative
information.
","Double-quantitative $\gamma^{\ast}-$fuzzy coverings approximation
  operators"
"  It is clear that one of the primary tools we can use to mitigate the
potential risk from a misbehaving AI system is the ability to turn the system
off. As the capabilities of AI systems improve, it is important to ensure that
such systems do not adopt subgoals that prevent a human from switching them
off. This is a challenge because many formulations of rational agents create
strong incentives for self-preservation. This is not caused by a built-in
instinct, but because a rational agent will maximize expected utility and
cannot achieve whatever objective it has been given if it is dead. Our goal is
to study the incentives an agent has to allow itself to be switched off. We
analyze a simple game between a human H and a robot R, where H can press R's
off switch but R can disable the off switch. A traditional agent takes its
reward function for granted: we show that such agents have an incentive to
disable the off switch, except in the special case where H is perfectly
rational. Our key insight is that for R to want to preserve its off switch, it
needs to be uncertain about the utility associated with the outcome, and to
treat H's actions as important observations about that utility. (R also has no
incentive to switch itself off in this setting.) We conclude that giving
machines an appropriate level of uncertainty about their objectives leads to
safer designs, and we argue that this setting is a useful generalization of the
classical AI paradigm of rational agents.
",The Off-Switch Game
"  Decision support systems help decision makers make better decisions in the
face of complex decision problems (e.g. investment or policy decisions).
Fisheries and Aquaculture is a domain where decision makers face such decisions
since they involve factors from many different scientific fields. No systematic
overview of literature describing decision support systems and their
application in fisheries and aquaculture has been conducted. This paper
summarizes scientific literature that describes decision support systems
applied to the domain of Fisheries and Aquaculture. We use an established
systematic mapping survey method to conduct our literature mapping. Our
research questions are: What decision support systems for fisheries and
aquaculture exists? What are the most investigated fishery and aquaculture
decision support systems topics and how have these changed over time? Do any
current DSS for fisheries provide real- time analytics? Do DSSes in Fisheries
and Aquaculture build their models using machine learning done on captured and
grounded data? The paper then detail how we employ the systematic mapping
method in answering these questions. This results in 27 papers being identified
as relevant and gives an exposition on the primary methods concluded in the
study for designing a decision support system. We provide an analysis of the
research done in the studies collected. We discovered that most literature does
not consider multiple aspects for multiple stakeholders in their work. In
addition we observed that little or no work has been done with real-time
analysis in these decision support systems.
","Decision Support Systems in Fisheries and Aquaculture: A systematic
  review"
"  This paper explores a novel way for analyzing the tournament structures to
find a best suitable one for the tournament under consideration. It concerns
about three aspects such as tournament conducting cost, competitiveness
development and ranking precision. It then proposes a new method using progress
tree to detect potential throwaway matches. The analysis performed using the
proposed method reveals the strengths and weaknesses of tournament structures.
As a conclusion, single elimination is best if we want to qualify one winner
only, all matches conducted are exciting in term of competitiveness. Double
elimination with proper seeding system is a better choice if we want to qualify
more winners. A reasonable number of extra matches need to be conducted in
exchange of being able to qualify top four winners. Round-robin gives reliable
ranking precision for all participants. However, its conduction cost is very
high, and it fails to maintain competitiveness development.
",An Analysis of Tournament Structure
"  Neutrosophic theory and applications have been expanding in all directions at
an astonishing rate especially after the introduction the journal entitled
Neutrosophic Sets and Systems. New theories, techniques, algorithms have been
rapidly developed. One of the most striking trends in the neutrosophic theory
is the hybridization of neutrosophic set with other potential sets such as
rough set, bipolar set, soft set, hesitant fuzzy set, etc. The different hybrid
structure such as rough neutrosophic set, single valued neutrosophic rough set,
bipolar neutrosophic set, single valued neutrosophic hesitant fuzzy set, etc.
are proposed in the literature in a short period of time. Neutrosophic set has
been a very important tool in all various areas of data mining, decision
making, e-learning, engineering, medicine, social science, and some more. The
book New Trends in Neutrosophic Theories and Applications focuses on theories,
methods, algorithms for decision making and also applications involving
neutrosophic information. Some topics deal with data mining, decision making,
e-learning, graph theory, medical diagnosis, probability theory, topology, and
some more.
",New Trends in Neutrosophic Theory and Applications
"  This paper discusses the semantics of weighted argumentation graphs that are
biplor, i.e. contain both attacks and support graphs. The work builds on
previous work by Amgoud, Ben-Naim et. al., which presents and compares several
semantics for argumentation graphs that contain only supports or only attacks
relationships, respectively.
",Bipolar Weighted Argumentation Graphs
"  Constraint Programming (CP) users need significant expertise in order to
model their problems appropriately, notably to select propagators and search
strategies. This puts the brakes on a broader uptake of CP. In this paper, we
introduce MICE, a complete Java CP modeler that can use any Mixed Integer
Linear Programming (MILP) solver as a solution technique. Our aim is to provide
an alternative tool for democratizing the ""CP-style"" modeling thanks to its
simplicity of use, with reasonable solving capabilities. Our contributions
include new decompositions of (reified) constraints and constraints on
numerical variables.
","""Model and Run"" Constraint Networks with a MILP Engine"
"  Reinforcement learning (RL) problems are often phrased in terms of Markov
decision processes (MDPs). In this thesis we go beyond MDPs and consider RL in
environments that are non-Markovian, non-ergodic and only partially observable.
Our focus is not on practical algorithms, but rather on the fundamental
underlying problems: How do we balance exploration and exploitation? How do we
explore optimally? When is an agent optimal? We follow the nonparametric
realizable paradigm.
  We establish negative results on Bayesian RL agents, in particular AIXI. We
show that unlucky or adversarial choices of the prior cause the agent to
misbehave drastically. Therefore Legg-Hutter intelligence and balanced Pareto
optimality, which depend crucially on the choice of the prior, are entirely
subjective. Moreover, in the class of all computable environments every policy
is Pareto optimal. This undermines all existing optimality properties for AIXI.
However, there are Bayesian approaches to general RL that satisfy objective
optimality guarantees: We prove that Thompson sampling is asymptotically
optimal in stochastic environments in the sense that its value converges to the
value of the optimal policy. We connect asymptotic optimality to regret given a
recoverability assumption on the environment that allows the agent to recover
from mistakes. Hence Thompson sampling achieves sublinear regret in these
environments.
  Our results culminate in a formal solution to the grain of truth problem: A
Bayesian agent acting in a multi-agent environment learns to predict the other
agents' policies if its prior assigns positive probability to them (the prior
contains a grain of truth). We construct a large but limit computable class
containing a grain of truth and show that agents based on Thompson sampling
over this class converge to play Nash equilibria in arbitrary unknown
computable multi-agent environments.
",Nonparametric General Reinforcement Learning
"  Bayesian inference as applied in a legal setting is about belief transfer and
involves a plurality of agents and communication protocols.
  A forensic expert (FE) may communicate to a trier of fact (TOF) first its
value of a certain likelihood ratio with respect to FE's belief state as
represented by a probability function on FE's proposition space. Subsequently
FE communicates its recently acquired confirmation that a certain evidence
proposition is true. Then TOF performs likelihood ratio transfer mediated
reasoning thereby revising their own belief state.
  The logical principles involved in likelihood transfer mediated reasoning are
discussed in a setting where probabilistic arithmetic is done within a meadow,
and with Adams conditioning placed in a central role.
",Adams Conditioning and Likelihood Ratio Transfer Mediated Inference
"  Sequence modeling with neural networks has lead to powerful models of
symbolic music data. We address the problem of exploiting these models to reach
creative musical goals, by combining with human input. To this end we
generalise previous work, which sampled Markovian sequence models under the
constraint that the sequence belong to the language of a given finite state
machine provided by the human. We consider more expressive non-Markov models,
thereby requiring approximate sampling which we provide in the form of an
efficient sequential Monte Carlo method. In addition we provide and compare
with a beam search strategy for conditional probability maximisation.
  Our algorithms are capable of convincingly re-harmonising famous musical
works. To demonstrate this we provide visualisations, quantitative experiments,
a human listening test and audio examples. We find both the sampling and
optimisation procedures to be effective, yet complementary in character. For
the case of highly permissive constraint sets, we find that sampling is to be
preferred due to the overly regular nature of the optimisation based results.
The generality of our algorithms permits countless other creative applications.
",Computer Assisted Composition with Recurrent Neural Networks
"  In the Markov decision process model, policies are usually evaluated by
expected cumulative rewards. As this decision criterion is not always suitable,
we propose in this paper an algorithm for computing a policy optimal for the
quantile criterion. Both finite and infinite horizons are considered. Finally
we experimentally evaluate our approach on random MDPs and on a data center
control problem.
",Optimizing Quantiles in Preference-based Markov Decision Processes
"  Stochastic network design is a general framework for optimizing network
connectivity. It has several applications in computational sustainability
including spatial conservation planning, pre-disaster network preparation, and
river network optimization. A common assumption in previous work has been made
that network parameters (e.g., probability of species colonization) are
precisely known, which is unrealistic in real- world settings. We therefore
address the robust river network design problem where the goal is to optimize
river connectivity for fish movement by removing barriers. We assume that fish
passability probabilities are known only imprecisely, but are within some
interval bounds. We then develop a planning approach that computes the policies
with either high robust ratio or low regret. Empirically, our approach scales
well to large river networks. We also provide insights into the solutions
generated by our robust approach, which has significantly higher robust ratio
than the baseline solution with mean parameter estimates.
",Robust Optimization for Tree-Structured Stochastic Network Design
"  Time-efficient link discovery is of central importance to implement the
vision of the Semantic Web. Some of the most rapid Link Discovery approaches
rely internally on planning to execute link specifications. In newer works,
linear models have been used to estimate the runtime the fastest planners.
However, no other category of models has been studied for this purpose so far.
In this paper, we study non-linear runtime estimation functions for runtime
estimation. In particular, we study exponential and mixed models for the
estimation of the runtimes of planners. To this end, we evaluate three
different models for runtime on six datasets using 400 link specifications. We
show that exponential and mixed models achieve better fits when trained but are
only to be preferred in some cases. Our evaluation also shows that the use of
better runtime approximation models has a positive impact on the overall
execution of link specifications.
",An Evaluation of Models for Runtime Approximation in Link Discovery
"  The Center of Gravity (COG) method is one of the most popular defuzzification
techniques of fuzzy mathematics. In earlier works the COG technique was
properly adapted to be used as an assessment model (RFAM)and several variations
of it (GRFAM, TFAM and TpFAM)were also constructed for the same purpose. In
this paper the outcomes of all these models are compared to the corresponding
outcomes of a traditional assessment method of the bi-valued logic, the Grade
Point Average (GPA) Index. Examples are also presented illustrating our
results.
","Comparison of the COG Defuzzification Technique and Its Variations to
  the GPA Index"
"  We show that the Bellman operator underlying the options framework leads to a
matrix splitting, an approach traditionally used to speed up convergence of
iterative solvers for large linear systems of equations. Based on standard
comparison theorems for matrix splittings, we then show how the asymptotic rate
of convergence varies as a function of the inherent timescales of the options.
This new perspective highlights a trade-off between asymptotic performance and
the cost of computation associated with building a good set of options.
",A Matrix Splitting Perspective on Planning with Options
"  We examine the complexity of inference in Bayesian networks specified by
logical languages. We consider representations that range from fragments of
propositional logic to function-free first-order logic with equality; in doing
so we cover a variety of plate models and of probabilistic relational models.
We study the complexity of inferences when network, query and domain are the
input (the inferential and the combined complexity), when the network is fixed
and query and domain are the input (the query/data complexity), and when the
network and query are fixed and the domain is the input (the domain
complexity). We draw connections with probabilistic databases and liftability
results, and obtain complexity classes that range from polynomial to
exponential levels.
","The Complexity of Bayesian Networks Specified by Propositional and
  Relational Languages"
"  If you are an artificial intelligence researcher, you should look to video
games as ideal testbeds for the work you do. If you are a video game developer,
you should look to AI for the technology that makes completely new types of
games possible. This chapter lays out the case for both of these propositions.
It asks the question ""what can video games do for AI"", and discusses how in
particular general video game playing is the ideal testbed for artificial
general intelligence research. It then asks the question ""what can AI do for
video games"", and lays out a vision for what video games might look like if we
had significantly more advanced AI at our disposal. The chapter is based on my
keynote at IJCCI 2015, and is written in an attempt to be accessible to a broad
audience.
","AI Researchers, Video Games Are Your Friends!"
"  In the classic Vehicle Routing Problem (VRP) a fleet of of vehicles has to
visit a set of customers while minimising the operations' costs. We study a
rich variant of the VRP featuring split deliveries, an heterogeneous fleet, and
vehicle-commodity incompatibility constraints. Our goal is twofold: define the
cheapest routing and the most adequate fleet.
  To do so, we split the problem into two interdependent components: a fleet
design component and a routing component. First, we define two Mixed Integer
Programming (MIP) formulations for each component. Then we discuss several
improvements in the form of valid cuts and symmetry breaking constraints.
  The main contribution of this paper is a comparison of the four resulting
models for this Rich VRP. We highlight their strengths and weaknesses with
extensive experiments.
  Finally, we explore a lightweight integration with Constraint Programming
(CP). We use a fast CP model which gives good solutions and use the solution to
warm-start our models.
",Fleet Size and Mix Split-Delivery Vehicle Routing
"  We examine non-dual relational extensions of rough set approximations and
find an extension which satisfies surprisingly many of the usual rough set
properties. We then use this definition to give an explanation for an
observation made by Samanta and Chakraborty in their recent paper [P. Samanta
and M.K. Chakraborty. Interface of rough set systems and modal logics: A
survey. Transactions on Rough Sets XIX, pages 114-137, 2015].
",On a Well-behaved Relational Generalisation of Rough Set Approximations
"  When faced with complex choices, users refine their own preference criteria
as they explore the catalogue of options. In this paper we propose an approach
to preference elicitation suited for this scenario. We extend Coactive
Learning, which iteratively collects manipulative feedback, to optionally query
example critiques. User critiques are integrated into the learning model by
dynamically extending the feature space. Our formulation natively supports
constructive learning tasks, where the option catalogue is generated
on-the-fly. We present an upper bound on the average regret suffered by the
learner. Our empirical analysis highlights the promise of our approach.
",Coactive Critiquing: Elicitation of Preferences and Features
"  In reinforcement learning, the reward function on current state and action is
widely used. When the objective is about the expectation of the (discounted)
total reward only, it works perfectly. However, if the objective involves the
total reward distribution, the result will be wrong. This paper studies
Value-at-Risk (VaR) problems in short- and long-horizon Markov decision
processes (MDPs) with two reward functions, which share the same expectations.
Firstly we show that with VaR objective, when the real reward function is
transition-based (with respect to action and both current and next states), the
simplified (state-based, with respect to action and current state only) reward
function will change the VaR. Secondly, for long-horizon MDPs, we estimate the
VaR function with the aid of spectral theory and the central limit theorem.
Thirdly, since the estimation method is for a Markov reward process with the
reward function on current state only, we present a transformation algorithm
for the Markov reward process with the reward function on current and next
states, in order to estimate the VaR function with an intact total reward
distribution.
","Transition-based versus State-based Reward Functions for MDPs with
  Value-at-Risk"
"  Knowledge Graphs (KG) constitute a flexible representation of complex
relationships between entities particularly useful for biomedical data. These
KG, however, are very sparse with many missing edges (facts) and the
visualisation of the mesh of interactions nontrivial. Here we apply a
compositional model to embed nodes and relationships into a vectorised semantic
space to perform graph completion. A visualisation tool based on Convolutional
Neural Networks and Self-Organised Maps (SOM) is proposed to extract high-level
insights from the KG. We apply this technique to a subset of CTD, containing
interactions of compounds with human genes / proteins and show that the
performance is comparable to the one obtained by structural models.
",Knowledge Representation in Graphs using Convolutional Neural Networks
"  Compositional models were introduce by Jirousek and Shenoy in the general
framework of valuation-based systems. They based their theory on an axiomatic
system of valuations involving not only the operations of combination and
marginalisation, but also of removal. They claimed that this systems covers
besides the classical case of discrete probability distributions, also the
cases of Gaussian densities and belief functions, and many other systems.
  Whereas their results on the compositional operator are correct, the
axiomatic basis is not sufficient to cover the examples claimed above. We
propose here a different axiomatic system of valuation algebras, which permits
a rigorous mathematical theory of compositional operators in valuation-based
systems and covers all the examples mentioned above. It extends the classical
theory of inverses in semigroup theory and places thereby the present theory
into its proper mathematical frame. Also this theory sheds light on the
different structures of valuation-based systems, like regular algebras
(represented by probability potentials), canncellative algebras (Gaussian
potentials) and general separative algebras (density functions).
","Inverses, Conditionals and Compositional Operators in Separative
  Valuation Algebra"
"  Hierarchical architectures are critical to the scalability of reinforcement
learning methods. Current hierarchical frameworks execute actions serially,
with macro-actions comprising sequences of primitive actions. We propose a
novel alternative to these control hierarchies based on concurrent execution of
many actions in parallel. Our scheme uses the concurrent compositionality
provided by the linearly solvable Markov decision process (LMDP) framework,
which naturally enables a learning agent to draw on several macro-actions
simultaneously to solve new tasks. We introduce the Multitask LMDP module,
which maintains a parallel distributed representation of tasks and may be
stacked to form deep hierarchies abstracted in space and time.
","Hierarchy through Composition with Linearly Solvable Markov Decision
  Processes"
"  It has been widely recognized that uncertainty is an inevitable aspect of
diagnosis and treatment of medical disorders. Such uncertainties hence, need to
be considered in computerized medical models. The existing medical modeling
techniques however, have mainly focused on capturing uncertainty associated
with diagnosis of medical disorders while ignoring uncertainty of treatments.
To tackle this issue, we have proposed using a fuzzy-based modeling and
description technique for capturing uncertainties in treatment plans. We have
further contributed a formal framework which allows for goal-oriented modeling
and analysis of medical treatments.
","GOTM: a Goal-Oriented Framework for Capturing Uncertainty of Medical
  Treatments"
"  Managing patients with multimorbidity often results in polypharmacy: the
prescription of multiple drugs. However, the long-term effects of specific
combinations of drugs and diseases are typically unknown. In particular, drugs
prescribed for one condition may result in adverse effects for the other. To
investigate which types of drugs may affect the further progression of
multimorbidity, we query models of diseases and prescriptions that are learned
from primary care data. State-of-the-art tractable Bayesian network
representations, on which such complex queries can be computed efficiently, are
employed for these large medical networks. Our results confirm that
prescriptions may lead to unintended negative consequences in further
development of multimorbidity in cardiovascular diseases. Moreover, a drug
treatment for one disease group may affect diseases of another group.
","Measuring Adverse Drug Effects on Multimorbity using Tractable Bayesian
  Networks"
"  Modeling an ontology is a hard and time-consuming task. Although
methodologies are useful for ontologists to create good ontologies, they do not
help with the task of evaluating the quality of the ontology to be reused. For
these reasons, it is imperative to evaluate the quality of the ontology after
constructing it or before reusing it. Few studies usually present only a set of
criteria and questions, but no guidelines to evaluate the ontology. The effort
to evaluate an ontology is very high as there is a huge dependence on the
evaluator's expertise to understand the criteria and questions in depth.
Moreover, the evaluation is still very subjective. This study presents a novel
methodology for ontology evaluation, taking into account three fundamental
principles: i) it is based on the Goal, Question, Metric approach for empirical
evaluation; ii) the goals of the methodologies are based on the roles of
knowledge representations combined with specific evaluation criteria; iii) each
ontology is evaluated according to the type of ontology. The methodology was
empirically evaluated using different ontologists and ontologies of the same
domain. The main contributions of this study are: i) defining a step-by-step
approach to evaluate the quality of an ontology; ii) proposing an evaluation
based on the roles of knowledge representations; iii) the explicit difference
of the evaluation according to the type of the ontology iii) a questionnaire to
evaluate the ontologies; iv) a statistical model that automatically calculates
the quality of the ontologies.
",FOCA: A Methodology for Ontology Evaluation
"  DeepMind Lab is a first-person 3D game platform designed for research and
development of general artificial intelligence and machine learning systems.
DeepMind Lab can be used to study how autonomous artificial agents may learn
complex tasks in large, partially observed, and visually diverse worlds.
DeepMind Lab has a simple and flexible API enabling creative task-designs and
novel AI-designs to be explored and quickly iterated upon. It is powered by a
fast and widely recognised game engine, and tailored for effective use by the
research community.
",DeepMind Lab
"  Assumption-Based Argumentation (ABA) is an argumentation framework that has
been proposed in the late 20th century. Since then, there was still no solver
implemented in a programming language which is easy to setup and no solver have
been interfaced to the web, which impedes the interests of the public. This
project aims to implement an ABA solver in a modern programming language that
performs reasonably well and interface it to the web for easier access by the
public. This project has demonstrated the novelty of development of an ABA
solver, that computes conflict-free, stable, admissible, grounded, ideal, and
complete semantics, in Python programming language which can be used via an
easy-to-use web interface for visualization of the argument and dispute trees.
Experiments were conducted to determine the project's best configurations and
to compare this project with proxdd, a state-of-the-art ABA solver, which has
no web interface and computes less number of semantics. From the results of the
experiments, this project's best configuration is achieved by utilizing
""pickle"" technique and tree caching technique. Using this project's best
configuration, this project achieved a lower average runtime compared to
proxdd. On other aspect, this project encountered more cases with exceptions
compared to proxdd, which might be caused by this project computing more
semantics and hence requires more resources to do so. Hence, it can be said
that this project run comparably well to the state-of-the-art ABA solver
proxdd. Future works of this project include computational complexity analysis
and efficiency analysis of algorithms implemented, implementation of more
semantics in argumentation framework, and usability testing of the web
interface.
",Web-based Argumentation
"  In many model-based diagnosis applications it is impossible to provide such a
set of observations and/or measurements that allow to identify the real cause
of a fault. Therefore, diagnosis systems often return many possible candidates,
leaving the burden of selecting the correct diagnosis to a user. Sequential
diagnosis techniques solve this problem by automatically generating a sequence
of queries to some oracle. The answers to these queries provide additional
information necessary to gradually restrict the search space by removing
diagnosis candidates inconsistent with the answers.
  During query computation, existing sequential diagnosis methods often require
the generation of many unnecessary query candidates and strongly rely on
expensive logical reasoners. We tackle this issue by devising efficient
heuristic query search methods. The proposed methods enable for the first time
a completely reasoner-free query generation while at the same time guaranteeing
optimality conditions, e.g. minimal cardinality or best understandability, of
the returned query that existing methods cannot realize. Hence, the performance
of this approach is independent of the (complexity of the) diagnosed system.
Experiments conducted using real-world problems show that the new approach is
highly scalable and outperforms existing methods by orders of magnitude.
",Scalable Computation of Optimized Queries for Sequential Diagnosis
"  We investigate a human-machine collaborative drawing environment in which an
autonomous agent sketches images while optionally allowing a user to directly
influence the agent's trajectory. We combine Monte Carlo Tree Search with image
classifiers and test both shallow models (e.g. multinomial logistic regression)
and deep Convolutional Neural Networks (e.g. LeNet, Inception v3). We found
that using the shallow model, the agent produces a limited variety of images,
which are noticably recogonisable by humans. However, using the deeper models,
the agent produces a more diverse range of images, and while the agent remains
very confident (99.99%) in having achieved its objective, to humans they mostly
resemble unrecognisable 'random' noise. We relate this to recent research which
also discovered that 'deep neural networks are easily fooled' \cite{Nguyen2015}
and we discuss possible solutions and future directions for the research.
","Collaborative creativity with Monte-Carlo Tree Search and Convolutional
  Neural Networks"
"  Ontohub is a repository engine for managing distributed heterogeneous
ontologies. The distributed nature enables communities to share and exchange
their contributions easily. The heterogeneous nature makes it possible to
integrate ontologies written in various ontology languages. Ontohub supports a
wide range of formal logical and ontology languages, as well as various
structuring and modularity constructs and inter-theory (concept) mappings,
building on the OMG-standardized DOL language. Ontohub repositories are
organised as Git repositories, thus inheriting all features of this popular
version control system. Moreover, Ontohub is the first repository engine
meeting a substantial amount of the requirements formulated in the context of
the Open Ontology Repository (OOR) initiative, including an API for federation
as well as support for logical inference and axiom selection.
",Ontohub: A semantic repository for heterogeneous ontologies
"  How to manage conflict is still an open issue in Dempster-Shafer evidence
theory. The correlation coefficient can be used to measure the similarity of
evidence in Dempster-Shafer evidence theory. However, existing correlation
coefficients of belief functions have some shortcomings. In this paper, a new
correlation coefficient is proposed with many desirable properties. One of its
applications is to measure the conflict degree among belief functions. Some
numerical examples and comparisons demonstrate the effectiveness of the
correlation coefficient.
",A correlation coefficient of belief functions
"  We investigate solving discrete optimisation problems using the estimation of
distribution (EDA) approach via a novel combination of deep belief
networks(DBN) and inductive logic programming (ILP).While DBNs are used to
learn the structure of successively better feasible solutions,ILP enables the
incorporation of domain-based background knowledge related to the goodness of
solutions.Recent work showed that ILP could be an effective way to use domain
knowledge in an EDA scenario.However,in a purely ILP-based EDA,sampling
successive populations is either inefficient or not straightforward.In our
Neuro-symbolic EDA,an ILP engine is used to construct a model for good
solutions using domain-based background knowledge.These rules are introduced as
Boolean features in the last hidden layer of DBNs used for EDA-based
optimization.This incorporation of logical ILP features requires some changes
while training and sampling from DBNs: (a)our DBNs need to be trained with data
for units at the input layer as well as some units in an otherwise hidden
layer, and (b)we would like the samples generated to be drawn from instances
entailed by the logical model.We demonstrate the viability of our approach on
instances of two optimisation problems: predicting optimal depth-of-win for the
KRK endgame,and jobshop scheduling.Our results are promising: (i)On each
iteration of distribution estimation,samples obtained with an ILP-assisted DBN
have a substantially greater proportion of good solutions than samples
generated using a DBN without ILP features, and (ii)On termination of
distribution estimation,samples obtained using an ILP-assisted DBN contain more
near-optimal samples than samples from a DBN without ILP features.These results
suggest that the use of ILP-constructed theories could be useful for
incorporating complex domain-knowledge into deep models for estimation of
distribution based procedures.
",Neuro-symbolic EDA-based Optimisation using ILP-enhanced DBNs
"  Evaluating agent performance when outcomes are stochastic and agents use
randomized strategies can be challenging when there is limited data available.
The variance of sampled outcomes may make the simple approach of Monte Carlo
sampling inadequate. This is the case for agents playing heads-up no-limit
Texas hold'em poker, where man-machine competitions have involved multiple days
of consistent play and still not resulted in statistically significant
conclusions even when the winner's margin is substantial. In this paper, we
introduce AIVAT, a low variance, provably unbiased value assessment tool that
uses an arbitrary heuristic estimate of state value, as well as the explicit
strategy of a subset of the agents. Unlike existing techniques which reduce the
variance from chance events, or only consider game ending actions, AIVAT
reduces the variance both from choices by nature and by players with a known
strategy. The resulting estimator in no-limit poker can reduce the number of
hands needed to draw statistical conclusions by more than a factor of 10.
","AIVAT: A New Variance Reduction Technique for Agent Evaluation in
  Imperfect Information Games"
"  This paper summarises how the ""SP theory of intelligence"" and its realisation
in the ""SP computer model"" simplifies and integrates concepts across artificial
intelligence and related areas, and thus provides a promising foundation for
the development of a general, human-level thinking machine, in accordance with
the main goal of research in artificial general intelligence.
  The key to this simplification and integration is the powerful concept of
""multiple alignment"", borrowed and adapted from bioinformatics. This concept
has the potential to be the ""double helix"" of intelligence, with as much
significance for human-level intelligence as has DNA for biological sciences.
  Strengths of the SP system include: versatility in the representation of
diverse kinds of knowledge; versatility in aspects of intelligence (including:
strengths in unsupervised learning; the processing of natural language; pattern
recognition at multiple levels of abstraction that is robust in the face of
errors in data; several kinds of reasoning (including: one-step `deductive'
reasoning; chains of reasoning; abductive reasoning; reasoning with
probabilistic networks and trees; reasoning with 'rules'; nonmonotonic
reasoning and reasoning with default values; Bayesian reasoning with
'explaining away'; and more); planning; problem solving; and more); seamless
integration of diverse kinds of knowledge and diverse aspects of intelligence
in any combination; and potential for application in several areas (including:
helping to solve nine problems with big data; helping to develop human-level
intelligence in autonomous robots; serving as a database with intelligence and
with versatility in the representation and integration of several forms of
knowledge; serving as a vehicle for medical knowledge and as an aid to medical
diagnosis; and several more).
","The SP Theory of Intelligence as a Foundation for the Development of a
  General, Human-Level Thinking Machine"
"  Optimization - minimization or maximization - in the lattice of subsets is a
frequent operation in Artificial Intelligence tasks. Examples are
subset-minimal model-based diagnosis, nonmonotonic reasoning by means of
circumscription, or preferred extensions in abstract argumentation. Finding the
optimum among many admissible solutions is often harder than finding admissible
solutions with respect to both computational complexity and methodology. This
paper addresses the former issue by means of an effective method for finding
subset-optimal solutions. It is based on the relationship between
cardinality-optimal and subset-optimal solutions, and the fact that many
logic-based declarative programming systems provide constructs for finding
cardinality-optimal solutions, for example maximum satisfiability (MaxSAT) or
weak constraints in Answer Set Programming (ASP). Clearly each
cardinality-optimal solution is also a subset-optimal one, and if the language
also allows for the addition of particular restricting constructs (both MaxSAT
and ASP do) then all subset-optimal solutions can be found by an iterative
computation of cardinality-optimal solutions. As a showcase, the computation of
preferred extensions of abstract argumentation frameworks using the proposed
method is studied.
","Solving Set Optimization Problems by Cardinality Optimization via Weak
  Constraints with an Application to Argumentation"
"  We propose to apply Simplicity Theory (ST) to model interest in creative
situations. ST has been designed to describe and predict interest in
communication. Here we use ST to derive a decision rule that we apply to a
simplified version of a creative game, the Poietic Generator. The decision rule
produces what can be regarded as an elementary form of creativity. This study
is meant as a proof of principle. It suggests that some creative actions may be
motivated by the search for unexpected simplicity.
","Role of Simplicity in Creative Behaviour: The Case of the Poietic
  Generator"
"  We formulate an integer program to solve a highly constrained academic
timetabling problem at the United States Merchant Marine Academy. The IP
instance that results from our real case study has approximately both 170,000
rows and columns and solves to optimality in 4--24 hours using a commercial
solver on a portable computer (near optimal feasible solutions were often found
in 4--12 hours). Our model is applicable to both high schools and small
colleges who wish to deviate from group scheduling. We also solve a necessary
preprocessing student subgrouping problem, which breaks up big groups of
students into small groups so they can optimally fit into small capacity
classes.
","Automated timetabling for small colleges and high schools using huge
  integer programs"
"  In this work we present an algorithm for composing monophonic melodies
similar in style to those of a given, phrase annotated, sample of melodies. For
implementation, a hybrid approach incorporating parametric Markov models of
higher order and a contour concept of phrases is used. This work is based on
the master thesis of Thayabaran Kathiresan (2015). An online listening test
conducted shows that enhancing a pure Markov model with musically relevant
context, like count and planed melody contour, improves the result
significantly.
","A hybrid approach to supervised machine learning for algorithmic melody
  composition"
"  This technical report describes the usage, syntax, semantics and core
algorithms of the probabilistic inductive logic programming framework PrASP.
PrASP is a research software which integrates non-monotonic reasoning based on
Answer Set Programming (ASP), probabilistic inference and parameter learning.
In contrast to traditional approaches to Probabilistic (Inductive) Logic
Programming, our framework imposes only little restrictions on probabilistic
logic programs. In particular, PrASP allows for ASP as well as First-Order
Logic syntax, and for the annotation of formulas with point probabilities as
well as interval probabilities. A range of widely configurable inference
algorithms can be combined in a pipeline-like fashion, in order to cover a
variety of use cases.
",PrASP Report
"  In this paper we introduce a fuzzy constraint linear discriminant analysis
(FC-LDA). The FC-LDA tries to minimize misclassification error based on
modified perceptron criterion that benefits handling the uncertainty near the
decision boundary by means of a fuzzy linear programming approach with fuzzy
resources. The method proposed has low computational complexity because of its
linear characteristics and the ability to deal with noisy data with different
degrees of tolerance. Obtained results verify the success of the algorithm when
dealing with different problems. Comparing FC-LDA and LDA shows superiority in
classification task.
",Fuzzy Constraints Linear Discriminant Analysis
"  Many robotic planning applications involve continuous actions with highly
non-linear constraints, which cannot be modeled using modern planners that
construct a propositional representation. We introduce STRIPStream: an
extension of the STRIPS language which can model these domains by supporting
the specification of blackbox generators to handle complex constraints. The
outputs of these generators interact with actions through possibly infinite
streams of objects and static predicates. We provide two algorithms which both
reduce STRIPStream problems to a sequence of finite-domain planning problems.
The representation and algorithms are entirely domain independent. We
demonstrate our framework on simple illustrative domains, and then on a
high-dimensional, continuous robotic task and motion planning domain.
",STRIPS Planning in Infinite Domains
"  In the past, several models of consciousness have become popular and have led
to the development of models for machine consciousness with varying degrees of
success and challenges for simulation and implementations. Moreover, affective
computing attributes that involve emotions, behavior and personality have not
been the focus of models of consciousness as they lacked motivation for
deployment in software applications and robots. The affective attributes are
important factors for the future of machine consciousness with the rise of
technologies that can assist humans. Personality and affection hence can give
an additional flavor for the computational model of consciousness in humanoid
robotics. Recent advances in areas of machine learning with a focus on deep
learning can further help in developing aspects of machine consciousness in
areas that can better replicate human sensory perceptions such as speech
recognition and vision. With such advancements, one encounters further
challenges in developing models that can synchronize different aspects of
affective computing. In this paper, we review some existing models of
consciousnesses and present an affective computational model that would enable
the human touch and feel for robotic systems.
",An affective computational model for machine consciousness
"  During the last decades, many cognitive architectures (CAs) have been
realized adopting different assumptions about the organization and the
representation of their knowledge level. Some of them (e.g. SOAR [Laird
(2012)]) adopt a classical symbolic approach, some (e.g. LEABRA [O'Reilly and
Munakata (2000)]) are based on a purely connectionist model, while others (e.g.
CLARION [Sun (2006)] adopt a hybrid approach combining connectionist and
symbolic representational levels. Additionally, some attempts (e.g. biSOAR)
trying to extend the representational capacities of CAs by integrating
diagrammatical representations and reasoning are also available [Kurup and
Chandrasekaran (2007)]. In this paper we propose a reflection on the role that
Conceptual Spaces, a framework developed by Peter G\""ardenfors [G\""ardenfors
(2000)] more than fifteen years ago, can play in the current development of the
Knowledge Level in Cognitive Systems and Architectures. In particular, we claim
that Conceptual Spaces offer a lingua franca that allows to unify and
generalize many aspects of the symbolic, sub-symbolic and diagrammatic
approaches (by overcoming some of their typical problems) and to integrate them
on a common ground. In doing so we extend and detail some of the arguments
explored by G\""ardenfors [G\""ardenfors (1997)] for defending the need of a
conceptual, intermediate, representation level between the symbolic and the
sub-symbolic one.
","Conceptual Spaces for Cognitive Architectures: A Lingua Franca for
  Different Levels of Representation"
"  In this paper, we tackle the problem of risk-averse route planning in a
transportation network with time-dependent and stochastic costs. To solve this
problem, we propose an adaptation of the A* algorithm that accommodates any
risk measure or decision criterion that is monotonic with first-order
stochastic dominance. We also present a case study of our algorithm on the
Manhattan, NYC, transportation network.
",Finding Risk-Averse Shortest Path with Time-dependent Stochastic Costs
"  In this paper, we present a link between preference-based and multiobjective
sequential decision-making. While transforming a multiobjective problem to a
preference-based one is quite natural, the other direction is a bit less
obvious. We present how this transformation (from preference-based to
multiobjective) can be done under the classic condition that preferences over
histories can be represented by additively decomposable utilities and that the
decision criterion to evaluate policies in a state is based on expectation.
This link yields a new source of multiobjective sequential decision-making
problems (i.e., when reward values are unknown) and justifies the use of
solving methods developed in one setting in the other one.
",From Preference-Based to Multiobjective Sequential Decision-Making
"  In this paper, a non-probabilistic method based on fuzzy logic is used to
update finite element models (FEMs). Model updating techniques use the measured
data to improve the accuracy of numerical models of structures. However, the
measured data are contaminated with experimental noise and the models are
inaccurate due to randomness in the parameters. This kind of aleatory
uncertainty is irreducible, and may decrease the accuracy of the finite element
model updating process. However, uncertainty quantification methods can be used
to identify the uncertainty in the updating parameters. In this paper, the
uncertainties associated with the modal parameters are defined as fuzzy
membership functions, while the model updating procedure is defined as an
optimization problem at each {\alpha}-cut level. To determine the membership
functions of the updated parameters, an objective function is defined and
minimized using two metaheuristic optimization algorithms: ant colony
optimization (ACO) and particle swarm optimization (PSO). A structural example
is used to investigate the accuracy of the fuzzy model updating strategy using
the PSO and ACO algorithms. Furthermore, the results obtained by the fuzzy
finite element model updating are compared with the Bayesian model updating
results.
","Fuzzy finite element model updating using metaheuristic optimization
  algorithms"
"  The high variance issue in unbiased policy-gradient methods such as VPG and
REINFORCE is typically mitigated by adding a baseline. However, the baseline
fitting itself suffers from the underfitting or the overfitting problem. In
this paper, we develop a K-fold method for baseline estimation in policy
gradient algorithms. The parameter K is the baseline estimation hyperparameter
that can adjust the bias-variance trade-off in the baseline estimates. We
demonstrate the usefulness of our approach via two state-of-the-art policy
gradient algorithms on three MuJoCo locomotive control tasks.
",A K-fold Method for Baseline Estimation in Policy Gradient Algorithms
"  Lifted probabilistic inference (Poole, 2003) and symbolic dynamic programming
for lifted stochastic planning (Boutilier et al, 2001) were introduced around
the same time as algorithmic efforts to use abstraction in stochastic systems.
Over the years, these ideas evolved into two distinct lines of research, each
supported by a rich literature. Lifted probabilistic inference focused on
efficient arithmetic operations on template-based graphical models under a
finite domain assumption while symbolic dynamic programming focused on
supporting sequential decision-making in rich quantified logical action models
and on open domain reasoning. Given their common motivation but different focal
points, both lines of research have yielded highly complementary innovations.
In this chapter, we aim to help close the gap between these two research areas
by providing an overview of lifted stochastic planning from the perspective of
probabilistic inference, showing strong connections to other chapters in this
book. This also allows us to define Generalized Lifted Inference as a paradigm
that unifies these areas and elucidates open problems for future research that
can benefit both lifted inference and stochastic planning.
",Stochastic Planning and Lifted Inference
"  Artificial intelligence has seen several breakthroughs in recent years, with
games often serving as milestones. A common feature of these games is that
players have perfect information. Poker is the quintessential game of imperfect
information, and a longstanding challenge problem in artificial intelligence.
We introduce DeepStack, an algorithm for imperfect information settings. It
combines recursive reasoning to handle information asymmetry, decomposition to
focus computation on the relevant decision, and a form of intuition that is
automatically learned from self-play using deep learning. In a study involving
44,000 hands of poker, DeepStack defeated with statistical significance
professional poker players in heads-up no-limit Texas hold'em. The approach is
theoretically sound and is shown to produce more difficult to exploit
strategies than prior approaches.
",DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker
"  We present a position paper advocating the notion that Stoic philosophy and
ethics can inform the development of ethical A.I. systems. This is in sharp
contrast to most work on building ethical A.I., which has focused on
Utilitarian or Deontological ethical theories. We relate ethical A.I. to
several core Stoic notions, including the dichotomy of control, the four
cardinal virtues, the ideal Sage, Stoic practices, and Stoic perspectives on
emotion or affect. More generally, we put forward an ethical view of A.I. that
focuses more on internal states of the artificial agent rather than on external
actions of the agent. We provide examples relating to near-term A.I. systems as
well as hypothetical superintelligent agents.
",Stoic Ethics for Artificial Agents
"  Forecasting the flow of crowds is of great importance to traffic management
and public safety, and very challenging as it is affected by many complex
factors, including spatial dependencies (nearby and distant), temporal
dependencies (closeness, period, trend), and external conditions (e.g., weather
and events). We propose a deep-learning-based approach, called ST-ResNet, to
collectively forecast two types of crowd flows (i.e. inflow and outflow) in
each and every region of a city. We design an end-to-end structure of ST-ResNet
based on unique properties of spatio-temporal data. More specifically, we
employ the residual neural network framework to model the temporal closeness,
period, and trend properties of crowd traffic. For each property, we design a
branch of residual convolutional units, each of which models the spatial
properties of crowd traffic. ST-ResNet learns to dynamically aggregate the
output of the three residual neural networks based on data, assigning different
weights to different branches and regions. The aggregation is further combined
with external factors, such as weather and day of the week, to predict the
final traffic of crowds in each and every region. We have developed a real-time
system based on Microsoft Azure Cloud, called UrbanFlow, providing the crowd
flow monitoring and forecasting in Guiyang City of China. In addition, we
present an extensive experimental evaluation using two types of crowd flows in
Beijing and New York City (NYC), where ST-ResNet outperforms nine well-known
baselines.
","Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual
  Networks"
"  The Internet of Things is arriving to our homes or cities through fields
already known like Smart Homes, Smart Cities, or Smart Towns. The monitoring of
environmental conditions of cities can help to adapt the indoor locations of
the cities in order to be more comfortable for people who stay there. A way to
improve the indoor conditions is an efficient temperature control, however, it
depends on many factors like the different combinations of outdoor temperature
and humidity. Therefore, adjusting the indoor temperature is not setting a
value according to other value. There are many more factors to take into
consideration, hence the traditional logic based in binary states cannot be
used. Many problems cannot be solved with a set of binary solutions and we need
a new way of development. Fuzzy logic is able to interpret many states, more
than two states, giving to computers the capacity to react in a similar way to
people. In this paper we will propose a new approach to control the temperature
using the Internet of Things together its platforms and fuzzy logic regarding
not only the indoor temperature but also the outdoor temperature and humidity
in order to save energy and to set a more comfortable environment for their
users. Finally, we will conclude that the fuzzy approach allows us to achieve
an energy saving around 40% and thus, save money.
","IoFClime: The fuzzy logic and the Internet of Things to control indoor
  temperature regarding the outdoor ambient conditions"
"  Cyber-Physical Systems in general, and Intelligent Transport Systems (ITS) in
particular use heterogeneous data sources combined with problem solving
expertise in order to make critical decisions that may lead to some form of
actions e.g., driver notifications, change of traffic light signals and braking
to prevent an accident. Currently, a major part of the decision process is done
by human domain experts, which is time-consuming, tedious and error-prone.
Additionally, due to the intrinsic nature of knowledge possession this decision
process cannot be easily replicated or reused. Therefore, there is a need for
automating the reasoning processes by providing computational systems a formal
representation of the domain knowledge and a set of methods to process that
knowledge. In this paper, we propose a knowledge model that can be used to
express both declarative knowledge about the systems' components, their
relations and their current state, as well as procedural knowledge representing
possible system behavior. In addition, we introduce a framework for knowledge
management and automated reasoning (KMARF). The idea behind KMARF is to
automatically select an appropriate problem solver based on formalized
reasoning expertise in the knowledge base, and convert a problem definition to
the corresponding format. This approach automates reasoning, thus reducing
operational costs, and enables reusability of knowledge and methods across
different domains. We illustrate the approach on a transportation planning use
case.
","A Framework for Knowledge Management and Automated Reasoning Applied on
  Intelligent Transport Systems"
"  Despite the recent progress in automatic theorem provers, proof engineers are
still suffering from the lack of powerful proof automation. In this position
paper we first report our proof strategy language based on a meta-tool
approach. Then, we propose an AI-based approach to drastically improve proof
automation for Isabelle, while identifying three major challenges we plan to
address for this objective.
",Towards Smart Proof Search for Isabelle
"  First-Order Logic (FOL) is widely regarded as one of the most important
foundations for knowledge representation. Nevertheless, in this paper, we argue
that FOL has several critical issues for this purpose. Instead, we propose an
alternative called assertional logic, in which all syntactic objects are
categorized as set theoretic constructs including individuals, concepts and
operators, and all kinds of knowledge are formalized by equality assertions. We
first present a primitive form of assertional logic that uses minimal assumed
knowledge and constructs. Then, we show how to extend it by definitions, which
are special kinds of knowledge, i.e., assertions. We argue that assertional
logic, although simpler, is more expressive and extensible than FOL. As a case
study, we show how assertional logic can be used to unify logic and
probability, and more building blocks in AI.
",From First-Order Logic to Assertional Logic
"  Since Leonard Savage's epoch-making ""Foundations of Statistics"", Subjective
Expected Utility Theory has been the presumptive model for decision-making.
Savage provided an act-based axiomatization of standard expected utility
theory. In this article, we provide a Savage-like axiomatization of nonstandard
expected utility theory. It corresponds to a weakening of Savage's 6th axiom.
",A Savage-Like Axiomatization for Nonstandard Expected Utility
"  A fuzzy clustering algorithm for multidimensional data is proposed in this
article. The data is described by vectors whose components are linguistic
variables defined in an ordinal scale. The obtained results confirm the
efficiency of the proposed approach.
",Fuzzy Clustering Data Given in the Ordinal Scale
"  In this paper we investigate the links between instantiated argumentation
systems and the axioms for non-monotonic reasoning described in [9] with the
aim of characterising the nature of argument based reasoning. In doing so, we
consider two possible interpretations of the consequence relation, and describe
which axioms are met by ASPIC+ under each of these interpretations. We then
consider the links between these axioms and the rationality postulates. Our
results indicate that argument based reasoning as characterised by ASPIC+ is -
according to the axioms of [9] - non-cumulative and non-monotonic, and
therefore weaker than the weakest non-monotonic reasoning systems they
considered possible. This weakness underpins ASPIC+'s success in modelling
other reasoning systems, and we conclude by considering the relationship
between ASPIC+ and other weak logical systems.
","On the links between argumentation-based reasoning and nonmonotonic
  reasoning"
"  The rapid advancement of machine learning techniques has re-energized
research into general artificial intelligence. While the idea of
domain-agnostic meta-learning is appealing, this emerging field must come to
terms with its relationship to human cognition and the statistics and structure
of the tasks humans perform. The position of this article is that only by
aligning our agents' abilities and environments with those of humans do we
stand a chance at developing general artificial intelligence (GAI). A broad
reading of the famous 'No Free Lunch' theorem is that there is no universally
optimal inductive bias or, equivalently, bias-free learning is impossible. This
follows from the fact that there are an infinite number of ways to extrapolate
data, any of which might be the one used by the data generating environment; an
inductive bias prefers some of these extrapolations to others, which lowers
performance in environments using these adversarial extrapolations. We may
posit that the optimal GAI is the one that maximally exploits the statistics of
its environment to create its inductive bias; accepting the fact that this
agent is guaranteed to be extremely sub-optimal for some alternative
environments. This trade-off appears benign when thinking about the environment
as being the physical universe, as performance on any fictive universe is
obviously irrelevant. But, we should expect a sharper inductive bias if we
further constrain our environment. Indeed, we implicitly do so by defining GAI
in terms of accomplishing that humans consider useful. One common version of
this is need the for 'common-sense reasoning', which implicitly appeals to the
statistics of physical universe as perceived by humans.
",Minimally Naturalistic Artificial Intelligence
"  Optimization is becoming a crucial element in industrial applications
involving sustainable alternative energy systems. During the design of such
systems, the engineer/decision maker would often encounter noise factors (e.g.
solar insolation and ambient temperature fluctuations) when their system
interacts with the environment. In this chapter, the sizing and design
optimization of the solar powered irrigation system was considered. This
problem is multivariate, noisy, nonlinear and multiobjective. This design
problem was tackled by first using the Fuzzy Type II approach to model the
noise factors. Consequently, the Bacterial Foraging Algorithm (BFA) (in the
context of a weighted sum framework) was employed to solve this multiobjective
fuzzy design problem. This method was then used to construct the approximate
Pareto frontier as well as to identify the best solution option in a fuzzy
setting. Comprehensive analyses and discussions were performed on the generated
numerical results with respect to the implemented solution methods.
","Multiobjective Optimization of Solar Powered Irrigation System with
  Fuzzy Type-2 Noise Modelling"
"  A compact information-rich representation of the environment, also called a
feature abstraction, can simplify a robot's task of mapping its raw sensory
inputs to useful action sequences. However, in environments that are
non-stationary and only partially observable, a single abstraction is probably
not sufficient to encode most variations. Therefore, learning multiple sets of
spatially or temporally local, modular abstractions of the inputs would be
beneficial. How can a robot learn these local abstractions without a teacher?
More specifically, how can it decide from where and when to start learning a
new abstraction? A recently proposed algorithm called Curious Dr. MISFA
addresses this problem. The algorithm is based on two underlying learning
principles called artificial curiosity and slowness. The former is used to make
the robot self-motivated to explore by rewarding itself whenever it makes
progress learning an abstraction; the later is used to update the abstraction
by extracting slowly varying components from raw sensory inputs. Curious Dr.
MISFA's application is, however, limited to discrete domains constrained by a
pre-defined state space and has design limitations that make it unstable in
certain situations. This paper presents a significant improvement that is
applicable to continuous environments, is computationally less expensive,
simpler to use with fewer hyper parameters, and stable in certain
non-stationary environments. We demonstrate the efficacy and stability of our
method in a vision-based robot simulator.
","Intrinsically Motivated Acquisition of Modular Slow Features for
  Humanoids in Continuous and Non-Stationary Environments"
"  Internship assignment is a complicated process for universities since it is
necessary to take into account a multiplicity of variables to establish a
compromise between companies' requirements and student competencies acquired
during the university training. These variables build up a complex relations
map that requires the formulation of an exhaustive and rigorous conceptual
scheme. In this research a domain ontological model is presented as support to
the student's decision making for opportunities of University studies level of
the University Lumiere Lyon 2 (ULL) education system. The ontology is designed
and created using methodological approach offering the possibility of improving
the progressive creation, capture and knowledge articulation. In this paper, we
draw a balance taking the demands of the companies across the capabilities of
the students. This will be done through the establishment of an ontological
model of an educational learners' profile and the internship postings which are
written in a free text and using uncontrolled vocabulary. Furthermore, we
outline the process of semantic matching which improves the quality of query
results.
",Ontology based system to guide internship assignment process
"  This article aims to achieve two goals: to show that probability is not the
only way of dealing with uncertainty (and even more, that there are kinds of
uncertainty which are for principled reasons not addressable with probabilistic
means); and to provide evidence that logic-based methods can well support
reasoning with uncertainty. For the latter claim, two paradigmatic examples are
presented: Logic Programming with Kleene semantics for modelling reasoning from
information in a discourse, to an interpretation of the state of affairs of the
intended model, and a neural-symbolic implementation of Input/Output logic for
dealing with uncertainty in dynamic normative contexts.
","Reasoning in Non-Probabilistic Uncertainty: Logic Programming and
  Neural-Symbolic Computing as Examples"
"  A network embedding is a representation of a large graph in a low-dimensional
space, where vertices are modeled as vectors. The objective of a good embedding
is to preserve the proximity between vertices in the original graph. This way,
typical search and mining methods can be applied in the embedded space with the
help of off-the-shelf multidimensional indexing approaches. Existing network
embedding techniques focus on homogeneous networks, where all vertices are
considered to belong to a single class.
","Heterogeneous Information Network Embedding for Meta Path based
  Proximity"
"  For agents and robots to become more useful, they must be able to quickly
learn from non-technical users. This paper investigates the problem of
interactively learning behaviors communicated by a human teacher using positive
and negative feedback. Much previous work on this problem has made the
assumption that people provide feedback for decisions that is dependent on the
behavior they are teaching and is independent from the learner's current
policy. We present empirical results that show this assumption to be
false---whether human trainers give a positive or negative feedback for a
decision is influenced by the learner's current policy. We argue that
policy-dependent feedback, in addition to being commonplace, enables useful
training strategies from which agents should benefit. Based on this insight, we
introduce Convergent Actor-Critic by Humans (COACH), an algorithm for learning
from policy-dependent feedback that converges to a local optimum. Finally, we
demonstrate that COACH can successfully learn multiple behaviors on a physical
robot, even with noisy image features.
",Interactive Learning from Policy-Dependent Human Feedback
"  We introduce the Binary Matrix Guessing Problem and provide two algorithms to
solve this problem. The first algorithm we introduce is Elementwise Probing
Algorithm (EPA) which is very fast under a score which utilizes Frobenius
Distance. The second algorithm is Additive Reinforcement Learning Algorithm
which combines ideas from perceptron algorithm and reinforcement learning
algorithm. This algorithm is significantly slower compared to first one, but
less restrictive and generalizes better. We compare computational performance
of both algorithms and provide numerical results.
  reason for withdrawal: Paper will be rewritten with experiments replicated on
verified and validated hardware and software.
",Binary Matrix Guessing Problem
"  The payload of communications satellites must go through a series of tests to
assert their ability to survive in space. Each test involves some equipment of
the payload to be active, which has an impact on the temperature of the
payload. Sequencing these tests in a way that ensures the thermal stability of
the payload and minimizes the overall duration of the test campaign is a very
important objective for satellite manufacturers. The problem can be decomposed
in two sub-problems corresponding to two objectives: First, the number of
distinct configurations necessary to run the tests must be minimized. This can
be modeled as packing the tests into configurations, and we introduce a set of
implied constraints to improve the lower bound of the model. Second, tests must
be sequenced so that the number of times an equipment unit has to be switched
on or off is minimized. We model this aspect using the constraint Switch, where
a buffer with limited capacity represents the currently active equipment units,
and we introduce an improvement of the propagation algorithm for this
constraint. We then introduce a search strategy in which we sequentially solve
the sub-problems (packing and sequencing). Experiments conducted on real and
random instances show the respective interest of our contributions.
","Constraint programming for planning test campaigns of communications
  satellites"
"  This paper focuses on modeling ride requests and their variations over
location and time, based on analyzing extensive real-world data from a
ride-sharing service. We introduce a graph model that captures the spatial and
temporal variability of ride requests and the potentials for ride pooling. We
discover these ride request graphs exhibit a well known property called
densification power law often found in real graphs modelling human behaviors.
We show the pattern of ride requests and the potential of ride pooling for a
city can be characterized by the densification factor of the ride request
graphs. Previous works have shown that it is possible to automatically generate
synthetic versions of these graphs that exhibit a given densification factor.
We present an algorithm for automatic generation of synthetic ride request
graphs that match quite well the densification factor of ride request graphs
from actual ride request data.
",Space-Time Graph Modeling of Ride Requests Based on Real-World Data
"  The ability to accurately predict and simulate human driving behavior is
critical for the development of intelligent transportation systems. Traditional
modeling methods have employed simple parametric models and behavioral cloning.
This paper adopts a method for overcoming the problem of cascading errors
inherent in prior approaches, resulting in realistic behavior that is robust to
trajectory perturbations. We extend Generative Adversarial Imitation Learning
to the training of recurrent policies, and we demonstrate that our model
outperforms rule-based controllers and maximum likelihood models in realistic
highway simulations. Our model both reproduces emergent behavior of human
drivers, such as lane change rate, while maintaining realistic control over
long time horizons.
",Imitating Driver Behavior with Generative Adversarial Networks
"  Modelling, specifying and reasoning about complex systems requires to process
in an integrated fashion declarative and procedural aspects of the target
domain. The paper reports on an experiment conducted with a propositional
version of Logic Programming Petri Nets (LPPNs), a notation extending Petri
Nets with logic programming constructs. Two semantics are presented: a
denotational semantics that fully maps the notation to ASP via Event Calculus;
and a hybrid operational semantics that process separately the causal
mechanisms via Petri nets, and the constraints associated to objects and to
events via Answer Set Programming (ASP). These two alternative specifications
enable an empirical evaluation in terms of computational efficiency.
Experimental results show that the hybrid semantics is more efficient w.r.t.
sequences, whereas the two semantics follows the same behaviour w.r.t.
branchings (although the denotational one performs better in absolute terms).
","Operationalizing Declarative and Procedural Knowledge: a Benchmark on
  Logic Programming Petri Nets (LPPNs)"
"  Autonomous software agents operating in dynamic environments need to
constantly reason about actions in pursuit of their goals, while taking into
consideration norms which might be imposed on those actions. Normative
practical reasoning supports agents making decisions about what is best for
them to (not) do in a given situation. What makes practical reasoning
challenging is the interplay between goals that agents are pursuing and the
norms that the agents are trying to uphold. We offer a formalisation to allow
agents to plan for multiple goals and norms in the presence of durative actions
that can be executed concurrently. We compare plans based on decision-theoretic
notions (i.e. utility) such that the utility gain of goals and utility loss of
norm violations are the basis for this comparison. The set of optimal plans
consists of plans that maximise the overall utility, each of which can be
chosen by the agent to execute. We provide an implementation of our proposal in
Answer Set Programming, thus allowing us to state the original problem in terms
of a logic program that can be queried for solutions with specific properties.
The implementation is proven to be sound and complete.
","Practical Reasoning with Norms for Autonomous Software Agents (Full
  Edition)"
"  When AI systems interact with humans in the loop, they are often called on to
provide explanations for their plans and behavior. Past work on plan
explanations primarily involved the AI system explaining the correctness of its
plan and the rationale for its decision in terms of its own model. Such
soliloquy is wholly inadequate in most realistic scenarios where the humans
have domain and task models that differ significantly from that used by the AI
system. We posit that the explanations are best studied in light of these
differing models. In particular, we show how explanation can be seen as a
""model reconciliation problem"" (MRP), where the AI system in effect suggests
changes to the human's model, so as to make its plan be optimal with respect to
that changed human model. We will study the properties of such explanations,
present algorithms for automatically computing them, and evaluate the
performance of the algorithms.
","Plan Explanations as Model Reconciliation: Moving Beyond Explanation as
  Soliloquy"
"  Automation and computer intelligence to support complex human decisions
becomes essential to manage large and distributed systems in the Cloud and IoT
era. Understanding the root cause of an observed symptom in a complex system
has been a major problem for decades. As industry dives into the IoT world and
the amount of data generated per year grows at an amazing speed, an important
question is how to find appropriate mechanisms to determine root causes that
can handle huge amounts of data or may provide valuable feedback in real-time.
While many survey papers aim at summarizing the landscape of techniques for
modelling system behavior and infering the root cause of a problem based in the
resulting models, none of those focuses on analyzing how the different
techniques in the literature fit growing requirements in terms of performance
and scalability. In this survey, we provide a review of root-cause analysis,
focusing on these particular aspects. We also provide guidance to choose the
best root-cause analysis strategy depending on the requirements of a particular
system and application.
",Survey on Models and Techniques for Root-Cause Analysis
"  Based on the in-depth analysis of the essence and features of vague
phenomena, this paper focuses on establishing the axiomatical foundation of
membership degree theory for vague phenomena, presents an axiomatic system to
govern membership degrees and their interconnections. On this basis, the
concept of vague partition is introduced, further, the concept of fuzzy set
introduced by Zadeh in 1965 is redefined based on vague partition from the
perspective of axiomatization. The thesis defended in this paper is that the
relationship among vague attribute values should be the starting point to
recognize and model vague phenomena from a quantitative view.
","Redefinition of the concept of fuzzy set based on vague partition from
  the perspective of axiomatization"
"  We introduce new diversification methods for zero-one optimization that
significantly extend strategies previously introduced in the setting of
metaheuristic search. Our methods incorporate easily implemented strategies for
partitioning assignments of values to variables, accompanied by processes
called augmentation and shifting which create greater flexibility and
generality. We then show how the resulting collection of diversified solutions
can be further diversified by means of permutation mappings, which equally can
be used to generate diversified collections of permutations for applications
such as scheduling and routing. These methods can be applied to non-binary
vectors by the use of binarization procedures and by Diversification-Based
Learning (DBL) procedures which also provide connections to applications in
clustering and machine learning. Detailed pseudocode and numerical
illustrations are provided to show the operation of our methods and the
collections of solutions they create.
",Diversification Methods for Zero-One Optimization
"  This article shows how the recent breakthroughs in Reinforcement Learning
(RL) that have enabled robots to learn to play arcade video games, walk or
assemble colored bricks, can be used to perform other tasks that are currently
at the core of engineering cyberphysical systems. We present the first use of
RL for the control of systems modeled by discretized non-linear Partial
Differential Equations (PDEs) and devise a novel algorithm to use
non-parametric control techniques for large multi-agent systems. We show how
neural network based RL enables the control of discretized PDEs whose
parameters are unknown, random, and time-varying. We introduce an algorithm of
Mutual Weight Regularization (MWR) which alleviates the curse of dimensionality
of multi-agent control schemes by sharing experience between agents while
giving each agent the opportunity to specialize its action policy so as to
tailor it to the local parameters of the part of the system it is located in.
","Expert Level control of Ramp Metering based on Multi-task Deep
  Reinforcement Learning"
"  Interaction information is one of the multivariate generalizations of mutual
information, which expresses the amount information shared among a set of
variables, beyond the information, which is shared in any proper subset of
those variables. Unlike (conditional) mutual information, which is always
non-negative, interaction information can be negative. We utilize this property
to find the direction of causal influences among variables in a triangle
topology under some mild assumptions.
","Interaction Information for Causal Inference: The Case of Directed
  Triangle"
"  We examine the meaning and the complexity of probabilistic logic programs
that consist of a set of rules and a set of independent probabilistic facts
(that is, programs based on Sato's distribution semantics). We focus on two
semantics, respectively based on stable and on well-founded models. We show
that the semantics based on stable models (referred to as the ""credal
semantics"") produces sets of probability models that dominate infinitely
monotone Choquet capacities, we describe several useful consequences of this
result. We then examine the complexity of inference with probabilistic logic
programs. We distinguish between the complexity of inference when a
probabilistic program and a query are given (the inferential complexity), and
the complexity of inference when the probabilistic program is fixed and the
query is given (the query complexity, akin to data complexity as used in
database theory). We obtain results on the inferential and query complexity for
acyclic, stratified, and cyclic propositional and relational programs,
complexity reaches various levels of the counting hierarchy and even
exponential levels.
",On the Semantics and Complexity of Probabilistic Logic Programs
"  The longest arc-preserving common subsequence problem is an NP-hard
combinatorial optimization problem from the field of computational biology.
This problem finds applications, in particular, in the comparison of
arc-annotated Ribonucleic acid (RNA) sequences. In this work we propose a
simple, hybrid evolutionary algorithm to tackle this problem. The most
important feature of this algorithm concerns a crossover operator based on
solution merging. In solution merging, two or more solutions to the problem are
merged, and an exact technique is used to find the best solution within this
union. It is experimentally shown that the proposed algorithm outperforms a
heuristic from the literature.
","A Hybrid Evolutionary Algorithm Based on Solution Merging for the
  Longest Arc-Preserving Common Subsequence Problem"
"  This survey explores Procedural Content Generation via Machine Learning
(PCGML), defined as the generation of game content using machine learning
models trained on existing content. As the importance of PCG for game
development increases, researchers explore new avenues for generating
high-quality content with or without human involvement; this paper addresses
the relatively new paradigm of using machine learning (in contrast with
search-based, solver-based, and constructive methods). We focus on what is most
often considered functional game content such as platformer levels, game maps,
interactive fiction stories, and cards in collectible card games, as opposed to
cosmetic content such as sprites and sound effects. In addition to using PCG
for autonomous generation, co-creativity, mixed-initiative design, and
compression, PCGML is suited for repair, critique, and content analysis because
of its focus on modeling existing content. We discuss various data sources and
representations that affect the resulting generated content. Multiple PCGML
methods are covered, including neural networks, long short-term memory (LSTM)
networks, autoencoders, and deep convolutional networks; Markov models,
$n$-grams, and multi-dimensional Markov chains; clustering; and matrix
factorization. Finally, we discuss open problems in the application of PCGML,
including learning from small datasets, lack of training data, multi-layered
learning, style-transfer, parameter tuning, and PCG as a game mechanic.
",Procedural Content Generation via Machine Learning (PCGML)
"  Safe interaction with human drivers is one of the primary challenges for
autonomous vehicles. In order to plan driving maneuvers effectively, the
vehicle's control system must infer and predict how humans will behave based on
their latent internal state (e.g., intentions and aggressiveness). This
research uses a simple model for human behavior with unknown parameters that
make up the internal states of the traffic participants and presents a method
for quantifying the value of estimating these states and planning with their
uncertainty explicitly modeled. An upper performance bound is established by an
omniscient Monte Carlo Tree Search (MCTS) planner that has perfect knowledge of
the internal states. A baseline lower bound is established by planning with
MCTS assuming that all drivers have the same internal state. MCTS variants are
then used to solve a partially observable Markov decision process (POMDP) that
models the internal state uncertainty to determine whether inferring the
internal state offers an advantage over the baseline. Applying this method to a
freeway lane changing scenario reveals that there is a significant performance
gap between the upper bound and baseline. POMDP planning techniques come close
to closing this gap, especially when important hidden model parameters are
correlated with measurable parameters.
","The Value of Inferring the Internal State of Traffic Participants for
  Autonomous Freeway Driving"
"  With the advent of modern computer networks, fault diagnosis has been a focus
of research activity. This paper reviews the history of fault diagnosis in
networks and discusses the main methods in information gathering section,
information analyzing section and diagnosing and revolving section of fault
diagnosis in networks. Emphasis will be placed upon knowledge-based methods
with discussing the advantages and shortcomings of the different methods. The
survey is concluded with a description of some open problems.
",Survey of modern Fault Diagnosis methods in networks
"  ASHACL, a variant of the W3C Shapes Constraint Language, is designed to
determine whether an RDF graph meets some conditions. These conditions are
grouped into shapes, which validate whether particular RDF terms each meet the
constraints of the shape. Shapes are themselves expressed as RDF triples in an
RDF graph, called a shapes graph.
",ASHACL: Alternative Shapes Constraint Language
"  We present a technique for automatically extracting mutual exclusion
invariants from temporal planning instances. It first identifies a set of
invariant templates by inspecting the lifted representation of the domain and
then checks these templates against properties that assure invariance. Our
technique builds on other approaches to invariant synthesis presented in the
literature, but departs from their limited focus on instantaneous actions by
addressing temporal domains. To deal with time, we formulate invariance
conditions that account for the entire structure of the actions and the
possible concurrent interactions between them. As a result, we construct a
significantly more comprehensive technique than previous methods, which is able
to find not only invariants for temporal domains, but also a broader set of
invariants for non-temporal domains. The experimental results reported in this
paper provide evidence that identifying a broader set of invariants results in
the generation of fewer multi-valued state variables with larger domains. We
show that, in turn, this reduction in the number of variables reflects
positively on the performance of a number of temporal planners that use a
variable/value representation by significantly reducing their running time.
","Extracting Lifted Mutual Exclusion Invariants from Temporal Planning
  Domains"
"  In this paper, we propose a new autonomous braking system based on deep
reinforcement learning. The proposed autonomous braking system automatically
decides whether to apply the brake at each time step when confronting the risk
of collision using the information on the obstacle obtained by the sensors. The
problem of designing brake control is formulated as searching for the optimal
policy in Markov decision process (MDP) model where the state is given by the
relative position of the obstacle and the vehicle's speed, and the action space
is defined as whether brake is stepped or not. The policy used for brake
control is learned through computer simulations using the deep reinforcement
learning method called deep Q-network (DQN). In order to derive desirable
braking policy, we propose the reward function which balances the damage
imposed to the obstacle in case of accident and the reward achieved when the
vehicle runs out of risk as soon as possible. DQN is trained for the scenario
where a vehicle is encountered with a pedestrian crossing the urban road.
Experiments show that the control agent exhibits desirable control behavior and
avoids collision without any mistake in various uncertain environments.
",Autonomous Braking System via Deep Reinforcement Learning
"  The technique of kernelization consists in extracting, from an instance of a
problem, an essentially equivalent instance whose size is bounded in a
parameter k. Besides being the basis for efficient param-eterized algorithms,
this method also provides a wealth of information to reason about in the
context of constraint programming. We study the use of kernelization for
designing propagators through the example of the Vertex Cover constraint. Since
the classic kernelization rules often correspond to dominance rather than
consistency, we introduce the notion of ""loss-less"" kernel. While our
preliminary experimental results show the potential of the approach, they also
show some of its limits. In particular, this method is more effective for
vertex covers of large and sparse graphs, as they tend to have, relatively,
smaller kernels.
",Propagation via Kernelization: The Vertex Cover Constraint
"  This paper has three main contributions to our understanding of fixed-depth
minimax search: (A) A new formulation for Stockman's SSS* algorithm, based on
Alpha-Beta, is presented. It solves all the perceived drawbacks of SSS*,
finally transforming it into a practical algorithm. In effect, we show that
SSS* = alpha-beta + ransposition tables. The crucial step is the realization
that transposition tables contain so-called solution trees, structures that are
used in best-first search algorithms like SSS*. Having created a practical
version, we present performance measurements with tournament game-playing
programs for three different minimax games, yielding results that contradict a
number of publications. (B) Based on the insights gained in our attempts at
understanding SSS*, we present a framework that facilitates the construction of
several best-first fixed- depth game-tree search algorithms, known and new. The
framework is based on depth-first null-window Alpha-Beta search, enhanced with
storage to allow for the refining of previous search results. It focuses
attention on the essential differences between algorithms. (C) We present a new
instance of the framework, MTD(f). It is well-suited for use with iterative
deepening, and performs better than algorithms that are currently used in most
state-of-the-art game-playing programs. We provide experimental evidence to
explain why MTD(f) performs better than the other fixed-depth minimax
algorithms.
",A Minimax Algorithm Better Than Alpha-beta?: No and Yes
"  In this paper we explore whether or not deep neural architectures can learn
to classify Boolean satisfiability (SAT). We devote considerable time to
discussing the theoretical properties of SAT. Then, we define a graph
representation for Boolean formulas in conjunctive normal form, and train
neural classifiers over general graph structures called Graph Neural Networks,
or GNNs, to recognize features of satisfiability. To the best of our knowledge
this has never been tried before. Our preliminary findings are potentially
profound. In a weakly-supervised setting, that is, without problem specific
feature engineering, Graph Neural Networks can learn features of
satisfiability.
",Graph Neural Networks and Boolean Satisfiability
"  The lack of diversity in a genetic algorithm's population may lead to a bad
performance of the genetic operators since there is not an equilibrium between
exploration and exploitation. In those cases, genetic algorithms present a fast
and unsuitable convergence.
  In this paper we develop a novel hybrid genetic algorithm which attempts to
obtain a balance between exploration and exploitation. It confronts the
diversity problem using the named greedy diversification operator. Furthermore,
the proposed algorithm applies a competition between parent and children so as
to exploit the high quality visited solutions. These operators are complemented
by a simple selection mechanism designed to preserve and take advantage of the
population diversity.
  Additionally, we extend our proposal to the field of memetic algorithms,
obtaining an improved model with outstanding results in practice.
  The experimental study shows the validity of the approach as well as how
important is taking into account the exploration and exploitation concepts when
designing an evolutionary algorithm.
","Genetic and Memetic Algorithm with Diversity Equilibrium based on Greedy
  Diversification"
"  This paper investigates the application of consensus clustering and
meta-clustering to the set of all possible partitions of a data set. We show
that when using a ""complement"" of Rand Index as a measure of cluster
similarity, the total-separation partition, putting each element in a separate
set, is chosen.
",On Seeking Consensus Between Document Similarity Measures
"  Researchers in answer set programming and constraint programming have spent
significant efforts in the development of hybrid languages and solving
algorithms combining the strengths of these traditionally separate fields.
These efforts resulted in a new research area: constraint answer set
programming. Constraint answer set programming languages and systems proved to
be successful at providing declarative, yet efficient solutions to problems
involving hybrid reasoning tasks. One of the main contributions of this paper
is the first comprehensive account of the constraint answer set language and
solver EZCSP, a mainstream representative of this research area that has been
used in various successful applications. We also develop an extension of the
transition systems proposed by Nieuwenhuis et al. in 2006 to capture Boolean
satisfiability solvers. We use this extension to describe the EZCSP algorithm
and prove formal claims about it. The design and algorithmic details behind
EZCSP clearly demonstrate that the development of the hybrid systems of this
kind is challenging. Many questions arise when one faces various design choices
in an attempt to maximize system's benefits. One of the key decisions that a
developer of a hybrid solver makes is settling on a particular integration
schema within its implementation. Thus, another important contribution of this
paper is a thorough case study based on EZCSP, focused on the various
integration schemas that it provides.
  Under consideration in Theory and Practice of Logic Programming (TPLP).
",Constraint Answer Set Solver EZCSP and Why Integration Schemas Matter
"  We develop T-SKIRT: a temporal, structured-knowledge, IRT-based method for
predicting student responses online. By explicitly accounting for student
learning and employing a structured, multidimensional representation of student
proficiencies, the model outperforms standard IRT-based methods on an online
response prediction task when applied to real responses collected from students
interacting with diverse pools of educational content.
","T-SKIRT: Online Estimation of Student Proficiency in an Adaptive
  Learning System"
"  In this article, we introduce a new conception of a family of esport games
called Samu Entropy to try to improve dataflow program graphs like the ones
that are based on Google's TensorFlow. Currently, the Samu Entropy project
specifies only requirements for new esport games to be developed with
particular attention to the investigation of the relationship between esport
and artificial intelligence. It is quite obvious that there is a very close and
natural relationship between esport games and artificial intelligence.
Furthermore, the project Samu Entropy focuses not only on using artificial
intelligence, but on creating AI in a new way. We present a reference game
called Face Battle that implements the Samu Entropy requirements.
",Entropy Non-increasing Games for the Improvement of Dataflow Programming
"  The Minimum Weight Dominating Set (MWDS) problem is an important
generalization of the Minimum Dominating Set (MDS) problem with extensive
applications. This paper proposes a new local search algorithm for the MWDS
problem, which is based on two new ideas. The first idea is a heuristic called
two-level configuration checking (CC2), which is a new variant of a recent
powerful configuration checking strategy (CC) for effectively avoiding the
recent search paths. The second idea is a novel scoring function based on the
frequency of being uncovered of vertices. Our algorithm is called CC2FS,
according to the names of the two ideas. The experimental results show that,
CC2FS performs much better than some state-of-the-art algorithms in terms of
solution quality on a broad range of MWDS benchmarks.
","Local Search for Minimum Weight Dominating Set with Two-Level
  Configuration Checking and Frequency Based Scoring Function"
"  Because of several technological limitations of traditional silicon based
computing, for past few years a paradigm shift, from silicon to carbon, is
occurring in computational world. DNA computing has been considered to be quite
promising in solving computational and reasoning problems by using DNA strands.
Resolution, an important aspect of automated theorem proving and mathematical
logic, is a rule of inference which leads to proof by contradiction technique
for sentences in propositional logic and first-order logic. This can also be
called refutation theorem-proving. In this paper we have shown how the theorem
proving with resolution refutation by DNA computation can be represented by the
semantics of process calculus and strand graph.
",Theorem Proving Based on Semantics of DNA Strand Graph
"  Generative model has been one of the most common approaches for solving the
Dialog State Tracking Problem with the capabilities to model the dialog
hypotheses in an explicit manner. The most important task in such Bayesian
networks models is constructing the most reliable user models by learning and
reflecting the training data into the probability distribution of user actions
conditional on networks states. This paper provides an overall picture of the
learning process in a Bayesian framework with an emphasize on the
state-of-the-art theoretical analyses of the Expectation Maximization learning
algorithm.
",The Dialog State Tracking Challenge with Bayesian Approach
"  Binary Knapsack Problem (BKP) is to select a subset of an element (item) set
with the highest value while keeping the total weight within the capacity of
the knapsack. This paper presents an integer programming model for a variation
of BKP where the value of each element may depend on selecting or ignoring
other elements. Strengths of such Value-Related Dependencies are assumed to be
imprecise and hard to specify. To capture this imprecision, we have proposed
modeling value-related dependencies using fuzzy graphs and their algebraic
structure.
","An Integer Programming Model for Binary Knapsack Problem with
  Value-Related Dependencies among Elements"
"  The field of Distributed Constraint Optimization has gained momentum in
recent years, thanks to its ability to address various applications related to
multi-agent cooperation. Nevertheless, solving Distributed Constraint
Optimization Problems (DCOPs) optimally is NP-hard. Therefore, in large-scale,
complex applications, incomplete DCOP algorithms are necessary. Current
incomplete DCOP algorithms suffer of one or more of the following limitations:
they (a) find local minima without providing quality guarantees; (b) provide
loose quality assessment; or (c) are unable to benefit from the structure of
the problem, such as domain-dependent knowledge and hard constraints.
Therefore, capitalizing on strategies from the centralized constraint solving
community, we propose a Distributed Large Neighborhood Search (D-LNS) framework
to solve DCOPs. The proposed framework (with its novel repair phase) provides
guarantees on solution quality, refining upper and lower bounds during the
iterative process, and can exploit domain-dependent structures. Our
experimental results show that D-LNS outperforms other incomplete DCOP
algorithms on both structured and unstructured problem instances.
",Solving DCOPs with Distributed Large Neighborhood Search
"  Devising an optimal strategy for navigation in a partially observable
environment is one of the key objectives in AI. One of the problem in this
context is the Canadian Traveler Problem (CTP). CTP is a navigation problem
where an agent is tasked to travel from source to target in a partially
observable weighted graph, whose edge might be blocked with a certain
probability and observing such blockage occurs only when reaching upon one of
the edges end points. The goal is to find a strategy that minimizes the
expected travel cost. The problem is known to be P$\#$ hard. In this work we
study the CTP theoretically and empirically. First, we study the Dep-CTP, a CTP
variant we introduce which assumes dependencies between the edges status. We
show that Dep-CTP is intractable, and further we analyze two of its subclasses
on disjoint paths graph. Second, we develop a general algorithm Gen-PAO that
optimally solve the CTP. Gen-PAO is capable of solving two other types of CTP
called Sensing-CTP and Expensive-Edges CTP. Since the CTP is intractable,
Gen-PAO use some pruning methods to reduce the space search for the optimal
solution. We also define some variants of Gen-PAO, compare their performance
and show some benefits of Gen-PAO over existing work.
",Theoretical and Experimental Analysis of the Canadian Traveler Problem
"  Though the word cognitive has a wide range of meanings we define cognitive
engineering as learning from brain to bolster engineering solutions. However,
giving an achievable framework to the process towards this has been a difficult
task. In this work we take the classic data information knowledge wisdom (DIKW)
framework to set some achievable goals and sub-goals towards cognitive
engineering. A layered framework like DIKW aligns nicely with the layered
structure of pre-frontal cortex. And breaking the task into sub-tasks based on
the layers also makes it easier to start developmental endeavours towards
achieving the final goal of a brain-inspired system.
",A DIKW Paradigm to Cognitive Engineering
"  Representation learning of knowledge graphs encodes entities and relation
types into a continuous low-dimensional vector space, learns embeddings of
entities and relation types. Most existing methods only concentrate on
knowledge triples, ignoring logic rules which contain rich background
knowledge. Although there has been some work aiming at leveraging both
knowledge triples and logic rules, they ignore the transitivity and
antisymmetry of logic rules. In this paper, we propose a novel approach to
learn knowledge representations with entities and ordered relations in
knowledges and logic rules. The key idea is to integrate knowledge triples and
logic rules, and approximately order the relation types in logic rules to
utilize the transitivity and antisymmetry of logic rules. All entries of the
embeddings of relation types are constrained to be non-negative. We translate
the general constrained optimization problem into an unconstrained optimization
problem to solve the non-negative matrix factorization. Experimental results
show that our model significantly outperforms other baselines on knowledge
graph completion task. It indicates that our model is capable of capturing the
transitivity and antisymmetry information, which is significant when learning
embeddings of knowledge graphs.
","Embedding Knowledge Graphs Based on Transitivity and Antisymmetry of
  Rules"
"  Effective teams are crucial for organisations, especially in environments
that require teams to be constantly created and dismantled, such as software
development, scientific experiments, crowd-sourcing, or the classroom. Key
factors influencing team performance are competences and personality of team
members. Hence, we present a computational model to compose proficient and
congenial teams based on individuals' personalities and their competences to
perform tasks of different nature. With this purpose, we extend Wilde's
post-Jungian method for team composition, which solely employs individuals'
personalities. The aim of this study is to create a model to partition agents
into teams that are balanced in competences, personality and gender. Finally,
we present some preliminary empirical results that we obtained when analysing
student performance. Results show the benefits of a more informed team
composition that exploits individuals' competences besides information about
their personalities.
",Synergistic Team Composition
"  We study the problem of learning probabilistic first-order logical rules for
knowledge base reasoning. This learning problem is difficult because it
requires learning the parameters in a continuous space as well as the structure
in a discrete space. We propose a framework, Neural Logic Programming, that
combines the parameter and structure learning of first-order logical rules in
an end-to-end differentiable model. This approach is inspired by a
recently-developed differentiable logic called TensorLog, where inference tasks
can be compiled into sequences of differentiable operations. We design a neural
controller system that learns to compose these operations. Empirically, our
method outperforms prior work on multiple knowledge base benchmark datasets,
including Freebase and WikiMovies.
",Differentiable Learning of Logical Rules for Knowledge Base Reasoning
"  In recent years prominent intellectuals have raised ethical concerns about
the consequences of artificial intelligence. One concern is that an autonomous
agent might modify itself to become ""superintelligent"" and, in supremely
effective pursuit of poorly specified goals, destroy all of humanity. This
paper considers and rejects the possibility of this outcome. We argue that this
scenario depends on an agent's ability to rapidly improve its ability to
predict its environment through self-modification. Using a Bayesian model of a
reasoning agent, we show that there are important limitations to how an agent
may improve its predictive ability through self-modification alone. We conclude
that concern about this artificial intelligence outcome is misplaced and better
directed at policy questions around data access and storage.
",Don't Fear the Reaper: Refuting Bostrom's Superintelligence Argument
"  Large computer-understandable proofs consist of millions of intermediate
logical steps. The vast majority of such steps originate from manually selected
and manually guided heuristics applied to intermediate goals. So far, machine
learning has generally not been used to filter or generate these steps. In this
paper, we introduce a new dataset based on Higher-Order Logic (HOL) proofs, for
the purpose of developing new machine learning-based theorem-proving
strategies. We make this dataset publicly available under the BSD license. We
propose various machine learning tasks that can be performed on this dataset,
and discuss their significance for theorem proving. We also benchmark a set of
simple baseline machine learning models suited for the tasks (including
logistic regression, convolutional neural networks and recurrent neural
networks). The results of our baseline models show the promise of applying
machine learning to HOL theorem proving.
","HolStep: A Machine Learning Dataset for Higher-order Logic Theorem
  Proving"
"  Machine-learning techniques have been recently used with spectacular results
to generate artefacts such as music or text. However, these techniques are
still unable to capture and generate artefacts that are convincingly
structured. In this paper we present an approach to generate structured musical
sequences. We introduce a mechanism for sampling efficiently variations of
musical sequences. Given a input sequence and a statistical model, this
mechanism samples a set of sequences whose distance to the input sequence is
approximately within specified bounds. This mechanism is implemented as an
extension of belief propagation, and uses local fields to bias the generation.
We show experimentally that sampled sequences are indeed closely correlated to
the standard musical similarity measure defined by Mongeau and Sankoff. We then
show how this mechanism can used to implement composition strategies that
enforce arbitrary structure on a musical lead sheet generation problem.
",Sampling Variations of Lead Sheets
"  Plan Recognition algorithms require to recognize a complete hierarchy
explaining the agent's actions and goals. While the output of such algorithms
is informative to the recognizer, the cost of its calculation is high in
run-time, space, and completeness. Moreover, performing plan recognition online
requires the observing agent to reason about future actions that have not yet
been seen and maintain a set of hypotheses to support all possible options.
This paper presents a new and efficient algorithm for online plan recognition
called SLIM (Semi-Lazy Inference Mechanism). It combines both a bottom-up and
top-down parsing processes, which allow it to commit only to the minimum
necessary actions in real-time, but still provide complete hypotheses post
factum. We show both theoretically and empirically that although the
computational cost of this process is still exponential, there is a significant
improvement in run-time when compared to a state of the art of plan recognition
algorithm.
",SLIM: Semi-Lazy Inference Mechanism for Plan Recognition
"  Plan recognition algorithms infer agents' plans from their observed actions.
Due to imperfect knowledge about the agent's behavior and the environment, it
is often the case that there are multiple hypotheses about an agent's plans
that are consistent with the observations, though only one of these hypotheses
is correct. This paper addresses the problem of how to disambiguate between
hypotheses, by querying the acting agent about whether a candidate plan in one
of the hypotheses matches its intentions. This process is performed
sequentially and used to update the set of possible hypotheses during the
recognition process. The paper defines the sequential plan recognition process
(SPRP), which seeks to reduce the number of hypotheses using a minimal number
of queries. We propose a number of policies for the SPRP which use maximum
likelihood and information gain to choose which plan to query. We show this
approach works well in practice on two domains from the literature,
significantly reducing the number of hypotheses using fewer queries than a
baseline approach. Our results can inform the design of future plan recognition
systems that interleave the recognition process with intelligent interventions
of their users.
",Sequential Plan Recognition
"  We introduce FeUdal Networks (FuNs): a novel architecture for hierarchical
reinforcement learning. Our approach is inspired by the feudal reinforcement
learning proposal of Dayan and Hinton, and gains power and efficacy by
decoupling end-to-end learning across multiple levels -- allowing it to utilise
different resolutions of time. Our framework employs a Manager module and a
Worker module. The Manager operates at a lower temporal resolution and sets
abstract goals which are conveyed to and enacted by the Worker. The Worker
generates primitive actions at every tick of the environment. The decoupled
structure of FuN conveys several benefits -- in addition to facilitating very
long timescale credit assignment it also encourages the emergence of
sub-policies associated with different goals set by the Manager. These
properties allow FuN to dramatically outperform a strong baseline agent on
tasks that involve long-term credit assignment or memorisation. We demonstrate
the performance of our proposed system on a range of tasks from the ATARI suite
and also from a 3D DeepMind Lab environment.
",FeUdal Networks for Hierarchical Reinforcement Learning
"  Bellemare et al. (2016) introduced the notion of a pseudo-count, derived from
a density model, to generalize count-based exploration to non-tabular
reinforcement learning. This pseudo-count was used to generate an exploration
bonus for a DQN agent and combined with a mixed Monte Carlo update was
sufficient to achieve state of the art on the Atari 2600 game Montezuma's
Revenge. We consider two questions left open by their work: First, how
important is the quality of the density model for exploration? Second, what
role does the Monte Carlo update play in exploration? We answer the first
question by demonstrating the use of PixelCNN, an advanced neural density model
for images, to supply a pseudo-count. In particular, we examine the intrinsic
difficulties in adapting Bellemare et al.'s approach when assumptions about the
model are violated. The result is a more practical and general algorithm
requiring no special apparatus. We combine PixelCNN pseudo-counts with
different agent architectures to dramatically improve the state of the art on
several hard Atari games. One surprising finding is that the mixed Monte Carlo
update is a powerful facilitator of exploration in the sparsest of settings,
including Montezuma's Revenge.
",Count-Based Exploration with Neural Density Models
"  In recent years, work has been done to develop the theory of General
Reinforcement Learning (GRL). However, there are few examples demonstrating
these results in a concrete way. In particular, there are no examples
demonstrating the known results regarding gener- alised discounting. We have
added to the GRL simulation platform AIXIjs the functionality to assign an
agent arbitrary discount functions, and an environment which can be used to
determine the effect of discounting on an agent's policy. Using this, we
investigate how geometric, hyperbolic and power discounting affect an informed
agent in a simple MDP. We experimentally reproduce a number of theoretical
results, and discuss some related subtleties. It was found that the agent's
behaviour followed what is expected theoretically, assuming appropriate
parameters were chosen for the Monte-Carlo Tree Search (MCTS) planning
algorithm.
","Generalised Discount Functions applied to a Monte-Carlo AImu
  Implementation"
"  As a well-known NP-hard problem, the Three-Index Assignment Problem (AP3) has
attracted lots of research efforts for developing heuristics. However, existing
heuristics either obtain less competitive solutions or consume too much time.
In this paper, a new heuristic named Approximate Muscle guided Beam Search
(AMBS) is developed to achieve a good trade-off between solution quality and
running time. By combining the approximate muscle with beam search, the
solution space size can be significantly decreased, thus the time for searching
the solution can be sharply reduced. Extensive experimental results on the
benchmark indicate that the new algorithm is able to obtain solutions with
competitive quality and it can be employed on instances with largescale. Work
of this paper not only proposes a new efficient heuristic, but also provides a
promising method to improve the efficiency of beam search.
",Approximate Muscle Guided Beam Search for Three-Index Assignment Problem
"  Although the problem of a critique of robotic behavior in near-unanimous
agreement to human norms seems intractable, a starting point of such an
ambition is a framework of the collection of knowledge a priori and experience
a posteriori categorized as a set of synthetical judgments available to the
intelligence, translated into computer code. If such a proposal were
successful, an algorithm with ethically traceable behavior and cogent
equivalence to human cognition is established. This paper will propose the
application of Kant's critique of reason to current programming constructs of
an autonomous intelligent system.
",A proposal for ethically traceable artificial intelligence
"  We investigate how to model exchangeability with choice functions.
Exchangeability is a structural assessment on a sequence of uncertain
variables. We show how such assessments are a special indifference assessment,
and how that leads to a counterpart of de Finetti's Representation Theorem,
both in a finite and a countable context.
",Exchangeable choice functions
"  Supplier selection is a typical multi-criteria decision making (MCDM) problem
and lots of uncertain information exist inevitably. To address this issue, a
new method was proposed based on interval data fusion. Our method follows the
original way to generate classical basic probability assignment(BPA) determined
by the distance among the evidences. However, the weights of criteria are kept
as interval numbers to generate interval BPAs and do the fusion of interval
BPAs. Finally, the order is ranked and the decision is made according to the
obtained interval BPAs. In this paper, a numerical example of supplier
selection is applied to verify the feasibility and validity of our method. The
new method is presented aiming at solving multiple-criteria decision-making
problems in which the weights of criteria or experts are described in fuzzy
data like linguistic terms or interval data.
",Evidential supplier selection based on interval data fusion
"  Recently, triggered by the impressive results in TV-games or game of Go by
Google DeepMind, end-to-end reinforcement learning (RL) is collecting
attentions. Although little is known, the author's group has propounded this
framework for around 20 years and already has shown various functions that
emerge in a neural network (NN) through RL. In this paper, they are introduced
again at this timing.
  ""Function Modularization"" approach is deeply penetrated subconsciously. The
inputs and outputs for a learning system can be raw sensor signals and motor
commands. ""State space"" or ""action space"" generally used in RL show the
existence of functional modules. That has limited reinforcement learning to
learning only for the action-planning module. In order to extend reinforcement
learning to learning of the entire function on a huge degree of freedom of a
massively parallel learning system and to explain or develop human-like
intelligence, the author has believed that end-to-end RL from sensors to motors
using a recurrent NN (RNN) becomes an essential key. Especially in the higher
functions, this approach is very effective by being free from the need to
decide their inputs and outputs.
  The functions that emerge, we have confirmed, through RL using a NN cover a
broad range from real robot learning with raw camera pixel inputs to
acquisition of dynamic functions in a RNN. Those are (1)image recognition,
(2)color constancy (optical illusion), (3)sensor motion (active recognition),
(4)hand-eye coordination and hand reaching movement, (5)explanation of brain
activities, (6)communication, (7)knowledge transfer, (8)memory, (9)selective
attention, (10)prediction, (11)exploration. The end-to-end RL enables the
emergence of very flexible comprehensive functions that consider many things in
parallel although it is difficult to give the boundary of each function
clearly.
","Functions that Emerge through End-to-End Reinforcement Learning - The
  Direction for Artificial General Intelligence -"
"  Evaluating a policy by deploying it in the real world can be risky and
costly. Off-policy policy evaluation (OPE) algorithms use historical data
collected from running a previous policy to evaluate a new policy, which
provides a means for evaluating a policy without requiring it to ever be
deployed. Importance sampling is a popular OPE method because it is robust to
partial observability and works with continuous states and actions. However,
the amount of historical data required by importance sampling can scale
exponentially with the horizon of the problem: the number of sequential
decisions that are made. We propose using policies over temporally extended
actions, called options, and show that combining these policies with importance
sampling can significantly improve performance for long-horizon problems. In
addition, we can take advantage of special cases that arise due to
options-based policies to further improve the performance of importance
sampling. We further generalize these special cases to a general covariance
testing rule that can be used to decide which weights to drop in an IS
estimate, and derive a new IS algorithm called Incremental Importance Sampling
that can provide significantly more accurate estimates for a broad class of
domains.
","Using Options and Covariance Testing for Long Horizon Off-Policy Policy
  Evaluation"
"  Communication is not only an action of choosing a signal, but needs to
consider the context and sensor signals. It also needs to decide what
information is communicated and how it is represented in or understood from
signals. Therefore, communication should be realized comprehensively together
with its purpose and other functions.
  The recent successful results in end-to-end reinforcement learning (RL) show
the importance of comprehensive learning and the usefulness of end-to-end RL.
Although little is known, we have shown that a variety of communications emerge
through RL using a (recurrent) neural network (NN). Here, three of them are
introduced.
  In the 1st one, negotiation to avoid conflicts among 4 randomly-picked agents
was learned. Each agent generates a binary signal from the output of its
recurrent NN (RNN), and receives 4 signals from the agents three times. After
learning, each agent made an appropriate final decision after negotiation for
any combination of 4 agents. Differentiation of individuality among the agents
also could be seen.
  The 2nd one focused on discretization of communication signal. A sender agent
perceives the receiver's location and generates a continuous signal twice by
its RNN. A receiver agent receives them sequentially, and moves according to
its RNN's output to reach the sender's location. When noises were added to the
signal, it was binarized through learning and 2-bit communication was
established.
  The 3rd one focused on end-to-end comprehensive communication. A sender
receives 1,785-pixel real camera image on which a real robot can be seen, and
sends two sounds whose frequencies are computed by its NN. A receiver receives
them, and two motion commands for the robot are generated by its NN. After
learning, though some preliminary learning was necessary for the sender, the
robot could reach the goal from any initial location.
","Communications that Emerge through Reinforcement Learning Using a
  (Recurrent) Neural Network"
"  Quantum decision systems are being increasingly considered for use in
artificial intelligence applications. Classical and quantum nodes can be
distinguished based on certain correlations in their states. This paper
investigates some properties of the states obtained in a decision tree
structure. How these correlations may be mapped to the decision tree is
considered. Classical tree representations and approximations to quantum states
are provided.
",On Quantum Decision Trees
"  It is well-known that any admissible unidirectional heuristic search
algorithm must expand all states whose $f$-value is smaller than the optimal
solution cost when using a consistent heuristic. Such states are called ""surely
expanded"" (s.e.). A recent study characterized s.e. pairs of states for
bidirectional search with consistent heuristics: if a pair of states is s.e.
then at least one of the two states must be expanded. This paper derives a
lower bound, VC, on the minimum number of expansions required to cover all s.e.
pairs, and present a new admissible front-to-end bidirectional heuristic search
algorithm, Near-Optimal Bidirectional Search (NBS), that is guaranteed to do no
more than 2VC expansions. We further prove that no admissible front-to-end
algorithm has a worst case better than 2VC. Experimental results show that NBS
competes with or outperforms existing bidirectional search algorithms, and
often outperforms A* as well.
","Front-to-End Bidirectional Heuristic Search with Near-Optimal Node
  Expansions"
"  Axioms can be used to model derived predicates in domain- independent
planning models. Formulating models which use axioms can sometimes result in
problems with much smaller search spaces and shorter plans than the original
model. Previous work on axiom-aware planners focused solely on state- space
search planners. We propose axiom-aware planners based on answer set
programming and integer programming. We evaluate them on PDDL domains with
axioms and show that they can exploit additional expressivity of axioms.
",Axioms in Model-based Planners
"  Recently, reinforcement learning has been successfully applied to the logical
game of Go, various Atari games, and even a 3D game, Labyrinth, though it
continues to have problems in sparse reward settings. It is difficult to
explore, but also difficult to exploit, a small number of successes when
learning policy. To solve this issue, the subgoal and option framework have
been proposed. However, discovering subgoals online is too expensive to be used
to learn options in large state spaces. We propose Micro-objective learning
(MOL) to solve this problem. The main idea is to estimate how important a state
is while training and to give an additional reward proportional to its
importance. We evaluated our algorithm in two Atari games: Montezuma's Revenge
and Seaquest. With three experiments to each game, MOL significantly improved
the baseline scores. Especially in Montezuma's Revenge, MOL achieved two times
better results than the previous state-of-the-art model.
","Micro-Objective Learning : Accelerating Deep Reinforcement Learning
  through the Discovery of Continuous Subgoals"
"  RoboCup offers a set of benchmark problems for Artificial Intelligence in
form of official world championships since 1997. The most tactical advanced and
richest in terms of behavioural complexity of these is the 2D Soccer Simulation
League, a simulated robotic soccer competition. BetaRun is a new attempt
combining both machine learning and manual programming approaches, with the
ultimate goal to arrive at a team that is trained entirely from observing and
playing games, and a new development based on agent2D.
","BetaRun Soccer Simulation League Team: Variety, Complexity, and Learning"
"  The problem of finding conflict-free trajectories for multiple agents of
identical circular shape, operating in shared 2D workspace, is addressed in the
paper and decoupled, e.g., prioritized, approach is used to solve this problem.
Agents' workspace is tessellated into the square grid on which any-angle moves
are allowed, e.g. each agent can move into an arbitrary direction as long as
this move follows the straight line segment whose endpoints are tied to the
distinct grid elements. A novel any-angle planner based on Safe Interval Path
Planning (SIPP) algorithm is proposed to find trajectories for an agent moving
amidst dynamic obstacles (other agents) on a grid. This algorithm is then used
as part of a prioritized multi-agent planner AA-SIPP(m). On the theoretical,
side we show that AA-SIPP(m) is complete under well-defined conditions. On the
experimental side, in simulation tests with up to 200 agents involved, we show
that our planner finds much better solutions in terms of cost (up to 20%)
compared to the planners relying on cardinal moves only.
",Any-Angle Pathfinding for Multiple Agents Based on SIPP Algorithm
"  In this paper we look into the problem of planning over hybrid domains, where
change can be both discrete and instantaneous, or continuous over time. In
addition, it is required that each state on the trajectory induced by the
execution of plans complies with a given set of global constraints. We approach
the computation of plans for such domains as the problem of searching over a
deterministic state model. In this model, some of the successor states are
obtained by solving numerically the so-called initial value problem over a set
of ordinary differential equations (ODE) given by the current plan prefix.
These equations hold over time intervals whose duration is determined
dynamically, according to whether zero crossing events take place for a set of
invariant conditions. The resulting planner, FS+, incorporates these features
together with effective heuristic guidance. FS+ does not impose any of the
syntactic restrictions on process effects often found on the existing
literature on Hybrid Planning. A key concept of our approach is that a clear
separation is struck between planning and simulation time steps. The former is
the time allowed to observe the evolution of a given dynamical system before
committing to a future course of action, whilst the later is part of the model
of the environment. FS+ is shown to be a robust planner over a diverse set of
hybrid domains, taken from the existing literature on hybrid planning and
systems.
","Numerical Integration and Dynamic Discretization in Heuristic Search
  Planning over Hybrid Domains"
"  ""Cognitive synergy"" refers to a dynamic in which multiple cognitive
processes, cooperating to control the same cognitive system, assist each other
in overcoming bottlenecks encountered during their internal processing.
Cognitive synergy has been posited as a key feature of real-world general
intelligence, and has been used explicitly in the design of the OpenCog
cognitive architecture. Here category theory and related concepts are used to
give a formalization of the cognitive synergy concept.
  A series of formal models of intelligent agents is proposed, with increasing
specificity and complexity: simple reinforcement learning agents; ""cognit""
agents with an abstract memory and processing model; hypergraph-based agents
(in which ""cognit"" operations are carried out via hypergraphs); hypergraph
agents with a rich language of nodes and hyperlinks (such as the OpenCog
framework provides); ""PGMC"" agents whose rich hypergraphs are endowed with
cognitive processes guided via Probabilistic Growth and Mining of Combinations;
and finally variations of the PrimeAGI design, which is currently being built
on top of OpenCog.
  A notion of cognitive synergy is developed for cognitive processes acting
within PGMC agents, based on developing a formal notion of ""stuckness,"" and
defining synergy as a relationship between cognitive processes in which they
can help each other out when they get stuck. It is proposed that cognitive
processes relating to each other synergetically, associate in a certain way
with functors that map into each other via natural transformations. Cognitive
synergy is proposed to correspond to a certain inequality regarding the
relative costs of different paths through certain commutation diagrams.
  Applications of this notion of cognitive synergy to particular cognitive
phenomena, and specific cognitive processes in the PrimeAGI design, are
discussed.
",Toward a Formal Model of Cognitive Synergy
"  A new model of symbol grounding is presented, in which the structures of
natural language, logical semantics, perception and action are represented
categorically, and symbol grounding is modeled via the composition of morphisms
between the relevant categories. This model gives conceptual insight into the
fundamentally systematic nature of symbol grounding, and also connects
naturally to practical real-world AI systems in current research and commercial
use. Specifically, it is argued that the structure of linguistic syntax can be
modeled as a certain asymmetric monoidal category, as e.g. implicit in the link
grammar formalism; the structure of spatiotemporal relationships and action
plans can be modeled similarly using ""image grammars"" and ""action grammars"";
and common-sense logical semantic structure can be modeled using
dependently-typed lambda calculus with uncertain truth values. Given these
formalisms, the grounding of linguistic descriptions in spatiotemporal
perceptions and coordinated actions consists of following morphisms from
language to logic through to spacetime and body (for comprehension), and vice
versa (for generation). The mapping is indicated between the spatial
relationships in the Region Connection Calculus and Allen Interval Algebra and
corresponding entries in the link grammar syntax parsing dictionary. Further,
the abstractions introduced here are shown to naturally model the structures
and systems currently being deployed in the context of using the OpenCog
cognitive architecture to control Hanson Robotics humanoid robots.
",Symbol Grounding via Chaining of Morphisms
"  A novel partial order is defined on the space of digraphs or hypergraphs,
based on assessing the cost of producing a graph via a sequence of elementary
transformations. Leveraging work by Knuth and Skilling on the foundations of
inference, and the structure of Heyting algebras on graph space, this partial
order is used to construct an intuitionistic probability measure that applies
to either digraphs or hypergraphs. As logical inference steps can be
represented as transformations on hypergraphs representing logical statements,
this also yields an intuitionistic probability measure on spaces of theorems.
The central result is also extended to yield intuitionistic probabilities based
on more general weighted rule systems defined over bicartesian closed
categories.
","Cost-Based Intuitionist Probabilities on Spaces of Graphs, Hypergraphs
  and Theorems"
"  In cooperative multiagent planning, it can often be beneficial for an agent
to make commitments about aspects of its behavior to others, allowing them in
turn to plan their own behaviors without taking the agent's detailed behavior
into account. Extending previous work in the Bayesian setting, we consider
instead a worst-case setting in which the agent has a set of possible
environments (MDPs) it could be in, and develop a commitment semantics that
allows for probabilistic guarantees on the agent's behavior in any of the
environments it could end up facing. Crucially, an agent receives observations
(of reward and state transitions) that allow it to potentially eliminate
possible environments and thus obtain higher utility by adapting its policy to
the history of observations. We develop algorithms and provide theory and some
preliminary empirical results showing that they ensure an agent meets its
commitments with history-dependent policies while minimizing maximum regret
over the possible environments.
","Minimizing Maximum Regret in Commitment Constrained Sequential Decision
  Making"
"  Both the ethics of autonomous systems and the problems of their technical
implementation have by now been studied in some detail. Less attention has been
given to the areas in which these two separate concerns meet. This paper,
written by both philosophers and engineers of autonomous systems, addresses a
number of issues in machine ethics that are located at precisely the
intersection between ethics and engineering. We first discuss the main
challenges which, in our view, machine ethics posses to moral philosophy. We
them consider different approaches towards the conceptual design of autonomous
systems and their implications on the ethics implementation in such systems.
Then we examine problematic areas regarding the specification and verification
of ethical behavior in autonomous systems, particularly with a view towards the
requirements of future legislation. We discuss transparency and accountability
issues that will be crucial for any future wide deployment of autonomous
systems in society. Finally we consider the, often overlooked, possibility of
intentional misuse of AI systems and the possible dangers arising out of
deliberately unethical design, implementation, and use of autonomous robots.
",Towards Moral Autonomous Systems
"  Dempster-Shafer theory of evidence is widely applied to uncertainty modelling
and knowledge reasoning because of its advantages in dealing with uncertain
information. But some conditions or requirements, such as exclusiveness
hypothesis and completeness constraint, limit the development and application
of that theory to a large extend. To overcome the shortcomings and enhance its
capability of representing the uncertainty, a novel model, called D numbers,
has been proposed recently. However, many key issues, for example how to
implement the combination of D numbers, remain unsolved. In the paper, we have
explored the combination of D Numbers from a perspective of conflict
redistribution, and proposed two combination rules being suitable for different
situations for the fusion of two D numbers. The proposed combination rules can
reduce to the classical Dempster's rule in Dempster-Shafer theory under a
certain conditions. Numerical examples and discussion about the proposed rules
are also given in the paper.
","Exploring the Combination Rules of D Numbers From a Perspective of
  Conflict Redistribution"
"  Recent methods have adapted the well-established AGM and belief base
frameworks for belief change to cover belief revision in logic programs. In
this study here, we present two new sets of belief change operators for logic
programs. They focus on preserving the explicit relationships expressed in the
rules of a program, a feature that is missing in purely semantic approaches
that consider programs only in their entirety. In particular, operators of the
latter class fail to satisfy preservation and support, two important properties
for belief change in logic programs required to ensure intuitive results.
  We address this shortcoming of existing approaches by introducing partial
meet and ensconcement constructions for logic program belief change, which
allow us to define syntax-preserving operators that satisfy preservation and
support. Our work is novel in that our constructions not only preserve more
information from a logic program during a change operation than existing ones,
but they also facilitate natural definitions of contraction operators, the
first in the field to the best of our knowledge.
  In order to evaluate the rationality of our operators, we translate the
revision and contraction postulates from the AGM and belief base frameworks to
the logic programming setting. We show that our operators fully comply with the
belief base framework and formally state the interdefinability between our
operators. We further propose an algorithm that is based on modularising a
logic program to reduce partial meet and ensconcement revisions or contractions
to performing the operation only on the relevant modules of that program.
Finally, we compare our approach to two state-of-the-art logic program revision
methods and demonstrate that our operators address the shortcomings of one and
generalise the other method.
",Syntax-Preserving Belief Change Operators for Logic Programs
"  In practice, a ranking of objects with respect to given set of criteria is of
considerable importance. However, due to lack of knowledge, information of time
pressure, decision makers might not be able to provide a (crisp) ranking of
objects from the top to the bottom. Instead, some objects might be ranked
equally, or better than other objects only to some degree. In such cases, a
generalization of crisp rankings to fuzzy rankings can be more useful. The aim
of the article is to introduce the notion of a fuzzy ranking and to discuss its
several properties, namely orderings, similarity and indecisiveness. The
proposed approach can be used both for group decision making or multiple
criteria decision making when uncertainty is involved.
",Fuzzy Rankings: Properties and Applications
"  Pairwise comparisons are an important tool of modern (multiple criteria)
decision making. Since human judgments are often inconsistent, many studies
focused on the ways how to express and measure this inconsistency, and several
inconsistency indices were proposed as an alternative to Saaty inconsistency
index and inconsistency ratio for reciprocal pairwise comparisons matrices.
This paper aims to: firstly, introduce a new measure of inconsistency of
pairwise comparisons and to prove its basic properties; secondly, to postulate
an additional axiom, an upper boundary axiom, to an existing set of axioms; and
the last, but not least, the paper provides proofs of satisfaction of this
additional axiom by selected inconsistency indices as well as it provides their
numerical comparison.
","On Inconsistency Indices and Inconsistency Axioms in Pairwise
  Comparisons"
"  Two-timescale Stochastic Approximation (SA) algorithms are widely used in
Reinforcement Learning (RL). Their iterates have two parts that are updated
using distinct stepsizes. In this work, we develop a novel recipe for their
finite sample analysis. Using this, we provide a concentration bound, which is
the first such result for a two-timescale SA. The type of bound we obtain is
known as `lock-in probability'. We also introduce a new projection scheme, in
which the time between successive projections increases exponentially. This
scheme allows one to elegantly transform a lock-in probability into a
convergence rate result for projected two-timescale SA. From this latter
result, we then extract key insights on stepsize selection. As an application,
we finally obtain convergence rates for the projected two-timescale RL
algorithms GTD(0), GTD2, and TDC.
","Finite Sample Analysis of Two-Timescale Stochastic Approximation with
  Applications to Reinforcement Learning"
"  Knowledge graph embedding aims at translating the knowledge graph into
numerical representations by transforming the entities and relations into
continuous low-dimensional vectors. Recently, many methods [1, 5, 3, 2, 6] have
been proposed to deal with this problem, but existing single-thread
implementations of them are time-consuming for large-scale knowledge graphs.
Here, we design a unified parallel framework to parallelize these methods,
which achieves a significant time reduction without influencing the accuracy.
We name our framework as ParaGraphE, which provides a library for parallel
knowledge graph embedding. The source code can be downloaded from
https://github.com/LIBBLE/LIBBLE-MultiThread/tree/master/ParaGraphE .
",ParaGraphE: A Library for Parallel Knowledge Graph Embedding
"  We discuss the computational complexity of approximating maximum a posteriori
inference in sum-product networks. We first show NP-hardness in trees of height
two by a reduction from maximum independent set; this implies
non-approximability within a sublinear factor. We show that this is a tight
bound, as we can find an approximation within a linear factor in networks of
height two. We then show that, in trees of height three, it is NP-hard to
approximate the problem within a factor $2^{f(n)}$ for any sublinear function
$f$ of the size of the input $n$. Again, this bound is tight, as we prove that
the usual max-product algorithm finds (in any network) approximations within
factor $2^{c \cdot n}$ for some constant $c < 1$. Last, we present a simple
algorithm, and show that it provably produces solutions at least as good as,
and potentially much better than, the max-product algorithm. We empirically
analyze the proposed algorithm against max-product using synthetic and
realistic networks.
","Approximation Complexity of Maximum A Posteriori Inference in
  Sum-Product Networks"
"  Since Alan Turing envisioned Artificial Intelligence (AI) [1], a major
driving force behind technical progress has been competition with human
cognition. Historical milestones have been frequently associated with computers
matching or outperforming humans in difficult cognitive tasks (e.g. face
recognition [2], personality classification [3], driving cars [4], or playing
video games [5]), or defeating humans in strategic zero-sum encounters (e.g.
Chess [6], Checkers [7], Jeopardy! [8], Poker [9], or Go [10]). In contrast,
less attention has been given to developing autonomous machines that establish
mutually cooperative relationships with people who may not share the machine's
preferences. A main challenge has been that human cooperation does not require
sheer computational power, but rather relies on intuition [11], cultural norms
[12], emotions and signals [13, 14, 15, 16], and pre-evolved dispositions
toward cooperation [17], common-sense mechanisms that are difficult to encode
in machines for arbitrary contexts. Here, we combine a state-of-the-art
machine-learning algorithm with novel mechanisms for generating and acting on
signals to produce a new learning algorithm that cooperates with people and
other machines at levels that rival human cooperation in a variety of
two-player repeated stochastic games. This is the first general-purpose
algorithm that is capable, given a description of a previously unseen game
environment, of learning to cooperate with people within short timescales in
scenarios previously unanticipated by algorithm designers. This is achieved
without complex opponent modeling or higher-order theories of mind, thus
showing that flexible, fast, and general human-machine cooperation is
computationally achievable using a non-trivial, but ultimately simple, set of
algorithmic mechanisms.
",Cooperating with Machines
"  Most games have, or can be generalised to have, a number of parameters that
may be varied in order to provide instances of games that lead to very
different player experiences. The space of possible parameter settings can be
seen as a search space, and we can therefore use a Random Mutation Hill
Climbing algorithm or other search methods to find the parameter settings that
induce the best games. One of the hardest parts of this approach is defining a
suitable fitness function. In this paper we explore the possibility of using
one of a growing set of General Video Game AI agents to perform automatic
play-testing. This enables a very general approach to game evaluation based on
estimating the skill-depth of a game. Agent-based play-testing is
computationally expensive, so we compare two simple but efficient optimisation
algorithms: the Random Mutation Hill-Climber and the Multi-Armed Bandit Random
Mutation Hill-Climber. For the test game we use a space-battle game in order to
provide a suitable balance between simulation speed and potential skill-depth.
Results show that both algorithms are able to rapidly evolve game versions with
significant skill-depth, but that choosing a suitable resampling number is
essential in order to combat the effects of noise.
",Evolving Game Skill-Depth using General Video Game AI Agents
"  Influence diagrams are a decision-theoretic extension of probabilistic
graphical models. In this paper we show how they can be used to solve the
Goddard problem. We present results of numerical experiments with this problem
and compare the solutions provided by influence diagrams with the optimal
solution.
",Solving the Goddard problem by an influence diagram
"  Research on human self-regulation has shown that people hold many goals
simultaneously and have complex self-regulation mechanisms to deal with this
goal conflict. Artificial autonomous systems may also need to find ways to cope
with conflicting goals. Indeed, the intricate interplay among different goals
may be critical to the design as well as long-term safety and stability of
artificial autonomous systems. I discuss some of the critical features of the
human self-regulation system and how it might be applied to an artificial
system. Furthermore, the implications of goal conflict for the reliability and
stability of artificial autonomous systems and ensuring their alignment with
human goals and ethics is examined.
",Goal Conflict in Designing an Autonomous Artificial System
"  Deliberating on large or continuous state spaces have been long standing
challenges in reinforcement learning. Temporal Abstraction have somewhat made
this possible, but efficiently planing using temporal abstraction still remains
an issue. Moreover using spatial abstractions to learn policies for various
situations at once while using temporal abstraction models is an open problem.
We propose here an efficient algorithm which is convergent under linear
function approximation while planning using temporally abstract actions. We
show how this algorithm can be used along with randomly generated option models
over multiple time scales to plan agents which need to act real time. Using
these randomly generated option models over multiple time scales are shown to
reduce number of decision epochs required to solve the given task, hence
effectively reducing the time needed for deliberation.
","Multi-Timescale, Gradient Descent, Temporal Difference Learning with
  Linear Options"
"  Robust belief revision methods are crucial in streaming data situations for
updating existing knowledge or beliefs with new incoming evidence. Bayes
conditioning is the primary mechanism in use for belief revision in data fusion
systems that use probabilistic inference. However, traditional conditioning
methods face several challenges due to inherent data/source imperfections in
big-data environments that harness soft (i.e., human or human-based) sources in
addition to hard (i.e., physics-based) sensors. The objective of this paper is
to investigate the most natural extension of Bayes conditioning that is
suitable for evidence updating in the presence of such uncertainties. By
viewing the evidence updating process as a thought experiment, an elegant
strategy is derived for robust evidence updating in the presence of extreme
uncertainties that are characteristic of big-data environments. In particular,
utilizing the Fagin-Halpern conditional notions, a natural extension to Bayes
conditioning is derived for evidence that takes the form of a general belief
function. The presented work differs fundamentally from the Conditional Update
Equation (CUE) and authors own extensions of it. An overview of this
development is provided via illustrative examples. Furthermore, insights into
parameter selection under various fusion contexts are also provided.
","Evidence Updating for Stream-Processing in Big-Data: Robust Conditioning
  in Soft and Hard Fusion Environments"
"  The advent of artificial intelligence has changed many disciplines such as
engineering, social science and economics. Artificial intelligence is a
computational technique which is inspired by natural intelligence such as the
swarming of birds, the working of the brain and the pathfinding of the ants.
These techniques have impact on economic theories. This book studies the impact
of artificial intelligence on economic theories, a subject that has not been
extensively studied. The theories that are considered are: demand and supply,
asymmetrical information, pricing, rational choice, rational expectation, game
theory, efficient market hypotheses, mechanism design, prospect, bounded
rationality, portfolio theory, rational counterfactual and causality. The
benefit of this book is that it evaluates existing theories of economics and
update them based on the developments in artificial intelligence field.
",Artificial Intelligence and Economic Theories
"  We present PEC, an Event Calculus (EC) style action language for reasoning
about probabilistic causal and narrative information. It has an action language
style syntax similar to that of the EC variant Modular-E. Its semantics is
given in terms of possible worlds which constitute possible evolutions of the
domain, and builds on that of EFEC, an epistemic extension of EC. We also
describe an ASP implementation of PEC and show the sense in which this is sound
and complete.
",Foundations for a Probabilistic Event Calculus
"  Privacy has traditionally been a major motivation for distributed problem
solving. Distributed Constraint Satisfaction Problem (DisCSP) as well as
Distributed Constraint Optimization Problem (DCOP) are fundamental models used
to solve various families of distributed problems. Even though several
approaches have been proposed to quantify and preserve privacy in such
problems, none of them is exempt from limitations. Here we approach the problem
by assuming that computation is performed among utilitarian agents. We
introduce a utilitarian approach where the utility of each state is estimated
as the difference between the reward for reaching an agreement on assignments
of shared variables and the cost of privacy loss. We investigate extensions to
solvers where agents integrate the utility function to guide their search and
decide which action to perform, defining thereby their policy. We show that
these extended solvers succeed in significantly reducing privacy loss without
significant degradation of the solution quality.
","Distributed Constraint Problems for Utilitarian Agents with Privacy
  Concerns, Recast as POMDPs"
"  Catastrophic forgetting is of special importance in reinforcement learning,
as the data distribution is generally non-stationary over time. We study and
compare several pseudorehearsal approaches for Q-learning with function
approximation in a pole balancing task. We have found that pseudorehearsal
seems to assist learning even in such very simple problems, given proper
initialization of the rehearsal parameters.
",Pseudorehearsal in value function approximation
"  The problem of automatically generating a computer program from some
specification has been studied since the early days of AI. Recently, two
competing approaches for automatic program learning have received significant
attention: (1) neural program synthesis, where a neural network is conditioned
on input/output (I/O) examples and learns to generate a program, and (2) neural
program induction, where a neural network generates new outputs directly using
a latent program representation.
  Here, for the first time, we directly compare both approaches on a
large-scale, real-world learning task. We additionally contrast to rule-based
program synthesis, which uses hand-crafted semantics to guide the program
generation. Our neural models use a modified attention RNN to allow encoding of
variable-sized sets of I/O pairs. Our best synthesis model achieves 92%
accuracy on a real-world test set, compared to the 34% accuracy of the previous
best neural synthesis approach. The synthesis model also outperforms a
comparable induction model on this task, but we more importantly demonstrate
that the strength of each approach is highly dependent on the evaluation metric
and end-user application. Finally, we show that we can train our neural models
to remain very robust to the type of noise expected in real-world data (e.g.,
typos), while a highly-engineered rule-based system fails entirely.
",RobustFill: Neural Program Learning under Noisy I/O
"  Diversification-Based Learning (DBL) derives from a collection of principles
and methods introduced in the field of metaheuristics that have broad
applications in computing and optimization. We show that the DBL framework goes
significantly beyond that of the more recent Opposition-based learning (OBL)
framework introduced in Tizhoosh (2005), which has become the focus of numerous
research initiatives in machine learning and metaheuristic optimization. We
unify and extend earlier proposals in metaheuristic search (Glover, 1997,
Glover and Laguna, 1997) to give a collection of approaches that are more
flexible and comprehensive than OBL for creating intensification and
diversification strategies in metaheuristic search. We also describe potential
applications of DBL to various subfields of machine learning and optimization.
",Diversification-Based Learning in Computing and Optimization
"  We extend the $ASPIC^+$ framework for structured argumentation so as to allow
applications of the reasoning by cases inference scheme for defeasible
arguments. Given an argument with conclusion `$A$ or $B$', an argument based on
$A$ with conclusion $C$, and an argument based on $B$ with conclusion $C$, we
allow the construction of an argument with conclusion $C$. We show how our
framework leads to different results than other approaches in non-monotonic
logic for dealing with disjunctive information, such as disjunctive default
theory or approaches based on the OR-rule (which allows to derive a defeasible
rule `If ($A$ or $B$) then $C$', given two defeasible rules `If $A$ then $C$'
and `If $B$ then $C$'). We raise new questions regarding the subtleties of
reasoning defeasibly with disjunctive information, and show that its
formalization is more intricate than one would presume.
",Reasoning by Cases in Structured Argumentation
"  Whether teaching in a classroom or a Massive Online Open Course it is crucial
to present the material in a way that benefits the audience as a whole. We
identify two important tasks to solve towards this objective, 1 group students
so that they can maximally benefit from peer interaction and 2 find an optimal
schedule of the educational material for each group. Thus, in this paper, we
solve the problem of team formation and content scheduling for education. Given
a time frame d, a set of students S with their required need to learn different
activities T and given k as the number of desired groups, we study the problem
of finding k group of students. The goal is to teach students within time frame
d such that their potential for learning is maximized and find the best
schedule for each group. We show this problem to be NP-hard and develop a
polynomial algorithm for it. We show our algorithm to be effective both on
synthetic as well as a real data set. For our experiments, we use real data on
students' grades in a Computer Science department. As part of our contribution,
we release a semi-synthetic dataset that mimics the properties of the real
data.
","Team Formation for Scheduling Educational Material in Massive Online
  Classes"
"  Based on a weighted knowledge graph to represent first-order knowledge and
combining it with a probabilistic model, we propose a methodology for the
creation of a medical knowledge network (MKN) in medical diagnosis. When a set
of symptoms is activated for a specific patient, we can generate a ground
medical knowledge network composed of symptom nodes and potential disease
nodes. By Incorporating a Boltzmann machine into the potential function of a
Markov network, we investigated the joint probability distribution of the MKN.
In order to deal with numerical symptoms, a multivariate inference model is
presented that uses conditional probability. In addition, the weights for the
knowledge graph were efficiently learned from manually annotated Chinese
Electronic Medical Records (CEMRs). In our experiments, we found numerically
that the optimum choice of the quality of disease node and the expression of
symptom variable can improve the effectiveness of medical diagnosis. Our
experimental results comparing a Markov logic network and the logistic
regression algorithm on an actual CEMR database indicate that our method holds
promise and that MKN can facilitate studies of intelligent diagnosis.
","Learning and inference in knowledge-based probabilistic model for
  medical diagnosis"
"  The exponential explosion of the set of patterns is one of the main
challenges in pattern mining. This challenge is approached by introducing a
constraint for pattern selection. One of the first constraints proposed in
pattern mining is support (frequency) of a pattern in a dataset. Frequency is
an anti-monotonic function, i.e., given an infrequent pattern, all its
superpatterns are not frequent. However, many other constraints for pattern
selection are neither monotonic nor anti-monotonic, which makes it difficult to
generate patterns satisfying these constraints.
  In order to deal with nonmonotonic constraints we introduce the notion of
""projection antimonotonicity"" and SOFIA algorithm that allow generating best
patterns for a class of nonmonotonic constraints. Cosine interest, robustness,
stability of closed itemsets, and the associated delta-measure are among these
constraints. SOFIA starts from light descriptions of transactions in dataset (a
small set of items in the case of itemset description) and then iteratively
adds more information to these descriptions (more items with indication of
tidsets they describe).
","Mining Best Closed Itemsets for Projection-antimonotonic Constraints in
  Polynomial Time"
"  Classical higher-order logic, when utilized as a meta-logic in which various
other (classical and non-classical) logics can be shallowly embedded, is well
suited for realising a universal logic reasoning approach. Universal logic
reasoning in turn, as envisioned already by Leibniz, may support the rigorous
formalisation and deep logical analysis of rational arguments within machines.
A respective universal logic reasoning framework is described and a range of
exemplary applications are discussed. In the future, universal logic reasoning
in combination with appropriate, controlled forms of rational argumentation may
serve as a communication layer between humans and intelligent machines.
","Universal Reasoning, Rational Argumentation and Human-Machine
  Interaction"
"  Self-paced learning (SPL) is a new methodology that simulates the learning
principle of humans/animals to start learning easier aspects of a learning
task, and then gradually take more complex examples into training. This
new-coming learning regime has been empirically substantiated to be effective
in various computer vision and pattern recognition tasks. Recently, it has been
proved that the SPL regime has a close relationship to a implicit self-paced
objective function. While this implicit objective could provide helpful
interpretations to the effectiveness, especially the robustness, insights under
the SPL paradigms, there are still no theoretical results strictly proved to
verify such relationship. To this issue, in this paper, we provide some
convergence results on this implicit objective of SPL. Specifically, we prove
that the learning process of SPL always converges to critical points of this
implicit objective under some mild conditions. This result verifies the
intrinsic relationship between SPL and this implicit objective, and makes the
previous robustness analysis on SPL complete and theoretically rational.
",On Convergence Property of Implicit Self-paced Objective
"  In this paper, we address the problem of how automated situation-awareness
can be achieved by learning real-world situations from ubiquitously generated
mobility data. Without semantic input about the time and space where situations
take place, this turns out to be a fundamental challenging problem.
Uncertainties also introduce technical challenges when data is generated in
irregular time intervals, being mixed with noise, and errors. Purely relying on
temporal patterns observable in mobility data, in this paper, we propose
Spaceprint, a fully automated algorithm for finding the repetitive pattern of
similar situations in spaces. We evaluate this technique by showing how the
latent variables describing the category, and the actual identity of a space
can be discovered from the extracted situation patterns. Doing so, we use
different real-world mobility datasets with data about the presence of mobile
entities in a variety of spaces. We also evaluate the performance of this
technique by showing its robustness against uncertainties.
",Spaceprint: a Mobility-based Fingerprinting Scheme for Public Spaces
"  Knowledge graph embedding aims to embed entities and relations of knowledge
graphs into low-dimensional vector spaces. Translating embedding methods regard
relations as the translation from head entities to tail entities, which achieve
the state-of-the-art results among knowledge graph embedding methods. However,
a major limitation of these methods is the time consuming training process,
which may take several days or even weeks for large knowledge graphs, and
result in great difficulty in practical applications. In this paper, we propose
an efficient parallel framework for translating embedding methods, called
ParTrans-X, which enables the methods to be paralleled without locks by
utilizing the distinguished structures of knowledge graphs. Experiments on two
datasets with three typical translating embedding methods, i.e., TransE [3],
TransH [17], and a more efficient variant TransE- AdaGrad [10] validate that
ParTrans-X can speed up the training process by more than an order of
magnitude.
",Efficient Parallel Translating Embedding For Knowledge Graphs
"  We present a novel heuristic approach that defines fuzzy geographical
descriptors using data gathered from a survey with human subjects. The
participants were asked to provide graphical interpretations of the descriptors
`north' and `south' for the Galician region (Spain). Based on these
interpretations, our approach builds fuzzy descriptors that are able to compute
membership degrees for geographical locations. We evaluated our approach in
terms of efficiency and precision. The fuzzy descriptors are meant to be used
as the cornerstones of a geographical referring expression generation algorithm
that is able to linguistically characterize geographical locations and regions.
This work is also part of a general research effort that intends to establish a
methodology which reunites the empirical studies traditionally practiced in
data-to-text and the use of fuzzy sets to model imprecision and vagueness in
words and expressions for text generation purposes.
",An Empirical Approach for Modeling Fuzzy Geographical Descriptors
"  Ontology alignment is widely-used to find the correspondences between
different ontologies in diverse fields.After discovering the alignments,several
performance scores are available to evaluate them.The scores typically require
the identified alignment and a reference containing the underlying actual
correspondences of the given ontologies.The current trend in the alignment
evaluation is to put forward a new score(e.g., precision, weighted precision,
etc.)and to compare various alignments by juxtaposing the obtained scores.
However,it is substantially provocative to select one measure among others for
comparison.On top of that, claiming if one system has a better performance than
one another cannot be substantiated solely by comparing two scalars.In this
paper,we propose the statistical procedures which enable us to theoretically
favor one system over one another.The McNemar's test is the statistical means
by which the comparison of two ontology alignment systems over one matching
task is drawn.The test applies to a 2x2 contingency table which can be
constructed in two different ways based on the alignments,each of which has
their own merits/pitfalls.The ways of the contingency table construction and
various apposite statistics from the McNemar's test are elaborated in minute
detail.In the case of having more than two alignment systems for comparison,
the family-wise error rate is expected to happen. Thus, the ways of preventing
such an error are also discussed.A directed graph visualizes the outcome of the
McNemar's test in the presence of multiple alignment systems.From this graph,
it is readily understood if one system is better than one another or if their
differences are imperceptible.The proposed statistical methodologies are
applied to the systems participated in the OAEI 2016 anatomy track, and also
compares several well-known similarity metrics for the same matching problem.
","Comparison of ontology alignment systems across single matching task via
  the McNemar's test"
"  In this paper, we present a new algorithm for parallel Monte Carlo tree
search (MCTS). It is based on the pipeline pattern and allows flexible
management of the control flow of the operations in parallel MCTS. The pipeline
pattern provides for the first structured parallel programming approach to
MCTS. Moreover, we propose a new lock-free tree data structure for parallel
MCTS which removes synchronization overhead. The Pipeline Pattern for Parallel
MCTS algorithm (called 3PMCTS), scales very well to higher numbers of cores
when compared to the existing methods.
",Structured Parallel Programming for Monte Carlo Tree Search
"  This chapter describes the history of metaheuristics in five distinct
periods, starting long before the first use of the term and ending a long time
in the future.
",A History of Metaheuristics
"  We propose a simulated annealing algorithm specifically tailored to optimise
total retrieval times in a multi-level warehouse under complex pre-batched
picking constraints. Experiments on real data from a picker-to-parts order
picking process in the warehouse of a European manufacturer show that optimal
storage assignments do not necessarily display features presumed in heuristics,
such as clustering of positively correlated items or ordering of items by
picking frequency.
  In an experiment run on more than 4000 batched orders with 1 to 150 items per
batch, the storage assignment suggested by the algorithm produces a 21\%
reduction in the total retrieval time with respect to a frequency-based storage
assignment.
","A simulated annealing approach to optimal storing in a multi-level
  warehouse"
"  TD(0) is one of the most commonly used algorithms in reinforcement learning.
Despite this, there is no existing finite sample analysis for TD(0) with
function approximation, even for the linear case. Our work is the first to
provide such results. Existing convergence rates for Temporal Difference (TD)
methods apply only to somewhat modified versions, e.g., projected variants or
ones where stepsizes depend on unknown problem parameters. Our analyses obviate
these artificial alterations by exploiting strong properties of TD(0). We
provide convergence rates both in expectation and with high-probability. The
two are obtained via different approaches that use relatively unknown, recently
developed stochastic approximation techniques.
",Finite Sample Analyses for TD(0) with Function Approximation
"  This paper suggests a new interpretation of the Dempster-Shafer theory in
terms of probabilistic interpretation of plausibility. A new rule of
combination of independent evidence is shown and its preservation of
interpretation is demonstrated.
","Transferrable Plausibility Model - A Probabilistic Interpretation of
  Mathematical Theory of Evidence"
"  An overview of current debates and contemporary research devoted to the
modeling of decision making processes and their facilitation directs attention
to the Analytic Hierarchy Process (AHP). At the core of the AHP are various
prioritization procedures (PPs) and consistency measures (CMs) for a Pairwise
Comparison Matrix (PCM) which, in a sense, reflects preferences of decision
makers. Certainly, when judgments about these preferences are perfectly
consistent (cardinally transitive), all PPs coincide and the quality of the
priority ratios (PRs) estimation is exemplary. However, human judgments are
very rarely consistent, thus the quality of PRs estimation may significantly
vary. The scale of these variations depends on the applied PP and utilized CM
for a PCM. This is why it is important to find out which PPs and which CMs for
a PCM lead directly to an improvement of the PRs estimation accuracy. The main
goal of this research is realized through the properly designed, coded and
executed seminal and sophisticated simulation algorithms in Wolfram Mathematica
8.0. These research results convince that the embedded in the AHP and commonly
applied, both genuine PP and CM for PCM may significantly deteriorate the
quality of PRs estimation; however, solutions proposed in this paper can
significantly improve the methodology.
","The quality of priority ratios estimation in relation to a selected
  prioritization procedure and consistency measure for a Pairwise Comparison
  Matrix"
"  The paper presents a novel view of the Dempster-Shafer belief function as a
measure of diversity in relational data bases. It is demonstrated that under
the interpretation The Dempster rule of evidence combination corresponds to the
join operator of the relational database theory. This rough-set based
interpretation is qualitative in nature and can represent a number of belief
function operators.
  The interpretation has the property that Given a definition of the belief
measure of objects in the interpretation domain we can perform operations in
this domain and the measure of the resulting object is derivable from measures
of component objects via belief operator. We demonstrated this property for
Dempster rule of combination, marginalization, Shafer's conditioning,
independent variables, Shenoy's notion of conditional independence of
variables.
  The interpretation is based on rough sets (in connection with decision
tables), but differs from previous interpretations of this type in that it
counts the diversity rather than frequencies in a decision table.
","Basic Formal Properties of A Relational Model of The Mathematical Theory
  of Evidence"
"  The media industry is increasingly personalizing the offering of contents in
attempt to better target the audience. This requires to analyze the
relationships that goes established between users and content they enjoy,
looking at one side to the content characteristics and on the other to the user
profile, in order to find the best match between the two. In this paper we
suggest to build that relationship using the Dempster-Shafer's Theory of
Evidence, proposing a reference model and illustrating its properties by means
of a toy example. Finally we suggest possible applications of the model for
tasks that are common in the modern media industry.
","Matching Media Contents with User Profiles by means of the
  Dempster-Shafer Theory"
"  Fundamental discrepancy between first order logic and statistical inference
(global versus local properties of universe) is shown to be the obstacle for
integration of logic and probability in L.p. logic of Bacchus. To overcome the
counterintuitiveness of L.p. behaviour, a 3-valued logic is proposed.
","Beliefs and Probability in Bacchus' l.p. Logic: A~3-Valued Logic
  Solution to Apparent Counter-intuition"
"  Business Intelligence and Analytics (BI&A) is the process of extracting and
predicting business-critical insights from data. Traditional BI focused on data
collection, extraction, and organization to enable efficient query processing
for deriving insights from historical data. With the rise of big data and cloud
computing, there are many challenges and opportunities for the BI. Especially
with the growing number of data sources, traditional BI\&A are evolving to
provide intelligence at different scales and perspectives - operational BI,
situational BI, self-service BI. In this survey, we review the evolution of
business intelligence systems in full scale from back-end architecture to and
front-end applications. We focus on the changes in the back-end architecture
that deals with the collection and organization of the data. We also review the
changes in the front-end applications, where analytic services and
visualization are the core components. Using a uses case from BI in Healthcare,
which is one of the most complex enterprises, we show how BI\&A will play an
important role beyond the traditional usage. The survey provides a holistic
view of Business Intelligence and Analytics for anyone interested in getting a
complete picture of the different pieces in the emerging next generation BI\&A
solutions.
",Next Generation Business Intelligence and Analytics: A Survey
"  CASP is an extension of ASP that allows for numerical constraints to be added
in the rules. PDDL+ is an extension of the PDDL standard language of automated
planning for modeling mixed discrete-continuous dynamics.
  In this paper, we present CASP solutions for dealing with PDDL+ problems,
i.e., encoding from PDDL+ to CASP, and extensions to the algorithm of the EZCSP
CASP solver in order to solve CASP programs arising from PDDL+ domains. An
experimental analysis, performed on well-known linear and non-linear variants
of PDDL+ domains, involving various configurations of the EZCSP solver, other
CASP solvers, and PDDL+ planners, shows the viability of our solution.
",CASP Solutions for Planning in Hybrid Domains
"  In this paper, we develop a novel paradigm, namely hypergraph shift, to find
robust graph modes by probabilistic voting strategy, which are semantically
sound besides the self-cohesiveness requirement in forming graph modes. Unlike
the existing techniques to seek graph modes by shifting vertices based on
pair-wise edges (i.e, an edge with $2$ ends), our paradigm is based on shifting
high-order edges (hyperedges) to deliver graph modes. Specifically, we convert
the problem of seeking graph modes as the problem of seeking maximizers of a
novel objective function with the aim to generate good graph modes based on
sifting edges in hypergraphs. As a result, the generated graph modes based on
dense subhypergraphs may more accurately capture the object semantics besides
the self-cohesiveness requirement. We also formally prove that our technique is
always convergent. Extensive empirical studies on synthetic and real world data
sets are conducted on clustering and graph matching. They demonstrate that our
techniques significantly outperform the existing techniques.
",Finding Modes by Probabilistic Hypergraphs Shifting
"  This paper is devoted to expressiveness of hypergraphs for which uncertainty
propagation by local computations via Shenoy/Shafer method applies. It is
demonstrated that for this propagation method for a given joint belief
distribution no valuation of hyperedges of a hypergraph may provide with
simpler hypergraph structure than valuation of hyperedges by conditional
distributions. This has vital implication that methods recovering belief
networks from data have no better alternative for finding the simplest
hypergraph structure for belief propagation. A method for recovery
tree-structured belief networks has been developed and specialized for
Dempster-Shafer belief functions
",Beliefs in Markov Trees - From Local Computations to Local Valuation
"  We develop our interpretation of the joint belief distribution and of
evidential updating that matches the following basic requirements:
  * there must exist an efficient method for reasoning within this framework
  * there must exist a clear correspondence between the contents of the
knowledge base and the real world
  * there must be a clear correspondence between the reasoning method and some
real world process
  * there must exist a clear correspondence between the results of the
reasoning process and the results of the real world process corresponding to
the reasoning process.
",Dempster-Shafer Belief Function - A New Interpretation
"  We propose a new task-specification language for Markov decision processes
that is designed to be an improvement over reward functions by being
environment independent. The language is a variant of Linear Temporal Logic
(LTL) that is extended to probabilistic specifications in a way that permits
approximations to be learned in finite time. We provide several small
environments that demonstrate the advantages of our geometric LTL (GLTL)
language and illustrate how it can be used to specify standard
reinforcement-learning tasks straightforwardly.
",Environment-Independent Task Specifications via GLTL
"  In this work we present a new agent architecture, called Reactor, which
combines multiple algorithmic and architectural contributions to produce an
agent with higher sample-efficiency than Prioritized Dueling DQN (Wang et al.,
2016) and Categorical DQN (Bellemare et al., 2017), while giving better
run-time performance than A3C (Mnih et al., 2016). Our first contribution is a
new policy evaluation algorithm called Distributional Retrace, which brings
multi-step off-policy updates to the distributional reinforcement learning
setting. The same approach can be used to convert several classes of multi-step
policy evaluation algorithms designed for expected value evaluation into
distributional ones. Next, we introduce the \b{eta}-leave-one-out policy
gradient algorithm which improves the trade-off between variance and bias by
using action values as a baseline. Our final algorithmic contribution is a new
prioritized replay algorithm for sequences, which exploits the temporal
locality of neighboring observations for more efficient replay prioritization.
Using the Atari 2600 benchmarks, we show that each of these innovations
contribute to both the sample efficiency and final agent performance. Finally,
we demonstrate that Reactor reaches state-of-the-art performance after 200
million frames and less than a day of training.
","The Reactor: A fast and sample-efficient Actor-Critic agent for
  Reinforcement Learning"
"  In this paper, we present a robotic prediction agent including a darkforest
Go engine, a fuzzy markup language (FML) assessment engine, an FML-based
decision support engine, and a robot engine for game of Go application. The
knowledge base and rule base of FML assessment engine are constructed by
referring the information from the darkforest Go engine located in NUTN and
OPU, for example, the number of MCTS simulations and winning rate prediction.
The proposed robotic prediction agent first retrieves the database of Go
competition website, and then the FML assessment engine infers the winning
possibility based on the information generated by darkforest Go engine. The
FML-based decision support engine computes the winning possibility based on the
partial game situation inferred by FML assessment engine. Finally, the robot
engine combines with the human-friendly robot partner PALRO, produced by
Fujisoft incorporated, to report the game situation to human Go players.
Experimental results show that the FML-based prediction agent can work
effectively.
",FML-based Prediction Agent and Its Application to Game of Go
"  The weighted Maximum Satisfiability problem (weighted MAX-SAT) is a NP-hard
problem with numerous applications arising in artificial intelligence. As an
efficient tool for heuristic design, the backbone has been applied to
heuristics design for many NP-hard problems. In this paper, we investigated the
computational complexity for retrieving the backbone in weighted MAX-SAT and
developed a new algorithm for solving this problem. We showed that it is
intractable to retrieve the full backbone under the assumption that . Moreover,
it is intractable to retrieve a fixed fraction of the backbone as well. And
then we presented a backbone guided local search (BGLS) with Walksat operator
for weighted MAX-SAT. BGLS consists of two phases: the first phase samples the
backbone information from local optima and the backbone phase conducts local
search under the guideline of backbone. Extensive experimental results on the
benchmark showed that BGLS outperforms the existing heuristics in both solution
quality and runtime.
","Approximating the Backbone in the Weighted Maximum Satisfiability
  Problem"
"  Catastrophic forgetting has a serious impact in reinforcement learning, as
the data distribution is generally sparse and non-stationary over time. The
purpose of this study is to investigate whether pseudorehearsal can increase
performance of an actor-critic agent with neural-network based policy selection
and function approximation in a pole balancing task and compare different
pseudorehearsal approaches. We expect that pseudorehearsal assists learning
even in such very simple problems, given proper initialization of the rehearsal
parameters.
",Pseudorehearsal in actor-critic agents
"  Intelligent systems sometimes need to infer the probable goals of people,
cars, and robots, based on partial observations of their motion. This paper
introduces a class of probabilistic programs for formulating and solving these
problems. The formulation uses randomized path planning algorithms as the basis
for probabilistic models of the process by which autonomous agents plan to
achieve their goals. Because these path planning algorithms do not have
tractable likelihood functions, new inference algorithms are needed. This paper
proposes two Monte Carlo techniques for these ""likelihood-free"" models, one of
which can use likelihood estimates from neural networks to accelerate
inference. The paper demonstrates efficacy on three simple examples, each using
under 50 lines of probabilistic code.
",Probabilistic programs for inferring the goals of autonomous agents
"  The advent of the Big Data hype and the consistent recollection of event logs
and real-time data from sensors, monitoring software and machine configuration
has generated a huge amount of time-varying data in about every sector of the
industry. Rule-based processing of such data has ceased to be relevant in many
scenarios where anomaly detection and pattern mining have to be entirely
accomplished by the machine. Since the early 2000s, the de-facto standard for
representing time series has been the Symbolic Aggregate approXimation (SAX).In
this document, we present a few algorithms using this representation for
anomaly detection and motif discovery, also known as pattern mining, in such
data. We propose a benchmark of anomaly detection algorithms using data from
Cloud monitoring software.
","Anomaly detection and motif discovery in symbolic representations of
  time series"
"  The paper discusses scientific and technological problems of dynamic
integrated expert systems development. Extensions of problem-oriented
methodology for dynamic integrated expert systems development are considered.
Attention is paid to the temporal knowledge representation and processing.
","Synergy of all-purpose static solver and temporal reasoning tools in
  dynamic integrated expert systems"
"  We introduce the first deep reinforcement learning agent that learns to beat
Atari games with the aid of natural language instructions. The agent uses a
multimodal embedding between environment observations and natural language to
self-monitor progress through a list of English instructions, granting itself
reward for completing instructions in addition to increasing the game score.
Our agent significantly outperforms Deep Q-Networks (DQNs), Asynchronous
Advantage Actor-Critic (A3C) agents, and the best agents posted to OpenAI Gym
on what is often considered the hardest Atari 2600 environment: Montezuma's
Revenge.
",Beating Atari with Natural Language Guided Reinforcement Learning
"  Extracting geographical tags from webpages is a well-motivated application in
many domains. In illicit domains with unusual language models, like human
trafficking, extracting geotags with both high precision and recall is a
challenging problem. In this paper, we describe a geotag extraction framework
in which context, constraints and the openly available Geonames knowledge base
work in tandem in an Integer Linear Programming (ILP) model to achieve good
performance. In preliminary empirical investigations, the framework improves
precision by 28.57% and F-measure by 36.9% on a difficult human trafficking
geotagging task compared to a machine learning-based baseline. The method is
already being integrated into an existing knowledge base construction system
widely used by US law enforcement agencies to combat human trafficking.
","Using Contexts and Constraints for Improved Geotagging of Human
  Trafficking Webpages"
"  We introduce the dependent doors problem as an abstraction for situations in
which one must perform a sequence of possibly dependent decisions, without
receiving feedback information on the effectiveness of previously made actions.
Informally, the problem considers a set of $d$ doors that are initially closed,
and the aim is to open all of them as fast as possible. To open a door, the
algorithm knocks on it and it might open or not according to some probability
distribution. This distribution may depend on which other doors are currently
open, as well as on which other doors were open during each of the previous
knocks on that door. The algorithm aims to minimize the expected time until all
doors open. Crucially, it must act at any time without knowing whether or which
other doors have already opened. In this work, we focus on scenarios where
dependencies between doors are both positively correlated and acyclic.The
fundamental distribution of a door describes the probability it opens in the
best of conditions (with respect to other doors being open or closed). We show
that if in two configurations of $d$ doors corresponding doors share the same
fundamental distribution, then these configurations have the same optimal
running time up to a universal constant, no matter what are the dependencies
between doors and what are the distributions. We also identify algorithms that
are optimal up to a universal constant factor. For the case in which all doors
share the same fundamental distribution we additionally provide a simpler
algorithm, and a formula to calculate its running time. We furthermore analyse
the price of lacking feedback for several configurations governed by standard
fundamental distributions. In particular, we show that the price is logarithmic
in $d$ for memoryless doors, but can potentially grow to be linear in $d$ for
other distributions.We then turn our attention to investigate precise bounds.
Even for the case of two doors, identifying the optimal sequence is an
intriguing combinatorial question. Here, we study the case of two cascading
memoryless doors. That is, the first door opens on each knock independently
with probability $p\_1$. The second door can only open if the first door is
open, in which case it will open on each knock independently with probability
$p\_2$. We solve this problem almost completely by identifying algorithms that
are optimal up to an additive term of 1.
","The Dependent Doors Problem: An Investigation into Sequential Decisions
  without Feedback"
"  The management of invasive mechanical ventilation, and the regulation of
sedation and analgesia during ventilation, constitutes a major part of the care
of patients admitted to intensive care units. Both prolonged dependence on
mechanical ventilation and premature extubation are associated with increased
risk of complications and higher hospital costs, but clinical opinion on the
best protocol for weaning patients off of a ventilator varies. This work aims
to develop a decision support tool that uses available patient information to
predict time-to-extubation readiness and to recommend a personalized regime of
sedation dosage and ventilator support. To this end, we use off-policy
reinforcement learning algorithms to determine the best action at a given
patient state from sub-optimal historical ICU data. We compare treatment
policies from fitted Q-iteration with extremely randomized trees and with
feedforward neural networks, and demonstrate that the policies learnt show
promise in recommending weaning protocols with improved outcomes, in terms of
minimizing rates of reintubation and regulating physiological stability.
","A Reinforcement Learning Approach to Weaning of Mechanical Ventilation
  in Intensive Care Units"
"  Humans can ground natural language commands to tasks at both abstract and
fine-grained levels of specificity. For instance, a human forklift operator can
be instructed to perform a high-level action, like ""grab a pallet"" or a
low-level action like ""tilt back a little bit."" While robots are also capable
of grounding language commands to tasks, previous methods implicitly assume
that all commands and tasks reside at a single, fixed level of abstraction.
Additionally, methods that do not use multiple levels of abstraction encounter
inefficient planning and execution times as they solve tasks at a single level
of abstraction with large, intractable state-action spaces closely resembling
real world complexity. In this work, by grounding commands to all the tasks or
subtasks available in a hierarchical planning framework, we arrive at a model
capable of interpreting language at multiple levels of specificity ranging from
coarse to more granular. We show that the accuracy of the grounding procedure
is improved when simultaneously inferring the degree of abstraction in language
used to communicate the task. Leveraging hierarchy also improves efficiency:
our proposed approach enables a robot to respond to a command within one second
on 90% of our tasks, while baselines take over twenty seconds on half the
tasks. Finally, we demonstrate that a real, physical robot can ground commands
at multiple levels of abstraction allowing it to efficiently plan different
subtasks within the same planning hierarchy.
","Accurately and Efficiently Interpreting Human-Robot Instructions of
  Varying Granularities"
"  Making high-quality decisions in strategic spatial planning is heavily
dependent on extracting knowledge from vast amounts of data. Although many
decision-making problems like developing urban areas require such perception
and reasoning, existing methods in this field usually neglect the deep
knowledge mined from geographic databases and are based on pure statistical
methods. Due to the large volume of data gathered in spatial databases, and the
uncertainty of spatial objects, mining association rules for high-level
knowledge representation is a challenging task. Few algorithms manage
geographical and non-geographical data using topological relations. In this
paper, a novel approach for spatial data mining based on the MOSES evolutionary
framework is presented which improves the classic genetic programming approach.
A hybrid architecture called GGeo is proposed to apply the MOSES mining rules
considering fuzzy topological relations from spatial data. The uncertainty and
fuzziness aspects are addressed using an enriched model of topological
relations by fuzzy region connection calculus. Moreover, to overcome the
problem of time-consuming fuzzy topological relationships calculations, this a
novel data pre-processing method is offered. GGeo analyses and learns from
geographical and non-geographical data and uses topological and distance
parameters, and returns a series of arithmetic-spatial formulas as
classification rules. The proposed approach is resistant to noisy data, and all
its stages run in parallel to increase speed. This approach may be used in
different spatial data classification problems as well as representing an
appropriate method of data analysis and economic policy making.
","A hybrid spatial data mining approach based on fuzzy topological
  relations and MOSES evolutionary algorithm"
"  General Video Game Artificial Intelligence is a general game playing
framework for Artificial General Intelligence research in the video-games
domain. In this paper, we propose for the first time a screen capture learning
agent for General Video Game AI framework. A Deep Q-Network algorithm was
applied and improved to develop an agent capable of learning to play different
games in the framework. After testing this algorithm using various games of
different categories and difficulty levels, the results suggest that our
proposed screen capture learning agent has the potential to learn many
different games using only a single learning algorithm.
",General Video Game AI: Learning from Screen Capture
"  Agent modelling involves considering how other agents will behave, in order
to influence your own actions. In this paper, we explore the use of agent
modelling in the hidden-information, collaborative card game Hanabi. We
implement a number of rule-based agents, both from the literature and of our
own devising, in addition to an Information Set Monte Carlo Tree Search
(IS-MCTS) agent. We observe poor results from IS-MCTS, so construct a new,
predictor version that uses a model of the agents with which it is paired. We
observe a significant improvement in game-playing strength from this agent in
comparison to IS-MCTS, resulting from its consideration of what the other
agents in a game would do. In addition, we create a flawed rule-based agent to
highlight the predictor's capabilities with such an agent.
",Evaluating and Modelling Hanabi-Playing Agents
"  Monte Carlo Tree Search techniques have generally dominated General Video
Game Playing, but recent research has started looking at Evolutionary
Algorithms and their potential at matching Tree Search level of play or even
outperforming these methods. Online or Rolling Horizon Evolution is one of the
options available to evolve sequences of actions for planning in General Video
Game Playing, but no research has been done up to date that explores the
capabilities of the vanilla version of this algorithm in multiple games. This
study aims to critically analyse the different configurations regarding
population size and individual length in a set of 20 games from the General
Video Game AI corpus. Distinctions are made between deterministic and
stochastic games, and the implications of using superior time budgets are
studied. Results show that there is scope for the use of these techniques,
which in some configurations outperform Monte Carlo Tree Search, and also
suggest that further research in these methods could boost their performance.
","Analysis of Vanilla Rolling Horizon Evolution Parameters in General
  Video Game Playing"
"  Stochastic Constraint Programming (SCP) is an extension of Constraint
Programming (CP) used for modelling and solving problems involving constraints
and uncertainty. SCP inherits excellent modelling abilities and filtering
algorithms from CP, but so far it has not been applied to large problems.
Reinforcement Learning (RL) extends Dynamic Programming to large stochastic
problems, but is problem-specific and has no generic solvers. We propose a
hybrid combining the scalability of RL with the modelling and constraint
filtering methods of CP. We implement a prototype in a CP system and
demonstrate its usefulness on SCP problems.
",Stochastic Constraint Programming as Reinforcement Learning
"  Data stream learning has been largely studied for extracting knowledge
structures from continuous and rapid data records. In the semantic Web, data is
interpreted in ontologies and its ordered sequence is represented as an
ontology stream. Our work exploits the semantics of such streams to tackle the
problem of concept drift i.e., unexpected changes in data distribution, causing
most of models to be less accurate as time passes. To this end we revisited (i)
semantic inference in the context of supervised stream learning, and (ii)
models with semantic embeddings. The experiments show accurate prediction with
data from Dublin and Beijing.
",Learning from Ontology Streams with Semantic Concept Drift
"  This work introduces a method to tune a sequence-based generative model for
molecular de novo design that through augmented episodic likelihood can learn
to generate structures with certain specified desirable properties. We
demonstrate how this model can execute a range of tasks such as generating
analogues to a query structure and generating compounds predicted to be active
against a biological target. As a proof of principle, the model is first
trained to generate molecules that do not contain sulphur. As a second example,
the model is trained to generate analogues to the drug Celecoxib, a technique
that could be used for scaffold hopping or library expansion starting from a
single molecule. Finally, when tuning the model towards generating compounds
predicted to be active against the dopamine receptor type 2, the model
generates structures of which more than 95% are predicted to be active,
including experimentally confirmed actives that have not been included in
either the generative model nor the activity prediction model.
",Molecular De Novo Design through Deep Reinforcement Learning
"  Vehicle climate control systems aim to keep passengers thermally comfortable.
However, current systems control temperature rather than thermal comfort and
tend to be energy hungry, which is of particular concern when considering
electric vehicles. This paper poses energy-efficient vehicle comfort control as
a Markov Decision Process, which is then solved numerically using
Sarsa({\lambda}) and an empirically validated, single-zone, 1D thermal model of
the cabin. The resulting controller was tested in simulation using 200 randomly
selected scenarios and found to exceed the performance of bang-bang,
proportional, simple fuzzy logic, and commercial controllers with 23%, 43%,
40%, 56% increase, respectively. Compared to the next best performing
controller, energy consumption is reduced by 13% while the proportion of time
spent thermally comfortable is increased by 23%. These results indicate that
this is a viable approach that promises to translate into substantial comfort
and energy improvements in the car.
",Reinforcement Learning-based Thermal Comfort Control for Vehicle Cabins
"  In this extended abstract, we propose Structured Production Systems (SPS),
which extend traditional production systems with well-formed syntactic
structures. Due to the richness of structures, structured production systems
significantly enhance the expressive power as well as the flexibility of
production systems, for instance, to handle uncertainty. We show that different
rule application strategies can be reduced into the basic one by utilizing
structures. Also, many fundamental approaches in computer science, including
automata, grammar and logic, can be captured by structured production systems.
",Structured Production System (extended abstract)
"  The area of computation called artificial intelligence (AI) is falsified by
describing a previous 1972 falsification of AI by British mathematical
physicist James Lighthill. How Lighthill's arguments continue to apply to
current AI is explained. It is argued that AI should use the Popperian
scientific method in which it is the duty of scientists to attempt to falsify
theories and if theories are falsified to replace or modify them. The paper
describes the Popperian method and discusses Paul Nurse's application of the
method to cell biology that also involves questions of mechanism and behavior.
It is shown how Lighthill's falsifying arguments especially combinatorial
explosion continue to apply to modern AI. Various skeptical arguments against
the assumptions of AI mostly by physicists especially against Hilbert's
philosophical programme that defined knowledge and truth as provable formal
sentences. John von Neumann's arguments from natural complexity against neural
networks and evolutionary algorithms are discussed. Next the game of chess is
discussed to show how modern chess experts have reacted to computer chess
programs. It is shown that currently chess masters can defeat any chess program
using Kasperov's arguments from his 1997 Deep Blue match and aftermath. The
game of 'go' and climate models are discussed to show computer applications
where combinatorial explosion may not apply. The paper concludes by advocating
studying computation as Peter Naur's Dataology.
","A Popperian Falsification of Artificial Intelligence -- Lighthill
  Defended"
"  Current measures of machine intelligence are either difficult to evaluate or
lack the ability to test a robot's problem-solving capacity in open worlds. We
propose a novel evaluation framework based on the formal notion of MacGyver
Test which provides a practical way for assessing the resilience and
resourcefulness of artificial agents.
","The MacGyver Test - A Framework for Evaluating Machine Resourcefulness
  and Creative Problem Solving"
"  A ranking is an ordered sequence of items, in which an item with higher
ranking score is more preferred than the items with lower ranking scores. In
many information systems, rankings are widely used to represent the preferences
over a set of items or candidates. The consensus measure of rankings is the
problem of how to evaluate the degree to which the rankings agree. The
consensus measure can be used to evaluate rankings in many information systems,
as quite often there is not ground truth available for evaluation.
  This paper introduces a novel approach for consensus measure of rankings by
using graph representation, in which the vertices or nodes are the items and
the edges are the relationship of items in the rankings. Such representation
leads to various algorithms for consensus measure in terms of different aspects
of rankings, including the number of common patterns, the number of common
patterns with fixed length and the length of the longest common patterns. The
proposed measure can be adopted for various types of rankings, such as full
rankings, partial rankings and rankings with ties. This paper demonstrates how
the proposed approaches can be used to evaluate the quality of rank aggregation
and the quality of top-$k$ rankings from Google and Bing search engines.
",Consensus measure of rankings
"  An Intelligent Personal Agent (IPA) is an agent that has the purpose of
helping the user to gain information through reliable resources with the help
of knowledge navigation techniques and saving time to search the best content.
The agent is also responsible for responding to the chat-based queries with the
help of Conversation Corpus. We will be testing different methods for optimal
query generation. To felicitate the ease of usage of the application, the agent
will be able to accept the input through Text (Keyboard), Voice (Speech
Recognition) and Server (Facebook) and output responses using the same method.
Existing chat bots reply by making changes in the input, but we will give
responses based on multiple SRT files. The model will learn using the human
dialogs dataset and will be able respond human-like. Responses to queries about
famous things (places, people, and words) can be provided using web scraping
which will enable the bot to have knowledge navigation features. The agent will
even learn from its past experiences supporting semi-supervised learning.
",Intelligent Personal Assistant with Knowledge Navigation
"  Current domain-independent, classical planners require symbolic models of the
problem domain and instance as input, resulting in a knowledge acquisition
bottleneck. Meanwhile, although deep learning has achieved significant success
in many fields, the knowledge is encoded in a subsymbolic representation which
is incompatible with symbolic systems such as planners. We propose LatPlan, an
unsupervised architecture combining deep learning and classical planning. Given
only an unlabeled set of image pairs showing a subset of transitions allowed in
the environment (training inputs), and a pair of images representing the
initial and the goal states (planning inputs), LatPlan finds a plan to the goal
state in a symbolic latent space and returns a visualized plan execution. The
contribution of this paper is twofold: (1) State Autoencoder, which finds a
propositional state representation of the environment using a Variational
Autoencoder. It generates a discrete latent vector from the images, based on
which a PDDL model can be constructed and then solved by an off-the-shelf
planner. (2) Action Autoencoder / Discriminator, a neural architecture which
jointly finds the action symbols and the implicit action models
(preconditions/effects), and provides a successor function for the implicit
graph search. We evaluate LatPlan using image-based versions of 3 planning
domains: 8-puzzle, Towers of Hanoi and LightsOut.
","Classical Planning in Deep Latent Space: Bridging the
  Subsymbolic-Symbolic Boundary"
"  A logical theory of regular double or multiple recurrence of eventualities,
which are regular patterns of occurrences that are repeated, in time, has been
developed within the context of temporal reasoning that enabled reasoning about
the problem of coincidence. i.e. if two complex eventualities, or eventuality
sequences consisting respectively of component eventualities x0, x1,....,xr and
y0, y1, ..,ys both recur over an interval k and all eventualities are of fixed
durations, is there a subinterval of k over which the occurrence xp and yq for
p between 1 and r and q between 1 and s coincide. We present the ideas behind a
new algorithm for detecting the coincidence of eventualities xp and yq within a
cycle of the double recurrence of x and y. The algorithm is based on the novel
concept of gcd partitions that requires the partitioning of each of the
incidences of both x and y into eventuality sequences each of which components
have a duration that is equal to the greatest common divisor of the durations
of x and y. The worst case running time of the partitioning algorithm is linear
in the maximum of the duration of x and that of y, while the worst case running
time of an algorithm exploring a complete cycle is quadratic in the durations
of x and y. Hence the partitioning algorithm works faster than the cyclical
exploration in the worst case.
","A Partitioning Algorithm for Detecting Eventuality Coincidence in
  Temporal Double recurrence"
"  In this paper we show how the defense relation among abstract arguments can
be used to encode the reasons for accepting arguments. After introducing a
novel notion of defenses and defense graphs, we propose a defense semantics
together with a new notion of defense equivalence of argument graphs, and
compare defense equivalence with standard equivalence and strong equivalence,
respectively. Then, based on defense semantics, we define two kinds of reasons
for accepting arguments, i.e., direct reasons and root reasons, and a notion of
root equivalence of argument graphs. Finally, we show how the notion of root
equivalence can be used in argumentation summarization.
","Defense semantics of argumentation: encoding reasons for accepting
  arguments"
"  Logical theories have been developed which have allowed temporal reasoning
about eventualities (a la Galton) such as states, processes, actions, events,
processes and complex eventualities such as sequences and recurrences of other
eventualities. This paper presents the problem of coincidence within the
framework of a first order logical theory formalising temporal multiple
recurrence of two sequences of fixed duration eventualities and presents a
solution to it The coincidence problem is described as: if two complex
eventualities (or eventuality sequences) consisting respectively of component
eventualities x0, x1,....,xr and y0, y1, ..,ys both recur over an interval k
and all eventualities are of fixed durations, is there a sub-interval of k over
which the incidence xt and yu for t between 0..r and s between 0..s coincide.
The solution presented here formalises the intuition that a solution can be
found by temporal projection over a cycle of the multiple recurrence of both
sequences.
",The Problem of Coincidence in A Theory of Temporal Multiple Recurrence
"  It is not rare that the performance of one metaheuristic algorithm can be
improved by incorporating ideas taken from another. In this article we present
how Simulated Annealing (SA) can be used to improve the efficiency of the Ant
Colony System (ACS) and Enhanced ACS when solving the Sequential Ordering
Problem (SOP). Moreover, we show how the very same ideas can be applied to
improve the convergence of a dedicated local search, i.e. the SOP-3-exchange
algorithm. A statistical analysis of the proposed algorithms both in terms of
finding suitable parameter values and the quality of the generated solutions is
presented based on a series of computational experiments conducted on SOP
instances from the well-known TSPLIB and SOPLIB2006 repositories. The proposed
ACS-SA and EACS-SA algorithms often generate solutions of better quality than
the ACS and EACS, respectively. Moreover, the EACS-SA algorithm combined with
the proposed SOP-3-exchange-SA local search was able to find 10 new best
solutions for the SOP instances from the SOPLIB2006 repository, thus improving
the state-of-the-art results as known from the literature. Overall, the best
known or improved solutions were found in 41 out of 48 cases.
",An improved Ant Colony System for the Sequential Ordering Problem
"  This paper describes a new evolutionary algorithm that is especially well
suited to AI-Assisted Game Design. The approach adopted in this paper is to use
observations of AI agents playing the game to estimate the game's quality. Some
of best agents for this purpose are General Video Game AI agents, since they
can be deployed directly on a new game without game-specific tuning; these
agents tend to be based on stochastic algorithms which give robust but noisy
results and tend to be expensive to run. This motivates the main contribution
of the paper: the development of the novel N-Tuple Bandit Evolutionary
Algorithm, where a model is used to estimate the fitness of unsampled points
and a bandit approach is used to balance exploration and exploitation of the
search space. Initial results on optimising a Space Battle game variant suggest
that the algorithm offers far more robust results than the Random Mutation Hill
Climber and a Biased Mutation variant, which are themselves known to offer
competitive performance across a range of problems. Subjective observations are
also given by human players on the nature of the evolved games, which indicate
a preference towards games generated by the N-Tuple algorithm.
",The N-Tuple Bandit Evolutionary Algorithm for Automatic Game Improvement
"  Imaging is a form of probabilistic belief change which could be employed for
both revision and update. In this paper, we propose a new framework for
probabilistic belief change based on imaging, called Expected Distance Imaging
(EDI). EDI is sufficiently general to define Bayesian conditioning and other
forms of imaging previously defined in the literature. We argue that, and
investigate how, EDI can be used for both revision and update. EDI's definition
depends crucially on a weight function whose properties are studied and whose
effect on belief change operations is analysed. Finally, four EDI
instantiations are proposed, two for revision and two for update, and
probabilistic rationality postulates are suggested for their analysis.
",Imagining Probabilistic Belief Change as Imaging (Technical Report)
"  Non-stationary domains, where unforeseen changes happen, present a challenge
for agents to find an optimal policy for a sequential decision making problem.
This work investigates a solution to this problem that combines Markov Decision
Processes (MDP) and Reinforcement Learning (RL) with Answer Set Programming
(ASP) in a method we call ASP(RL). In this method, Answer Set Programming is
used to find the possible trajectories of an MDP, from where Reinforcement
Learning is applied to learn the optimal policy of the problem. Results show
that ASP(RL) is capable of efficiently finding the optimal solution of an MDP
representing non-stationary domains.
",Answer Set Programming for Non-Stationary Markov Decision Processes
"  This work presents a model for the Tramp Ship Scheduling problem including
berth allocation considerations, motivated by a real case of a shipping
company. The aim is to determine the travel schedule for each vessel
considering multiple docking and multiple time windows at the berths. This work
is innovative due to the consideration of both spatial and temporal attributes
during the scheduling process. The resulting model is formulated as a
mixed-integer linear programming problem, and a heuristic method to deal with
multiple vessel schedules is also presented. Numerical experimentation is
performed to highlight the benefits of the proposed approach and the
applicability of the heuristic. Conclusions and recommendations for further
research are provided.
","Tramp Ship Scheduling Problem with Berth Allocation Considerations and
  Time-dependent Constraints"
"  Logics of limited belief aim at enabling computationally feasible reasoning
in highly expressive representation languages. These languages are often
dialects of first-order logic with a weaker form of logical entailment that
keeps reasoning decidable or even tractable. While a number of such logics have
been proposed in the past, they tend to remain for theoretical analysis only
and their practical relevance is very limited. In this paper, we aim to go
beyond the theory. Building on earlier work by Liu, Lakemeyer, and Levesque, we
develop a logic of limited belief that is highly expressive while remaining
decidable in the first-order and tractable in the propositional case and
exhibits some characteristics that make it attractive for an implementation. We
introduce a reasoning system that employs this logic as representation language
and present experimental results that showcase the benefit of limited belief.
",A Reasoning System for a First-Order Logic of Limited Belief
"  Logic-based event recognition systems infer occurrences of events in time
using a set of event definitions in the form of first-order rules. The Event
Calculus is a temporal logic that has been used as a basis in event recognition
applications, providing among others, direct connections to machine learning,
via Inductive Logic Programming (ILP). OLED is a recently proposed ILP system
that learns event definitions in the form of Event Calculus theories, in a
single pass over a data stream. In this work we present a version of OLED that
allows for distributed, online learning. We evaluate our approach on a
benchmark activity recognition dataset and show that we can significantly
reduce training times, exchanging minimal information between processing nodes.
",Distributed Online Learning of Event Definitions
"  The concept of evolving intelligent system (EIS) provides an effective avenue
for data stream mining because it is capable of coping with two prominent
issues: online learning and rapidly changing environments. We note at least
three uncharted territories of existing EISs: data uncertainty, temporal system
dynamic, redundant data streams. This book chapter aims at delivering a
concrete solution of this problem with the algorithmic development of a novel
learning algorithm, namely PANFIS++. PANFIS++ is a generalized version of the
PANFIS by putting forward three important components: 1) An online active
learning scenario is developed to overcome redundant data streams. This module
allows to actively select data streams for the training process, thereby
expediting execution time and enhancing generalization performance, 2) PANFIS++
is built upon an interval type-2 fuzzy system environment, which incorporates
the so-called footprint of uncertainty. This component provides a degree of
tolerance for data uncertainty. 3) PANFIS++ is structured under a recurrent
network architecture with a self-feedback loop. This is meant to tackle the
temporal system dynamic. The efficacy of the PANFIS++ has been numerically
validated through numerous real-world and synthetic case studies, where it
delivers the highest predictive accuracy while retaining the lowest complexity.
",PANFIS++: A Generalized Approach to Evolving Learning
"  As manufacturing processes become increasingly automated, so should tool
condition monitoring (TCM) as it is impractical to have human workers monitor
the state of the tools continuously. Tool condition is crucial to ensure the
good quality of products: Worn tools affect not only the surface quality but
also the dimensional accuracy, which means higher reject rate of the products.
Therefore, there is an urgent need to identify tool failures before it occurs
on the fly. While various versions of intelligent tool condition monitoring
have been proposed, most of them suffer from a cognitive nature of traditional
machine learning algorithms. They focus on the how to learn process without
paying attention to other two crucial issues: what to learn, and when to learn.
The what to learn and the when to learn provide self regulating mechanisms to
select the training samples and to determine time instants to train a model. A
novel tool condition monitoring approach based on a psychologically plausible
concept, namely the metacognitive scaffolding theory, is proposed and built
upon a recently published algorithm, recurrent classifier (rClass). The
learning process consists of three phases: what to learn, how to learn, when to
learn and makes use of a generalized recurrent network structure as a cognitive
component. Experimental studies with real-world manufacturing data streams were
conducted where rClass demonstrated the highest accuracy while retaining the
lowest complexity over its counterparts.
",Metacognitive Learning Approach for Online Tool Condition Monitoring
"  How to handle uncertainty in medical diagnosis is an open issue. In this
paper, a new decision making methodology based on Z-numbers is presented.
Firstly, the experts' opinions are represented by Z-numbers. Z-number is an
ordered pair of fuzzy numbers denoted as Z = (A, B). Then, a new method for
ranking fuzzy numbers is proposed. And based on the proposed fuzzy number
ranking method, a novel method is presented to transform the Z-numbers into
Basic Probability Assignment (BPA). As a result, the information from different
sources is combined by the Dempster' combination rule. The final decision
making is more reasonable due to the advantage of information fusion. Finally,
two experiments, risk analysis and medical diagnosis, are illustrated to show
the efficiency of the proposed methodology.
",A New Medical Diagnosis Method Based on Z-Numbers
"  This paper uses anthropic reasoning to argue for a reduced likelihood that
superintelligent AI will come into existence in the future. To make this
argument, a new principle is introduced: the Super-Strong Self-Sampling
Assumption (SSSSA), building on the Self-Sampling Assumption (SSA) and the
Strong Self-Sampling Assumption (SSSA). SSA uses as its sample the relevant
observers, whereas SSSA goes further by using observer-moments. SSSSA goes
further still and weights each sample proportionally, according to the size of
a mind in cognitive terms. SSSSA is required for human observer-samples to be
typical, given by how much non-human animals outnumber humans. Given SSSSA, the
assumption that humans experience typical observer-samples relies on a future
where superintelligent AI does not dominate, which in turn reduces the
likelihood of it being created at all.
","An Anthropic Argument against the Future Existence of Superintelligent
  Artificial Intelligence"
"  Shepard's Universal Law of Generalization offered a compelling case for the
first physics-like law in cognitive science that should hold for all
intelligent agents in the universe. Shepard's account is based on a rational
Bayesian model of generalization, providing an answer to the question of why
such a law should emerge. Extending this account to explain how humans use
multiple examples to make better generalizations requires an additional
assumption, called the size principle: hypotheses that pick out fewer objects
should make a larger contribution to generalization. The degree to which this
principle warrants similarly law-like status is far from conclusive. Typically,
evaluating this principle has not been straightforward, requiring additional
assumptions. We present a new method for evaluating the size principle that is
more direct, and apply this method to a diverse array of datasets. Our results
provide support for the broad applicability of the size principle.
",Evidence for the size principle in semantic and perceptual domains
"  Recently introduced composition operator for credal sets is an analogy of
such operators in probability, possibility, evidence and valuation-based
systems theories. It was designed to construct multidimensional models (in the
framework of credal sets) from a system of low- dimensional credal sets. In
this paper we study its potential from the computational point of view
utilizing methods of polyhedral geometry.
",Composition of Credal Sets via Polyhedral Geometry
"  Social abstract argumentation is a principled way to assign values to
conflicting (weighted) arguments. In this note we discuss the important
property of the uniqueness of the model.
",A note on the uniqueness of models in social abstract argumentation
"  In most common settings of Markov Decision Process (MDP), an agent evaluate a
policy based on expectation of (discounted) sum of rewards. However in many
applications this criterion might not be suitable from two perspective: first,
in risk aversion situation expectation of accumulated rewards is not robust
enough, this is the case when distribution of accumulated reward is heavily
skewed; another issue is that many applications naturally take several
objective into consideration when evaluating a policy, for instance in
autonomous driving an agent needs to balance speed and safety when choosing
appropriate decision. In this paper, we consider evaluating a policy based on a
sequence of quantiles it induces on a set of target states, our idea is to
reformulate the original problem into a multi-objective MDP problem with
lexicographic preference naturally defined. For computation of finding an
optimal policy, we proposed an algorithm \textbf{FLMDP} that could solve
general multi-objective MDP with lexicographic reward preference.
","Solving Multi-Objective MDP with Lexicographic Preference: An
  application to stochastic planning with multiple quantile objective"
"  Critical node problems involve identifying a subset of critical nodes from an
undirected graph whose removal results in optimizing a pre-defined measure over
the residual graph. As useful models for a variety of practical applications,
these problems are computational challenging. In this paper, we study the
classic critical node problem (CNP) and introduce an effective memetic
algorithm for solving CNP. The proposed algorithm combines a double
backbone-based crossover operator (to generate promising offspring solutions),
a component-based neighborhood search procedure (to find high-quality local
optima) and a rank-based pool updating strategy (to guarantee a healthy
population). Specially, the component-based neighborhood search integrates two
key techniques, i.e., two-phase node exchange strategy and node weighting
scheme. The double backbone-based crossover extends the idea of general
backbone-based crossovers. Extensive evaluations on 42 synthetic and real-world
benchmark instances show that the proposed algorithm discovers 21 new upper
bounds and matches 18 previous best-known upper bounds. We also demonstrate the
relevance of our algorithm for effectively solving a variant of the classic
CNP, called the cardinality-constrained critical node problem. Finally, we
investigate the usefulness of each key algorithmic component.
",Memetic search for identifying critical nodes in sparse graphs
"  We present a rational analysis of curiosity, proposing that people's
curiosity is driven by seeking stimuli that maximize their ability to make
appropriate responses in the future. This perspective offers a way to unify
previous theories of curiosity into a single framework. Experimental results
confirm our model's predictions, showing how the relationship between curiosity
and confidence can change significantly depending on the nature of the
environment. Please refer to https://psyarxiv.com/wg5m6/ for a more updated
version of this manuscript with a more detailed modeling section with extensive
experiments.
",A rational analysis of curiosity
"  Turing test was long considered the measure for artificial intelligence. But
with the advances in AI, it has proved to be insufficient measure. We can now
aim to mea- sure machine intelligence like we measure human intelligence. One
of the widely accepted measure of intelligence is standardized math and science
test. In this paper, we explore the progress we have made towards the goal of
making a machine smart enough to pass the standardized test. We see the
challenges and opportunities posed by the domain, and note that we are quite
some ways from actually making a system as smart as a even a middle school
scholar.
",A Survey of Question Answering for Math and Science Problem
"  We present the third generation of the constraint answer set system clingcon,
combining Answer Set Programming (ASP) with finite domain constraint processing
(CP). While its predecessors rely on a black-box approach to hybrid solving by
integrating the CP solver gecode, the new clingcon system pursues a lazy
approach using dedicated constraint propagators to extend propagation in the
underlying ASP solver clasp. No extension is needed for parsing and grounding
clingcon's hybrid modeling language since both can be accommodated by the new
generic theory handling capabilities of the ASP grounder gringo. As a whole,
clingcon 3 is thus an extension of the ASP system clingo 5, which itself relies
on the grounder gringo and the solver clasp. The new approach of clingcon
offers a seamless integration of CP propagation into ASP solving that benefits
from the whole spectrum of clasp's reasoning modes, including for instance
multi-shot solving and advanced optimization techniques. This is accomplished
by a lazy approach that unfolds the representation of constraints and adds it
to that of the logic program only when needed. Although the unfolding is
usually dictated by the constraint propagators during solving, it can already
be partially (or even totally) done during preprocessing. Moreover, clingcon's
constraint preprocessing and propagation incorporate several well established
CP techniques that greatly improve its performance. We demonstrate this via an
extensive empirical evaluation contrasting, first, the various techniques in
the context of CSP solving and, second, the new clingcon system with other
hybrid ASP systems. Under consideration in Theory and Practice of Logic
Programming (TPLP)
",Clingcon: The Next Generation
"  The pancake puzzle is a classic optimization problem that has become a
standard benchmark for heuristic search algorithms. In this paper, we provide
full proofs regarding the local search topology of the gap heuristic for the
pancake puzzle. First, we show that in any non-goal state in which there is no
move that will decrease the number of gaps, there is a move that will keep the
number of gaps constant. We then classify any state in which the number of gaps
cannot be decreased in a single action into two groups: those requiring 2
actions to decrease the number of gaps, and those which require 3 actions to
decrease the number of gaps.
","A Formal Characterization of the Local Search Topology of the Gap
  Heuristic"
"  In many tasks related to reasoning about consequences of a logical theory, it
is desirable to decompose the theory into a number of weakly-related or
independent components. However, a theory may represent knowledge that is
subject to change, as a result of executing actions that have effects on some
of the initial properties mentioned in the theory. Having once computed a
decomposition of a theory, it is advantageous to know whether a decomposition
has to be computed again in the newly-changed theory (obtained from taking into
account changes resulting from execution of an action). In the paper, we
address this problem in the scope of the situation calculus, where a change of
an initial theory is related to the notion of progression. Progression provides
a form of forward reasoning; it relies on forgetting values of those
properties, which are subject to change, and computing new values for them. We
consider decomposability and inseparability, two component properties known
from the literature, and contribute by 1) studying the conditions when these
properties are preserved and 2) when they are lost wrt progression and the
related operation of forgetting. To show the latter, we demonstrate the
boundaries using a number of negative examples. To show the former, we identify
cases when these properties are preserved under forgetting and progression of
initial theories in local-effect basic action theories of the situation
calculus. Our paper contributes to bridging two different communities in
Knowledge Representation, namely research on modularity and research on
reasoning about actions.
",Progression of Decomposed Local-Effect Action Theories
"  We propose a new mechanism for integration of OWL ontologies using semantic
import relations. In contrast to the standard OWL importing, we do not require
all axioms of the imported ontologies to be taken into account for reasoning
tasks, but only their logical implications over a chosen signature. This
property comes natural in many ontology integration scenarios, especially when
the number of ontologies is large. In this paper, we study the complexity of
reasoning over ontologies with semantic import relations and establish a range
of tight complexity bounds for various fragments of OWL.
",On the Complexity of Semantic Integration of OWL Ontologies
"  The brain's self-monitoring of activities, including internal activities -- a
functionality that we refer to as awareness -- has been suggested as a key
element of consciousness. Here we investigate whether the presence of an
inner-eye-like process (monitor) that supervises the activities of a number of
subsystems (operative agents) engaged in the solution of a problem can improve
the problem-solving efficiency of the system. The problem is to find the global
maximum of a NK fitness landscape and the performance is measured by the time
required to find that maximum. The operative agents explore blindly the fitness
landscape and the monitor provides them with feedback on the quality (fitness)
of the proposed solutions. This feedback is then used by the operative agents
to bias their searches towards the fittest regions of the landscape. We find
that a weak feedback between the monitor and the operative agents improves the
performance of the system, regardless of the difficulty of the problem, which
is gauged by the number of local maxima in the landscape. For easy problems
(i.e., landscapes without local maxima), the performance improves monotonically
as the feedback strength increases, but for difficult problems, there is an
optimal value of the feedback strength beyond which the system performance
degrades very rapidly.
",Awareness improves problem-solving performance
"  User opinions expressed in the form of ratings can influence an individual's
view of an item. However, the true quality of an item is often obfuscated by
user biases, and it is not obvious from the observed ratings the importance
different users place on different aspects of an item. We propose a
probabilistic modeling of the observed aspect ratings to infer (i) each user's
aspect bias and (ii) latent intrinsic quality of an item. We model multi-aspect
ratings as ordered discrete data and encode the dependency between different
aspects by using a latent Gaussian structure. We handle the
Gaussian-Categorical non-conjugacy using a stick-breaking formulation coupled
with P\'{o}lya-Gamma auxiliary variable augmentation for a simple, fully
Bayesian inference. On two real world datasets, we demonstrate the predictive
ability of our model and its effectiveness in learning explainable user biases
to provide insights towards a more reliable product quality estimation.
",Quantifying Aspect Bias in Ordinal Ratings using a Bayesian Approach
"  Local consistencies stronger than arc consistency have received a lot of
attention since the early days of CSP research. %because of the strong pruning
they can achieve. However, they have not been widely adopted by CSP solvers.
This is because applying such consistencies can sometimes result in
considerably smaller search tree sizes and therefore in important speed-ups,
but in other cases the search space reduction may be small, causing severe run
time penalties. Taking advantage of recent advances in parallelization, we
propose a novel approach for the application of strong local consistencies
(SLCs) that can improve their performance by largely preserving the speed-ups
they offer in cases where they are successful, and eliminating the run time
penalties in cases where they are unsuccessful. This approach is presented in
the form of two search algorithms. Both algorithms consist of a master search
process, which is a typical CSP solver, and a number of slave processes, with
each one implementing a SLC method. The first algorithm runs the different SLCs
synchronously at each node of the search tree explored in the master process,
while the second one can run them asynchronously at different nodes of the
search tree. Experimental results demonstrate the benefits of the proposed
method.
","Exploiting the Pruning Power of Strong Local Consistencies Through
  Parallelization"
"  We develop the theory and practice of an approach to modelling and
probabilistic inference in causal networks that is suitable when
application-specific or analysis-specific constraints should inform such
inference or when little or no data for the learning of causal network
structure or probability values at nodes are available. Constrained Bayesian
Networks generalize a Bayesian Network such that probabilities can be symbolic,
arithmetic expressions and where the meaning of the network is constrained by
finitely many formulas from the theory of the reals. A formal semantics for
constrained Bayesian Networks over first-order logic of the reals is given,
which enables non-linear and non-convex optimisation algorithms that rely on
decision procedures for this logic, and supports the composition of several
constrained Bayesian Networks. A non-trivial case study in arms control, where
few or no data are available to assess the effectiveness of an arms inspection
process, evaluates our approach. An open-access prototype implementation of
these foundations and their algorithms uses the SMT solver Z3 as decision
procedure, leverages an open-source package for Bayesian inference to symbolic
computation, and is evaluated experimentally.
","Constrained Bayesian Networks: Theory, Optimization, and Applications"
"  In this paper, we constructed a model to determine weights of criterias and
presented a solution for determining the optimal alternative by using the
constructed model and relationship analysis between criterias in fuzzy group
decision-making problem with different forms of preference information of
decision makers on criterias.
","A Method for Determining Weights of Criterias and Alternative of Fuzzy
  Group Decision Making Problem"
"  Expectation for the emergence of higher functions is getting larger in the
framework of end-to-end reinforcement learning using a recurrent neural
network. However, the emergence of ""thinking"" that is a typical higher function
is difficult to realize because ""thinking"" needs non fixed-point, flow-type
attractors with both convergence and transition dynamics. Furthermore, in order
to introduce ""inspiration"" or ""discovery"" in ""thinking"", not completely random
but unexpected transition should be also required.
  By analogy to ""chaotic itinerancy"", we have hypothesized that ""exploration""
grows into ""thinking"" through learning by forming flow-type attractors on
chaotic random-like dynamics. It is expected that if rational dynamics are
learned in a chaotic neural network (ChNN), coexistence of rational state
transition, inspiration-like state transition and also random-like exploration
for unknown situation can be realized.
  Based on the above idea, we have proposed new reinforcement learning using a
ChNN as an actor. The positioning of exploration is completely different from
the conventional one. The chaotic dynamics inside the ChNN produces exploration
factors by itself. Since external random numbers for stochastic action
selection are not used, exploration factors cannot be isolated from the output.
Therefore, the learning method is also completely different from the
conventional one.
  At each non-feedback connection, one variable named causality trace takes in
and maintains the input through the connection according to the change in its
output. Using the trace and TD error, the weight is updated.
  In this paper, as the result of a recent simple task to see whether the new
learning works or not, it is shown that a robot with two wheels and two visual
sensors reaches a target while avoiding an obstacle after learning though there
are still many rooms for improvement.
","New Reinforcement Learning Using a Chaotic Neural Network for Emergence
  of ""Thinking"" - ""Exploration"" Grows into ""Thinking"" through Learning -"
"  The domain of text-based adventure games has been recently established as a
new challenge of creating the agent that is both able to understand natural
language, and acts intelligently in text-described environments.
  In this paper, we present our approach to tackle the problem. Our agent,
named Golovin, takes advantage of the limited game domain. We use genre-related
corpora (including fantasy books and decompiled games) to create language
models suitable to this domain. Moreover, we embed mechanisms that allow us to
specify, and separately handle, important tasks as fighting opponents, managing
inventory, and navigating on the game map.
  We validated usefulness of these mechanisms, measuring agent's performance on
the set of 50 interactive fiction games. Finally, we show that our agent plays
on a level comparable to the winner of the last year Text-Based Adventure AI
Competition.
",Text-based Adventures of the Golovin AI Agent
"  This paper describes a method for identification of the informative variables
in the information system with discrete decision variables. It is targeted
specifically towards discovery of the variables that are non-informative when
considered alone, but are informative when the synergistic interactions between
multiple variables are considered. To this end, the mutual entropy of all
possible k-tuples of variables with decision variable is computed. Then, for
each variable the maximal information gain due to interactions with other
variables is obtained. For non-informative variables this quantity conforms to
the well known statistical distributions. This allows for discerning truly
informative variables from non-informative ones. For demonstration of the
approach, the method is applied to several synthetic datasets that involve
complex multidimensional interactions between variables. It is capable of
identifying most important informative variables, even in the case when the
dimensionality of the analysis is smaller than the true dimensionality of the
problem. What is more, the high sensitivity of the algorithm allows for
detection of the influence of nuisance variables on the response variable.
","All-relevant feature selection using multidimensional filters with
  exhaustive search"
"  This paper proposes a design of hierarchical fuzzy inference tree (HFIT). An
HFIT produces an optimum treelike structure, i.e., a natural hierarchical
structure that accommodates simplicity by combining several low-dimensional
fuzzy inference systems (FISs). Such a natural hierarchical structure provides
a high degree of approximation accuracy. The construction of HFIT takes place
in two phases. Firstly, a nondominated sorting based multiobjective genetic
programming (MOGP) is applied to obtain a simple tree structure (a low
complexity model) with a high accuracy. Secondly, the differential evolution
algorithm is applied to optimize the obtained tree's parameters. In the derived
tree, each node acquires a different input's combination, where the
evolutionary process governs the input's combination. Hence, HFIT nodes are
heterogeneous in nature, which leads to a high diversity among the rules
generated by the HFIT. Additionally, the HFIT provides an automatic feature
selection because it uses MOGP for the tree's structural optimization that
accepts inputs only relevant to the knowledge contained in data. The HFIT was
studied in the context of both type-1 and type-2 FISs, and its performance was
evaluated through six application problems. Moreover, the proposed
multiobjective HFIT was compared both theoretically and empirically with
recently proposed FISs methods from the literature, such as McIT2FIS,
TSCIT2FNN, SIT2FNN, RIT2FNS-WB, eT2FIS, MRIT2NFS, IT2FNN-SVR, etc. From the
obtained results, it was found that the HFIT provided less complex and highly
accurate models compared to the models produced by the most of other methods.
Hence, the proposed HFIT is an efficient and competitive alternative to the
other FISs for function approximation and feature selection.
",Multiobjective Programming for Type-2 Hierarchical Fuzzy Inference Trees
"  Based on Alan Turing's proposition on AI and computing machinery, which
shaped Computing as we know it today, the new AI computing machinery should
comprise a universal computer and a universal learning machine. The later
should understand linear algebra natively to overcome the slowdown of Moore's
law. In such a universal learnig machine, a computing unit does not need to
keep the legacy of a universal computing core. The data can be distributed to
the computing units, and the results can be collected from them through
Collective Streaming, reminiscent of Collective Communication in
Supercomputing. It is not necessary to use a GPU-like deep memory hierarchy,
nor a TPU-like fine-grain mesh.
","AI, Native Supercomputing and The Revival of Moore's Law"
"  Outlier detection is the identification of points in a dataset that do not
conform to the norm. Outlier detection is highly sensitive to the choice of the
detection algorithm and the feature subspace used by the algorithm. Extracting
domain-relevant insights from outliers needs systematic exploration of these
choices since diverse outlier sets could lead to complementary insights. This
challenge is especially acute in an interactive setting, where the choices must
be explored in a time-constrained manner. In this work, we present REMIX, the
first system to address the problem of outlier detection in an interactive
setting. REMIX uses a novel mixed integer programming (MIP) formulation for
automatically selecting and executing a diverse set of outlier detectors within
a time limit. This formulation incorporates multiple aspects such as (i) an
upper limit on the total execution time of detectors (ii) diversity in the
space of algorithms and features, and (iii) meta-learning for evaluating the
cost and utility of detectors. REMIX provides two distinct ways for the analyst
to consume its results: (i) a partitioning of the detectors explored by REMIX
into perspectives through low-rank non-negative matrix factorization; each
perspective can be easily visualized as an intuitive heatmap of experiments
versus outliers, and (ii) an ensembled set of outliers which combines outlier
scores from all detectors. We demonstrate the benefits of REMIX through
extensive empirical validation on real-world data.
",REMIX: Automated Exploration for Interactive Outlier Detection
"  In this work, we present a methodology that enables an agent to make
efficient use of its exploratory actions by autonomously identifying possible
objectives in its environment and learning them in parallel. The identification
of objectives is achieved using an online and unsupervised adaptive clustering
algorithm. The identified objectives are learned (at least partially) in
parallel using Q-learning. Using a simulated agent and environment, it is shown
that the converged or partially converged value function weights resulting from
off-policy learning can be used to accumulate knowledge about multiple
objectives without any additional exploration. We claim that the proposed
approach could be useful in scenarios where the objectives are initially
unknown or in real world scenarios where exploration is typically a time and
energy intensive process. The implications and possible extensions of this work
are also briefly discussed.
","Identification and Off-Policy Learning of Multiple Objectives Using
  Adaptive Clustering"
"  The field of Statistical Relational Learning (SRL) is concerned with learning
probabilistic models from relational data. Learned SRL models are typically
represented using some kind of weighted logical formulas, which make them
considerably more interpretable than those obtained by e.g. neural networks. In
practice, however, these models are often still difficult to interpret
correctly, as they can contain many formulas that interact in non-trivial ways
and weights do not always have an intuitive meaning. To address this, we
propose a new SRL method which uses possibilistic logic to encode relational
models. Learned models are then essentially stratified classical theories,
which explicitly encode what can be derived with a given level of certainty.
Compared to Markov Logic Networks (MLNs), our method is faster and produces
considerably more interpretable models.
","Induction of Interpretable Possibilistic Logic Theories from Relational
  Data"
"  Ontology-based data access (OBDA) is a popular approach for integrating and
querying multiple data sources by means of a shared ontology. The ontology is
linked to the sources using mappings, which assign views over the data to
ontology predicates. Motivated by the need for OBDA systems supporting
database-style aggregate queries, we propose a bag semantics for OBDA, where
duplicate tuples in the views defined by the mappings are retained, as is the
case in standard databases. We show that bag semantics makes conjunctive query
answering in OBDA coNP-hard in data complexity. To regain tractability, we
consider a rather general class of queries and show its rewritability to a
generalisation of the relational calculus to bags.
",The Bag Semantics of Ontology-Based Data Access
"  Action planning using learned and differentiable forward models of the world
is a general approach which has a number of desirable properties, including
improved sample complexity over model-free RL methods, reuse of learned models
across different tasks, and the ability to perform efficient gradient-based
optimization in continuous action spaces. However, this approach does not apply
straightforwardly when the action space is discrete. In this work, we show that
it is in fact possible to effectively perform planning via backprop in discrete
action spaces, using a simple paramaterization of the actions vectors on the
simplex combined with input noise when training the forward model. Our
experiments show that this approach can match or outperform model-free RL and
discrete planning methods on gridworld navigation tasks in terms of performance
and/or planning time while using limited environment interactions, and can
additionally be used to perform model-based control in a challenging new task
where the action space combines discrete and continuous actions. We furthermore
propose a policy distillation approach which yields a fast policy network which
can be used at inference time, removing the need for an iterative planning
procedure.
",Model-Based Planning with Discrete and Continuous Actions
"  The Maximum Balanced Biclique Problem is a well-known graph model with
relevant applications in diverse domains. This paper introduces a novel
algorithm, which combines an effective constraint-based tabu search procedure
and two dedicated graph reduction techniques. We verify the effectiveness of
the algorithm on 30 classical random benchmark graphs and 25 very large
real-life sparse graphs from the popular Koblenz Network Collection (KONECT).
The results show that the algorithm improves the best-known results (new lower
bounds) for 10 classical benchmarks and obtains the optimal solutions for 14
KONECT instances.
","Combining tabu search and graph reduction to solve the maximum balanced
  biclique problem"
"  The stochastic shortest path problem (SSP) is a highly expressive model for
probabilistic planning. The computational hardness of SSPs has sparked interest
in determinization-based planners that can quickly solve large problems.
However, existing methods employ a simplistic approach to determinization. In
particular, they ignore the possibility of tailoring the determinization to the
specific characteristics of the target domain. In this work we examine this
question, by showing that learning a good determinization for a planning domain
can be done efficiently and can improve performance. Moreover, we show how to
directly incorporate probabilistic reasoning into the planning problem when a
good determinization is not sufficient by itself. Based on these insights, we
introduce a planner, FF-LAO*, that outperforms state-of-the-art probabilistic
planners on several well-known competition benchmarks.
",Generalizing the Role of Determinization in Probabilistic Planning
"  Answer Set Programming (ASP) is a powerful modeling formalism for
combinatorial problems. However, writing ASP models is not trivial. We propose
a novel method, called Sketched Answer Set Programming (SkASP), aiming at
supporting the user in resolving this issue. The user writes an ASP program
while marking uncertain parts open with question marks. In addition, the user
provides a number of positive and negative examples of the desired program
behaviour. The sketched model is rewritten into another ASP program, which is
solved by traditional methods. As a result, the user obtains a functional and
reusable ASP program modelling her problem. We evaluate our approach on 21 well
known puzzles and combinatorial problems inspired by Karp's 21 NP-complete
problems and demonstrate a use-case for a database application based on ASP.
",Sketched Answer Set Programming
"  For most reinforcement learning approaches, the learning is performed by
maximizing an accumulative reward that is expectedly and manually defined for
specific tasks. However, in real world, rewards are emergent phenomena from the
complex interactions between agents and environments. In this paper, we propose
an implicit generic reward model for reinforcement learning. Unlike those
rewards that are manually defined for specific tasks, such implicit reward is
task independent. It only comes from the deviation from the agents' previous
experiences.
",Experience enrichment based task independent reward model
"  Reinforcement learning is a general and powerful framework with which to
study and implement artificial intelligence. Recent advances in deep learning
have enabled RL algorithms to achieve impressive performance in restricted
domains such as playing Atari video games (Mnih et al., 2015) and, recently,
the board game Go (Silver et al., 2016). However, we are still far from
constructing a generally intelligent agent. Many of the obstacles and open
questions are conceptual: What does it mean to be intelligent? How does one
explore and learn optimally in general, unknown environments? What, in fact,
does it mean to be optimal in the general sense? The universal Bayesian agent
AIXI (Hutter, 2005) is a model of a maximally intelligent agent, and plays a
central role in the sub-field of general reinforcement learning (GRL).
Recently, AIXI has been shown to be flawed in important ways; it doesn't
explore enough to be asymptotically optimal (Orseau, 2010), and it can perform
poorly with certain priors (Leike and Hutter, 2015). Several variants of AIXI
have been proposed to attempt to address these shortfalls: among them are
entropy-seeking agents (Orseau, 2011), knowledge-seeking agents (Orseau et al.,
2013), Bayes with bursts of exploration (Lattimore, 2013), MDL agents (Leike,
2016a), Thompson sampling (Leike et al., 2016), and optimism (Sunehag and
Hutter, 2015). We present AIXIjs, a JavaScript implementation of these GRL
agents. This implementation is accompanied by a framework for running
experiments against various environments, similar to OpenAI Gym (Brockman et
al., 2016), and a suite of interactive demos that explore different properties
of the agents, similar to REINFORCEjs (Karpathy, 2015). We use AIXIjs to
present numerous experiments illustrating fundamental properties of, and
differences between, these agents.
",AIXIjs: A Software Demo for General Reinforcement Learning
"  In this paper $\ast$--compatible extensions of fuzzy relations are studied,
generalizing some results obtained by Duggan in case of crisp relations. From
this general result are obtained as particular cases fuzzy versions of some
important extension theorems for crisp relations (Szpilrajn, Hansson,
Suzumura). Two notions of consistent closure of a fuzzy relation are
introduced.
",Compatible extensions and consistent closures: a fuzzy approach
"  In this paper we consider the nature of the machine intelligences we have
created in the context of our human intelligence. We suggest that the
fundamental difference between human and machine intelligence comes down to
\emph{embodiment factors}. We define embodiment factors as the ratio between an
entity's ability to communicate information vs compute information. We
speculate on the role of embodiment factors in driving our own intelligence and
consciousness. We briefly review dual process models of cognition and cast
machine intelligence within that framework, characterising it as a dominant
System Zero, which can drive behaviour through interfacing with us
subconsciously. Driven by concerns about the consequence of such a system we
suggest prophylactic courses of action that could be considered. Our main
conclusion is that it is \emph{not} sentient intelligence we should fear but
\emph{non-sentient} intelligence.
",Living Together: Mind and Machine Intelligence
"  The human reasoning process is seldom a one-way process from an input leading
to an output. Instead, it often involves a systematic deduction by ruling out
other possible outcomes as a self-checking mechanism. In this paper, we
describe the design of a hybrid neural network for logical learning that is
similar to the human reasoning through the introduction of an auxiliary input,
namely the indicators, that act as the hints to suggest logical outcomes. We
generate these indicators by digging into the hidden information buried
underneath the original training data for direct or indirect suggestions. We
used the MNIST data to demonstrate the design and use of these indicators in a
convolutional neural network. We trained a series of such hybrid neural
networks with variations of the indicators. Our results show that these hybrid
neural networks are very robust in generating logical outcomes with inherently
higher prediction accuracy than the direct use of the original input and output
in apparent models. Such improved predictability with reassured logical
confidence is obtained through the exhaustion of all possible indicators to
rule out all illogical outcomes, which is not available in the apparent models.
Our logical learning process can effectively cope with the unknown unknowns
using a full exploitation of all existing knowledge available for learning. The
design and implementation of the hints, namely the indicators, become an
essential part of artificial intelligence for logical learning. We also
introduce an ongoing application setup for this hybrid neural network in an
autonomous grasping robot, namely as_DeepClaw, aiming at learning an optimized
grasping pose through logical learning.
",Logical Learning Through a Hybrid Neural Network with Auxiliary Inputs
"  Many network optimization problems can be formulated as stochastic network
design problems in which edges are present or absent stochastically.
Furthermore, protective actions can guarantee that edges will remain present.
We consider the problem of finding the optimal protection strategy under a
budget limit in order to maximize some connectivity measurements of the
network. Previous approaches rely on the assumption that edges are independent.
In this paper, we consider a more realistic setting where multiple edges are
not independent due to natural disasters or regional events that make the
states of multiple edges stochastically correlated. We use Markov Random Fields
to model the correlation and define a new stochastic network design framework.
We provide a novel algorithm based on Sample Average Approximation (SAA)
coupled with a Gibbs or XOR sampler. The experimental results on real road
network data show that the policies produced by SAA with the XOR sampler have
higher quality and lower variance compared to SAA with Gibbs sampler.
",XOR-Sampling for Network Design with Correlated Stochastic Events
"  Applying deep reinforcement learning (RL) on real systems suffers from slow
data sampling. We propose an enhanced generative adversarial network (EGAN) to
initialize an RL agent in order to achieve faster learning. The EGAN utilizes
the relation between states and actions to enhance the quality of data samples
generated by a GAN. Pre-training the agent with the EGAN shows a steeper
learning curve with a 20% improvement of training time in the beginning of
learning, compared to no pre-training, and an improvement compared to training
with GAN by about 5% with smaller variations. For real time systems with sparse
and slow data sampling the EGAN could be used to speed up the early phases of
the training process.
","Enhanced Experience Replay Generation for Efficient Reinforcement
  Learning"
"  Explaining and reasoning about processes which underlie observed black-box
phenomena enables the discovery of causal mechanisms, derivation of suitable
abstract representations and the formulation of more robust predictions. We
propose to learn high level functional programs in order to represent abstract
models which capture the invariant structure in the observed data. We introduce
the $\pi$-machine (program-induction machine) -- an architecture able to induce
interpretable LISP-like programs from observed data traces. We propose an
optimisation procedure for program learning based on backpropagation, gradient
descent and A* search. We apply the proposed method to three problems: system
identification of dynamical systems, explaining the behaviour of a DQN agent
and learning by demonstration in a human-robot interaction scenario. Our
experimental results show that the $\pi$-machine can efficiently induce
interpretable programs from individual data traces.
",Explaining Transition Systems through Program Induction
"  Sequential decision making problems, such as structured prediction, robotic
control, and game playing, require a combination of planning policies and
generalisation of those plans. In this paper, we present Expert Iteration
(ExIt), a novel reinforcement learning algorithm which decomposes the problem
into separate planning and generalisation tasks. Planning new policies is
performed by tree search, while a deep neural network generalises those plans.
Subsequently, tree search is improved by using the neural network policy to
guide search, increasing the strength of new plans. In contrast, standard deep
Reinforcement Learning algorithms rely on a neural network not only to
generalise plans, but to discover them too. We show that ExIt outperforms
REINFORCE for training a neural network to play the board game Hex, and our
final tree search agent, trained tabula rasa, defeats MoHex 1.0, the most
recent Olympiad Champion player to be publicly released.
",Thinking Fast and Slow with Deep Learning and Tree Search
"  In this paper we present a methodology and discuss some implementation issues
for a project on statistical/expert approach to data analysis and knowledge
acquisition. We discuss some general assumptions underlying the project.
Further, the requirements for a user-friendly computer assistant are specified
along with the nature of tools aiding the researcher. Next we show some aspects
of belief network approach and Dempster-Shafer (DST) methodology introduced in
practice to system SEAD. Specifically we present the application of DS
methodology to belief revision problem. Further a concept of an interface to
probabilistic and DS belief networks enabling a user to understand the
communication with a belief network based reasoning system is presented
","Knowledge Acquisition, Representation \& Manipulation in Decision
  Support Systems"
"  Randomized experiments have been used to assist decision-making in many
areas. They help people select the optimal treatment for the test population
with certain statistical guarantee. However, subjects can show significant
heterogeneity in response to treatments. The problem of customizing treatment
assignment based on subject characteristics is known as uplift modeling,
differential response analysis, or personalized treatment learning in
literature. A key feature for uplift modeling is that the data is unlabeled. It
is impossible to know whether the chosen treatment is optimal for an individual
subject because response under alternative treatments is unobserved. This
presents a challenge to both the training and the evaluation of uplift models.
In this paper we describe how to obtain an unbiased estimate of the key
performance metric of an uplift model, the expected response. We present a new
uplift algorithm which creates a forest of randomized trees. The trees are
built with a splitting criterion designed to directly optimize their uplift
performance based on the proposed evaluation method. Both the evaluation method
and the algorithm apply to arbitrary number of treatments and general response
types. Experimental results on synthetic data and industry-provided data show
that our algorithm leads to significant performance improvement over other
applicable methods.
",Uplift Modeling with Multiple Treatments and General Response Types
"  The explosive growth of the location-enabled devices coupled with the
increasing use of Internet services has led to an increasing awareness of the
importance and usage of geospatial information in many applications. The
navigation apps (often called Maps), use a variety of available data sources to
calculate and predict the travel time as well as several options for routing in
public transportation, car or pedestrian modes. This paper evaluates the
pedestrian mode of Maps apps in three major smartphone operating systems
(Android, iOS and Windows Phone). In the paper, we will show that the Maps apps
on iOS, Android and Windows Phone in pedestrian mode, predict travel time
without learning from the individual's movement profile. In addition, we will
exemplify that those apps suffer from a specific data quality issue which
relates to the absence of information about location and type of pedestrian
crossings. Finally, we will illustrate learning from movement profile of
individuals using various predictive analytics models to improve the accuracy
of travel time estimation.
","Predictive Analytics for Enhancing Travel Time Estimation in Navigation
  Apps of Apple, Google, and Microsoft"
"  In this paper we explore the theoretical boundaries of planning in a setting
where no model of the agent's actions is given. Instead of an action model, a
set of successfully executed plans are given and the task is to generate a plan
that is safe, i.e., guaranteed to achieve the goal without failing. To this
end, we show how to learn a conservative model of the world in which actions
are guaranteed to be applicable. This conservative model is then given to an
off-the-shelf classical planner, resulting in a plan that is guaranteed to
achieve the goal. However, this reduction from a model-free planning to a
model-based planning is not complete: in some cases a plan will not be found
even when such exists. We analyze the relation between the number of observed
plans and the likelihood that our conservative approach will indeed fail to
solve a solvable problem. Our analysis show that the number of trajectories
needed scales gracefully.
","Efficient, Safe, and Probably Approximately Complete Learning of Action
  Models"
"  Semantic Image Interpretation (SII) is the task of extracting structured
semantic descriptions from images. It is widely agreed that the combined use of
visual data and background knowledge is of great importance for SII. Recently,
Statistical Relational Learning (SRL) approaches have been developed for
reasoning under uncertainty and learning in the presence of data and rich
knowledge. Logic Tensor Networks (LTNs) are an SRL framework which integrates
neural networks with first-order fuzzy logic to allow (i) efficient learning
from noisy data in the presence of logical constraints, and (ii) reasoning with
logical formulas describing general properties of the data. In this paper, we
develop and apply LTNs to two of the main tasks of SII, namely, the
classification of an image's bounding boxes and the detection of the relevant
part-of relations between objects. To the best of our knowledge, this is the
first successful application of SRL to such SII tasks. The proposed approach is
evaluated on a standard image processing benchmark. Experiments show that the
use of background knowledge in the form of logical constraints can improve the
performance of purely data-driven approaches, including the state-of-the-art
Fast Region-based Convolutional Neural Networks (Fast R-CNN). Moreover, we show
that the use of logical background knowledge adds robustness to the learning
system when errors are present in the labels of the training data.
",Logic Tensor Networks for Semantic Image Interpretation
"  In reinforcement learning, we often define goals by specifying rewards within
desirable states. One problem with this approach is that we typically need to
redefine the rewards each time the goal changes, which often requires some
understanding of the solution in the agents environment. When humans are
learning to complete tasks, we regularly utilize alternative sources that guide
our understanding of the problem. Such task representations allow one to
specify goals on their own terms, thus providing specifications that can be
appropriately interpreted across various environments. This motivates our own
work, in which we represent goals in environments that are different from the
agents. We introduce Cross-Domain Perceptual Reward (CDPR) functions, learned
rewards that represent the visual similarity between an agents state and a
cross-domain goal image. We report results for learning the CDPRs with a deep
neural network and using them to solve two tasks with deep reinforcement
learning.
",Cross-Domain Perceptual Reward Functions
"  With applications to many disciplines, the traveling salesman problem (TSP)
is a classical computer science optimization problem with applications to
industrial engineering, theoretical computer science, bioinformatics, and
several other disciplines. In recent years, there have been a plethora of novel
approaches for approximate solutions ranging from simplistic greedy to
cooperative distributed algorithms derived from artificial intelligence. In
this paper, we perform an evaluation and analysis of cornerstone algorithms for
the Euclidean TSP. We evaluate greedy, 2-opt, and genetic algorithms. We use
several datasets as input for the algorithms including a small dataset, a
mediumsized dataset representing cities in the United States, and a synthetic
dataset consisting of 200 cities to test algorithm scalability. We discover
that the greedy and 2-opt algorithms efficiently calculate solutions for
smaller datasets. Genetic algorithm has the best performance for optimality for
medium to large datasets, but generally have longer runtime. Our
implementations is public available.
","An Empirical Analysis of Approximation Algorithms for the Euclidean
  Traveling Salesman Problem"
"  We study the notion of robustness in stable matching problems. We first
define robustness by introducing (a,b)-supermatches. An $(a,b)$-supermatch is a
stable matching in which if $a$ pairs break up it is possible to find another
stable matching by changing the partners of those $a$ pairs and at most $b$
other pairs. In this context, we define the most robust stable matching as a
$(1,b)$-supermatch where b is minimum. We show that checking whether a given
stable matching is a $(1,b)$-supermatch can be done in polynomial time. Next,
we use this procedure to design a constraint programming model, a local search
approach, and a genetic algorithm to find the most robust stable matching. Our
empirical evaluation on large instances show that local search outperforms the
other approaches.
",Finding Robust Solutions to Stable Marriage
"  The quadratic unconstrained binary optimization (QUBO) problem arises in
diverse optimization applications ranging from Ising spin problems to classical
problems in graph theory and binary discrete optimization. The use of
preprocessing to transform the graph representing the QUBO problem into a
smaller equivalent graph is important for improving solution quality and time
for both exact and metaheuristic algorithms and is a step towards mapping large
scale QUBO to hardware graphs used in quantum annealing computers. In an
earlier paper (Lewis and Glover, 2016) a set of rules was introduced that
achieved significant QUBO reductions as verified through computational testing.
Here this work is extended with additional rules that provide further
reductions that succeed in exactly solving 10% of the benchmark QUBO problems.
An algorithm and associated data structures to efficiently implement the entire
set of rules is detailed and computational experiments are reported that
demonstrate their efficacy.
","Logical and Inequality Implications for Reducing the Size and Complexity
  of Quadratic Unconstrained Binary Optimization Problems"
"  We introduce a new flexible paradigm of grounding and solving in Answer Set
Programming (ASP), which we refer to as multi-shot ASP solving, and present its
implementation in the ASP system clingo.
  Multi-shot ASP solving features grounding and solving processes that deal
with continuously changing logic programs. In doing so, they remain operative
and accommodate changes in a seamless way. For instance, such processes allow
for advanced forms of search, as in optimization or theory solving, or
interaction with an environment, as in robotics or query-answering. Common to
them is that the problem specification evolves during the reasoning process,
either because data or constraints are added, deleted, or replaced. This
evolutionary aspect adds another dimension to ASP since it brings about state
changing operations. We address this issue by providing an operational
semantics that characterizes grounding and solving processes in multi-shot ASP
solving. This characterization provides a semantic account of grounder and
solver states along with the operations manipulating them.
  The operative nature of multi-shot solving avoids redundancies in relaunching
grounder and solver programs and benefits from the solver's learning
capacities. clingo accomplishes this by complementing ASP's declarative input
language with control capacities. On the declarative side, a new directive
allows for structuring logic programs into named and parameterizable
subprograms. The grounding and integration of these subprograms into the
solving process is completely modular and fully controllable from the
procedural side. To this end, clingo offers a new application programming
interface that is conveniently accessible via scripting languages.
",Multi-shot ASP solving with clingo
"  The Quadratic Unconstrained Binary Optimization problem (QUBO) has become a
unifying model for representing a wide range of combinatorial optimization
problems, and for linking a variety of disciplines that face these problems. A
new class of quantum annealing computer that maps QUBO onto a physical qubit
network structure with specific size and edge density restrictions is
generating a growing interest in ways to transform the underlying QUBO
structure into an equivalent graph having fewer nodes and edges. In this paper
we present rules for reducing the size of the QUBO matrix by identifying
variables whose value at optimality can be predetermined. We verify that the
reductions improve both solution quality and time to solution and, in the case
of metaheuristic methods where optimal solutions cannot be guaranteed, the
quality of solutions obtained within reasonable time limits.
  We discuss the general QUBO structural characteristics that can take
advantage of these reduction techniques and perform careful experimental design
and analysis to identify and quantify the specific characteristics most
affecting reduction. The rules make it possible to dramatically improve
solution times on a new set of problems using both the exact Cplex solver and a
tabu search metaheuristic.
","Quadratic Unconstrained Binary Optimization Problem Preprocessing:
  Theory and Empirical Analysis"
"  In this work we present strategies for (optimal) measurement selection in
model-based sequential diagnosis. In particular, assuming a set of leading
diagnoses being given, we show how queries (sets of measurements) can be
computed and optimized along two dimensions: expected number of queries and
cost per query. By means of a suitable decoupling of two optimizations and a
clever search space reduction the computations are done without any inference
engine calls. For the full search space, we give a method requiring only a
polynomial number of inferences and guaranteeing query properties existing
methods cannot provide. Evaluation results using real-world problems indicate
that the new method computes (virtually) optimal queries instantly
independently of the size and complexity of the considered diagnosis problems.
","Inexpensive Cost-Optimized Measurement Proposal for Sequential
  Model-Based Diagnosis"
"  Abstraction is a fundamental tool for reasoning about complex systems.
Program abstraction has been utilized to great effect for analyzing
deterministic programs. At the heart of program abstraction is the relationship
between a concrete program, which is difficult to analyze, and an abstract
program, which is more tractable. Program abstractions, however, are typically
not probabilistic. We generalize non-deterministic program abstractions to
probabilistic program abstractions by explicitly quantifying the
non-deterministic choices. Our framework upgrades key definitions and
properties of abstractions to the probabilistic context. We also discuss
preliminary ideas for performing inference on probabilistic abstractions and
general probabilistic programs.
",Probabilistic Program Abstractions
"  Intuitively, obedience -- following the order that a human gives -- seems
like a good property for a robot to have. But, we humans are not perfect and we
may give orders that are not best aligned to our preferences. We show that when
a human is not perfectly rational then a robot that tries to infer and act
according to the human's underlying preferences can always perform better than
a robot that simply follows the human's literal order. Thus, there is a
tradeoff between the obedience of a robot and the value it can attain for its
owner. We investigate how this tradeoff is impacted by the way the robot infers
the human's preferences, showing that some methods err more on the side of
obedience than others. We then analyze how performance degrades when the robot
has a misspecified model of the features that the human cares about or the
level of rationality of the human. Finally, we study how robots can start
detecting such model misspecification. Overall, our work suggests that there
might be a middle ground in which robots intelligently decide when to obey
human orders, but err on the side of obedience.
",Should Robots be Obedient?
"  The act of persuasion, a key component in rhetoric argumentation, may be
viewed as a dynamics modifier. We extend Dung's frameworks with acts of
persuasion among agents, and consider interactions among attack, persuasion and
defence that have been largely unheeded so far. We characterise basic notions
of admissibilities in this framework, and show a way of enriching them through,
effectively, CTL (computation tree logic) encoding, which also permits
importation of the theoretical results known to the logic into our
argumentation frameworks. Our aim is to complement the growing interest in
coordination of static and dynamic argumentation.
",Abstract Argumentation / Persuasion / Dynamics
"  There are two common approaches for optimizing the performance of a machine:
genetic algorithms and machine learning. A genetic algorithm is applied over
many generations whereas machine learning works by applying feedback until the
system meets a performance threshold. Though these are methods that typically
operate separately, we combine evolutionary adaptation and machine learning
into one approach. Our focus is on machines that can learn during their
lifetime, but instead of equipping them with a machine learning algorithm we
aim to let them evolve their ability to learn by themselves. We use evolvable
networks of probabilistic and deterministic logic gates, known as Markov
Brains, as our computational model organism. The ability of Markov Brains to
learn is augmented by a novel adaptive component that can change its
computational behavior based on feedback. We show that Markov Brains can indeed
evolve to incorporate these feedback gates to improve their adaptability to
variable environments. By combining these two methods, we now also implemented
a computational model that can be used to study the evolution of learning.
",Machine Learned Learning Machines
"  Artificial Intelligence aims to provide computer programs with commonsense
knowledge to reason about our world. This paper offers a new practical approach
towards automated commonsense reasoning with first-order logic (FOL)
ontologies. We propose a new black-box testing methodology of FOL SUMO-based
ontologies by exploiting WordNet and its mapping into SUMO. Our proposal
includes a method for the (semi-)automatic creation of a very large benchmark
of competency questions and a procedure for its automated evaluation by using
automated theorem provers (ATPs). Applying different quality criteria, our
testing proposal enables a successful evaluation of a) the competency of
several translations of SUMO into FOL and b) the performance of various
automated ATPs. Finally, we also provide a fine-grained and complete analysis
of the commonsense reasoning competency of current FOL SUMO-based ontologies.
",Black-box Testing of First-Order Logic Ontologies Using WordNet
"  Formal ontologies are axiomatizations in a logic-based formalism. The
development of formal ontologies, and their important role in the Semantic Web
area, is generating considerable research on the use of automated reasoning
techniques and tools that help in ontology engineering. One of the main aims is
to refine and to improve axiomatizations for enabling automated reasoning tools
to efficiently infer reliable information. Defects in the axiomatization can
not only cause wrong inferences, but can also hinder the inference of expected
information, either by increasing the computational cost of, or even
preventing, the inference. In this paper, we introduce a novel, fully automatic
white-box testing framework for first-order logic ontologies. Our methodology
is based on the detection of inference-based redundancies in the given
axiomatization. The application of the proposed testing method is fully
automatic since a) the automated generation of tests is guided only by the
syntax of axioms and b) the evaluation of tests is performed by automated
theorem provers. Our proposal enables the detection of defects and serves to
certify the grade of suitability --for reasoning purposes-- of every axiom. We
formally define the set of tests that are generated from any axiom and prove
that every test is logically related to redundancies in the axiom from which
the test has been generated. We have implemented our method and used this
implementation to automatically detect several non-trivial defects that were
hidden in various first-order logic ontologies. Throughout the paper we provide
illustrative examples of these defects, explain how they were found, and how
each proof --given by an automated theorem-prover-- provides useful hints on
the nature of each defect. Additionally, by correcting all the detected
defects, we have obtained an improved version of one of the tested ontologies:
Adimen-SUMO.
",Automatic White-Box Testing of First-Order Logic Ontologies
"  Though a belief network (a representation of the joint probability
distribution, see [3]) and a causal network (a representation of causal
relationships [14]) are intended to mean different things, they are closely
related. Both assume an underlying dag (directed acyclic graph) structure of
relations among variables and if Markov condition and faithfulness condition
[15] are met, then a causal network is in fact a belief network. The difference
comes to appearance when we recover belief network and causal network structure
from data.
  A causal network structure may be impossible to recover completely from data
as not all directions of causal links may be uniquely determined [15].
Fortunately, if we deal with causally sufficient sets of variables (that is
whenever significant influence variables are not omitted from observation),
then there exists the possibility to identify the family of belief networks a
causal network belongs to [16]. Regrettably, to our knowledge, a similar result
is not directly known for causally insufficient sets of variables. Spirtes,
Glymour and Scheines developed a CI algorithm to handle this situation, but it
leaves some important questions open.
  The big open question is whether or not the bidirectional edges (that is
indications of a common cause) are the only ones necessary to develop a belief
network out of the product of CI, or must there be some other hidden variables
added (e.g. by guessing). This paper is devoted to settling this question.
",Learning Belief Network Structure From Data under Causal Insufficiency
"  Games have always been popular testbeds for Artificial Intelligence (AI). In
the last decade, we have seen the rise of the Multiple Online Battle Arena
(MOBA) games, which are the most played games nowadays. In spite of this, there
are few works that explore MOBA as a testbed for AI Research. In this paper we
present and discuss the main features and opportunities offered by MOBA games
to Game AI Research. We describe the various challenges faced along the game
and also propose a discrete model that can be used to better understand and
explore the game. With this, we aim to encourage the use of MOBA as a novel
research platform for Game AI.
",MOBA: a New Arena for Game AI
"  Many state-of-the-art reinforcement learning (RL) algorithms typically assume
that the environment is an ergodic Markov Decision Process (MDP). In contrast,
the field of universal reinforcement learning (URL) is concerned with
algorithms that make as few assumptions as possible about the environment. The
universal Bayesian agent AIXI and a family of related URL algorithms have been
developed in this setting. While numerous theoretical optimality results have
been proven for these agents, there has been no empirical investigation of
their behavior to date. We present a short and accessible survey of these URL
algorithms under a unified notation and framework, along with results of some
experiments that qualitatively illustrate some properties of the resulting
policies, and their relative performance on partially-observable gridworld
environments. We also present an open-source reference implementation of the
algorithms which we hope will facilitate further understanding of, and
experimentation with, these ideas.
",Universal Reinforcement Learning Algorithms: Survey and Experiments
"  There are many goals for an AI that could become dangerous if the AI becomes
superintelligent or otherwise powerful. Much work on the AI control problem has
been focused on constructing AI goals that are safe even for such AIs. This
paper looks at an alternative approach: defining a general concept of `low
impact'. The aim is to ensure that a powerful AI which implements low impact
will not modify the world extensively, even if it is given a simple or
dangerous goal. The paper proposes various ways of defining and grounding low
impact, and discusses methods for ensuring that the AI can still be allowed to
have a (desired) impact despite the restriction. The end of the paper addresses
known issues with this approach and avenues for future research.
",Low Impact Artificial Intelligences
"  We present a new system S for handling uncertainty in a quantified modal
logic (first-order modal logic). The system is based on both probability theory
and proof theory. The system is derived from Chisholm's epistemology. We
concretize Chisholm's system by grounding his undefined and primitive (i.e.
foundational) concept of reasonablenes in probability and proof theory. S can
be useful in systems that have to interact with humans and provide
justifications for their uncertainty. As a demonstration of the system, we
apply the system to provide a solution to the lottery paradox. Another
advantage of the system is that it can be used to provide uncertainty values
for counterfactual statements. Counterfactuals are statements that an agent
knows for sure are false. Among other cases, counterfactuals are useful when
systems have to explain their actions to users. Uncertainties for
counterfactuals fall out naturally from our system.
  Efficient reasoning in just simple first-order logic is a hard problem.
Resolution-based first-order reasoning systems have made significant progress
over the last several decades in building systems that have solved non-trivial
tasks (even unsolved conjectures in mathematics). We present a sketch of a
novel algorithm for reasoning that extends first-order resolution.
  Finally, while there have been many systems of uncertainty for propositional
logics, first-order logics and propositional modal logics, there has been very
little work in building systems of uncertainty for first-order modal logics.
The work described below is in progress; and once finished will address this
lack.
",Strength Factors: An Uncertainty System for a Quantified Modal Logic
"  Experience replay is one of the most commonly used approaches to improve the
sample efficiency of reinforcement learning algorithms. In this work, we
propose an approach to select and replay sequences of transitions in order to
accelerate the learning of a reinforcement learning agent in an off-policy
setting. In addition to selecting appropriate sequences, we also artificially
construct transition sequences using information gathered from previous
agent-environment interactions. These sequences, when replayed, allow value
function information to trickle down to larger sections of the
state/state-action space, thereby making the most of the agent's experience. We
demonstrate our approach on modified versions of standard reinforcement
learning tasks such as the mountain car and puddle world problems and
empirically show that it enables better learning of value functions as compared
to other forms of experience replay. Further, we briefly discuss some of the
possible extensions to this work, as well as applications and situations where
this approach could be particularly useful.
",Experience Replay Using Transition Sequences
"  Clause Learning is one of the most important components of a conflict driven
clause learning (CDCL) SAT solver that is effective on industrial instances.
Since the number of learned clauses is proved to be exponential in the worse
case, it is necessary to identify the most relevant clauses to maintain and
delete the irrelevant ones. As reported in the literature, several learned
clauses deletion strategies have been proposed. However the diversity in both
the number of clauses to be removed at each step of reduction and the results
obtained with each strategy creates confusion to determine which criterion is
better. Thus, the problem to select which learned clauses are to be removed
during the search step remains very challenging. In this paper, we propose a
novel approach to identify the most relevant learned clauses without favoring
or excluding any of the proposed measures, but by adopting the notion of
dominance relationship among those measures. Our approach bypasses the problem
of the diversity of results and reaches a compromise between the assessments of
these measures. Furthermore, the proposed approach also avoids another
non-trivial problem which is the amount of clauses to be deleted at each
reduction of the learned clause database.
","Towards Learned Clauses Database Reduction Strategies Based on Dominance
  Relationship"
"  While knowledge representation and reasoning are considered the keys for
human-level artificial intelligence, connectionist networks have been shown
successful in a broad range of applications due to their capacity for robust
learning and flexible inference under uncertainty. The idea of representing
symbolic knowledge in connectionist networks has been well-received and
attracted much attention from research community as this can establish a
foundation for integration of scalable learning and sound reasoning. In
previous work, there exist a number of approaches that map logical inference
rules with feed-forward propagation of artificial neural networks (ANN).
However, the discriminative structure of an ANN requires the separation of
input/output variables which makes it difficult for general reasoning where any
variables should be inferable. Other approaches address this issue by employing
generative models such as symmetric connectionist networks, however, they are
difficult and convoluted. In this paper we propose a novel method to represent
propositional formulas in restricted Boltzmann machines which is less complex,
especially in the cases of logical implications and Horn clauses. An
integration system is then developed and evaluated in real datasets which shows
promising results.
","Propositional Knowledge Representation and Reasoning in Restricted
  Boltzmann Machines"
"  Recent progress in Reinforcement Learning (RL), fueled by its combination,
with Deep Learning has enabled impressive results in learning to interact with
complex virtual environments, yet real-world applications of RL are still
scarce. A key limitation is data efficiency, with current state-of-the-art
approaches requiring millions of training samples. A promising way to tackle
this problem is to augment RL with learning from human demonstrations. However,
human demonstration data is not yet readily available. This hinders progress in
this direction. The present work addresses this problem as follows. We (i)
collect and describe a large dataset of human Atari 2600 replays -- the largest
and most diverse such data set publicly released to date, (ii) illustrate an
example use of this dataset by analyzing the relation between demonstration
quality and imitation learning performance, and (iii) outline possible research
directions that are opened up by our work.
",The Atari Grand Challenge Dataset
"  Multi-start algorithms are a common and effective tool for metaheuristic
searches. In this paper we amplify multi-start capabilities by employing the
parallel processing power of the graphics processer unit (GPU) to quickly
generate a diverse starting set of solutions for the Unconstrained Binary
Quadratic Optimization Problem which are evaluated and used to implement
screening methods to select solutions for further optimization. This method is
implemented as an initial high quality solution generation phase prior to a
secondary steepest ascent search and a comparison of results to best known
approaches on benchmark unconstrained binary quadratic problems demonstrates
that GPU-enabled diversified multi-start with screening quickly yields very
good results.
","A Diversified Multi-Start Algorithm for Unconstrained Binary Quadratic
  Problems Leveraging the Graphics Processor Unit"
"  In [1], we introduced mechanical learning and proposed 2 approaches to
mechanical learning. Here, we follow one such approach to well describe the
objects and the processes of learning. We discuss 2 kinds of patterns:
objective and subjective pattern. Subjective pattern is crucial for learning
machine. We prove that for any objective pattern we can find a proper
subjective pattern based upon least base patterns to express the objective
pattern well. X-form is algebraic expression for subjective pattern. Collection
of X-forms form internal representation space, which is center of learning
machine. We discuss learning by teaching and without teaching. We define data
sufficiency by X-form. We then discussed some learning strategies. We show, in
each strategy, with sufficient data, and with certain capabilities, learning
machine indeed can learn any pattern (universal learning machine). In appendix,
with knowledge of learning machine, we try to view deep learning from a
different angle, i.e. its internal representation space and its learning
dynamics.
",Descriptions of Objectives and Processes of Mechanical Learning
"  We introduce a diversified top-k partial MaxSAT problem, a combination of
partial MaxSAT problem and enumeration problem. Given a partial MaxSAT formula
F and a positive integer k, the diversified top-k partial MaxSAT is to find k
maximal solutions for F such that the k maximal solutions satisfy the maximum
number of soft clauses of F. This problem can be widely used in many
applications including community detection, sensor place, motif discovery, and
combinatorial testing. We prove the problem is NP-hard and propose an approach
for solving the problem. The concrete idea of the approach is to design an
encoding EE which reduces diversified top-k partial MaxSAT problem into partial
MaxSAT problem, and then solve the resulting problem with state-of-art solvers.
In addition, we present an algorithm MEMKC exactly solving the diversified
top-k partial MaxSAT. Through several experiments we show that our approach can
be successfully applied to the interesting problem.
",Diversified Top-k Partial MaxSAT Solving
"  As robots begin to cohabit with humans in semi-structured environments, the
need arises to understand instructions involving rich variability---for
instance, learning to ground symbols in the physical world. Realistically, this
task must cope with small datasets consisting of a particular users' contextual
assignment of meaning to terms. We present a method for processing a raw stream
of cross-modal input---i.e., linguistic instructions, visual perception of a
scene and a concurrent trace of 3D eye tracking fixations---to produce the
segmentation of objects with a correspondent association to high-level
concepts. To test our framework we present experiments in a table-top object
manipulation scenario. Our results show our model learns the user's notion of
colour and shape from a small number of physical demonstrations, generalising
to identifying physical referents for novel combinations of the words.
",Grounding Symbols in Multi-Modal Instructions
"  The growing adoption of IT-systems for modeling and executing (business)
processes or services has thrust the scientific investigation towards
techniques and tools which support more complex forms of process analysis. Many
of them, such as conformance checking, process alignment, mining and
enhancement, rely on complete observation of past (tracked and logged)
executions. In many real cases, however, the lack of human or IT-support on all
the steps of process execution, as well as information hiding and abstraction
of model and data, result in incomplete log information of both data and
activities. This paper tackles the issue of automatically repairing traces with
missing information by notably considering not only activities but also data
manipulated by them. Our technique recasts such a problem in a reachability
problem and provides an encoding in an action language which allows to
virtually use any state-of-the-art planning to return solutions.
",Enhancing workflow-nets with data for trace completion
"  Deep neural networks are able to solve tasks across a variety of domains and
modalities of data. Despite many empirical successes, we lack the ability to
clearly understand and interpret the learned internal mechanisms that
contribute to such effective behaviors or, more critically, failure modes. In
this work, we present a general method for visualizing an arbitrary neural
network's inner mechanisms and their power and limitations. Our dataset-centric
method produces visualizations of how a trained network attends to components
of its inputs. The computed ""attention masks"" support improved interpretability
by highlighting which input attributes are critical in determining output. We
demonstrate the effectiveness of our framework on a variety of deep neural
network architectures in domains from computer vision, natural language
processing, and reinforcement learning. The primary contribution of our
approach is an interpretable visualization of attention that provides unique
insights into the network's underlying decision-making process irrespective of
the data modality.
",Modeling Latent Attention Within Neural Networks
"  Existing methods for dealing with knowledge updates differ greatly depending
on the underlying knowledge representation formalism. When Classical Logic is
used, updates are typically performed by manipulating the knowledge base on the
model-theoretic level. On the opposite side of the spectrum stand the semantics
for updating Answer-Set Programs that need to rely on rule syntax. Yet, a
unifying perspective that could embrace both these branches of research is of
great importance as it enables a deeper understanding of all involved methods
and principles and creates room for their cross-fertilisation, ripening and
further development.
  This paper bridges the seemingly irreconcilable approaches to updates. It
introduces a novel monotonic characterisation of rules, dubbed RE-models, and
shows it to be a more suitable semantic foundation for rule updates than
SE-models. Then it proposes a generic scheme for specifying semantic rule
update operators, based on the idea of viewing a program as the set of sets of
RE-models of its rules; updates are performed by introducing additional
interpretations - exceptions - to the sets of RE-models of rules in the
original program. The introduced scheme is used to define rule update operators
that are closely related to both classical update principles and traditional
approaches to rules updates, and serve as a basis for a solution to the
long-standing problem of state condensing, showing how they can be equivalently
defined as binary operators on some class of logic programs.
  Finally, the essence of these ideas is extracted to define an abstract
framework for exception-based update operators, viewing a knowledge base as the
set of sets of models of its elements, which can capture a wide range of both
model- and formula-based classical update operators, and thus serves as the
first firm formal ground connecting classical and rule updates.
",Exception-Based Knowledge Updates
"  While several matrix factorization (MF) and tensor factorization (TF) models
have been proposed for knowledge base (KB) inference, they have rarely been
compared across various datasets. Is there a single model that performs well
across datasets? If not, what characteristics of a dataset determine the
performance of MF and TF models? Is there a joint TF+MF model that performs
robustly on all datasets? We perform an extensive evaluation to compare popular
KB inference models across popular datasets in the literature. In addition to
answering the questions above, we remove a limitation in the standard
evaluation protocol for MF models, propose an extension to MF models so that
they can better handle out-of-vocabulary (OOV) entity pairs, and develop a
novel combination of TF and MF models. We also analyze and explain the results
based on models and dataset characteristics. Our best model is robust, and
obtains strong results across all datasets.
",Joint Matrix-Tensor Factorization for Knowledge Base Inference
"  Humans are expert in the amount of sensory data they deal with each moment.
Human brain not only analyses these data but also starts synthesizing new
information from the existing data. The current age Big-data systems are needed
not just to analyze data but also to come up new interpretation. We believe
that the pivotal ability in human brain which enables us to do this is what is
known as ""intuition"". Here, we present an intuition based architecture for big
data analysis and synthesis.
","ICABiDAS: Intuition Centred Architecture for Big Data Analysis and
  Synthesis"
"  In many robotic applications, some aspects of the system dynamics can be
modeled accurately while others are difficult to obtain or model. We present a
novel reinforcement learning (RL) method for continuous state and action spaces
that learns with partial knowledge of the system and without active
exploration. It solves linearly-solvable Markov decision processes (L-MDPs),
which are well suited for continuous state and action spaces, based on an
actor-critic architecture. Compared to previous RL methods for L-MDPs and path
integral methods which are model based, the actor-critic learning does not need
a model of the uncontrolled dynamics and, importantly, transition noise levels;
however, it requires knowing the control dynamics for the problem. We evaluate
our method on two synthetic test problems, and one real-world problem in
simulation and using real traffic data. Our experiments demonstrate improved
learning and policy performance.
","Actor-Critic for Linearly-Solvable Continuous MDP with Partially Known
  Dynamics"
"  Pathfinding is a very popular area in computer game development. While
two-dimensional (2D) pathfinding is widely applied in most of the popular game
engines, little implementation of real three-dimensional (3D) pathfinding can
be found. This research presents a dynamic search space optimization algorithm
which can be applied to tessellate 3D search space unevenly, significantly
reducing the total number of resulting nodes. The algorithm can be used with
popular pathfinding algorithms in 3D game engines. Furthermore, a simplified
standalone 3D pathfinding algorithm is proposed in this paper. The proposed
algorithm relies on ray-casting or line vision to generate a feasible path
during runtime without requiring division of the search space into a 3D grid.
Both of the proposed algorithms are simulated on Unreal Engine to show
innerworkings and resultant path comparison with A*. The advantages and
shortcomings of the proposed algorithms are also discussed along with future
directions.
","3D Pathfinding and Collision Avoidance Using Uneven Search-space
  Quantization and Visual Cone Search"
"  Non-stationary domains, that change in unpredicted ways, are a challenge for
agents searching for optimal policies in sequential decision-making problems.
This paper presents a combination of Markov Decision Processes (MDP) with
Answer Set Programming (ASP), named {\em Online ASP for MDP} (oASP(MDP)), which
is a method capable of constructing the set of domain states while the agent
interacts with a changing environment. oASP(MDP) updates previously obtained
policies, learnt by means of Reinforcement Learning (RL), using rules that
represent the domain changes observed by the agent. These rules represent a set
of domain constraints that are processed as ASP programs reducing the search
space. Results show that oASP(MDP) is capable of finding solutions for problems
in non-stationary domains without interfering with the action-value function
approximation process.
","A method for the online construction of the set of states of a Markov
  Decision Process using Answer Set Programming"
"  Symbolic has been long considered as a language of human intelligence while
neural networks have advantages of robust computation and dealing with noisy
data. The integration of neural-symbolic can offer better learning and
reasoning while providing a means for interpretability through the
representation of symbolic knowledge. Although previous works focus intensively
on supervised feedforward neural networks, little has been done for the
unsupervised counterparts. In this paper we show how to integrate symbolic
knowledge into unsupervised neural networks. We exemplify our approach with
knowledge in different forms, including propositional logic for DNA promoter
prediction and first-order logic for understanding family relationship.
",Unsupervised Neural-Symbolic Integration
"  Epistemic logic with non-standard knowledge operators, especially the
""knowing-value"" operator, has recently gathered much attention. With the
""knowing-value"" operator, we can express knowledge of individual variables, but
not of the relations between them in general. In this paper, we propose a new
operator Kf to express knowledge of the functional dependencies between
variables. The semantics of this Kf operator uses a function domain which
imposes a constraint on what counts as a functional dependency relation. By
adjusting this function domain, different interesting logics arise, and in this
paper we axiomatize three such logics in a single agent setting. Then we show
how these three logics can be unified by allowing the function domain to vary
relative to different agents and possible worlds. A multiagent axiomatization
is given in this case.
",Epistemic Logic with Functional Dependency Operator
"  We propose a new General Game Playing (GGP) language called Regular
Boardgames (RBG), which is based on the theory of regular languages. The
objective of RBG is to join key properties as expressiveness, efficiency, and
naturalness of the description in one GGP formalism, compensating certain
drawbacks of the existing languages. This often makes RBG more suitable for
various research and practical developments in GGP. While dedicated mostly for
describing board games, RBG is universal for the class of all finite
deterministic turn-based games with perfect information. We establish
foundations of RBG, and analyze it theoretically and experimentally, focusing
on the efficiency of reasoning. Regular Boardgames is the first GGP language
that allows efficient encoding and playing games with complex rules and with
large branching factor (e.g.\ amazons, arimaa, large chess variants, go,
international checkers, paper soccer).
",Regular Boardgames
"  As intelligent systems are increasingly making decisions that directly affect
society, perhaps the most important upcoming research direction in AI is to
rethink the ethical implications of their actions. Means are needed to
integrate moral, societal and legal values with technological developments in
AI, both during the design process as well as part of the deliberation
algorithms employed by these systems. In this paper, we describe leading ethics
theories and propose alternative ways to ensure ethical behavior by artificial
systems. Given that ethics are dependent on the socio-cultural context and are
often only implicit in deliberation processes, methodologies are needed to
elicit the values held by designers and stakeholders, and to make these
explicit leading to better understanding and trust on artificial autonomous
systems.
",Responsible Autonomy
"  The conditioning in the Dempster-Shafer Theory of Evidence has been defined
(by Shafer \cite{Shafer:90} as combination of a belief function and of an
""event"" via Dempster rule.
  On the other hand Shafer \cite{Shafer:90} gives a ""probabilistic""
interpretation of a belief function (hence indirectly its derivation from a
sample). Given the fact that conditional probability distribution of a
sample-derived probability distribution is a probability distribution derived
from a subsample (selected on the grounds of a conditioning event), the paper
investigates the empirical nature of the Dempster- rule of combination.
  It is demonstrated that the so-called ""conditional"" belief function is not a
belief function given an event but rather a belief function given manipulation
of original empirical data.\\ Given this, an interpretation of belief function
different from that of Shafer is proposed. Algorithms for construction of
belief networks from data are derived for this interpretation.
",What Does a Belief Function Believe In ?
"  Multiplayer Online Battle Arena (MOBA) is one of the most played game genres
nowadays. With the increasing growth of this genre, it becomes necessary to
develop effective intelligent agents to play alongside or against human
players. In this paper we address the problem of agent development for MOBA
games. We implement a two-layered architecture agent that handles both
navigation and game mechanics. This architecture relies on the use of Influence
Maps, a widely used approach for tactical analysis. Several experiments were
performed using {\em League of Legends} as a testbed, and show promising
results in this highly dynamic real-time context.
",On the Development of Intelligent Agents for MOBA Games
"  We present a new preprocessing algorithm for embedding the nodes of a given
edge-weighted undirected graph into a Euclidean space. The Euclidean distance
between any two nodes in this space approximates the length of the shortest
path between them in the given graph. Later, at runtime, a shortest path
between any two nodes can be computed with A* search using the Euclidean
distances as heuristic. Our preprocessing algorithm, called FastMap, is
inspired by the data mining algorithm of the same name and runs in near-linear
time. Hence, FastMap is orders of magnitude faster than competing approaches
that produce a Euclidean embedding using Semidefinite Programming. FastMap also
produces admissible and consistent heuristics and therefore guarantees the
generation of shortest paths. Moreover, FastMap applies to general undirected
graphs for which many traditional heuristics, such as the Manhattan Distance
heuristic, are not well defined. Empirically, we demonstrate that A* search
using the FastMap heuristic is competitive with A* search using other
state-of-the-art heuristics, such as the Differential heuristic.
",The FastMap Algorithm for Shortest Path Computations
"  Multi-Agent Path Finding (MAPF) is an NP-hard problem well studied in
artificial intelligence and robotics. It has many real-world applications for
which existing MAPF solvers use various heuristics. However, these solvers are
deterministic and perform poorly on ""hard"" instances typically characterized by
many agents interfering with each other in a small region. In this paper, we
enhance MAPF solvers with randomization and observe that they exhibit
heavy-tailed distributions of runtimes on hard instances. This leads us to
develop simple rapid randomized restart (RRR) strategies with the intuition
that, given a hard instance, multiple short runs have a better chance of
solving it compared to one long run. We validate this intuition through
experiments and show that our RRR strategies indeed boost the performance of
state-of-the-art MAPF solvers such as iECBS and M*.
",Rapid Randomized Restarts for Multi-Agent Path Finding Solvers
"  Drawing an inspiration from behavioral studies of human decision making, we
propose here a general parametric framework for multi-armed bandit problem,
which extends the standard Thompson Sampling approach to incorporate reward
processing biases associated with several neurological and psychiatric
conditions, including Parkinson's and Alzheimer's diseases,
attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain.
We demonstrate empirically that the proposed parametric approach can often
outperform the baseline Thompson Sampling on a variety of datasets. Moreover,
from the behavioral modeling perspective, our parametric framework can be
viewed as a first step towards a unifying computational model capturing reward
processing abnormalities across multiple mental conditions.
",Bandit Models of Human Behavior: Reward Processing in Mental Disorders
"  This paper is concerned with the apparent greatest weakness of the
Mathematical Theory of Evidence (MTE) of Shafer \cite{Shafer:76}, which has
been strongly criticized by Wasserman \cite{Wasserman:92ijar} - the
relationship to frequencies.
  Weaknesses of various proposals of probabilistic interpretation of MTE belief
functions are demonstrated.
  A new frequency-based interpretation is presented overcoming various
drawbacks of earlier interpretations.
",Evidence Against Evidence Theory (?!)
"  MOBAs represent a huge segment of online gaming and are growing as both an
eSport and a casual genre. The natural starting point for AI researchers
interested in MOBAs is to develop an AI to play the game better than a human -
but MOBAs have many more challenges besides adversarial AI. In this paper we
introduce the reader to the wider context of MOBA culture, propose a range of
challenges faced by the community today, and posit concrete AI projects that
can be undertaken to begin solving them.
",Off The Beaten Lane: AI Challenges In MOBAs Beyond Player Control
"  In this research, we investigate the subject of path-finding. A pruned
version of visibility graph based on Candidate Vertices is formulated, followed
by a new visibility check technique. Such combination enables us to quickly
identify the useful vertices and thus find the optimal path more efficiently.
The algorithm proposed is demonstrated on various path-finding cases. The
performance of the new technique on visibility graphs is compared to the
traditional A* on Grids, Theta* and A* on Visibility Graphs in terms of path
length, number of nodes evaluated, as well as computational time. The key
algorithmic contribution is that the new approach combines the merits of
grid-based method and visibility graph-based method and thus yields better
overall performance.
","A Focal Any-Angle Path-finding Algorithm Based on A* on Visibility
  Graphs"
"  Over 13 months in 2016-17 the FCC conducted an ""incentive auction"" to
repurpose radio spectrum from broadcast television to wireless internet. In the
end, the auction yielded $19.8 billion, $10.05 billion of which was paid to 175
broadcasters for voluntarily relinquishing their licenses across 14 UHF
channels. Stations that continued broadcasting were assigned potentially new
channels to fit as densely as possible into the channels that remained. The
government netted more than $7 billion (used to pay down the national debt)
after covering costs. A crucial element of the auction design was the
construction of a solver, dubbed SATFC, that determined whether sets of
stations could be ""repacked"" in this way; it needed to run every time a station
was given a price quote. This paper describes the process by which we built
SATFC. We adopted an approach we dub ""deep optimization"", taking a data-driven,
highly parametric, and computationally intensive approach to solver design.
More specifically, to build SATFC we designed software that could pair both
complete and local-search SAT-encoded feasibility checking with a wide range of
domain-specific techniques. We then used automatic algorithm configuration
techniques to construct a portfolio of eight complementary algorithms to be run
in parallel, aiming to achieve good performance on instances that arose in
proprietary auction simulations. To evaluate the impact of our solver in this
paper, we built an open-source reverse auction simulator. We found that within
the short time budget required in practice, SATFC solved more than 95% of the
problems it encountered. Furthermore, the incentive auction paired with SATFC
produced nearly optimal allocations in a restricted setting and substantially
outperformed other alternatives at national scale.
",Deep Optimization for Spectrum Repacking
"  We consider the task of evaluating a policy for a Markov decision process
(MDP). The standard unbiased technique for evaluating a policy is to deploy the
policy and observe its performance. We show that the data collected from
deploying a different policy, commonly called the behavior policy, can be used
to produce unbiased estimates with lower mean squared error than this standard
technique. We derive an analytic expression for the optimal behavior policy ---
the behavior policy that minimizes the mean squared error of the resulting
estimates. Because this expression depends on terms that are unknown in
practice, we propose a novel policy evaluation sub-problem, behavior policy
search: searching for a behavior policy that reduces mean squared error. We
present a behavior policy search algorithm and empirically demonstrate its
effectiveness in lowering the mean squared error of policy performance
estimates.
",Data-Efficient Policy Evaluation Through Behavior Policy Search
"  This is a contribution to the formalization of the concept of agents in
multivariate Markov chains. Agents are commonly defined as entities that act,
perceive, and are goal-directed. In a multivariate Markov chain (e.g. a
cellular automaton) the transition matrix completely determines the dynamics.
This seems to contradict the possibility of acting entities within such a
system. Here we present definitions of actions and perceptions within
multivariate Markov chains based on entity-sets. Entity-sets represent a
largely independent choice of a set of spatiotemporal patterns that are
considered as all the entities within the Markov chain. For example, the
entity-set can be chosen according to operational closure conditions or
complete specific integration. Importantly, the perception-action loop also
induces an entity-set and is a multivariate Markov chain. We then show that our
definition of actions leads to non-heteronomy and that of perceptions
specialize to the usual concept of perception in the perception-action loop.
",Action and perception for spatiotemporal patterns
"  Constrained counting is important in domains ranging from artificial
intelligence to software analysis. There are already a few approaches for
counting models over various types of constraints. Recently, hashing-based
approaches achieve both theoretical guarantees and scalability, but still rely
on solution enumeration. In this paper, a new probabilistic polynomial time
approximate model counter is proposed, which is also a hashing-based universal
framework, but with only satisfiability queries. A variant with a dynamic
stopping criterion is also presented. Empirical evaluation over benchmarks on
propositional logic formulas and SMT(BV) formulas shows that the approach is
promising.
",A New Probabilistic Algorithm for Approximate Model Counting
"  The population in Sweden is growing rapidly due to immigration. In this
light, the issue of infrastructure upgrades to provide telecommunication
services is of importance. New antennas can be installed at hot spots of user
demand, which will require an investment, and/or the clientele expansion can be
carried out in a planned manner to promote the exploitation of the
infrastructure in the less loaded geographical zones. In this paper, we explore
the second alternative. Informally speaking, the term Infrastructure-Stressing
describes a user who stays in the zones of high demand, which are prone to
produce service failures, if further loaded. We have studied the
Infrastructure-Stressing population in the light of their correlation with
geo-demographic segments. This is motivated by the fact that specific
geo-demographic segments can be targeted via marketing campaigns. Fuzzy logic
is applied to create an interface between big data, numeric methods for
processing big data and a manager.
",Fuzzy Recommendations in Marketing Campaigns
"  In this paper we provide a first analysis of the research questions that
arise when dealing with the problem of communicating pieces of formal
argumentation through natural language interfaces. It is a generally held
opinion that formal models of argumentation naturally capture human argument,
and some preliminary studies have focused on justifying this view.
Unfortunately, the results are not only inconclusive, but seem to suggest that
explaining formal argumentation to humans is a rather articulated task.
Graphical models for expressing argumentation-based reasoning are appealing,
but often humans require significant training to use these tools effectively.
We claim that natural language interfaces to formal argumentation systems offer
a real alternative, and may be the way forward for systems that capture human
argument.
",On Natural Language Generation of Formal Argumentation
"  The recent adaptation of deep neural network-based methods to reinforcement
learning and planning domains has yielded remarkable progress on individual
tasks. Nonetheless, progress on task-to-task transfer remains limited. In
pursuit of efficient and robust generalization, we introduce the Schema
Network, an object-oriented generative physics simulator capable of
disentangling multiple causes of events and reasoning backward through causes
to achieve goals. The richly structured architecture of the Schema Network can
learn the dynamics of an environment directly from data. We compare Schema
Networks with Asynchronous Advantage Actor-Critic and Progressive Networks on a
suite of Breakout variations, reporting results on training efficiency and
zero-shot generalization, consistently demonstrating faster, more robust
learning and better transfer. We argue that generalizing from limited data and
learning causal relationships are essential abilities on the path toward
generally intelligent systems.
","Schema Networks: Zero-shot Transfer with a Generative Causal Model of
  Intuitive Physics"
"  The highly influential framework of conceptual spaces provides a geometric
way of representing knowledge. It aims at bridging the gap between symbolic and
subsymbolic processing. Instances are represented by points in a
high-dimensional space and concepts are represented by convex regions in this
space. In this paper, we present our approach towards grounding the dimensions
of a conceptual space in latent spaces learned by an InfoGAN from unlabeled
data.
",Towards Grounding Conceptual Spaces in Neural Representations
"  Inductive Logic Programming (ILP) combines rule-based and statistical
artificial intelligence methods, by learning a hypothesis comprising a set of
rules given background knowledge and constraints for the search space. We focus
on extending the XHAIL algorithm for ILP which is based on Answer Set
Programming and we evaluate our extensions using the Natural Language
Processing application of sentence chunking. With respect to processing natural
language, ILP can cater for the constant change in how we use language on a
daily basis. At the same time, ILP does not require huge amounts of training
examples such as other statistical methods and produces interpretable results,
that means a set of rules, which can be analysed and tweaked if necessary. As
contributions we extend XHAIL with (i) a pruning mechanism within the
hypothesis generalisation algorithm which enables learning from larger
datasets, (ii) a better usage of modern solver technology using recently
developed optimisation methods, and (iii) a time budget that permits the usage
of suboptimal results. We evaluate these improvements on the task of sentence
chunking using three datasets from a recent SemEval competition. Results show
that our improvements allow for learning on bigger datasets with results that
are of similar quality to state-of-the-art systems on the same task. Moreover,
we compare the hypotheses obtained on datasets to gain insights on the
structure of each dataset.
","Improving Scalability of Inductive Logic Programming via Pruning and
  Best-Effort Optimisation"
"  We study the problem of cooperative multi-agent reinforcement learning with a
single joint reward signal. This class of learning problems is difficult
because of the often large combined action and observation spaces. In the fully
centralized and decentralized approaches, we find the problem of spurious
rewards and a phenomenon we call the ""lazy agent"" problem, which arises due to
partial observability. We address these problems by training individual agents
with a novel value decomposition network architecture, which learns to
decompose the team value function into agent-wise value functions. We perform
an experimental evaluation across a range of partially-observable multi-agent
domains and show that learning such value-decompositions leads to superior
results, in particular when combined with weight sharing, role information and
information channels.
",Value-Decomposition Networks For Cooperative Multi-Agent Learning
"  Many tourist applications provide a personalized tourist agenda with the list
of recommended activities to the user. These applications must undoubtedly deal
with the constraints and preferences that define the user interests. Among
these preferences, we can find those that define the travel style of the user,
such as the rhythm of the trip, the number of visits to include in the tour or
the priority to visits of special interest for the user. In this paper, we deal
with the task of creating a customized tourist agenda as a planning and
scheduling application capable of conveniently scheduling the most appropriate
goals (visits) so as to maximize the user satisfaction with the tourist route.
This paper makes an analysis of the meaning of the travel style preferences and
compares the quality of the solutions obtained by two different solvers, a
PDDL-based planner and a Constraint Satisfaction Problem solver. We also define
several quality metrics and perform extensive experiments in order to evaluate
the results obtained with both solvers.
","Evaluating the quality of tourist agendas customized to different travel
  styles"
"  This paper focuses on preserving the privacy of sensitive patterns when
inducing decision trees. We adopt a record augmentation approach for hiding
sensitive classification rules in binary datasets. Such a hiding methodology is
preferred over other heuristic solutions like output perturbation or
cryptographic techniques - which restrict the usability of the data - since the
raw data itself is readily available for public use. We show some key lemmas
which are related to the hiding process and we also demonstrate the methodology
with an example and an indicative experiment using a prototype hiding tool.
",Data set operations to hide decision tree rules
"  The use of semi-autonomous and autonomous robotic assistants to aid in care
of the elderly is expected to ease the burden on human caretakers, with
small-stage testing already occurring in a variety of countries. Yet, it is
likely that these robots will need to request human assistance via
teleoperation when domain expertise is needed for a specific task. As
deployment of robotic assistants moves to scale, mapping these requests for
human aid to the teleoperators themselves will be a difficult online
optimization problem. In this paper, we design a system that allocates requests
to a limited number of teleoperators, each with different specialities, in an
online fashion. We generalize a recent model of online job scheduling with a
worst-case competitive-ratio bound to our setting. Next, we design a scalable
machine-learning-based teleoperator-aware task scheduling algorithm and show,
experimentally, that it performs well when compared to an omniscient optimal
scheduling algorithm.
",Learning to Schedule Deadline- and Operator-Sensitive Tasks
"  Evolution sculpts both the body plans and nervous systems of agents together
over time. In contrast, in AI and robotics, a robot's body plan is usually
designed by hand, and control policies are then optimized for that fixed
design. The task of simultaneously co-optimizing the morphology and controller
of an embodied robot has remained a challenge. In psychology, the theory of
embodied cognition posits that behavior arises from a close coupling between
body plan and sensorimotor control, which suggests why co-optimizing these two
subsystems is so difficult: most evolutionary changes to morphology tend to
adversely impact sensorimotor control, leading to an overall decrease in
behavioral performance. Here, we further examine this hypothesis and
demonstrate a technique for ""morphological innovation protection"", which
temporarily reduces selection pressure on recently morphologically-changed
individuals, thus enabling evolution some time to ""readapt"" to the new
morphology with subsequent control policy mutations. We show the potential for
this method to avoid local optima and converge to similar highly fit
morphologies across widely varying initial conditions, while sustaining fitness
improvements further into optimization. While this technique is admittedly only
the first of many steps that must be taken to achieve scalable optimization of
embodied machines, we hope that theoretical insight into the cause of
evolutionary stagnation in current methods will help to enable the automation
of robot design and behavioral training -- while simultaneously providing a
testbed to investigate the theory of embodied cognition.
",Scalable Co-Optimization of Morphology and Control in Embodied Machines
"  In this report, we provide a comparative analysis of different techniques for
user intent classification towards the task of app recommendation. We analyse
the performance of different models and architectures for multi-label
classification over a dataset with a relative large number of classes and only
a handful examples of each class. We focus, in particular, on memory network
architectures, and compare how well the different versions perform under the
task constraints. Since the classifier is meant to serve as a module in a
practical dialog system, it needs to be able to work with limited training data
and incorporate new data on the fly. We devise a 1-shot learning task to test
the models under the above constraint. We conclude that relatively simple
versions of memory networks perform better than other approaches. Although, for
tasks with very limited data, simple non-parametric methods perform comparably,
without needing the extra training data.
","User Intent Classification using Memory Networks: A Comparative Analysis
  for a Limited Data Scenario"
"  This paper presents preliminary results of our work with a major financial
company, where we try to use methods of plan recognition in order to
investigate the interactions of a costumer with the company's online interface.
In this paper, we present the first steps of integrating a plan recognition
algorithm in a real-world application for detecting and analyzing the
interactions of a costumer. It uses a novel approach for plan recognition from
bare-bone UI data, which reasons about the plan library at the lowest
recognition level in order to define the relevancy of actions in our domain,
and then uses it to perform plan recognition.
  We present preliminary results of inference on three different use-cases
modeled by domain experts from the company, and show that this approach manages
to decrease the overload of information required from an analyst to evaluate a
costumer's session - whether this is a malicious or benign session, whether the
intended tasks were completed, and if not - what actions are expected next.
",Session Analysis using Plan Recognition
"  The highly influential framework of conceptual spaces provides a geometric
way of representing knowledge. Instances are represented by points in a
high-dimensional space and concepts are represented by convex regions in this
space. After pointing out a problem with the convexity requirement, we propose
a formalization of conceptual spaces based on fuzzy star-shaped sets. Our
formalization uses a parametric definition of concepts and extends the original
framework by adding means to represent correlations between different domains
in a geometric way. Moreover, we define computationally efficient operations on
concepts (intersection, union, and projection onto a subspace) and show that
these operations can support both learning and reasoning processes.
",A Thorough Formalization of Conceptual Spaces
"  Motor adaptation displays a structure-learning effect: adaptation to a new
perturbation occurs more quickly when the subject has prior exposure to
perturbations with related structure. Although this `learning-to-learn' effect
is well documented, its underlying computational mechanisms are poorly
understood. We present a new model of motor structure learning, approaching it
from the point of view of deep reinforcement learning. Previous work outside of
motor control has shown how recurrent neural networks can account for
learning-to-learn effects. We leverage this insight to address motor learning,
by importing it into the setting of model-based reinforcement learning. We
apply the resulting processing architecture to empirical findings from a
landmark study of structure learning in target-directed reaching (Braun et al.,
2009), and discuss its implications for a wider range of learning-to-learn
phenomena.
",Structure Learning in Motor Control:A Deep Reinforcement Learning Model
"  There is significant concern that technological advances, especially in
Robotics and Artificial Intelligence (AI), could lead to high levels of
unemployment in the coming decades. Studies have estimated that around half of
all current jobs are at risk of automation. To look into this issue in more
depth, we surveyed experts in Robotics and AI about the risk, and compared
their views with those of non-experts. Whilst the experts predicted a
significant number of occupations were at risk of automation in the next two
decades, they were more cautious than people outside the field in predicting
occupations at risk. Their predictions were consistent with their estimates for
when computers might be expected to reach human level performance across a wide
range of skills. These estimates were typically decades later than those of the
non-experts. Technological barriers may therefore provide society with more
time to prepare for an automated future than the public fear. In addition,
public expectations may need to be dampened about the speed of progress to be
expected in Robotics and AI.
",Expert and Non-Expert Opinion about Technological Unemployment
"  This paper introduces a new framework for real-time decision making in video
games. An Ensemble agent is a compound agent composed of multiple agents, each
with its own tasks or goals to achieve. Usually when dealing with real-time
decision making, reactive agents are used; that is agents that return a
decision based on the current state. While reactive agents are very fast, most
games require more than just a rule-based agent to achieve good results.
Deliberative agents---agents that use a forward model to search future
states---are very useful in games with no hard time limit, such as Go or
Backgammon, but generally take too long for real-time games. The Ensemble
framework addresses this issue by allowing the agent to be both deliberative
and reactive at the same time. This is achieved by breaking up the game-play
into logical roles and having highly focused components for each role, with
each component disregarding anything outwith its own role. Reactive agents can
be used where a reactive agent is suited to the role, and where a deliberative
approach is required, branching is kept to a minimum by the removal of all
extraneous factors, enabling an informed decision to be made within a much
smaller time-frame. An Arbiter is used to combine the component results,
allowing high performing agents to be created from simple, efficient
components.
",Ensemble Framework for Real-time Decision Making
"  Presented is a Julia meta-program that discovers compact theories from data
if they exist. It writes candidate theories in Julia and then validates:
tossing the bad theories and keeping the good theories. Compactness is measured
by a metric: such as the number of space-time derivatives. The underlying
algorithm is applicable to a wide variety of combinatorics problems and
compactness serves to cut down the search space.
",On the enumeration of sentences by compactness
"  We propose a new system for generating art. The system generates art by
looking at art and learning about style; and becomes creative by increasing the
arousal potential of the generated art by deviating from the learned styles. We
build over Generative Adversarial Networks (GAN), which have shown the ability
to learn to generate novel images simulating a given distribution. We argue
that such networks are limited in their ability to generate creative products
in their original design. We propose modifications to its objective to make it
capable of generating creative art by maximizing deviation from established
styles and minimizing deviation from art distribution. We conducted experiments
to compare the response of human subjects to the generated art with their
response to art created by artists. The results show that human subjects could
not distinguish art generated by the proposed system from art generated by
contemporary artists and shown in top art fairs. Human subjects even rated the
generated images higher on various scales.
","CAN: Creative Adversarial Networks, Generating ""Art"" by Learning About
  Styles and Deviating from Style Norms"
"  Explaining the behavior of a black box machine learning model at the instance
level is useful for building trust. However, it is also important to understand
how the model behaves globally. Such an understanding provides insight into
both the data on which the model was trained and the patterns that it learned.
We present here an approach that learns if-then rules to globally explain the
behavior of black box machine learning models that have been used to solve
classification problems. The approach works by first extracting conditions that
were important at the instance level and then evolving rules through a genetic
algorithm with an appropriate fitness function. Collectively, these rules
represent the patterns followed by the model for decisioning and are useful for
understanding its behavior. We demonstrate the validity and usefulness of the
approach by interpreting black box models created using publicly available data
sets as well as a private digital marketing data set.
",MAGIX: Model Agnostic Globally Interpretable Explanations
"  There has been a recent resurgence in the area of explainable artificial
intelligence as researchers and practitioners seek to make their algorithms
more understandable. Much of this research is focused on explicitly explaining
decisions or actions to a human observer, and it should not be controversial to
say that looking at how humans explain to each other can serve as a useful
starting point for explanation in artificial intelligence. However, it is fair
to say that most work in explainable artificial intelligence uses only the
researchers' intuition of what constitutes a `good' explanation. There exists
vast and valuable bodies of research in philosophy, psychology, and cognitive
science of how people define, generate, select, evaluate, and present
explanations, which argues that people employ certain cognitive biases and
social expectations towards the explanation process. This paper argues that the
field of explainable artificial intelligence should build on this existing
research, and reviews relevant papers from philosophy, cognitive
psychology/science, and social psychology, which study these topics. It draws
out some important findings, and discusses ways that these can be infused with
work on explainable artificial intelligence.
","Explanation in Artificial Intelligence: Insights from the Social
  Sciences"
"  Domain adaptation deals with adapting classifiers trained on data from a
source distribution, to work effectively on data from a target distribution. In
this paper, we introduce the Nonlinear Embedding Transform (NET) for
unsupervised domain adaptation. The NET reduces cross-domain disparity through
nonlinear domain alignment. It also embeds the domain-aligned data such that
similar data points are clustered together. This results in enhanced
classification. To determine the parameters in the NET model (and in other
unsupervised domain adaptation models), we introduce a validation procedure by
sampling source data points that are similar in distribution to the target
data. We test the NET and the validation procedure using popular image datasets
and compare the classification results across competitive procedures for
unsupervised domain adaptation.
","Model Selection with Nonlinear Embedding for Unsupervised Domain
  Adaptation"
"  We introduce a new count-based optimistic exploration algorithm for
Reinforcement Learning (RL) that is feasible in environments with
high-dimensional state-action spaces. The success of RL algorithms in these
domains depends crucially on generalisation from limited training experience.
Function approximation techniques enable RL agents to generalise in order to
estimate the value of unvisited states, but at present few methods enable
generalisation regarding uncertainty. This has prevented the combination of
scalable RL algorithms with efficient exploration strategies that drive the
agent to reduce its uncertainty. We present a new method for computing a
generalised state visit-count, which allows the agent to estimate the
uncertainty associated with any state. Our \phi-pseudocount achieves
generalisation by exploiting same feature representation of the state space
that is used for value function approximation. States that have less frequently
observed features are deemed more uncertain. The \phi-Exploration-Bonus
algorithm rewards the agent for exploring in feature space rather than in the
untransformed state space. The method is simpler and less computationally
expensive than some previous proposals, and achieves near state-of-the-art
results on high-dimensional RL benchmarks.
",Count-Based Exploration in Feature Space for Reinforcement Learning
"  In Markov Decision Processes (MDPs), the reward obtained in a state depends
on the properties of the last state and action. This state dependency makes it
difficult to reward more interesting long-term behaviors, such as always
closing a door after it has been opened, or providing coffee only following a
request. Extending MDPs to handle such non-Markovian reward function was the
subject of two previous lines of work, both using variants of LTL to specify
the reward function and then compiling the new model back into a Markovian
model. Building upon recent progress in the theories of temporal logics over
finite traces, we adopt LDLf for specifying non-Markovian rewards and provide
an elegant automata construction for building a Markovian model, which extends
that of previous work and offers strong minimality and compositionality
guarantees.
","Specifying Non-Markovian Rewards in MDPs Using LDL on Finite Traces
  (Preliminary Version)"
"  In this paper, random forests are proposed for operating devices diagnostics
in the presence of a variable number of features. In various contexts, like
large or difficult-to-access monitored areas, wired sensor networks providing
features to achieve diagnostics are either very costly to use or totally
impossible to spread out. Using a wireless sensor network can solve this
problem, but this latter is more subjected to flaws. Furthermore, the networks'
topology often changes, leading to a variability in quality of coverage in the
targeted area. Diagnostics at the sink level must take into consideration that
both the number and the quality of the provided features are not constant, and
that some politics like scheduling or data aggregation may be developed across
the network. The aim of this article is ($1$) to show that random forests are
relevant in this context, due to their flexibility and robustness, and ($2$) to
provide first examples of use of this method for diagnostics based on data
provided by a wireless sensor network.
","Random Forests for Industrial Device Functioning Diagnostics Using
  Wireless Sensor Networks"
"  Temporal landmarks have been proved to be a helpful mechanism to deal with
temporal planning problems, specifically to improve planners performance and
handle problems with deadline constraints. In this paper, we show the strength
of using temporal landmarks to handle the state trajectory constraints of
PDDL3.0. We analyze the formalism of TempLM, a temporal planner particularly
aimed at solving planning problems with deadlines, and we present a detailed
study that exploits the underlying temporal landmark-based mechanism of TempLM
for representing and reasoning with trajectory constraints.
",Handling PDDL3.0 State Trajectory Constraints with Temporal Landmarks
"  The task of learning to pick a single preferred example out a finite set of
examples, an ""optimal choice problem"", is a supervised machine learning problem
with complex, structured input. Problems of optimal choice emerge often in
various practical applications. We formalize the problem, show that it does not
satisfy the assumptions of statistical learning theory, yet it can be solved
efficiently in some cases. We propose two approaches to solve the problem. Both
of them reach good solutions on real life data from a signal processing
application.
",Optimal choice: new machine learning problem and its solution
"  Over the years complexity theorists have proposed many structural parameters
to explain the surprising efficiency of conflict-driven clause-learning (CDCL)
SAT solvers on a wide variety of large industrial Boolean instances. While some
of these parameters have been studied empirically, until now there has not been
a unified comparative study of their explanatory power on a comprehensive
benchmark. We correct this state of affairs by conducting a large-scale
empirical evaluation of CDCL SAT solver performance on nearly 7000 industrial
and crafted formulas against several structural parameters such as backdoors,
treewidth, backbones, and community structure.
  Our study led us to several results. First, we show that while such
parameters only weakly correlate with CDCL solving time, certain combinations
of them yield much better regression models. Second, we show how some
parameters can be used as a ""lens"" to better understand the efficiency of
different solving heuristics. Finally, we propose a new complexity-theoretic
parameter, which we call learning-sensitive with restarts (LSR) backdoors, that
extends the notion of learning-sensitive (LS) backdoors to incorporate restarts
and discuss algorithms to compute them. We mathematically prove that for
certain class of instances minimal LSR-backdoors are exponentially smaller than
minimal-LS backdoors.
",Relating Complexity-theoretic Parameters with SAT Solver Performance
"  In Constraint Programming (CP) a portfolio solver combines a variety of
different constraint solvers for solving a given problem. This fairly recent
approach enables to significantly boost the performance of single solvers,
especially when multicore architectures are exploited. In this work we give a
brief overview of the portfolio solver sunny-cp, and we discuss its performance
in the MiniZinc Challenge---the annual international competition for CP
solvers---where it won two gold medals in 2015 and 2016. Under consideration in
Theory and Practice of Logic Programming (TPLP)
",SUNNY-CP and the MiniZinc Challenge
"  Learning relations based on evidence from knowledge bases relies on
processing the available relation instances. Many relations, however, have
clear domain and range, which we hypothesize could help learn a better, more
generalizing, model. We include such information in the RESCAL model in the
form of a regularization factor added to the loss function that takes into
account the types (categories) of the entities that appear as arguments to
relations in the knowledge base. We note increased performance compared to the
baseline model in terms of mean reciprocal rank and hits@N, N = 1, 3, 10.
Furthermore, we discover scenarios that significantly impact the effectiveness
of the type regularizer.
",Learning Knowledge Graph Embeddings with Type Regularizer
"  Research in UAV scheduling has obtained an emerging interest from scientists
in the optimization field. When the scheduling itself has established a strong
root since the 19th century, works on UAV scheduling in indoor environment has
come forth in the latest decade. Several works on scheduling UAV operations in
indoor (two and three dimensional) and outdoor environments are reported. In
this paper, a further study on UAV scheduling in three dimensional indoor
environment is investigated. Dealing with indoor environment\textemdash where
humans, UAVs, and other elements or infrastructures are likely to coexist in
the same space\textemdash draws attention towards the safety of the operations.
In relation to the battery level, a preserved battery level leads to safer
operations, promoting the UAV to have a decent remaining power level. A
methodology which consists of a heuristic approach based on Restful Task
Assignment Algorithm, incorporated with Particle Swarm Optimization Algorithm,
is proposed. The motivation is to preserve the battery level throughout the
operations, which promotes less possibility in having failed UAVs on duty. This
methodology is tested with 54 benchmark datasets stressing on 4 different
aspects: geographical distance, number of tasks, number of predecessors, and
slack time. The test results and their characteristics in regard to the
proposed methodology are discussed and presented.
",Indoor UAV scheduling with Restful Task Assignment Algorithm
"  This paper proposes a new algorithm for recovery of belief network structure
from data handling hidden variables. It consists essentially in an extension of
the CI algorithm of Spirtes et al. by restricting the number of conditional
dependencies checked up to k variables and in an extension of the original CI
by additional steps transforming so called partial including path graph into a
belief network. Its correctness is demonstrated.
",Restricted Causal Inference Algorithm
"  Several domains have adopted the increasing use of IoT-based devices to
collect sensor data for generating abstractions and perceptions of the real
world. This sensor data is multi-modal and heterogeneous in nature. This
heterogeneity induces interoperability issues while developing cross-domain
applications, thereby restricting the possibility of reusing sensor data to
develop new applications. As a solution to this, semantic approaches have been
proposed in the literature to tackle problems related to interoperability of
sensor data. Several ontologies have been proposed to handle different aspects
of IoT-based sensor data collection, ranging from discovering the IoT sensors
for data collection to applying reasoning on the collected sensor data for
drawing inferences. In this paper, we survey these existing semantic ontologies
to provide an overview of the recent developments in this field. We highlight
the fundamental ontological concepts (e.g., sensor-capabilities and
context-awareness) required for an IoT-based application, and survey the
existing ontologies which include these concepts. Based on our study, we also
identify the shortcomings of currently available ontologies, which serves as a
stepping stone to state the need for a common unified ontology for the IoT
domain.
",A study of existing Ontologies in the IoT-domain
"  In multi-agent path finding (MAPF) the task is to find non-conflicting paths
for multiple agents. In this paper we focus on finding suboptimal solutions for
MAPF for the sum-of-costs variant. Recently, a SAT-based approached was
developed to solve this problem and proved beneficial in many cases when
compared to other search-based solvers. In this paper, we present SAT-based
unbounded- and bounded-suboptimal algorithms and compare them to relevant
algorithms. Experimental results show that in many case the SAT-based solver
significantly outperforms the search-based solvers.
","Modifying Optimal SAT-based Approach to Multi-agent Path-finding Problem
  to Suboptimal Variants"
"  This paper describes a roadmap for the development of the ""SP Machine"", based
on the ""SP Theory of Intelligence"" and its realisation in the ""SP Computer
Model"". The SP Machine will be developed initially as a software virtual
machine with high levels of parallel processing, hosted on a high-performance
computer. The system should help users visualise knowledge structures and
processing. Research is needed into how the system may discover low-level
features in speech and in images. Strengths of the SP System in the processing
of natural language may be augmented, in conjunction with the further
development of the SP System's strengths in unsupervised learning. Strengths of
the SP System in pattern recognition may be developed for computer vision. Work
is needed on the representation of numbers and the performance of arithmetic
processes. A computer model is needed of ""SP-Neural"", the version of the SP
Theory expressed in terms of neurons and their inter-connections. The SP
Machine has potential in many areas of application, several of which may be
realised on short-to-medium timescales.
","A Roadmap for the Development of the ""SP Machine"" for Artificial
  Intelligence"
"  Artificial Intelligence methods to solve continuous- control tasks have made
significant progress in recent years. However, these algorithms have important
limitations and still need significant improvement to be used in industry and
real- world applications. This means that this area is still in an active
research phase. To involve a large number of research groups, standard
benchmarks are needed to evaluate and compare proposed algorithms. In this
paper, we propose a physical environment benchmark framework to facilitate
collaborative research in this area by enabling different research groups to
integrate their designed benchmarks in a unified cloud-based repository and
also share their actual implemented benchmarks via the cloud. We demonstrate
the proposed framework using an actual implementation of the classical
mountain-car example and present the results obtained using a Reinforcement
Learning algorithm.
",OPEB: Open Physical Environment Benchmark for Artificial Intelligence
"  This paper addresses the challenge of viewing and navigating Bayesian
networks as their structural size and complexity grow. Starting with a review
of the state of the art of visualizing Bayesian networks, an area which has
largely been passed over, we improve upon existing visualizations in three
ways. First, we apply a disciplined approach to the graphic design of the basic
elements of the Bayesian network. Second, we propose a technique for direct,
visual comparison of posterior distributions resulting from alternative
evidence sets. Third, we leverage a central mathematical tool in information
theory, to assist the user in finding variables of interest in the network, and
to reduce visual complexity where unimportant. We present our methods applied
to two modestly large Bayesian networks constructed from real-world data sets.
Results suggest the new techniques can be a useful tool for discovering
information flow phenomena, and also for qualitative comparisons of different
evidence configurations, especially in large probabilistic networks.
",Visualizing the Consequences of Evidence in Bayesian Networks
"  The set-based concept approach has been suggested as a means to
simultaneously explore different design concepts, which are meaningful sub-sets
of the entire set of solutions. Previous efforts concerning the suggested
approach focused on either revealing the global front (s-Pareto front), of all
the concepts, or on finding the concepts' fronts, within a relaxation zone. In
contrast, here the aim is to reveal which of the concepts have at least one
solution with a performance vector within a pre-defined window-of-interest
(WOI). This paper provides the rational for this new concept-based exploration
problem, and suggests a WOI-based rather than Pareto-based multi-objective
evolutionary algorithm. The proposed algorithm, which simultaneously explores
different concepts, is tested using a recently suggested concept-based
benchmarking approach. The numerical study of this paper shows that the
algorithm can cope with various numerical difficulties in a simultaneous way,
which outperforms a sequential exploration approach.
","Window-of-interest based Multi-objective Evolutionary Search for
  Satisficing Concepts"
"  In this paper, we propose ELF, an Extensive, Lightweight and Flexible
platform for fundamental reinforcement learning research. Using ELF, we
implement a highly customizable real-time strategy (RTS) engine with three game
environments (Mini-RTS, Capture the Flag and Tower Defense). Mini-RTS, as a
miniature version of StarCraft, captures key game dynamics and runs at 40K
frame-per-second (FPS) per core on a Macbook Pro notebook. When coupled with
modern reinforcement learning methods, the system can train a full-game bot
against built-in AIs end-to-end in one day with 6 CPUs and 1 GPU. In addition,
our platform is flexible in terms of environment-agent communication
topologies, choices of RL methods, changes in game parameters, and can host
existing C/C++-based game environments like Arcade Learning Environment. Using
ELF, we thoroughly explore training parameters and show that a network with
Leaky ReLU and Batch Normalization coupled with long-horizon training and
progressive curriculum beats the rule-based built-in AI more than $70\%$ of the
time in the full game of Mini-RTS. Strong performance is also achieved on the
other two games. In game replays, we show our agents learn interesting
strategies. ELF, along with its RL platform, is open-sourced at
https://github.com/facebookresearch/ELF.
","ELF: An Extensive, Lightweight and Flexible Research Platform for
  Real-time Strategy Games"
"  We propose Black Box Explanations through Transparent Approximations (BETA),
a novel model agnostic framework for explaining the behavior of any black-box
classifier by simultaneously optimizing for fidelity to the original model and
interpretability of the explanation. To this end, we develop a novel objective
function which allows us to learn (with optimality guarantees), a small number
of compact decision sets each of which explains the behavior of the black box
model in unambiguous, well-defined regions of feature space. Furthermore, our
framework also is capable of accepting user input when generating these
approximations, thus allowing users to interactively explore how the black-box
model behaves in different subspaces that are of interest to the user. To the
best of our knowledge, this is the first approach which can produce global
explanations of the behavior of any given black box model through joint
optimization of unambiguity, fidelity, and interpretability, while also
allowing users to explore model behavior based on their preferences.
Experimental evaluation with real-world datasets and user studies demonstrates
that our approach can generate highly compact, easy-to-understand, yet accurate
approximations of various kinds of predictive models compared to
state-of-the-art baselines.
",Interpretable & Explorable Approximations of Black Box Models
"  Causation discovery without manipulation is considered a crucial problem to a
variety of applications. The state-of-the-art solutions are applicable only
when large numbers of samples are available or the problem domain is
sufficiently small. Motivated by the observations of the local sparsity
properties on causal structures, we propose a general Split-and-Merge
framework, named SADA, to enhance the scalability of a wide class of causation
discovery algorithms. In SADA, the variables are partitioned into subsets, by
finding causal cut on the sparse causal structure over the variables. By
running mainstream causation discovery algorithms as basic causal solvers on
the subproblems, complete causal structure can be reconstructed by combining
the partial results. SADA benefits from the recursive division technique, since
each small subproblem generates more accurate result under the same number of
samples. We theoretically prove that SADA always reduces the scales of problems
without sacrifice on accuracy, under the condition of local causal sparsity and
reliable conditional independence tests. We also present sufficient condition
to accuracy enhancement by SADA, even when the conditional independence tests
are vulnerable. Extensive experiments on both simulated and real-world datasets
verify the improvements on scalability and accuracy by applying SADA together
with existing causation discovery algorithms.
","SADA: A General Framework to Support Robust Causation Discovery with
  Theoretical Guarantee"
"  In typical reinforcement learning (RL), the environment is assumed given and
the goal of the learning is to identify an optimal policy for the agent taking
actions through its interactions with the environment. In this paper, we extend
this setting by considering the environment is not given, but controllable and
learnable through its interaction with the agent at the same time. This
extension is motivated by environment design scenarios in the real-world,
including game design, shopping space design and traffic signal design.
Theoretically, we find a dual Markov decision process (MDP) w.r.t. the
environment to that w.r.t. the agent, and derive a policy gradient solution to
optimizing the parametrized environment. Furthermore, discontinuous
environments are addressed by a proposed general generative framework. Our
experiments on a Maze game design task show the effectiveness of the proposed
algorithms in generating diverse and challenging Mazes against various agent
settings.
","Learning to Design Games: Strategic Environments in Reinforcement
  Learning"
"  We formulate a strong equivalence between machine learning, artificial
intelligence methods and the formulation of statistical data assimilation as
used widely in physical and biological sciences. The correspondence is that
layer number in the artificial network setting is the analog of time in the
data assimilation setting. Within the discussion of this equivalence we show
that adding more layers (making the network deeper) is analogous to adding
temporal resolution in a data assimilation framework.
  How one can find a candidate for the global minimum of the cost functions in
the machine learning context using a method from data assimilation is
discussed. Calculations on simple models from each side of the equivalence are
reported.
  Also discussed is a framework in which the time or layer label is taken to be
continuous, providing a differential equation, the Euler-Lagrange equation,
which shows that the problem being solved is a two point boundary value problem
familiar in the discussion of variational methods. The use of continuous layers
is denoted ""deepest learning"". These problems respect a symplectic symmetry in
continuous time/layer phase space. Both Lagrangian versions and Hamiltonian
versions of these problems are presented. Their well-studied implementation in
a discrete time/layer, while respected the symplectic structure, is addressed.
The Hamiltonian version provides a direct rationale for back propagation as a
solution method for the canonical momentum.
","Machine Learning, Deepest Learning: Statistical Data Assimilation
  Problems"
"  Many practical problems are characterized by a preference relation over
admissible solutions, where preferred solutions are minimal in some sense. For
example, a preferred diagnosis usually comprises a minimal set of reasons that
is sufficient to cause the observed anomaly. Alternatively, a minimal
correction subset comprises a minimal set of reasons whose deletion is
sufficient to eliminate the observed anomaly. Circumscription formalizes such
preference relations by associating propositional theories with minimal models.
The resulting enumeration problem is addressed here by means of a new algorithm
taking advantage of unsatisfiable core analysis. Empirical evidence of the
efficiency of the algorithm is given by comparing the performance of the
resulting solver, CIRCUMSCRIPTINO, with HCLASP, CAMUS MCS, LBX and MCSLS on the
enumeration of minimal models for problems originating from practical
applications.
  This paper is under consideration for acceptance in TPLP.
","Model enumeration in propositional circumscription via unsatisfiable
  core analysis"
"  This paper proposes a new fuzzy assessing procedure with application in
management decision making. The proposed fuzzy approach build the membership
functions for system characteristics of a standby repairable system. This
method is used to extract a family of conventional crisp intervals from the
fuzzy repairable system for the desired system characteristics. This can be
determined with a set of nonlinear parametric programing using the membership
functions. When system characteristics are governed by the membership
functions, more information is provided for use by management, and because the
redundant system is extended to the fuzzy environment, general repairable
systems are represented more accurately and the analytic results are more
useful for designers and practitioners. Also beside standby, active redundancy
systems are used in many cases so this article has many practical instances.
Different from other studies, our model provides, a good estimated value based
on uncertain environments, a comparison discussion of using fuzzy theory and
conventional method and also a comparison between parallel (active redundancy)
and series system in fuzzy world when we have standby redundancy. When the
membership function intervals cannot be inverted explicitly, system management
or designers can specify the system characteristics of interest, perform
numerical calculations, examine the corresponding {\alpha}-cuts, and use this
information to develop or improve system processes.
",Application of Fuzzy Assessing for Reliability Decision Making
"  Trust region methods, such as TRPO, are often used to stabilize policy
optimization algorithms in reinforcement learning (RL). While current trust
region strategies are effective for continuous control, they typically require
a prohibitively large amount of on-policy interaction with the environment. To
address this problem, we propose an off-policy trust region method, Trust-PCL.
The algorithm is the result of observing that the optimal policy and state
values of a maximum reward objective with a relative-entropy regularizer
satisfy a set of multi-step pathwise consistencies along any path. Thus,
Trust-PCL is able to maintain optimization stability while exploiting
off-policy data to improve sample efficiency. When evaluated on a number of
continuous control tasks, Trust-PCL improves the solution quality and sample
efficiency of TRPO.
",Trust-PCL: An Off-Policy Trust Region Method for Continuous Control
"  Hybrid MKNF knowledge bases have been considered one of the dominant
approaches to combining open world ontology languages with closed world
rule-based languages. Currently, the only known inference methods are based on
the approach of guess-and-verify, while most modern SAT/ASP solvers are built
under the DPLL architecture. The central impediment here is that it is not
clear what constitutes a constraint propagator, a key component employed in any
DPLL-based solver. In this paper, we address this problem by formulating the
notion of unfounded sets for nondisjunctive hybrid MKNF knowledge bases, based
on which we propose and study two new well-founded operators. We show that by
employing a well-founded operator as a constraint propagator, a sound and
complete DPLL search engine can be readily defined. We compare our approach
with the operator based on the alternating fixpoint construction by Knorr et al
[2011] and show that, when applied to arbitrary partial partitions, the new
well-founded operators not only propagate more truth values but also circumvent
the non-converging behavior of the latter. In addition, we study the
possibility of simplifying a given hybrid MKNF knowledge base by employing a
well-founded operator, and show that, out of the two operators proposed in this
paper, the weaker one can be applied for this purpose and the stronger one
cannot. These observations are useful in implementing a grounder for hybrid
MKNF knowledge bases, which can be applied before the computation of MKNF
models.
  The paper is under consideration for acceptance in TPLP.
",Well-Founded Operators for Normal Hybrid MKNF Knowledge Bases
"  The reinforcement learning paradigm allows, in principle, for complex
behaviours to be learned directly from simple reward signals. In practice,
however, it is common to carefully hand-design the reward function to encourage
a particular solution, or to derive it from demonstration data. In this paper
explore how a rich environment can help to promote the learning of complex
behavior. Specifically, we train agents in diverse environmental contexts, and
find that this encourages the emergence of robust behaviours that perform well
across a suite of tasks. We demonstrate this principle for locomotion --
behaviours that are known for their sensitivity to the choice of reward. We
train several simulated bodies on a diverse set of challenging terrains and
obstacles, using a simple reward function based on forward progress. Using a
novel scalable variant of policy gradient reinforcement learning, our agents
learn to run, jump, crouch and turn as required by the environment without
explicit reward-based guidance. A visual depiction of highlights of the learned
behavior can be viewed following https://youtu.be/hx_bgoTF7bs .
",Emergence of Locomotion Behaviours in Rich Environments
"  The highly influential framework of conceptual spaces provides a geometric
way of representing knowledge. Instances are represented by points in a
high-dimensional space and concepts are represented by regions in this space.
Our recent mathematical formalization of this framework is capable of
representing correlations between different domains in a geometric way. In this
paper, we extend our formalization by providing quantitative mathematical
definitions for the notions of concept size, subsethood, implication,
similarity, and betweenness. This considerably increases the representational
power of our formalization by introducing measurable ways of describing
relations between concepts.
",Measuring Relations Between Concepts In Conceptual Spaces
"  We investigate a generalisation of the coherent choice functions considered
by Seidenfeld et al. (2010), by sticking to the convexity axiom but imposing no
Archimedeanity condition. We define our choice functions on vector spaces of
options, which allows us to incorporate as special cases both Seidenfeld et
al.'s (2010) choice functions on horse lotteries and sets of desirable gambles
(Quaeghebeur, 2014), and to investigate their connections. We show that choice
functions based on sets of desirable options (gambles) satisfy Seidenfeld's
convexity axiom only for very particular types of sets of desirable options,
which are in a one-to-one relationship with the lexicographic probabilities. We
call them lexicographic choice functions. Finally, we prove that these choice
functions can be used to determine the most conservative convex choice function
associated with a given binary relation.
",Lexicographic choice functions
"  A number of intriguing decision scenarios revolve around partitioning a
collection of objects to optimize some application specific objective function.
This problem is generally referred to as the Object Partitioning Problem (OPP)
and is known to be NP-hard. We here consider a particularly challenging version
of OPP, namely, the Stochastic On-line Equi-Partitioning Problem (SO-EPP). In
SO-EPP, the target partitioning is unknown and has to be inferred purely from
observing an on-line sequence of object pairs. The paired objects belong to the
same partition with probability $p$ and to different partitions with
probability $1-p$, with $p$ also being unknown. As an additional complication,
the partitions are required to be of equal cardinality. Previously, only
sub-optimal solution strategies have been proposed for SO- EPP. In this paper,
we propose the first optimal solution strategy. In brief, the scheme that we
propose, BN-EPP, is founded on a Bayesian network representation of SO-EPP
problems. Based on probabilistic reasoning, we are not only able to infer the
underlying object partitioning with optimal accuracy. We are also able to
simultaneously infer $p$, allowing us to accelerate learning as object pairs
arrive. Furthermore, our scheme is the first to support arbitrary constraints
on the partitioning (Constrained SO-EPP). Being optimal, BN-EPP provides
superior performance compared to existing solution schemes. We additionally
introduce Walk-BN-EPP, a novel WalkSAT inspired algorithm for solving large
scale BN-EPP problems. Finally, we provide a BN-EPP based solution to the
problem of order picking, a representative real-life application of BN-EPP.
","An Optimal Bayesian Network Based Solution Scheme for the Constrained
  Stochastic On-line Equi-Partitioning Problem"
"  Representing knowledge as high-dimensional vectors in a continuous semantic
vector space can help overcome the brittleness and incompleteness of
traditional knowledge bases. We present a method for performing deductive
reasoning directly in such a vector space, combining analogy, association, and
deduction in a straightforward way at each step in a chain of reasoning,
drawing on knowledge from diverse sources and ontologies.
","Deductive and Analogical Reasoning on a Semantically Embedded Knowledge
  Graph"
"  This paper introduces the Intentional Unintentional (IU) agent. This agent
endows the deep deterministic policy gradients (DDPG) agent for continuous
control with the ability to solve several tasks simultaneously. Learning to
solve many tasks simultaneously has been a long-standing, core goal of
artificial intelligence, inspired by infant development and motivated by the
desire to build flexible robot manipulators capable of many diverse behaviours.
We show that the IU agent not only learns to solve many tasks simultaneously
but it also learns faster than agents that target a single task at-a-time. In
some cases, where the single task DDPG method completely fails, the IU agent
successfully solves the task. To demonstrate this, we build a playroom
environment using the MuJoCo physics engine, and introduce a grounded formal
language to automatically generate tasks.
","The Intentional Unintentional Agent: Learning to Solve Many Continuous
  Control Tasks Simultaneously"
"  While general game playing is an active field of research, the learning of
game design has tended to be either a secondary goal of such research or it has
been solely the domain of humans. We propose a field of research, Automated
Game Design Learning (AGDL), with the direct purpose of learning game designs
directly through interaction with games in the mode that most people experience
games: via play. We detail existing work that touches the edges of this field,
describe current successful projects in AGDL and the theoretical foundations
that enable them, point to promising applications enabled by AGDL, and discuss
next steps for this exciting area of study. The key moves of AGDL are to use
game programs as the ultimate source of truth about their own design, and to
make these design properties available to other systems and avenues of inquiry.
",Automated Game Design Learning
"  We propose and evaluate a new technique for learning hybrid automata
automatically by observing the runtime behavior of a dynamical system. Working
from a sequence of continuous state values and predicates about the
environment, CHARDA recovers the distinct dynamic modes, learns a model for
each mode from a given set of templates, and postulates causal guard conditions
which trigger transitions between modes. Our main contribution is the use of
information-theoretic measures (1)~as a cost function for data segmentation and
model selection to penalize over-fitting and (2)~to determine the likely causes
of each transition. CHARDA is easily extended with different classes of model
templates, fitting methods, or predicates. In our experiments on a complex
videogame character, CHARDA successfully discovers a reasonable
over-approximation of the character's true behaviors. Our results also compare
favorably against recent work in automatically learning probabilistic timed
automata in an aircraft domain: CHARDA exactly learns the modes of these
simpler automata.
",CHARDA: Causal Hybrid Automata Recovery via Dynamic Analysis
"  Proceedings of the 2017 AdKDD and TargetAd Workshop held in conjunction with
the 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining Halifax,
Nova Scotia, Canada.
",Proceedings of the 2017 AdKDD & TargetAd Workshop
"  The real-time strategy game StarCraft has proven to be a challenging
environment for artificial intelligence techniques, and as a result, current
state-of-the-art solutions consist of numerous hand-crafted modules. In this
paper, we show how macromanagement decisions in StarCraft can be learned
directly from game replays using deep learning. Neural networks are trained on
789,571 state-action pairs extracted from 2,005 replays of highly skilled
players, achieving top-1 and top-3 error rates of 54.6% and 22.9% in predicting
the next build action. By integrating the trained network into UAlbertaBot, an
open source StarCraft bot, the system can significantly outperform the game's
built-in Terran bot, and play competitively against UAlbertaBot with a fixed
rush strategy. To our knowledge, this is the first time macromanagement tasks
are learned directly from replays in StarCraft. While the best hand-crafted
strategies are still the state-of-the-art, the deep network approach is able to
express a wide range of different strategies and thus improving the network's
performance further with deep reinforcement learning is an immediately
promising avenue for future research. Ultimately this approach could lead to
strong StarCraft bots that are less reliant on hard-coded strategies.
",Learning Macromanagement in StarCraft from Replays using Deep Learning
"  We propose a novel method for automatic program synthesis. P-Tree Programming
represents the program search space through a single probabilistic prototype
tree. From this prototype tree we form program instances which we evaluate on a
given problem. The error values from the evaluations are propagated through the
prototype tree. We use them to update the probability distributions that
determine the symbol choices of further instances. The iterative method is
applied to several symbolic regression benchmarks from the literature. It
outperforms standard Genetic Programming to a large extend. Furthermore, it
relies on a concise set of parameters which are held constant for all problems.
The algorithm can be employed for most of the typical computational
intelligence tasks such as classification, automatic program induction, and
symbolic regression.
",P-Tree Programming
"  Jumping has been an important mechanic since its introduction in Donkey Kong.
It has taken a variety of forms and shown up in numerous games, with each jump
having a different feel. In this paper, we use a modified Nintendo
Entertainment System (NES) emulator to semi-automatically run experiments on a
large subset (30%) of NES platform games. We use these experiments to build
models of jumps from different developers, series, and games across the history
of the console. We then examine these models to gain insights into different
forms of jumping and their associated feel.
",Mechanics Automatically Recognized via Interactive Observation: Jumping
"  Several approaches of structuring (factorization, decomposition) of
Dempster-Shafer joint belief functions from literature are reviewed with
special emphasis on their capability to capture independence from the point of
view of the claim that belief functions generalize bayes notion of probability.
  It is demonstrated that Zhu and Lee's {Zhu:93} logical networks and Smets'
{Smets:93} directed acyclic graphs are unable to capture statistical
dependence/independence of bayesian networks {Pearl:88}. On the other hand,
though Shenoy and Shafer's hypergraphs can explicitly represent bayesian
network factorization of bayesian belief functions, they disclaim any need for
representation of independence of variables in belief functions.
  Cano et al. {Cano:93} reject the hypergraph representation of Shenoy and
Shafer just on grounds of missing representation of variable independence, but
in their frameworks some belief functions factorizable in Shenoy/Shafer
framework cannot be factored.
  The approach in {Klopotek:93f} on the other hand combines the merits of both
Cano et al. and of Shenoy/Shafer approach in that for Shenoy/Shafer approach no
simpler factorization than that in {Klopotek:93f} approach exists and on the
other hand all independences among variables captured in Cano et al. framework
and many more are captured in {Klopotek:93f} approach.%
","Independence, Conditionality and Structure of Dempster-Shafer Belief
  Functions"
"  Mathematical Theory of Evidence called also Dempster-Shafer Theory (DST) is
known as a foundation for reasoning when knowledge is expressed at various
levels of detail. Though much research effort has been committed to this theory
since its foundation, many questions remain open. One of the most important
open questions seems to be the relationship between frequencies and the
Mathematical Theory of Evidence. The theory is blamed to leave frequencies
outside (or aside of) its framework. The seriousness of this accusation is
obvious: (1) no experiment may be run to compare the performance of DST-based
models of real world processes against real world data, (2) data may not serve
as foundation for construction of an appropriate belief model.
  In this paper we develop a frequentist interpretation of the DST bringing to
fall the above argument against DST. An immediate consequence of it is the
possibility to develop algorithms acquiring automatically DST belief models
from data. We propose three such algorithms for various classes of belief model
structures: for tree structured belief networks, for poly-tree belief networks
and for general type belief networks.
","Identification and Interpretation of Belief Structure in Dempster-Shafer
  Theory"
"  We provide a novel notion of what it means to be interpretable, looking past
the usual association with human understanding. Our key insight is that
interpretability is not an absolute concept and so we define it relative to a
target model, which may or may not be a human. We define a framework that
allows for comparing interpretable procedures by linking it to important
practical aspects such as accuracy and robustness. We characterize many of the
current state-of-the-art interpretable methods in our framework portraying its
general applicability.
",A Formal Framework to Characterize Interpretability of Procedures
"  Game maps are useful for human players, general-game-playing agents, and
data-driven procedural content generation. These maps are generally made by
hand-assembling manually-created screenshots of game levels. Besides being
tedious and error-prone, this approach requires additional effort for each new
game and level to be mapped. The results can still be hard for humans or
computational systems to make use of, privileging visual appearance over
semantic information. We describe a software system, Mappy, that produces a
good approximation of a linked map of rooms given a Nintendo Entertainment
System game program and a sequence of button inputs exploring its world. In
addition to visual maps, Mappy outputs grids of tiles (and how they change over
time), positions of non-tile objects, clusters of similar rooms that might in
fact be the same room, and a set of links between these rooms. We believe this
is a necessary step towards developing larger corpora of high-quality
semantically-annotated maps for PCG via machine learning and other
applications.
",Automatic Mapping of NES Games with Mappy
"  Programming by Optimization tools perform automatic software configuration
according to the specification supplied by a software developer. Developers
specify design spaces for program components, and the onerous task of
determining which configuration best suits a given use case is determined using
automated analysis tools and optimization heuristics. However, in current
approaches to Programming by Optimization, design space specification and
exploration relies on external configuration algorithms, executable wrappers
and fragile, preprocessed programming language extensions.
  Here we show that the architectural pattern of Dependency Injection provides
a superior alternative to the traditional Programming by Optimization pipeline.
We demonstrate that configuration tools based on Dependency Injection fit
naturally into the software development process, while requiring less overhead
than current wrapper-based mechanisms. Furthermore, the structural
correspondence between Dependency Injection and context-free grammars yields a
new class of evolutionary metaheuristics for automated algorithm configuration.
We found that the new heuristics significantly outperform existing
configuration algorithms on many problems of interest (in one case by two
orders of magnitude). We anticipate that these developments will make
Programming by Optimization immediately applicable to a large number of
enterprise software projects.
",Dependency Injection for Programming by Optimization
"  Answer Set Programming (ASP) is a well-established declarative paradigm. One
of the successes of ASP is the availability of efficient systems.
State-of-the-art systems are based on the ground+solve approach. In some
applications this approach is infeasible because the grounding of one or few
constraints is expensive. In this paper, we systematically compare alternative
strategies to avoid the instantiation of problematic constraints, that are
based on custom extensions of the solver. Results on real and synthetic
benchmarks highlight some strengths and weaknesses of the different strategies.
(Under consideration for acceptance in TPLP, ICLP 2017 Special Issue.)
","Constraints, Lazy Constraints, or Propagators in ASP Solving: An
  Empirical Analysis"
"  The recent series 5 of the ASP system clingo provides generic means to
enhance basic Answer Set Programming (ASP) with theory reasoning capabilities.
We instantiate this framework with different forms of linear constraints,
discuss the respective implementations, and present techniques of how to use
these constraints in a reactive context. More precisely, we introduce
extensions to clingo with difference and linear constraints over integers and
reals, respectively, and realize them in complementary ways. Finally, we
empirically evaluate the resulting clingo derivatives clingo[dl] and clingo[lp]
on common fragments and contrast them to related ASP systems.
  This paper is under consideration for acceptance in TPLP.
",Clingo goes Linear Constraints over Reals and Integers
"  This paper verifies a result of {Shenoy:94} concerning graphoidal structure
of Shenoy's notion of independence for Dempster-Shafer theory of belief
functions. Shenoy proved that his notion of independence has graphoidal
properties for positive normal valuations.
  The requirement of strict positive normal valuations as prerequisite for
application of graphoidal properties excludes a wide class of DS belief
functions. It excludes especially so-called probabilistic belief functions. It
is demonstrated that the requirement of positiveness of valuation may be
weakened in that it may be required that commonality function is non-zero for
singleton sets instead, and the graphoidal properties for independence of
belief function variables are then preserved. This means especially that
probabilistic belief functions with all singleton sets as focal points possess
graphoidal properties for independence.
",On (Anti)Conditional Independence in Dempster-Shafer Theory
"  Advances in Artificial Intelligence require progress across all of computer
science.
","Advances in Artificial Intelligence Require Progress Across all of
  Computer Science"
"  Reliability assessment of distribution system, based on historical data and
probabilistic methods, leads to an unreliable estimation of reliability indices
since the data for the distribution components are usually inaccurate or
unavailable. Fuzzy logic is an efficient method to deal with the uncertainty in
reliability inputs. In this paper, the ENS index along with other commonly used
indices in reliability assessment are evaluated for the distribution system
using fuzzy logic. Accordingly, the influential variables on the failure rate
and outage duration time of the distribution components, which are natural or
human-made, are explained using proposed fuzzy membership functions. The
reliability indices are calculated and compared for different cases of the
system operations by simulation on the IEEE RBTS Bus 2. The results of
simulation show how utilities can significantly improve the reliability of
their distribution system by considering the risk of the influential variables.
","Reliability Assessment of Distribution System Using Fuzzy Logic for
  Modelling of Transformer and Line Uncertainties"
"  Hidden variables are well known sources of disturbance when recovering belief
networks from data based only on measurable variables. Hence models assuming
existence of hidden variables are under development.
  This paper presents a new algorithm ""accelerating"" the known CI algorithm of
Spirtes, Glymour and Scheines {Spirtes:93}. We prove that this algorithm does
not produces (conditional) independencies not present in the data if
statistical independence test is reliable.
  This result is to be considered as non-trivial since e.g. the same claim
fails to be true for FCI algorithm, another ""accelerator"" of CI, developed in
{Spirtes:93}.
",Fast Restricted Causal Inference
"  Among the many anticipated roles for robots in the future is that of being a
human teammate. Aside from all the technological hurdles that have to be
overcome with respect to hardware and control to make robots fit to work with
humans, the added complication here is that humans have many conscious and
subconscious expectations of their teammates - indeed, we argue that teaming is
mostly a cognitive rather than physical coordination activity. This introduces
new challenges for the AI and robotics community and requires fundamental
changes to the traditional approach to the design of autonomy. With this in
mind, we propose an update to the classical view of the intelligent agent
architecture, highlighting the requirements for mental modeling of the human in
the deliberative process of the autonomous agent. In this article, we outline
briefly the recent efforts of ours, and others in the community, towards
developing cognitive teammates along these guidelines.
",AI Challenges in Human-Robot Cognitive Teaming
"  In this paper, we demonstrate the application of Fuzzy Markup Language (FML)
to construct an FML-based Dynamic Assessment Agent (FDAA), and we present an
FML-based Human-Machine Cooperative System (FHMCS) for the game of Go. The
proposed FDAA comprises an intelligent decision-making and learning mechanism,
an intelligent game bot, a proximal development agent, and an intelligent
agent. The intelligent game bot is based on the open-source code of Facebook
Darkforest, and it features a representational state transfer application
programming interface mechanism. The proximal development agent contains a
dynamic assessment mechanism, a GoSocket mechanism, and an FML engine with a
fuzzy knowledge base and rule base. The intelligent agent contains a GoSocket
engine and a summarization agent that is based on the estimated win rate,
real-time simulation number, and matching degree of predicted moves.
Additionally, the FML for player performance evaluation and linguistic
descriptions for game results commentary are presented. We experimentally
verify and validate the performance of the FDAA and variants of the FHMCS by
testing five games in 2016 and 60 games of Google Master Go, a new version of
the AlphaGo program, in January 2017. The experimental results demonstrate that
the proposed FDAA can work effectively for Go applications.
","FML-based Dynamic Assessment Agent for Human-Machine Cooperative System
  on Game of Go"
"  It is quite exceptional, if it ever happens, that a new conceptual domain be
built from scratch. Usually, it is developed and mastered in interaction, both
positive and negative, with other more operational existing domains. Few
reasoning mechanisms have been proposed to account for the interplay of
different conceptual domains and the transfer of information from one to
another. Analogical reasoning is one, blending is another. This paper presents
a new mechanism, called 'tunnel effect', that may explain, in part, how
scientists and students reason while constructing a new conceptual domain. One
experimental study with high school students and analyses from the history of
science, particularly about the birth of classical thermodynamics, provide
evidence and illustrate this mechanism. The knowledge organization, processes
and conditions for its appearance are detailed and put into the perspective of
a computational model. Specifically, we put forward the hypothesis that two
levels of knowledge, notional and conceptual, cooperate in the scientific
discovery process when a new conceptual domain is being built. The type of
conceptual learning that can be associated with tunnel effect is discussed and
a thorough comparison is made with analogical reasoning in order to underline
the main features of the new proposed mechanism.
","Tunnel Effects in Cognition: A new Mechanism for Scientific Discovery
  and Education"
"  Can we evolve better training data for machine learning algorithms? To
investigate this question we use population-based optimisation algorithms to
generate artificial surrogate training data for naive Bayes for regression. We
demonstrate that the generalisation performance of naive Bayes for regression
models is enhanced by training them on the artificial data as opposed to the
real data. These results are important for two reasons. Firstly, naive Bayes
models are simple and interpretable but frequently underperform compared to
more complex ""black box"" models, and therefore new methods of enhancing
accuracy are called for. Secondly, the idea of using the real training data
indirectly in the construction of the artificial training data, as opposed to
directly for model training, is a novel twist on the usual machine learning
paradigm.
","Improving Naive Bayes for Regression with Optimised Artificial Surrogate
  Data"
"  Management of chronic diseases such as heart failure (HF) is a major public
health problem. A standard approach to managing chronic diseases by medical
community is to have a committee of experts develop guidelines that all
physicians should follow. Due to their complexity, these guidelines are
difficult to implement and are adopted slowly by the medical community at
large. We have developed a physician advisory system that codes the entire set
of clinical practice guidelines for managing HF using answer set
programming(ASP). In this paper we show how abductive reasoning can be deployed
to find missing symptoms and conditions that the patient must exhibit in order
for a treatment prescribed by a physician to work effectively. Thus, if a
physician does not make an appropriate recommendation or makes a non-adherent
recommendation, our system will advise the physician about symptoms and
conditions that must be in effect for that recommendation to apply. It is under
consideration for acceptance in TPLP.
","Improving Adherence to Heart Failure Management Guidelines via Abductive
  Reasoning"
"  An approach for coalition formation of multi-agent pursuit based on neural
network and AGRMF model is proposed.This paper constructs a novel neural work
called AGRMF-ANN which consists of feature extraction part and group generation
part. On one hand,The convolutional layers of feature extraction part can
abstract the features of agent group role membership function(AGRMF) for all of
the groups,on the other hand,those features will be fed to the group generation
part based on self-organizing map(SOM) layer which is used to group the
pursuers with similar features in the same group. Besides, we also come up the
group attractiveness function(GAF) to evaluate the quality of groups and the
pursuers contribution in order to adjust the main ability indicators of AGRMF
and other weight of all neural network. The simulation experiment showed that
this proposal can improve the effectiveness of coalition formation for
multi-agent pursuit and ability to adopt pursuit-evasion problem with the scale
of pursuer team growing.
","Coalition formation for Multi-agent Pursuit based on Neural Network and
  AGRMF Model"
"  Among the myriad of desirable properties discussed in the context of
forgetting in Answer Set Programming (ASP), strong persistence naturally
captures its essence. Recently, it has been shown that it is not always
possible to forget a set of atoms from a program while obeying this property,
and a precise criterion regarding what can be forgotten has been presented,
accompanied by a class of forgetting operators that return the correct result
when forgetting is possible.
  However, it is an open question what to do when we have to forget a set of
atoms, but cannot without violating this property. In this paper, we address
this issue and investigate three natural alternatives to forget when forgetting
without violating strong persistence is not possible, which turn out to
correspond to the different possible relaxations of the characterization of
strong persistence. Additionally, we discuss their preferable usage, shed light
on the relation between forgetting and notions of relativized equivalence
established earlier in the context of ASP, and present a detailed study on
their computational complexity.
","When You Must Forget: beyond strong persistence when forgetting in
  answer set programming"
"  The highly influential framework of conceptual spaces provides a geometric
way of representing knowledge. Instances are represented by points and concepts
are represented by regions in a (potentially) high-dimensional space. Based on
our recent formalization, we present a comprehensive implementation of the
conceptual spaces framework that is not only capable of representing concepts
with inter-domain correlations, but that also offers a variety of operations on
these concepts.
",A Comprehensive Implementation of Conceptual Spaces
"  Machine Learning has been a big success story during the AI resurgence. One
particular stand out success relates to learning from a massive amount of data.
In spite of early assertions of the unreasonable effectiveness of data, there
is increasing recognition for utilizing knowledge whenever it is available or
can be created purposefully. In this paper, we discuss the indispensable role
of knowledge for deeper understanding of content where (i) large amounts of
training data are unavailable, (ii) the objects to be recognized are complex,
(e.g., implicit entities and highly subjective content), and (iii) applications
need to use complementary or related data in multiple modalities/media. What
brings us to the cusp of rapid progress is our ability to (a) create relevant
and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP
techniques. Using diverse examples, we seek to foretell unprecedented progress
in our ability for deeper understanding and exploitation of multimodal data and
continued incorporation of knowledge in learning techniques.
","Knowledge will Propel Machine Understanding of Content: Extrapolating
  from Current Examples"
"  This work proposes a formulation of propositional logic, named Eigenlogic,
using quantum observables as propositions. The eigenvalues of these operators
are the truth-values and the associated eigenvectors the interpretations of the
propositional system. Fuzzy logic arises naturally when considering vectors
outside the eigensystem, the fuzzy membership function is obtained by the Born
rule of the logical observable.This approach is then applied in the context of
quantum robots using simple behavioral agents represented by Braitenberg
vehicles. Processing with non-classical logic such as multivalued logic, fuzzy
logic and the quantum Eigenlogic permits to enlarge the behavior possibilities
and the associated decisions of these simple agents.
","Eigenlogic: Interpretable Quantum Observables with applications to Fuzzy
  Behavior of Vehicular Robots"
"  One promising trend in digital system integration consists of boosting
on-chip communication performance by means of silicon photonics, thus
materializing the so-called Optical Networks-on-Chip (ONoCs). Among them,
wavelength routing can be used to route a signal to destination by univocally
associating a routing path to the wavelength of the optical carrier. Such
wavelengths should be chosen so to minimize interferences among optical
channels and to avoid routing faults. As a result, physical parameter selection
of such networks requires the solution of complex constrained optimization
problems. In previous work, published in the proceedings of the International
Conference on Computer-Aided Design, we proposed and solved the problem of
computing the maximum parallelism obtainable in the communication between any
two endpoints while avoiding misrouting of optical signals. The underlying
technology, only quickly mentioned in that paper, is Answer Set Programming
(ASP). In this work, we detail the ASP approach we used to solve such problem.
  Another important design issue is to select the wavelengths of optical
carriers such that they are spread across the available spectrum, in order to
reduce the likelihood that, due to imperfections in the manufacturing process,
unintended routing faults arise. We show how to address such problem in
Constraint Logic Programming on Finite Domains (CLP(FD)).
  This paper is under consideration for possible publication on Theory and
Practice of Logic Programming.
","Logic Programming approaches for routing fault-free and
  maximally-parallel Wavelength Routed Optical Networks on Chip (Application
  paper)"
"  Bayesian Filtering for plan and activity recognition is challenging for
scenarios that contain many observation equivalent entities (i.e. entities that
produce the same observations). This is due to the combinatorial explosion in
the number of hypotheses that need to be tracked. However, this class of
problems exhibits a certain symmetry that can be exploited for state space
representation and inference. We analyze current state of the art methods and
find that none of them completely fits the requirements arising in this problem
class. We sketch a novel inference algorithm that provides a solution by
incorporating concepts from Lifted Inference algorithms, Probabilistic Multiset
Rewriting Systems, and Computational State Space Models. Two experiments
confirm that this novel algorithm has the potential to perform efficient
probabilistic inference on this problem class.
",Sequential Lifted Bayesian Filtering in Multiset Rewriting Systems
"  Predictive business process monitoring refers to the act of making
predictions about the future state of ongoing cases of a business process,
based on their incomplete execution traces and logs of historical (completed)
traces. Motivated by the increasingly pervasive availability of fine-grained
event data about business process executions, the problem of predictive process
monitoring has received substantial attention in the past years. In particular,
a considerable number of methods have been put forward to address the problem
of outcome-oriented predictive process monitoring, which refers to classifying
each ongoing case of a process according to a given set of possible categorical
outcomes - e.g., Will the customer complain or not? Will an order be delivered,
canceled or withdrawn? Unfortunately, different authors have used different
datasets, experimental settings, evaluation measures and baselines to assess
their proposals, resulting in poor comparability and an unclear picture of the
relative merits and applicability of different methods. To address this gap,
this article presents a systematic review and taxonomy of outcome-oriented
predictive process monitoring methods, and a comparative experimental
evaluation of eleven representative methods using a benchmark covering 24
predictive process monitoring tasks based on nine real-life event logs.
",Outcome-Oriented Predictive Process Monitoring: Review and Benchmark
"  Automated planning remains one of the most general paradigms in Artificial
Intelligence, providing means of solving problems coming from a wide variety of
domains. One of the key factors restricting the applicability of planning is
its computational complexity resulting from exponentially large search spaces.
Heuristic approaches are necessary to solve all but the simplest problems. In
this work, we explore the possibility of obtaining domain-independent heuristic
functions using machine learning. This is a part of a wider research program
whose objective is to improve practical applicability of planning in systems
for which the planning domains evolve at run time. The challenge is therefore
the learning of (corrections of) domain-independent heuristics that can be
reused across different planning domains.
",Towards learning domain-independent planning heuristics
"  Answer Set Programming (ASP) is a well-established declarative problem
solving paradigm which became widely used in AI and recognized as a powerful
tool for knowledge representation and reasoning (KRR), especially for its high
expressiveness and the ability to deal also with incomplete knowledge.
  Recently, thanks to the availability of a number of robust and efficient
implementations, ASP has been increasingly employed in a number of different
domains, and used for the development of industrial-level and enterprise
applications. This made clear the need for proper development tools and
interoperability mechanisms for easing interaction and integration with
external systems in the widest range of real-world scenarios, including mobile
applications and educational contexts.
  In this work we present a framework for integrating the KRR capabilities of
ASP into generic applications. We show the use of the framework by illustrating
proper specializations for some relevant ASP systems over different platforms,
including the mobile setting; furthermore, the potential of the framework for
educational purposes is illustrated by means of the development of several
ASP-based applications.
","A Framework for Easing the Development of Applications Embedding Answer
  Set Programming"
"  Because preferences naturally arise and play an important role in many
real-life decisions, they are at the backbone of various fields. In particular
preferences are increasingly used in almost all matching procedures-based
applications. In this work we highlight the benefit of using AI insights on
preferences in a large scale application, namely the French Admission
Post-Baccalaureat Platform (APB). Each year APB allocates hundreds of thousands
first year applicants to universities. This is done automatically by matching
applicants preferences to university seats. In practice, APB can be unable to
distinguish between applicants which leads to the introduction of random
selection. This has created frustration in the French public since randomness,
even used as a last mean does not fare well with the republican egalitarian
principle. In this work, we provide a solution to this problem. We take
advantage of recent AI Preferences Theory results to show how to enhance APB in
order to improve expressiveness of applicants preferences and reduce their
exposure to random decisions.
","Preference Reasoning in Matching Procedures: Application to the
  Admission Post-Baccalaureat Platform"
"  In adversarial training, a set of models learn together by pursuing competing
goals, usually defined on single data instances. However, in relational
learning and other non-i.i.d domains, goals can also be defined over sets of
instances. For example, a link predictor for the is-a relation needs to be
consistent with the transitivity property: if is-a(x_1, x_2) and is-a(x_2, x_3)
hold, is-a(x_1, x_3) needs to hold as well. Here we use such assumptions for
deriving an inconsistency loss, measuring the degree to which the model
violates the assumptions on an adversarially-generated set of examples. The
training objective is defined as a minimax problem, where an adversary finds
the most offending adversarial examples by maximising the inconsistency loss,
and the model is trained by jointly minimising a supervised loss and the
inconsistency loss on the adversarial examples. This yields the first method
that can use function-free Horn clauses (as in Datalog) to regularise any
neural link predictor, with complexity independent of the domain size. We show
that for several link prediction models, the optimisation problem faced by the
adversary has efficient closed-form solutions. Experiments on link prediction
benchmarks indicate that given suitable prior knowledge, our method can
significantly improve neural link predictors on all relevant metrics.
",Adversarial Sets for Regularising Neural Link Predictors
"  In recent work, we proved that the domain recursion inference rule makes
domain-lifted inference possible on several relational probability models
(RPMs) for which the best known time complexity used to be exponential. We also
identified two classes of RPMs for which inference becomes domain lifted when
using domain recursion. These two classes subsume the largest lifted classes
that were previously known. In this paper, we show that domain recursion can
also be applied to models with existential quantifiers. Currently, all lifted
inference algorithms assume that existential quantifiers have been removed in
pre-processing by Skolemization. We show that besides introducing potentially
inconvenient negative weights, Skolemization may increase the time complexity
of inference. We give two example models where domain recursion can replace
Skolemization, avoids the need for dealing with negative numbers, and reduces
the time complexity of inference. These two examples may be interesting from
three theoretical aspects: 1- they provide a better and deeper understanding of
domain recursion and, in general, (lifted) inference, 2- they may serve as
evidence that there are larger classes of models for which domain recursion can
satisfyingly replace Skolemization, and 3- they may serve as evidence that
better Skolemization techniques exist.
",Domain Recursion for Lifted Inference with Existential Quantifiers
"  Training robots for operation in the real world is a complex, time consuming
and potentially expensive task. Despite significant success of reinforcement
learning in games and simulations, research in real robot applications has not
been able to match similar progress. While sample complexity can be reduced by
training policies in simulation, such policies can perform sub-optimally on the
real platform given imperfect calibration of model dynamics. We present an
approach -- supplemental to fine tuning on the real robot -- to further benefit
from parallel access to a simulator during training and reduce sample
requirements on the real robot. The developed approach harnesses auxiliary
rewards to guide the exploration for the real world agent based on the
proficiency of the agent in simulation and vice versa. In this context, we
demonstrate empirically that the reciprocal alignment for both agents provides
further benefit as the agent in simulation can adjust to optimize its behaviour
for states commonly visited by the real-world agent.
",Mutual Alignment Transfer Learning
"  The theory of belief functions is an effective tool to deal with the multiple
uncertain information. In recent years, many evidence combination rules have
been proposed in this framework, such as the conjunctive rule, the cautious
rule, the PCR (Proportional Conflict Redistribution) rules and so on. These
rules can be adopted for different types of sources. However, most of these
rules are not applicable when the number of sources is large. This is due to
either the complexity or the existence of an absorbing element (such as the
total conflict mass function for the conjunctive-based rules when applied on
unreliable evidence). In this paper, based on the assumption that the majority
of sources are reliable, a combination rule for a large number of sources,
named LNS (stands for Large Number of Sources), is proposed on the basis of a
simple idea: the more common ideas one source shares with others, the
morereliable the source is. This rule is adaptable for aggregating a large
number of sources among which some are unreliable. It will keep the spirit of
the conjunctive rule to reinforce the belief on the focal elements with which
the sources are in agreement. The mass on the empty set will be kept as an
indicator of the conflict. Moreover, it can be used to elicit the major opinion
among the experts. The experimental results on synthetic mass functionsverify
that the rule can be effectively used to combine a large number of mass
functions and to elicit the major opinion.
",Evidence combination for a large number of sources
"  Processing and publishing the data of the historical sciences in the semantic
web is an interesting challenge in which the representation of temporal aspects
plays a key role. We propose in this paper a model of temporal knowledge
representation adapted to work on historical documents. This model is based on
the notion of fluent that is represented in RDF graphs. We show how this model
allows to represent the knowledge necessary to the historians and how it can be
used to reason on this knowledge using the SWRL and SPARQL languages. This
model is being used in a project to digitize, study and publish the manuscripts
of linguist Ferdinand de Saussure.
","Un mod\`ele pour la repr\'esentation des connaissances temporelles dans
  les documents historiques"
"  ProbLog is a state-of-art combination of logic programming and probabilities;
in particular ProbLog offers parameter learning through a variant of the EM
algorithm. However, the resulting learning algorithm is rather slow, even when
the data are complete. In this short paper we offer some insights that lead to
orders of magnitude improvements in ProbLog's parameter learning speed with
complete data.
",Speeding-up ProbLog's Parameter Learning
"  Manufacturers of safety-critical systems must make the case that their
product is sufficiently safe for public deployment. Much of this case often
relies upon critical event outcomes from real-world testing, requiring
manufacturers to be strategic about how they allocate testing resources in
order to maximize their chances of demonstrating system safety. This work
frames the partially observable and belief-dependent problem of test scheduling
as a Markov decision process, which can be solved efficiently to yield
closed-loop manufacturer testing policies. By solving for policies over a wide
range of problem formulations, we are able to provide high-level guidance for
manufacturers and regulators on issues relating to the testing of
safety-critical systems. This guidance spans an array of topics, including
circumstances under which manufacturers should continue testing despite
observed incidents, when manufacturers should test aggressively, and when
regulators should increase or reduce the real-world testing requirements for an
autonomous vehicle.
",Closed-Loop Policies for Operational Tests of Safety-Critical Systems
"  Sequential pattern mining algorithms are widely used to explore care pathways
database, but they generate a deluge of patterns, mostly redundant or useless.
Clinicians need tools to express complex mining queries in order to generate
less but more significant patterns. These algorithms are not versatile enough
to answer complex clinician queries. This article proposes to apply a
declarative pattern mining approach based on Answer Set Programming paradigm.
It is exemplified by a pharmaco-epidemiological study investigating the
possible association between hospitalization for seizure and antiepileptic drug
switch from a french medico-administrative database.
",Declarative Sequential Pattern Mining of Care Pathways
"  We introduce $\mathcal{DLR}^+$, an extension of the n-ary propositionally
closed description logic $\mathcal{DLR}$ to deal with attribute-labelled tuples
(generalising the positional notation), projections of relations, and global
and local objectification of relations, able to express inclusion, functional,
key, and external uniqueness dependencies. The logic is equipped with both TBox
and ABox axioms. We show how a simple syntactic restriction on the appearance
of projections sharing common attributes in a $\mathcal{DLR}^+$ knowledge base
makes reasoning in the language decidable with the same computational
complexity as $\mathcal{DLR}$. The obtained $\mathcal{DLR}^\pm$ n-ary
description logic is able to encode more thoroughly conceptual data models such
as EER, UML, and ORM.
","A Decidable Very Expressive Description Logic for Databases (Extended
  Version)"
"  Statistical Relational Models and, more recently, Probabilistic Programming,
have been making strides towards an integration of logic and probabilistic
reasoning. A natural expectation for this project is that a probabilistic logic
reasoning algorithm reduces to a logic reasoning algorithm when provided a
model that only involves 0-1 probabilities, exhibiting all the advantages of
logic reasoning such as short-circuiting, intelligibility, and the ability to
provide proof trees for a query answer. In fact, we can take this further and
require that these characteristics be present even for probabilistic models
with probabilities \emph{near} 0 and 1, with graceful degradation as the model
becomes more uncertain. We also seek inference that has amortized constant time
complexity on a model's size (even if still exponential in the induced width of
a more directly relevant portion of it) so that it can be applied to huge
knowledge bases of which only a relatively small portion is relevant to typical
queries. We believe that, among the probabilistic reasoning algorithms, Belief
Propagation is the most similar to logic reasoning: messages are propagated
among neighboring variables, and the paths of message-passing are similar to
proof trees. However, Belief Propagation is either only applicable to tree
models, or approximate (and without guarantees) for precision and convergence.
In this paper we present work in progress on an Anytime Exact Belief
Propagation algorithm that is very similar to Belief Propagation but is exact
even for graphical models with cycles, while exhibiting soft short-circuiting,
amortized constant time complexity in the model size, and which can provide
probabilistic proof trees.
",Anytime Exact Belief Propagation
"  An abstract argumentation framework can be used to model the argumentative
stance of an agent at a high level of abstraction, by indicating for every pair
of arguments that is being considered in a debate whether the first attacks the
second. When modelling a group of agents engaged in a debate, we may wish to
aggregate their individual argumentation frameworks to obtain a single such
framework that reflects the consensus of the group. Even when agents disagree
on many details, there may well be high-level agreement on important semantic
properties, such as the acceptability of a given argument. Using techniques
from social choice theory, we analyse under what circumstances such semantic
properties agreed upon by the individual agents can be preserved under
aggregation.
","Preservation of Semantic Properties during the Aggregation of Abstract
  Argumentation Frameworks"
"  We propose a general and model-free approach for Reinforcement Learning (RL)
on real robotics with sparse rewards. We build upon the Deep Deterministic
Policy Gradient (DDPG) algorithm to use demonstrations. Both demonstrations and
actual interactions are used to fill a replay buffer and the sampling ratio
between demonstrations and transitions is automatically tuned via a prioritized
replay mechanism. Typically, carefully engineered shaping rewards are required
to enable the agents to efficiently explore on high dimensional control
problems such as robotics. They are also required for model-based acceleration
methods relying on local solvers such as iLQG (e.g. Guided Policy Search and
Normalized Advantage Function). The demonstrations replace the need for
carefully engineered rewards, and reduce the exploration problem encountered by
classical RL approaches in these domains. Demonstrations are collected by a
robot kinesthetically force-controlled by a human demonstrator. Results on four
simulated insertion tasks show that DDPG from demonstrations out-performs DDPG,
and does not require engineered rewards. Finally, we demonstrate the method on
a real robotics task consisting of inserting a clip (flexible object) into a
rigid object.
","Leveraging Demonstrations for Deep Reinforcement Learning on Robotics
  Problems with Sparse Rewards"
"  Lifted inference algorithms commonly exploit symmetries in a probabilistic
graphical model (PGM) for efficient inference. However, existing algorithms for
Boolean-valued domains can identify only those pairs of states as symmetric, in
which the number of ones and zeros match exactly (count symmetries). Moreover,
algorithms for lifted inference in multi-valued domains also compute a
multi-valued extension of count symmetries only. These algorithms miss many
symmetries in a domain. In this paper, we present first algorithms to compute
non-count symmetries in both Boolean-valued and multi-valued domains. Our
methods can also find symmetries between multi-valued variables that have
different domain cardinalities. The key insight in the algorithms is that they
change the unit of symmetry computation from a variable to a variable-value
(VV) pair. Our experiments find that exploiting these symmetries in MCMC can
obtain substantial computational gains over existing algorithms.
",Non-Count Symmetries in Boolean & Multi-Valued Prob. Graphical Models
"  We propose a new type of self-aware systems inspired by ideas from
higher-order theories of consciousness. First, we discussed the crucial
distinction between introspection and reflexion. Then, we focus on
computational reflexion as a mechanism by which a computer program can inspect
its own code at every stage of the computation. Finally, we provide a formal
definition and a proof-of-concept implementation of computational reflexion,
viewed as an enriched form of program interpretation and a way to dynamically
""augment"" a computational process.
",Providing Self-Aware Systems with Reflexivity
"  In this article we study the transfer learning model of action advice under a
budget. We focus on reinforcement learning teachers providing action advice to
heterogeneous students playing the game of Pac-Man under a limited advice
budget. First, we examine several critical factors affecting advice quality in
this setting, such as the average performance of the teacher, its variance and
the importance of reward discounting in advising. The experiments show the
non-trivial importance of the coefficient of variation (CV) as a statistic for
choosing policies that generate advice. The CV statistic relates variance to
the corresponding mean. Second, the article studies policy learning for
distributing advice under a budget. Whereas most methods in the relevant
literature rely on heuristics for advice distribution we formulate the problem
as a learning one and propose a novel RL algorithm capable of learning when to
advise, adapting to the student and the task at hand. Furthermore, we argue
that learning to advise under a budget is an instance of a more generic
learning problem: Constrained Exploitation Reinforcement Learning.
",Learning to Teach Reinforcement Learning Agents
"  In dialogical argumentation it is often assumed that the involved parties
always correctly identify the intended statements posited by each other,
realize all of the associated relations, conform to the three acceptability
states (accepted, rejected, undecided), adjust their views when new and correct
information comes in, and that a framework handling only attack relations is
sufficient to represent their opinions. Although it is natural to make these
assumptions as a starting point for further research, removing them or even
acknowledging that such removal should happen is more challenging for some of
these concepts than for others. Probabilistic argumentation is one of the
approaches that can be harnessed for more accurate user modelling. The
epistemic approach allows us to represent how much a given argument is believed
by a given person, offering us the possibility to express more than just three
agreement states. It is equipped with a wide range of postulates, including
those that do not make any restrictions concerning how initial arguments should
be viewed, thus potentially being more adequate for handling beliefs of the
people that have not fully disclosed their opinions in comparison to Dung's
semantics. The constellation approach can be used to represent the views of
different people concerning the structure of the framework we are dealing with,
including cases in which not all relations are acknowledged or when they are
seen differently than intended. Finally, bipolar argumentation frameworks can
be used to express both positive and negative relations between arguments. In
this paper we describe the results of an experiment in which participants
judged dialogues in terms of agreement and structure. We compare our findings
with the aforementioned assumptions as well as with the constellation and
epistemic approaches to probabilistic argumentation and bipolar argumentation.
","Empirical Evaluation of Abstract Argumentation: Supporting the Need for
  Bipolar and Probabilistic Approaches"
"  We introduce a model that learns to convert simple hand drawings into
graphics programs written in a subset of \LaTeX. The model combines techniques
from deep learning and program synthesis. We learn a convolutional neural
network that proposes plausible drawing primitives that explain an image. These
drawing primitives are like a trace of the set of primitive commands issued by
a graphics program. We learn a model that uses program synthesis techniques to
recover a graphics program from that trace. These programs have constructs like
variable bindings, iterative loops, or simple kinds of conditionals. With a
graphics program in hand, we can correct errors made by the deep network,
measure similarity between drawings by use of similar high-level geometric
structures, and extrapolate drawings. Taken together these results are a step
towards agents that induce useful, human-readable programs from perceptual
input.
",Learning to Infer Graphics Programs from Hand-Drawn Images
"  ANGELINA is an automated game design system which has previously been built
as a single software block which designs games from start to finish. In this
paper we outline a roadmap for the development of a new version of ANGELINA,
designed to iterate on games in different ways to produce a continuous creative
process that will improve the quality of its work, but more importantly improve
the perception of the software as being an independently creative piece of
software. We provide an initial report of the system's structure here as well
as results from the first working module of the system.
",A Vision For Continuous Automated Game Design
"  I propose the purpose our concept of actual causation serves is minimizing
various cost in intervention practice. Actual causation has three features:
nonredundant sufficiency, continuity and abnormality; these features correspond
to the minimization of exploitative cost, exploratory cost and risk cost in
intervention practice. Incorporating these three features, a definition of
actual causation is given. I test the definition in 66 causal cases from actual
causation literature and show that this definition's application fit intuition
better than some other causal modelling based definitions.
",Cost and Actual Causation
"  The combination of argumentation and probability paves the way to new
accounts of qualitative and quantitative uncertainty, thereby offering new
theoretical and applicative opportunities. Due to a variety of interests,
probabilistic argumentation is approached in the literature with different
frameworks, pertaining to structured and abstract argumentation, and with
respect to diverse types of uncertainty, in particular the uncertainty on the
credibility of the premises, the uncertainty about which arguments to consider,
and the uncertainty on the acceptance status of arguments or statements.
Towards a general framework for probabilistic argumentation, we investigate a
labelling-oriented framework encompassing a basic setting for rule-based
argumentation and its (semi-) abstract account, along with diverse types of
uncertainty. Our framework provides a systematic treatment of various kinds of
uncertainty and of their relationships and allows us to back or question
assertions from the literature.
",A Labelling Framework for Probabilistic Argumentation
"  Explaining and reasoning about processes which underlie observed black-box
phenomena enables the discovery of causal mechanisms, derivation of suitable
abstract representations and the formulation of more robust predictions. We
propose to learn high level functional programs in order to represent abstract
models which capture the invariant structure in the observed data. We introduce
the $\pi$-machine (program-induction machine) -- an architecture able to induce
interpretable LISP-like programs from observed data traces. We propose an
optimisation procedure for program learning based on backpropagation, gradient
descent and A* search. We apply the proposed method to two problems: system
identification of dynamical systems and explaining the behaviour of a DQN
agent. Our results show that the $\pi$-machine can efficiently induce
interpretable programs from individual data traces.
",Using Program Induction to Interpret Transition System Dynamics
"  Hierarchical reinforcement learning methods offer a powerful means of
planning flexible behavior in complicated domains. However, learning an
appropriate hierarchical decomposition of a domain into subtasks remains a
substantial challenge. We present a novel algorithm for subtask discovery,
based on the recently introduced multitask linearly-solvable Markov decision
process (MLMDP) framework. The MLMDP can perform never-before-seen tasks by
representing them as a linear combination of a previously learned basis set of
tasks. In this setting, the subtask discovery problem can naturally be posed as
finding an optimal low-rank approximation of the set of tasks the agent will
face in a domain. We use non-negative matrix factorization to discover this
minimal basis set of tasks, and show that the technique learns intuitive
decompositions in a variety of domains. Our method has several qualitatively
desirable features: it is not limited to learning subtasks with single goal
states, instead learning distributed patterns of preferred states; it learns
qualitatively different hierarchical decompositions in the same domain
depending on the ensemble of tasks the agent will face; and it may be
straightforwardly iterated to obtain deeper hierarchical decompositions.
",Hierarchical Subtask Discovery With Non-Negative Matrix Factorization
"  Human aware planning requires an agent to be aware of the intentions,
capabilities and mental model of the human in the loop during its decision
process. This can involve generating plans that are explicable to a human
observer as well as the ability to provide explanations when such plans cannot
be generated. This has led to the notion ""multi-model planning"" which aim to
incorporate effects of human expectation in the deliberative process of a
planner - either in the form of explicable task planning or explanations
produced thereof. In this paper, we bring these two concepts together and show
how a planner can account for both these needs and achieve a trade-off during
the plan generation process itself by means of a model-space search method
MEGA. This in effect provides a comprehensive perspective of what it means for
a decision making agent to be ""human-aware"" by bringing together existing
principles of planning under the umbrella of a single plan generation process.
We situate our discussion specifically keeping in mind the recent work on
explicable planning and explanation generation, and illustrate these concepts
in modified versions of two well known planning domains, as well as a
demonstration on a robot involved in a typical search and reconnaissance task
with an external supervisor.
",Balancing Explicability and Explanation in Human-Aware Planning
"  We study a novel outlier detection problem that aims to identify abnormal
input-output associations in data, whose instances consist of multi-dimensional
input (context) and output (responses) pairs. We present our approach that
works by analyzing data in the conditional (input--output) relation space,
captured by a decomposable probabilistic model. Experimental results
demonstrate the ability of our approach in identifying multivariate conditional
outliers.
",Detection of Abnormal Input-Output Associations
"  The multi-armed bandit problem forms the foundation for solving a wide range
of on-line stochastic optimization problems through a simple, yet effective
mechanism. One simply casts the problem as a gambler that repeatedly pulls one
out of N slot machine arms, eliciting random rewards. Learning of reward
probabilities is then combined with reward maximization, by carefully balancing
reward exploration against reward exploitation. In this paper, we address a
particularly intriguing variant of the multi-armed bandit problem, referred to
as the {\it Stochastic Point Location (SPL) Problem}. The gambler is here only
told whether the optimal arm (point) lies to the ""left"" or to the ""right"" of
the arm pulled, with the feedback being erroneous with probability $1-\pi$.
This formulation thus captures optimization in continuous action spaces with
both {\it informative} and {\it deceptive} feedback. To tackle this class of
problems, we formulate a compact and scalable Bayesian representation of the
solution space that simultaneously captures both the location of the optimal
arm as well as the probability of receiving correct feedback. We further
introduce the accompanying Thompson Sampling guided Stochastic Point Location
(TS-SPL) scheme for balancing exploration against exploitation. By learning
$\pi$, TS-SPL also supports {\it deceptive} environments that are lying about
the direction of the optimal arm. This, in turn, allows us to solve the
fundamental Stochastic Root Finding (SRF) Problem. Empirical results
demonstrate that our scheme deals with both deceptive and informative
environments, significantly outperforming competing algorithms both for SRF and
SPL.
","Thompson Sampling Guided Stochastic Searching on the Line for Deceptive
  Environments with Applications to Root-Finding Problems"
"  We release a dataset of 65646 StarCraft replays that contains 1535 million
frames and 496 million player actions. We provide full game state data along
with the original replays that can be viewed in StarCraft. The game state data
was recorded every 3 frames which ensures suitability for a wide variety of
machine learning tasks such as strategy classification, inverse reinforcement
learning, imitation learning, forward modeling, partial information extraction,
and others. We use TorchCraft to extract and store the data, which standardizes
the data format for both reading from replays and reading directly from the
game. Furthermore, the data can be used on different operating systems and
platforms. The dataset contains valid, non-corrupted replays only and its
quality and diversity was ensured by a number of heuristics. We illustrate the
diversity of the data with various statistics and provide examples of tasks
that benefit from the dataset. We make the dataset available at
https://github.com/TorchCraft/StarData . En Taro Adun!
",STARDATA: A StarCraft AI Research Dataset
"  We study the following problem: given a labeled dataset and a specific
datapoint x, how did the i-th feature influence the classification for x? We
identify a family of numerical influence measures - functions that, given a
datapoint x, assign a numeric value phi_i(x) to every feature i, corresponding
to how altering i's value would influence the outcome for x. This family, which
we term monotone influence measures (MIM), is uniquely derived from a set of
desirable properties, or axioms. The MIM family constitutes a provably sound
methodology for measuring feature influence in classification domains; the
values generated by MIM are based on the dataset alone, and do not make any
queries to the classifier. While this requirement naturally limits the scope of
our framework, we demonstrate its effectiveness on data.
","Axiomatic Characterization of Data-Driven Influence Measures for
  Classification"
"  Given an environment with continuous state spaces and discrete actions, we
investigate using a Double Deep Q-learning Reinforcement Agent to find optimal
policies using the LunarLander-v2 OpenAI gym environment.
","Investigating Reinforcement Learning Agents for Continuous State Space
  Environments"
"  Reinforcement learning is a proven technique for an agent to learn a task.
However, when learning a task using reinforcement learning, the agent cannot
distinguish the characteristics of the environment from those of the task. This
makes it harder to transfer skills between tasks in the same environment.
Furthermore, this does not reduce risk when training for a new task. In this
paper, we introduce an approach to decouple the environment characteristics
from the task-specific ones, allowing an agent to develop a sense of survival.
We evaluate our approach in an environment where an agent must learn a sequence
of collection tasks, and show that decoupled learning allows for a safer
utilization of prior knowledge.
",Decoupled Learning of Environment Characteristics for Safe Exploration
"  There have been a number of developments in measuring inconsistency in
logic-based representations of knowledge. In contrast, the development of
inconsistency measures for computational models of argument has been limited.
To address this shortcoming, this paper provides a general framework for
measuring inconsistency in abstract argumentation, together with some proposals
for specific measures, and a consideration of measuring inconsistency in
logic-based instantiations of argument graphs, including a review of some
existing proposals and a consideration of how existing logic-based measures of
inconsistency can be applied.
",Measuring Inconsistency in Argument Graphs
"  Hierarchically structured agent plans are important for efficient planning
and acting, and they also serve (among other things) to produce ""richer""
classical plans, composed not just of a sequence of primitive actions, but also
""abstract"" ones representing the supplied hierarchies. A crucial step for this
and other approaches is deriving precondition and effect ""summaries"" from a
given plan hierarchy. This paper provides mechanisms to do this for more
pragmatic and conventional hierarchies than in the past. To this end, we
formally define the notion of a precondition and an effect for a hierarchical
plan; we present data structures and algorithms for automatically deriving this
information; and we analyse the properties of the presented algorithms. We
conclude the paper by detailing how our algorithms may be used together with a
classical planner in order to obtain abstract plans.
",Addendum to: Summary Information for Reasoning About Hierarchical Plans
"  The notion of commitment is widely studied as a high-level abstraction for
modeling multiagent interaction. An important challenge is supporting flexible
decentralized enactments of commitment specifications. In this paper, we
combine recent advances on specifying commitments and information protocols.
Specifically, we contribute Tosca, a technique for automatically synthesizing
information protocols from commitment specifications. Our main result is that
the synthesized protocols support commitment alignment, which is the idea that
agents must make compatible inferences about their commitments despite
decentralization.
",Tosca: Operationalizing Commitments Over Information Protocols
"  Knowledge graphs and vector space models are robust knowledge representation
techniques with individual strengths and weaknesses. Vector space models excel
at determining similarity between concepts, but are severely constrained when
evaluating complex dependency relations and other logic-based operations that
are a strength of knowledge graphs. We describe the VKG structure that helps
unify knowledge graphs and vector representation of entities, and enables
powerful inference methods and search capabilities that combine their
complementary strengths. We analogize this to thinking `fast' in vector space
along with thinking 'slow' and `deeply' by reasoning over the knowledge graph.
We have created a query processing engine that takes complex queries and
decomposes them into subqueries optimized to run on the respective knowledge
graph or vector view of a VKG. We show that the VKG structure can process
specific queries that are not efficiently handled by vector spaces or knowledge
graphs alone. We also demonstrate and evaluate the VKG structure and the query
processing engine by developing a system called Cyber-All-Intel for knowledge
extraction, representation and querying in an end-to-end pipeline grounded in
the cybersecurity informatics domain.
","Thinking, Fast and Slow: Combining Vector Spaces and Knowledge Graphs"
"  Bike sharing systems' popularity has consistently been rising during the past
years. Managing and maintaining these emerging systems are indispensable parts
of these systems. Visualizing the current operations can assist in getting a
better grasp on the performance of the system. In this paper, a data mining
approach is used to identify and visualize some important factors related to
bike-share operations and management. To consolidate the data, we cluster
stations that have a similar pickup and drop-off profiles during weekdays and
weekends. We provide the temporal profile of the center of each cluster which
can be used as a simple and practical approach for approximating the number of
pickups and drop-offs of the stations. We also define two indices based on
stations' shortages and surpluses that reflect the degree of balancing aid a
station needs. These indices can help stakeholders improve the quality of the
bike-share user experience in at-least two ways. It can act as a complement to
balancing optimization efforts, and it can identify stations that need
expansion. We mine the District of Columbia's regional bike-share data and
discuss the findings of this data set. We examine the bike-share system during
different quarters of the year and during both peak and non-peak hours.
Findings reflect that on weekdays most of the pickups and drop-offs happen
during the morning and evening peaks whereas on weekends pickups and drop-offs
are spread out throughout the day. We also show that throughout the day, more
than 40% of the stations are relatively self-balanced. Not worrying about these
stations during ordinary days can allow the balancing efforts to focus on a
fewer stations and therefore potentially improve the efficiency of the
balancing optimization models.
","Understanding and Visualizing the District of Columbia Capital Bikeshare
  System Using Data Analysis for Balancing Purposes"
"  As demand drives systems to generalize to various domains and problems, the
study of multitask, transfer and lifelong learning has become an increasingly
important pursuit. In discrete domains, performance on the Atari game suite has
emerged as the de facto benchmark for assessing multitask learning. However, in
continuous domains there is a lack of agreement on standard multitask
evaluation environments which makes it difficult to compare different
approaches fairly. In this work, we describe a benchmark set of tasks that we
have developed in an extendable framework based on OpenAI Gym. We run a simple
baseline using Trust Region Policy Optimization and release the framework
publicly to be expanded and used for the systematic comparison of multitask,
transfer, and lifelong learning in continuous domains.
",Benchmark Environments for Multitask Learning in Continuous Domains
"  This paper continues the research that considers a new cognitive model based
strongly on the human brain. In particular, it considers the neural binding
structure of an earlier paper. It also describes some new methods in the areas
of image processing and behaviour simulation. The work is all based on earlier
research by the author and the new additions are intended to fit in with the
overall design. For image processing, a grid-like structure is used with 'full
linking'. Each cell in the classifier grid stores a list of all other cells it
gets associated with and this is used as the learned image that new input is
compared to. For the behaviour metric, a new prediction equation is suggested,
as part of a simulation, that uses feedback and history to dynamically
determine its course of action. While the new methods are from widely different
topics, both can be compared with the binary-analog type of interface that is
the main focus of the paper. It is suggested that the simplest of linking
between a tree and ensemble can explain neural binding and variable signal
strengths.
",New Ideas for Brain Modelling 4
"  Sum-product networks (SPNs) are a class of probabilistic graphical models
that allow tractable marginal inference. However, the maximum a posteriori
(MAP) inference in SPNs is NP-hard. We investigate MAP inference in SPNs from
both theoretical and algorithmic perspectives. For the theoretical part, we
reduce general MAP inference to its special case without evidence and hidden
variables; we also show that it is NP-hard to approximate the MAP problem to
$2^{n^\epsilon}$ for fixed $0 \leq \epsilon < 1$, where $n$ is the input size.
For the algorithmic part, we first present an exact MAP solver that runs
reasonably fast and could handle SPNs with up to 1k variables and 150k arcs in
our experiments. We then present a new approximate MAP solver with a good
balance between speed and accuracy, and our comprehensive experiments on
real-world datasets show that it has better overall performance than existing
approximate solvers.
",Maximum A Posteriori Inference in Sum-Product Networks
"  There is sufficient information in the far-field of a radiating dipole
antenna to rediscover the Maxwell Equations and the wave equations of light,
including the speed of light $c.$ TheoSea is a Julia program that does this in
about a second, and the key insight is that the compactness of theories drives
the search. The program is a computational embodiment of the scientific method:
observation, consideration of candidate theories, and validation.
",TheoSea: Marching Theory to Light
"  T-distributed stochastic neighbor embedding (tSNE) is a popular and
prize-winning approach for dimensionality reduction and visualizing
high-dimensional data. However, tSNE is non-parametric: once visualization is
built, tSNE is not designed to incorporate additional data into existing
representation. It highly limits the applicability of tSNE to the scenarios
where data are added or updated over time (like dashboards or series of data
snapshots).
  In this paper we propose, analyze and evaluate LION-tSNE (Local Interpolation
with Outlier coNtrol) - a novel approach for incorporating new data into tSNE
representation. LION-tSNE is based on local interpolation in the vicinity of
training data, outlier detection and a special outlier mapping algorithm. We
show that LION-tSNE method is robust both to outliers and to new samples from
existing clusters. We also discuss multiple possible improvements for special
cases.
  We compare LION-tSNE to a comprehensive list of possible benchmark approaches
that include multiple interpolation techniques, gradient descent for new data,
and neural network approximation.
","Visualizing and Exploring Dynamic High-Dimensional Datasets with
  LION-tSNE"
"  The cognitive framework of conceptual spaces [3] provides geometric means for
representing knowledge. A conceptual space is a high-dimensional space whose
dimensions are partitioned into so-called domains. Within each domain, the
Euclidean metric is used to compute distances. Distances in the overall space
are computed by applying the Manhattan metric to the intra-domain distances.
Instances are represented as points in this space and concepts are represented
by regions. In this paper, we derive a formula for the size of a hyperball
under the combined metric of a conceptual space. One can think of such a
hyperball as the set of all points having a certain minimal similarity to the
hyperball's center.
",The Size of a Hyperball in a Conceptual Space
"  A* is a best-first search algorithm for finding optimal-cost paths in graphs.
A* benefits significantly from parallelism because in many applications, A* is
limited by memory usage, so distributed memory implementations of A* that use
all of the aggregate memory on the cluster enable problems that can not be
solved by serial, single-machine implementations to be solved. We survey
approaches to parallel A*, focusing on decentralized approaches to A* which
partition the state space among processors. We also survey approaches to
parallel, limited-memory variants of A* such as parallel IDA*.
",A Survey of Parallel A*
"  The General AI Challenge is an initiative to encourage the wider artificial
intelligence community to focus on important problems in building intelligent
machines with more general scope than is currently possible. The challenge
comprises of multiple rounds, with the first round focusing on gradual
learning, i.e. the ability to re-use already learned knowledge for efficiently
learning to solve subsequent problems. In this article, we will present details
of the first round of the challenge, its inspiration and aims. We also outline
a more formal description of the challenge and present a preliminary analysis
of its curriculum, based on ideas from computational mechanics. We believe,
that such formalism will allow for a more principled approach towards
investigating tasks in the challenge, building new curricula and for
potentially improving consequent challenge rounds.
",General AI Challenge - Round One: Gradual Learning
"  Machine learning algorithms are everywhere, ranging from simple data analysis
and pattern recognition tools used across the sciences to complex systems that
achieve super-human performance on various tasks. Ensuring that they are
well-behaved---that they do not, for example, cause harm to humans or act in a
racist or sexist way---is therefore not a hypothetical problem to be dealt with
in the future, but a pressing one that we address here. We propose a new
framework for designing machine learning algorithms that simplifies the problem
of specifying and regulating undesirable behaviors. To show the viability of
this new framework, we use it to create new machine learning algorithms that
preclude the sexist and harmful behaviors exhibited by standard machine
learning algorithms in our experiments. Our framework for designing machine
learning algorithms simplifies the safe and responsible application of machine
learning.
",On Ensuring that Intelligent Machines Are Well-Behaved
"  Among the local consistency techniques used for solving constraint networks,
path-consistency (PC) has received a great deal of attention. However,
enforcing PC is computationally expensive and sometimes even unnecessary.
Directional path-consistency (DPC) is a weaker notion of PC that considers a
given variable ordering and can thus be enforced more efficiently than PC. This
paper shows that DPC (the DPC enforcing algorithm of Dechter and Pearl) decides
the constraint satisfaction problem (CSP) of a constraint language if it is
complete and has the variable elimination property (VEP). However, we also show
that no complete VEP constraint language can have a domain with more than 2
values. We then present a simple variant of the DPC algorithm, called DPC*, and
show that the CSP of a constraint language can be decided by DPC* if it is
closed under a majority operation. In fact, DPC* is sufficient for guaranteeing
backtrack-free search for such constraint networks. Examples of majority-closed
constraint classes include the classes of connected row-convex (CRC)
constraints and tree-preserving constraints, which have found applications in
various domains, such as scene labeling, temporal reasoning, geometric
reasoning, and logical filtering. Our experimental evaluations show that DPC*
significantly outperforms the state-of-the-art algorithms for solving
majority-closed constraints.
",Exploring Directional Path-Consistency for Solving Constraint Networks
"  Data analytics helps basketball teams to create tactics. However, manual data
collection and analytics are costly and ineffective. Therefore, we applied a
deep bidirectional long short-term memory (BLSTM) and mixture density network
(MDN) approach. This model is not only capable of predicting a basketball
trajectory based on real data, but it also can generate new trajectory samples.
It is an excellent application to help coaches and players decide when and
where to shoot. Its structure is particularly suitable for dealing with time
series problems. BLSTM receives forward and backward information at the same
time, while stacking multiple BLSTMs further increases the learning ability of
the model. Combined with BLSTMs, MDN is used to generate a multi-modal
distribution of outputs. Thus, the proposed model can, in principle, represent
arbitrary conditional probability distributions of output variables. We tested
our model with two experiments on three-pointer datasets from NBA SportVu data.
In the hit-or-miss classification experiment, the proposed model outperformed
other models in terms of the convergence speed and accuracy. In the trajectory
generation experiment, eight model-generated trajectories at a given time
closely matched real trajectories.
","Applying Deep Bidirectional LSTM and Mixture Density Network for
  Basketball Trajectory Prediction"
"  In this paper, a new type of 3D bin packing problem (BPP) is proposed, in
which a number of cuboid-shaped items must be put into a bin one by one
orthogonally. The objective is to find a way to place these items that can
minimize the surface area of the bin. This problem is based on the fact that
there is no fixed-sized bin in many real business scenarios and the cost of a
bin is proportional to its surface area. Our research shows that this problem
is NP-hard. Based on previous research on 3D BPP, the surface area is
determined by the sequence, spatial locations and orientations of items. Among
these factors, the sequence of items plays a key role in minimizing the surface
area. Inspired by recent achievements of deep reinforcement learning (DRL)
techniques, especially Pointer Network, on combinatorial optimization problems
such as TSP, a DRL-based method is applied to optimize the sequence of items to
be packed into the bin. Numerical results show that the method proposed in this
paper achieve about 5% improvement than heuristic method.
","Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning
  Method"
"  Knowledge graphs are large, useful, but incomplete knowledge repositories.
They encode knowledge through entities and relations which define each other
through the connective structure of the graph. This has inspired methods for
the joint embedding of entities and relations in continuous low-dimensional
vector spaces, that can be used to induce new edges in the graph, i.e., link
prediction in knowledge graphs. Learning these representations relies on
contrasting positive instances with negative ones. Knowledge graphs include
only positive relation instances, leaving the door open for a variety of
methods for selecting negative examples. In this paper we present an empirical
study on the impact of negative sampling on the learned embeddings, assessed
through the task of link prediction. We use state-of-the-art knowledge graph
embeddings -- \rescal , TransE, DistMult and ComplEX -- and evaluate on
benchmark datasets -- FB15k and WN18. We compare well known methods for
negative sampling and additionally propose embedding based sampling methods. We
note a marked difference in the impact of these sampling methods on the two
datasets, with the ""traditional"" corrupting positives method leading to best
results on WN18, while embedding based methods benefiting the task on FB15k.
","Analysis of the Impact of Negative Sampling on Link Prediction in
  Knowledge Graphs"
"  In this article, we present a survey of recent advances in passive human
behaviour recognition in indoor areas using the channel state information (CSI)
of commercial WiFi systems. Movement of human body causes a change in the
wireless signal reflections, which results in variations in the CSI. By
analyzing the data streams of CSIs for different activities and comparing them
against stored models, human behaviour can be recognized. This is done by
extracting features from CSI data streams and using machine learning techniques
to build models and classifiers. The techniques from the literature that are
presented herein have great performances, however, instead of the machine
learning techniques employed in these works, we propose to use deep learning
techniques such as long-short term memory (LSTM) recurrent neural network
(RNN), and show the improved performance. We also discuss about different
challenges such as environment change, frame rate selection, and multi-user
scenario, and suggest possible directions for future work.
",A Survey of Human Activity Recognition Using WiFi CSI
"  We present a new approach to learning for planning, where knowledge acquired
while solving a given set of planning problems is used to plan faster in
related, but new problem instances. We show that a deep neural network can be
used to learn and represent a \emph{generalized reactive policy} (GRP) that
maps a problem instance and a state to an action, and that the learned GRPs
efficiently solve large classes of challenging problem instances. In contrast
to prior efforts in this direction, our approach significantly reduces the
dependence of learning on handcrafted domain knowledge or feature selection.
Instead, the GRP is trained from scratch using a set of successful execution
traces. We show that our approach can also be used to automatically learn a
heuristic function that can be used in directed search algorithms. We evaluate
our approach using an extensive suite of experiments on two challenging
planning problem domains and show that our approach facilitates learning
complex decision making policies and powerful heuristic functions with minimal
human input. Videos of our results are available at goo.gl/Hpy4e3.
",Learning Generalized Reactive Policies using Deep Neural Networks
"  Most natural language processing tasks can be formulated as the approximated
nearest neighbor search problem, such as word analogy, document similarity,
machine translation. Take the question-answering task as an example, given a
question as the query, the goal is to search its nearest neighbor in the
training dataset as the answer. However, existing methods for approximate
nearest neighbor search problem may not perform well owing to the following
practical challenges: 1) there are noise in the data; 2) the large scale
dataset yields a huge retrieval space and high search time complexity.
  In order to solve these problems, we propose a novel approximate nearest
neighbor search framework which i) projects the data to a subspace based
spectral analysis which eliminates the influence of noise; ii) partitions the
training dataset to different groups in order to reduce the search space.
Specifically, the retrieval space is reduced from $O(n)$ to $O(\log n)$ (where
$n$ is the number of data points in the training dataset). We prove that the
retrieved nearest neighbor in the projected subspace is the same as the one in
the original feature space. We demonstrate the outstanding performance of our
framework on real-world natural language processing tasks.
",Subspace Approximation for Approximate Nearest Neighbor Search in NLP
"  Dependency graph, as a heterogeneous graph representing the intrinsic
relationships between different pairs of system entities, is essential to many
data analysis applications, such as root cause diagnosis, intrusion detection,
etc. Given a well-trained dependency graph from a source domain and an immature
dependency graph from a target domain, how can we extract the entity and
dependency knowledge from the source to enhance the target? One way is to
directly apply a mature dependency graph learned from a source domain to the
target domain. But due to the domain variety problem, directly using the source
dependency graph often can not achieve good performance. Traditional transfer
learning methods mainly focus on numerical data and are not applicable.
  In this paper, we propose ACRET, a knowledge transfer based model for
accelerating dependency graph learning from heterogeneous categorical event
streams. In particular, we first propose an entity estimation model to filter
out irrelevant entities from the source domain based on entity embedding and
manifold learning. Only the entities with statistically high correlations are
transferred to the target domain. On the surviving entities, we propose a
dependency construction model for constructing the unbiased dependency
relationships by solving a two-constraint optimization problem. The
experimental results on synthetic and real-world datasets demonstrate the
effectiveness and efficiency of ACRET. We also apply ACRET to a real enterprise
security system for intrusion detection. Our method is able to achieve superior
detection performance at least 20 days lead lag time in advance with more than
70% accuracy.
","Accelerating Dependency Graph Learning from Heterogeneous Categorical
  Event Streams via Knowledge Transfer"
"  In this article, we review recent Deep Learning advances in the context of
how they have been applied to play different types of video games such as
first-person shooters, arcade games, and real-time strategy games. We analyze
the unique requirements that different game genres pose to a deep learning
system and highlight important open challenges in the context of applying these
machine learning methods to video games, such as general game playing, dealing
with extremely large decision spaces and sparse rewards.
",Deep Learning for Video Game Playing
"  Humans develop a common sense of style compatibility between items based on
their attributes. We seek to automatically answer questions like ""Does this
shirt go well with that pair of jeans?"" In order to answer these kinds of
questions, we attempt to model human sense of style compatibility in this
paper. The basic assumption of our approach is that most of the important
attributes for a product in an online store are included in its title
description. Therefore it is feasible to learn style compatibility from these
descriptions. We design a Siamese Convolutional Neural Network architecture and
feed it with title pairs of items, which are either compatible or incompatible.
Those pairs will be mapped from the original space of symbolic words into some
embedded style space. Our approach takes only words as the input with few
preprocessing and there is no laborious and expensive feature engineering.
",Deep Style Match for Complementary Recommendation
"  We consider the problem of rational uncertainty about unproven mathematical
statements, remarked on by G\""odel and others. Using Bayesian-inspired
arguments we build a normative model of fair bets under deductive uncertainty
which draws from both probability and the theory of algorithms. We comment on
connections to Zeilberger's notion of ""semi-rigorous proofs"", particularly that
inherent subjectivity would be present. We also discuss a financial view with
models of arbitrage where traders have limited computational resources.
",Plausibility and probability in deductive reasoning
"  The notions of disintegration and Bayesian inversion are fundamental in
conditional probability theory. They produce channels, as conditional
probabilities, from a joint state, or from an already given channel (in
opposite direction). These notions exist in the literature, in concrete
situations, but are presented here in abstract graphical formulations. The
resulting abstract descriptions are used for proving basic results in
conditional probability theory. The existence of disintegration and Bayesian
inversion is discussed for discrete probability, and also for measure-theoretic
probability --- via standard Borel spaces and via likelihoods. Finally, the
usefulness of disintegration and Bayesian inversion is illustrated in several
examples.
",Disintegration and Bayesian Inversion via String Diagrams
"  Semantics based knowledge representations such as ontologies are found to be
very useful in automatically generating meaningful factual questions.
Determining the difficulty level of these system generated questions is helpful
to effectively utilize them in various educational and professional
applications. The existing approaches for finding the difficulty level of
factual questions are very simple and are limited to a few basic principles. We
propose a new methodology for this problem by considering an educational theory
called Item Response Theory (IRT). In the IRT, knowledge proficiency of end
users (learners) are considered for assigning difficulty levels, because of the
assumptions that a given question is perceived differently by learners of
various proficiencies. We have done a detailed study on the features (factors)
of a question statement which could possibly determine its difficulty level for
three learner categories (experts, intermediates and beginners). We formulate
ontology based metrics for the same. We then train three logistic regression
models to predict the difficulty level corresponding to the three learner
categories.
",Difficulty-level Modeling of Ontology-based Factual Questions
"  We explain how the prototype automatic chess problem composer, Chesthetica,
successfully composed a rare and interesting chess problem using the new
Digital Synaptic Neural Substrate (DSNS) computational creativity approach.
This problem represents a greater challenge from a creative standpoint because
the checkmate is not always clear and the method of winning even less so.
Creating a decisive chess problem of this type without the aid of an omniscient
7-piece endgame tablebase (and one that also abides by several chess
composition conventions) would therefore be a challenge for most human players
and composers working on their own. The fact that a small computer with
relatively low processing power and memory was sufficient to compose such a
problem using the DSNS approach in just 10 days is therefore noteworthy. In
this report we document the event and result in some detail. It lends
additional credence to the DSNS as a viable new approach in the field of
computational creativity. In particular, in areas where human-like creativity
is required for targeted or specific problems with no clear path to the
solution.
",A Computer Composes A Fabled Problem: Four Knights vs. Queen
"  We introduce a novel method to train agents of reinforcement learning (RL) by
sharing knowledge in a way similar to the concept of using a book. The recorded
information in the form of a book is the main means by which humans learn
knowledge. Nevertheless, the conventional deep RL methods have mainly focused
either on experiential learning where the agent learns through interactions
with the environment from the start or on imitation learning that tries to
mimic the teacher. Contrary to these, our proposed book learning shares key
information among different agents in a book-like manner by delving into the
following two characteristic features: (1) By defining the linguistic function,
input states can be clustered semantically into a relatively small number of
core clusters, which are forwarded to other RL agents in a prescribed manner.
(2) By defining state priorities and the contents for recording, core
experiences can be selected and stored in a small container. We call this
container as `BOOK'. Our method learns hundreds to thousand times faster than
the conventional methods by learning only a handful of core cluster
information, which shows that deep RL agents can effectively learn through the
shared knowledge from other agents.
","BOOK: Storing Algorithm-Invariant Episodes for Deep Reinforcement
  Learning"
"  We introduce an online active exploration algorithm for data-efficiently
learning an abstract symbolic model of an environment. Our algorithm is divided
into two parts: the first part quickly generates an intermediate Bayesian
symbolic model from the data that the agent has collected so far, which the
agent can then use along with the second part to guide its future exploration
towards regions of the state space that the model is uncertain about. We show
that our algorithm outperforms random and greedy exploration policies on two
different computer game domains. The first domain is an Asteroids-inspired game
with complex dynamics but basic logical structure. The second is the Treasure
Game, with simpler dynamics but more complex logical structure.
",Active Exploration for Learning Symbolic Representations
"  We consider the fundamental question: how a legacy ""student"" Artificial
Intelligent (AI) system could learn from a legacy ""teacher"" AI system or a
human expert without complete re-training and, most importantly, without
requiring significant computational resources. Here ""learning"" is understood as
an ability of one system to mimic responses of the other and vice-versa. We
call such learning an Artificial Intelligence knowledge transfer. We show that
if internal variables of the ""student"" Artificial Intelligent system have the
structure of an $n$-dimensional topological vector space and $n$ is
sufficiently high then, with probability close to one, the required knowledge
transfer can be implemented by simple cascades of linear functionals. In
particular, for $n$ sufficiently large, with probability close to one, the
""student"" system can successfully and non-iteratively learn $k\ll n$ new
examples from the ""teacher"" (or correct the same number of mistakes) at the
cost of two additional inner products. The concept is illustrated with an
example of knowledge transfer from a pre-trained convolutional neural network
to a simple linear classifier with HOG features.
",Knowledge Transfer Between Artificial Intelligence Systems
"  This paper contains analysis and extension of exploiters-based knowledge
extraction methods, which allow generation of new knowledge, based on the basic
ones. The main achievement of the paper is useful features of some universal
exploiters proof, which allow extending set of basic classes and set of basic
relations by finite set of new classes of objects and relations among them,
which allow creating of complete lattice. Proposed approach gives an
opportunity to compute quantity of new classes, which can be generated using
it, and quantity of different types, which each of obtained classes describes;
constructing of defined hierarchy of classes with determined subsumption
relation; avoidance of some problems of inheritance and more efficient
restoring of basic knowledge within the database.
",Object-Oriented Knowledge Extraction using Universal Exploiters
"  We discuss that how the majority of traditional modeling approaches are
following the idealism point of view in scientific modeling, which follow the
set theoretical notions of models based on abstract universals. We show that
while successful in many classical modeling domains, there are fundamental
limits to the application of set theoretical models in dealing with complex
systems with many potential aspects or properties depending on the
perspectives. As an alternative to abstract universals, we propose a conceptual
modeling framework based on concrete universals that can be interpreted as a
category theoretical approach to modeling. We call this modeling framework
pre-specific modeling. We further, discuss how a certain group of mathematical
and computational methods, along with ever-growing data streams are able to
operationalize the concept of pre-specific modeling.
","Computational Machines in a Coexistence with Concrete Universals and
  Data Streams"
"  A commonly used technique for managing AI complexity in real-time strategy
(RTS) games is to use action and/or state abstractions. High-level abstractions
can often lead to good strategic decision making, but tactical decision quality
may suffer due to lost details. A competing method is to sample the search
space which often leads to good tactical performance in simple scenarios, but
poor high-level planning.
  We propose to use a deep convolutional neural network (CNN) to select among a
limited set of abstract action choices, and to utilize the remaining
computation time for game tree search to improve low level tactics. The CNN is
trained by supervised learning on game states labelled by Puppet Search, a
strategic search algorithm that uses action abstractions. The network is then
used to select a script --- an abstract action --- to produce low level actions
for all units. Subsequently, the game tree search algorithm improves the
tactical actions of a subset of units using a limited view of the game state
only considering units close to opponent units.
  Experiments in the microRTS game show that the combined algorithm results in
higher win-rates than either of its two independent components and other
state-of-the-art microRTS agents.
  To the best of our knowledge, this is the first successful application of a
convolutional network to play a full RTS game on standard game maps, as
previous work has focused on sub-problems, such as combat, or on very small
maps.
","Combining Strategic Learning and Tactical Search in Real-Time Strategy
  Games"
"  We propose that operator induction serves as an adequate model of perception.
We explain how to reduce universal agent models to operator induction. We
propose a universal measure of operator induction fitness, and show how it can
be used in a reinforcement learning model and a homeostasis (self-preserving)
agent based on the free energy principle. We show that the action of the
homeostasis agent can be explained by the operator induction model.
","Ultimate Intelligence Part III: Measures of Intelligence, Perception and
  Intelligent Agents"
"  We present theoretical analysis and a suite of tests and procedures for
addressing a broad class of redundant and misleading association rules we call
\emph{specious rules}. Specious dependencies, also known as \emph{spurious},
\emph{apparent}, or \emph{illusory associations}, refer to a well-known
phenomenon where marginal dependencies are merely products of interactions with
other variables and disappear when conditioned on those variables.
  The most extreme example is Yule-Simpson's paradox where two variables
present positive dependence in the marginal contingency table but negative in
all partial tables defined by different levels of a confounding factor. It is
accepted wisdom that in data of any nontrivial dimensionality it is infeasible
to control for all of the exponentially many possible confounds of this nature.
In this paper, we consider the problem of specious dependencies in the context
of statistical association rule mining. We define specious rules and show they
offer a unifying framework which covers many types of previously proposed
redundant or misleading association rules. After theoretical analysis, we
introduce practical algorithms for detecting and pruning out specious
association rules efficiently under many key goodness measures, including
mutual information and exact hypergeometric probabilities. We demonstrate that
the procedure greatly reduces the number of associations discovered, providing
an elegant and effective solution to the problem of association mining
discovering large numbers of misleading and redundant rules.
","Specious rules: an efficient and effective unifying method for removing
  misleading and uninformative patterns in association rule mining"
"  We describe a method to use discrete human feedback to enhance the
performance of deep learning agents in virtual three-dimensional environments
by extending deep-reinforcement learning to model the confidence and
consistency of human feedback. This enables deep reinforcement learning
algorithms to determine the most appropriate time to listen to the human
feedback, exploit the current policy model, or explore the agent's environment.
Managing the trade-off between these three strategies allows DRL agents to be
robust to inconsistent or intermittent human feedback. Through experimentation
using a synthetic oracle, we show that our technique improves the training
speed and overall performance of deep reinforcement learning in navigating
three-dimensional environments using Minecraft. We further show that our
technique is robust to highly innacurate human feedback and can also operate
when no human feedback is given.
","Explore, Exploit or Listen: Combining Human Feedback and Policy Model to
  Speed up Deep Reinforcement Learning in 3D Worlds"
"  Data based judgments go into artificial intelligence applications but they
undergo paradoxical reversal when seemingly unnecessary additional data is
provided. Examples of this are Simpson's reversal and the disjunction effect
where the beliefs about the data change once it is presented or aggregated
differently. Sometimes the significance of the difference can be evaluated
using statistical tests such as Pearson's chi-squared or Fisher's exact test,
but this may not be helpful in threshold-based decision systems that operate
with incomplete information. To mitigate risks in the use of algorithms in
decision-making, we consider the question of modeling of beliefs. We argue that
evidence supports that beliefs are not classical statistical variables and they
should, in the general case, be considered as superposition states of disjoint
or polar outcomes. We analyze the disjunction effect from the perspective of
the belief as a quantum vector.
",Probability Reversal and the Disjunction Effect in Reasoning Systems
"  In Information fusion, the conflict is an important concept. Indeed,
combining several imperfect experts or sources allows conflict. In the theory
of belief functions, this notion has been discussed a lot. The mass appearing
on the empty set during the conjunctive combination rule is generally
considered as conflict, but that is not really a conflict. Some measures of
conflict have been proposed and some approaches have been proposed in order to
manage this conflict or to decide with conflicting mass functions. We recall in
this chapter some of them and we propose a discussion to consider the conflict
in information fusion with the theory of belief functions.
",Conflict management in information fusion with belief functions
"  We compare Tetrad (Java) algorithms to the other public software packages BNT
(Bayes Net Toolbox, Matlab), pcalg (R), bnlearn (R) on the \vanilla"" task of
recovering DAG structure to the extent possible from data generated recursively
from linear, Gaussian structure equation models (SEMs) with no latent
variables, for random graphs, with no additional knowledge of variable order or
adjacency structure, and without additional specification of intervention
information. Each one of the above packages offers at least one implementation
suitable to this purpose. We compare them on adjacency and orientation accuracy
as well as time performance, for fixed datasets. We vary the number of
variables, the number of samples, and the density of graph, for a total of 27
combinations, averaging all statistics over 10 runs, for a total of 270
datasets. All runs are carried out on the same machine and on their native
platforms. An interactive visualization tool is provided for the reader who
wishes to know more than can be documented explicitly in this report.
","A Comparison of Public Causal Search Packages on Linear, Gaussian Data
  with No Latent Variables"
"  Ordered weighted averaging (OWA) operators have been widely used in decision
making these past few years. An important issue facing the OWA operators' users
is the determination of the OWA weights. This paper introduces an OWA
determination method based on truncated distributions that enables intuitive
generation of OWA weights according to a certain level of risk and trade-off.
These two dimensions are represented by the two first moments of the truncated
distribution. We illustrate our approach with the well-know normal distribution
and the definition of a continuous parabolic decision-strategy space. We
finally study the impact of the number of criteria on the results.
",Generating OWA weights using truncated distributions
"  In this paper, we introduce the problem of denoting and deriving the
complexity of workflows (plans, schedules) in collaborative, planner-assisted
settings where humans and agents are trying to jointly solve a task. The
interactions -- and hence the workflows that connect the human and the agents
-- may differ according to the domain and the kind of agents. We adapt insights
from prior work in human-agent teaming and workflow analysis to suggest metrics
for workflow complexity. The main motivation behind this work is to highlight
metrics for human comprehensibility of plans and schedules. The planning
community has seen its fair share of work on the synthesis of plans that take
diversity into account -- what value do such plans hold if their generation is
not guided at least in part by metrics that reflect the ease of engaging with
and using those plans?
","Workflow Complexity for Collaborative Interactions: Where are the
  Metrics? -- A Challenge"
"  Recent work has shown that temporally extended actions (options) can be
learned fully end-to-end as opposed to being specified in advance. While the
problem of ""how"" to learn options is increasingly well understood, the question
of ""what"" good options should be has remained elusive. We formulate our answer
to what ""good"" options should be in the bounded rationality framework (Simon,
1957) through the notion of deliberation cost. We then derive practical
gradient-based learning algorithms to implement this objective. Our results in
the Arcade Learning Environment (ALE) show increased performance and
interpretability.
","When Waiting is not an Option : Learning Options with a Deliberation
  Cost"
"  Reinforcement learning (RL), while often powerful, can suffer from slow
learning speeds, particularly in high dimensional spaces. The autonomous
decomposition of tasks and use of hierarchical methods hold the potential to
significantly speed up learning in such domains. This paper proposes a novel
practical method that can autonomously decompose tasks, by leveraging
association rule mining, which discovers hidden relationship among entities in
data mining. We introduce a novel method called ARM-HSTRL (Association Rule
Mining to extract Hierarchical Structure of Tasks in Reinforcement Learning).
It extracts temporal and structural relationships of sub-goals in RL, and
multi-task RL. In particular,it finds sub-goals and relationship among them. It
is shown the significant efficiency and performance of the proposed method in
two main topics of RL.
","Autonomous Extracting a Hierarchical Structure of Tasks in Reinforcement
  Learning and Multi-task Reinforcement Learning"
"  The performance of many hard combinatorial problem solvers depends strongly
on their parameter settings, and since manual parameter tuning is both tedious
and suboptimal the AI community has recently developed several algorithm
configuration (AC) methods to automatically address this problem. While all
existing AC methods start the configuration process of an algorithm A from
scratch for each new type of benchmark instances, here we propose to exploit
information about A's performance on previous benchmarks in order to warmstart
its configuration on new types of benchmarks. We introduce two complementary
ways in which we can exploit this information to warmstart AC methods based on
a predictive model. Experiments for optimizing a very flexible modern SAT
solver on twelve different instance sets show that our methods often yield
substantial speedups over existing AC methods (up to 165-fold) and can also
find substantially better configurations given the same compute budget.
",Warmstarting of Model-based Algorithm Configuration
"  We present KBLRN, a framework for end-to-end learning of knowledge base
representations from latent, relational, and numerical features. KBLRN
integrates feature types with a novel combination of neural representation
learning and probabilistic product of experts models. To the best of our
knowledge, KBLRN is the first approach that learns representations of knowledge
bases by integrating latent, relational, and numerical features. We show that
instances of KBLRN outperform existing methods on a range of knowledge base
completion tasks. We contribute a novel data sets enriching commonly used
knowledge base completion benchmarks with numerical features. The data sets are
available under a permissive BSD-3 license. We also investigate the impact
numerical features have on the KB completion performance of KBLRN.
","KBLRN : End-to-End Learning of Knowledge Base Representations with
  Latent, Relational, and Numerical Features"
"  Conversational AI systems are becoming famous in day to day lives. In this
paper, we are trying to address the following key question: To identify whether
design, as well as development efforts for search oriented conversational AI
are successful or not.It is tricky to define 'success' in the case of
conversational AI and equally tricky part is to use appropriate metrics for the
evaluation of conversational AI. We propose four different perspectives namely
user experience, information retrieval, linguistic and artificial intelligence
for the evaluation of conversational AI systems. Additionally, background
details of conversational AI systems are provided including desirable
characteristics of personal assistants, differences between chatbot and an AI
based personal assistant. An importance of personalization and how it can be
achieved is explained in detail. Current challenges in the development of an
ideal conversational AI (personal assistant) are also highlighted along with
guidelines for achieving personalized experience for users.
",Perspectives for Evaluating Conversational AI
"  Time series prediction is of great significance in many applications and has
attracted extensive attention from the data mining community. Existing work
suggests that for many problems, the shape in the current time series may
correlate an upcoming shape in the same or another series. Therefore, it is a
promising strategy to associate two recurring patterns as a rule's antecedent
and consequent: the occurrence of the antecedent can foretell the occurrence of
the consequent, and the learned shape of consequent will give accurate
predictions. Earlier work employs symbolization methods, but the symbolized
representation maintains too little information of the original series to mine
valid rules. The state-of-the-art work, though directly manipulating the
series, fails to segment the series precisely for seeking
antecedents/consequents, resulting in inaccurate rules in common scenarios. In
this paper, we propose a novel motif-based rule discovery method, which
utilizes motif discovery to accurately extract frequently occurring consecutive
subsequences, i.e. motifs, as antecedents/consequents. It then investigates the
underlying relationships between motifs by matching motifs as rule candidates
and ranking them based on the similarities. Experimental results on real open
datasets show that the proposed approach outperforms the baseline method by
23.9%. Furthermore, it extends the applicability from single time series to
multiple ones.
",Motif-based Rule Discovery for Predicting Real-valued Time Series
"  This paper proposes a formal cognitive framework for problem solving based on
category theory. We introduce cognitive categories, which are categories with
exactly one morphism between any two objects. Objects in these categories are
interpreted as states and morphisms as transformations between states.
Moreover, cognitive problems are reduced to the specification of two objects in
a cognitive category: an outset (i.e. the current state of the system) and a
goal (i.e. the desired state). Cognitive systems transform the target system by
means of generators and evaluators. Generators realize cognitive operations
over a system by grouping morphisms, whilst evaluators group objects as a way
to generalize outsets and goals to partially defined states. Meta-cognition
emerges when the whole cognitive system is self-referenced as sub-states in the
cognitive category, whilst learning must always be considered as a
meta-cognitive process to maintain consistency. Several examples grounded in
basic AI methods are provided as well.
",General problem solving with category theory
"  Deep reinforcement learning is revolutionizing the artificial intelligence
field. Currently, it serves as a good starting point for constructing
intelligent autonomous systems which offer a better knowledge of the visual
world. It is possible to scale deep reinforcement learning with the use of deep
learning and do amazing tasks such as use of pixels in playing video games. In
this paper, key concepts of deep reinforcement learning including reward
function, differences between reinforcement learning and supervised learning
and models for implementation of reinforcement are discussed. Key challenges
related to the implementation of reinforcement learning in conversational AI
domain are identified as well as discussed in detail. Various conversational
models which are based on deep reinforcement learning (as well as deep
learning) are also discussed. In summary, this paper discusses key aspects of
deep reinforcement learning which are crucial for designing an efficient
conversational AI.
",Deep Reinforcement Learning for Conversational AI
"  We develop a reinforcement learning based search assistant which can assist
users through a set of actions and sequence of interactions to enable them
realize their intent. Our approach caters to subjective search where the user
is seeking digital assets such as images which is fundamentally different from
the tasks which have objective and limited search modalities. Labeled
conversational data is generally not available in such search tasks and
training the agent through human interactions can be time consuming. We propose
a stochastic virtual user which impersonates a real user and can be used to
sample user behavior efficiently to train the agent which accelerates the
bootstrapping of the agent. We develop A3C algorithm based context preserving
architecture which enables the agent to provide contextual assistance to the
user. We compare the A3C agent with Q-learning and evaluate its performance on
average rewards and state values it obtains with the virtual user in validation
episodes. Our experiments show that the agent learns to achieve higher rewards
and better states.
","Improving Search through A3C Reinforcement Learning based Conversational
  Agent"
"  Planning problems in partially observable environments cannot be solved
directly with convolutional networks and require some form of memory. But, even
memory networks with sophisticated addressing schemes are unable to learn
intelligent reasoning satisfactorily due to the complexity of simultaneously
learning to access memory and plan. To mitigate these challenges we introduce
the Memory Augmented Control Network (MACN). The proposed network architecture
consists of three main parts. The first part uses convolutions to extract
features and the second part uses a neural network-based planning module to
pre-plan in the environment. The third part uses a network controller that
learns to store those specific instances of past information that are necessary
for planning. The performance of the network is evaluated in discrete grid
world environments for path planning in the presence of simple and complex
obstacles. We show that our network learns to plan and can generalize to new
environments.
",Memory Augmented Control Networks
"  In the propositional setting, the marginal problem is to find a
(maximum-entropy) distribution that has some given marginals. We study this
problem in a relational setting and make the following contributions. First, we
compare two different notions of relational marginals. Second, we show a
duality between the resulting relational marginal problems and the maximum
likelihood estimation of the parameters of relational models, which generalizes
a well-known duality from the propositional setting. Third, by exploiting the
relational marginal formulation, we present a statistically sound method to
learn the parameters of relational models that will be applied in settings
where the number of constants differs between the training and test data.
Furthermore, based on a relational generalization of marginal polytopes, we
characterize cases where the standard estimators based on feature's number of
true groundings needs to be adjusted and we quantitatively characterize the
consequences of these adjustments. Fourth, we prove bounds on expected errors
of the estimated parameters, which allows us to lower-bound, among other
things, the effective sample size of relational training data.
",Relational Marginal Problems: Theory and Estimation
"  As computational power has continued to increase, and sensors have become
more accurate, the corresponding advent of systems that are at once cognitive
and immersive has arrived. These \textit{cognitive and immersive systems}
(CAISs) fall squarely into the intersection of AI with HCI/HRI: such systems
interact with and assist the human agents that enter them, in no small part
because such systems are infused with AI able to understand and reason about
these humans and their knowledge, beliefs, goals, communications, plans, etc.
We herein explain our approach to engineering CAISs. We emphasize the capacity
of a CAIS to develop and reason over a `theory of the mind' of its human
partners. This capacity entails that the AI in question has a sophisticated
model of the beliefs, knowledge, goals, desires, emotions, etc.\ of these
humans. To accomplish this engineering, a formal framework of very high
expressivity is needed. In our case, this framework is a \textit{cognitive
event calculus}, a particular kind of quantified multi-operator modal logic,
and a matching high-expressivity automated reasoner and planner. To explain,
advance, and to a degree validate our approach, we show that a calculus of this
type satisfies a set of formal requirements, and can enable a CAIS to
understand a psychologically tricky scenario couched in what we call the
\textit{cognitive polysolid framework} (CPF). We also formally show that a room
that satisfies these requirements can have a useful property we term
\emph{expectation of usefulness}. CPF, a sub-class of \textit{cognitive
microworlds}, includes machinery able to represent and plan over not merely
blocks and actions (such as seen in the primitive `blocks worlds' of old), but
also over agents and their mental attitudes about both other agents and
inanimate objects.
","Toward Cognitive and Immersive Systems: Experiments in a Cognitive
  Microworld"
"  A value learning system has incentives to follow shutdown instructions,
assuming the shutdown instruction provides information (in the technical sense)
about which actions lead to valuable outcomes. However, this assumption is not
robust to model mis-specification (e.g., in the case of programmer errors). We
demonstrate this by presenting some Supervised POMDP scenarios in which errors
in the parameterized reward function remove the incentive to follow shutdown
commands. These difficulties parallel those discussed by Soares et al. (2015)
in their paper on corrigibility. We argue that it is important to consider
systems that follow shutdown commands under some weaker set of assumptions
(e.g., that one small verified module is correctly implemented; as opposed to
an entire prior probability distribution and/or parameterized reward function).
We discuss some difficulties with simple ways to attempt to attain these sorts
of guarantees in a value learning framework.
",Incorrigibility in the CIRL Framework
"  Recently, evolving networks are becoming a suitable form to model many
real-world complex systems, due to their peculiarities to represent the systems
and their constituting entities, the interactions between the entities and the
time-variability of their structure and properties. Designing computational
models able to analyze evolving networks becomes relevant in many applications.
The goal of this research project is to evaluate the possible contribution of
temporal pattern mining techniques in the analysis of evolving networks. In
particular, we aim at exploiting available snapshots for the recognition of
valuable and potentially useful knowledge about the temporal dynamics exhibited
by the network over the time, without making any prior assumption about the
underlying evolutionary schema. Pattern-based approaches of temporal pattern
mining can be exploited to detect and characterize changes exhibited by a
network over the time, starting from observed snapshots.
",Temporal Pattern Mining from Evolving Networks
"  Objective: Electronic medical records (EMRs) contain an amount of medical
knowledge which can be used for clinical decision support (CDS). Our objective
is a general system that can extract and represent these knowledge contained in
EMRs to support three CDS tasks: test recommendation, initial diagnosis, and
treatment plan recommendation, with the given condition of one patient.
Methods: We extracted four kinds of medical entities from records and
constructed an EMR-based medical knowledge network (EMKN), in which nodes are
entities and edges reflect their co-occurrence in a single record. Three
bipartite subgraphs (bi-graphs) were extracted from the EMKN to support each
task. One part of the bi-graph was the given condition (e.g., symptoms), and
the other was the condition to be inferred (e.g., diseases). Each bi-graph was
regarded as a Markov random field to support the inference. Three lazy energy
functions and one parameter-based energy function were proposed, as well as two
knowledge representation learning-based energy functions, which can provide a
distributed representation of medical entities. Three measures were utilized
for performance evaluation. Results: On the initial diagnosis task, 80.11% of
the test records identified at least one correct disease from top 10
candidates. Test and treatment recommendation results were 87.88% and 92.55%,
respectively. These results altogether indicate that the proposed system
outperformed the baseline methods. The distributed representation of medical
entities does reflect similarity relationships in regards to knowledge level.
Conclusion: Combining EMKN and MRF is an effective approach for general medical
knowledge representation and inference. Different tasks, however, require
designing their energy functions individually.
","EMR-based medical knowledge representation and inference via Markov
  random fields and distributed representation learning"
"  State-of-the-art knowledge compilers generate deterministic subsets of DNNF,
which have been recently shown to be exponentially less succinct than DNNF. In
this paper, we propose a new method to compile DNNFs without enforcing
determinism necessarily. Our approach is based on compiling deterministic DNNFs
with the addition of auxiliary variables to the input formula. These variables
are then existentially quantified from the deterministic structure in linear
time, which would lead to a DNNF that is equivalent to the input formula and
not necessarily deterministic. On the theoretical side, we show that the new
method could generate exponentially smaller DNNFs than deterministic ones, even
by adding a single auxiliary variable. Further, we show that various existing
techniques that introduce auxiliary variables to the input formulas can be
employed in our framework. On the practical side, we empirically demonstrate
that our new method can significantly advance DNNF compilation on certain
benchmarks.
",On Compiling DNNFs without Determinism
"  Decentralized receding horizon control (D-RHC) provides a mechanism for
coordination in multi-agent settings without a centralized command center.
However, combining a set of different goals, costs, and constraints to form an
efficient optimization objective for D-RHC can be difficult. To allay this
problem, we use a meta-learning process -- cost adaptation -- which generates
the optimization objective for D-RHC to solve based on a set of human-generated
priors (cost and constraint functions) and an auxiliary heuristic. We use this
adaptive D-RHC method for control of mesh-networked swarm agents. This
formulation allows a wide range of tasks to be encoded and can account for
network delays, heterogeneous capabilities, and increasingly large swarms
through the adaptation mechanism. We leverage the Unity3D game engine to build
a simulator capable of introducing artificial networking failures and delays in
the swarm. Using the simulator we validate our method on an example coordinated
exploration task. We demonstrate that cost adaptation allows for more efficient
and safer task completion under varying environment conditions and increasingly
large swarm sizes. We release our simulator and code to the community for
future work.
",Cost Adaptation for Robust Decentralized Swarm Behaviour
"  This paper maps out the relation between different approaches for handling
preferences in argumentation with strict rules and defeasible assumptions by
offering translations between them. The systems we compare are: non-prioritized
defeats i.e. attacks, preference-based defeats, and preference-based defeats
extended with reverse defeat.
",Assumption-Based Approaches to Reasoning with Priorities
"  In this paper we focus on the unconstrained binary quadratic optimization
model, maximize x^t Qx, x binary, and consider the problem of identifying
optimal solutions that are robust with respect to perturbations in the Q
matrix.. We are motivated to find robust, or stable, solutions because of the
uncertainty inherent in the big data origins of Q and limitations in computer
numerical precision, particularly in a new class of quantum annealing
computers. Experimental design techniques are used to generate a diverse subset
of possible scenarios, from which robust solutions are identified. An
illustrative example with practical application to business decision making is
examined. The approach presented also generates a surface response equation
which is used to estimate upper bounds in constant time for Q instantiations
within the scenario extremes. In addition, a theoretical framework for the
robustness of individual x_i variables is considered by examining the range of
Q values over which the x_i are predetermined.
",Robust Optimization of Unconstrained Binary Quadratic Problems
"  Local search is a basic building block in memetic algorithms. Guided Local
Search (GLS) can improve the efficiency of local search. By changing the guide
function, GLS guides a local search to escape from locally optimal solutions
and find better solutions. The key component of GLS is its penalizing mechanism
which determines which feature is selected to penalize when the search is
trapped in a locally optimal solution. The original GLS penalizing mechanism
only makes use of the cost and the current penalty value of each feature. It is
well known that many combinatorial optimization problems have a big valley
structure, i.e., the better a solution is, the more the chance it is closer to
a globally optimal solution. This paper proposes to use big valley structure
assumption to improve the GLS penalizing mechanism. An improved GLS algorithm
called Elite Biased GLS (EB-GLS) is proposed. EB-GLS records and maintains an
elite solution as an estimate of the globally optimal solutions, and reduces
the chance of penalizing the features in this solution. We have systematically
tested the proposed algorithm on the symmetric traveling salesman problem.
Experimental results show that EB-GLS is significantly better than GLS.
","EB-GLS: An Improved Guided Local Search Based on the Big Valley
  Structure"
"  We make an important connection to existing results in econometrics to
describe an alternative formulation of inverse reinforcement learning (IRL). In
particular, we describe an algorithm using Conditional Choice Probabilities
(CCP), which are maximum likelihood estimates of the policy estimated from
expert demonstrations, to solve the IRL problem. Using the language of
structural econometrics, we re-frame the optimal decision problem and introduce
an alternative representation of value functions due to (Hotz and Miller 1993).
In addition to presenting the theoretical connections that bridge the IRL
literature between Economics and Robotics, the use of CCPs also has the
practical benefit of reducing the computational cost of solving the IRL
problem. Specifically, under the CCP representation, we show how one can avoid
repeated calls to the dynamic programming subroutine typically used in IRL. We
show via extensive experimentation on standard IRL benchmarks that CCP-IRL is
able to outperform MaxEnt-IRL, with as much as a 5x speedup and without
compromising on the quality of the recovered reward function.
",Inverse Reinforcement Learning with Conditional Choice Probabilities
"  Graph is an important data representation which appears in a wide diversity
of real-world scenarios. Effective graph analytics provides users a deeper
understanding of what is behind the data, and thus can benefit a lot of useful
applications such as node classification, node recommendation, link prediction,
etc. However, most graph analytics methods suffer the high computation and
space cost. Graph embedding is an effective yet efficient way to solve the
graph analytics problem. It converts the graph data into a low dimensional
space in which the graph structural information and graph properties are
maximally preserved. In this survey, we conduct a comprehensive review of the
literature in graph embedding. We first introduce the formal definition of
graph embedding as well as the related concepts. After that, we propose two
taxonomies of graph embedding which correspond to what challenges exist in
different graph embedding problem settings and how the existing work address
these challenges in their solutions. Finally, we summarize the applications
that graph embedding enables and suggest four promising future research
directions in terms of computation efficiency, problem settings, techniques and
application scenarios.
","A Comprehensive Survey of Graph Embedding: Problems, Techniques and
  Applications"
"  The ""Loving AI"" project involves developing software enabling humanoid robots
to interact with people in loving and compassionate ways, and to promote
people' self-understanding and self-transcendence. Currently the project
centers on the Hanson Robotics robot ""Sophia"" -- specifically, on supplying
Sophia with personality content and cognitive, linguistic, perceptual and
behavioral content aimed at enabling loving interactions supportive of human
self-transcendence. In September 2017 a small pilot study was conducted,
involving the Sophia robot leading human subjects through dialogues and
exercises focused on meditation, visualization and relaxation. The pilot was an
apparent success, qualitatively demonstrating the viability of the approach and
the ability of appropriate human-robot interaction to increase human well-being
and advance human consciousness.
",Humanoid Robots as Agents of Human Consciousness Expansion
"  Traffic flow prediction is an important research issue for solving the
traffic congestion problem in an Intelligent Transportation System (ITS).
Traffic congestion is one of the most serious problems in a city, which can be
predicted in advance by analyzing traffic flow patterns. Such prediction is
possible by analyzing the real-time transportation data from correlative roads
and vehicles. This article first gives a brief introduction to the
transportation data, and surveys the state-of-the-art prediction methods. Then,
we verify whether or not the prediction performance is able to be improved by
fitting actual data to optimize the parameters of the prediction model which is
used to predict the traffic flow. Such verification is conducted by comparing
the optimized time series prediction model with the normal time series
prediction model. This means that in the era of big data, accurate use of the
data becomes the focus of studying the traffic flow prediction to solve the
congestion problem. Finally, experimental results of a case study are provided
to verify the existence of such performance improvement, while the research
challenges of this data-analytics-based prediction are presented and discussed.
",When Traffic Flow Prediction Meets Wireless Big Data Analytics
"  This paper contains analysis of concept of a class within different
object-oriented knowledge representation models. The main attention is paid to
structure of the class and its efficiency in the context of data storage, using
object-relational mapping. The main achievement of the paper is extension of
concept of homogeneous class of objects by introducing concepts of single-core
and multi-core inhomogeneous classes of objects, which allow simultaneous
defining of a few different types within one class of objects, avoiding
duplication of properties and methods in representation of types, decreasing
sizes of program codes and providing more efficient information storage in the
databases. In addition, the paper contains results of experiment, which show
that data storage in relational database, using proposed extensions of the
class, in some cases is more efficient in contrast to usage of homogeneous
classes of objects.
","Object-Oriented Knowledge Representation and Data Storage Using
  Inhomogeneous Classes"
"  To resolve conflicts among norms, various nonmonotonic formalisms can be used
to perform prioritized normative reasoning. Meanwhile, formal argumentation
provides a way to represent nonmonotonic logics. In this paper, we propose a
representation of prioritized normative reasoning by argumentation. Using
hierarchical abstract normative systems, we define three kinds of prioritized
normative reasoning approaches, called Greedy, Reduction, and Optimization.
Then, after formulating an argumentation theory for a hierarchical abstract
normative system, we show that for a totally ordered hierarchical abstract
normative system, Greedy and Reduction can be represented in argumentation by
applying the weakest link and the last link principles respectively, and
Optimization can be represented by introducing additional defeats capturing the
idea that for each argument that contains a norm not belonging to the maximal
obeyable set then this argument should be rejected.
",Prioritized Norms in Formal Argumentation
"  Adversarial attacks are known to succeed on classifiers, but it has been an
open question whether more complex vision systems are vulnerable. In this
paper, we study adversarial examples for vision and language models, which
incorporate natural language understanding and complex structures such as
attention, localization, and modular architectures. In particular, we
investigate attacks on a dense captioning model and on two visual question
answering (VQA) models. Our evaluation shows that we can generate adversarial
examples with a high success rate (i.e., > 90%) for these models. Our work
sheds new light on understanding adversarial attacks on vision systems which
have a language component and shows that attention, bounding box localization,
and compositional internal structures are vulnerable to adversarial attacks.
These observations will inform future work towards building effective defenses.
","Fooling Vision and Language Models Despite Localization and Attention
  Mechanism"
"  The process of building ontologies is a difficult task that involves
collaboration between ontology developers and domain experts and requires an
ongoing interaction between them. This collaboration is made more difficult,
because they tend to use different tool sets, which can hamper this
interaction. In this paper, we propose to decrease this distance between domain
experts and ontology developers by creating more readable forms of ontologies,
and further to enable editing in normal office environments. Building on a
programmatic ontology development environment, such as Tawny-OWL, we are now
able to generate these readable/editable from the raw ontological source and
its embedded comments. We have this translation to HTML for reading; this
environment provides rich hyperlinking as well as active features such as
hiding the source code in favour of comments. We are now working on translation
to a Word document that also enables editing. Taken together this should
provide a significant new route for collaboration between the ontologist and
domain specialist.
",User and Developer Interaction with Editable and Readable Ontologies
"  In the context of fitness coaching or for rehabilitation purposes, the motor
actions of a human participant must be observed and analyzed for errors in
order to provide effective feedback. This task is normally carried out by human
coaches, and it needs to be solved automatically in technical applications that
are to provide automatic coaching (e.g. training environments in VR). However,
most coaching systems only provide coarse information on movement quality, such
as a scalar value per body part that describes the overall deviation from the
correct movement. Further, they are often limited to static body postures or
rather simple movements of single body parts. While there are many approaches
to distinguish between different types of movements (e.g., between walking and
jumping), the detection of more subtle errors in a motor performance is less
investigated. We propose a novel approach to classify errors in sports or
rehabilitation exercises such that feedback can be delivered in a rapid and
detailed manner: Homogeneous sub-sequences of exercises are first temporally
aligned via Dynamic Time Warping. Next, we extract a feature vector from the
aligned sequences, which serves as a basis for feature selection using Random
Forests. The selected features are used as input for Support Vector Machines,
which finally classify the movement errors. We compare our algorithm to a well
established state-of-the-art approach in time series classification, 1-Nearest
Neighbor combined with Dynamic Time Warping, and show our algorithm's
superiority regarding classification quality as well as computational cost.
","Automatic Error Analysis of Human Motor Performance for Interactive
  Coaching in Virtual Reality"
"  This paper introduces a Fuzzy Logic framework for scene learning, recognition
and similarity detection, where scenes are taught via human examples. The
framework allows a robot to: (i) deal with the intrinsic vagueness associated
with determining spatial relations among objects; (ii) infer similarities and
dissimilarities in a set of scenes, and represent them in a hierarchical
structure represented in a Fuzzy ontology. In this paper, we briefly formalize
our approach and we provide a few use cases by way of illustration.
Nevertheless, we discuss how the framework can be used in real-world scenarios.
","Scene learning, recognition and similarity detection in a fuzzy ontology
  via human examples"
"  Predicting traffic conditions has been recently explored as a way to relieve
traffic congestion. Several pioneering approaches have been proposed based on
traffic observations of the target location as well as its adjacent regions,
but they obtain somewhat limited accuracy due to lack of mining road topology.
To address the effect attenuation problem, we propose to take account of the
traffic of surrounding locations(wider than adjacent range). We propose an
end-to-end framework called DeepTransport, in which Convolutional Neural
Networks (CNN) and Recurrent Neural Networks (RNN) are utilized to obtain
spatial-temporal traffic information within a transport network topology. In
addition, attention mechanism is introduced to align spatial and temporal
information. Moreover, we constructed and released a real-world large traffic
condition dataset with 5-minute resolution. Our experiments on this dataset
demonstrate our method captures the complex relationship in temporal and
spatial domain. It significantly outperforms traditional statistical methods
and a state-of-the-art deep learning method.
","DeepTransport: Learning Spatial-Temporal Dependency for Traffic
  Condition Forecasting"
"  Reward engineering is an important aspect of reinforcement learning. Whether
or not the user's intentions can be correctly encapsulated in the reward
function can significantly impact the learning outcome. Current methods rely on
manually crafted reward functions that often require parameter tuning to obtain
the desired behavior. This operation can be expensive when exploration requires
systems to interact with the physical world. In this paper, we explore the use
of temporal logic (TL) to specify tasks in reinforcement learning. TL formula
can be translated to a real-valued function that measures its level of
satisfaction against a trajectory. We take advantage of this function and
propose temporal logic policy search (TLPS), a model-free learning technique
that finds a policy that satisfies the TL specification. A set of simulated
experiments are conducted to evaluate the proposed approach.
","A Policy Search Method For Temporal Logic Specified Reinforcement
  Learning Tasks"
"  Goal recognition is the problem of inferring the goal of an agent, based on
its observed actions. An inspiring approach - plan recognition by planning
(PRP) - uses off-the-shelf planners to dynamically generate plans for given
goals, eliminating the need for the traditional plan library. However, existing
PRP formulation is inherently inefficient in online recognition, and cannot be
used with motion planners for continuous spaces. In this paper, we utilize a
different PRP formulation which allows for online goal recognition, and for
application in continuous spaces. We present an online recognition algorithm,
where two heuristic decision points may be used to improve run-time
significantly over existing work. We specify heuristics for continuous domains,
prove guarantees on their use, and empirically evaluate the algorithm over
hundreds of experiments in both a 3D navigational environment and a cooperative
robotic team task.
",Heuristic Online Goal Recognition in Continuous Domains
"  The container pre-marshalling problem (CPMP) is concerned with the
re-ordering of containers in container terminals during off-peak times so that
containers can be quickly retrieved when the port is busy. The problem has
received significant attention in the literature and is addressed by a large
number of exact and heuristic methods. Existing methods for the CPMP heavily
rely on problem-specific components (e.g., proven lower bounds) that need to be
developed by domain experts with knowledge of optimization techniques and a
deep understanding of the problem at hand. With the goal to automate the costly
and time-intensive design of heuristics for the CPMP, we propose a new method
called Deep Learning Heuristic Tree Search (DLTS). It uses deep neural networks
to learn solution strategies and lower bounds customized to the CPMP solely
through analyzing existing (near-) optimal solutions to CPMP instances. The
networks are then integrated into a tree search procedure to decide which
branch to choose next and to prune the search tree. DLTS produces the highest
quality heuristic solutions to the CPMP to date with gaps to optimality below
2% on real-world sized instances.
","Deep Learning Assisted Heuristic Tree Search for the Container
  Pre-marshalling Problem"
"  Although artificial intelligence is currently one of the most interesting
areas in scientific research, the potential threats posed by emerging AI
systems remain a source of persistent controversy. To address the issue of AI
threat, this study proposes a standard intelligence model that unifies AI and
human characteristics in terms of four aspects of knowledge, i.e., input,
output, mastery, and creation. Using this model, we observe three challenges,
namely, expanding of the von Neumann architecture; testing and ranking the
intelligence quotient of naturally and artificially intelligent systems,
including humans, Google, Bing, Baidu, and Siri; and finally, the dividing of
artificially intelligent systems into seven grades from robots to Google Brain.
Based on this, we conclude that AlphaGo belongs to the third grade.
",Intelligence Quotient and Intelligence Grade of Artificial Intelligence
"  As AI is increasingly being adopted into application solutions, the challenge
of supporting interaction with humans is becoming more apparent. Partly this is
to support integrated working styles, in which humans and intelligent systems
cooperate in problem-solving, but also it is a necessary step in the process of
building trust as humans migrate greater responsibility to such systems. The
challenge is to find effective ways to communicate the foundations of AI-driven
behaviour, when the algorithms that drive it are far from transparent to
humans. In this paper we consider the opportunities that arise in AI planning,
exploiting the model-based representations that form a familiar and common
basis for communication with users, while acknowledging the gap between
planning algorithms and human problem-solving.
",Explainable Planning
"  Business Process Management (BPM) is a central element of today
organizations. Despite over the years its main focus has been the support of
processes in highly controlled domains, nowadays many domains of interest to
the BPM community are characterized by ever-changing requirements,
unpredictable environments and increasing amounts of data that influence the
execution of process instances. Under such dynamic conditions, BPM systems must
increase their level of automation to provide the reactivity and flexibility
necessary for process management. On the other hand, the Artificial
Intelligence (AI) community has concentrated its efforts on investigating
dynamic domains that involve active control of computational entities and
physical devices (e.g., robots, software agents, etc.). In this context,
Automated Planning, which is one of the oldest areas in AI, is conceived as a
model-based approach to synthesize autonomous behaviours in automated way from
a model. In this paper, we discuss how automated planning techniques can be
leveraged to enable new levels of automation and support for business
processing, and we show some concrete examples of their successful application
to the different stages of the BPM life cycle.
",What Automated Planning can do for Business Process Management
"  Deep reinforcement learning for multi-agent cooperation and competition has
been a hot topic recently. This paper focuses on cooperative multi-agent
problem based on actor-critic methods under local observations settings. Multi
agent deep deterministic policy gradient obtained state of art results for some
multi-agent games, whereas, it cannot scale well with growing amount of agents.
In order to boost scalability, we propose a parameter sharing deterministic
policy gradient method with three variants based on neural networks, including
actor-critic sharing, actor sharing and actor sharing with partially shared
critic. Benchmarks from rllab show that the proposed method has advantages in
learning speed and memory efficiency, well scales with growing amount of
agents, and moreover, it can make full use of reward sharing and
exchangeability if possible.
","Parameter Sharing Deep Deterministic Policy Gradient for Cooperative
  Multi-agent Reinforcement Learning"
"  Partially observable Markov decision processes (POMDPs) are widely used in
probabilistic planning problems in which an agent interacts with an environment
using noisy and imprecise sensors. We study a setting in which the sensors are
only partially defined and the goal is to synthesize ""weakest"" additional
sensors, such that in the resulting POMDP, there is a small-memory policy for
the agent that almost-surely (with probability~1) satisfies a reachability
objective. We show that the problem is NP-complete, and present a symbolic
algorithm by encoding the problem into SAT instances. We illustrate trade-offs
between the amount of memory of the policy and the number of additional sensors
on a simple example. We have implemented our approach and consider three
classical POMDP examples from the literature, and show that in all the examples
the number of sensors can be significantly decreased (as compared to the
existing solutions in the literature) without increasing the complexity of the
policies.
",Sensor Synthesis for POMDPs with Reachability Objectives
"  We characterize three notions of explainable AI that cut across research
fields: opaque systems that offer no insight into its algo- rithmic mechanisms;
interpretable systems where users can mathemat- ically analyze its algorithmic
mechanisms; and comprehensible systems that emit symbols enabling user-driven
explanations of how a conclusion is reached. The paper is motivated by a corpus
analysis of NIPS, ACL, COGSCI, and ICCV/ECCV paper titles showing differences
in how work on explainable AI is positioned in various fields. We close by
introducing a fourth notion: truly explainable systems, where automated
reasoning is central to output crafted explanations without requiring human
post processing as final step of the generative process.
","What Does Explainable AI Really Mean? A New Conceptualization of
  Perspectives"
"  Personal Health Systems (PHS) are mobile solutions tailored to monitoring
patients affected by chronic non communicable diseases. A patient affected by a
chronic disease can generate large amounts of events. Type 1 Diabetic patients
generate several glucose events per day, ranging from at least 6 events per day
(under normal monitoring) to 288 per day when wearing a continuous glucose
monitor (CGM) that samples the blood every 5 minutes for several days. This is
a large number of events to monitor for medical doctors, in particular when
considering that they may have to take decisions concerning adjusting the
treatment, which may impact the life of the patients for a long time. Given the
need to analyse such a large stream of data, doctors need a simple approach
towards physiological time series that allows them to promptly transfer their
knowledge into queries to identify interesting patterns in the data. Achieving
this with current technology is not an easy task, as on one hand it cannot be
expected that medical doctors have the technical knowledge to query databases
and on the other hand these time series include thousands of events, which
requires to re-think the way data is indexed. In order to tackle the knowledge
representation and efficiency problem, this contribution presents the kd-tree
cached event calculus (\ceckd) an event calculus extension for knowledge
engineering of temporal rules capable to handle many thousands events produced
by a diabetic patient. \ceckd\ is built as a support to a graphical interface
to represent monitoring rules for diabetes type 1. In addition, the paper
evaluates the \ceckd\ with respect to the cached event calculus (CEC) to show
how indexing events using kd-trees improves scalability with respect to the
current state of the art.
",Indexing the Event Calculus with Kd-trees to Monitor Diabetes
"  A key challenge in the legal domain is the adaptation and representation of
the legal knowledge expressed through texts, in order for legal practitioners
and researchers to access this information easier and faster to help with
compliance related issues. One way to approach this goal is in the form of a
taxonomy of legal concepts. While this task usually requires a manual
construction of terms and their relations by domain experts, this paper
describes a methodology to automatically generate a taxonomy of legal noun
concepts. We apply and compare two approaches on a corpus consisting of
statutory instruments for UK, Wales, Scotland and Northern Ireland laws.
",Automatic Taxonomy Generation - A Use-Case in the Legal Domain
"  The infamous exploration-exploitation dilemma is one of the oldest and most
important problems in reinforcement learning (RL). Deliberate and effective
exploration is necessary for RL agents to succeed in most environments.
However, until very recently even very sophisticated RL algorithms employed
simple, undirected exploration strategies in large-scale RL tasks.
  We introduce a new optimistic count-based exploration algorithm for RL that
is feasible in high-dimensional MDPs. The success of RL algorithms in these
domains depends crucially on generalization from limited training experience.
Function approximation techniques enable RL agents to generalize in order to
estimate the value of unvisited states, but at present few methods have
achieved generalization about the agent's uncertainty regarding unvisited
states. We present a new method for computing a generalized state visit-count,
which allows the agent to estimate the uncertainty associated with any state.
  In contrast to existing exploration techniques, our
$\phi$-$\textit{pseudocount}$ achieves generalization by exploiting the feature
representation of the state space that is used for value function
approximation. States that have less frequently observed features are deemed
more uncertain. The resulting $\phi$-$\textit{Exploration-Bonus}$ algorithm
rewards the agent for exploring in feature space rather than in the original
state space. This method is simpler and less computationally expensive than
some previous proposals, and achieves near state-of-the-art results on
high-dimensional RL benchmarks. In particular, we report world-class results on
several notoriously difficult Atari 2600 video games, including Montezuma's
Revenge.
",Exploration in Feature Space for Reinforcement Learning
"  Measuring the performance of solar energy and heat transfer systems requires
a lot of time, economic cost and manpower. Meanwhile, directly predicting their
performance is challenging due to the complicated internal structures.
Fortunately, a knowledge-based machine learning method can provide a promising
prediction and optimization strategy for the performance of energy systems. In
this Chapter, the authors will show how they utilize the machine learning
models trained from a large experimental database to perform precise prediction
and optimization on a solar water heater (SWH) system. A new energy system
optimization strategy based on a high-throughput screening (HTS) process is
proposed. This Chapter consists of: i) Comparative studies on varieties of
machine learning models (artificial neural networks (ANNs), support vector
machine (SVM) and extreme learning machine (ELM)) to predict the performances
of SWHs; ii) Development of an ANN-based software to assist the quick
prediction and iii) Introduction of a computational HTS method to design a
high-performance SWH system.
","Performance Prediction and Optimization of Solar Water Heater via a
  Knowledge-Based Machine Learning Method"
"  People can think in auditory, visual and tactile forms of language, so can
machines principally. But is it possible for them to think in radio language?
According to a first principle presented for general intelligence, i.e. the
principle of language's relativity, the answer may give an exceptional solution
for robot astronauts to talk with each other in space exploration.
",Can Machines Think in Radio Language?
"  One significant simplification in most previous work on robot learning is the
closed-world assumption where the robot is assumed to know ahead of time a
complete set of predicates describing the state of the physical world. However,
robots are not likely to have a complete model of the world especially when
learning a new task. To address this problem, this extended abstract gives a
brief introduction to our on-going work that aims to enable the robot to
acquire new state representations through language communication with humans.
","Interactive Learning of State Representation through Natural Language
  Instruction and Explanation"
"  Macro-management is an important problem in StarCraft, which has been studied
for a long time. Various datasets together with assorted methods have been
proposed in the last few years. But these datasets have some defects for
boosting the academic and industrial research: 1) There're neither standard
preprocessing, parsing and feature extraction procedures nor predefined
training, validation and test set in some datasets. 2) Some datasets are only
specified for certain tasks in macro-management. 3) Some datasets are either
too small or don't have enough labeled data for modern machine learning
algorithms such as deep neural networks. So most previous methods are trained
with various features, evaluated on different test sets from the same or
different datasets, making it difficult to be compared directly. To boost the
research of macro-management in StarCraft, we release a new dataset MSC based
on the platform SC2LE. MSC consists of well-designed feature vectors,
pre-defined high-level actions and final result of each match. We also split
MSC into training, validation and test set for the convenience of evaluation
and comparison. Besides the dataset, we propose a baseline model and present
initial baseline results for global state evaluation and build order
prediction, which are two of the key tasks in macro-management. Various
downstream tasks and analyses of the dataset are also described for the sake of
research on macro-management in StarCraft II. Homepage:
https://github.com/wuhuikai/MSC.
",MSC: A Dataset for Macro-Management in StarCraft II
"  Reasoning about causes and effects naturally arises in the engineering of
safety-critical systems. A classical example is Fault Tree Analysis, a
deductive technique used for system safety assessment, whereby an undesired
state is reduced to the set of its immediate causes. The design of fault
management systems also requires reasoning on causality relationships. In
particular, a fail-operational system needs to ensure timely detection and
identification of faults, i.e. recognize the occurrence of run-time faults
through their observable effects on the system. Even more complex scenarios
arise when multiple faults are involved and may interact in subtle ways.
  In this work, we propose a formal approach to fault management for complex
systems. We first introduce the notions of fault tree and minimal cut sets. We
then present a formal framework for the specification and analysis of
diagnosability, and for the design of fault detection and identification (FDI)
components. Finally, we review recent advances in fault propagation analysis,
based on the Timed Failure Propagation Graphs (TFPG) formalism.
","Causality and Temporal Dependencies in the Design of Fault Management
  Systems"
"  This work handles the inverse reinforcement learning (IRL) problem where only
a small number of demonstrations are available from a demonstrator for each
high-dimensional task, insufficient to estimate an accurate reward function.
Observing that each demonstrator has an inherent reward for each state and the
task-specific behaviors mainly depend on a small number of key states, we
propose a meta IRL algorithm that first models the reward function for each
task as a distribution conditioned on a baseline reward function shared by all
tasks and dependent only on the demonstrator, and then finds the most likely
reward function in the distribution that explains the task-specific behaviors.
We test the method in a simulated environment on path planning tasks with
limited demonstrations, and show that the accuracy of the learned reward
function is significantly improved. We also apply the method to analyze the
motion of a patient under rehabilitation.
","Meta Inverse Reinforcement Learning via Maximum Reward Sharing for Human
  Motion Analysis"
"  Reinforcement learning algorithms can train agents that solve problems in
complex, interesting environments. Normally, the complexity of the trained
agent is closely related to the complexity of the environment. This suggests
that a highly capable agent requires a complex environment for training. In
this paper, we point out that a competitive multi-agent environment trained
with self-play can produce behaviors that are far more complex than the
environment itself. We also point out that such environments come with a
natural curriculum, because for any skill level, an environment full of agents
of this level will have the right level of difficulty. This work introduces
several competitive multi-agent environments where agents compete in a 3D world
with simulated physics. The trained agents learn a wide variety of complex and
interesting skills, even though the environment themselves are relatively
simple. The skills include behaviors such as running, blocking, ducking,
tackling, fooling opponents, kicking, and defending using both arms and legs. A
highlight of the learned behaviors can be found here: https://goo.gl/eR7fbX
",Emergent Complexity via Multi-Agent Competition
"  The recent breakthroughs of deep reinforcement learning (DRL) technique in
Alpha Go and playing Atari have set a good example in handling large state and
actions spaces of complicated control problems. The DRL technique is comprised
of (i) an offline deep neural network (DNN) construction phase, which derives
the correlation between each state-action pair of the system and its value
function, and (ii) an online deep Q-learning phase, which adaptively derives
the optimal action and updates value estimates. In this paper, we first present
the general DRL framework, which can be widely utilized in many applications
with different optimization objectives. This is followed by the introduction of
three specific applications: the cloud computing resource allocation problem,
the residential smart grid task scheduling problem, and building HVAC system
optimal control problem. The effectiveness of the DRL technique in these three
cyber-physical applications have been validated. Finally, this paper
investigates the stochastic computing-based hardware implementations of the DRL
framework, which consumes a significant improvement in area efficiency and
power consumption compared with binary-based implementation counterparts.
","Deep Reinforcement Learning: Framework, Applications, and Embedded
  Implementations"
"  Most recently proposed methods for Neural Program Induction work under the
assumption of having a large set of input/output (I/O) examples for learning
any underlying input-output mapping. This paper aims to address the problem of
data and computation efficiency of program induction by leveraging information
from related tasks. Specifically, we propose two approaches for cross-task
knowledge transfer to improve program induction in limited-data scenarios. In
our first proposal, portfolio adaptation, a set of induction models is
pretrained on a set of related tasks, and the best model is adapted towards the
new task using transfer learning. In our second approach, meta program
induction, a $k$-shot learning approach is used to make a model generalize to
new tasks without additional training. To test the efficacy of our methods, we
constructed a new benchmark of programs written in the Karel programming
language. Using an extensive experimental evaluation on the Karel benchmark, we
demonstrate that our proposals dramatically outperform the baseline induction
method that does not use knowledge transfer. We also analyze the relative
performance of the two approaches and study conditions in which they perform
best. In particular, meta induction outperforms all existing approaches under
extreme data sparsity (when a very small number of examples are available),
i.e., fewer than ten. As the number of available I/O examples increase (i.e. a
thousand or more), portfolio adapted program induction becomes the best
approach. For intermediate data sizes, we demonstrate that the combined method
of adapted meta program induction has the strongest performance.
",Neural Program Meta-Induction
"  We present a novel formalization of counterfactual conditionals in a
quantified modal logic. Counterfactual conditionals play a vital role in
ethical and moral reasoning. Prior work has shown that moral reasoning systems
(and more generally, theory-of-mind reasoning systems) should be at least as
expressive as first-order (quantified) modal logic (QML) to be well-behaved.
While existing work on moral reasoning has focused on counterfactual-free QML
moral reasoning, we present a fully specified and implemented formal system
that includes counterfactual conditionals. We validate our model with two
projects. In the first project, we demonstrate that our system can be used to
model a complex moral principle, the doctrine of double effect. In the second
project, we use the system to build a data-set with true and false
counterfactuals as licensed by our theory, which we believe can be useful for
other researchers. This project also shows that our model can be
computationally feasible.
",Counterfactual Conditionals in Quantified Modal Logic
"  The ever increasing prevalence of publicly available structured data on the
World Wide Web enables new applications in a variety of domains. In this paper,
we provide a conceptual approach that leverages such data in order to explain
the input-output behavior of trained artificial neural networks. We apply
existing Semantic Web technologies in order to provide an experimental proof of
concept.
","Explaining Trained Neural Networks with Semantic Web Technologies: First
  Steps"
"  Games with large branching factors pose a significant challenge for game tree
search algorithms. In this paper, we address this problem with a sampling
strategy for Monte Carlo Tree Search (MCTS) algorithms called {\em na\""{i}ve
sampling}, based on a variant of the Multi-armed Bandit problem called {\em
Combinatorial Multi-armed Bandits} (CMAB). We analyze the theoretical
properties of several variants of {\em na\""{i}ve sampling}, and empirically
compare it against the other existing strategies in the literature for CMABs.
We then evaluate these strategies in the context of real-time strategy (RTS)
games, a genre of computer games characterized by their very large branching
factors. Our results show that as the branching factor grows, {\em na\""{i}ve
sampling} outperforms the other sampling strategies.
",Combinatorial Multi-armed Bandits for Real-Time Strategy Games
"  This paper describes and motivates a new decision theory known as functional
decision theory (FDT), as distinct from causal decision theory and evidential
decision theory. Functional decision theorists hold that the normative
principle for action is to treat one's decision as the output of a fixed
mathematical function that answers the question, ""Which output of this very
function would yield the best outcome?"" Adhering to this principle delivers a
number of benefits, including the ability to maximize wealth in an array of
traditional decision-theoretic and game-theoretic problems where CDT and EDT
perform poorly. Using one simple and coherent decision rule, functional
decision theorists (for example) achieve more utility than CDT on Newcomb's
problem, more utility than EDT on the smoking lesion problem, and more utility
than both in Parfit's hitchhiker problem. In this paper, we define FDT, explore
its prescriptions in a number of different decision problems, compare it to CDT
and EDT, and give philosophical justifications for FDT as a normative theory of
decision-making.
",Functional Decision Theory: A New Theory of Instrumental Rationality
"  Networks are fundamental models for data used in practically every
application domain. In most instances, several implicit or explicit choices
about the network definition impact the translation of underlying data to a
network representation, and the subsequent question(s) about the underlying
system being represented. Users of downstream network data may not even be
aware of these choices or their impacts. We propose a task-focused network
model selection methodology which addresses several key challenges. Our
approach constructs network models from underlying data and uses minimum
description length (MDL) criteria for selection. Our methodology measures
efficiency, a general and comparable measure of the network's performance of a
local (i.e. node-level) predictive task of interest. Selection on efficiency
favors parsimonious (e.g. sparse) models to avoid overfitting and can be
applied across arbitrary tasks and representations. We show stability,
sensitivity, and significance testing in our methodology.
",Network Model Selection Using Task-Focused Minimum Description Length
"  A key question in causal inference analyses is how to find subgroups with
elevated treatment effects. This paper takes a machine learning approach and
introduces a generative model, Causal Rule Sets (CRS), for interpretable
subgroup discovery. A CRS model uses a small set of short decision rules to
capture a subgroup where the average treatment effect is elevated. We present a
Bayesian framework for learning a causal rule set. The Bayesian model consists
of a prior that favors simple models for better interpretability as well as
avoiding overfitting, and a Bayesian logistic regression that captures the
likelihood of data, characterizing the relation between outcomes, attributes,
and subgroup membership. The Bayesian model has tunable parameters that can
characterize subgroups with various sizes, providing users with more flexible
choices of models from the \emph{treatment efficient frontier}. We find maximum
a posteriori models using iterative discrete Monte Carlo steps in the joint
solution space of rules sets and parameters. To improve search efficiency, we
provide theoretically grounded heuristics and bounding strategies to prune and
confine the search space. Experiments show that the search algorithm can
efficiently recover true underlying subgroups. We apply CRS on public and
real-world datasets from domains where interpretability is indispensable. We
compare CRS with state-of-the-art rule-based subgroup discovery models. Results
show that CRS achieved consistently competitive performance on datasets from
various domains, represented by high treatment efficient frontiers.
","Causal Rule Sets for Identifying Subgroups with Enhanced Treatment
  Effect"
"  How can a delivery robot navigate reliably to a destination in a new office
building, with minimal prior information? To tackle this challenge, this paper
introduces a two-level hierarchical approach, which integrates model-free deep
learning and model-based path planning. At the low level, a neural-network
motion controller, called the intention-net, is trained end-to-end to provide
robust local navigation. The intention-net maps images from a single monocular
camera and ""intentions"" directly to robot controls. At the high level, a path
planner uses a crude map, e.g., a 2-D floor plan, to compute a path from the
robot's current location to the goal. The planned path provides intentions to
the intention-net. Preliminary experiments suggest that the learned motion
controller is robust against perceptual uncertainty and by integrating with a
path planner, it generalizes effectively to new environments and goals.
","Intention-Net: Integrating Planning and Deep Learning for Goal-Directed
  Autonomous Navigation"
"  Because of the increasing availability of spatiotemporal data, a variety of
data-analytic applications have become possible. Characterizing driving
context, where context may be thought of as a combination of location and time,
is a new challenging application. An example of such a characterization is
finding the correlation between driving behavior and traffic conditions. This
contextual information enables analysts to validate observation-based
hypotheses about the driving of an individual. In this paper, we present
DriveContext, a novel framework to find the characteristics of a context, by
extracting significant driving patterns (e.g., a slow-down), and then
identifying the set of potential causes behind patterns (e.g., traffic
congestion). Our experimental results confirm the feasibility of the framework
in identifying meaningful driving patterns, with improvements in comparison
with the state-of-the-art. We also demonstrate how the framework derives
interesting characteristics for different contexts, through real-world
examples.
",Characterizing Driving Context from Driver Behavior
"  We use decision trees to build a helpdesk agent reference network to
facilitate the on-the-job advising of junior or less experienced staff on how
to better address telecommunication customer fault reports. Such reports
generate field measurements and remote measurements which, when coupled with
location data and client attributes, and fused with organization-level
statistics, can produce models of how support should be provided. Beyond
decision support, these models can help identify staff who can act as advisors,
based on the quality, consistency and predictability of dealing with complex
troubleshooting reports. Advisor staff models are then used to guide less
experienced staff in their decision making; thus, we advocate the deployment of
a simple mechanism which exploits the availability of staff with a sound track
record at the helpdesk to act as dormant tutors.
",Decision Trees for Helpdesk Advisor Graphs
"  This paper focuses on preserving the privacy of sensitive pat-terns when
inducing decision trees. We adopt a record aug-mentation approach for hiding
sensitive classification rules in binary datasets. Such a hiding methodology is
preferred over other heuristic solutions like output perturbation or
crypto-graphic techniques - which restrict the usability of the data - since
the raw data itself is readily available for public use. In this paper, we
propose a look ahead approach using linear Diophantine equations in order to
add the appropriate number of instances while minimally disturbing the initial
entropy of the nodes.
","On Using Linear Diophantine Equations to Tune the extent of Look Ahead
  while Hiding Decision Tree Rules"
"  Go gaming is a struggle for territory control between rival, black and white,
stones on a board. We model the Go dynamics in a game by means of the Ising
model whose interaction coefficients reflect essential rules and tactics
employed in Go to build long-term strategies. At any step of the game, the
energy functional of the model provides the control degree (strength) of a
player over the board. A close fit between predictions of the model with actual
games is obtained.
",Go game formal revealing by Ising model
"  Apprenticeship learning (AL) is a kind of Learning from Demonstration
techniques where the reward function of a Markov Decision Process (MDP) is
unknown to the learning agent and the agent has to derive a good policy by
observing an expert's demonstrations. In this paper, we study the problem of
how to make AL algorithms inherently safe while still meeting its learning
objective. We consider a setting where the unknown reward function is assumed
to be a linear combination of a set of state features, and the safety property
is specified in Probabilistic Computation Tree Logic (PCTL). By embedding
probabilistic model checking inside AL, we propose a novel
counterexample-guided approach that can ensure safety while retaining
performance of the learnt policy. We demonstrate the effectiveness of our
approach on several challenging AL scenarios where safety is essential.
",Safety-Aware Apprenticeship Learning
"  Little by little, newspapers are revealing the bright future that Artificial
Intelligence (AI) is building. Intelligent machines will help everywhere.
However, this bright future has a dark side: a dramatic job market contraction
before its unpredictable transformation. Hence, in a near future, large numbers
of job seekers will need financial support while catching up with these novel
unpredictable jobs. This possible job market crisis has an antidote inside. In
fact, the rise of AI is sustained by the biggest knowledge theft of the recent
years. Learning AI machines are extracting knowledge from unaware skilled or
unskilled workers by analyzing their interactions. By passionately doing their
jobs, these workers are digging their own graves.
  In this paper, we propose Human-in-the-loop Artificial Intelligence (HIT-AI)
as a fairer paradigm for Artificial Intelligence systems. HIT-AI will reward
aware and unaware knowledge producers with a different scheme: decisions of AI
systems generating revenues will repay the legitimate owners of the knowledge
used for taking those decisions. As modern Robin Hoods, HIT-AI researchers
should fight for a fairer Artificial Intelligence that gives back what it
steals.
",Human-in-the-loop Artificial Intelligence
"  A core business in the fashion industry is the understanding and prediction
of customer needs and trends. Search engines and social networks are at the
same time a fundamental bridge and a costly middleman between the customer's
purchase intention and the retailer. To better exploit Europe's distinctive
characteristics e.g., multiple languages, fashion and cultural differences, it
is pivotal to reduce retailers' dependence to search engines. This goal can be
achieved by harnessing various data channels (manufacturers and distribution
networks, online shops, large retailers, social media, market observers, call
centers, press/magazines etc.) that retailers can leverage in order to gain
more insight about potential buyers, and on the industry trends as a whole.
This can enable the creation of novel on-line shopping experiences, the
detection of influencers, and the prediction of upcoming fashion trends.
  In this paper, we provide an overview of the main research challenges and an
analysis of the most promising technological solutions that we are
investigating in the FashionBrain project.
","FashionBrain Project: A Vision for Understanding Europe's Fashion Data
  Universe"
"  The semantic web has received many contributions of researchers as ontologies
which, in this context, i.e. within RDF linked data, are formalized
conceptualizations that might use different protocols, such as RDFS, OWL DL and
OWL FULL. In this article, we describe new expressive techniques which were
found necessary after elaborating dozens of OWL ontologies for the scientific
academy, the State and the civil society. They consist in: 1) stating possible
uses a property might have without incurring into axioms or restrictions; 2)
assigning a level of priority for an element (class, property, triple); 3)
correct depiction in diagrams of relations between classes, between individuals
which are imperative, and between individuals which are optional; 4) a
convenient association between OWL classes and SKOS concepts. We propose
specific rules to accomplish these enhancements and exemplify both its use and
the difficulties that arise because these techniques are currently not
established as standards to the ontology designer.
",Enhancements of linked data expressiveness for ontologies
"  Vagueness and uncertainty management is counted among one of the challenges
that remain unresolved in systems that generate texts from non-linguistic data,
known as data-to-text systems. In the last decade, work in fuzzy linguistic
summarization and description of data has raised the interest of using fuzzy
sets to model and manage the imprecision of human language in data-to-text
systems. However, despite some research in this direction, there has not been
an actual clear discussion and justification on how fuzzy sets can contribute
to data-to-text for modeling vagueness and uncertainty in words and
expressions. This paper intends to bridge this gap by answering the following
questions: What does vagueness mean in fuzzy sets theory? What does vagueness
mean in data-to-text contexts? In what ways can fuzzy sets theory contribute to
improve data-to-text systems? What are the challenges that researchers from
both disciplines need to address for a successful integration of fuzzy sets
into data-to-text systems? In what cases should the use of fuzzy sets be
avoided in D2T? For this, we review and discuss the state of the art of
vagueness modeling in natural language generation and data-to-text, describe
potential and actual usages of fuzzy sets in data-to-text contexts, and provide
some additional insights about the engineering of data-to-text systems that
make use of fuzzy set-based techniques.
","On modeling vagueness and uncertainty in data-to-text systems through
  fuzzy sets"
"  The literature on Multiple Criteria Decision Analysis (MCDA) proposes several
methods in order to sort alternatives evaluated on several attributes into
ordered classes. Non Compensatory Sorting models (NCS) assign alternatives to
classes based on the way they compare to multicriteria profiles separating the
consecutive classes. Previous works have proposed approaches to learn the
parameters of a NCS model based on a learning set. Exact approaches based on
mixed integer linear programming ensures that the learning set is best
restored, but can only handle datasets of limited size. Heuristic approaches
can handle large learning sets, but do not provide any guarantee about the
inferred model. In this paper, we propose an alternative formulation to learn a
NCS model. This formulation, based on a SAT problem, guarantees to find a model
fully consistent with the learning set (whenever it exists), and is
computationally much more efficient than existing exact MIP approaches.
","An efficient SAT formulation for learning multiple criteria
  non-compensatory sorting rules from examples"
"  Providing elderly and people with special needs, including those suffering
from physical disabilities and chronic diseases, with the possibility of
retaining their independence at best is one of the most important challenges
our society is expected to face. Assistance models based on the home care
paradigm are being adopted rapidly in almost all industrialized and emerging
countries. Such paradigms hypothesize that it is necessary to ensure that the
so-called Activities of Daily Living are correctly and regularly performed by
the assisted person to increase the perception of an improved quality of life.
This chapter describes the computational inference engine at the core of
Arianna, a system able to understand whether an assisted person performs a
given set of ADL and to motivate him/her in performing them through a
speech-mediated motivational dialogue, using a set of nearables to be installed
in an apartment, plus a wearable to be worn or fit in garments.
","Towards a new paradigm for assistive technology at home: research
  challenges, design issues and performance assessment"
"  Representing domain knowledge is crucial for any task. There has been a wide
range of techniques developed to represent this knowledge, from older logic
based approaches to the more recent deep learning based techniques (i.e.
embeddings). In this paper, we discuss some of these methods, focusing on the
representational expressiveness tradeoffs that are often made. In particular,
we focus on the the ability of various techniques to encode `partial knowledge'
- a key component of successful knowledge systems. We introduce and describe
the concepts of `ensembles of embeddings' and `aggregate embeddings' and
demonstrate how they allow for partial knowledge.
",Partial Knowledge In Embeddings
"  Border crossing delays cause problems like huge economics loss and heavy
environmental pollutions. To understand more about the nature of border
crossing delay, this study applies a dictionary-based compression algorithm to
process the historical Niagara Frontier border wait times data. It can identify
the abnormal spatial-temporal patterns for both passenger vehicles and trucks
at three bridges connecting US and Canada. Furthermore, it provides a
quantitate anomaly score to rank the wait times patterns across the three
bridges for each vehicle type and each direction. By analyzing the top three
most abnormal patterns, we find that there are at least two factors
contributing the anomaly of the patterns. The weekends and holidays may cause
unusual heave congestions at the three bridges at the same time, and the
freight transportation demand may be uneven from Canada to the USA at Peace
Bridge and Lewiston-Queenston Bridge, which may lead to a high anomaly score.
By calculating the frequency of the top 5% abnormal patterns by hour of the
day, the results show that for cars from the USA to Canada, the frequency of
abnormal waiting time patterns is the highest during noon while for trucks in
the same direction, it is the highest during the afternoon peak hours. For
Canada to US direction, the frequency of abnormal border wait time patterns for
both cars and trucks reaches to the peak during the afternoon. The analysis of
abnormal spatial-temporal wait times patterns is promising to improve the
border crossing management
","Abnormal Spatial-Temporal Pattern Analysis for Niagara Frontier Border
  Wait Times"
"  Skills learned through (deep) reinforcement learning often generalizes poorly
across domains and re-training is necessary when presented with a new task. We
present a framework that combines techniques in \textit{formal methods} with
\textit{reinforcement learning} (RL). The methods we provide allows for
convenient specification of tasks with logical expressions, learns hierarchical
policies (meta-controller and low-level controllers) with well-defined
intrinsic rewards, and construct new skills from existing ones with little to
no additional exploration. We evaluate the proposed methods in a simple grid
world simulation as well as a more complicated kitchen environment in AI2Thor
","Automata-Guided Hierarchical Reinforcement Learning for Skill
  Composition"
"  While deep reinforcement learning (deep RL) agents are effective at
maximizing rewards, it is often unclear what strategies they use to do so. In
this paper, we take a step toward explaining deep RL agents through a case
study using Atari 2600 environments. In particular, we focus on using saliency
maps to understand how an agent learns and executes a policy. We introduce a
method for generating useful saliency maps and use it to show 1) what strong
agents attend to, 2) whether agents are making decisions for the right or wrong
reasons, and 3) how agents evolve during learning. We also test our method on
non-expert human subjects and find that it improves their ability to reason
about these agents. Overall, our results show that saliency information can
provide significant insight into an RL agent's decisions and learning behavior.
",Visualizing and Understanding Atari Agents
"  Background: In silico drug-target interaction (DTI) prediction plays an
integral role in drug repositioning: the discovery of new uses for existing
drugs. One popular method of drug repositioning is network-based DTI
prediction, which uses complex network theory to predict DTIs from a
drug-target network. Currently, most network-based DTI prediction is based on
machine learning methods such as Restricted Boltzmann Machines (RBM) or Support
Vector Machines (SVM). These methods require additional information about the
characteristics of drugs, targets and DTIs, such as chemical structure, genome
sequence, binding types, causes of interactions, etc., and do not perform
satisfactorily when such information is unavailable. We propose a new,
alternative method for DTI prediction that makes use of only network topology
information attempting to solve this problem.
  Results: We compare our method for DTI prediction against the well-known RBM
approach. We show that when applied to the MATADOR database, our approach based
on node neighborhoods yield higher precision for high-ranking predictions than
RBM when no information regarding DTI types is available.
  Conclusion: This demonstrates that approaches purely based on network
topology provide a more suitable approach to DTI prediction in the many
real-life situations where little or no prior knowledge is available about the
characteristics of drugs, targets, or their interactions.
","Erratum: Link prediction in drug-target interactions network using
  similarity indices"
"  It is often argued that an agent making decisions on behalf of two or more
principals who have different utility functions should adopt a {\em
Pareto-optimal} policy, i.e., a policy that cannot be improved upon for one
agent without making sacrifices for another. A famous theorem of Harsanyi shows
that, when the principals have a common prior on the outcome distributions of
all policies, a Pareto-optimal policy for the agent is one that maximizes a
fixed, weighted linear combination of the principals' utilities.
  In this paper, we show that Harsanyi's theorem does not hold for principals
with different priors, and derive a more precise generalization which does
hold, which constitutes our main result. In this more general case, the
relative weight given to each principal's utility should evolve over time
according to how well the agent's observations conform with that principal's
prior. The result has implications for the design of contracts, treaties, joint
ventures, and robots.
","Servant of Many Masters: Shifting priorities in Pareto-optimal
  sequential decision-making"
"  There has been much discussion of the right to explanation in the EU General
Data Protection Regulation, and its existence, merits, and disadvantages.
Implementing a right to explanation that opens the black box of algorithmic
decision-making faces major legal and technical barriers. Explaining the
functionality of complex algorithmic decision-making systems and their
rationale in specific cases is a technically challenging problem. Some
explanations may offer little meaningful information to data subjects, raising
questions around their value. Explanations of automated decisions need not
hinge on the general public understanding how algorithmic systems function.
Even though such interpretability is of great importance and should be pursued,
explanations can, in principle, be offered without opening the black box.
Looking at explanations as a means to help a data subject act rather than
merely understand, one could gauge the scope and content of explanations
according to the specific goal or action they are intended to support. From the
perspective of individuals affected by automated decision-making, we propose
three aims for explanations: (1) to inform and help the individual understand
why a particular decision was reached, (2) to provide grounds to contest the
decision if the outcome is undesired, and (3) to understand what would need to
change in order to receive a desired result in the future, based on the current
decision-making model. We assess how each of these goals finds support in the
GDPR. We suggest data controllers should offer a particular type of
explanation, unconditional counterfactual explanations, to support these three
aims. These counterfactual explanations describe the smallest change to the
world that can be made to obtain a desirable outcome, or to arrive at the
closest possible world, without needing to explain the internal logic of the
system.
","Counterfactual Explanations without Opening the Black Box: Automated
  Decisions and the GDPR"
"  Teachers intentionally pick the most informative examples to show their
students. However, if the teacher and student are neural networks, the examples
that the teacher network learns to give, although effective at teaching the
student, are typically uninterpretable. We show that training the student and
teacher iteratively, rather than jointly, can produce interpretable teaching
strategies. We evaluate interpretability by (1) measuring the similarity of the
teacher's emergent strategies to intuitive strategies in each domain and (2)
conducting human experiments to evaluate how effective the teacher's strategies
are at teaching humans. We show that the teacher network learns to select or
generate interpretable, pedagogical examples to teach rule-based,
probabilistic, boolean, and hierarchical concepts.
",Interpretable and Pedagogical Examples
"  Dom/wdeg is one of the best performing heuristics for dynamic variable
ordering in backtrack search [Boussemart et al., 2004]. As originally defined,
this heuristic increments the weight of the constraint that causes a domain
wipeout (i.e., a dead-end) when enforcing arc consistency during search. ""The
process of weighting constraints with dom/wdeg is not defined when more than
one constraint lead to a domain wipeout [Vion et al., 2011]."" In this paper, we
investigate how weights should be updated in the context of two high-level
consistencies, namely, singleton (POAC) and relational consistencies (RNIC). We
propose, analyze, and empirically evaluate several strategies for updating the
weights. We statistically compare the proposed strategies and conclude with our
recommendations.
","Weight-Based Variable Ordering in the Context of High-Level
  Consistencies"
"  Rather than learning new control policies for each new task, it is possible,
when tasks share some structure, to compose a ""meta-policy"" from previously
learned policies. This paper reports results from experiments using Deep
Reinforcement Learning on a continuous-state, discrete-action autonomous
driving simulator. We explore how Deep Neural Networks can represent
meta-policies that switch among a set of previously learned policies,
specifically in settings where the dynamics of a new scenario are composed of a
mixture of previously learned dynamics and where the state observation is
possibly corrupted by sensing noise. We also report the results of experiments
varying dynamics mixes, distractor policies, magnitudes/distributions of
sensing noise, and obstacles. In a fully observed experiment, the meta-policy
learning algorithm achieves 2.6x the reward achieved by the next best policy
composition technique with 80% less exploration. In a partially observed
experiment, the meta-policy learning algorithm converges after 50 iterations
while a direct application of RL fails to converge even after 200 iterations.
","Composing Meta-Policies for Autonomous Driving Using Hierarchical Deep
  Reinforcement Learning"
"  The next leap on the internet has already started as Semantic Web. At its
core, Semantic Web transforms the document oriented web to a data oriented web
enriched with semantics embedded as metadata. This change in perspective
towards the web offers numerous benefits for vast amount of data intensive
industries that are bound to the web and its related applications. The
industries are diverse as they range from Oil & Gas exploration to the
investigative journalism, and everything in between. This paper discusses eight
different industries which currently reap the benefits of Semantic Web. The
paper also offers a future outlook into Semantic Web applications and discusses
the areas in which Semantic Web would play a key role in the future.
",Semantic Web Today: From Oil Rigs to Panama Papers
"  Roguelike games generally feature exploration problems as a critical, yet
often repetitive element of gameplay. Automated approaches, however, face
challenges in terms of optimality, as well as due to incomplete information,
such as from the presence of secret doors. This paper presents an algorithmic
approach to exploration of roguelike dungeon environments. Our design aims to
minimize exploration time, balancing coverage and discovery of secret areas
with resource cost. Our algorithm is based on the concept of occupancy maps
popular in robotics, adapted to encourage efficient discovery of secret access
points. Through extensive experimentation on NetHack maps we show that this
technique is significantly more efficient than simpler greedy approaches and an
existing automated player. We further investigate optimized parameterization
for the algorithm through a comprehensive data analysis. These results point
towards better automation for players as well as heuristics applicable to fully
automated gameplay.
",Exploration in NetHack With Secret Discovery
"  Computational models of decisionmaking must contend with the variance of
context and any number of possible decisions that a defined strategic actor can
make at a given time. Relying on cognitive science theory, the authors have
created an algorithm that captures the orientation of the actor towards an
object and arrays the possible decisions available to that actor based on their
given intersubjective orientation. This algorithm, like a traditional K-means
clustering algorithm, relies on a core-periphery structure that gives the
likelihood of moves as those closest to the cluster's centroid. The result is
an algorithm that enables unsupervised classification of an array of decision
points belonging to an actor's present state and deeply rooted in cognitive
science theory.
",CogSciK: Clustering for Cognitive Science Motivated Decision Making
"  Program synthesis is a class of regression problems where one seeks a
solution, in the form of a source-code program, mapping the inputs to their
corresponding outputs exactly. Due to its precise and combinatorial nature,
program synthesis is commonly formulated as a constraint satisfaction problem,
where input-output examples are encoded as constraints and solved with a
constraint solver. A key challenge of this formulation is scalability: while
constraint solvers work well with a few well-chosen examples, a large set of
examples can incur significant overhead in both time and memory. We describe a
method to discover a subset of examples that is both small and representative:
the subset is constructed iteratively, using a neural network to predict the
probability of unchosen examples conditioned on the chosen examples in the
subset, and greedily adding the least probable example. We empirically evaluate
the representativeness of the subsets constructed by our method, and
demonstrate such subsets can significantly improve synthesis time and
stability.
",Selecting Representative Examples for Program Synthesis
"  Ontology engineering is a hard and error-prone task, in which small changes
may lead to errors, or even produce an inconsistent ontology. As ontologies
grow in size, the need for automated methods for repairing inconsistencies
while preserving as much of the original knowledge as possible increases. Most
previous approaches to this task are based on removing a few axioms from the
ontology to regain consistency. We propose a new method based on weakening
these axioms to make them less restrictive, employing the use of refinement
operators. We introduce the theoretical framework for weakening DL ontologies,
propose algorithms to repair ontologies based on the framework, and provide an
analysis of the computational complexity. Through an empirical analysis made
over real-life ontologies, we show that our approach preserves significantly
more of the original knowledge of the ontology than removing axioms.
",Repairing Ontologies via Axiom Weakening
"  This paper explores the entertainment experience and learning experience in
Scrabble. It proposes a new measure from the educational point of view, which
we call learning coefficient, based on the balance between the learner's skill
and the challenge in Scrabble. Scrabble variants, generated using different
size of board and dictionary, are analyzed with two measures of game refinement
and learning coefficient. The results show that 13x13 Scrabble yields the best
entertainment experience and 15x15 (standard) Scrabble with 4% of original
dictionary size yields the most effective environment for language learners.
Moreover, 15x15 Scrabble with 10% of original dictionary size has a good
balance between entertainment and learning experience.
","First Results from Using Game Refinement Measure and Learning
  Coefficient in Scrabble"
"  A temporally abstract action, or an option, is specified by a policy and a
termination condition: the policy guides option behavior, and the termination
condition roughly determines its length. Generally, learning with longer
options (like learning with multi-step returns) is known to be more efficient.
However, if the option set for the task is not ideal, and cannot express the
primitive optimal policy exactly, shorter options offer more flexibility and
can yield a better solution. Thus, the termination condition puts learning
efficiency at odds with solution quality. We propose to resolve this dilemma by
decoupling the behavior and target terminations, just like it is done with
policies in off-policy learning. To this end, we give a new algorithm,
Q(\beta), that learns the solution with respect to any termination condition,
regardless of how the options actually terminate. We derive Q(\beta) by casting
learning with options into a common framework with well-studied multi-step
off-policy learning. We validate our algorithm empirically, and show that it
holds up to its motivating claims.
",Learning with Options that Terminate Off-Policy
"  The study and understanding of human behaviour is relevant to computer
science, artificial intelligence, neural computation, cognitive science,
philosophy, psychology, and several other areas. Presupposing cognition as
basis of behaviour, among the most prominent tools in the modelling of
behaviour are computational-logic systems, connectionist models of cognition,
and models of uncertainty. Recent studies in cognitive science, artificial
intelligence, and psychology have produced a number of cognitive models of
reasoning, learning, and language that are underpinned by computation. In
addition, efforts in computer science research have led to the development of
cognitive computational systems integrating machine learning and automated
reasoning. Such systems have shown promise in a range of applications,
including computational biology, fault diagnosis, training and assessment in
simulators, and software verification. This joint survey reviews the personal
ideas and views of several researchers on neural-symbolic learning and
reasoning. The article is organised in three parts: Firstly, we frame the scope
and goals of neural-symbolic computation and have a look at the theoretical
foundations. We then proceed to describe the realisations of neural-symbolic
computation, systems, and applications. Finally we present the challenges
facing the area and avenues for further research.
",Neural-Symbolic Learning and Reasoning: A Survey and Interpretation
"  Here we examine the paperclip apocalypse concern for artificial general
intelligence (or AGI) whereby a superintelligent AI with a simple goal (ie.,
producing paperclips) accumulates power so that all resources are devoted
towards that simple goal and are unavailable for any other use. We provide
conditions under which a paper apocalypse can arise but also show that, under
certain architectures for recursive self-improvement of AIs, that a paperclip
AI may refrain from allowing power capabilities to be developed. The reason is
that such developments pose the same control problem for the AI as they do for
humans (over AIs) and hence, threaten to deprive it of resources for its
primary goal.
",Self-Regulating Artificial General Intelligence
"  Juba recently proposed a formulation of learning abductive reasoning from
examples, in which both the relative plausibility of various explanations, as
well as which explanations are valid, are learned directly from data. The main
shortcoming of this formulation of the task is that it assumes access to
full-information (i.e., fully specified) examples; relatedly, it offers no role
for declarative background knowledge, as such knowledge is rendered redundant
in the abduction task by complete information. In this work, we extend the
formulation to utilize such partially specified examples, along with
declarative background knowledge about the missing data. We show that it is
possible to use implicitly learned rules together with the explicitly given
declarative knowledge to support hypotheses in the course of abduction. We
observe that when a small explanation exists, it is possible to obtain a
much-improved guarantee in the challenging exception-tolerant setting. Such
small, human-understandable explanations are of particular interest for
potential applications of the task.
",Learning Abduction under Partial Observability
"  In this work we introduce a new framework for performing temporal predictions
in the presence of uncertainty. It is based on a simple idea of disentangling
components of the future state which are predictable from those which are
inherently unpredictable, and encoding the unpredictable components into a
low-dimensional latent variable which is fed into a forward model. Our method
uses a supervised training objective which is fast and easy to train. We
evaluate it in the context of video prediction on multiple datasets and show
that it is able to consistently generate diverse predictions without the need
for alternating minimization over a latent space or adversarial training.
",Prediction Under Uncertainty with Error-Encoding Networks
"  The efficient solution of state space search problems is often attempted by
guiding search algorithms with heuristics (estimates of the distance from any
state to the goal). A popular way for creating heuristic functions is by using
an abstract version of the state space. However, the quality of
abstraction-based heuristic functions, and thus the speed of search, can suffer
from spurious transitions, i.e., state transitions in the abstract state space
for which no corresponding transitions in the reachable component of the
original state space exist. Our first contribution is a quantitative study
demonstrating that the harmful effects of spurious transitions on heuristic
functions can be substantial, in terms of both the increase in the number of
abstract states and the decrease in the heuristic values, which may slow down
search. Our second contribution is an empirical study on the benefits of
removing a certain kind of spurious transition, namely those that involve
states with a pair of mutually exclusive (mutex) variablevalue assignments. In
the context of state space planning, a mutex pair is a pair of variable-value
assignments that does not occur in any reachable state. Detecting mutex pairs
is a problem that has been addressed frequently in the planning literature. Our
study shows that there are cases in which mutex detection helps to eliminate
harmful spurious transitions to a large extent and thus to speed up search
substantially.
","An Empirical Study of the Effects of Spurious Transitions on
  Abstraction-based Heuristics"
"  Tree projections provide a unifying framework to deal with most structural
decomposition methods of constraint satisfaction problems (CSPs). Within this
framework, a CSP instance is decomposed into a number of sub-problems, called
views, whose solutions are either already available or can be computed
efficiently. The goal is to arrange portions of these views in a tree-like
structure, called tree projection, which determines an efficiently solvable CSP
instance equivalent to the original one. Deciding whether a tree projection
exists is NP-hard. Solution methods have therefore been proposed in the
literature that do not require a tree projection to be given, and that either
correctly decide whether the given CSP instance is satisfiable, or return that
a tree projection actually does not exist. These approaches had not been
generalized so far on CSP extensions for optimization problems, where the goal
is to compute a solution of maximum value/minimum cost. The paper fills the
gap, by exhibiting a fixed-parameter polynomial-time algorithm that either
disproves the existence of tree projections or computes an optimal solution,
with the parameter being the size of the expression of the objective function
to be optimized over all possible solutions (and not the size of the whole
constraint formula, used in related works). Tractability results are also
established for the problem of returning the best K solutions. Finally,
parallel algorithms for such optimization problems are proposed and analyzed.
Given that the classes of acyclic hypergraphs, hypergraphs of bounded
treewidth, and hypergraphs of bounded generalized hypertree width are all
covered as special cases of the tree projection framework, the results in this
paper directly apply to these classes. These classes are extensively considered
in the CSP setting, as well as in conjunctive database query evaluation and
optimization.
","Tree Projections and Constraint Optimization Problems: Fixed-Parameter
  Tractability and Parallel Algorithms"
"  Inspired by the magic sets for Datalog, we present a novel goal-driven
approach for answering queries over terminating existential rules with equality
(aka TGDs and EGDs). Our technique improves the performance of query answering
by pruning the consequences that are not relevant for the query. This is
challenging in our setting because equalities can potentially affect all
predicates in a dataset. We address this problem by combining the existing
singularization technique with two new ingredients: an algorithm for
identifying the rules relevant to a query and a new magic sets algorithm. We
show empirically that our technique can significantly improve the performance
of query answering, and that it can mean the difference between answering a
query in a few seconds or not being able to process the query at all.
",Goal-Driven Query Answering for Existential Rules with Equality
"  Knowledge graphs are useful for many artificial intelligence (AI) tasks.
However, knowledge graphs often have missing facts. To populate the graphs,
knowledge graph embedding models have been developed. Knowledge graph embedding
models map entities and relations in a knowledge graph to a vector space and
predict unknown triples by scoring candidate triples. TransE is the first
translation-based method and it is well known because of its simplicity and
efficiency for knowledge graph completion. It employs the principle that the
differences between entity embeddings represent their relations. The principle
seems very simple, but it can effectively capture the rules of a knowledge
graph. However, TransE has a problem with its regularization. TransE forces
entity embeddings to be on a sphere in the embedding vector space. This
regularization warps the embeddings and makes it difficult for them to fulfill
the abovementioned principle. The regularization also affects adversely the
accuracies of the link predictions. On the other hand, regularization is
important because entity embeddings diverge by negative sampling without it.
This paper proposes a novel embedding model, TorusE, to solve the
regularization problem. The principle of TransE can be defined on any Lie
group. A torus, which is one of the compact Lie groups, can be chosen for the
embedding space to avoid regularization. To the best of our knowledge, TorusE
is the first model that embeds objects on other than a real or complex vector
space, and this paper is the first to formally discuss the problem of
regularization of TransE. Our approach outperforms other state-of-the-art
approaches such as TransE, DistMult and ComplEx on a standard link prediction
task. We show that TorusE is scalable to large-size knowledge graphs and is
faster than the original TransE.
",TorusE: Knowledge Graph Embedding on a Lie Group
"  Model-Based Diagnosis deals with the identification of the real cause of a
system's malfunction based on a formal system model and observations of the
system behavior. When a malfunction is detected, there is usually not enough
information available to pinpoint the real cause and one needs to discriminate
between multiple fault hypotheses (called diagnoses). To this end, Sequential
Diagnosis approaches ask an oracle for additional system measurements.
  This work presents strategies for (optimal) measurement selection in
model-based sequential diagnosis. In particular, assuming a set of leading
diagnoses being given, we show how queries (sets of measurements) can be
computed and optimized along two dimensions: expected number of queries and
cost per query. By means of a suitable decoupling of two optimizations and a
clever search space reduction the computations are done without any inference
engine calls. For the full search space, we give a method requiring only a
polynomial number of inferences and show how query properties can be guaranteed
which existing methods do not provide. Evaluation results using real-world
problems indicate that the new method computes (virtually) optimal queries
instantly independently of the size and complexity of the considered diagnosis
problems and outperforms equally general methods not exploiting the proposed
theory by orders of magnitude.
","A Generally Applicable, Highly Scalable Measurement Computation and
  Optimization Approach to Sequential Model-Based Diagnosis"
"  It is possible that powerful and potentially dangerous artificial
intelligence (AI) might be developed in the future. An Oracle is a design which
aims to restrain the impact of a potentially dangerous AI by restricting the
agent to no actions besides answering questions. Unfortunately, most Oracles
will be motivated to gain more control over the world by manipulating users
through the content of their answers, and Oracles of potentially high
intelligence might be very successful at this
\citep{DBLP:journals/corr/AlfonsecaCACAR16}. In this paper we present two
designs for Oracles which, even under pessimistic assumptions, will not
manipulate their users into releasing them and yet will still be incentivised
to provide their users with helpful answers. The first design is the
counterfactual Oracle -- which choses its answer as if it expected nobody to
ever read it. The second design is the low-bandwidth Oracle -- which is limited
by the quantity of information it can transmit.
",Good and safe uses of AI Oracles
"  In order for neural networks to learn complex languages or grammars, they
must have sufficient computational power or resources to recognize or generate
such languages. Though many approaches have been discussed, one ob- vious
approach to enhancing the processing power of a recurrent neural network is to
couple it with an external stack memory - in effect creating a neural network
pushdown automata (NNPDA). This paper discusses in detail this NNPDA - its
construction, how it can be trained and how useful symbolic information can be
extracted from the trained network.
  In order to couple the external stack to the neural network, an optimization
method is developed which uses an error function that connects the learning of
the state automaton of the neural network to the learning of the operation of
the external stack. To minimize the error function using gradient descent
learning, an analog stack is designed such that the action and storage of
information in the stack are continuous. One interpretation of a continuous
stack is the probabilistic storage of and action on data. After training on
sample strings of an unknown source grammar, a quantization procedure extracts
from the analog stack and neural network a discrete pushdown automata (PDA).
Simulations show that in learning deterministic context-free grammars - the
balanced parenthesis language, 1*n0*n, and the deterministic Palindrome - the
extracted PDA is correct in the sense that it can correctly recognize unseen
strings of arbitrary length. In addition, the extracted PDAs can be shown to be
identical or equivalent to the PDAs of the source grammars which were used to
generate the training strings.
","The Neural Network Pushdown Automaton: Model, Stack and Learning
  Simulations"
"  Predicting travel times of vehicles in urban settings is a useful and
tangible quantity of interest in the context of intelligent transportation
systems. We address the problem of travel time prediction in arterial roads
using data sampled from probe vehicles. There is only a limited literature on
methods using data input from probe vehicles. The spatio-temporal dependencies
captured by existing data driven approaches are either too detailed or very
simplistic. We strike a balance of the existing data driven approaches to
account for varying degrees of influence a given road may experience from its
neighbors, while controlling the number of parameters to be learnt.
Specifically, we use a NoisyOR conditional probability distribution (CPD) in
conjunction with a dynamic bayesian network (DBN) to model state transitions of
various roads. We propose an efficient algorithm to learn model parameters. We
propose an algorithm for predicting travel times on trips of arbitrary
durations. Using synthetic and real world data traces we demonstrate the
superior performance of the proposed method under different traffic conditions.
","Predicting vehicular travel times by modeling heterogeneous influences
  between arterial roads"
"  The goal of a traditional Markov decision process (MDP) is to maximize
expected cumulativereward over a defined horizon (possibly infinite). In many
applications, however, a decision maker may beinterested in optimizing a
specific quantile of the cumulative reward instead of its expectation. In this
paperwe consider the problem of optimizing the quantiles of the cumulative
rewards of a Markov decision process(MDP), which we refer to as a quantile
Markov decision process (QMDP). We provide analytical resultscharacterizing the
optimal QMDP value function and present a dynamic programming-based algorithm
tosolve for the optimal policy. The algorithm also extends to the MDP problem
with a conditional value-at-risk(CVaR) objective. We illustrate the practical
relevance of our model by evaluating it on an HIV treatmentinitiation problem,
where patients aim to balance the potential benefits and risks of the
treatment.
",Quantile Markov Decision Process
"  Knowledge bases (KB) constructed through information extraction from text
play an important role in query answering and reasoning. In this work, we study
a particular reasoning task, the problem of discovering causal relationships
between entities, known as causal discovery. There are two contrasting types of
approaches to discovering causal knowledge. One approach attempts to identify
causal relationships from text using automatic extraction techniques, while the
other approach infers causation from observational data. However, extractions
alone are often insufficient to capture complex patterns and full observational
data is expensive to obtain. We introduce a probabilistic method for fusing
noisy extractions with observational data to discover causal knowledge. We
propose a principled approach that uses the probabilistic soft logic (PSL)
framework to encode well-studied constraints to recover long-range patterns and
consistent predictions, while cheaply acquired extractions provide a proxy for
unseen observations. We apply our method gene regulatory networks and show the
promise of exploiting KB signals in causal discovery, suggesting a critical,
new area of research.
",Using Noisy Extractions to Discover Causal Knowledge
"  Knowing the reflection of game theory and ethics, we develop a mathematical
representation to bridge the gap between the concepts in moral philosophy
(e.g., Kantian and Utilitarian) and AI ethics industry technology standard
(e.g., IEEE P7000 standard series for Ethical AI). As an application, we
demonstrate how human value can be obtained from the experimental game theory
(e.g., trust game experiment) so as to build an ethical AI. Moreover, an
approach to test the ethics (rightness or wrongness) of a given AI algorithm by
using an iterated Prisoner's Dilemma Game experiment is discussed as an
example. Compared with existing mathematical frameworks and testing method on
AI ethics technology, the advantages of the proposed approach are analyzed.
",Using experimental game theory to transit human values to ethical AI
"  Ethics of algorithms is an emerging topic in various disciplines such as
social science, law, and philosophy, but also artificial intelligence (AI). The
value alignment problem expresses the challenge of (machine) learning values
that are, in some way, aligned with human requirements or values. In this paper
I argue for looking at how humans have formalized and communicated values, in
professional codes of ethics, and for exploring declarative decision-theoretic
ethical programs (DDTEP) to formalize codes of ethics. This renders machine
ethical reasoning and decision-making, as well as learning, more transparent
and hopefully more accountable. The paper includes proof-of-concept examples of
known toy dilemmas and gatekeeping domains such as archives and libraries.
","From Algorithmic Black Boxes to Adaptive White Boxes: Declarative
  Decision-Theoretic Ethical Programs as Codes of Ethics"
"  A major target of linguistics and cognitive science has been to understand
what class of learning systems can acquire the key structures of natural
language. Until recently, the computational requirements of language have been
used to argue that learning is impossible without a highly constrained
hypothesis space. Here, we describe a learning system that is maximally
unconstrained, operating over the space of all computations, and is able to
acquire several of the key structures present natural language from positive
evidence alone. The model successfully acquires regular (e.g. $(ab)^n$),
context-free (e.g. $a^n b^n$, $x x^R$), and context-sensitive (e.g.
$a^nb^nc^n$, $a^nb^mc^nd^m$, $xx$) formal languages. Our approach develops the
concept of factorized programs in Bayesian program induction in order to help
manage the complexity of representation. We show in learning, the model
predicts several phenomena empirically observed in human grammar acquisition
experiments.
",One Model for the Learning of Language
"  In the context of SAT solvers, Shatter is a popular tool for symmetry
breaking on CNF formulas. Nevertheless, little has been said about its use in
the context of AllSAT problems: problems where we are interested in listing all
the models of a Boolean formula. AllSAT has gained much popularity in recent
years due to its many applications in domains like model checking, data mining,
etc. One example of a particularly transparent application of AllSAT to other
fields of computer science is computational Ramsey theory. In this paper we
study the effect of incorporating Shatter to the workflow of using Boolean
formulas to generate all possible edge colorings of a graph avoiding prescribed
monochromatic subgraphs. Generating complete sets of colorings is an important
building block in computational Ramsey theory. We identify two drawbacks in the
na\""ive use of Shatter to break the symmetries of Boolean formulas encoding
Ramsey-type problems for graphs: a ""blow-up"" in the number of models and the
generation of incomplete sets of colorings. The issues presented in this work
are not intended to discourage the use of Shatter as a preprocessing tool for
AllSAT problems in combinatorial computing but to help researchers properly use
this tool by avoiding these potential pitfalls. To this end, we provide
strategies and additional tools to cope with the negative effects of using
Shatter for AllSAT. While the specific application addressed in this paper is
that of Ramsey-type problems, the analysis we carry out applies to many other
areas in which highly-symmetrical Boolean formulas arise and we wish to find
all of their models.
",Exploring the Use of Shatter for AllSAT Through Ramsey-Type Problems
"  Esports has emerged as a popular genre for players as well as spectators,
supporting a global entertainment industry. Esports analytics has evolved to
address the requirement for data-driven feedback, and is focused on
cyber-athlete evaluation, strategy and prediction. Towards the latter, previous
work has used match data from a variety of player ranks from hobbyist to
professional players. However, professional players have been shown to behave
differently than lower ranked players. Given the comparatively limited supply
of professional data, a key question is thus whether mixed-rank match datasets
can be used to create data-driven models which predict winners in professional
matches and provide a simple in-game statistic for viewers and broadcasters.
Here we show that, although there is a slightly reduced accuracy, mixed-rank
datasets can be used to predict the outcome of professional matches, with
suitably optimized configurations.
","Win Prediction in Esports: Mixed-Rank Match Prediction in Multi-player
  Online Battle Arena Games"
"  Very important breakthroughs in data centric deep learning algorithms led to
impressive performance in transactional point applications of Artificial
Intelligence (AI) such as Face Recognition, or EKG classification. With all due
appreciation, however, knowledge blind data only machine learning algorithms
have severe limitations for non-transactional AI applications, such as medical
diagnosis beyond the EKG results. Such applications require deeper and broader
knowledge in their problem solving capabilities, e.g. integrating anatomy and
physiology knowledge with EKG results and other patient findings. Following a
review and illustrations of such limitations for several real life AI
applications, we point at ways to overcome them. The proposed Wikipedia for
Smart Machines initiative aims at building repositories of software structures
that represent humanity science & technology knowledge in various parts of
life; knowledge that we all learn in schools, universities and during our
professional life. Target readers for these repositories are smart machines;
not human. AI software developers will have these Reusable Knowledge structures
readily available, hence, the proposed name ReKopedia. Big Data is by now a
mature technology, it is time to focus on Big Knowledge. Some will be derived
from data, some will be obtained from mankind gigantic repository of knowledge.
Wikipedia for smart machines along with the new Double Deep Learning approach
offer a paradigm for integrating datacentric deep learning algorithms with
algorithms that leverage deep knowledge, e.g. evidential reasoning and
causality reasoning. For illustration, a project is described to produce
ReKopedia knowledge modules for medical diagnosis of about 1,000 disorders.
Data is important, but knowledge deep, basic, and commonsense is equally
important.
",Wikipedia for Smart Machines and Double Deep Machine Learning
"  The efficient use of limited computational resources is an essential
ingredient of intelligence. Selecting computations optimally according to
rational metareasoning would achieve this, but this is computationally
intractable. Inspired by psychology and neuroscience, we propose the first
concrete and domain-general learning algorithm for approximating the optimal
selection of computations: Bayesian metalevel policy search (BMPS). We derive
this general, sample-efficient search algorithm for a computation-selecting
metalevel policy based on the insight that the value of information lies
between the myopic value of information and the value of perfect information.
We evaluate BMPS on three increasingly difficult metareasoning problems: when
to terminate computation, how to allocate computation between competing
options, and planning. Across all three domains, BMPS achieved near-optimal
performance and compared favorably to previously proposed metareasoning
heuristics. Finally, we demonstrate the practical utility of BMPS in an
emergency management scenario, even accounting for the overhead of
metareasoning.
",Learning to select computations
"  Constant structure closed semantic systems are the systems each element of
which receives its definition through the correspondent unchangeable set of
other elements of the system. Discrete time means here that the definitions of
the elements change iteratively and simultaneously based on the ""neighbor
portraits"" from the previous iteration. I prove that the iterative redefinition
process in such class of systems will quickly degenerate into a series of
pairwise isomorphic states and discuss some directions of further research.
",The destiny of constant structure discrete time closed semantic systems
"  Artificial Intelligence (AI) has been used extensively in automatic decision
making in a broad variety of scenarios, ranging from credit ratings for loans
to recommendations of movies. Traditional design guidelines for AI models focus
essentially on accuracy maximization, but recent work has shown that
economically irrational and socially unacceptable scenarios of discrimination
and unfairness are likely to arise unless these issues are explicitly
addressed. This undesirable behavior has several possible sources, such as
biased datasets used for training that may not be detected in black-box models.
After pointing out connections between such bias of AI and the problem of
induction, we focus on Popper's contributions after Hume's, which offer a
logical theory of preferences. An AI model can be preferred over others on
purely rational grounds after one or more attempts at refutation based on
accuracy and fairness. Inspired by such epistemological principles, this paper
proposes a structured approach to mitigate discrimination and unfairness caused
by bias in AI systems. In the proposed computational framework, models are
selected and enhanced after attempts at refutation. To illustrate our
discussion, we focus on hiring decision scenarios where an AI system filters in
which job applicants should go to the interview phase.
","Modeling Epistemological Principles for Bias Mitigation in AI Systems:
  An Illustration in Hiring Decisions"
"  There are many methodologies and techniques for easing the task of ontology
building. Here we describe the intersection of two of these: ontology
normalisation and fully programmatic ontology development. The first of these
describes a standardized organisation for an ontology, with singly inherited
self-standing entities, and a number of small taxonomies of refining entities.
The former are described and defined in terms of the latter and used to manage
the polyhierarchy of the self-standing entities. Fully programmatic development
is a technique where an ontology is developed using a domain-specific language
within a programming language, meaning that as well defining ontological
entities, it is possible to add arbitrary patterns or new syntax within the
same environment. We describe how new patterns can be used to enable a new
style of ontology development that we call hypernormalisation.
","Facets, Tiers and Gems: Ontology Patterns for Hypernormalisation"
"  In practical situations, there are many dynamic covering information systems
with variations of attributes, but there are few studies on related
family-based attribute reduction of dynamic covering information systems. In
this paper, we first investigate updated mechanisms of constructing attribute
reducts for consistent and inconsistent covering information systems when
varying attribute sets by using related families. Then we employ examples to
illustrate how to compute attribute reducts of dynamic covering information
systems with variations of attribute sets. Finally, the experimental results
illustrates that the related family-based methods are effective to perform
attribute reduction of dynamic covering information systems when attribute sets
are varying with time.
","Related family-based attribute reduction of covering information systems
  when varying attribute sets"
"  Hierarchical abstractions, also known as options -- a type of temporally
extended action (Sutton et. al. 1999) that enables a reinforcement learning
agent to plan at a higher level, abstracting away from the lower-level details.
In this work, we learn reusable options whose parameters can vary, encouraging
different behaviors, based on the current situation. In principle, these
behaviors can include vigor, defence or even risk-averseness. These are some
examples of what we refer to in the broader context as Situational Awareness
(SA). We incorporate SA, in the form of vigor, into hierarchical RL by defining
and learning situationally aware options in a Probabilistic Goal Semi-Markov
Decision Process (PG-SMDP). This is achieved using our Situationally Aware
oPtions (SAP) policy gradient algorithm which comes with a theoretical
convergence guarantee. We learn reusable options in different scenarios in a
RoboCup soccer domain (i.e., winning/losing). These options learn to execute
with different levels of vigor resulting in human-like behaviours such as
`time-wasting' in the winning scenario. We show the potential of the agent to
exit bad local optima using reusable options in RoboCup. Finally, using SAP,
the agent mitigates feature-based model misspecification in a Bottomless Pit of
Death domain.
",Situationally Aware Options
"  This paper is concerned with learning to solve tasks that require a chain of
interdependent steps of relational inference, like answering complex questions
about the relationships between objects, or solving puzzles where the smaller
elements of a solution mutually constrain each other. We introduce the
recurrent relational network, a general purpose module that operates on a graph
representation of objects. As a generalization of Santoro et al. [2017]'s
relational network, it can augment any neural network model with the capacity
to do many-step relational reasoning. We achieve state of the art results on
the bAbI textual question-answering dataset with the recurrent relational
network, consistently solving 20/20 tasks. As bAbI is not particularly
challenging from a relational reasoning point of view, we introduce
Pretty-CLEVR, a new diagnostic dataset for relational reasoning. In the
Pretty-CLEVR set-up, we can vary the question to control for the number of
relational reasoning steps that are required to obtain the answer. Using
Pretty-CLEVR, we probe the limitations of multi-layer perceptrons, relational
and recurrent relational networks. Finally, we show how recurrent relational
networks can learn to solve Sudoku puzzles from supervised training data, a
challenging task requiring upwards of 64 steps of relational reasoning. We
achieve state-of-the-art results amongst comparable methods by solving 96.6% of
the hardest Sudoku puzzles.
",Recurrent Relational Networks
"  Action abstractions restrict the number of legal actions available during
search in multi-unit real-time adversarial games, thus allowing algorithms to
focus their search on a set of promising actions. Optimal strategies derived
from un-abstracted spaces are guaranteed to be no worse than optimal strategies
derived from action-abstracted spaces. In practice, however, due to real-time
constraints and the state space size, one is only able to derive good
strategies in un-abstracted spaces in small-scale games. In this paper we
introduce search algorithms that use an action abstraction scheme we call
asymmetric abstraction. Asymmetric abstractions retain the un-abstracted
spaces' theoretical advantage over regularly abstracted spaces while still
allowing the search algorithms to derive effective strategies, even in
large-scale games. Empirical results on combat scenarios that arise in a
real-time strategy game show that our search algorithms are able to
substantially outperform state-of-the-art approaches.
","Asymmetric Action Abstractions for Multi-Unit Control in Adversarial
  Real-Time Games"
"  We agree with Lake and colleagues on their list of key ingredients for
building humanlike intelligence, including the idea that model-based reasoning
is essential. However, we favor an approach that centers on one additional
ingredient: autonomy. In particular, we aim toward agents that can both build
and exploit their own internal models, with minimal human hand-engineering. We
believe an approach centered on autonomous learning has the greatest chance of
success as we scale toward real-world complexity, tackling domains for which
ready-made formal models are not available. Here we survey several important
examples of the progress that has been made toward building autonomous agents
with humanlike abilities, and highlight some outstanding challenges.
","Building Machines that Learn and Think for Themselves: Commentary on
  Lake et al., Behavioral and Brain Sciences, 2017"
"  The best improvisational theatre actors can make any scene partner, of any
skill level or ability, appear talented and proficient in the art form, and
thus ""make them shine"". To challenge this improvisational paradigm, we built an
artificial intelligence (AI) trained to perform live shows alongside human
actors for human audiences. Over the course of 30 performances to a combined
audience of almost 3000 people, we have refined theatrical games which involve
combinations of human and (at times, adversarial) AI actors. We have developed
specific scene structures to include audience participants in interesting ways.
Finally, we developed a complete show structure that submitted the audience to
a Turing test and observed their suspension of disbelief, which we believe is
key for human/non-human theatre co-creation.
",Improvised Comedy as a Turing Test
"  We propose the cascade attribute learning network (CALNet), which can learn
attributes in a control task separately and assemble them together. Our
contribution is twofold: first we propose attribute learning in reinforcement
learning (RL). Attributes used to be modeled using constraint functions or
terms in the objective function, making it hard to transfer. Attribute
learning, on the other hand, models these task properties as modules in the
policy network. We also propose using novel cascading compensative networks in
the CALNet to learn and assemble attributes. Using the CALNet, one can zero
shoot an unseen task by separately learning all its attributes, and assembling
the attribute modules. We have validated the capacity of our model on a wide
variety of control problems with attributes in time, position, velocity and
acceleration phases.
",Cascade Attribute Learning Network
"  Adversarial decision making is a particular type of decision making problem
where the gain a decision maker obtains as a result of his decisions is
affected by the actions taken by others. Representation of alternatives'
evaluations and methods to find the optimal alternative are two important
aspects in the adversarial decision making. The aim of this study is to develop
a general framework for solving the adversarial decision making issue under
uncertain environment. By combining fuzzy set theory, game theory and D numbers
theory (DNT), a DNT based game-theoretic framework for adversarial decision
making under fuzzy environment is presented. Within the proposed framework or
model, fuzzy set theory is used to model the uncertain evaluations of decision
makers to alternatives, the non-exclusiveness among fuzzy evaluations are taken
into consideration by using DNT, and the conflict of interests among decision
makers is considered in a two-person non-constant sum game theory perspective.
An illustrative application is given to demonstrate the effectiveness of the
proposed model. This work, on one hand, has developed an effective framework
for adversarial decision making under fuzzy environment; One the other hand, it
has further improved the basis of DNT as a generalization of Dempster-Shafer
theory for uncertainty reasoning.
","D numbers theory based game-theoretic framework in adversarial decision
  making under fuzzy environment"
"  A common assumption in machine learning is that training data are i.i.d.
samples from some distribution. Processes that generate i.i.d. samples are, in
a sense, uninformative---they produce data without regard to how good this data
is for learning. By contrast, cognitive science research has shown that when
people generate training data for others (i.e., teaching), they deliberately
select examples that are helpful for learning. Because the data is more
informative, learning can require less data. Interestingly, such examples are
most effective when learners know that the data were pedagogically generated
(as opposed to randomly generated). We call this pedagogical learning---when a
learner assumes that evidence comes from a helpful teacher. In this work, we
ask how pedagogical learning might work for machine learning algorithms.
Studying this question requires understanding how people actually teach complex
concepts with examples, so we conducted a behavioral study examining how people
teach regular expressions using example strings. We found that teachers'
examples contain powerful clustering structure that can greatly facilitate
learning. We then develop a model of teaching and show a proof of concept that
using this model inside of a learner can improve performance.
",Pedagogical learning
"  Interval Pairwise Comparison Matrices have been widely used to account for
uncertain statements concerning the preferences of decision makers. Several
approaches have been proposed in the literature, such as multiplicative and
fuzzy interval matrices. In this paper, we propose a general unified approach
to Interval Pairwise Comparison Matrices, based on Abelian linearly ordered
groups. In this framework, we generalize some consistency conditions provided
for multiplicative and/or fuzzy interval pairwise comparison matrices and
provide inclusion relations between them. Then, we provide a concept of
distance between intervals that, together with a notion of mean defined over
real continuous Abelian linearly ordered groups, allows us to provide a
consistency index and an indeterminacy index. In this way, by means of suitable
isomorphisms between Abelian linearly ordered groups, we will be able to
compare the inconsistency and the indeterminacy of different kinds of Interval
Pairwise Comparison Matrices, e.g. multiplicative, additive, and fuzzy, on a
unique Cartesian coordinate system.
",A general unified framework for interval pairwise comparison matrices
"  Artificial Intelligence is a central topic in the computer science
curriculum. From the year 2011 a project-based learning methodology based on
computer games has been designed and implemented into the intelligence
artificial course at the University of the Bio-Bio. The project aims to develop
software-controlled agents (bots) which are programmed by using heuristic
algorithms seen during the course. This methodology allows us to obtain good
learning results, however several challenges have been founded during its
implementation.
  In this paper we show how linguistic descriptions of data can help to provide
students and teachers with technical and personalized feedback about the
learned algorithms. Algorithm behavior profile and a new Turing test for
computer games bots based on linguistic modelling of complex phenomena are also
proposed in order to deal with such challenges.
  In order to show and explore the possibilities of this new technology, a web
platform has been designed and implemented by one of authors and its
incorporation in the process of assessment allows us to improve the teaching
learning process.
","How linguistic descriptions of data can help to the teaching-learning
  process in higher education, case of study: artificial intelligence"
"  We introduce and analyze an extension to the matching problem on a weighted
bipartite graph: Assignment with Type Constraints. The two parts of the graph
are partitioned into subsets called types and blocks; we seek a matching with
the largest sum of weights under the constraint that there is a pre-specified
cap on the number of vertices matched in every type-block pair. Our primary
motivation stems from the public housing program of Singapore, accounting for
over 70% of its residential real estate. To promote ethnic diversity within its
housing projects, Singapore imposes ethnicity quotas: each new housing
development comprises blocks of flats and each ethnicity-based group in the
population must not own more than a certain percentage of flats in a block.
Other domains using similar hard capacity constraints include matching
prospective students to schools or medical residents to hospitals. Limiting
agents' choices for ensuring diversity in this manner naturally entails some
welfare loss. One of our goals is to study the trade-off between diversity and
social welfare in such settings. We first show that, while the classic
assignment program is polynomial-time computable, adding diversity constraints
makes it computationally intractable; however, we identify a
$\tfrac{1}{2}$-approximation algorithm, as well as reasonable assumptions on
the weights that permit poly-time algorithms. Next, we provide two upper bounds
on the price of diversity -- a measure of the loss in welfare incurred by
imposing diversity constraints -- as functions of natural problem parameters.
We conclude the paper with simulations based on publicly available data from
two diversity-constrained allocation problems -- Singapore Public Housing and
Chicago School Choice -- which shed light on how the constrained maximization
as well as lottery-based variants perform in practice.
",The Price of Quota-based Diversity in Assignment Problems
"  This paper presents the Crossmodal Attentive Skill Learner (CASL), integrated
with the recently-introduced Asynchronous Advantage Option-Critic (A2OC)
architecture [Harb et al., 2017] to enable hierarchical reinforcement learning
across multiple sensory inputs. We provide concrete examples where the approach
not only improves performance in a single task, but accelerates transfer to new
tasks. We demonstrate the attention mechanism anticipates and identifies useful
latent features, while filtering irrelevant sensor modalities during execution.
We modify the Arcade Learning Environment [Bellemare et al., 2013] to support
audio queries, and conduct evaluations of crossmodal learning in the Atari 2600
game Amidar. Finally, building on the recent work of Babaeizadeh et al. [2017],
we open-source a fast hybrid CPU-GPU implementation of CASL.
",Crossmodal Attentive Skill Learner
"  Hypernym identification of open-domain entities is crucial for taxonomy
construction as well as many higher-level applications. Current methods suffer
from either low precision or low recall. To decrease the difficulty of this
problem, we adopt a classification-based method. We pre-define a concept
taxonomy and classify an entity to one of its leaf concept, based on the name
and description information of the entity. A convolutional neural network
classifier and a K-means clustering module are adopted for classification. We
applied this system to 2.1 million Baidu Baike entities, and 1.1 million of
them were successfully identified with a precision of 99.36%.
",Classification of entities via their descriptive sentences
"  Particle swarm optimization comes under lot of changes after James Kennedy
and Russell Eberhart first proposes the idea in 1995. The changes has been done
mainly on Inertia parameters in velocity updating equation so that the
convergence rate will be higher. We are proposing a novel approach where
particles movement will not be depend on its velocity rather it will be decided
by constrained biased random walk of particles. In random walk every particles
movement based on two significant parameters, one is random process like toss
of a coin and other is how much displacement a particle should have. In our
approach we exploit this idea by performing a biased random operation and based
on the outcome of that random operation, PSO particles choose the direction of
the path and move non-uniformly into the solution space. This constrained,
non-uniform movement helps the random walking particle to converge quicker then
classical PSO. In our constrained biased random walking approach, we no longer
needed velocity term (Vi), rather we introduce a new parameter (K) which is a
probabilistic function. No global best particle (PGbest), local best particle
(PLbest), Constriction parameter (W) are required rather we use a new term
called Ptarg which is loosely influenced by PGbest.We test our algorithm on
five different benchmark functions, and also compare its performance with
classical PSO and Quantum Particle Swarm Optimization (QPSO).This new approach
have been shown significantly better than basic PSO and sometime outperform
QPSO in terms of convergence, search space, number of iterations.
",A Modification of Particle Swarm Optimization using Random Walk
"  This paper presents a proof-of concept study for demonstrating the viability
of building collaboration among multiple agents through standard Q learning
algorithm embedded in particle swarm optimisation. Collaboration is formulated
to be achieved among the agents via some sort competition, where the agents are
expected to balance their action in such a way that none of them drifts away of
the team and none intervene any fellow neighbours territory. Particles are
devised with Q learning algorithm for self training to learn how to act as
members of a swarm and how to produce collaborative/collective behaviours. The
produced results are supportive to the algorithmic structures suggesting that a
substantive collaboration can be build via proposed learning algorithm.
","A reinforcement learning algorithm for building collaboration in
  multi-agent systems"
"  In online advertising, our aim is to match the advertisers with the most
relevant users to optimize the campaign performance. In the pursuit of
achieving this goal, multiple data sources provided by the advertisers or
third-party data providers are utilized to choose the set of users according to
the advertisers' targeting criteria. In this paper, we present a framework that
can be applied to assess the quality of such data sources in large scale. This
framework efficiently evaluates the similarity of a specific data source
categorization to that of the ground truth, especially for those cases when the
ground truth is accessible only in aggregate, and the user-level information is
anonymized or unavailable due to privacy reasons. We propose multiple
methodologies within this framework, present some preliminary assessment
results, and evaluate how the methodologies compare to each other. We also
present two use cases where we can utilize the data quality assessment results:
the first use case is targeting specific user categories, and the second one is
forecasting the desirable audiences we can reach for an online advertising
campaign with pre-set targeting criteria.
",Towards Data Quality Assessment in Online Advertising
"  We draw upon a previously largely untapped literature on human collective
intelligence as a source of inspiration for improving deep learning. Implicit
in many algorithms that attempt to solve Deep Reinforcement Learning (DRL)
tasks is the network of processors along which parameter values are shared. So
far, existing approaches have implicitly utilized fully-connected networks, in
which all processors are connected. However, the scientific literature on human
collective intelligence suggests that complete networks may not always be the
most effective information network structures for distributed search through
complex spaces. Here we show that alternative topologies can improve deep
neural network training: we find that sparser networks learn higher rewards
faster, leading to learning improvements at lower communication costs.
","Improved Learning in Evolution Strategies via Sparser Inter-Agent
  Network Topologies"
"  Embedding knowledge graphs (KGs) into continuous vector spaces is a focus of
current research. Combining such an embedding model with logic rules has
recently attracted increasing attention. Most previous attempts made a one-time
injection of logic rules, ignoring the interactive nature between embedding
learning and logical inference. And they focused only on hard rules, which
always hold with no exception and usually require extensive manual effort to
create or validate. In this paper, we propose Rule-Guided Embedding (RUGE), a
novel paradigm of KG embedding with iterative guidance from soft rules. RUGE
enables an embedding model to learn simultaneously from 1) labeled triples that
have been directly observed in a given KG, 2) unlabeled triples whose labels
are going to be predicted iteratively, and 3) soft rules with various
confidence levels extracted automatically from the KG. In the learning process,
RUGE iteratively queries rules to obtain soft labels for unlabeled triples, and
integrates such newly labeled triples to update the embedding model. Through
this iterative procedure, knowledge embodied in logic rules may be better
transferred into the learned embeddings. We evaluate RUGE in link prediction on
Freebase and YAGO. Experimental results show that: 1) with rule knowledge
injected iteratively, RUGE achieves significant and consistent improvements
over state-of-the-art baselines; and 2) despite their uncertainties,
automatically extracted soft rules are highly beneficial to KG embedding, even
those with moderate confidence levels. The code and data used for this paper
can be obtained from https://github.com/iieir-km/RUGE.
",Knowledge Graph Embedding with Iterative Guidance from Soft Rules
"  We present a differentiable framework capable of learning a wide variety of
compositions of simple policies that we call skills. By recursively composing
skills with themselves, we can create hierarchies that display complex
behavior. Skill networks are trained to generate skill-state embeddings that
are provided as inputs to a trainable composition function, which in turn
outputs a policy for the overall task. Our experiments on an environment
consisting of multiple collect and evade tasks show that this architecture is
able to quickly build complex skills from simpler ones. Furthermore, the
learned composition function displays some transfer to unseen combinations of
skills, allowing for zero-shot generalizations.
",Learning to Compose Skills
"  Lindenmayer systems (L-systems) are a formal grammar system that iteratively
rewrites all symbols of a string, in parallel. When visualized with a graphical
interpretation, the images have self-similar shapes that appear frequently in
nature, and they have been particularly successful as a concise, reusable
technique for simulating plants. The L-system inference problem is to find an
L-system to simulate a given plant. This is currently done mainly by experts,
but this process is limited by the availability of experts, the complexity that
may be solved by humans, and time. This paper introduces the Plant Model
Inference Tool (PMIT) that infers deterministic context-free L-systems from an
initial sequence of strings generated by the system using a genetic algorithm.
PMIT is able to infer more complex systems than existing approaches. Indeed,
while existing approaches are limited to L-systems with a total sum of 20
combined symbols in the productions, PMIT can infer almost all L-systems tested
where the total sum is 140 symbols. This was validated using a test bed of 28
previously developed L-system models, in addition to models created
artificially by bootstrapping larger models.
",New Techniques for Inferring L-Systems Using Genetic Algorithm
"  Learning Automata (LA) are considered as one of the most powerful tools in
the field of reinforcement learning. The family of estimator algorithms is
proposed to improve the convergence rate of LA and has made great achievements.
However, the estimators perform poorly on estimating the reward probabilities
of actions in the initial stage of the learning process of LA. In this
situation, a lot of rewards would be added to the probabilities of non-optimal
actions. Thus, a large number of extra iterations are needed to compensate for
these wrong rewards. In order to improve the speed of convergence, we propose a
new P-model absorbing learning automaton by utilizing a double competitive
strategy which is designed for updating the action probability vector. In this
way, the wrong rewards can be corrected instantly. Hence, the proposed Double
Competitive Algorithm overcomes the drawbacks of existing estimator algorithms.
A refined analysis is presented to show the $\epsilon-optimality$ of the
proposed scheme. The extensive experimental results in benchmark environments
demonstrate that our proposed learning automata perform more efficiently than
the most classic LA $SE_{RI}$ and the current fastest LA $DGCPA^{*}$.
",A double competitive strategy based learning automata algorithm
"  The task of decision-making under uncertainty is daunting, especially for
problems which have significant complexity. Healthcare policy makers across the
globe are facing problems under challenging constraints, with limited tools to
help them make data driven decisions. In this work we frame the process of
finding an optimal malaria policy as a stochastic multi-armed bandit problem,
and implement three agent based strategies to explore the policy space. We
apply a Gaussian Process regression to the findings of each agent, both for
comparison and to account for stochastic results from simulating the spread of
malaria in a fixed population. The generated policy spaces are compared with
published results to give a direct reference with human expert decisions for
the same simulated population. Our novel approach provides a powerful resource
for policy makers, and a platform which can be readily extended to capture
future more nuanced policy spaces.
",Novel Exploration Techniques (NETs) for Malaria Policy Interventions
"  In his seminal book `The Inmates are Running the Asylum: Why High-Tech
Products Drive Us Crazy And How To Restore The Sanity' [2004, Sams
Indianapolis, IN, USA], Alan Cooper argues that a major reason why software is
often poorly designed (from a user perspective) is that programmers are in
charge of design decisions, rather than interaction designers. As a result,
programmers design software for themselves, rather than for their target
audience, a phenomenon he refers to as the `inmates running the asylum'. This
paper argues that explainable AI risks a similar fate. While the re-emergence
of explainable AI is positive, this paper argues most of us as AI researchers
are building explanatory agents for ourselves, rather than for the intended
users. But explainable AI is more likely to succeed if researchers and
practitioners understand, adopt, implement, and improve models from the vast
and valuable bodies of research in philosophy, psychology, and cognitive
science, and if evaluation of these models is focused more on people than on
technology. From a light scan of literature, we demonstrate that there is
considerable scope to infuse more results from the social and behavioural
sciences into explainable AI, and present some key results from these fields
that are relevant to explainable AI.
","Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to
  Stop Worrying and Love the Social and Behavioural Sciences"
"  Humans are able to identify a referred visual object in a complex scene via a
few rounds of natural language communications. Success communication requires
both parties to engage and learn to adapt for each other. In this paper, we
introduce an interactive training method to improve the natural language
conversation system for a visual grounding task. During interactive training,
both agents are reinforced by the guidance from a common reward function. The
parametrized reward function also cooperatively updates itself via
interactions, and contribute to accomplishing the task. We evaluate the method
on GuessWhat?! visual grounding task, and significantly improve the task
success rate. However, we observe language drifting problem during training and
propose to use reward engineering to improve the interpretability for the
generated conversations. Our result also indicates evaluating goal-ended visual
conversation tasks require semantic relevant metrics beyond task success rate.
",Interactive Reinforcement Learning for Object Grounding via Self-Talking
"  This paper briefly elaborates on a development in (applied) fuzzy logic that
has taken place in the last couple of decades, namely, the complementation or
even replacement of the traditional knowledge-based approach to fuzzy
rule-based systems design by a data-driven one. It is argued that the classical
rule-based modeling paradigm is actually more amenable to the knowledge-based
approach, for which it has originally been conceived, while being less apt to
data-driven model design. An important reason that prevents fuzzy (rule-based)
systems from being leveraged in large-scale applications is the flat structure
of rule bases, along with the local nature of fuzzy rules and their limited
ability to express complex dependencies between variables. This motivates
alternative approaches to fuzzy systems modeling, in which functional
dependencies can be represented more flexibly and more compactly in terms of
hierarchical structures.
","From knowledge-based to data-driven modeling of fuzzy rule-based
  systems: A critical reflection"
"  The goal of this Random Walks project is to code and experiment the Markov
Chain Monte Carlo (MCMC) method for the problem of graph coloring. In this
report, we present the plots of cost function \(\mathbf{H}\) by varying the
parameters like \(\mathbf{q}\) (Number of colors that can be used in coloring)
and \(\mathbf{c}\) (Average node degree). The results are obtained by using
simulated annealing scheme, where the temperature (inverse of
\(\mathbf{\beta}\)) parameter in the MCMC is lowered progressively.
",Simulated Annealing Algorithm for Graph Coloring
"  To realize human-like robot intelligence, a large-scale cognitive
architecture is required for robots to understand the environment through a
variety of sensors with which they are equipped. In this paper, we propose a
novel framework named Serket that enables the construction of a large-scale
generative model and its inference easily by connecting sub-modules to allow
the robots to acquire various capabilities through interaction with their
environments and others. We consider that large-scale cognitive models can be
constructed by connecting smaller fundamental models hierarchically while
maintaining their programmatic independence. Moreover, connected modules are
dependent on each other, and parameters are required to be optimized as a
whole. Conventionally, the equations for parameter estimation have to be
derived and implemented depending on the models. However, it becomes harder to
derive and implement those of a larger scale model. To solve these problems, in
this paper, we propose a method for parameter estimation by communicating the
minimal parameters between various modules while maintaining their programmatic
independence. Therefore, Serket makes it easy to construct large-scale models
and estimate their parameters via the connection of modules. Experimental
results demonstrated that the model can be constructed by connecting modules,
the parameters can be optimized as a whole, and they are comparable with the
original models that we have proposed.
","SERKET: An Architecture for Connecting Stochastic Models to Realize a
  Large-Scale Cognitive Model"
"  The task of end-to-end relation extraction consists of two sub-tasks: i)
identifying entity mentions along with their types and ii) recognizing semantic
relations among the entity mention pairs. %Identifying entity mentions along
with their types and recognizing semantic relations among the entity mentions,
are two very important problems in Information Extraction. It has been shown
that for better performance, it is necessary to address these two sub-tasks
jointly. We propose an approach for simultaneous extraction of entity mentions
and relations in a sentence, by using inference in Markov Logic Networks (MLN).
We learn three different classifiers : i) local entity classifier, ii) local
relation classifier and iii) ""pipeline"" relation classifier which uses
predictions of the local entity classifier. Predictions of these classifiers
may be inconsistent with each other. We represent these predictions along with
some domain knowledge using weighted first-order logic rules in an MLN and
perform joint inference over the MLN to obtain a global output with minimum
inconsistencies. Experiments on the ACE (Automatic Content Extraction) 2004
dataset demonstrate that our approach of joint extraction using MLNs
outperforms the baselines of individual classifiers. Our end-to-end relation
extraction performance is better than 2 out of 3 previous results reported on
the ACE 2004 dataset.
",End-to-End Relation Extraction using Markov Logic Networks
"  The present document is an excerpt of an essay that I wrote as part of my
application material to graduate school in Computer Science (with a focus on
Artificial Intelligence), in 1986. I was not invited by any of the schools that
received it, so I became a theoretical physicist instead. The essay's full
title was ""Some Topics in Philosophy and Computer Science"". I am making this
text (unchanged from 1985, preserving the typesetting as much as possible)
available now in memory of Jerry Fodor, whose writings had influenced me
significantly at the time (even though I did not always agree).
",The mind as a computational system
"  Recent advances in visual activity recognition have raised the possibility of
applications such as automated video surveillance. Effective approaches for
such problems however require the ability to recognize the plans of agents from
video information. Although traditional plan recognition algorithms depend on
access to sophisticated planning domain models, one recent promising direction
involves learning approximated (or shallow) domain models directly from the
observed activity sequences DUP. One limitation is that such approaches expect
observed action sequences as inputs. In many cases involving vision/sensing
from raw data, there is considerable uncertainty about the specific action at
any given time point. The most we can expect in such cases is probabilistic
information about the action at that point. The input will then be sequences of
such observed action distributions. In this work, we address the problem of
constructing an effective data-interface that allows a plan recognition module
to directly handle such observation distributions. Such an interface works like
a bridge between the low-level perception module, and the high-level plan
recognition module. We propose two approaches. The first involves resampling
the distribution sequences to single action sequences, from which we could
learn an action affinity model based on learned action (word) embeddings for
plan recognition. The second is to directly learn action distribution
embeddings by our proposed Distr2vec (distribution to vector) model, to
construct an affinity model for plan recognition.
","Recognizing Plans by Learning Embeddings from Observed Action
  Distributions"
"  This paper presents a non-manual design engineering method based on heuristic
search algorithm to search for candidate agents in the solution space which
formed by artificial intelligence agents modeled on the base of
bionics.Compared with the artificial design method represented by meta-learning
and the bionics method represented by the neural architecture chip,this method
is more feasible for realizing artificial general intelligence,and it has a
much better interaction with cognitive neuroscience;at the same time,the
engineering method is based on the theoretical hypothesis that the final
learning algorithm is stable in certain scenarios,and has generalization
ability in various scenarios.The paper discusses the theory preliminarily and
proposes the possible correlation between the theory and the fixed-point
theorem in the field of mathematics.Limited by the author's knowledge
level,this correlation is proposed only as a kind of conjecture.
","A Heuristic Search Algorithm Using the Stability of Learning Algorithms
  in Certain Scenarios as the Fitness Function: An Artificial General
  Intelligence Engineering Approach"
"  Feature selection is an important preprocessing step for classification
problems. It deals with selecting near optimal features in the original
dataset. Feature selection is an NP-hard problem, so meta-heuristics can be
more efficient than exact methods. In this work, Ant Lion Optimizer (ALO),
which is a recent metaheuristic algorithm, is employed as a wrapper feature
selection method. Six variants of ALO are proposed where each employ a transfer
function to map a continuous search space to a discrete search space. The
performance of the proposed approaches is tested on eighteen UCI datasets and
compared to a number of existing approaches in the literature: Particle Swarm
Optimization, Gravitational Search Algorithm, and two existing ALO-based
approaches. Computational experiments show that the proposed approaches
efficiently explore the feature space and select the most informative features,
which help to improve the classification accuracy.
","S-Shaped vs. V-Shaped Transfer Functions for Antlion Optimization
  Algorithm in Feature Selection Problems"
"  Nintendo's Super Smash Bros. Melee fighting game can be emulated on modern
hardware allowing us to inspect internal memory states, such as character
positions. We created an AI that avoids being hit by training using these
internal memory states and outputting controller button presses. After training
on a month's worth of Melee matches, our best agent learned to avoid the
toughest AI built into the game for a full minute 74.6% of the time.
","Nintendo Super Smash Bros. Melee: An ""Untouchable"" Agent"
"  The Hard Problem of consciousness has been dismissed as an illusion. By
showing that computers are capable of experiencing, we show that they are at
least rudimentarily conscious with potential to eventually reach
superconsciousness. The main contribution of the paper is a test for confirming
certain subjective experiences in a tested agent. We follow with analysis of
benefits and problems with conscious machines and implications of such
capability on future of computing, machine rights and artificial intelligence
safety.
",Detecting Qualia in Natural and Artificial Agents
"  Eigenoptions (EOs) have been recently introduced as a promising idea for
generating a diverse set of options through the graph Laplacian, having been
shown to allow efficient exploration. Despite its initial promising results, a
couple of issues in current algorithms limit its application, namely: (1) EO
methods require two separate steps (eigenoption discovery and reward
maximization) to learn a control policy, which can incur a significant amount
of storage and computation; (2) EOs are only defined for problems with discrete
state-spaces and; (3) it is not easy to take the environment's reward function
into consideration when discovering EOs. To addresses these issues, we
introduce an algorithm termed eigenoption-critic (EOC) based on the
Option-critic (OC) framework [Bacon17], a general hierarchical reinforcement
learning (RL) algorithm that allows learning the intra-option policies
simultaneously with the policy over options. We also propose a generalization
of EOC to problems with continuous state-spaces through the Nystr\""om
approximation. EOC can also be seen as extending OC to nonstationary settings,
where the discovered options are not tailored for a single task.
",The Eigenoption-Critic Framework
"  This paper proposes a low-cost, easily realizable strategy to equip a
reinforcement learning (RL) agent the capability of behaving ethically. Our
model allows the designers of RL agents to solely focus on the task to achieve,
without having to worry about the implementation of multiple trivial ethical
patterns to follow. Based on the assumption that the majority of human
behavior, regardless which goals they are achieving, is ethical, our design
integrates human policy with the RL policy to achieve the target objective with
less chance of violating the ethical code that human beings normally obey.
","A Low-Cost Ethics Shaping Approach for Designing Reinforcement Learning
  Agents"
"  The modeling of emergent swarm intelligence constitutes a major challenge and
it has been tacked in a number of different ways. However, existing approaches
fail to capture the nature of swarm intelligence and they are either too
abstract for practical application or not generic enough to describe the
various types of emergence phenomena. In this paper, a contradiction-centric
model for swarm intelligence is proposed, in which individuals determine their
behaviors based on their internal contradictions whilst they associate and
interact to update their contradictions. The model hypothesizes that 1) the
emergence of swarm intelligence is rooted in the development of individuals'
internal contradictions and the interactions taking place between individuals
and the environment, and 2) swarm intelligence is essentially a combinative
reflection of the configurations of individuals' internal contradictions and
the distributions of these contradictions across individuals. The model is
formally described and five swarm intelligence systems are studied to
illustrate its broad applicability. The studies confirm the generic character
of the model and its effectiveness for describing the emergence of various
kinds of swarm intelligence; and they also demonstrate that the model is
straightforward to apply, without the need for complicated computations.
",A Generic Model for Swarm Intelligence and Its Validations
"  Computation has changed the world more than any previous expressions of
knowledge. In its particular algorithmic embodiment, it offers a perspective,
within which the digital computer (one of many possible) exercises a role
reminiscent of theology. Since it is closed to meaning, algorithmic digital
computation can at most mimic the creative aspects of life. AI, in the
perspective of time, proved to be less an acronym for artificial intelligence
and more of automating tasks associated with intelligence. The entire
development led to the hypostatized role of the machine: outputting nothing
else but reality, including that of the humanity that made the machine happen.
The convergence machine called deep learning is only the latest form through
which the deterministic theology of the machine claims more than what extremely
effective data processing actually is. A new understanding of complexity, as
well as the need to distinguish between the reactive nature of the artificial
and the anticipatory nature of the living are suggested as practical responses
to the challenges posed by machine theology.
",In folly ripe. In reason rotten. Putting machine theology to rest
"  Using Deep Reinforcement Learning (DRL) can be a promising approach to handle
various tasks in the field of (simulated) autonomous driving. However, recent
publications mainly consider learning in unusual driving environments. This
paper presents Driving School for Autonomous Agents (DSA^2), a software for
validating DRL algorithms in more usual driving environments based on
artificial and realistic road networks. We also present the results of applying
DSA^2 for handling the task of driving on a straight road while regulating the
velocity of one vehicle according to different speed limits.
","Simulated Autonomous Driving on Realistic Road Networks using Deep
  Reinforcement Learning"
"  In this paper, we will show that (1) the results about the fuzzy reasoning
algoritm obtained in the paper ""Computer Sciences Vol. 34, No.4, pp.145-148,
2007"" according to the paper ""IEEE Transactions On systems, Man and
cybernetics, 18, pp.1049-1056, 1988"" are correct; (2) example 2 in the paper
""An Algorithm of General Fuzzy Inference With The Reductive Property"" presented
by He Ying-Si, Quan Hai-Jin and Deng Hui-Wen according to the paper ""An
approximate analogical reasoning approach based on similarity measures""
presented by Tursken I.B. and Zhong zhao is incorrect; (3) the mistakes in
their paper are modified and then a calculation example of FMT is supplemented.
","Consideration on Example 2 of ""An Algorithm of General Fuzzy
  InferenceWith The Reductive Property"""
"  We examine the issue of stability of probability in reasoning about complex
systems with uncertainty in structure. Normally, propositions are viewed as
probability functions on an abstract random graph where it is implicitly
assumed that the nodes of the graph have stable properties. But what if some of
the nodes change their characteristics? This is a situation that cannot be
covered by abstractions of either static or dynamic sets when these changes
take place at regular intervals. We propose the use of sets with elements that
change, and modular forms are proposed to account for one type of such change.
An expression for the dependence of the mean on the probability of the
switching elements has been determined. The system is also analyzed from the
perspective of decision between different hypotheses. Such sets are likely to
be of use in complex system queries and in analysis of surveys.
",Reasoning in Systems with Elements that Randomly Switch Characteristics
"  This paper presents a framework for intrinsic point of interest discovery
from trajectory databases. Intrinsic points of interest are regions of a
geospatial area innately defined by the spatial and temporal aspects of
trajectory data, and can be of varying size, shape, and resolution. Any
trajectory database exhibits such points of interest, and hence are intrinsic,
as compared to most other point of interest definitions which are said to be
extrinsic, as they require trajectory metadata, external knowledge about the
region the trajectories are observed, or other application-specific
information. Spatial and temporal aspects are qualities of any trajectory
database, making the framework applicable to data from any domain and of any
resolution. The framework is developed under recent developments on the
consistency of nonparametric hierarchical density estimators and enables the
possibility of formal statistical inference and evaluation over such intrinsic
points of interest. Comparisons of the POIs uncovered by the framework in
synthetic truth data to thousands of parameter settings for common POI
discovery methods show a marked improvement in fidelity without the need to
tune any parameters by hand.
",Intrinsic Point of Interest Discovery from Trajectory Data
"  Inverse Reinforcement Learning (IRL) is the task of learning a single reward
function given a Markov Decision Process (MDP) without defining the reward
function, and a set of demonstrations generated by humans/experts. However, in
practice, it may be unreasonable to assume that human behaviors can be
explained by one reward function since they may be inherently inconsistent.
Also, demonstrations may be collected from various users and aggregated to
infer and predict user's behaviors. In this paper, we introduce the
Non-parametric Behavior Clustering IRL algorithm to simultaneously cluster
demonstrations and learn multiple reward functions from demonstrations that may
be generated from more than one behaviors. Our method is iterative: It
alternates between clustering demonstrations into different behavior clusters
and inverse learning the reward functions until convergence. It is built upon
the Expectation-Maximization formulation and non-parametric clustering in the
IRL setting. Further, to improve the computation efficiency, we remove the need
of completely solving multiple IRL problems for multiple clusters during the
iteration steps and introduce a resampling technique to avoid generating too
many unlikely clusters. We demonstrate the convergence and efficiency of the
proposed method through learning multiple driver behaviors from demonstrations
generated from a grid-world environment and continuous trajectories collected
from autonomous robot cars using the Gazebo robot simulator.
",Inverse Reinforce Learning with Nonparametric Behavior Clustering
"  Inverse reinforcement learning (IRL) attempts to infer human rewards or
preferences from observed behavior. Since human planning systematically
deviates from rationality, several approaches have been tried to account for
specific human shortcomings. However, the general problem of inferring the
reward function of an agent of unknown rationality has received little
attention. Unlike the well-known ambiguity problems in IRL, this one is
practically relevant but cannot be resolved by observing the agent's policy in
enough environments. This paper shows (1) that a No Free Lunch result implies
it is impossible to uniquely decompose a policy into a planning algorithm and
reward function, and (2) that even with a reasonable simplicity prior/Occam's
razor on the set of decompositions, we cannot distinguish between the true
decomposition and others that lead to high regret. To address this, we need
simple `normative' assumptions, which cannot be deduced exclusively from
observations.
","Occam's razor is insufficient to infer the preferences of irrational
  agents"
"  With the increasing commoditization of computer vision, speech recognition
and machine translation systems and the widespread deployment of learning-based
back-end technologies such as digital advertising and intelligent
infrastructures, AI (Artificial Intelligence) has moved from research labs to
production. These changes have been made possible by unprecedented levels of
data and computation, by methodological advances in machine learning, by
innovations in systems software and architectures, and by the broad
accessibility of these technologies.
  The next generation of AI systems promises to accelerate these developments
and increasingly impact our lives via frequent interactions and making (often
mission-critical) decisions on our behalf, often in highly personalized
contexts. Realizing this promise, however, raises daunting challenges. In
particular, we need AI systems that make timely and safe decisions in
unpredictable environments, that are robust against sophisticated adversaries,
and that can process ever increasing amounts of data across organizations and
individuals without compromising confidentiality. These challenges will be
exacerbated by the end of the Moore's Law, which will constrain the amount of
data these technologies can store and process. In this paper, we propose
several open research directions in systems, architectures, and security that
can address these challenges and help unlock AI's potential to improve lives
and society.
",A Berkeley View of Systems Challenges for AI
"  There have been numerous breakthroughs with reinforcement learning in the
recent years, perhaps most notably on Deep Reinforcement Learning successfully
playing and winning relatively advanced computer games. There is undoubtedly an
anticipation that Deep Reinforcement Learning will play a major role when the
first AI masters the complicated game plays needed to beat a professional
Real-Time Strategy game player. For this to be possible, there needs to be a
game environment that targets and fosters AI research, and specifically Deep
Reinforcement Learning. Some game environments already exist, however, these
are either overly simplistic such as Atari 2600 or complex such as Starcraft II
from Blizzard Entertainment. We propose a game environment in between Atari
2600 and Starcraft II, particularly targeting Deep Reinforcement Learning
algorithm research. The environment is a variant of Tower Line Wars from
Warcraft III, Blizzard Entertainment. Further, as a proof of concept that the
environment can harbor Deep Reinforcement algorithms, we propose and apply a
Deep Q-Reinforcement architecture. The architecture simplifies the state space
so that it is applicable to Q-learning, and in turn improves performance
compared to current state-of-the-art methods. Our experiments show that the
proposed architecture can learn to play the environment well, and score 33%
better than standard Deep Q-learning which in turn proves the usefulness of the
game environment.
",Towards a Deep Reinforcement Learning Approach for Tower Line Wars
"  `Indifference' refers to a class of methods used to control reward based
agents. Indifference techniques aim to achieve one or more of three distinct
goals: rewards dependent on certain events (without the agent being motivated
to manipulate the probability of those events), effective disbelief (where
agents behave as if particular events could never happen), and seamless
transition from one reward function to another (with the agent acting as if
this change is unanticipated). This paper presents several methods for
achieving these goals in the POMDP setting, establishing their uses, strengths,
and requirements. These methods of control work even when the implications of
the agent's reward are otherwise not fully understood.
",'Indifference' methods for managing agent rewards
"  The rapid development of artificial intelligence has brought the artificial
intelligence threat theory as well as the problem about how to evaluate the
intelligence level of intelligent products. Both need to find a quantitative
method to evaluate the intelligence level of intelligence systems, including
human intelligence. Based on the standard intelligence system and the extended
Von Neumann architecture, this paper proposes General IQ, Service IQ and Value
IQ evaluation methods for intelligence systems, depending on different
evaluation purposes. Among them, the General IQ of intelligence systems is to
answer the question of whether the artificial intelligence can surpass the
human intelligence, which is reflected in putting the intelligence systems on
an equal status and conducting the unified evaluation. The Service IQ and Value
IQ of intelligence systems are used to answer the question of how the
intelligent products can better serve the human, reflecting the intelligence
and required cost of each intelligence system as a product in the process of
serving human.
",Three IQs of AI Systems and their Testing Methods
"  Evolution strategies (ES) are a family of black-box optimization algorithms
able to train deep neural networks roughly as well as Q-learning and policy
gradient methods on challenging deep reinforcement learning (RL) problems, but
are much faster (e.g. hours vs. days) because they parallelize better. However,
many RL problems require directed exploration because they have reward
functions that are sparse or deceptive (i.e. contain local optima), and it is
unknown how to encourage such exploration with ES. Here we show that algorithms
that have been invented to promote directed exploration in small-scale evolved
neural networks via populations of exploring agents, specifically novelty
search (NS) and quality diversity (QD) algorithms, can be hybridized with ES to
improve its performance on sparse or deceptive deep RL tasks, while retaining
scalability. Our experiments confirm that the resultant new algorithms, NS-ES
and two QD algorithms, NSR-ES and NSRA-ES, avoid local optima encountered by ES
to achieve higher performance on Atari and simulated robots learning to walk
around a deceptive trap. This paper thus introduces a family of fast, scalable
algorithms for reinforcement learning that are capable of directed exploration.
It also adds this new family of exploration algorithms to the RL toolbox and
raises the interesting possibility that analogous algorithms with multiple
simultaneous paths of exploration might also combine well with existing RL
algorithms outside ES.
","Improving Exploration in Evolution Strategies for Deep Reinforcement
  Learning via a Population of Novelty-Seeking Agents"
"  From our experiences in the past, we have seen that the growth of cities is
very much dependent on the transportation networks. In mega cities,
transportation networks determine to a significant extent as to where the
people will move and houses will be built. Hence, transportation network data
is crucial to an urban growth prediction system. Existing works have used
manually derived distance based features based on the road networks to build
models on urban growth. But due to the non-generic and laborious nature of the
manual feature engineering process, we can shift to End-to-End systems which do
not rely on manual feature engineering. In this paper, we propose a method to
integrate road network data to an existing Rule based End-to-End framework
without manual feature engineering. Our method employs recurrent neural
networks to represent road networks in a structured way such that it can be
plugged into the previously proposed End-to-End framework. The proposed
approach enhances the performance in terms of Figure of Merit, Producer's
accuracy, User's accuracy and Overall accuracy of the existing Rule based
End-to-End framework.
","Learning Representations from Road Network for End-to-End Urban Growth
  Simulation"
"  In the context of public transport modeling and simulation, we address the
problem of mismatch between simulated transit trips and observed ones. We point
to the weakness of the current travel demand modeling process; the trips it
generates are over-optimistic and do not reflect the real passenger choices. We
introduce the notion of mini activities the travelers do during the trips; they
can explain the deviation of simulated trips from the observed trips. We
propose to mine the smart card data to extract the mini activities. We develop
a technique to integrate them in the generated trips and learn such an
integration from two available sources, the trip history and trip planner
recommendations. For an input travel demand, we build a Markov chain over the
trip collection and apply the Monte Carlo Markov Chain algorithm to integrate
mini activities in such a way that the selected characteristics converge to the
desired distributions. We test our method in different settings on the
passenger trip collection of Nancy, France. We report experimental results
demonstrating a very important mismatch reduction.
",Mining Smart Card Data for Travelers' Mini Activities
"  This paper proposes a novel column generation framework for combinatorial
software testing. In particular, it combines Mathematical Programming and
Constraint Programming in a hybrid decomposition to generate covering arrays.
The approach allows generating parameterized test cases with coverage
guarantees between parameter interactions of a given application. Compared to
exhaustive testing, combinatorial test case generation reduces the number of
tests to run significantly. Our column generation algorithm is generic and can
accommodate mixed coverage arrays over heterogeneous alphabets. The algorithm
is realized in practice as a cloud service and recognized as one of the five
winners of the company-wide cloud application challenge at Oracle. The service
is currently helping software developers from a range of different product
teams in their testing efforts while exposing declarative constraint models and
hybrid optimization techniques to a broader audience.
","Column Generation for Interaction Coverage in Combinatorial Software
  Testing"
"  Learning policies for complex tasks that require multiple different skills is
a major challenge in reinforcement learning (RL). It is also a requirement for
its deployment in real-world scenarios. This paper proposes a novel framework
for efficient multi-task reinforcement learning. Our framework trains agents to
employ hierarchical policies that decide when to use a previously learned
policy and when to learn a new skill. This enables agents to continually
acquire new skills during different stages of training. Each learned task
corresponds to a human language description. Because agents can only access
previously learned skills through these descriptions, the agent can always
provide a human-interpretable description of its choices. In order to help the
agent learn the complex temporal dependencies necessary for the hierarchical
policy, we provide it with a stochastic temporal grammar that modulates when to
rely on previously learned skills and when to execute new skills. We validate
our approach on Minecraft games designed to explicitly test the ability to
reuse previously learned skills while simultaneously learning new skills.
","Hierarchical and Interpretable Skill Acquisition in Multi-task
  Reinforcement Learning"
"  Many tasks in artificial intelligence require the collaboration of multiple
agents. We exam deep reinforcement learning for multi-agent domains. Recent
research efforts often take the form of two seemingly conflicting perspectives,
the decentralized perspective, where each agent is supposed to have its own
controller; and the centralized perspective, where one assumes there is a
larger model controlling all agents. In this regard, we revisit the idea of the
master-slave architecture by incorporating both perspectives within one
framework. Such a hierarchical structure naturally leverages advantages from
one another. The idea of combining both perspectives is intuitive and can be
well motivated from many real world systems, however, out of a variety of
possible realizations, we highlights three key ingredients, i.e. composed
action representation, learnable communication and independent reasoning. With
network designs to facilitate these explicitly, our proposal consistently
outperforms latest competing methods both in synthetic experiments and when
applied to challenging StarCraft micromanagement tasks.
","Revisiting the Master-Slave Architecture in Multi-Agent Deep
  Reinforcement Learning"
"  Catastrophic forgetting has a significant negative impact in reinforcement
learning. The purpose of this study is to investigate how pseudorehearsal can
change performance of an actor-critic agent with neural-network function
approximation. We tested agent in a pole balancing task and compared different
pseudorehearsal approaches. We have found that pseudorehearsal can assist
learning and decrease forgetting.
","Pseudorehearsal in actor-critic agents with neural network function
  approximation"
"  We present DPIQN, a deep policy inference Q-network that targets multi-agent
systems composed of controllable agents, collaborators, and opponents that
interact with each other. We focus on one challenging issue in such
systems---modeling agents with varying strategies---and propose to employ
""policy features"" learned from raw observations (e.g., raw images) of
collaborators and opponents by inferring their policies. DPIQN incorporates the
learned policy features as a hidden vector into its own deep Q-network (DQN),
such that it is able to predict better Q values for the controllable agents
than the state-of-the-art deep reinforcement learning models. We further
propose an enhanced version of DPIQN, called deep recurrent policy inference
Q-network (DRPIQN), for handling partial observability. Both DPIQN and DRPIQN
are trained by an adaptive training procedure, which adjusts the network's
attention to learn the policy features and its own Q-values at different phases
of the training process. We present a comprehensive analysis of DPIQN and
DRPIQN, and highlight their effectiveness and generalizability in various
multi-agent settings. Our models are evaluated in a classic soccer game
involving both competitive and collaborative scenarios. Experimental results
performed on 1 vs. 1 and 2 vs. 2 games show that DPIQN and DRPIQN demonstrate
superior performance to the baseline DQN and deep recurrent Q-network (DRQN)
models. We also explore scenarios in which collaborators or opponents
dynamically change their policies, and show that DPIQN and DRPIQN do lead to
better overall performance in terms of stability and mean scores.
",A Deep Policy Inference Q-Network for Multi-Agent Systems
"  We present a framework combining hierarchical and multi-agent deep
reinforcement learning approaches to solve coordination problems among a
multitude of agents using a semi-decentralized model. The framework extends the
multi-agent learning setup by introducing a meta-controller that guides the
communication between agent pairs, enabling agents to focus on communicating
with only one other agent at any step. This hierarchical decomposition of the
task allows for efficient exploration to learn policies that identify globally
optimal solutions even as the number of collaborating agents increases. We show
promising initial experimental results on a simulated distributed scheduling
problem.
","Federated Control with Hierarchical Multi-Agent Deep Reinforcement
  Learning"
"  The Internet of Things (IoT) is continuously growing to connect billions of
smart devices anywhere and anytime in an Internet-like structure, which enables
a variety of applications, services and interactions between human and objects.
In the future, the smart devices are supposed to be able to autonomously
discover a target device with desired features and generate a set of entirely
new services and applications that are not supervised or even imagined by human
beings. The pervasiveness of smart devices, as well as the heterogeneity of
their design and functionalities, raise a major concern: How can a smart device
efficiently discover a desired target device? In this paper, we propose a
Social-Aware and Distributed (SAND) scheme that achieves a fast, scalable and
efficient device discovery in the IoT. The proposed SAND scheme adopts a novel
device ranking criteria that measures the device's degree, social relationship
diversity, clustering coefficient and betweenness. Based on the device ranking
criteria, the discovery request can be guided to travel through critical
devices that stand at the major intersections of the network, and thus quickly
reach the desired target device by contacting only a limited number of
intermediate devices. With the help of such an intelligent device discovery as
SAND, the IoT devices, as well as other computing facilities, software and data
on the Internet, can autonomously establish new social connections with each
other as human being do. They can formulate self-organized computing groups to
perform required computing tasks, facilitate a fusion of a variety of computing
service, network service and data to generate novel applications and services,
evolve from the individual aritificial intelligence to the collaborative
intelligence, and eventually enable the birth of a robot society.
","Intelligent Device Discovery in the Internet of Things - Enabling the
  Robot Society"
"  Minimizing adverse reactions caused by drug-drug interactions has always been
a momentous research topic in clinical pharmacology. Detecting all possible
interactions through clinical studies before a drug is released to the market
is a demanding task. The power of big data is opening up new approaches to
discover various drug-drug interactions. However, these discoveries contain a
huge amount of noise and provide knowledge bases far from complete and
trustworthy ones to be utilized. Most existing studies focus on predicting
binary drug-drug interactions between drug pairs but ignore other interactions.
In this paper, we propose a novel framework, called PRD, to predict drug-drug
interactions. The framework uses the graph embedding that can overcome data
incompleteness and sparsity issues to achieve multiple DDI label prediction.
First, a large-scale drug knowledge graph is generated from different sources.
Then, the knowledge graph is embedded with comprehensive biomedical text into a
common low dimensional space. Finally, the learned embeddings are used to
efficiently compute rich DDI information through a link prediction process. To
validate the effectiveness of the proposed framework, extensive experiments
were conducted on real-world datasets. The results demonstrate that our model
outperforms several state-of-the-art baseline methods in terms of capability
and accuracy.
","Predicting Rich Drug-Drug Interactions via Biomedical Knowledge Graphs
  and Text Jointly Embedding"
"  Recent developments have established the vulnerability of deep Reinforcement
Learning (RL) to policy manipulation attacks via adversarial perturbations. In
this paper, we investigate the robustness and resilience of deep RL to
training-time and test-time attacks. Through experimental results, we
demonstrate that under noncontiguous training-time attacks, Deep Q-Network
(DQN) agents can recover and adapt to the adversarial conditions by reactively
adjusting the policy. Our results also show that policies learned under
adversarial perturbations are more robust to test-time attacks. Furthermore, we
compare the performance of $\epsilon$-greedy and parameter-space noise
exploration methods in terms of robustness and resilience against adversarial
perturbations.
","Whatever Does Not Kill Deep Reinforcement Learning, Makes It Stronger"
"  Research in analogical reasoning suggests that higher-order cognitive
functions such as abstract reasoning, far transfer, and creativity are founded
on recognizing structural similarities among relational systems. Here we
integrate theories of analogy with the computational framework of reinforcement
learning (RL). We propose a psychology theory that is a computational synergy
between analogy and RL, in which analogical comparison provides the RL learning
algorithm with a measure of relational similarity, and RL provides feedback
signals that can drive analogical learning. Simulation results support the
power of this approach.
","Reinforcement Learning with Analogical Similarity to Guide Schema
  Induction and Attention"
"  The game Starcraft is one of the most interesting arenas to test new machine
learning and computational intelligence techniques; however, StarCraft matches
take a long time and creating a good dataset for training can be hard. Besides,
analyzing match logs to extract the main characteristics can also be done in
many different ways to the point that extracting and processing data itself can
take an inordinate amount of time and of course, depending on what you choose,
can bias learning algorithms. In this paper we present a simplified dataset
extracted from the set of matches published by Robinson and Watson, which we
have called RedDwarfData, containing several thousand matches processed to
frames, so that temporal studies can also be undertaken. This dataset is
available from GitHub under a free license. An initial analysis and appraisal
of these matches is also made.
",RedDwarfData: a simplified dataset of StarCraft matches
"  The DeepMind Control Suite is a set of continuous control tasks with a
standardised structure and interpretable rewards, intended to serve as
performance benchmarks for reinforcement learning agents. The tasks are written
in Python and powered by the MuJoCo physics engine, making them easy to use and
modify. We include benchmarks for several learning algorithms. The Control
Suite is publicly available at https://www.github.com/deepmind/dm_control . A
video summary of all tasks is available at http://youtu.be/rAai4QzcYbs .
",DeepMind Control Suite
"  As a generalization of Dempster-Shafer theory, the theory of D numbers is a
new theoretical framework for uncertainty reasoning. Measuring the uncertainty
of knowledge or information represented by D numbers is an unsolved issue in
that theory. In this paper, inspired by distance based uncertainty measures for
Dempster-Shafer theory, a total uncertainty measure for a D number is proposed
based on its belief intervals. The proposed total uncertainty measure can
simultaneously capture the discord, and non-specificity, and non-exclusiveness
involved in D numbers. And some basic properties of this total uncertainty
measure, including range, monotonicity, generalized set consistency, are also
presented.
",A total uncertainty measure for D numbers based on belief intervals
"  ViZDoom is a robust, first-person shooter reinforcement learning environment,
characterized by a significant degree of latent state information. In this
paper, double-Q learning and prioritized experience replay methods are tested
under a certain ViZDoom combat scenario using a competitive deep recurrent
Q-network (DRQN) architecture. In addition, an ensembling technique known as
snapshot ensembling is employed using a specific annealed learning rate to
observe differences in ensembling efficacy under these two methods. Annealed
learning rates are important in general to the training of deep neural network
models, as they shake up the status-quo and counter a model's tending towards
local optima. While both variants show performance exceeding those of built-in
AI agents of the game, the known stabilizing effects of double-Q learning are
illustrated, and priority experience replay is again validated in its
usefulness by showing immediate results early on in agent development, with the
caveat that value overestimation is accelerated in this case. In addition, some
unique behaviors are observed to develop for priority experience replay (PER)
and double-Q (DDQ) variants, and snapshot ensembling of both PER and DDQ proves
a valuable method for improving performance of the ViZDoom Marine.
","ViZDoom: DRQN with Prioritized Experience Replay, Double-Q Learning, &
  Snapshot Ensembling"
"  We examine implemented systems for ethical machine reasoning with a view to
identifying the practical challenges (as opposed to philosophical challenges)
posed by the area. We identify a need for complex ethical machine reasoning not
only to be multi-objective, proactive, and scrutable but that it must draw on
heterogeneous evidential reasoning. We also argue that, in many cases, it needs
to operate in real time and be verifiable. We propose a general architecture
involving a declarative ethical arbiter which draws upon multiple evidential
reasoners each responsible for a particular ethical feature of the system's
environment. We claim that this architecture enables some separation of
concerns among the practical challenges that ethical machine reasoning poses.
",Practical Challenges in Explicit Ethical Machine Reasoning
"  In fact, there exist three genres of intelligence architectures: logics (e.g.
\textit{Random Forest, A$^*$ Searching}), neurons (e.g. \textit{CNN, LSTM}) and
probabilities (e.g. \textit{Naive Bayes, HMM}), all of which are incompatible
to each other. However, to construct powerful intelligence systems with various
methods, we propose the intelligence graph (short as \textbf{\textit{iGraph}}),
which is composed by both of neural and probabilistic graph, under the
framework of forward-backward propagation. By the paradigm of iGraph, we design
a recommendation model with semantic principle. First, the probabilistic
distributions of categories are generated from the embedding representations of
users/items, in the manner of neurons. Second, the probabilistic graph infers
the distributions of features, in the manner of probabilities. Last, for the
recommendation diversity, we perform an expectation computation then conduct a
logic judgment, in the manner of logics. Experimentally, we beat the
state-of-the-art baselines and verify our conclusions.
",Intelligence Graph
"  In the age of algorithms, I focus on the question of how to ensure algorithms
that will take over many of our familiar archival and library tasks, will
behave according to human ethical norms that have evolved over many years. I
start by characterizing physical archives in the context of related
institutions such as libraries and museums. In this setting I analyze how
ethical principles, in particular about access to information, have been
formalized and communicated in the form of ethical codes, or: codes of
conducts. After that I describe two main developments: digitalization, in which
physical aspects of the world are turned into digital data, and
algorithmization, in which intelligent computer programs turn this data into
predictions and decisions. Both affect interactions that were once physical but
now digital. In this new setting I survey and analyze the ethical aspects of
algorithms and how they shape a vision on the future of archivists and
librarians, in the form of algorithmic documentalists, or: codementalists.
Finally I outline a general research strategy, called IntERMEeDIUM, to obtain
algorithms that obey are human ethical values encoded in code of ethics.
","Gatekeeping Algorithms with Human Ethical Bias: The ethics of algorithms
  in archives, libraries and society"
"  Individual and group decisions are complex, often involving choosing an apt
alternative from a multitude of options. Evaluating pairwise comparisons breaks
down such complex decision problems into tractable ones. Pairwise comparison
matrices (PCMs) are regularly used to solve multiple-criteria decision-making
(MCDM) problems, for example, using Saaty's analytic hierarchy process (AHP)
framework. However, there are two significant drawbacks of using PCMs. First,
humans evaluate PCMs in an inconsistent manner. Second, not all entries of a
large PCM can be reliably filled by human decision makers. We address these two
issues by first establishing a novel connection between PCMs and
time-irreversible Markov processes. Specifically, we show that every PCM
induces a family of dissipative maximum path entropy random walks (MERW) over
the set of alternatives. We show that only `consistent' PCMs correspond to
detailed balanced MERWs. We identify the non-equilibrium entropy production in
the induced MERWs as a metric of inconsistency of the underlying PCMs. Notably,
the entropy production satisfies all of the recently laid out criteria for
reasonable consistency indices. We also propose an approach to use incompletely
filled PCMs in AHP. Potential future avenues are discussed as well.
  keywords: analytic hierarchy process, markov chains, maximum entropy
","Entropy production rate as a criterion for inconsistency in decision
  theory"
"  Our approach is basically a coherence approach, but we avoid the well-known
pitfalls of coherence theories of truth. Consistency is replaced by
reliability, which expresses support and attack, and, in principle, every
theory (or agent, message) counts. At the same time, we do not require a
priviledged access to ""reality"". A centerpiece of our approach is that we
attribute reliability also to agents, messages, etc., so an unreliable source
of information will be less important in future. Our ideas can also be extended
to value systems, and even actions, e.g., of animals.
",A Reliability Theory of Truth
"  Symbolic Regression tries to find a mathematical expression that describes
the relationship of a set of explanatory variables to a measured variable. The
main objective is to find a model that minimizes the error and, optionally,
that also minimizes the expression size. A smaller expression can be seen as an
interpretable model considered a reliable decision model. This is often
performed with Genetic Programming which represents their solution as
expression trees. The shortcoming of this algorithm lies on this representation
that defines a rugged search space and contains expressions of any size and
difficulty. These pose as a challenge to find the optimal solution under
computational constraints. This paper introduces a new data structure, called
Interaction-Transformation (IT), that constrains the search space in order to
exclude a region of larger and more complicated expressions. In order to test
this data structure, it was also introduced an heuristic called SymTree. The
obtained results show evidence that SymTree are capable of obtaining the
optimal solution whenever the target function is within the search space of the
IT data structure and competitive results when it is not. Overall, the
algorithm found a good compromise between accuracy and simplicity for all the
generated models.
",A Greedy Search Tree Heuristic for Symbolic Regression
"  For pattern recognition like image recognition, it has become clear that each
machine-learning dictionary data actually became data in probability space
belonging to Euclidean space. However, the distances in the Euclidean space and
the distances in the probability space are separated and ununified when machine
learning is introduced in the pattern recognition. There is still a problem
that it is impossible to directly calculate an accurate matching relation
between the sampling data of the read image and the learned dictionary data. In
this research, we focused on the reason why the distance is changed and the
extent of change when passing through the probability space from the original
Euclidean distance among data belonging to multiple probability spaces
containing Euclidean space. By finding the reason of the cause of the distance
error and finding the formula expressing the error quantitatively, a possible
distance formula to unify Euclidean space and probability space is found. Based
on the results of this research, the relationship between machine-learning
dictionary data and sampling data was clearly understood for pattern
recognition. As a result, the calculation of collation among data and
machine-learning to compete mutually between data are cleared, and complicated
calculations became unnecessary. Finally, using actual pattern recognition
data, experimental demonstration of a possible distance formula to unify
Euclidean space and probability space discovered by this research was carried
out, and the effectiveness of the result was confirmed.
","Distance formulas capable of unifying Euclidian space and probability
  space"
"  We present a dockerized version of a real-time strategy game StarCraft: Brood
War, commonly used as a domain for AI research, with a pre-installed collection
of AI developement tools supporting all the major types of StarCraft bots. This
provides a convenient way to deploy StarCraft AIs on numerous hosts at once and
across multiple platforms despite limited OS support of StarCraft. In this
technical report, we describe the design of our Docker images and present a few
use cases.
","Multi-platform Version of StarCraft: Brood War in a Docker Container:
  Technical Report"
"  The Winograd Schema Challenge (WSC) is a test of machine intelligence,
designed to be an improvement on the Turing test. A Winograd Schema consists of
a sentence and a corresponding question. To successfully answer these
questions, one requires the use of commonsense knowledge and reasoning. This
work focuses on extracting common sense knowledge which can be used to generate
answers for the Winograd schema challenge. Common sense knowledge is extracted
based on events (or actions) and their participants; called Event-Based
Conditional Commonsense (ECC). I propose an approach using Narrative Event
Chains [Chambers et al., 2008] to extract ECC knowledge. These are stored in
templates, to be later used for answering the WSC questions. This approach
works well with respect to a subset of WSC tasks.
",Winograd Schema - Knowledge Extraction Using Narrative Chains
"  Concept-cognitive learning (CCL) is a hot topic in recent years, and it has
attracted much attention from the communities of formal concept analysis,
granular computing and cognitive computing. However, the relationship among
cognitive computing (CC), concept-cognitive computing (CCC), CCL and
concept-cognitive learning model (CCLM) is not clearly described. To this end,
we first explain the relationship of CC, CCC, CCL and CCLM. Then, we propose a
generalized concept-cognitive learning (GCCL) from the point of view of machine
learning. Finally, experiments on some data sets are conducted to verify the
feasibility of concept formation and concept-cognitive process of GCCL.
",A generalized concept-cognitive learning: A machine learning viewpoint
"  We present a study in Distributed Deep Reinforcement Learning (DDRL) focused
on scalability of a state-of-the-art Deep Reinforcement Learning algorithm
known as Batch Asynchronous Advantage ActorCritic (BA3C). We show that using
the Adam optimization algorithm with a batch size of up to 2048 is a viable
choice for carrying out large scale machine learning computations. This,
combined with careful reexamination of the optimizer's hyperparameters, using
synchronous training on the node level (while keeping the local, single node
part of the algorithm asynchronous) and minimizing the memory footprint of the
model, allowed us to achieve linear scaling for up to 64 CPU nodes. This
corresponds to a training time of 21 minutes on 768 CPU cores, as opposed to 10
hours when using a single node with 24 cores achieved by a baseline single-node
implementation.
","Distributed Deep Reinforcement Learning: Learn how to play Atari games
  in 21 minutes"
"  We propose a deep learning model - Probabilistic Prognostic Estimates of
Survival in Metastatic Cancer Patients (PPES-Met) for estimating short-term
life expectancy (3 months) of the patients by analyzing free-text clinical
notes in the electronic medical record, while maintaining the temporal visit
sequence. In a single framework, we integrated semantic data mapping and neural
embedding technique to produce a text processing method that extracts relevant
information from heterogeneous types of clinical notes in an unsupervised
manner, and we designed a recurrent neural network to model the temporal
dependency of the patient visits. The model was trained on a large dataset
(10,293 patients) and validated on a separated dataset (1818 patients). Our
method achieved an area under the ROC curve (AUC) of 0.89. To provide
explain-ability, we developed an interactive graphical tool that may improve
physician understanding of the basis for the model's predictions. The high
accuracy and explain-ability of the PPES-Met model may enable our model to be
used as a decision support tool to personalize metastatic cancer treatment and
provide valuable assistance to the physicians.
","Abstract: Probabilistic Prognostic Estimates of Survival in Metastatic
  Cancer Patients"
"  Experience replay allows a reinforcement learning agent to train on samples
from a large amount of the most recent experiences. A simple in-RAM experience
replay stores these most recent experiences in a list in RAM, and then copies
sampled batches to the GPU for training. I moved this list to the GPU, thus
creating an in-GPU experience replay, and a training step that no longer has
inputs copied from the CPU. I trained an agent to play Super Smash Bros. Melee,
using internal game memory values as inputs and outputting controller button
presses. A single state in Melee contains 27 floats, so the full experience
replay fits on a single GPU. For a batch size of 128, the in-GPU experience
replay trained twice as fast as the in-RAM experience replay. As far as I know,
this is the first in-GPU implementation of experience replay. Finally, I note a
few ideas for fitting the experience replay inside the GPU when the environment
state requires more memory.
",Deep In-GPU Experience Replay
"  We present a formalization and computational implementation of the second
formulation of Kant's categorical imperative. This ethical principle requires
an agent to never treat someone merely as a means but always also as an end.
Here we interpret this principle in terms of how persons are causally affected
by actions. We introduce Kantian causal agency models in which moral patients,
actions, goals, and causal influence are represented, and we show how to
formalize several readings of Kant's categorical imperative that correspond to
Kant's concept of strict and wide duties towards oneself and others. Stricter
versions handle cases where an action directly causally affects oneself or
others, whereas the wide version maximizes the number of persons being treated
as an end. We discuss limitations of our formalization by pointing to one of
Kant's cases that the machinery cannot handle in a satisfying way.
","A Formalization of Kant's Second Formulation of the Categorical
  Imperative"
"  This short paper describes our ongoing research on Greenhouse - a
zero-positive machine learning system for time-series anomaly detection.
","Greenhouse: A Zero-Positive Machine Learning System for Time-Series
  Anomaly Detection"
"  Classical anomaly detection is principally concerned with point-based
anomalies, anomalies that occur at a single data point. In this paper, we
present a new mathematical model to express range-based anomalies, anomalies
that occur over a range (or period) of time.
",Precision and Recall for Range-Based Anomaly Detection
"  Methods for learning optimal policies in autonomous agents often assume that
the way the domain is conceptualised---its possible states and actions and
their causal structure---is known in advance and does not change during
learning. This is an unrealistic assumption in many scenarios, because new
evidence can reveal important information about what is possible, possibilities
that the agent was not aware existed prior to learning. We present a model of
an agent which both discovers and learns to exploit unforeseen possibilities
using two sources of evidence: direct interaction with the world and
communication with a domain expert. We use a combination of probabilistic and
symbolic reasoning to estimate all components of the decision problem,
including its set of random variables and their causal dependencies. Agent
simulations show that the agent converges on optimal polices even when it
starts out unaware of factors that are critical to behaving optimally.
",Reasoning about Unforeseen Possibilities During Policy Learning
"  Recently, width-based planning methods have been shown to yield
state-of-the-art results in the Atari 2600 video games. For this, the states
were associated with the (RAM) memory states of the simulator. In this work, we
consider the same planning problem but using the screen instead. By using the
same visual inputs, the planning results can be compared with those of humans
and learning methods. We show that the planning approach, out of the box and
without training, results in scores that compare well with those obtained by
humans and learning methods, and moreover, by developing an episodic, rollout
version of the IW(k) algorithm, we show that such scores can be obtained in
almost real time.
",Planning with Pixels in (Almost) Real Time
"  Pairwise comparison matrices often exhibit inconsistency, therefore many
indices have been suggested to measure their deviation from a consistent
matrix. A set of axioms has been proposed recently that is required to be
satisfied by any reasonable inconsistency index. This set seems to be not
exhaustive as illustrated by an example, hence it is expanded by adding two new
properties. All axioms are considered on the set of triads, pairwise comparison
matrices with three alternatives, which is the simplest case of inconsistency.
We choose the logically independent properties and prove that they
characterize, that is, uniquely determine the inconsistency ranking induced by
most inconsistency indices that coincide on this restricted domain. Since
triads play a prominent role in a number of inconsistency indices, our results
can also contribute to the measurement of inconsistency for pairwise comparison
matrices with more than three alternatives.
",Axiomatizations of inconsistency indices for triads
"  We consider the task of program synthesis in the presence of a reward
function over the output of programs, where the goal is to find programs with
maximal rewards. We employ an iterative optimization scheme, where we train an
RNN on a dataset of K best programs from a priority queue of the generated
programs so far. Then, we synthesize new programs and add them to the priority
queue by sampling from the RNN. We benchmark our algorithm, called priority
queue training (or PQT), against genetic algorithm and reinforcement learning
baselines on a simple but expressive Turing complete programming language
called BF. Our experimental results show that our simple PQT algorithm
significantly outperforms the baselines. By adding a program length penalty to
the reward function, we are able to synthesize short, human readable programs.
",Neural Program Synthesis with Priority Queue Training
"  Partially Observable Markov Decision Processes (POMDPs) are rich environments
often used in machine learning. But the issue of information and causal
structures in POMDPs has been relatively little studied. This paper presents
the concepts of equivalent and counterfactually equivalent POMDPs, where agents
cannot distinguish which environment they are in though any observations and
actions. It shows that any POMDP is counterfactually equivalent, for any finite
number of turns, to a deterministic POMDP with all uncertainty concentrated
into the initial state. This allows a better understanding of POMDP
uncertainty, information, and learning.
","Counterfactual equivalence for POMDPs, and underlying deterministic
  environments"
"  The highly influential framework of conceptual spaces provides a geometric
way of representing knowledge. Instances are represented by points in a
similarity space and concepts are represented by convex regions in this space.
After pointing out a problem with the convexity requirement, we propose a
formalization of conceptual spaces based on fuzzy star-shaped sets. Our
formalization uses a parametric definition of concepts and extends the original
framework by adding means to represent correlations between different domains
in a geometric way. Moreover, we define various operations for our
formalization, both for creating new concepts from old ones and for measuring
relations between concepts. We present an illustrative toy-example and sketch a
research project on concept formation that is based on both our formalization
and its implementation.
","Formalized Conceptual Spaces with a Geometric Representation of
  Correlations"
"  Deep reinforcement learning has achieved great strides in solving challenging
motion control tasks. Recently, there has been significant work on methods for
exploiting the data gathered during training, but there has been less work on
how to best generate the data to learn from. For continuous action domains, the
most common method for generating exploratory actions involves sampling from a
Gaussian distribution centred around the mean action output by a policy.
Although these methods can be quite capable, they do not scale well with the
dimensionality of the action space, and can be dangerous to apply on hardware.
We consider learning a forward dynamics model to predict the result,
($x_{t+1}$), of taking a particular action, ($u$), given a specific observation
of the state, ($x_{t}$). With this model we perform internal look-ahead
predictions of outcomes and seek actions we believe have a reasonable chance of
success. This method alters the exploratory action space, thereby increasing
learning speed and enables higher quality solutions to difficult problems, such
as robotic locomotion and juggling.
",Model-Based Action Exploration for Learning Dynamic Motion Skills
"  Human speech is the most important part of General Artificial Intelligence
and subject of much research. The hypothesis proposed in this article provides
explanation of difficulties that modern science tackles in the field of human
brain simulation. The hypothesis is based on the author's conviction that the
brain of any given person has different ability to process and store
information. Therefore, the approaches that are currently used to create
General Artificial Intelligence have to be altered.
",Multilayered Model of Speech
"  The goal of the Internet of Things (IoT) is to transform any thing around us,
such as a trash can or a street light, into a smart thing. A smart thing has
the ability of sensing, processing, communicating and/or actuating. In order to
achieve the goal of a smart IoT application, such as minimizing waste
transportation costs or reducing energy consumption, the smart things in the
application scenario must cooperate with each other without a centralized
control. Inspired by known approaches to design swarm of cooperative and
autonomous robots, we modeled our smart things based on the embodied cognition
concept. Each smart thing is a physical agent with a body composed of a
microcontroller, sensors and actuators, and a brain that is represented by an
artificial neural network. This type of agent is commonly called an embodied
agent. The behavior of these embodied agents is autonomously configured through
an evolutionary algorithm that is triggered according to the application
performance. To illustrate, we have designed three homogeneous prototypes for
smart street lights based on an evolved network. This application has shown
that the proposed approach results in a feasible way of modeling decentralized
smart things with self-developed and cooperative capabilities.
",Engineering Cooperative Smart Things based on Embodied Cognition
"  We introduce a new computational model of moral decision making, drawing on a
recent theory of commonsense moral learning via social dynamics. Our model
describes moral dilemmas as a utility function that computes trade-offs in
values over abstract moral dimensions, which provide interpretable parameter
values when implemented in machine-led ethical decision-making. Moreover,
characterizing the social structures of individuals and groups as a
hierarchical Bayesian model, we show that a useful description of an
individual's moral values - as well as a group's shared values - can be
inferred from a limited amount of observed data. Finally, we apply and evaluate
our approach to data from the Moral Machine, a web application that collects
human judgments on moral dilemmas involving autonomous vehicles.
",A Computational Model of Commonsense Moral Decision Making
"  Successful completion of reasoning task requires the agent to have relevant
prior knowledge or some given context of the world dynamics. Usually, the
information provided to the system for a reasoning task is just the query or
some supporting story, which is often not enough for common reasoning tasks.
The goal here is that, if the information provided along the question is not
sufficient to correctly answer the question, the model should choose k most
relevant documents that can aid its inference process. In this work, the model
dynamically selects top k most relevant memory candidates that can be used to
successfully solve reasoning tasks. Experiments were conducted on a subset of
Winograd Schema Challenge (WSC) problems to show that the proposed model has
the potential for commonsense reasoning. The WSC is a test of machine
intelligence, designed to be an improvement on the Turing test.
",Top k Memory Candidates in Memory Networks for Common Sense Reasoning
"  Systems are typically made from simple components regardless of their
complexity. While the function of each part is easily understood, higher order
functions are emergent properties and are notoriously difficult to explain. In
networked systems, both digital and biological, each component receives inputs,
performs a simple computation, and creates an output. When these components
have multiple outputs, we intuitively assume that the outputs are causally
dependent on the inputs but are themselves independent of each other given the
state of their shared input. However, this intuition can be violated for
components with probabilistic logic, as these typically cannot be decomposed
into separate logic gates with one output each. This violation of conditional
independence on the past system state is equivalent to instantaneous
interaction --- the idea is that some information between the outputs is not
coming from the inputs and thus must have been created instantaneously. Here we
compare evolved artificial neural systems with and without instantaneous
interaction across several task environments. We show that systems without
instantaneous interactions evolve faster, to higher final levels of
performance, and require fewer logic components to create a densely connected
cognitive machinery.
","The Role of Conditional Independence in the Evolution of Intelligent
  Systems"
"  While the philosophical literature has extensively studied how decisions
relate to arguments, reasons and justifications, decision theory almost
entirely ignores the latter notions and rather focuses on preference and
belief. In this article, we argue that decision theory can largely benefit from
explicitly taking into account the stance that decision-makers take towards
arguments and counter-arguments. To that end, we elaborate a formal framework
aiming to integrate the role of arguments and argumentation in decision theory
and decision aid. We start from a decision situation, where an individual
requests decision support. In this context, we formally define, as a
commendable basis for decision-aid, this individual's deliberated judgment,
popularized by Rawls. We explain how models of deliberated judgment can be
validated empirically. We then identify conditions upon which the existence of
a valid model can be taken for granted, and analyze how these conditions can be
relaxed. We then explore the significance of our proposed framework for
decision aiding practice. We argue that our concept of deliberated judgment
owes its normative credentials both to its normative foundations (the idea of
rationality based on arguments) and to its reference to empirical reality (the
stance that real, empirical individuals hold towards arguments and
counter-arguments, on due reflection). We then highlight that our framework
opens promising avenues for future research involving both philosophical and
decision theoretic approaches, as well as empirical implementations.
",A formal framework for deliberated judgment
"  The concept of innateness is rarely discussed in the context of artificial
intelligence. When it is discussed, or hinted at, it is often the context of
trying to reduce the amount of innate machinery in a given system. In this
paper, I consider as a test case a recent series of papers by Silver et al
(Silver et al., 2017a) on AlphaGo and its successors that have been presented
as an argument that a ""even in the most challenging of domains: it is possible
to train to superhuman level, without human examples or guidance"", ""starting
tabula rasa.""
  I argue that these claims are overstated, for multiple reasons. I close by
arguing that artificial intelligence needs greater attention to innateness, and
I point to some proposals about what that innateness might look like.
","Innateness, AlphaZero, and Artificial Intelligence"
"  Recent advances in deep learning have allowed artificial agents to rival
human-level performance on a wide range of complex tasks; however, the ability
of these networks to learn generalizable strategies remains a pressing
challenge. This critical limitation is due in part to two factors: the opaque
information representation in deep neural networks and the complexity of the
task environments in which they are typically deployed. Here we propose a novel
Hierarchical Q-Network (HQN) motivated by theories of the hierarchical
organization of the human prefrontal cortex, that attempts to identify lower
dimensional patterns in the value landscape that can be exploited to construct
an internal model of rules in simple environments. We draw on combinatorial
games, where there exists a single optimal strategy for winning that
generalizes across other features of the game, to probe the strategy
generalization of the HQN and other reinforcement learning (RL) agents using
variations of Wythoff's game. Traditional RL approaches failed to reach
satisfactory performance on variants of Wythoff's Game; however, the HQN
learned heuristic-like strategies that generalized across changes in board
configuration. More importantly, the HQN allowed for transparent inspection of
the agent's internal model of the game following training. Our results show how
a biologically inspired hierarchical learner can facilitate learning abstract
rules to promote robust and flexible action policies in simplified training
environments with clearly delineated optimal strategies.
","Learning model-based strategies in simple environments with hierarchical
  q-networks"
"  Starting from the observation that rational closure has the undesirable
property of being an ""all or nothing"" mechanism, we here propose a
multipreferential semantics, which enriches the preferential semantics
underlying rational closure in order to separately deal with the inheritance of
different properties in an ontology with exceptions. We provide a
multipreference closure mechanism which is sound with respect to the
multipreference semantics.
","Reasoning about multiple aspects in DLs: Semantics and Closure
  Construction"
"  We present CHALET, a 3D house simulator with support for navigation and
manipulation. CHALET includes 58 rooms and 10 house configuration, and allows
to easily create new house and room layouts. CHALET supports a range of common
household activities, including moving objects, toggling appliances, and
placing objects inside closeable containers. The environment and actions
available are designed to create a challenging domain to train and evaluate
autonomous agents, including for tasks that combine language, vision, and
planning in a dynamic environment.
",CHALET: Cornell House Agent Learning Environment
"  This paper describes the application of comparison training (CT) for
automatic feature weight tuning, with the final objective of improving the
evaluation functions used in Chinese chess programs. First, we propose an
n-tuple network to extract features, since n-tuple networks require very little
expert knowledge through its large numbers of features, while simulta-neously
allowing easy access. Second, we propose a novel evalua-tion method that
incorporates tapered eval into CT. Experiments show that with the same features
and the same Chinese chess program, the automatically tuned comparison training
feature weights achieved a win rate of 86.58% against the weights that were
hand-tuned. The above trained version was then improved by adding additional
features, most importantly n-tuple features. This improved version achieved a
win rate of 81.65% against the trained version without additional features.
",Comparison Training for Computer Chinese Chess
"  We propose a curiosity reward based on information theory principles and
consistent with the animal instinct to maintain certain critical parameters
within a bounded range. Our experimental validation shows the added value of
the additional homeostatic drive to enhance the overall information gain of a
reinforcement learning agent interacting with a complex environment using
continuous actions. Our method builds upon two ideas: i) To take advantage of a
new Bellman-like equation of information gain and ii) to simplify the
computation of the local rewards by avoiding the approximation of complex
distributions over continuous states and actions.
",Curiosity-driven reinforcement learning with homeostatic regulation
"  The foundations of all methodologies for the measurement and verification
(M&V) of energy savings are based on the same five key principles: accuracy,
completeness, conservatism, consistency and transparency. The most widely
accepted methodologies tend to generalise M&V so as to ensure applicability
across the spectrum of energy conservation measures (ECM's). These do not
provide a rigid calculation procedure to follow. This paper aims to bridge the
gap between high-level methodologies and the practical application of modelling
algorithms, with a focus on the industrial buildings sector. This is achieved
with the development of a novel, machine learning supported methodology for M&V
2.0 which enables accurate quantification of savings.
  A novel and computationally efficient feature selection algorithm and
powerful machine learning regression algorithms are employed to maximise the
effectiveness of available data. The baseline period energy consumption is
modelled using artificial neural networks, support vector machines, k-nearest
neighbours and multiple ordinary least squares regression. Improved knowledge
discovery and an expanded boundary of analysis allow more complex energy
systems be analysed, thus increasing the applicability of M&V. A case study in
a large biomedical manufacturing facility is used to demonstrate the
methodology's ability to accurately quantify the savings under real-world
conditions. The ECM was found to result in 604,527 kWh of energy savings with
57% uncertainty at a confidence interval of 68%. 20 baseline energy models are
developed using an exhaustive approach with the optimal model being used to
quantify savings. The range of savings estimated with each model are presented
and the acceptability of uncertainty is reviewed. The case study demonstrates
the ability of the methodology to perform M&V to an acceptable standard in
challenging circumstances.
","Development and application of a machine learning supported methodology
  for measurement and verification (M&V) 2.0"
"  This paper investigates estimating the variance of a temporal-difference
learning agent's update target. Most reinforcement learning methods use an
estimate of the value function, which captures how good it is for the agent to
be in a particular state and is mathematically expressed as the expected sum of
discounted future rewards (called the return). These values can be
straightforwardly estimated by averaging batches of returns using Monte Carlo
methods. However, if we wish to update the agent's value estimates during
learning--before terminal outcomes are observed--we must use a different
estimation target called the {\lambda}-return, which truncates the return with
the agent's own estimate of the value function. Temporal difference learning
methods estimate the expected {\lambda}-return for each state, allowing these
methods to update online and incrementally, and in most cases achieve better
generalization error and faster learning than Monte Carlo methods. Naturally
one could attempt to estimate higher-order moments of the {\lambda}-return.
This paper is about estimating the variance of the {\lambda}-return. Prior work
has shown that given estimates of the variance of the {\lambda}-return,
learning systems can be constructed to (1) mitigate risk in action selection,
and (2) automatically adapt the parameters of the learning process itself to
improve performance. Unfortunately, existing methods for estimating the
variance of the {\lambda}-return are complex and not well understood
empirically. We contribute a method for estimating the variance of the
{\lambda}-return directly using policy evaluation methods from reinforcement
learning. Our approach is significantly simpler than prior methods that
independently estimate the second moment of the {\lambda}-return. Empirically
our new approach behaves at least as well as existing approaches, but is
generally more robust.
","Directly Estimating the Variance of the {\lambda}-Return Using
  Temporal-Difference Methods"
"  In this paper, we study the problem of discovering the Markov blanket (MB) of
a target variable from multiple interventional datasets. Datasets attained from
interventional experiments contain richer causal information than passively
observed data (observational data) for MB discovery. However, almost all
existing MB discovery methods are designed for finding MBs from a single
observational dataset. To identify MBs from multiple interventional datasets,
we face two challenges: (1) unknown intervention variables; (2) nonidentical
data distributions. To tackle the challenges, we theoretically analyze (a)
under what conditions we can find the correct MB of a target variable, and (b)
under what conditions we can identify the causes of the target variable via
discovering its MB. Based on the theoretical analysis, we propose a new
algorithm for discovering MBs from multiple interventional datasets, and
present the conditions/assumptions which assure the correctness of the
algorithm. To our knowledge, this work is the first to present the theoretical
analyses about the conditions for MB discovery in multiple interventional
datasets and the algorithm to find the MBs in relation to the conditions. Using
benchmark Bayesian networks and real-world datasets, the experiments have
validated the effectiveness and efficiency of the proposed algorithm in the
paper.
",Discovering Markov Blanket from Multiple interventional Datasets
"  Automated planning is a major topic of research in artificial intelligence,
and enjoys a long and distinguished history. The classical paradigm assumes a
distinguished initial state, comprised of a set of facts, and is defined over a
set of actions which change that state in one way or another. Planning in many
real-world settings, however, is much more involved: an agent's knowledge is
almost never simply a set of facts that are true, and actions that the agent
intends to execute never operate the way they are supposed to. Thus,
probabilistic planning attempts to incorporate stochastic models directly into
the planning process. In this article, we briefly report on probabilistic
planning through the lens of probabilistic programming: a programming paradigm
that aims to ease the specification of structured probability distributions. In
particular, we provide an overview of the features of two systems, HYPE and
ALLEGRO, which emphasise different strengths of probabilistic programming that
are particularly useful for complex modelling issues raised in probabilistic
planning. Among other things, with these systems, one can instantiate planning
problems with growing and shrinking state spaces, discrete and continuous
probability distributions, and non-unique prior distributions in a first-order
setting.
",Probabilistic Planning by Probabilistic Programming
"  To solve the text-based question and answering task that requires relational
reasoning, it is necessary to memorize a large amount of information and find
out the question relevant information from the memory. Most approaches were
based on external memory and four components proposed by Memory Network. The
distinctive component among them was the way of finding the necessary
information and it contributes to the performance. Recently, a simple but
powerful neural network module for reasoning called Relation Network (RN) has
been introduced. We analyzed RN from the view of Memory Network, and realized
that its MLP component is able to reveal the complicate relation between
question and object pair. Motivated from it, we introduce which uses MLP to
find out relevant information on Memory Network architecture. It shows new
state-of-the-art results in jointly trained bAbI-10k story-based question
answering tasks and bAbI dialog-based question answering tasks.
","Finding ReMO (Related Memory Object): A Simple Neural Architecture for
  Text based Reasoning"
"  Knowledge graphs contain rich relational structures of the world, and thus
complement data-driven machine learning in heterogeneous data. One of the most
effective methods in representing knowledge graphs is to embed symbolic
relations and entities into continuous spaces, where relations are
approximately linear translation between projected images of entities in the
relation space. However, state-of-the-art relation projection methods such as
TransR, TransD or TransSparse do not model the correlation between relations,
and thus are not scalable to complex knowledge graphs with thousands of
relations, both in computational demand and in statistical robustness. To this
end we introduce TransF, a novel translation-based method which mitigates the
burden of relation projection by explicitly modeling the basis subspaces of
projection matrices. As a result, TransF is far more light weight than the
existing projection methods, and is robust when facing a high number of
relations. Experimental results on the canonical link prediction task show that
our proposed model outperforms competing rivals by a large margin and achieves
state-of-the-art performance. Especially, TransF improves by 9%/5% in the
head/tail entity prediction task for N-to-1/1-to-N relations over the best
performing translation-based method.
",Knowledge Graph Embedding with Multiple Relation Projections
"  An intelligent robot agent based on domain ontology, machine learning
mechanism, and Fuzzy Markup Language (FML) for students and robot co-learning
is presented in this paper. The machine-human co-learning model is established
to help various students learn the mathematical concepts based on their
learning ability and performance. Meanwhile, the robot acts as a teacher's
assistant to co-learn with children in the class. The FML-based knowledge base
and rule base are embedded in the robot so that the teachers can get feedback
from the robot on whether students make progress or not. Next, we inferred
students' learning performance based on learning content's difficulty and
students' ability, concentration level, as well as teamwork sprit in the class.
Experimental results show that learning with the robot is helpful for
disadvantaged and below-basic children. Moreover, the accuracy of the
intelligent FML-based agent for student learning is increased after machine
learning mechanism.
","Ontology-based Fuzzy Markup Language Agent for Student and Robot
  Co-Learning"
"  We address the problem of deploying a reinforcement learning (RL) agent on a
physical system such as a datacenter cooling unit or robot, where critical
constraints must never be violated. We show how to exploit the typically smooth
dynamics of these systems and enable RL algorithms to never violate constraints
during learning. Our technique is to directly add to the policy a safety layer
that analytically solves an action correction formulation per each state. The
novelty of obtaining an elegant closed-form solution is attained due to a
linearized model, learned on past trajectories consisting of arbitrary actions.
This is to mimic the real-world circumstances where data logs were generated
with a behavior policy that is implausible to describe mathematically; such
cases render the known safety-aware off-policy methods inapplicable. We
demonstrate the efficacy of our approach on new representative physics-based
environments, and prevail where reward shaping fails by maintaining zero
constraint violations.
",Safe Exploration in Continuous Action Spaces
"  Semantic Web Rule Language (SWRL) combines OWL (Web Ontology Language)
ontologies with Horn Logic rules of the Rule Markup Language (RuleML) family.
Being supported by ontology editors, rule engines and ontology reasoners, it
has become a very popular choice for developing rule-based applications on top
of ontologies. However, SWRL is probably not go-ing to become a WWW Consortium
standard, prohibiting industrial acceptance. On the other hand, SPIN (SPARQL
Inferencing Notation) has become a de-facto industry standard to rep-resent
SPARQL rules and constraints on Semantic Web models, building on the widespread
acceptance of SPARQL (SPARQL Protocol and RDF Query Language). In this paper,
we ar-gue that the life of existing SWRL rule-based ontology applications can
be prolonged by con-verting them to SPIN. To this end, we have developed the
SWRL2SPIN tool in Prolog that transforms SWRL rules into SPIN rules,
considering the object-orientation of SPIN, i.e. linking rules to the
appropriate ontology classes and optimizing them, as derived by analysing the
rule conditions.
","SWRL2SPIN: A tool for transforming SWRL rule bases in OWL ontologies to
  object-oriented SPIN rules"
"  The development of artificial general intelligence is considered by many to
be inevitable. What such intelligence does after becoming aware is not so
certain. To that end, research suggests that the likelihood of artificial
general intelligence becoming hostile to humans is significant enough to
warrant inquiry into methods to limit such potential. Thus, containment of
artificial general intelligence is a timely and meaningful research topic.
While there is limited research exploring possible containment strategies, such
work is bounded by the underlying field the strategies draw upon. Accordingly,
we set out to construct an ontology to describe necessary elements in any
future containment technology. Using existing academic literature, we developed
a single domain ontology containing five levels, 32 codes, and 32 associated
descriptors. Further, we constructed ontology diagrams to demonstrate intended
relationships. We then identified humans, AGI, and the cyber world as novel
agent objects necessary for future containment activities. Collectively, the
work addresses three critical gaps: (a) identifying and arranging fundamental
constructs; (b) situating AGI containment within cyber science; and (c)
developing scientific rigor within the field.
","A Cyber Science Based Ontology for Artificial General Intelligence
  Containment"
"  Effective collaboration between humans and AI-based systems requires
effective modeling of the human in the loop, both in terms of the mental state
as well as the physical capabilities of the latter. However, these models can
also open up pathways for manipulating and exploiting the human in the hopes of
achieving some greater good, especially when the intent or values of the AI and
the human are not aligned or when they have an asymmetrical relationship with
respect to knowledge or computation power. In fact, such behavior does not
necessarily require any malicious intent but can rather be borne out of
cooperative scenarios. It is also beyond simple misinterpretation of intents,
as in the case of value alignment problems, and thus can be effectively
engineered if desired. Such techniques already exist and pose several
unresolved ethical and moral questions with regards to the design of autonomy.
In this paper, we illustrate some of these issues in a teaming scenario and
investigate how they are perceived by participants in a thought experiment.
","Algorithms for the Greater Good! On Mental Modeling and Acceptable
  Symbiosis in Human-AI Collaboration"
"  Generalized planning is concerned with the characterization and computation
of plans that solve many instances at once. In the standard formulation, a
generalized plan is a mapping from feature or observation histories into
actions, assuming that the instances share a common pool of features and
actions. This assumption, however, excludes the standard relational planning
domains where actions and objects change across instances. In this work, we
extend the standard formulation of generalized planning to such domains. This
is achieved by projecting the actions over the features, resulting in a common
set of abstract actions which can be tested for soundness and completeness, and
which can be used for generating general policies such as ""if the gripper is
empty, pick the clear block above x and place it on the table"" that achieve the
goal clear(x) in any Blocksworld instance. In this policy, ""pick the clear
block above x"" is an abstract action that may represent the action Unstack(a,
b) in one situation and the action Unstack(b, c) in another. Transformations
are also introduced for computing such policies by means of fully observable
non-deterministic (FOND) planners. The value of generalized representations for
learning general policies is also discussed.
","Features, Projections, and Representation Change for Generalized
  Planning"
"  In this paper, we consider a modified version of the control problem in a
model free Markov decision process (MDP) setting with large state and action
spaces. The control problem most commonly addressed in the contemporary
literature is to find an optimal policy which maximizes the value function,
i.e., the long run discounted reward of the MDP. The current settings also
assume access to a generative model of the MDP with the hidden premise that
observations of the system behaviour in the form of sample trajectories can be
obtained with ease from the model. In this paper, we consider a modified
version, where the cost function is the expectation of a non-convex function of
the value function without access to the generative model. Rather, we assume
that a sample trajectory generated using a priori chosen behaviour policy is
made available. In this restricted setting, we solve the modified control
problem in its true sense, i.e., to find the best possible policy given this
limited information. We propose a stochastic approximation algorithm based on
the well-known cross entropy method which is data (sample trajectory)
efficient, stable, robust as well as computationally and storage efficient. We
provide a proof of convergence of our algorithm to a policy which is globally
optimal relative to the behaviour policy. We also present experimental results
to corroborate our claims and we demonstrate the superiority of the solution
produced by our algorithm compared to the state-of-the-art algorithms under
appropriately chosen behaviour policy.
","An Incremental Off-policy Search in a Model-free Markov Decision Process
  Using a Single Sample Path"
"  Deep learning relies on a very specific kind of neural networks: those
superposing several neural layers. In the last few years, deep learning
achieved major breakthroughs in many tasks such as image analysis, speech
recognition, natural language processing, and so on. Yet, there is no
theoretical explanation of this success. In particular, it is not clear why the
deeper the network, the better it actually performs.
  We argue that the explanation is intimately connected to a key feature of the
data collected from our surrounding universe to feed the machine learning
algorithms: large non-parallelizable logical depth. Roughly speaking, we
conjecture that the shortest computational descriptions of the universe are
algorithms with inherently large computation times, even when a large number of
computers are available for parallelization. Interestingly, this conjecture,
combined with the folklore conjecture in theoretical computer science that $ P
\neq NC$, explains the success of deep learning.
",Deep Learning Works in Practice. But Does it Work in Theory?
"  We present a model for exact recursive Bayesian filtering based on lifted
multiset states. Combining multisets with lifting makes it possible to
simultaneously exploit multiple strategies for reducing inference complexity
when compared to list-based grounded state representations. The core idea is to
borrow the concept of Maximally Parallel Multiset Rewriting Systems and to
enhance it by concepts from Rao-Blackwellization and Lifted Inference, giving a
representation of state distributions that enables efficient inference. In
worlds where the random variables that define the system state are exchangeable
-- where the identity of entities does not matter -- it automatically uses a
representation that abstracts from ordering (achieving an exponential reduction
in complexity) -- and it automatically adapts when observations or system
dynamics destroy exchangeability by breaking symmetry.
",Lifted Filtering via Exchangeable Decomposition
"  In this paper we develop a new family of Ordered Weighted Averaging (OWA)
operators. Weight vector is obtained from a desired orness of the operator.
Using Faulhaber's formulas we obtain direct and simple expressions for the
weight vector without any iteration loop. With the exception of one weight, the
remaining follow a straight line relation. As a result, a fast and robust
algorithm is developed. The resulting weight vector is suboptimal according
with the Maximum Entropy criterion, but it is very close to the optimal.
Comparisons are done with other procedures.
",A family of OWA operators based on Faulhaber's formulas
"  Deceptive games are games where the reward structure or other aspects of the
game are designed to lead the agent away from a globally optimal policy. While
many games are already deceptive to some extent, we designed a series of games
in the Video Game Description Language (VGDL) implementing specific types of
deception, classified by the cognitive biases they exploit. VGDL games can be
run in the General Video Game Artificial Intelligence (GVGAI) Framework, making
it possible to test a variety of existing AI agents that have been submitted to
the GVGAI Competition on these deceptive games. Our results show that all
tested agents are vulnerable to several kinds of deception, but that different
agents have different weaknesses. This suggests that we can use deception to
understand the capabilities of a game-playing algorithm, and game-playing
algorithms to characterize the deception displayed by a game.
",Deceptive Games
"  When humans perform inductive learning, they often enhance the process with
background knowledge. With the increasing availability of well-formed
collaborative knowledge bases, the performance of learning algorithms could be
significantly enhanced if a way were found to exploit these knowledge bases. In
this work, we present a novel algorithm for injecting external knowledge into
induction algorithms using feature generation. Given a feature, the algorithm
defines a new learning task over its set of values, and uses the knowledge base
to solve the constructed learning task. The resulting classifier is then used
as a new feature for the original problem. We have applied our algorithm to the
domain of text classification using large semantic knowledge bases. We have
shown that the generated features significantly improve the performance of
existing learning algorithms.
",Recursive Feature Generation for Knowledge-based Learning
"  The study and publication of historical scientific manuscripts are com- plex
tasks that involve, among others, the explicit representation of the text mean-
ings and reasoning on temporal entities. In this paper we present the first
results of an interdisciplinary project dedicated to the study of Saussure's
manuscripts. These results aim to fulfill requirements elaborated with
Saussurean humanists. They comprise a model for the representation of
time-varying statements and time-varying domain knowledge (in particular
terminologies) as well as imple- mentation techniques for the semantic indexing
of manuscripts and for temporal reasoning on knowledge extracted from the
manuscripts.
",A Semantic Model for Historical Manuscripts
"  Spatio-temporal prediction is a key type of tasks in urban computing, e.g.,
traffic flow and air quality. Adequate data is usually a prerequisite,
especially when deep learning is adopted. However, the development levels of
different cities are unbalanced, and still many cities suffer from data
scarcity. To address the problem, we propose a novel cross-city transfer
learning method for deep spatio-temporal prediction tasks, called RegionTrans.
RegionTrans aims to effectively transfer knowledge from a data-rich source city
to a data-scarce target city. More specifically, we first learn an inter-city
region matching function to match each target city region to a similar source
city region. A neural network is designed to effectively extract region-level
representation for spatio-temporal prediction. Finally, an optimization
algorithm is proposed to transfer learned features from the source city to the
target city with the region matching function. Using citywide crowd flow
prediction as a demonstration experiment, we verify the effectiveness of
RegionTrans. Results show that RegionTrans can outperform the state-of-the-art
fine-tuning deep spatio-temporal prediction models by reducing up to 10.7%
prediction error.
",Cross-City Transfer Learning for Deep Spatio-Temporal Prediction
"  Recent years have seen a boom in interest in machine learning systems that
can provide a human-understandable rationale for their predictions or
decisions. However, exactly what kinds of explanation are truly
human-interpretable remains poorly understood. This work advances our
understanding of what makes explanations interpretable in the specific context
of verification. Suppose we have a machine learning system that predicts X, and
we provide rationale for this prediction X. Given an input, an explanation, and
an output, is the output consistent with the input and the supposed rationale?
Via a series of user-studies, we identify what kinds of increases in complexity
have the greatest effect on the time it takes for humans to verify the
rationale, and which seem relatively insensitive.
","How do Humans Understand Explanations from Machine Learning Systems? An
  Evaluation of the Human-Interpretability of Explanation"
"  Models of a phenomenon are often developed by examining it under different
experimental conditions, or measurement contexts. The resultant probabilistic
models assume that the underlying random variables, which define a measurable
set of outcomes, can be defined independent of the measurement context. The
phenomenon is deemed contextual when this assumption fails. Contextuality is an
important issue in quantum physics. However, there has been growing speculation
that it manifests outside the quantum realm with human cognition being a
particularly prominent area of investigation. This article contributes the
foundations of a probabilistic programming language that allows convenient
exploration of contextuality in wide range of applications relevant to
cognitive science and artificial intelligence. Specific syntax is proposed to
allow the specification of ""measurement contexts"". Each such context delivers a
partial model of the phenomenon based on the associated experimental condition
described by the measurement context. The probabilistic program is translated
into a hypergraph in a modular way. Recent theoretical results from the field
of quantum physics show that contextuality can be equated with the possibility
of constructing a probabilistic model on the resulting hypergraph. The use of
hypergraphs opens the door for a theoretically succinct and efficient
computational semantics sensitive to modelling both contextual and
non-contextual phenomena. Finally, this article raises awareness of
contextuality beyond quantum physics and to contribute formal methods to detect
its presence by means of hypergraph semantics.
","Modelling contextuality by probabilistic programs with hypergraph
  semantics"
"  Recent work in explanation generation for decision making agents has looked
at how unexplained behavior of autonomous systems can be understood in terms of
differences in the model of the system and the human's understanding of the
same, and how the explanation process as a result of this mismatch can be then
seen as a process of reconciliation of these models. Existing algorithms in
such settings, while having been built on contrastive, selective and social
properties of explanations as studied extensively in the psychology literature,
have not, to the best of our knowledge, been evaluated in settings with actual
humans in the loop. As such, the applicability of such explanations to human-AI
and human-robot interactions remains suspect. In this paper, we set out to
evaluate these explanation generation algorithms in a series of studies in a
mock search and rescue scenario with an internal semi-autonomous robot and an
external human commander. We demonstrate to what extent the properties of these
algorithms hold as they are evaluated by humans, and how the dynamics of trust
between the human and the robot evolve during the process of these
interactions.
",Plan Explanations as Model Reconciliation -- An Empirical Study
"  Perception and reasoning are basic human abilities that are seamlessly
connected as part of human intelligence. However, in current machine learning
systems, the perception and reasoning modules are incompatible. Tasks requiring
joint perception and reasoning ability are difficult to accomplish autonomously
and still demand human intervention. Inspired by the way language experts
decoded Mayan scripts by joining two abilities in an abductive manner, this
paper proposes the abductive learning framework. The framework learns
perception and reasoning simultaneously with the help of a trial-and-error
abductive process. We present the Neural-Logical Machine as an implementation
of this novel learning framework. We demonstrate that--using human-like
abductive learning--the machine learns from a small set of simple hand-written
equations and then generalizes well to complex equations, a feat that is beyond
the capability of state-of-the-art neural network models. The abductive
learning framework explores a new direction for approaching human-level
learning ability.
","Tunneling Neural Perception and Logic Reasoning through Abductive
  Learning"
"  We consider a team of reinforcement learning agents that concurrently learn
to operate in a common environment. We identify three properties - adaptivity,
commitment, and diversity - which are necessary for efficient coordinated
exploration and demonstrate that straightforward extensions to single-agent
optimistic and posterior sampling approaches fail to satisfy them. As an
alternative, we propose seed sampling, which extends posterior sampling in a
manner that meets these requirements. Simulation results investigate how
per-agent regret decreases as the number of agents grows, establishing
substantial advantages of seed sampling over alternative exploration schemes.
",Coordinated Exploration in Concurrent Reinforcement Learning
"  This paper describes a problem arising in sea exploration, where the aim is
to schedule the expedition of a ship for collecting information about the
resources on the seafloor. The aim is to collect data by probing on a set of
carefully chosen locations, so that the information available is optimally
enriched. This problem has similarities with the orienteering problem, where
the aim is to plan a time-limited trip for visiting a set of vertices,
collecting a prize at each of them, in such a way that the total value
collected is maximum. In our problem, the score at each vertex is associated
with an estimation of the level of the resource on the given surface, which is
done by regression using Gaussian processes. Hence, there is a correlation
among scores on the selected vertices; this is a first difference with respect
to the standard orienteering problem. The second difference is the location of
each vertex, which in our problem is a freely chosen point on a given surface.
","The Sea Exploration Problem: Data-driven Orienteering on a Continuous
  Surface"
"  Reinforcement learning in environments with many action-state pairs is
challenging. At issue is the number of episodes needed to thoroughly search the
policy space. Most conventional heuristics address this search problem in a
stochastic manner. This can leave large portions of the policy space unvisited
during the early training stages. In this paper, we propose an
uncertainty-based, information-theoretic approach for performing guided
stochastic searches that more effectively cover the policy space. Our approach
is based on the value of information, a criterion that provides the optimal
trade-off between expected costs and the granularity of the search process. The
value of information yields a stochastic routine for choosing actions during
learning that can explore the policy space in a coarse to fine manner. We
augment this criterion with a state-transition uncertainty factor, which guides
the search process into previously unexplored regions of the policy space.
","Guided Policy Exploration for Markov Decision Processes using an
  Uncertainty-Based Value-of-Information Criterion"
"  Cycles of attacking arguments pose non-trivial issues in Dung style
argumentation theory, apparent behavioural difference between odd and even
length cycles being a notable one. While a few methods were proposed for
treating them, to - in particular - enable selection of acceptable arguments in
an odd-length cycle when Dung semantics could select none, so far the issues
have been observed from a purely argument-graph-theoretic perspective. Per
contra, we consider argument graphs together with a certain lattice like
semantic structure over arguments e.g. ontology. As we show, the
semantic-argumentgraphic hybrid theory allows us to apply abstract
interpretation, a widely known methodology in static program analysis, to
formal argumentation. With this, even where no arguments in a cycle could be
selected sensibly, we could say more about arguments acceptability of an
argument framework that contains it. In a certain sense, we can verify Dung
extensions with respect to a semantic structure in this hybrid theory, to
consolidate our confidence in their suitability. By defining the theory, and by
making comparisons to existing approaches, we ultimately discover that whether
Dung semantics, or an alternative semantics such as cf2, is adequate or
problematic depends not just on an argument graph but also on the semantic
relation among the arguments in the graph.
","Abstractly Interpreting Argumentation Frameworks for Sharpening
  Extensions"
"  We focus on learning the desired objective function for a robot. Although
trajectory demonstrations can be very informative of the desired objective,
they can also be difficult for users to provide. Answers to comparison queries,
asking which of two trajectories is preferable, are much easier for users, and
have emerged as an effective alternative. Unfortunately, comparisons are far
less informative. We propose that there is much richer information that users
can easily provide and that robots ought to leverage. We focus on augmenting
comparisons with feature queries, and introduce a unified formalism for
treating all answers as observations about the true desired reward. We derive
an active query selection algorithm, and test these queries in simulation and
on real users. We find that richer, feature-augmented queries can extract more
information faster, leading to robots that better match user preferences in
their behavior.
","Learning from Richer Human Guidance: Augmenting Comparison-Based
  Learning with Feature Queries"
"  All artificial Intelligence (AI) systems make errors. These errors are
unexpected, and differ often from the typical human mistakes (""non-human""
errors). The AI errors should be corrected without damage of existing skills
and, hopefully, avoiding direct human expertise. This paper presents an initial
summary report of project taking new and systematic approach to improving the
intellectual effectiveness of the individual AI by communities of AIs. We
combine some ideas of learning in heterogeneous multiagent systems with new and
original mathematical approaches for non-iterative corrections of errors of
legacy AI systems. The mathematical foundations of AI non-destructive
correction are presented and a series of new stochastic separation theorems is
proven. These theorems provide a new instrument for the development, analysis,
and assessment of machine learning methods and algorithms in high dimension.
They demonstrate that in high dimensions and even for exponentially large
samples, linear classifiers in their classical Fisher's form are powerful
enough to separate errors from correct responses with high probability and to
provide efficient solution to the non-destructive corrector problem. In
particular, we prove some hypotheses formulated in our paper `Stochastic
Separation Theorems' (Neural Networks, 94, 255--259, 2017), and answer one
general problem published by Donoho and Tanner in 2009.
",Augmented Artificial Intelligence: a Conceptual Framework
"  This research proposes a novel indicator-based hybrid evolutionary approach
that combines approximate and exact algorithms. We apply it to a new
bi-criteria formulation of the travelling thief problem, which is known to the
Evolutionary Computation community as a benchmark multi-component optimisation
problem that interconnects two classical NP-hard problems: the travelling
salesman problem and the 0-1 knapsack problem. Our approach employs the exact
dynamic programming algorithm for the underlying Packing-While-Travelling (PWT)
problem as a subroutine within a bi-objective evolutionary algorithm. This
design takes advantage of the data extracted from Pareto fronts generated by
the dynamic program to achieve better solutions. Furthermore, we develop a
number of novel indicators and selection mechanisms to strengthen synergy of
the two algorithmic components of our approach. The results of computational
experiments show that the approach is capable to outperform the
state-of-the-art results for the single-objective case of the problem.
","Evolutionary Computation plus Dynamic Programming for the Bi-Objective
  Travelling Thief Problem"
"  Learning a Bayesian networks with bounded treewidth is important for reducing
the complexity of the inferences. We present a novel anytime algorithm (k-MAX)
method for this task, which scales up to thousands of variables. Through
extensive experiments we show that it consistently yields higher-scoring
structures than its competitors on complete data sets. We then consider the
problem of structure learning from incomplete data sets. This can be addressed
by structural EM, which however is computationally very demanding. We thus
adopt the novel k-MAX algorithm in the maximization step of structural EM,
obtaining an efficient computation of the expected sufficient statistics. We
test the resulting structural EM method on the task of imputing missing data,
comparing it against the state-of-the-art approach based on random forests. Our
approach achieves the same imputation accuracy of the competitors, but in about
one tenth of the time. Furthermore we show that it has worst-case complexity
linear in the input size, and that it is easily parallelizable.
","Efficient Learning of Bounded-Treewidth Bayesian Networks from Complete
  and Incomplete Data Sets"
"  Within the context of video games the notion of perfectly rational agents can
be undesirable as it leads to uninteresting situations, where humans face tough
adversarial decision makers. Current frameworks for stochastic games and
reinforcement learning prohibit tuneable strategies as they seek optimal
performance. In this paper, we enable such tuneable behaviour by generalising
soft Q-learning to stochastic games, where more than one agent interact
strategically. We contribute both theoretically and empirically. On the theory
side, we show that games with soft Q-learning exhibit a unique value and
generalise team games and zero-sum games far beyond these two extremes to cover
a continuous spectrum of gaming behaviour. Experimentally, we show how tuning
agents' constraints affect performance and demonstrate, through a neural
network architecture, how to reliably balance games with high-dimensional
representations.
",Balancing Two-Player Stochastic Games with Soft Q-Learning
"  We propose to use a supervised machine learning technique to track the
location of a mobile agent in real time. Hidden Markov Models are used to build
artificial intelligence that estimates the unknown position of a mobile target
moving in a defined environment. This narrow artificial intelligence performs
two distinct tasks. First, it provides real-time estimation of the mobile
agent's position using the forward algorithm. Second, it uses the Baum-Welch
algorithm as a statistical learning tool to gain knowledge of the mobile
target. Finally, an experimental environment is proposed, namely a video game
that we use to test our artificial intelligence. We present statistical and
graphical results to illustrate the efficiency of our method.
","Narrow Artificial Intelligence with Machine Learning for Real-Time
  Estimation of a Mobile Agents Location Using Hidden Markov Models"
"  We study the problem of off-policy evaluation (OPE) in reinforcement learning
(RL), where the goal is to estimate the performance of a policy from the data
generated by another policy(ies). In particular, we focus on the doubly robust
(DR) estimators that consist of an importance sampling (IS) component and a
performance model, and utilize the low (or zero) bias of IS and low variance of
the model at the same time. Although the accuracy of the model has a huge
impact on the overall performance of DR, most of the work on using the DR
estimators in OPE has been focused on improving the IS part, and not much on
how to learn the model. In this paper, we propose alternative DR estimators,
called more robust doubly robust (MRDR), that learn the model parameter by
minimizing the variance of the DR estimator. We first present a formulation for
learning the DR model in RL. We then derive formulas for the variance of the DR
estimator in both contextual bandits and RL, such that their gradients
w.r.t.~the model parameters can be estimated from the samples, and propose
methods to efficiently minimize the variance. We prove that the MRDR estimators
are strongly consistent and asymptotically optimal. Finally, we evaluate MRDR
in bandits and RL benchmark problems, and compare its performance with the
existing methods.
",More Robust Doubly Robust Off-policy Evaluation
"  Graph planning gives rise to fundamental algorithmic questions such as
shortest path, traveling salesman problem, etc. A classical problem in discrete
planning is to consider a weighted graph and construct a path that maximizes
the sum of weights for a given time horizon $T$. However, in many scenarios,
the time horizon is not fixed, but the stopping time is chosen according to
some distribution such that the expected stopping time is $T$. If the stopping
time distribution is not known, then to ensure robustness, the distribution is
chosen by an adversary, to represent the worst-case scenario.
  A stationary plan for every vertex always chooses the same outgoing edge. For
fixed horizon or fixed stopping-time distribution, stationary plans are not
sufficient for optimality. Quite surprisingly we show that when an adversary
chooses the stopping-time distribution with expected stopping time $T$, then
stationary plans are sufficient. While computing optimal stationary plans for
fixed horizon is NP-complete, we show that computing optimal stationary plans
under adversarial stopping-time distribution can be achieved in polynomial
time. Consequently, our polynomial-time algorithm for adversarial stopping time
also computes an optimal plan among all possible plans.
",Graph Planning with Expected Finite Horizon
"  The questions in a crowdsourcing task typically exhibit varying degrees of
difficulty and subjectivity. Their joint effects give rise to the variation in
responses to the same question by different crowd-workers. This variation is
low when the question is easy to answer and objective, and high when it is
difficult and subjective. Unfortunately, current quality control methods for
crowdsourcing consider only the question difficulty to account for the
variation. As a result,these methods cannot distinguish workers personal
preferences for different correct answers of a partially subjective question
from their ability/expertise to avoid objectively wrong answers for that
question. To address this issue, we present a probabilistic model which (i)
explicitly encodes question difficulty as a model parameter and (ii) implicitly
encodes question subjectivity via latent preference factors for crowd-workers.
We show that question subjectivity induces grouping of crowd-workers, revealed
through clustering of their latent preferences. Moreover, we develop a
quantitative measure of the subjectivity of a question. Experiments show that
our model(1) improves the performance of both quality control for crowd-sourced
answers and next answer prediction for crowd-workers,and (2) can potentially
provide coherent rankings of questions in terms of their difficulty and
subjectivity, so that task providers can refine their designs of the
crowdsourcing tasks, e.g. by removing highly subjective questions or
inappropriately difficult questions.
","Distinguishing Question Subjectivity from Difficulty for Improved
  Crowdsourcing"
"  The Complex Event Recognition (CER) group is a research team, affiliated with
the National Centre of Scientific Research ""Demokritos"" in Greece. The CER
group works towards advanced and efficient methods for the recognition of
complex events in a multitude of large, heterogeneous and interdependent data
streams. Its research covers multiple aspects of complex event recognition,
from efficient detection of patterns on event streams to handling uncertainty
and noise in streams, and machine learning techniques for inferring interesting
patterns. Lately, it has expanded to methods for forecasting the occurrence of
events. It was founded in 2009 and currently hosts 3 senior researchers, 5 PhD
students and works regularly with under-graduate students.
",The Complex Event Recognition Group
"  The paper analyzes the problem of judgments or preferences subsequent to
initial analysis by autonomous agents in a hierarchical system where the higher
level agents does not have access to group size information. We propose methods
that reduce instances of preference reversal of the kind encountered in
Simpson's paradox.
",Reasoning in a Hierarchical System with Missing Group Size Information
"  The primary aim of the study is to introduce APLOCO method which is developed
for the solution of multicriteria decision making problems both theoretically
and practically. In this context, application subject of APLACO constitutes
evaluation of investment potential of different cities in metropolitan status
in Turkey. The secondary purpose of the study is to identify the independent
variables affecting the factories in the operating phase and to estimate the
effect levels of independent variables on the dependent variable in the
organized industrial zones (OIZs), whose mission is to reduce regional
development disparities and to mobilize local production dynamics. For this
purpose, the effect levels of independent variables on dependent variables have
been determined using the multilayer perceptron (MLP) method, which has a wide
use in artificial neural networks (ANNs). The effect levels derived from MLP
have been then used as the weight levels of the decision criteria in APLOCO.
The independent variables included in MLP are also used as the decision
criteria in APLOCO. According to the results obtained from APLOCO, Istanbul
city is the best alternative in term of the investment potential and other
alternatives are Manisa, Denizli, Izmir, Kocaeli, Bursa, Ankara, Adana, and
Antalya, respectively. Although APLOCO is used to solve the ranking problem in
order to show application process in the paper, it can be employed easily in
the solution of classification and selection problems. On the other hand, the
study also shows a rare example of the nested usage of APLOCO which is one of
the methods of operation research as well as MLP used in determination of
weights.
","A New Multi Criteria Decision Making Method: Approach of Logarithmic
  Concept (APLOCO)"
"  It is undeniable that artificial intelligence (AI) and blockchain concepts
are spreading at a phenomenal rate. Both technologies have distinct degree of
technological complexity and multi-dimensional business implications. However,
a common misunderstanding about blockchain concept, in particular, is that
blockchain is decentralized and is not controlled by anyone. But the underlying
development of a blockchain system is still attributed to a cluster of core
developers. Take smart contract as an example, it is essentially a collection
of codes (or functions) and data (or states) that are programmed and deployed
on a blockchain (say, Ethereum) by different human programmers. It is thus,
unfortunately, less likely to be free of loopholes and flaws. In this article,
through a brief overview about how artificial intelligence could be used to
deliver bug-free smart contract so as to achieve the goal of blockchain 2.0, we
to emphasize that the blockchain implementation can be assisted or enhanced via
various AI techniques. The alliance of AI and blockchain is expected to create
numerous possibilities.
",Blockchain and Artificial Intelligence
"  We use model-free reinforcement learning, extensive simulation, and transfer
learning to develop a continuous control algorithm that has good zero-shot
performance in a real physical environment. We train a simulated agent to act
optimally across a set of similar environments, each with dynamics drawn from a
prior distribution. We propose that the agent is able to adjust its actions
almost immediately, based on small set of observations. This robust and
adaptive behavior is enabled by using a policy gradient algorithm with an Long
Short Term Memory (LSTM) function approximation. Finally, we train an agent to
navigate a two-dimensional environment with uncertain dynamics and noisy
observations. We demonstrate that this agent has good zero-shot performance in
a real physical environment. Our preliminary results indicate that the agent is
able to infer the environmental dynamics after only a few timesteps, and adjust
its actions accordingly.
","Learning Robust and Adaptive Real-World Continuous Control Using
  Simulation and Transfer Learning"
"  Bike sharing provides an environment-friendly way for traveling and is
booming all over the world. Yet, due to the high similarity of user travel
patterns, the bike imbalance problem constantly occurs, especially for dockless
bike sharing systems, causing significant impact on service quality and company
revenue. Thus, it has become a critical task for bike sharing systems to
resolve such imbalance efficiently. In this paper, we propose a novel deep
reinforcement learning framework for incentivizing users to rebalance such
systems. We model the problem as a Markov decision process and take both
spatial and temporal features into consideration. We develop a novel deep
reinforcement learning algorithm called Hierarchical Reinforcement Pricing
(HRP), which builds upon the Deep Deterministic Policy Gradient algorithm.
Different from existing methods that often ignore spatial information and rely
heavily on accurate prediction, HRP captures both spatial and temporal
dependencies using a divide-and-conquer structure with an embedded localized
module. We conduct extensive experiments to evaluate HRP, based on a dataset
from Mobike, a major Chinese dockless bike sharing company. Results show that
HRP performs close to the 24-timeslot look-ahead optimization, and outperforms
state-of-the-art methods in both service level and bike distribution. It also
transfers well when applied to unseen areas.
","A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike
  Sharing Systems"
"  This working note discusses the topic of story generation, with a view to
identifying the knowledge required to understand aviation incident narratives
(which have structural similarities to stories), following the premise that to
understand aviation incidents, one should at least be able to generate examples
of them. We give a brief overview of aviation incidents and their relation to
stories, and then describe two of our earlier attempts (using `scripts' and
`story grammars') at incident generation which did not evolve promisingly.
Following this, we describe a simple incident generator which did work (at a
`toy' level), using a `world simulation' approach. This generator is based on
Meehan's TALE-SPIN story generator (1977). We conclude with a critique of the
approach.
",Story Generation and Aviation Incident Representation
"  Several tasks in artificial intelligence require to be able to find models
about knowledge dynamics. They include belief revision, fusion and belief
merging, and abduction. In this paper we exploit the algebraic framework of
mathematical morphology in the context of propositional logic, and define
operations such as dilation or erosion of a set of formulas. We derive concrete
operators, based on a semantic approach, that have an intuitive interpretation
and that are formally well behaved, to perform revision, fusion and abduction.
Computation and tractability are addressed, and simple examples illustrate the
typical results that can be obtained.
","Morphologic for knowledge dynamics: revision, fusion, abduction"
"  This paper presents a framework for generating adventure games from open
data. Focusing on the murder mystery type of adventure games, the generator is
able to transform open data from Wikipedia articles, OpenStreetMap and images
from Wikimedia Commons into WikiMysteries. Every WikiMystery game revolves
around the murder of a person with a Wikipedia article and populates the game
with suspects who must be arrested by the player if guilty of the murder or
absolved if innocent. Starting from only one person as the victim, an extensive
generative pipeline finds suspects, their alibis, and paths connecting them
from open data, transforms open data into cities, buildings, non-player
characters, locks and keys and dialog options. The paper describes in detail
each generative step, provides a specific playthrough of one WikiMystery where
Albert Einstein is murdered, and evaluates the outcomes of games generated for
the 100 most influential people of the 20th century.
",Who Killed Albert Einstein? From Open Data to Murder Mystery Games
"  Despite the recent successes of deep neural networks in various fields such
as image and speech recognition, natural language processing, and reinforcement
learning, we still face big challenges in bringing the power of numeric
optimization to symbolic reasoning. Researchers have proposed different avenues
such as neural machine translation for proof synthesis, vectorization of
symbols and expressions for representing symbolic patterns, and coupling of
neural back-ends for dimensionality reduction with symbolic front-ends for
decision making. However, these initial explorations are still only point
solutions, and bear other shortcomings such as lack of correctness guarantees.
In this paper, we present our approach of casting symbolic reasoning as games,
and directly harnessing the power of deep reinforcement learning in the style
of Alpha(Go) Zero on symbolic problems. Using the Boolean Satisfiability (SAT)
problem as showcase, we demonstrate the feasibility of our method, and the
advantages of modularity, efficiency, and correctness guarantees.
","From Gameplay to Symbolic Reasoning: Learning SAT Solver Heuristics in
  the Style of Alpha(Go) Zero"
"  A reliable modeling of uncertain evidence in Bayesian networks based on a
set-valued quantification is proposed. Both soft and virtual evidences are
considered. We show that evidence propagation in this setup can be reduced to
standard updating in an augmented credal network, equivalent to a set of
consistent Bayesian networks. A characterization of the computational
complexity for this task is derived together with an efficient exact procedure
for a subclass of instances. In the case of multiple uncertain evidences over
the same variable, the proposed procedure can provide a set-valued version of
the geometric approach to opinion pooling.
","Reliable Uncertain Evidence Modeling in Bayesian Networks by Credal
  Networks"
"  Integrated task and motion planning has emerged as a challenging problem in
sequential decision making, where a robot needs to compute high-level strategy
and low-level motion plans for solving complex tasks. While high-level
strategies require decision making over longer time-horizons and scales, their
feasibility depends on low-level constraints based upon the geometries and
continuous dynamics of the environment. The hybrid nature of this problem makes
it difficult to scale; most existing approaches focus on deterministic, fully
observable scenarios. We present a new approach where the high-level decision
problem occurs in a stochastic setting and can be modeled as a Markov decision
process. In contrast to prior efforts, we show that complete MDP policies, or
contingent behaviors, can be computed effectively in an anytime fashion. Our
algorithm continuously improves the quality of the solution and is guaranteed
to be probabilistically complete. We evaluate the performance of our approach
on a challenging, realistic test problem: autonomous aircraft inspection. Our
results show that we can effectively compute consistent task and motion
policies for the most likely execution-time outcomes using only a fraction of
the computation required to develop the complete task and motion policy.
",An Anytime Algorithm for Task and Motion MDPs
"  We introduce and discuss, through a computational algebraic geometry
approach, the automatic reasoning handling of propositions that are
simultaneously true and false over some relevant collections of instances. A
rigorous, algorithmic criterion is presented for detecting such cases, and its
performance is exemplified through the implementation of this test on the
dynamic geometry program GeoGebra.
","Detecting truth, just on parts"
"  After the recent groundbreaking results of AlphaGo, we have seen a strong
interest in reinforcement learning in game playing. General Game Playing (GGP)
provides a good testbed for reinforcement learning. In GGP, a specification of
games rules is given. GGP problems can be solved by reinforcement learning.
Q-learning is one of the canonical reinforcement learning methods, and has been
used by (Banerjee & Stone, IJCAI 2007) in GGP. In this paper we implement
Q-learning in GGP for three small-board games (Tic-Tac-Toe, Connect Four, Hex),
to allow comparison to Banerjee et al. As expected, Q-learning converges,
although much slower than MCTS. Borrowing an idea from MCTS, we enhance
Q-learning with Monte Carlo Search, to give QM-learning. This enhancement
improves the performance of pure Q-learning. We believe that QM-learning can
also be used to improve performance of reinforcement learning further for
larger games, something which we will test in future work.
",Monte Carlo Q-learning for General Game Playing
"  The use of artificial intelligence intelligencein medicine can be traced back
to 1968 when Paycha published his paper Le diagnostic a l'aide d'intelligences
artificielle, presentation de la premiere machine diagnostri. Few years later
Shortliffe et al. presented an expert system named Mycin which was able to
identify bacteria causing severe blood infections and to recommend antibiotics.
Despite the fact that Mycin outperformed members of the Stanford medical school
in the reliability of diagnosis it was never used in practice due to a legal
issue who do you sue if it gives a wrong diagnosis?. However only in 2016 when
the artificial intelligence software built into the IBM Watson AI platform
correctly diagnosed and proposed an effective treatment for a 60-year-old
womans rare form of leukemia the AI use in medicine become really popular.On of
first papers presenting the use of AI in paediatrics was published in 1984. The
paper introduced a computer-assisted medical decision making system called
SHELP.
",Artificial intelligence and pediatrics: A synthetic mini review
"  Users of AI systems may rely upon them to produce plans for achieving desired
objectives. Such AI systems should be able to compute obfuscated plans whose
execution in adversarial situations protects privacy, as well as legible plans
which are easy for team members to understand in cooperative situations. We
develop a unified framework that addresses these dual problems by computing
plans with a desired level of comprehensibility from the point of view of a
partially informed observer. For adversarial settings, our approach produces
obfuscated plans with observations that are consistent with at least k goals
from a set of decoy goals. By slightly varying our framework, we present an
approach for goal legibility in cooperative settings which produces plans that
achieve a goal while being consistent with at most j goals from a set of
confounding goals. In addition, we show how the observability of the observer
can be controlled to either obfuscate or clarify the next actions in a plan
when the goal is known to the observer. We present theoretical results on the
complexity analysis of our problems. We demonstrate the execution of obfuscated
and legible plans in a cooking domain using a physical robot Fetch. We also
provide an empirical evaluation to show the feasibility and usefulness of our
approaches using IPC domains.
","A Unified Framework for Planning in Adversarial and Cooperative
  Environments"
"  Planning under uncertainty is critical for robust robot performance in
uncertain, dynamic environments, but it incurs high computational cost.
State-of-the-art online search algorithms, such as DESPOT, have vastly improved
the computational efficiency of planning under uncertainty and made it a
valuable tool for robotics in practice. This work takes one step further by
leveraging both CPU and GPU parallelization in order to achieve near real-time
online planning performance for complex tasks with large state, action, and
observation spaces. Specifically, we propose Hybrid Parallel DESPOT
(HyP-DESPOT), a massively parallel online planning algorithm that integrates
CPU and GPU parallelism in a multi-level scheme. It performs parallel DESPOT
tree search by simultaneously traversing multiple independent paths using
multi-core CPUs and performs parallel Monte-Carlo simulations at the leaf nodes
of the search tree using GPUs. Experimental results show that HyP-DESPOT speeds
up online planning by up to several hundred times, compared with the original
DESPOT algorithm, in several challenging robotic tasks in simulation.
","HyP-DESPOT: A Hybrid Parallel Algorithm for Online Planning under
  Uncertainty"
"  We consider the multi-vehicle one-to-one pickup and delivery problem with
split loads, a NP-hard problem linked with a variety of applications for bulk
product transportation, bike-sharing systems and inventory re-balancing. This
problem is notoriously difficult due to the interaction of two challenging
vehicle routing attributes, ""pickups and deliveries"" and ""split deliveries"".
This possibly leads to optimal solutions of a size that grows exponentially
with the instance size, containing multiple visits per customer pair, even in
the same route. To solve this problem, we propose an iterated local search
metaheuristic as well as a branch-and-price algorithm. The core of the
metaheuristic consists of a new large neighborhood search, which reduces the
problem of finding the best insertion combination of a pickup and delivery pair
into a route (with possible splits) to a resource-constrained shortest path and
knapsack problem. Similarly, the branch-and-price algorithm uses sophisticated
labeling techniques, route relaxations, pre-processing and branching rules for
an efficient resolution. Our computational experiments on classical
single-vehicle instances demonstrate the excellent performance of the
metaheuristic, which produces new best known solutions for 92 out of 93 test
instances, and outperforms all previous algorithms. Experimental results on new
multi-vehicle instances with distance constraints are also reported. The
branch-and-price algorithm produces optimal solutions for instances with up to
20 pickup-and-delivery pairs, and very accurate solutions are found by the
metaheuristic.
","Large Neighborhood-Based Metaheuristic and Branch-and-Price for the
  Pickup and Delivery Problem with Split Loads"
"  Air Traffic Flow and Capacity Management (ATFCM) is one of the constituent
parts of Air Traffic Management (ATM). The goal of ATFCM is to make airport and
airspace capacity meet traffic demand and, when capacity opportunities are
exhausted, optimise traffic flows to meet the available capacity. One of the
key enablers of ATFCM is the accurate estimation of future traffic demand. The
available information (schedules, flight plans, etc.) and its associated level
of uncertainty differ across the different ATFCM planning phases, leading to
qualitative differences between the types of forecasting that are feasible at
each time horizon. While abundant research has been conducted on tactical
trajectory prediction (i.e., during the day of operations), trajectory
prediction in the pre-tactical phase, when few or no flight plans are
available, has received much less attention. As a consequence, the methods
currently in use for pre-tactical traffic forecast are still rather
rudimentary, often resulting in suboptimal ATFCM decision making. This paper
proposes a machine learning approach for the prediction of airlines route
choices between two airports as a function of route characteristics, such as
flight efficiency, air navigation charges and expected level of congestion.
Different predictive models based on multinomial logistic regression and
decision trees are formulated and calibrated with historical traffic data, and
a critical evaluation of each model is conducted. We analyse the predictive
power of each model in terms of its ability to forecast traffic volumes at the
level of charging zones, proving significant potential to enhance pre-tactical
traffic forecast. We conclude by discussing the limitations and room for
improvement of the proposed approach, as well as the future developments
required to produce reliable traffic forecasts at a higher spatial and temporal
resolution.
",A Machine Learning Approach to Air Traffic Route Choice Modelling
"  Hierarchical learning (HL) is key to solving complex sequential decision
problems with long horizons and sparse rewards. It allows learning agents to
break-up large problems into smaller, more manageable subtasks. A common
approach to HL, is to provide the agent with a number of high-level skills that
solve small parts of the overall problem. A major open question, however, is
how to identify a suitable set of reusable skills. We propose a principled
approach that uses human demonstrations to infer a set of subgoals based on
changes in the demonstration dynamics. Using these subgoals, we decompose the
learning problem into an abstract high-level representation and a set of
low-level subtasks. The abstract description captures the overall problem
structure, while subtasks capture desired skills. We demonstrate that we can
jointly optimize over both levels of learning. We show that the resulting
method significantly outperforms previous baselines on two challenging
problems: the Atari 2600 game Montezuma's Revenge, and a simulated robotics
problem moving the ant robot through a maze.
",Learning High-level Representations from Demonstrations
"  We address the problem of inferring the causal direction between two
variables by comparing the least-squares errors of the predictions in both
possible directions. Under the assumption of an independence between the
function relating cause and effect, the conditional noise distribution, and the
distribution of the cause, we show that the errors are smaller in causal
direction if both variables are equally scaled and the causal relation is close
to deterministic. Based on this, we provide an easily applicable algorithm that
only requires a regression in both possible causal directions and a comparison
of the errors. The performance of the algorithm is compared with various
related causal inference methods in different artificial and real-world data
sets.
",Analysis of cause-effect inference by comparing regression errors
"  The paper describes the architecture of the intelligence system for automated
design of ontological knowledge bases of domain areas and the software model of
the management GUI (Graphical User Interface) subsystem
","The problem of the development ontology-driven architecture of
  intellectual software systems"
"  Creation of the information systems and tools for scientific research and
development support has always been one of the central directions of the
development of computer science. The main features of the modern evolution of
scientific research and development are the transdisciplinary approach and the
deep intellectualisation of all stages of the life cycle of formulation and
solution of scientific problems. The theoretical and practical aspects of the
development of perspective complex knowledge-oriented information systems and
their components are considered in the paper. The analysis of existing
scientific information systems (or current research information systems, CRIS)
and synthesis of general principles of design of the research and development
workstation environment of a researcher and its components are carried out in
the work. The functional components of knowledge-oriented information system
research and development workstation environment of a researcher are designed.
Designed and developed functional components of knowledge-oriented information
system developing research and development workstation environment,including
functional models and software implementation of the software subsystem for
creation and use of ontological knowledge base for research fellow
publications, as part of personalized knowledge base of scientific researcher.
Research in modern conditions of e-Science paradigm requires pooling scientific
community and intensive exchange of research results that may be achieved
through the use of scientific information systems. research and development
workstation environment allows to solve problems of contructivisation and
formalisation of knowledge representation, obtained during the research process
and collective accomplices interaction.
","Design and software implementation of subsystems for creating and using
  the ontological base of a research scientist"
"  The article describes the technique for designing a domain ontology, shows
the flowchart of algorithm design and example of constructing a fragment of the
ontology of the subject area of Computer Science is considered.
",Technique for designing a domain ontology
"  The article presents an overview of current specialized ontology engineering
tools, as well as texts' annotation tools based on ontologies. The main
functions and features of these tools, their advantages and disadvantages are
discussed. A systematic comparative analysis of means for engineering
ontologies is presented.
",Integrated Tools for Engineering Ontologies
"  This paper describes the design principles of methodology of
knowledge-oriented information systems based on ontological approach. Such
systems implement technology subject-oriented extraction of knowledge from the
set of natural language texts and their formal and logical presentation and
application processing
","Principles of design and software development models of
  ontological-driven computer systems"
"  This article represents one of the contemporary trends in the application of
the latest methods of information and communication technology for medicine
through an expert system helps the doctor to diagnose some chest diseases which
is important because of the frequent spread of chest diseases nowadays in
addition to the overlap symptoms of these diseases, which is difficult to right
diagnose by doctors with several algorithms: Forward Chaining, Backward
Chaining, Neural Network(Back Propagation). However, this system cannot replace
the doctor function, but it can help the doctor to avoid wrong diagnosis and
treatments. It can also be developed in such a way to help the novice doctors.
",Expert System for Diagnosis of Chest Diseases Using Neural Networks
"  This paper describes a method for generative player modeling and its
application to the automatic testing of game content using archetypal player
models called procedural personas. Theoretically grounded in psychological
decision theory, procedural personas are implemented using a variation of Monte
Carlo Tree Search (MCTS) where the node selection criteria are developed using
evolutionary computation, replacing the standard UCB1 criterion of MCTS. Using
these personas we demonstrate how generative player models can be applied to a
varied corpus of game levels and demonstrate how different play styles can be
enacted in each level. In short, we use artificially intelligent personas to
construct synthetic playtesters. The proposed approach could be used as a tool
for automatic play testing when human feedback is not readily available or when
quick visualization of potential interactions is necessary. Possible
applications include interactive tools during game development or procedural
content generation systems where many evaluations must be conducted within a
short time span.
","Automated Playtesting with Procedural Personas through MCTS with Evolved
  Heuristics"
"  We present a formal analysis of Douglas Hofstadter's concept of
\emph{superrationality}. We start by defining superrationally justifiable
actions, and study them in symmetric games. We then model the beliefs of the
players, in a way that leads them to different choices than the usual
assumption of rationality by restricting the range of conceivable choices.
These beliefs are captured in the formal notion of \emph{type} drawn from
epistemic game theory. The theory of coalgebras is used to frame type spaces
and to account for the existence of some of them. We find conditions that
guarantee superrational outcomes.
",Superrational types
"  There is a growing interest within the AI research community to develop
autonomous systems capable of explaining their behavior to users. One aspect of
the explanation generation problem that has yet to receive much attention is
the task of explaining plans to users whose level of expertise differ from that
of the explainer. We propose an approach for addressing this problem by
representing the user's model as an abstraction of the domain model that the
planner uses. We present algorithms for generating minimal explanations in
cases where this abstract human model is not known. We reduce the problem of
generating explanation to a search over the space of abstract models and
investigate possible greedy approximations for minimal explanations. We also
empirically show that our approach can efficiently compute explanations for a
variety of problems.
","Hierarchical Expertise-Level Modeling for User Specific Robot-Behavior
  Explanations"
"  In this paper we construct preimage attack on the truncated variant of the
MD4 hash function. Specifically, we study the MD4-39 function defined by the
first 39 steps of the MD4 algorithm. We suggest a new attack on MD4-39, which
develops the ideas proposed by H. Dobbertin in 1998. Namely, the special
relaxation constraints are introduced in order to simplify the equations
corresponding to the problem of finding a preimage for an arbitrary MD4-39 hash
value. The equations supplemented with the relaxation constraints are then
reduced to the Boolean Satisfiability Problem (SAT) and solved using the
state-of-the-art SAT solvers. We show that the effectiveness of a set of
relaxation constraints can be evaluated using the black-box function of a
special kind. Thus, we suggest automatic method of relaxation constraints
generation by applying the black-box optimization to this function. The
proposed method made it possible to find new relaxation constraints that
contribute to a SAT-based preimage attack on MD4-39 which significantly
outperforms the competition.
","Using Automatic Generation of Relaxation Constraints to Improve the
  Preimage Attack on 39-step MD4"
"  This paper introduces epistemic graphs as a generalization of the epistemic
approach to probabilistic argumentation. In these graphs, an argument can be
believed or disbelieved up to a given degree, thus providing a more
fine--grained alternative to the standard Dung's approaches when it comes to
determining the status of a given argument. Furthermore, the flexibility of the
epistemic approach allows us to both model the rationale behind the existing
semantics as well as completely deviate from them when required. Epistemic
graphs can model both attack and support as well as relations that are neither
support nor attack. The way other arguments influence a given argument is
expressed by the epistemic constraints that can restrict the belief we have in
an argument with a varying degree of specificity. The fact that we can specify
the rules under which arguments should be evaluated and we can include
constraints between unrelated arguments permits the framework to be more
context--sensitive. It also allows for better modelling of imperfect agents,
which can be important in multi--agent applications.
","Epistemic Graphs for Representing and Reasoning with Positive and
  Negative Influences of Arguments"
"  Theory of mind (ToM; Premack & Woodruff, 1978) broadly refers to humans'
ability to represent the mental states of others, including their desires,
beliefs, and intentions. We propose to train a machine to build such models
too. We design a Theory of Mind neural network -- a ToMnet -- which uses
meta-learning to build models of the agents it encounters, from observations of
their behaviour alone. Through this process, it acquires a strong prior model
for agents' behaviour, as well as the ability to bootstrap to richer
predictions about agents' characteristics and mental states using only a small
number of behavioural observations. We apply the ToMnet to agents behaving in
simple gridworld environments, showing that it learns to model random,
algorithmic, and deep reinforcement learning agents from varied populations,
and that it passes classic ToM tasks such as the ""Sally-Anne"" test (Wimmer &
Perner, 1983; Baron-Cohen et al., 1985) of recognising that others can hold
false beliefs about the world. We argue that this system -- which autonomously
learns how to model other agents in its world -- is an important step forward
for developing multi-agent AI systems, for building intermediating technology
for machine-human interaction, and for advancing the progress on interpretable
AI.
",Machine Theory of Mind
"  We present the first class of policy-gradient algorithms that work with both
state-value and policy function-approximation, and are guaranteed to converge
under off-policy training. Our solution targets problems in reinforcement
learning where the action representation adds to the-curse-of-dimensionality;
that is, with continuous or large action sets, thus making it infeasible to
estimate state-action value functions (Q functions). Using state-value
functions helps to lift the curse and as a result naturally turn our
policy-gradient solution into classical Actor-Critic architecture whose Actor
uses state-value function for the update. Our algorithms, Gradient Actor-Critic
and Emphatic Actor-Critic, are derived based on the exact gradient of averaged
state-value function objective and thus are guaranteed to converge to its
optimal solution, while maintaining all the desirable properties of classical
Actor-Critic methods with no additional hyper-parameters. To our knowledge,
this is the first time that convergent off-policy learning methods have been
extended to classical Actor-Critic methods with function approximation.
","Convergent Actor-Critic Algorithms Under Off-Policy Training and
  Function Approximation"
"  Description Logics (DLs) under Rational Closure (RC) is a well-known
framework for non-monotonic reasoning in DLs. In this paper, we address the
concept subsumption decision problem under RC for nominal safe
$\mathcal{ELO}_\bot$, a notable and practically important DL representative of
the OWL 2 profile OWL 2 EL.
  Our contribution here is to define a polynomial time subsumption procedure
for nominal safe $\mathcal{ELO}_\bot$ under RC that relies entirely on a series
of classical, monotonic $\mathcal{EL}_\bot$ subsumption tests. Therefore, any
existing classical monotonic $\mathcal{EL}_\bot$ reasoner can be used as a
black box to implement our method. We then also adapt the method to one of the
known extensions of RC for DLs, namely Defeasible Inheritance-based DLs without
losing the computational tractability.
","A Polynomial Time Subsumption Algorithm for Nominal Safe
  $\mathcal{ELO}_\bot$ under Rational Closure"
"  We study invariant local expansion operators for conflict-free and admissible
sets in Abstract Argumentation Frameworks (AFs). Such operators are directly
applied on AFs, and are invariant with respect to a chosen ""semantics"" (that is
w.r.t. each of the conflict free/admissible set of arguments). Accordingly, we
derive a definition of robustness for AFs in terms of the number of times such
operators can be applied without producing any change in the chosen semantics.
","On Looking for Local Expansion Invariants in Argumentation Semantics: a
  Preliminary Report"
"  Real-time bidding (RTB) is an important mechanism in online display
advertising, where a proper bid for each page view plays an essential role for
good marketing results. Budget constrained bidding is a typical scenario in RTB
where the advertisers hope to maximize the total value of the winning
impressions under a pre-set budget constraint. However, the optimal bidding
strategy is hard to be derived due to the complexity and volatility of the
auction environment. To address these challenges, in this paper, we formulate
budget constrained bidding as a Markov Decision Process and propose a
model-free reinforcement learning framework to resolve the optimization
problem. Our analysis shows that the immediate reward from environment is
misleading under a critical resource constraint. Therefore, we innovate a
reward function design methodology for the reinforcement learning problems with
constraints. Based on the new reward design, we employ a deep neural network to
learn the appropriate reward so that the optimal policy can be learned
effectively. Different from the prior model-based work, which suffers from the
scalability problem, our framework is easy to be deployed in large-scale
industrial applications. The experimental evaluations demonstrate the
effectiveness of our framework on large-scale real datasets.
","Budget Constrained Bidding by Model-free Reinforcement Learning in
  Display Advertising"
"  The assignment of weights to attacks in a classical Argumentation Framework
allows to compute semantics by taking into account the different importance of
each argument. We represent a Weighted Argumentation Framework by a non-binary
matrix, and we characterize the basic extensions (such as w-admissible, w-
stable, w-complete) by analysing sub-blocks of this matrix. Also, we show how
to reduce the matrix into another one of smaller size, that is equivalent to
the original one for the determination of extensions. Furthermore, we provide
two algorithms that allow to build incrementally w-grounded and w-preferred
extensions starting from a w-admissible extension.
","A Matrix Approach for Weighted Argumentation Frameworks: a Preliminary
  Report"
"  With an increasing demand from emerging logistics businesses, Vehicle Routing
Problem with Private fleet and common Carrier (VRPPC) has been introduced to
manage package delivery services from a supplier to customers. However, almost
all of existing studies focus on the deterministic problem that assumes all
parameters are known perfectly at the time when the planning and routing
decisions are made. In reality, some parameters are random and unknown.
Therefore, in this paper, we consider VRPPC with hard time windows and random
demand, called Optimal Delivery Planning (ODP). The proposed ODP aims to
minimize the total package delivery cost while meeting the customer time window
constraints. We use stochastic integer programming to formulate the
optimization problem incorporating the customer demand uncertainty. Moreover,
we evaluate the performance of the ODP using test data from benchmark dataset
and from actual Singapore road map.
","Optimal Stochastic Delivery Planning in Full-Truckload and
  Less-Than-Truckload Delivery"
"  Reasoning systems with too simple a model of the world and human intent are
unable to consider potential negative side effects of their actions and modify
their plans to avoid them (e.g., avoiding potential errors). However,
hand-encoding the enormous and subtle body of facts that constitutes common
sense into a knowledge base has proved too difficult despite decades of work.
Distributed semantic vector spaces learned from large text corpora, on the
other hand, can learn representations that capture shades of meaning of
common-sense concepts and perform analogical and associational reasoning in
ways that knowledge bases are too rigid to perform, by encoding concepts and
the relations between them as geometric structures. These have, however, the
disadvantage of being unreliable, poorly understood, and biased in their view
of the world by the source material. This chapter will discuss how these
approaches may be combined in a way that combines the best properties of each
for understanding the world and human intentions in a richer way.
",Semantic Vector Spaces for Broadening Consideration of Consequences
"  Reinforcement learning (RL) agents improve through trial-and-error, but when
reward is sparse and the agent cannot discover successful action sequences,
learning stagnates. This has been a notable problem in training deep RL agents
to perform web-based tasks, such as booking flights or replying to emails,
where a single mistake can ruin the entire sequence of actions. A common remedy
is to ""warm-start"" the agent by pre-training it to mimic expert demonstrations,
but this is prone to overfitting. Instead, we propose to constrain exploration
using demonstrations. From each demonstration, we induce high-level ""workflows""
which constrain the allowable actions at each time step to be similar to those
in the demonstration (e.g., ""Step 1: click on a textbox; Step 2: enter some
text""). Our exploration policy then learns to identify successful workflows and
samples actions that satisfy these workflows. Workflows prune out bad
exploration directions and accelerate the agent's ability to discover rewards.
We use our approach to train a novel neural policy designed to handle the
semi-structured nature of websites, and evaluate on a suite of web tasks,
including the recent World of Bits benchmark. We achieve new state-of-the-art
results, and show that workflow-guided exploration improves sample efficiency
over behavioral cloning by more than 100x.
","Reinforcement Learning on Web Interfaces Using Workflow-Guided
  Exploration"
"  This paper proposes an agent with particle swarm optimization (PSO) based on
a Fuzzy Markup Language (FML) for students learning performance evaluation and
educational applications, and the proposed agent is according to the response
data from a conventional test and an item response theory. First, we apply a
GS-based parameter estimation mechanism to estimate the items parameters
according to the response data, and then to compare its results with those of
an IRT-based Bayesian parameter estimation mechanism. In addition, we propose a
static-IRT test assembly mechanism to assemble a form for the conventional
test. The presented FML-based dynamic assessment mechanism infers the
probability of making a correct response to the item for a student with various
abilities. Moreover, this paper also proposes a novel PFML learning mechanism
for optimizing the parameters between items and students. Finally, we adopt a
K-fold cross validation mechanism to evaluate the performance of the proposed
agent. Experimental results show that the novel PFML learning mechanism for the
parameter estimation and learning optimization performs favorably. We believe
the proposed PFML will be a reference for education research and pedagogy and
an important co-learning mechanism for future human-machine educational
applications.
","PSO-based Fuzzy Markup Language for Student Learning Performance
  Evaluation and Educational Application"
"  I apply recent work on ""learning to think"" (2015) and on PowerPlay (2011) to
the incremental training of an increasingly general problem solver, continually
learning to solve new tasks without forgetting previous skills. The problem
solver is a single recurrent neural network (or similar general purpose
computer) called ONE. ONE is unusual in the sense that it is trained in various
ways, e.g., by black box optimization / reinforcement learning / artificial
evolution as well as supervised / unsupervised learning. For example, ONE may
learn through neuroevolution to control a robot through environment-changing
actions, and learn through unsupervised gradient descent to predict future
inputs and vector-valued reward signals as suggested in 1990. User-given tasks
can be defined through extra goal-defining input patterns, also proposed in
1990. Suppose ONE has already learned many skills. Now a copy of ONE can be
re-trained to learn a new skill, e.g., through neuroevolution without a
teacher. Here it may profit from re-using previously learned subroutines, but
it may also forget previous skills. Then ONE is retrained in PowerPlay style
(2011) on stored input/output traces of (a) ONE's copy executing the new skill
and (b) previous instances of ONE whose skills are still considered worth
memorizing. Simultaneously, ONE is retrained on old traces (even those of
unsuccessful trials) to become a better predictor, without additional expensive
interaction with the enviroment. More and more control and prediction skills
are thus collapsed into ONE, like in the chunker-automatizer system of the
neural history compressor (1991). This forces ONE to relate partially analogous
skills (with shared algorithmic information) to each other, creating common
subroutines in form of shared subnetworks of ONE, to greatly speed up
subsequent learning of additional, novel but algorithmically related skills.
",One Big Net For Everything
"  Enhancing evacuee safety is a key factor in reducing the number of injuries
and deaths that result from earthquakes. One way this can be achieved is by
training occupants. Virtual Reality (VR) and Serious Games (SGs), represent
novel techniques that may overcome the limitations of traditional training
approaches. VR and SGs have been examined in the fire emergency context,
however, their application to earthquake preparedness has not yet been
extensively examined. We provide a theoretical discussion of the advantages and
limitations of using VR SGs to investigate how building occupants behave during
earthquake evacuations and to train building occupants to cope with such
emergencies. We explore key design components for developing a VR SG framework:
(a) what features constitute an earthquake event, (b) which building types can
be selected and represented within the VR environment, (c) how damage to the
building can be determined and represented, (d) how non-player characters (NPC)
can be designed, and (e) what level of interaction there can be between NPC and
the human participants. We illustrate the above by presenting the Auckland City
Hospital, New Zealand as a case study, and propose a possible VR SG training
tool to enhance earthquake preparedness in public buildings.
","Prototyping Virtual Reality Serious Games for Building Earthquake
  Preparedness: The Auckland City Hospital Case Study"
"  Antifragile systems grow measurably better in the presence of hazards. This
is in contrast to fragile systems which break down in the presence of hazards,
robust systems that tolerate hazards up to a certain degree, and resilient
systems that -- like self-healing systems -- revert to their earlier expected
behavior after a period of convalescence. The notion of antifragility was
introduced by Taleb for economics systems, but its applicability has been
illustrated in biological and engineering domains as well. In this paper, we
propose an architecture that imparts antifragility to intelligent autonomous
systems, specifically those that are goal-driven and based on AI-planning. We
argue that this architecture allows the system to self-improve by uncovering
new capabilities obtained either through the hazards themselves (opportunistic)
or through deliberation (strategic). An AI planning-based case study of an
autonomous wheeled robot is presented. We show that with the proposed
architecture, the robot develops antifragile behaviour with respect to an oil
spill hazard.
",Antifragility for Intelligent Autonomous Systems
"  This paper offers a multi-disciplinary review of knowledge acquisition
methods in human activity systems. The review captures the degree of
involvement of various types of agencies in the knowledge acquisition process,
and proposes a classification with three categories of methods: the human
agent, the human-inspired agent, and the autonomous machine agent methods. In
the first two categories, the acquisition of knowledge is seen as a cognitive
task analysis exercise, while in the third category knowledge acquisition is
treated as an autonomous knowledge-discovery endeavour. The motivation for this
classification stems from the continuous change over time of the structure,
meaning and purpose of human activity systems, which are seen as the factor
that fuelled researchers' and practitioners' efforts in knowledge acquisition
for more than a century.
  We show through this review that the KA field is increasingly active due to
the higher and higher pace of change in human activity, and conclude by
discussing the emergence of a fourth category of knowledge acquisition methods,
which are based on red-teaming and co-evolution.
","A Multi-Disciplinary Review of Knowledge Acquisition Methods: From Human
  to Autonomous Eliciting Agents"
"  We study planning problems where autonomous agents operate inside
environments that are subject to uncertainties and not fully observable.
Partially observable Markov decision processes (POMDPs) are a natural formal
model to capture such problems. Because of the potentially huge or even
infinite belief space in POMDPs, synthesis with safety guarantees is, in
general, computationally intractable. We propose an approach that aims to
circumvent this difficulty: in scenarios that can be partially or fully
simulated in a virtual environment, we actively integrate a human user to
control an agent. While the user repeatedly tries to safely guide the agent in
the simulation, we collect data from the human input. Via behavior cloning, we
translate the data into a strategy for the POMDP. The strategy resolves all
nondeterminism and non-observability of the POMDP, resulting in a discrete-time
Markov chain (MC). The efficient verification of this MC gives quantitative
insights into the quality of the inferred human strategy by proving or
disproving given system specifications. For the case that the quality of the
strategy is not sufficient, we propose a refinement method using
counterexamples presented to the human. Experiments show that by including
humans into the POMDP verification loop we improve the state of the art by
orders of magnitude in terms of scalability.
","Human-in-the-Loop Synthesis for Partially Observable Markov Decision
  Processes"
"  This article provides a brief introduction to the ""Theory of Intelligence""
and its realisation in the ""SP Computer Model"". The overall goal of the SP
programme of research, in accordance with long-established principles in
science, has been the simplification and integration of observations and
concepts across artificial intelligence, mainstream computing, mathematics, and
human learning, perception, and cognition. In broad terms, the SP system is a
brain-like system that takes in ""New"" information through its senses and stores
some or all of it as ""Old"" information. A central idea in the system is the
powerful concept of ""SP-multiple-alignment"", borrowed and adapted from
bioinformatics. This the key to the system's versatility in aspects of
intelligence, in the representation of diverse kinds of knowledge, and in the
seamless integration of diverse aspects of intelligence and diverse kinds of
knowledge, in any combination. There are many potential benefits and
applications of the SP system. It is envisaged that the system will be
developed as the ""SP Machine"", which will initially be a software virtual
machine, hosted on a high-performance computer, a vehicle for further research
and a step towards the development of an industrial-strength SP Machine.
",Introduction to the SP theory of intelligence
"  The aim of behaviour change is to help people to change aspects of their
behaviour for the better (e.g., to decrease calorie intake, to drink in
moderation, to take more exercise, to complete a course of antibiotics once
started, etc.). In current persuasion technology for behaviour change, the
emphasis is on helping people to explore their issues (e.g., through
questionnaires or game playing) or to remember to follow a behaviour change
plan (e.g., diaries and email reminders). However, recent developments in
computational persuasion are leading to an argument-centric approach to
persuasion that can potentially be harnessed in behaviour change applications.
In this paper, we review developments in computational persuasion, and then
focus on domain modelling as a key component. We present a multi-dimensional
approach to domain modelling. At the core of this proposal is an ontology which
provides a representation of key factors, in particular kinds of belief, which
we have identified in the behaviour change literature as being important in
diverse behaviour change initiatives. Our proposal for domain modelling is
intended to facilitate the acquisition and representation of the arguments that
can be used in persuasion dialogues, together with meta-level information about
them which can be used by the persuader to make strategic choices of argument
to present.
","Domain Modelling in Computational Persuasion for Behaviour Change in
  Healthcare"
"  Deep reinforcement learning has emerged as a powerful tool for a variety of
learning tasks, however deep nets typically exhibit forgetting when learning
multiple tasks in sequence. To mitigate forgetting, we propose an experience
replay process that augments the standard FIFO buffer and selectively stores
experiences in a long-term memory. We explore four strategies for selecting
which experiences will be stored: favoring surprise, favoring reward, matching
the global training distribution, and maximizing coverage of the state space.
We show that distribution matching successfully prevents catastrophic
forgetting, and is consistently the best approach on all domains tested. While
distribution matching has better and more consistent performance, we identify
one case in which coverage maximization is beneficial - when tasks that receive
less trained are more important. Overall, our results show that selective
experience replay, when suitable selection algorithms are employed, can prevent
catastrophic forgetting.
",Selective Experience Replay for Lifelong Learning
"  General Video Game Playing (GVGP) aims at designing an agent that is capable
of playing multiple video games with no human intervention. In 2014, The
General Video Game AI (GVGAI) competition framework was created and released
with the purpose of providing researchers a common open-source and easy to use
platform for testing their AI methods with potentially infinity of games
created using Video Game Description Language (VGDL). The framework has been
expanded into several tracks during the last few years to meet the demand of
different research directions. The agents are required either to play multiple
unknown games with or without access to game simulations, or to design new game
levels or rules. This survey paper presents the VGDL, the GVGAI framework,
existing tracks, and reviews the wide use of GVGAI framework in research,
education and competitions five years after its birth. A future plan of
framework improvements is also described.
","General Video Game AI: a Multi-Track Framework for Evaluating Agents,
  Games and Content Generation Algorithms"
"  Bidding optimization is one of the most critical problems in online
advertising. Sponsored search (SS) auction, due to the randomness of user query
behavior and platform nature, usually adopts keyword-level bidding strategies.
In contrast, the display advertising (DA), as a relatively simpler scenario for
auction, has taken advantage of real-time bidding (RTB) to boost the
performance for advertisers. In this paper, we consider the RTB problem in
sponsored search auction, named SS-RTB. SS-RTB has a much more complex dynamic
environment, due to stochastic user query behavior and more complex bidding
policies based on multiple keywords of an ad. Most previous methods for DA
cannot be applied. We propose a reinforcement learning (RL) solution for
handling the complex dynamic environment. Although some RL methods have been
proposed for online advertising, they all fail to address the ""environment
changing"" problem: the state transition probabilities vary between two days.
Motivated by the observation that auction sequences of two days share similar
transition patterns at a proper aggregation level, we formulate a robust MDP
model at hour-aggregation level of the auction data and propose a
control-by-model framework for SS-RTB. Rather than generating bid prices
directly, we decide a bidding model for impressions of each hour and perform
real-time bidding accordingly. We also extend the method to handle the
multi-agent problem. We deployed the SS-RTB system in the e-commerce search
auction platform of Alibaba. Empirical experiments of offline evaluation and
online A/B test demonstrate the effectiveness of our method.
",Deep Reinforcement Learning for Sponsored Search Real-time Bidding
"  The tasks that an agent will need to solve often are not known during
training. However, if the agent knows which properties of the environment are
important then, after learning how its actions affect those properties, it may
be able to use this knowledge to solve complex tasks without training
specifically for them. Towards this end, we consider a setup in which an
environment is augmented with a set of user defined attributes that
parameterize the features of interest. We propose a method that learns a policy
for transitioning between ""nearby"" sets of attributes, and maintains a graph of
possible transitions. Given a task at test time that can be expressed in terms
of a target set of attributes, and a current state, our model infers the
attributes of the current state and searches over paths through attribute space
to get a high level plan, and then uses its low level policy to execute the
plan. We show in 3D block stacking, grid-world games, and StarCraft that our
model is able to generalize to longer, more complex tasks at test time by
composing simpler learned policies.
",Composable Planning with Attributes
"  Relation detection is a core component for Knowledge Base Question Answering
(KBQA). In this paper, we propose a KB relation detection model via multi-view
matching which utilizes more useful information extracted from question and KB.
The matching inside each view is through multiple perspectives to compare two
input texts thoroughly. All these components are designed in an end-to-end
trainable neural network model. Experiments on SimpleQuestions and WebQSP yield
state-of-the-art results.
",Knowledge Base Relation Detection via Multi-View Matching
"  Automatic chess problem or puzzle composition typically involves generating
and testing various different positions, sometimes using particular piece sets.
Once a position has been generated, it is then usually tested for positional
legality based on the game rules. However, it is useful to be able to estimate
what the search space size for particular piece combinations is to begin with.
So if a desirable chess problem was successfully generated by examining
'merely' 100,000 or so positions in a theoretical search space of about 100
billion, this would imply the composing approach used was quite viable and
perhaps even impressive. In this article, I explain a method of calculating the
size of this search space using a combinatorics and permutations approach.
While the mathematics itself may already be established, a precise method and
justification of applying it with regard to the chessboard and chess pieces has
not been documented, to the best of our knowledge. Additionally, the method
could serve as a useful starting point for further estimations of search space
size which filter out positions for legality and rotation, depending on how the
automatic composer is allowed to place pieces on the board (because this
affects its total search space size).
",Estimating Total Search Space Size for Specific Piece Sets in Chess
"  Simulation is an appealing option for validating the safety of autonomous
vehicles. Generative Adversarial Imitation Learning (GAIL) has recently been
shown to learn representative human driver models. These human driver models
were learned through training in single-agent environments, but they have
difficulty in generalizing to multi-agent driving scenarios. We argue these
difficulties arise because observations at training and test time are sampled
from different distributions. This difference makes such models unsuitable for
the simulation of driving scenes, where multiple agents must interact
realistically over long time horizons. We extend GAIL to address these
shortcomings through a parameter-sharing approach grounded in curriculum
learning. Compared with single-agent GAIL policies, policies generated by our
PS-GAIL method prove superior at interacting stably in a multi-agent setting
and capturing the emergent behavior of human drivers.
",Multi-Agent Imitation Learning for Driving Simulation
"  Businesses are naturally interested in detecting anomalies in their internal
processes, because these can be indicators for fraud and inefficiencies. Within
the domain of business intelligence, classic anomaly detection is not very
frequently researched. In this paper, we propose a method, using autoencoders,
for detecting and analyzing anomalies occurring in the execution of a business
process. Our method does not rely on any prior knowledge about the process and
can be trained on a noisy dataset already containing the anomalies. We
demonstrate its effectiveness by evaluating it on 700 different datasets and
testing its performance against three state-of-the-art anomaly detection
methods. This paper is an extension of our previous work from 2016 [30].
Compared to the original publication we have further refined the approach in
terms of performance and conducted an elaborate evaluation on more
sophisticated datasets including real-life event logs from the Business Process
Intelligence Challenges of 2012 and 2017. In our experiments our approach
reached an F1 score of 0.87, whereas the best unaltered state-of-the-art
approach reached an F1 score of 0.72. Furthermore, our approach can be used to
analyze the detected anomalies in terms of which event within one execution of
the process causes the anomaly.
",Analyzing Business Process Anomalies Using Autoencoders
"  We consider the problem of exploration in meta reinforcement learning. Two
new meta reinforcement learning algorithms are suggested: E-MAML and
E-$\text{RL}^2$. Results are presented on a novel environment we call `Krazy
World' and a set of maze environments. We show E-MAML and E-$\text{RL}^2$
deliver better performance on tasks where exploration is important.
","Some Considerations on Learning to Explore via Meta-Reinforcement
  Learning"
"  The considered problem is how to optimally allocate a set of jobs to
technicians of different skills such that the number of technicians of each
skill does not exceed the number of persons with that skill designation. The
key motivation is the quick sensitivity analysis in terms of the workforce size
which is quite necessary in many industries in the presence of unexpected work
orders. A time-indexed mathematical model is proposed to minimize the total
weighted completion time of the jobs. The proposed model is decomposed into a
number of single-skill sub-problems so that each one is a combination of a
series of nested binary Knapsack problems. A heuristic procedure is proposed to
solve the problem. Our experimental results, based on a real-world case study,
reveal that the proposed method quickly produces a schedule statistically close
to the optimal one while the classical optimal procedure is very
time-consuming.
","A Swift Heuristic Method for Work Order Scheduling under the
  Skilled-Workforce Constraint"
"  With the growing integration of smartphones into our daily lives, and their
increased ease of use, mobile games have become highly popular across all
demographics. People listen to music, play games or read the news while in
transit or bridging gap times. While mobile gaming is gaining popularity,
mobile expression of creativity is still in its early stages. We present here a
new type of mobile app -- fluidic games -- and illustrate our iterative
approach to their design. This new type of app seamlessly integrates
exploration of the design space into the actual user experience of playing the
game, and aims to enrich the user experience. To better illustrate the game
domain and our approach, we discuss one specific fluidic game, which is
available as a commercial product. We also briefly discuss open challenges such
as player support and how generative techniques can aid the exploration of the
game space further.
",Exploring Novel Game Spaces with Fluidic Games
"  Under dynamic conditions on bridges, we need a real-time management. To this
end, this paper presents a rule-based decision support system in which the
necessary rules are extracted from simulation results made by Aimsun traffic
micro-simulation software. Then, these rules are generalized by the aid of
fuzzy rule generation algorithms. Then, they are trained by a set of supervised
and the unsupervised learning algorithms to get an ability to make decision in
real cases. As a pilot case study, Nasr Bridge in Tehran is simulated in Aimsun
and WEKA data mining software is used to execute the learning algorithms. Based
on this experiment, the accuracy of the supervised algorithms to generalize the
rules is greater than 80%. In addition, CART decision tree and sequential
minimal optimization (SMO) provides 100% accuracy for normal data and these
algorithms are so reliable for crisis management on bridge. This means that, it
is possible to use such machine learning methods to manage bridges in the
real-time conditions.
","A real-time decision support system for bridge management based on the
  rules generalized by CART decision tree and SMO algorithms"
"  The aim of this paper is to introduce a new framework for defining abductive
reasoning operators based on a notion of retraction in arbitrary logics defined
as satisfaction systems. We show how this framework leads to the design of
explanatory relations satisfying properties of abductive reasoning, and discuss
its application to several logics. This extends previous work on propositional
logics where retraction was defined as a morphological erosion. Here weaker
properties are required for retraction, leading to a larger set of suitable
operators for abduction for different logics.
","Explanatory relations in arbitrary logics based on satisfaction systems,
  cutting and retraction"
"  There currently exists a wide range of techniques to model and evolve
artificial players for games. Existing techniques range from black box neural
networks to entirely hand-designed solutions. In this paper, we demonstrate the
feasibility of a genetic programming framework using human controller input to
derive meaningful artificial players which can, later on, be optimised by hand.
The current state of the art in game character design relies heavily on human
designers to manually create and edit scripts and rules for game characters. To
address this manual editing bottleneck, current computational intelligence
techniques approach the issue with fully autonomous character generators,
replacing most of the design process using black box solutions such as neural
networks or the like. Our GP approach to this problem creates character
controllers which can be further authored and developed by a designer it also
offers designers to included their play style without the need to use a
programming language. This keeps the designer in the loop while reducing
repetitive manual labour. Our system also provides insights into how players
express themselves in games and into deriving appropriate models for
representing those insights. We present our framework, supporting findings and
open challenges.
",A Genetic Programming Framework for 2D Platform AI
"  This paper describes a process for combining patterns and features, to guide
a search process and make predictions. It is based on the functionality that a
human brain might have, which is a highly distributed network of simple
neuronal components that can apply some level of matching and cross-referencing
over retrieved patterns. The process uses memory in a dynamic way and it is
directed through the pattern matching. The paper firstly describes the
mechanisms for neuronal search, memory and prediction. The paper then presents
a formal language for defining cognitive processes, that is, pattern-based
sequences and transitions. The language can define an outer framework for
concept sets that are linked to perform the cognitive act. The language also
has a mathematical basis, allowing for the rule construction to be consistent.
Now, both static memory and dynamic process hierarchies can be built as tree
structures. The new information can also be used to further integrate the
cognitive model and the ensemble-hierarchy structure becomes an essential part.
A theory about linking can suggest that nodes in different regions link
together when generally they represent the same thing.
",New Ideas for Brain Modelling 5
"  This paper proposes an intent-aware multi-agent planning framework as well as
a learning algorithm. Under this framework, an agent plans in the goal space to
maximize the expected utility. The planning process takes the belief of other
agents' intents into consideration. Instead of formulating the learning problem
as a partially observable Markov decision process (POMDP), we propose a simple
but effective linear function approximation of the utility function. It is
based on the observation that for humans, other people's intents will pose an
influence on our utility for a goal. The proposed framework has several major
advantages: i) it is computationally feasible and guaranteed to converge. ii)
It can easily integrate existing intent prediction and low-level planning
algorithms. iii) It does not suffer from sparse feedbacks in the action space.
We experiment our algorithm in a real-world problem that is non-episodic, and
the number of agents and goals can vary over time. Our algorithm is trained in
a scene in which aerial robots and humans interact, and tested in a novel scene
with a different environment. Experimental results show that our algorithm
achieves the best performance and human-like behaviors emerge during the
dynamic process.
",Intent-aware Multi-agent Reinforcement Learning
"  Plan recognition aims to discover target plans (i.e., sequences of actions)
behind observed actions, with history plan libraries or domain models in hand.
Previous approaches either discover plans by maximally ""matching"" observed
actions to plan libraries, assuming target plans are from plan libraries, or
infer plans by executing domain models to best explain the observed actions,
assuming that complete domain models are available. In real world applications,
however, target plans are often not from plan libraries, and complete domain
models are often not available, since building complete sets of plans and
complete domain models are often difficult or expensive. In this paper we view
plan libraries as corpora and learn vector representations of actions using the
corpora, we then discover target plans based on the vector representations.
Specifically, we propose two approaches, DUP and RNNPlanner, to discover target
plans based on vector representations of actions. DUP explores the EM-style
framework to capture local contexts of actions and discover target plans by
optimizing the probability of target plans, while RNNPlanner aims to leverage
long-short term contexts of actions based on RNNs (recurrent neural networks)
framework to help recognize target plans. In the experiments, we empirically
show that our approaches are capable of discovering underlying plans that are
not from plan libraries, without requiring domain models provided. We
demonstrate the effectiveness of our approaches by comparing its performance to
traditional plan recognition approaches in three planning domains. We also
compare DUP and RNNPlanner to see their advantages and disadvantages.
",Discovering Underlying Plans Based on Shallow Models
"  The Cognitive Theory of True Conditions (CTTC) is a proposal to design the
implementation of cognitive abilities and to describe the model-theoretic
semantics of symbolic cognitive architectures. The CTTC is formulated
mathematically using the multi-optional many-sorted past present future(MMPPF)
structures. This article discussed how decision-making processes are described
in the CTTC.
",Decision-making processes in the Cognitive Theory of True Conditions
"  Ontologies are critical sources of semantic information for many application
domains. Hence, there are ontologies proposed and utilized for domains such as
medicine, chemical engineering, and electrical energy. In this paper, we
present an improved and extended version of a wind energy ontology previously
proposed. First, the ontology is restructured to increase its understandability
and coverage. Secondly, it is enriched with new concepts, crisp/fuzzy
attributes, and instances to increase its usability in semantic applications
regarding wind energy. The ultimate ontology is utilized within a Web-based
semantic portal application for wind energy, in order to showcase its
contribution in a genuine application. Hence, the current study is a
significant to wind and thereby renewable energy informatics, with the
presented publicly-available wind energy ontology and the implemented
proof-of-concept system.
",OntoWind: An Improved and Extended Wind Energy Ontology
"  The analytic philosophy of Robert Brandom, based on the ideas of pragmatism,
paints a picture of sapience, through inferentialism. In this paper, we present
a theory, that utilizes essential elements of Brandom's philosophy, towards the
objective of achieving strong-AI. We do this by connecting the constitutive
elements of reinforcement learning and the Game Of Giving and Asking For
Reasons. Further, following Brandom's prescriptive thoughts, we restructure the
popular reinforcement learning algorithm A3C, and show that RL algorithms can
be tuned towards the objective of strong-AI.
",A Brandom-ian view of Reinforcement Learning towards strong-AI
"  In multiagent environments, the capability of learning is important for an
agent to behave appropriately in face of unknown opponents and dynamic
environment. From the system designer's perspective, it is desirable if the
agents can learn to coordinate towards socially optimal outcomes, while also
avoiding being exploited by selfish opponents. To this end, we propose a novel
gradient ascent based algorithm (SA-IGA) which augments the basic
gradient-ascent algorithm by incorporating social awareness into the policy
update process. We theoretically analyze the learning dynamics of SA-IGA using
dynamical system theory and SA-IGA is shown to have linear dynamics for a wide
range of games including symmetric games. The learning dynamics of two
representative games (the prisoner's dilemma game and the coordination game)
are analyzed in details. Based on the idea of SA-IGA, we further propose a
practical multiagent learning algorithm, called SA-PGA, based on Q-learning
update rule. Simulation results show that SA-PGA agent can achieve higher
social welfare than previous social-optimality oriented Conditional Joint
Action Learner (CJAL) and also is robust against individually rational
opponents by reaching Nash equilibrium solutions.
","SA-IGA: A Multiagent Reinforcement Learning Method Towards Socially
  Optimal Outcomes"
"  We present the MAC network, a novel fully differentiable neural network
architecture, designed to facilitate explicit and expressive reasoning. MAC
moves away from monolithic black-box neural architectures towards a design that
encourages both transparency and versatility. The model approaches problems by
decomposing them into a series of attention-based reasoning steps, each
performed by a novel recurrent Memory, Attention, and Composition (MAC) cell
that maintains a separation between control and memory. By stringing the cells
together and imposing structural constraints that regulate their interaction,
MAC effectively learns to perform iterative reasoning processes that are
directly inferred from the data in an end-to-end approach. We demonstrate the
model's strength, robustness and interpretability on the challenging CLEVR
dataset for visual reasoning, achieving a new state-of-the-art 98.9% accuracy,
halving the error rate of the previous best model. More importantly, we show
that the model is computationally-efficient and data-efficient, in particular
requiring 5x less data than existing models to achieve strong results.
",Compositional Attention Networks for Machine Reasoning
"  The enormous amount of data to be represented using large graphs exceeds in
some cases the resources of a conventional computer. Edges in particular can
take up a considerable amount of memory as compared to the number of nodes.
However, rigorous edge storage might not always be essential to be able to draw
the needed conclusions. A similar problem takes records with many variables and
attempts to extract the most discernible features. It is said that the
""dimension"" of this data is reduced. Following an approach with the same
objective in mind, we can map a graph representation to a k-dimensional space
and answer queries of neighboring nodes by measuring Euclidean distances. The
accuracy of our answers would decrease but would be compensated for by fuzzy
logic which gives an idea about the likelihood of error. This method allows for
reasonable representation in memory while maintaining a fair amount of useful
information. Promising preliminary results are obtained and reported by testing
the proposed approach on a number of Facebook graphs.
","Concise Fuzzy Representation of Big Graphs: a Dimensionality Reduction
  Approach"
"  Artificial Intelligence (AI) started out with an ambition to reproduce the
human mind, but, as the sheer scale of that ambition became manifest, it
quickly retreated into either studying specialized intelligent behaviours, or
proposing over-arching architectural concepts for interfacing specialized
intelligent behaviour components, conceived of as agents in a kind of
organization. This agent-based modeling paradigm, in turn, proves to have
interesting applications in understanding, simulating, and predicting the
behaviour of social and legal structures on an aggregate level. For these
reasons, this chapter examines a number of relevant cross-cutting concerns,
conceptualizations, modeling problems and design challenges in large-scale
distributed Artificial Intelligence, as well as in institutional systems, and
identifies potential grounds for novel advances.
","Institutional Metaphors for Designing Large-Scale Distributed AI versus
  AI Techniques for Running Institutions"
"  Highly automated driving requires precise models of traffic participants.
Many state of the art models are currently based on machine learning
techniques. Among others, the required amount of labeled data is one major
challenge. An autonomous learning process addressing this problem is proposed.
The initial models are iteratively refined in three steps: (1) detection and
context identification, (2) novelty detection and active learning and (3)
online model adaption.
","Highly Automated Learning for Improved Active Safety of Vulnerable Road
  Users"
"  We present a formal language with expressions denoting general symbol
structures and queries which access information in those structures. A
sequence-to-sequence network processing this language learns to encode symbol
structures and query them. The learned representation (approximately) shares a
simple linearity property with theoretical techniques for performing this task.
",Learning and analyzing vector encoding of symbolic representations
"  Since Artificial Intelligence (AI) software uses techniques like deep
lookahead search and stochastic optimization of huge neural networks to fit
mammoth datasets, it often results in complex behavior that is difficult for
people to understand. Yet organizations are deploying AI algorithms in many
mission-critical settings. To trust their behavior, we must make AI
intelligible, either by using inherently interpretable models or by developing
new methods for explaining and controlling otherwise overwhelmingly complex
decisions using local approximation, vocabulary alignment, and interactive
explanation. This paper argues that intelligibility is essential, surveys
recent work on building such systems, and highlights key directions for
research.
",The Challenge of Crafting Intelligible Intelligence
"  This article explores the ideas that went into George Boole's development of
an algebra for logical inference in his book The Laws of Thought. We explore in
particular his wife Mary Boole's claim that he was deeply influenced by Indian
logic and argue that his work was more than a framework for processing
propositions. By exploring parallels between his work and Indian logic, we are
able to explain several peculiarities of this work.
",On the Algebra in Boole's Laws of Thought
"  Due to the good performance of current SAT (satisfiability) and Max-SAT
(maximum ssatisfiability) solvers, many real-life optimization problems such as
scheduling can be solved by encoding them into Max-SAT. In this paper we tackle
the course timetabling problem of the department of mathematics, Cairo
University by encoding it into Max-SAT. Generating timetables for the
department by hand has proven to be cumbersome and the generated timetable
almost always contains conflicts. We show how the constraints can be modelled
as a Max-SAT instance.
",Solving the Course-timetabling Problem of Cairo University Using Max-SAT
"  Fractal AI is a theory for general artificial intelligence. It allows
deriving new mathematical tools that constitute the foundations for a new kind
of stochastic calculus, by modelling information using cellular automaton-like
structures instead of smooth functions. In the repository included we are
presenting a new Agent, derived from the first principles of the theory, which
is capable of solving Atari games several orders of magnitude more efficiently
than other similar techniques, like Monte Carlo Tree Search. The code provided
shows how it is now possible to beat some of the current State of The Art
benchmarks on Atari games, without previous learning and using less than 1000
samples to calculate each one of the actions when standard MCTS uses 3 Million
samples. Among other things, Fractal AI makes it possible to generate a huge
database of top performing examples with a very little amount of computation
required, transforming Reinforcement Learning into a supervised problem. The
algorithm presented is capable of solving the exploration vs exploitation
dilemma on both the discrete and continuous cases, while maintaining control
over any aspect of the behaviour of the Agent. From a general approach, new
techniques presented here have direct applications to other areas such as
Non-equilibrium thermodynamics, chemistry, quantum physics, economics,
information theory, and non-linear control theory.
",Fractal AI: A fragile theory of intelligence
"  This paper presents an overview of the sixth AIBIRDS competition, held at the
26th International Joint Conference on Artificial Intelligence. This
competition tasked participants with developing an intelligent agent which can
play the physics-based puzzle game Angry Birds. This game uses a sophisticated
physics engine that requires agents to reason and predict the outcome of
actions with only limited environmental information. Agents entered into this
competition were required to solve a wide assortment of previously unseen
levels within a set time limit. The physical reasoning and planning required to
solve these levels are very similar to those of many real-world problems. This
year's competition featured some of the best agents developed so far and even
included several new AI techniques such as deep reinforcement learning. Within
this paper we describe the framework, rules, submitted agents and results for
this competition. We also provide some background information on related work
and other video game AI competitions, as well as discussing some potential
ideas for future AIBIRDS competitions and agent improvements.
",The 2017 AIBIRDS Competition
"  Freight train services in a railway network system are generally divided into
two categories: one is the unscheduled train, whose operating frequency
fluctuates with origin-destination (OD) demands; the other is the scheduled
train, which is running based on regular timetable just like the passenger
trains. The timetable will be released to the public if determined and it would
not be influenced by OD demands. Typically, the total capacity of scheduled
trains can usually satisfy the predicted demands of express cargos in average.
However, the demands are changing in practice. Therefore, how to distribute the
shipments between different stations to unscheduled and scheduled train
services has become an important research field in railway transportation. This
paper focuses on the coordinated optimization of the rail express cargos
distribution in two service networks. On the premise of fully utilizing the
capacity of scheduled service network first, we established a Car-to-Train
(CTT) assignment model to assign rail express cargos to scheduled and
unscheduled trains scientifically. The objective function is to maximize the
net income of transporting the rail express cargos. The constraints include the
capacity restriction on the service arcs, flow balance constraints, logical
relationship constraint between two groups of decision variables and the due
date constraint. The last constraint is to ensure that the total transportation
time of a shipment would not be longer than its predefined due date. Finally,
we discuss the linearization techniques to simplify the model proposed in this
paper, which make it possible for obtaining global optimal solution by using
the commercial software.
","A Study of Car-to-Train Assignment Problem for Rail Express Cargos on
  Scheduled and Unscheduled Train Service Network"
"  Relaxed models are abstract problem descriptions generated by ignoring
constraints that are present in base-level problems. They play an important
role in planning and search algorithms, as it has been shown that the length of
an optimal solution to a relaxed model yields a monotone heuristic for an A?
search of a base-level problem. Optimal solutions to a relaxed model may be
computed algorithmically or by search in a further relaxed model, leading to a
search that explores a hierarchy of relaxed models. In this paper, we review
the traditional definition of problem relaxation and show that searching in the
abstraction hierarchy created by problem relaxation will not reduce the
computational effort required to find optimal solutions to the base- level
problem, unless the relaxed problem found in the hierarchy can be transformed
by some optimization (e.g., subproblem factoring). Specifically, we prove that
any A* search of the base-level using a heuristic h2 will largely dominate an
A* search of the base-level using a heuristic h1, if h1 must be computed by an
A* search of the relaxed model using h2.
","A New Result on the Complexity of Heuristic Estimates for the A*
  Algorithm"
"  Reward shaping allows reinforcement learning (RL) agents to accelerate
learning by receiving additional reward signals. However, these signals can be
difficult to design manually, especially for complex RL tasks. We propose a
simple and general approach that determines the reward of pre-defined events by
their rarity alone. Here events become less rewarding as they are experienced
more often, which encourages the agent to continually explore new types of
events as it learns. The adaptiveness of this reward function results in a form
of automated curriculum learning that does not have to be specified by the
experimenter. We demonstrate that this \emph{Rarity of Events} (RoE) approach
enables the agent to succeed in challenging VizDoom scenarios without access to
the extrinsic reward from the environment. Furthermore, the results demonstrate
that RoE learns a more versatile policy that adapts well to critical changes in
the environment. Rewarding events based on their rarity could help in many
unsolved RL environments that are characterized by sparse extrinsic rewards but
a plethora of known event types.
",Automated Curriculum Learning by Rewarding Temporally Rare Events
"  In this paper, we proposed VeSC-CoL (Version Space Cardinality based Concept
Learning) to deal with concept learning on extremely imbalanced datasets,
especially when cross-validation is not a viable option. VeSC-CoL uses version
space cardinality as a measure for model quality to replace cross-validation.
Instead of naive enumeration of the version space, Ordered Binary Decision
Diagram and Boolean Satisfiability are used to compute the version space.
Experiments show that VeSC-CoL can accurately learn the target concept when
computational resource is allowed.
",A Concept Learning Tool Based On Calculating Version Space Cardinality
"  This paper introduces an innovative approach for handling 2D compound
hypotheses within the Belief Function Theory framework. We propose a
polygon-based generic rep- resentation which relies on polygon clipping
operators. This approach allows us to account in the computational cost for the
precision of the representation independently of the cardinality of the
discernment frame. For the BBA combination and decision making, we propose
efficient algorithms which rely on hashes for fast lookup, and on a topological
ordering of the focal elements within a directed acyclic graph encoding their
interconnections. Additionally, an implementation of the functionalities
proposed in this paper is provided as an open source library. Experimental
results on a pedestrian localization problem are reported. The experiments show
that the solution is accurate and that it fully benefits from the scalability
of the 2D search space granularity provided by our representation.
","2CoBel : An Efficient Belief Function Extension for Two-dimensional
  Continuous Spaces"
"  In this work we study a rational extension $SROEL^R T$ of the low complexity
description logic SROEL, which underlies the OWL EL ontology language. The
extension involves a typicality operator T, whose semantics is based on Lehmann
and Magidor's ranked models and allows for the definition of defeasible
inclusions. We consider both rational entailment and minimal entailment. We
show that deciding instance checking under minimal entailment is in general
$\Pi^P_2$-hard, while, under rational entailment, instance checking can be
computed in polynomial time. We develop a Datalog calculus for instance
checking under rational entailment and exploit it, with stratified negation,
for computing the rational closure of simple KBs in polynomial time.
","Defeasible Reasoning in SROEL: from Rational Entailment to Rational
  Closure"
"  Conversation interfaces (CIs), or chatbots, are a popular form of intelligent
agents that engage humans in task-oriented or informal conversation. In this
position paper and demonstration, we argue that chatbots working in dynamic
environments, like with sensor data, can not only serve as a promising platform
to research issues at the intersection of learning, reasoning, representation
and execution for goal-directed autonomy; but also handle non-trivial business
applications. We explore the underlying issues in the context of Water Advisor,
a preliminary multi-modal conversation system that can access and explain water
quality data.
",On Chatbots Exhibiting Goal-Directed Autonomy in Dynamic Environments
"  The Turing Machine has two implicit properties that depend on its underlying
notion of computing: the format is fully determinate and computations are
information preserving. Distributed representations lack these properties and
cannot be fully captured by Turing's standard model. To address this limitation
a distributed extension of the Turing Machine is introduced in this paper. In
the extended machine, functions and abstractions are expressed extensionally
and computations are entropic. The machine is applied to the definition of an
associative memory, with its corresponding memory register, recognition and
retrieval operations. The memory is tested with an experiment for storing and
recognizing hand written digits with satisfactory results. The experiment can
be seen as a proof of concept that information can be stored and processed
effectively in a highly distributed fashion using a symbolic but not fully
determinate format. The new machine augments the symbolic mode of computing
with consequences on the way Church Thesis is understood. The paper is
concluded with a discussion of some implications of the extended machine for
Artificial Intelligence and Cognition.
",A Distributed Extension of the Turing Machine
"  The recent successes of AI have captured the wildest imagination of both the
scientific communities and the general public. Robotics and AI amplify human
potentials, increase productivity and are moving from simple reasoning towards
human-like cognitive abilities. Current AI technologies are used in a set area
of applications, ranging from healthcare, manufacturing, transport, energy, to
financial services, banking, advertising, management consulting and government
agencies. The global AI market is around 260 billion USD in 2016 and it is
estimated to exceed 3 trillion by 2024. To understand the impact of AI, it is
important to draw lessons from it's past successes and failures and this white
paper provides a comprehensive explanation of the evolution of AI, its current
status and future directions.
",Artificial Intelligence and Robotics
"  As multicore computing is now standard, it seems irresponsible for
constraints researchers to ignore the implications of it. Researchers need to
address a number of issues to exploit parallelism, such as: investigating which
constraint algorithms are amenable to parallelisation; whether to use shared
memory or distributed computation; whether to use static or dynamic
decomposition; and how to best exploit portfolios and cooperating search. We
review the literature, and see that we can sometimes do quite well, some of the
time, on some instances, but we are far from a general solution. Yet there
seems to be little overall guidance that can be given on how best to exploit
multicore computers to speed up constraint solving. We hope at least that this
survey will provide useful pointers to future researchers wishing to correct
this situation.
  Under consideration in Theory and Practice of Logic Programming (TPLP).
",A Review of Literature on Parallel Constraint Solving
"  Committee selection with diversity or distributional constraints is a
ubiquitous problem. However, many of the formal approaches proposed so far have
certain drawbacks including (1) computationally intractability in general, and
(2) inability to suggest a solution for certain instances where the hard
constraints cannot be met. We propose a practical and polynomial-time algorithm
for diverse committee selection that draws on the idea of using soft bounds and
satisfies natural axioms.
",A Rule for Committee Selection with Soft Diversity Constraints
"  Navigating through unstructured environments is a basic capability of
intelligent creatures, and thus is of fundamental interest in the study and
development of artificial intelligence. Long-range navigation is a complex
cognitive task that relies on developing an internal representation of space,
grounded by recognisable landmarks and robust visual processing, that can
simultaneously support continuous self-localisation (""I am here"") and a
representation of the goal (""I am going there""). Building upon recent research
that applies deep reinforcement learning to maze navigation problems, we
present an end-to-end deep reinforcement learning approach that can be applied
on a city scale. Recognising that successful navigation relies on integration
of general policies with locale-specific knowledge, we propose a dual pathway
architecture that allows locale-specific features to be encapsulated, while
still enabling transfer to multiple cities. We present an interactive
navigation environment that uses Google StreetView for its photographic content
and worldwide coverage, and demonstrate that our learning method allows agents
to learn to navigate multiple cities and to traverse to target destinations
that may be kilometres away. The project webpage http://streetlearn.cc contains
a video summarising our research and showing the trained agent in diverse city
environments and on the transfer task, the form to request the StreetLearn
dataset and links to further resources. The StreetLearn environment code is
available at https://github.com/deepmind/streetlearn
",Learning to Navigate in Cities Without a Map
"  Synthesizing physiologically-accurate human movement in a variety of
conditions can help practitioners plan surgeries, design experiments, or
prototype assistive devices in simulated environments, reducing time and costs
and improving treatment outcomes. Because of the large and complex solution
spaces of biomechanical models, current methods are constrained to specific
movements and models, requiring careful design of a controller and hindering
many possible applications. We sought to discover if modern optimization
methods efficiently explore these complex spaces. To do this, we posed the
problem as a competition in which participants were tasked with developing a
controller to enable a physiologically-based human model to navigate a complex
obstacle course as quickly as possible, without using any experimental data.
They were provided with a human musculoskeletal model and a physics-based
simulation environment. In this paper, we discuss the design of the
competition, technical difficulties, results, and analysis of the top
controllers. The challenge proved that deep reinforcement learning techniques,
despite their high computational cost, can be successfully employed as an
optimization method for synthesizing physiologically feasible motion in
high-dimensional biomechanical systems.
","Learning to Run challenge: Synthesizing physiologically accurate motion
  using deep reinforcement learning"
"  In the encoding of many real-world problems to propositional satisfiability,
the cardinality constraint is a recurrent constraint that needs to be managed
effectively. Several efficient encodings have been proposed while missing that
such a constraint can be involved in a more general propositional formulation.
To avoid combinatorial explosion, Tseitin principle usually used to translate
such general propositional formula to Conjunctive Normal Form (CNF), introduces
fresh propositional variables to represent sub-formulas and/or complex
contraints. Thanks to Plaisted and Greenbaum improvement, the polarity of the
sub-formula $\Phi$ is taken into account leading to conditional constraints of
the form $y\rightarrow \Phi$, or $\Phi\rightarrow y$, where $y$ is a fresh
propositional variable. In the case where $\Phi$ represents a cardinality
constraint, such translation leads to conditional cardinality constraints
subject of the present paper. We first show that when all the clauses encoding
the cardinality constraint are augmented with an additional new variable, most
of the well-known encodings cease to maintain the generalized arc consistency
property. Then, we consider some of these encodings and show how they can be
extended to recover such important property. An experimental validation is
conducted on a SAT-based pattern mining application, where such conditional
cardinality constraints is a cornerstone, showing the relevance of our proposed
approach.
",Efficient Encodings of Conditional Cardinality Constraints
"  Fuzzy relation equations (FRE)are associated with the composition of binary
fuzzy relations. In the present work FRE are used as a tool for studying the
process of learning a new subject matter by a student class. A classroom
application and other csuitable examples connected to the student learning of
the derivative are also presented illustrating our results and useful
conclusions are obtained.
",A Study of Student Learning Skills Using Fuzzy Relation Equations
"  The theory of grey systems plays an important role in science,engineering and
in the everyday life in general for handling approximate data. In the present
paper grey numbers are used as a tool for assessing with linguistic expressions
the mean performance of a group of objects participating in a certain activity.
Two applications to student and football player assessment are also presented
illustrating our results.
",Application of Grey Numbers to Assessment Processes
"  Techniques combining machine learning with translation to automated reasoning
have recently become an important component of formal proof assistants. Such
""hammer"" tech- niques complement traditional proof assistant automation as
implemented by tactics and decision procedures. In this paper we present a
unified proof assistant automation approach which attempts to automate the
selection of appropriate tactics and tactic-sequences com- bined with an
optimized small-scale hammering approach. We implement the technique as a
tactic-level automation for HOL4: TacticToe. It implements a modified
A*-algorithm directly in HOL4 that explores different tactic-level proof paths,
guiding their selection by learning from a large number of previous
tactic-level proofs. Unlike the existing hammer methods, TacticToe avoids
translation to FOL, working directly on the HOL level. By combining tactic
prediction and premise selection, TacticToe is able to re-prove 39 percent of
7902 HOL4 theorems in 5 seconds whereas the best single HOL(y)Hammer strategy
solves 32 percent in the same amount of time.
",Learning to Reason with HOL4 tactics
"  In order to build agents with a rich understanding of their environment, one
key objective is to endow them with a grasp of intuitive physics; an ability to
reason about three-dimensional objects, their dynamic interactions, and
responses to forces. While some work on this problem has taken the approach of
building in components such as ready-made physics engines, other research aims
to extract general physical concepts directly from sensory data. In the latter
case, one challenge that arises is evaluating the learning system. Research on
intuitive physics knowledge in children has long employed a violation of
expectations (VOE) method to assess children's mastery of specific physical
concepts. We take the novel step of applying this method to artificial learning
systems. In addition to introducing the VOE technique, we describe a set of
probe datasets inspired by classic test stimuli from developmental psychology.
We test a baseline deep learning system on this battery, as well as on a
physics learning dataset (""IntPhys"") recently posed by another research group.
Our results show how the VOE technique may provide a useful tool for tracking
physics knowledge in future research.
",Probing Physics Knowledge Using Tools from Developmental Psychology
"  This chapter offers an accessible introduction to the channel-based approach
to Bayesian probability theory. This framework rests on algebraic and logical
foundations, inspired by the methodologies of programming language semantics.
It offers a uniform, structured and expressive language for describing Bayesian
phenomena in terms of familiar programming concepts, like channel, predicate
transformation and state transformation. The introduction also covers inference
in Bayesian networks, which will be modelled by a suitable calculus of string
diagrams.
",The Logical Essentials of Bayesian Reasoning
"  The highly influential framework of conceptual spaces provides a geometric
way of representing knowledge. Instances are represented by points in a
high-dimensional space and concepts are represented by regions in this space.
In this article, we extend our recent mathematical formalization of this
framework by providing quantitative mathematical definitions for measuring
relations between concepts: We develop formal ways for computing concept size,
subsethood, implication, similarity, and betweenness. This considerably
increases the representational capabilities of our formalization and makes it
the most thorough and comprehensive formalization of conceptual spaces
developed so far.
","Formal Ways for Measuring Relations between Concepts in Conceptual
  Spaces"
"  Predictive process monitoring has recently gained traction in academia and is
maturing also in companies. However, with the growing body of research, it
might be daunting for companies to navigate in this domain in order to find,
provided certain data, what can be predicted and what methods to use. The main
objective of this paper is developing a value-driven framework for classifying
existing work on predictive process monitoring. This objective is achieved by
systematically identifying, categorizing, and analyzing existing approaches for
predictive process monitoring. The review is then used to develop a
value-driven framework that can support organizations to navigate in the
predictive process monitoring field and help them to find value and exploit the
opportunities enabled by these analysis techniques.
",Predictive Process Monitoring Methods: Which One Suits Me Best?
"  Partially Observable Markov Decision Processes (POMDPs) offer an elegant
framework to model sequential decision making in uncertain environments.
Solving POMDPs online is an active area of research and given the size of
real-world problems approximate solvers are used. Recently, a few approaches
have been suggested for solving POMDPs by using MDP solvers in conjunction with
imitation learning. MDP based POMDP solvers work well for some cases, while
catastrophically failing for others. The main failure point of such solvers is
the lack of motivation for MDP solvers to gain information, since under their
assumption the environment is either already known as much as it can be or the
uncertainty will disappear after the next step. However for solving POMDP
problems gaining information can lead to efficient solutions. In this paper we
derive a set of conditions where MDP based POMDP solvers are provably
sub-optimal. We then use the well-known tiger problem to demonstrate such
sub-optimality. We show that multi-resolution, budgeted information gathering
cannot be addressed using MDP based POMDP solvers. The contribution of the
paper helps identify the properties of a POMDP problem for which the use of MDP
based POMDP solvers is inappropriate, enabling better design choices.
","Hindsight is Only 50/50: Unsuitability of MDP based Approximate POMDP
  Solvers for Multi-resolution Information Gathering"
"  This paper examines common assumptions regarding the decision-making internal
environment for intelligent agents and investigates issues related to
processing of memory and belief states to help obtain better understanding of
the responses. In specific, we consider order effects and discuss both
classical and non-classical explanations for them. We also consider implicit
cognition and explore if certain inaccessible states may be best modeled as
quantum states. We propose that the hypothesis that quantum states are at the
basis of order effects be tested on large databases such as those related to
medical treatment and drug efficacy. A problem involving a maze network is
considered and comparisons made between classical and quantum decision
scenarios for it.
",Order Effects for Queries in Intelligent Systems
"  We describe a class calculus that is expressive enough to describe and
improve its own learning process. It can design and debug programs that satisfy
given input/output constraints, based on its ontology of previously learned
programs. It can improve its own model of the world by checking the actual
results of the actions of its robotic activators. For instance, it could check
the black box of a car crash to determine if it was probably caused by electric
failure, a stuck electronic gate, dark ice, or some other condition that it
must add to its ontology in order to meet its sub-goal of preventing such
crashes in the future. Class algebra basically defines the eval/eval-1 Galois
connection between the residuated Boolean algebras of 1. equivalence classes
and super/sub classes of class algebra type expressions, and 2. a residual
Boolean algebra of biclique relationships. It distinguishes which formulas are
equivalent, entailed, or unrelated, based on a simplification algorithm that
may be thought of as producing a unique pair of Karnaugh maps that describe the
rough sets of maximal bicliques of relations. Such maps divide the
n-dimensional space of up to 2n-1 conjunctions of up to n propositions into
clopen (i.e. a closed set of regions and their boundaries) causal sets. This
class algebra is generalized to type-2 fuzzy class algebra by using relative
frequencies as probabilities. It is also generalized to a class calculus
involving assignments that change the states of programs.
  INDEX TERMS 4-valued Boolean Logic, Artificial Intelligence, causal sets,
class algebra, consciousness, intelligent design, IS-A hierarchy, mathematical
logic, meta-theory, pointless topological space, residuated lattices, rough
sets, type-2 fuzzy sets
",A Mathematical Framework for Superintelligent Machines
"  Our paradigm for the use of artificial agents to teach requires among other
things that they persist through time in their interaction with human students,
in such a way that they ""teleport"" or ""migrate"" from an embodiment at one time
t to a different embodiment at later time t'. In this short paper, we report on
initial steps toward the formalization of such teleportation, in order to
enable an overseeing AI system to establish, mechanically, and verifiably, that
the human students in question will likely believe that the very same
artificial agent has persisted across such times despite the different
embodiments.
",Toward Formalizing Teleportation of Pedagogical Artificial Agents
"  The AGINAO is a project to create a human-level artificial general
intelligence system (HL AGI) embodied in the Aldebaran Robotics' NAO humanoid
robot. The dynamical and open-ended cognitive engine of the robot is
represented by an embedded and multi-threaded control program, that is
self-crafted rather than hand-crafted, and is executed on a simulated Universal
Turing Machine (UTM). The actual structure of the cognitive engine emerges as a
result of placing the robot in a natural preschool-like environment and running
a core start-up system that executes self-programming of the cognitive layer on
top of the core layer. The data from the robot's sensory devices supplies the
training samples for the machine learning methods, while the commands sent to
actuators enable testing hypotheses and getting a feedback. The individual
self-created subroutines are supposed to reflect the patterns and concepts of
the real world, while the overall program structure reflects the spatial and
temporal hierarchy of the world dependencies. This paper focuses on the details
of the self-programming approach, limiting the discussion of the applied
cognitive architecture to a necessary minimum.
",The AGINAO Self-Programming Engine
"  AGINAO builds its cognitive engine by applying self-programming techniques to
create a hierarchy of interconnected codelets - the tiny pieces of code
executed on a virtual machine. These basic processing units are evaluated for
their applicability and fitness with a notion of reward calculated from
self-information gain of binary partitioning of the codelet's input
state-space. This approach, however, is useless for the evaluation of
actuators. Instead, a model is proposed in which actuators are evaluated by
measuring the impact that an activation of an effector, and consequently the
feedback from the robot sensors, has on average reward received by the
processing units.
",Evaluating Actuators in a Purely Information-Theory Based Reward Model
"  Personalization is very powerful in improving the effectiveness of health
interventions. Reinforcement learning (RL) algorithms are suitable for learning
these tailored interventions from sequential data collected about individuals.
However, learning can be very fragile. The time to learn intervention policies
is limited as disengagement from the user can occur quickly. Also, in e-Health
intervention timing can be crucial before the optimal window passes. We present
an approach that learns tailored personalization policies for groups of users
by combining RL and clustering. The benefits are two-fold: speeding up the
learning to prevent disengagement while maintaining a high level of
personalization. Our clustering approach utilizes dynamic time warping to
compare user trajectories consisting of states and rewards. We apply online and
batch RL to learn policies over clusters of individuals and introduce our
self-developed and publicly available simulator for e-Health interventions to
evaluate our approach. We compare our methods with an e-Health intervention
benchmark. We demonstrate that batch learning outperforms online learning for
our setting. Furthermore, our proposed clustering approach for RL finds
near-optimal clusterings which lead to significantly better policies in terms
of cumulative reward compared to learning a policy per individual or learning
one non-personalized policy across all individuals. Our findings also indicate
that the learned policies accurately learn to send interventions at the right
moments and that the users workout more and at the right times of the day.
","A clustering-based reinforcement learning approach for tailored
  personalization of e-Health interventions"
"  An autonomous agent embodied in a humanoid robot, in order to learn from the
overwhelming flow of raw and noisy sensory, has to effectively reduce the high
spatial-temporal data dimensionality. In this paper we propose a novel method
of unsupervised feature extraction and selection with binary space
partitioning, followed by a computation of information gain that is interpreted
as intrinsic reward, then applied as immediate-reward signal for the
reinforcement-learning. The space partitioning is executed by tiny codelets
running on a simulated Turing Machine. The features are represented by concept
nodes arranged in a hierarchy, in which those of a lower level become the input
vectors of a higher level.
",Binary Space Partitioning as Intrinsic Reward
"  A characteristic of existing predictive process monitoring techniques is to
first construct a predictive model based on past process executions, and then
use it to predict the future of new ongoing cases, without the possibility of
updating it with new cases when they complete their execution. This can make
predictive process monitoring too rigid to deal with the variability of
processes working in real environments that continuously evolve and/or exhibit
new variant behaviors over time. As a solution to this problem, we propose the
use of algorithms that allow the incremental construction of the predictive
model. These incremental learning algorithms update the model whenever new
cases become available so that the predictive model evolves over time to fit
the current circumstances. The algorithms have been implemented using different
case encoding strategies and evaluated on a number of real and synthetic
datasets. The results provide a first evidence of the potential of incremental
learning strategies for predicting process monitoring in real environments, and
of the impact of different case encoding strategies in this setting.
","Incremental Predictive Process Monitoring: How to Deal with the
  Variability of Real Environments"
"  We suggest that the analysis of incomplete contracting developed by law and
economics researchers can provide a useful framework for understanding the AI
alignment problem and help to generate a systematic approach to finding
solutions. We first provide an overview of the incomplete contracting
literature and explore parallels between this work and the problem of AI
alignment. As we emphasize, misalignment between principal and agent is a core
focus of economic analysis. We highlight some technical results from the
economics literature on incomplete contracts that may provide insights for AI
alignment researchers. Our core contribution, however, is to bring to bear an
insight that economists have been urged to absorb from legal scholars and other
behavioral scientists: the fact that human contracting is supported by
substantial amounts of external structure, such as generally available
institutions (culture, law) that can supply implied terms to fill the gaps in
incomplete contracts. We propose a research agenda for AI alignment work that
focuses on the problem of how to build AI that can replicate the human
cognitive processes that connect individual incomplete contracts with this
supporting external structure.
",Incomplete Contracting and AI Alignment
"  Knowledge Graphs (KGs) are becoming essential to information systems that
require access to structured data. Several approaches have been recently
proposed, for obtaining vector representations of KGs suitable for Machine
Learning tasks, based on identifying and extracting relevant graph
substructures using uniform and biased random walks. However, such approaches
lead to representations comprising mostly ""popular"", instead of ""relevant"",
entities in the KG. In KGs, in which different types of entities often exist
(such as in Linked Open Data), a given target entity may have its own distinct
set of most ""relevant"" nodes and edges. We propose specificity as an accurate
measure of identifying most relevant, entity-specific, nodes and edges. We
develop a scalable method based on bidirectional random walks to compute
specificity. Our experimental evaluation results show that specificity-based
biased random walks extract more ""meaningful"" (in terms of size and relevance)
RDF substructures compared to the state-of-the-art and, the graph embedding
learned from the extracted substructures, outperform existing techniques in the
task of entity recommendation in DBpedia.
","Not all Embeddings are created Equal: Extracting Entity-specific
  Substructures for RDF Graph Embedding"
"  As e-learning systems become more prevalent, there is a growing need for them
to accommodate individual differences between students. This paper addresses
the problem of how to personalize educational content to students in order to
maximize their learning gains over time. We present a new computational
approach to this problem called MAPLE (Multi-Armed Bandits based
Personalization for Learning Environments) that combines difficulty ranking
with multi-armed bandits. Given a set of target questions MAPLE estimates the
expected learning gains for each question and uses an exploration-exploitation
strategy to choose the next question to pose to the student. It maintains a
personalized ranking over the difficulties of question in the target set which
is used in two ways: First, to obtain initial estimates over the learning gains
for the set of questions. Second, to update the estimates over time based on
the students responses. We show in simulations that MAPLE was able to improve
students' learning gains compared to approaches that sequence questions in
increasing level of difficulty, or rely on content experts. When implemented in
a live e-learning system in the wild, MAPLE showed promising results. This work
demonstrates the efficacy of using stochastic approaches to the sequencing
problem when augmented with information about question difficulty.
","Combining Difficulty Ranking with Multi-Armed Bandits to Sequence
  Educational Content"
"  Inspired by findings of sensorimotor coupling in humans and animals, there
has recently been a growing interest in the interaction between action and
perception in robotic systems [Bogh et al., 2016]. Here we consider perception
and action as two serial information channels with limited
information-processing capacity. We follow [Genewein et al., 2015] and
formulate a constrained optimization problem that maximizes utility under
limited information-processing capacity in the two channels. As a solution we
obtain an optimal perceptual channel and an optimal action channel that are
coupled such that perceptual information is optimized with respect to
downstream processing in the action module. The main novelty of this study is
that we propose an online optimization procedure to find bounded-optimal
perception and action channels in parameterized serial perception-action
systems. In particular, we implement the perceptual channel as a multi-layer
neural network and the action channel as a multinomial distribution. We
illustrate our method in a NAO robot simulator with a simplified cup lifting
task.
","An information-theoretic on-line update principle for perception-action
  coupling"
"  Recent approaches to goal recognition have progressively relaxed the
assumptions about the amount and correctness of domain knowledge and available
observations, yielding accurate and efficient algorithms. These approaches,
however, assume completeness and correctness of the domain theory against which
their algorithms match observations: this is too strong for most real-world
domains. In this paper, we develop goal recognition techniques that are capable
of recognizing goals using \textit{incomplete} (and possibly incorrect) domain
theories. We show the efficiency and accuracy of our approaches empirically
against a large dataset of goal and plan recognition problems with incomplete
domains.
",Heuristic Approaches for Goal Recognition in Incomplete Domain Models
"  In the framework of MDP, although the general reward function takes three
arguments-current state, action, and successor state; it is often simplified to
a function of two arguments-current state and action. The former is called a
transition-based reward function, whereas the latter is called a state-based
reward function. When the objective involves the expected cumulative reward
only, this simplification works perfectly. However, when the objective is
risk-sensitive, this simplification leads to an incorrect value. We present
state-augmentation transformations (SATs), which preserve the reward sequences
as well as the reward distributions and the optimal policy in risk-sensitive
reinforcement learning. In risk-sensitive scenarios, firstly we prove that, for
every MDP with a stochastic transition-based reward function, there exists an
MDP with a deterministic state-based reward function, such that for any given
(randomized) policy for the first MDP, there exists a corresponding policy for
the second MDP, such that both Markov reward processes share the same reward
sequence. Secondly we illustrate that two situations require the proposed SATs
in an inventory control problem. One could be using Q-learning (or other
learning methods) on MDPs with transition-based reward functions, and the other
could be using methods, which are for the Markov processes with a deterministic
state-based reward functions, on the Markov processes with general reward
functions. We show the advantage of the SATs by considering Value-at-Risk as an
example, which is a risk measure on the reward distribution instead of the
measures (such as mean and variance) of the distribution. We illustrate the
error in the reward distribution estimation from the direct use of Q-learning,
and show how the SATs enable a variance formula to work on Markov processes
with general reward functions.
","State-Augmentation Transformations for Risk-Sensitive Reinforcement
  Learning"
"  In this paper we introduce a new class of tuple-generating dependencies
(TGDs) called triangularly-guarded TGDs, which are TGDs with certain
restrictions on the atomic derivation track embedded in the underlying rule
set. We show that conjunctive query answering under this new class of TGDs is
decidable. We further show that this new class strictly contains some other
decidable classes such as weak-acyclic, guarded, sticky and shy, which, to the
best of our knowledge, provides a unified representation of all these
aforementioned classes.
","A New Decidable Class of Tuple Generating Dependencies: The
  Triangularly-Guarded Class"
"  Extracting temporal relations (before, after, overlapping, etc.) is a key
aspect of understanding events described in natural language. We argue that
this task would gain from the availability of a resource that provides prior
knowledge in the form of the temporal order that events usually follow. This
paper develops such a resource -- a probabilistic knowledge base acquired in
the news domain -- by extracting temporal relations between events from the New
York Times (NYT) articles over a 20-year span (1987--2007). We show that
existing temporal extraction systems can be improved via this resource. As a
byproduct, we also show that interesting statistics can be retrieved from this
resource, which can potentially benefit other time-aware tasks. The proposed
system and resource are both publicly available.
","Improving Temporal Relation Extraction with a Globally Acquired
  Statistical Resource"
"  Simultaneously utilizing several complementary solvers is a simple yet
effective strategy for solving computationally hard problems. However, manually
building such solver portfolios typically requires considerable domain
knowledge and plenty of human effort. As an alternative, automatic construction
of parallel portfolios (ACPP) aims at automatically building effective parallel
portfolios based on a given problem instance set and a given rich design space.
One promising way to solve the ACPP problem is to explicitly group the
instances into different subsets and promote a component solver to handle each
of them.This paper investigates solving ACPP from this perspective, and
especially studies how to obtain a good instance grouping.The experimental
results showed that the parallel portfolios constructed by the proposed method
could achieve consistently superior performances to the ones constructed by the
state-of-the-art ACPP methods,and could even rival sophisticated hand-designed
parallel solvers.
","Automatic Construction of Parallel Portfolios via Explicit Instance
  Grouping"
"  Automated vehicles are deemed to be the key element for the intelligent
transportation system in the future. Many studies have been made to improve the
Automated vehicles' ability of environment recognition and vehicle control,
while the attention paid to decision making is not enough though the decision
algorithms so far are very preliminary. Therefore, a framework of the
decision-making training and learning is put forward in this paper. It consists
of two parts: the deep reinforcement learning training program and the
high-fidelity virtual simulation environment. Then the basic microscopic
behavior, car-following, is trained within this framework. In addition,
theoretical analysis and experiments were conducted on setting reward function
for accelerating training using deep reinforcement learning. The results show
that on the premise of driving comfort, the efficiency of the trained Automated
vehicle increases 7.9% compared to the classical traffic model, intelligent
driver model. Later on, on a more complex three-lane section, we trained the
integrated model combines both car-following and lane-changing behavior, the
average speed further grows 2.4%. It indicates that our framework is effective
for Automated vehicle's decision-making learning.
","Automated vehicle's behavior decision making using deep reinforcement
  learning and high-fidelity simulation environment"
"  Tasks such as social network analysis, human behavior recognition, or
modeling biochemical reactions, can be solved elegantly by using the
probabilistic inference framework. However, standard probabilistic inference
algorithms work at a propositional level, and thus cannot capture the
symmetries and redundancies that are present in these tasks. Algorithms that
exploit those symmetries have been devised in different research fields, for
example by the lifted inference-, multiple object tracking-, and modeling and
simulation-communities. The common idea, that we call state space abstraction,
is to perform inference over compact representations of sets of symmetric
states. Although they are concerned with a similar topic, the relationship
between these approaches has not been investigated systematically. This survey
provides the following contributions. We perform a systematic literature review
to outline the state of the art in probabilistic inference methods exploiting
symmetries. From an initial set of more than 4,000 papers, we identify 116
relevant papers. Furthermore, we provide new high-level categories that
classify the approaches, based on common properties of the approaches. The
research areas underlying each of the categories are introduced concisely.
Researchers from different fields that are confronted with a state space
explosion problem in a probabilistic system can use this classification to
identify possible solutions. Finally, based on this conceptualization, we
identify potentials for future research, as some relevant application domains
are not addressed by current approaches.
","State-Space Abstractions for Probabilistic Inference: A Systematic
  Review"
"  This paper builds on the recent ASPIC+ formalism, to develop a general
framework for argumentation with preferences. We motivate a revised definition
of conflict free sets of arguments, adapt ASPIC+ to accommodate a broader range
of instantiating logics, and show that under some assumptions, the resulting
framework satisfies key properties and rationality postulates. We then show
that the generalised framework accommodates Tarskian logic instantiations
extended with preferences, and then study instantiations of the framework by
classical logic approaches to argumentation. We conclude by arguing that
ASPIC+'s modelling of defeasible inference rules further testifies to the
generality of the framework, and then examine and counter recent critiques of
Dung's framework and its extensions to accommodate preferences.
",A General Account of Argumentation with Preferences
"  A prominent approach to implementing ontology-mediated queries (OMQs) is to
rewrite into a first-order query, which is then executed using a conventional
SQL database system. We consider the case where the ontology is formulated in
the description logic EL and the actual query is a conjunctive query and show
that rewritings of such OMQs can be efficiently computed in practice, in a
sound and complete way. Our approach combines a reduction with a decomposed
backwards chaining algorithm for OMQs that are based on the simpler atomic
queries, also illuminating the relationship between first-order rewritings of
OMQs based on conjunctive and on atomic queries. Experiments with real-world
ontologies show promising results.
","Computing FO-Rewritings in EL in Practice: from Atomic to Conjunctive
  Queries"
"  In order to make the task, description of planning domains and problems, more
comprehensive for non-experts in planning, the visual representation has been
used in planning domain modeling in recent years. However, current knowledge
engineering tools with visual modeling, like itSIMPLE (Vaquero et al. 2012) and
VIZ (Vodr\'a\v{z}ka and Chrpa 2010), are less efficient than the traditional
method of hand-coding by a PDDL expert using a text editor, and rarely involved
in finetuning planning domains depending on the plan validation. Aim at this,
we present an integrated development environment KAVI for planning domain
modeling inspired by itSIMPLE and VIZ. KAVI using an abstract domain knowledge
base to improve the efficiency of planning domain visual modeling. By
integrating planners and a plan validator, KAVI proposes a method to fine-tune
planning domains based on the plan validation.
",An Integrated Development Environment for Planning Domain Modeling
"  Spatial information is often expressed using qualitative terms such as
natural language expressions instead of coordinates; reasoning over such terms
has several practical applications, such as bus routes planning. Representing
and reasoning on trajectories is a specific case of qualitative spatial
reasoning that focuses on moving objects and their paths. In this work, we
propose two versions of a trajectory calculus based on the allowed properties
over trajectories, where trajectories are defined as a sequence of
non-overlapping regions of a partitioned map. More specifically, if a given
trajectory is allowed to start and finish at the same region, 6 base relations
are defined (TC-6). If a given trajectory should have different start and
finish regions but cycles are allowed within, 10 base relations are defined
(TC-10). Both versions of the calculus are implemented as ASP programs; we
propose several different encodings, including a generalised program capable of
encoding any qualitative calculus in ASP. All proposed encodings are
experimentally evaluated using a real-world dataset. Experiment results show
that the best performing implementation can scale up to an input of 250
trajectories for TC-6 and 150 trajectories for TC-10 for the problem of
discovering a consistent configuration, a significant improvement compared to
previous ASP implementations for similar qualitative spatial and temporal
calculi. This manuscript is under consideration for acceptance in TPLP.
","A Trajectory Calculus for Qualitative Spatial Reasoning Using Answer Set
  Programming"
"  Planning with preferences has been employed extensively to quickly generate
high-quality plans. However, it may be difficult for the human expert to supply
this information without knowledge of the reasoning employed by the planner and
the distribution of planning problems. We consider the problem of actively
eliciting preferences from a human expert during the planning process.
Specifically, we study this problem in the context of the Hierarchical Task
Network (HTN) planning framework as it allows easy interaction with the human.
Our experimental results on several diverse planning domains show that the
preferences gathered using the proposed approach improve the quality and speed
of the planner, while reducing the burden on the human expert.
",Preference-Guided Planning: An Active Elicitation Approach
"  This paper presents the statistical model for Ticker [1], a novel
probabilistic stereophonic single-switch text entry method for
visually-impaired users with motor disabilities who rely on single-switch
scanning systems to communicate. All terminology and notation are defined in
[1].
","The Statistical Model for Ticker, an Adaptive Single-Switch Text-Entry
  Method for Visually Impaired Users"
"  The question whether an ontology can safely be replaced by another, possibly
simpler, one is fundamental for many ontology engineering and maintenance
tasks. It underpins, for example, ontology versioning, ontology modularization,
forgetting, and knowledge exchange. What safe replacement means depends on the
intended application of the ontology. If, for example, it is used to query
data, then the answers to any relevant ontology-mediated query should be the
same over any relevant data set; if, in contrast, the ontology is used for
conceptual reasoning, then the entailed subsumptions between concept
expressions should coincide. This gives rise to different notions of ontology
inseparability such as query inseparability and concept inseparability, which
generalize corresponding notions of conservative extensions. We survey results
on various notions of inseparability in the context of description logic
ontologies, discussing their applications, useful model-theoretic
characterizations, algorithms for determining whether two ontologies are
inseparable (and, sometimes, for computing the difference between them if they
are not), and the computational complexity of this problem.
","Inseparability and Conservative Extensions of Description Logic
  Ontologies: A Survey"
"  We describe a set of techniques to generate queries automatically based on
one or more ingested, input corpuses. These queries require no a priori domain
knowledge, and hence no human domain experts. Thus, these auto-generated
queries help address the epistemological question of how we know what we know,
or more precisely in this case, how an AI system with ingested data knows what
it knows. These auto-generated queries can also be used to identify and remedy
problem areas in ingested material -- areas for which the knowledge of the AI
system is incomplete or even erroneous. Similarly, the proposed techniques
facilitate tests of AI capability -- both in terms of coverage and accuracy. By
removing humans from the main learning loop, our approach also allows more
effective scaling of AI and cognitive capabilities to provide (1) broader
coverage in a single domain such as health or geology; and (2) more rapid
deployment to new domains. The proposed techniques also allow ingested
knowledge to be extended naturally. Our investigations are early, and this
paper provides a description of the techniques. Assessment of their efficacy is
our next step for future work.
",Understanding AI Data Repositories with Automatic Query Generation
"  This paper describes a new algorithm for exact Bayesian inference that is
based on a recently proposed compositional semantics of Bayesian networks in
terms of channels. The paper concentrates on the ideas behind this algorithm,
involving a linearisation (`stretching') of the Bayesian network, followed by a
combination of forward state transformation and backward predicate
transformation, while evidence is accumulated along the way. The performance of
a prototype implementation of the algorithm in Python is briefly compared to a
standard implementation (pgmpy): first results show competitive performance.
",A Channel-based Exact Inference Algorithm for Bayesian Networks
"  Expert diagnostic support systems have been extensively studied. The
practical applications of these systems in real-world scenarios have been
somewhat limited due to well-understood shortcomings, such as lack of
extensibility. More recently, machine-learned models for medical diagnosis have
gained momentum, since they can learn and generalize patterns found in very
large datasets like electronic health records. These models also have
shortcomings - in particular, there is no easy way to incorporate prior
knowledge from existing literature or experts. In this paper, we present a
method to merge both approaches by using expert systems as generative models
that create simulated data on which models can be learned. We demonstrate that
such a learned model not only preserves the original properties of the expert
systems but also addresses some of their limitations. Furthermore, we show how
this approach can also be used as the starting point to combine expert
knowledge with knowledge extracted from other data sources, such as electronic
health records.
","Learning from the experts: From expert systems to machine-learned
  diagnosis models"
"  With the recent availability of Electronic Health Records (EHR) and great
opportunities they offer for advancing medical informatics, there has been
growing interest in mining EHR for improving quality of care. Disease diagnosis
due to its sensitive nature, huge costs of error, and complexity has become an
increasingly important focus of research in past years. Existing studies model
EHR by capturing co-occurrence of clinical events to learn their latent
embeddings. However, relations among clinical events carry various semantics
and contribute differently to disease diagnosis which gives precedence to a
more advanced modeling of heterogeneous data types and relations in EHR data
than existing solutions. To address these issues, we represent how
high-dimensional EHR data and its rich relationships can be suitably translated
into HeteroMed, a heterogeneous information network for robust medical
diagnosis. Our modeling approach allows for straightforward handling of missing
values and heterogeneity of data. HeteroMed exploits metapaths to capture
higher level and semantically important relations contributing to disease
diagnosis. Furthermore, it employs a joint embedding framework to tailor
clinical event representations to the disease diagnosis goal. To the best of
our knowledge, this is the first study to use Heterogeneous Information Network
for modeling clinical data and disease diagnosis. Experimental results of our
study show superior performance of HeteroMed compared to prior methods in
prediction of exact diagnosis codes and general disease cohorts. Moreover,
HeteroMed outperforms baseline models in capturing similarities of clinical
events which are examined qualitatively through case studies.
",HeteroMed: Heterogeneous Information Network for Medical Diagnosis
"  The tabu and restart are two fundamental strategies for local search. In this
paper, we improve the local search algorithms for solving the Maximum Weight
Clique (MWC) problem by introducing new tabu and restart strategies. Both the
tabu and restart strategies proposed are based on the notion of a local search
scenario, which involves not only a candidate solution but also the tabu status
and unlocking relationship. Compared to the strategy of configuration checking,
our tabu mechanism discourages forming a cycle of unlocking operations. Our new
restart strategy is based on the re-occurrence of a local search scenario
instead of that of a candidate solution. Experimental results show that the
resulting MWC solver outperforms several state-of-the-art solvers on the
DIMACS, BHOSLIB, and two benchmarks from practical applications.
",Advancing Tabu and Restart in Local Search for Maximum Weight Cliques
"  Robots need task planning algorithms to sequence actions toward accomplishing
goals that are impossible through individual actions. Off-the-shelf task
planners can be used by intelligent robotics practitioners to solve a variety
of planning problems. However, many different planners exist, each with
different strengths and weaknesses, and there are no general rules for which
planner would be best to apply to a given problem.
  In this article, we empirically compare the performance of state-of-the-art
planners that use either the Planning Domain Description Language (PDDL), or
Answer Set Programming (ASP) as the underlying action language. PDDL is
designed for task planning, and PDDL-based planners are widely used for a
variety of planning problems. ASP is designed for knowledge-intensive
reasoning, but can also be used for solving task planning problems. Given
domain encodings that are as similar as possible, we find that PDDL-based
planners perform better on problems with longer solutions, and ASP-based
planners are better on tasks with a large number of objects or in which complex
reasoning is required to reason about action preconditions and effects. The
resulting analysis can inform selection among general purpose planning systems
for particular robot task planning domains.
","Task Planning in Robotics: an Empirical Comparison of PDDL-based and
  ASP-based Systems"
"  In this paper we discuss some of the issues concerning the Memory and Content
aspects in the recent debate on the identification of a Standard Model of the
Mind (Laird, Lebiere, and Rosenbloom in press). In particular, we focus on the
representational models concerning the Declarative Memories of current
Cognitive Architectures (CAs). In doing so we outline some of the main problems
affecting the current CAs and suggest that the Conceptual Spaces, a
representational framework developed by Gardenfors, is worth-considering to
address such problems. Finally, we briefly analyze the alternative
representational assumptions employed in the three CAs constituting the current
baseline for the Standard Model (i.e. SOAR, ACT-R and Sigma). In doing so, we
point out the respective differences and discuss their implications in the
light of the analyzed problems.
",Representational Issues in the Debate on the Standard Model of the Mind
"  Telematics data is becoming increasingly available due to the ubiquity of
devices that collect data during drives, for different purposes, such as usage
based insurance (UBI), fleet management, navigation of connected vehicles, etc.
Consequently, a variety of data-analytic applications have become feasible that
extract valuable insights from the data. In this paper, we address the
especially challenging problem of discovering behavior-based driving patterns
from only externally observable phenomena (e.g. vehicle's speed). We present a
trajectory segmentation approach capable of discovering driving patterns as
separate segments, based on the behavior of drivers. This segmentation approach
includes a novel transformation of trajectories along with a dynamic
programming approach for segmentation. We apply the segmentation approach on a
real-word, rich dataset of personal car trajectories provided by a major
insurance company based in Columbus, Ohio. Analysis and preliminary results
show the applicability of approach for finding significant driving patterns.
",Discovery of Driving Patterns by Trajectory Segmentation
"  The design of video game levels is a complex and critical task. Levels need
to elicit fun and challenge while avoiding frustration at all costs. In this
paper, we present a framework to assist designers in the creation of levels for
2D platformers. Our framework provides designers with a toolbox (i) to create
2D platformer levels, (ii) to estimate the difficulty and probability of
success of single jump actions (the main mechanics of platformer games), and
(iii) a set of metrics to evaluate the difficulty and probability of completion
of entire levels. At the end, we present the results of a set of experiments we
carried out with human players to validate the metrics included in our
framework.
",An Integrated Framework for AI Assisted Level Design in 2D Platformers
"  This paper describes an avenue for artificial and computational intelligence
techniques applied within games research to be deployed for purposes of
physical therapy. We provide an overview of prototypical research focussed on
the application of motion sensor input devices and virtual reality equipment
for rehabilitation of motor impairment an issue typical of patient's of
traumatic brain injuries. We highlight how advances in procedural content
generation and player modelling can stimulate development in this area by
improving quality of rehabilitation programmes and measuring patient
performance.
",Intelligent Physiotherapy Through Procedural Content Generation
"  Policy gradient methods are often applied to reinforcement learning in
continuous multiagent games. These methods perform local search in the
joint-action space, and as we show, they are susceptable to a game-theoretic
pathology known as relative overgeneralization. To resolve this issue, we
propose Multiagent Soft Q-learning, which can be seen as the analogue of
applying Q-learning to continuous controls. We compare our method to MADDPG, a
state-of-the-art approach, and show that our method achieves better
coordination in multiagent cooperative tasks, converging to better local optima
in the joint action space.
",Multiagent Soft Q-Learning
"  We describe an application of Answer Set Programming to the understanding of
narratives about stereotypical activities, demonstrated via question answering.
Substantial work in this direction was done by Erik Mueller, who modeled
stereotypical activities as scripts. His systems were able to understand a good
number of narratives, but could not process texts describing exceptional
scenarios. We propose addressing this problem by using a theory of intentions
developed by Blount, Gelfond, and Balduccini. We present a methodology in which
we substitute scripts by activities (i.e., hierarchical plans associated with
goals) and employ the concept of an intentional agent to reason about both
normal and exceptional scenarios. We exemplify the application of this
methodology by answering questions about a number of restaurant stories. This
paper is under consideration for acceptance in TPLP.
","An ASP Methodology for Understanding Narratives about Stereotypical
  Activities"
"  This paper explores the problem of task learning and planning, contributing
the Action-Category Representation (ACR) to improve computational performance
of both Planning and Reinforcement Learning (RL). ACR is an algorithm-agnostic,
abstract data representation that maps objects to action categories (groups of
actions), inspired by the psychological concept of action codes. We validate
our approach in StarCraft and Lightworld domains; our results demonstrate
several benefits of ACR relating to improved computational performance of
planning and RL, by reducing the action space for the agent.
","Action Categorization for Computationally Improved Task Learning and
  Planning"
"  In this paper, we introduce an alternative approach to Temporal Answer Set
Programming that relies on a variation of Temporal Equilibrium Logic (TEL) for
finite traces. This approach allows us to even out the expressiveness of TEL
over infinite traces with the computational capacity of (incremental) Answer
Set Programming (ASP). Also, we argue that finite traces are more natural when
reasoning about action and change. As a result, our approach is readily
implementable via multi-shot ASP systems and benefits from an extension of
ASP's full-fledged input language with temporal operators. This includes future
as well as past operators whose combination offers a rich temporal modeling
language. For computation, we identify the class of temporal logic programs and
prove that it constitutes a normal form for our approach. Finally, we outline
two implementations, a generic one and an extension of clingo.
",Temporal Answer Set Programming on Finite Traces
"  We introduce the asprilo [1] framework to facilitate experimental studies of
approaches addressing complex dynamic applications. For this purpose, we have
chosen the domain of robotic intra-logistics. This domain is not only highly
relevant in the context of today's fourth industrial revolution but it moreover
combines a multitude of challenging issues within a single uniform framework.
This includes multi-agent planning, reasoning about action, change, resources,
strategies, etc. In return, asprilo allows users to study alternative solutions
as regards effectiveness and scalability. Although asprilo relies on Answer Set
Programming and Python, it is readily usable by any system complying with its
fact-oriented interface format. This makes it attractive for benchmarking and
teaching well beyond logic programming. More precisely, asprilo consists of a
versatile benchmark generator, solution checker and visualizer as well as a
bunch of reference encodings featuring various ASP techniques. Importantly, the
visualizer's animation capabilities are indispensable for complex scenarios
like intra-logistics in order to inspect valid as well as invalid solution
candidates. Also, it allows for graphically editing benchmark layouts that can
be used as a basis for generating benchmark suites.
  [1] asprilo stands for Answer Set Programming for robotic intra-logistics
",Experimenting with robotic intra-logistics domains
"  Automated storage and retrieval systems are principal components of modern
production and warehouse facilities. In particular, automated guided vehicles
nowadays substitute human-operated pallet trucks in transporting production
materials between storage locations and assembly stations. While low-level
control systems take care of navigating such driverless vehicles along
programmed routes and avoid collisions even under unforeseen circumstances, in
the common case of multiple vehicles sharing the same operation area, the
problem remains how to set up routes such that a collection of transport tasks
is accomplished most effectively. We address this prevalent problem in the
context of car assembly at Mercedes-Benz Ludwigsfelde GmbH, a large-scale
producer of commercial vehicles, where routes for automated guided vehicles
used in the production process have traditionally been hand-coded by human
engineers. Such ad-hoc methods may suffice as long as a running production
process remains in place, while any change in the factory layout or production
targets necessitates tedious manual reconfiguration, not to mention the missing
portability between different production plants. Unlike this, we propose a
declarative approach based on Answer Set Programming to optimize the routes
taken by automated guided vehicles for accomplishing transport tasks. The
advantages include a transparent and executable problem formalization, provable
optimality of routes relative to objective criteria, as well as elaboration
tolerance towards particular factory layouts and production targets. Moreover,
we demonstrate that our approach is efficient enough to deal with the transport
tasks evolving in realistic production processes at the car factory of
Mercedes-Benz Ludwigsfelde GmbH.
","Routing Driverless Transport Vehicles in Car Assembly with Answer Set
  Programming"
"  Partially-observable Markov decision processes (POMDPs) with discounted-sum
payoff are a standard framework to model a wide range of problems related to
decision making under uncertainty. Traditionally, the goal has been to obtain
policies that optimize the expectation of the discounted-sum payoff. A key
drawback of the expectation measure is that even low probability events with
extreme payoff can significantly affect the expectation, and thus the obtained
policies are not necessarily risk-averse. An alternate approach is to optimize
the probability that the payoff is above a certain threshold, which allows
obtaining risk-averse policies, but ignores optimization of the expectation. We
consider the expectation optimization with probabilistic guarantee (EOPG)
problem, where the goal is to optimize the expectation ensuring that the payoff
is above a given threshold with at least a specified probability. We present
several results on the EOPG problem, including the first algorithm to solve it.
","Expectation Optimization with Probabilistic Guarantees in POMDPs with
  Discounted-sum Objectives"
"  We show how a bi-directional grammar can be used to specify and verbalise
answer set programs in controlled natural language. We start from a program
specification in controlled natural language and translate this specification
automatically into an executable answer set program. The resulting answer set
program can be modified following certain naming conventions and the revised
version of the program can then be verbalised in the same subset of natural
language that was used as specification language. The bi-directional grammar is
parametrised for processing and generation, deals with referring expressions,
and exploits symmetries in the data structure of the grammar rules whenever
these grammar rules need to be duplicated. We demonstrate that verbalisation
requires sentence planning in order to aggregate similar structures with the
aim to improve the readability of the generated specification. Without
modifications, the generated specification is always semantically equivalent to
the original one; our bi-directional grammar is the first one that allows for
semantic round-tripping in the context of controlled natural language
processing. This paper is under consideration for acceptance in TPLP.
","Specifying and Verbalising Answer Set Programs in Controlled Natural
  Language"
"  Autonomously training interpretable control strategies, called policies,
using pre-existing plant trajectory data is of great interest in industrial
applications. Fuzzy controllers have been used in industry for decades as
interpretable and efficient system controllers. In this study, we introduce a
fuzzy genetic programming (GP) approach called fuzzy GP reinforcement learning
(FGPRL) that can select the relevant state features, determine the size of the
required fuzzy rule set, and automatically adjust all the controller parameters
simultaneously. Each GP individual's fitness is computed using model-based
batch reinforcement learning (RL), which first trains a model using available
system samples and subsequently performs Monte Carlo rollouts to predict each
policy candidate's performance. We compare FGPRL to an extended version of a
related method called fuzzy particle swarm reinforcement learning (FPSRL),
which uses swarm intelligence to tune the fuzzy policy parameters. Experiments
using an industrial benchmark show that FGPRL is able to autonomously learn
interpretable fuzzy policies with high control performance.
","Generating Interpretable Fuzzy Controllers using Particle Swarm
  Optimization and Genetic Programming"
"  Attacks in cyber-physical systems (CPS) which manipulate sensor readings can
cause enormous physical damage if undetected. Detection of attacks on sensors
is crucial to mitigate this issue. We study supervised regression as a means to
detect anomalous sensor readings, where each sensor's measurement is predicted
as a function of other sensors. We show that several common learning approaches
in this context are still vulnerable to \emph{stealthy attacks}, which
carefully modify readings of compromised sensors to cause desired damage while
remaining undetected. Next, we model the interaction between the CPS defender
and attacker as a Stackelberg game in which the defender chooses detection
thresholds, while the attacker deploys a stealthy attack in response. We
present a heuristic algorithm for finding an approximately optimal threshold
for the defender in this game, and show that it increases system resilience to
attacks without significantly increasing the false alarm rate.
",Adversarial Regression for Detecting Attacks in Cyber-Physical Systems
"  We present a probabilistic extension of action language BC+. Just like BC+ is
defined as a high-level notation of answer set programs for describing
transition systems, the proposed language, which we call pBC+, is defined as a
high-level notation of LPMLN programs---a probabilistic extension of answer set
programs. We show how probabilistic reasoning about transition systems, such as
prediction, postdiction, and planning problems, as well as probabilistic
diagnosis for dynamic domains, can be modeled in pBC+ and computed using an
implementation of LPMLN.
",A Probabilistic Extension of Action Language BC+
"  Logic Programs with Ordered Disjunction (LPOD) is an extension of standard
answer set programs to handle preference using the construct of ordered
disjunction, and CR-Prolog2 is an extension of standard answer set programs
with consistency restoring rules and LPOD-like ordered disjunction. We present
reductions of each of these languages into the standard ASP language, which
gives us an alternative way to understand the extensions in terms of the
standard ASP language.
",Translating LPOD and CR-Prolog2 into Standard Answer Set Programs
"  Most researchers regard AI as a static function without memory. This is one
of the few articles where AI is seen as a device with memory. When we have
memory, we can ask ourselves: ""Where am I?"", and ""What is going on?"" When we
have no memory, we have to assume that we are always in the same place and that
the world is always in the same state.
",How does the AI understand what's going on
"  The development of Artificial General Intelligence (AGI) promises to be a
major event. Along with its many potential benefits, it also raises serious
safety concerns (Bostrom, 2014). The intention of this paper is to provide an
easily accessible and up-to-date collection of references for the emerging
field of AGI safety. A significant number of safety problems for AGI have been
identified. We list these, and survey recent research on solving them. We also
cover works on how best to think of AGI from the limited knowledge we have
today, predictions for when AGI will first be created, and what will happen
after its creation. Finally, we review the current public policy on AGI.
",AGI Safety Literature Review
"  The algorithm selection problem is to choose the most suitable algorithm for
solving a given problem instance. It leverages the complementarity between
different approaches that is present in many areas of AI. We report on the
state of the art in algorithm selection, as defined by the Algorithm Selection
competitions in 2015 and 2017. The results of these competitions show how the
state of the art improved over the years. We show that although performance in
some cases is very good, there is still room for improvement in other cases.
Finally, we provide insights into why some scenarios are hard, and pose
challenges to the community on how to advance the current state of the art.
",The Algorithm Selection Competitions 2015 and 2017
"  Several recently proposed methods aim to learn conceptual space
representations from large text collections. These learned representations
asso- ciate each object from a given domain of interest with a point in a
high-dimensional Euclidean space, but they do not model the concepts from this
do- main, and can thus not directly be used for catego- rization and related
cognitive tasks. A natural solu- tion is to represent concepts as Gaussians,
learned from the representations of their instances, but this can only be
reliably done if sufficiently many in- stances are given, which is often not
the case. In this paper, we introduce a Bayesian model which addresses this
problem by constructing informative priors from background knowledge about how
the concepts of interest are interrelated with each other. We show that this
leads to substantially better pre- dictions in a knowledge base completion
task.
",Learning Conceptual Space Representations of Interrelated Concepts
"  Humans often learn how to perform tasks via imitation: they observe others
perform a task, and then very quickly infer the appropriate actions to take
based on their observations. While extending this paradigm to autonomous agents
is a well-studied problem in general, there are two particular aspects that
have largely been overlooked: (1) that the learning is done from observation
only (i.e., without explicit action information), and (2) that the learning is
typically done very quickly. In this work, we propose a two-phase, autonomous
imitation learning technique called behavioral cloning from observation (BCO),
that aims to provide improved performance with respect to both of these
aspects. First, we allow the agent to acquire experience in a self-supervised
fashion. This experience is used to develop a model which is then utilized to
learn a particular task by observing an expert perform that task without the
knowledge of the specific actions taken. We experimentally compare BCO to
imitation learning methods, including the state-of-the-art, generative
adversarial imitation learning (GAIL) technique, and we show comparable task
performance in several different simulation domains while exhibiting increased
learning speed after expert trajectories become available.
",Behavioral Cloning from Observation
"  We present a framework for the partitioning of a spatial trajectory in a
sequence of segments based on spatial density and temporal criteria. The result
is a set of temporally separated clusters interleaved by sub-sequences of
unclustered points. A major novelty is the proposal of an outlier or noise
model based on the distinction between intra-cluster (local noise) and
inter-cluster noise (transition): the local noise models the temporary absence
from a residence while the transition the definitive departure towards a next
residence. We analyze in detail the properties of the model and present a
comprehensive solution for the extraction of temporally ordered clusters. The
effectiveness of the solution is evaluated first qualitatively and next
quantitatively by contrasting the segmentation with ground truth. The ground
truth consists of a set of trajectories of labeled points simulating animal
movement. Moreover, we show that the approach can streamline the discovery of
additional derived patterns, by presenting a novel technique for the analysis
of periodic movement. From a methodological perspective, a valuable aspect of
this research is that it combines the theoretical investigation with the
application and external validation of the segmentation framework. This paves
the way to an effective deployment of the solution in broad and challenging
fields such as e-science.
",Cluster-based trajectory segmentation with local noise
"  Existing Semantic Desktops are still reproached for being too complicated to
use or not scaling well. Besides, a real ""killer app"" is still missing. In this
paper, we present a new prototype inspired by NEPOMUK and its successors having
a semantic graph and ontologies as its basis. In addition, we introduce the
idea of context spaces that users can directly interact with and work on. To
make them available in all applications without further ado, the system is
transparently integrated using mostly standard protocols complemented by a
sidebar for advanced features. By exploiting collected context information and
applying Managed Forgetting features (like hiding, condensation or deletion),
the system is able to dynamically reorganize itself, which also includes a kind
of tidy-up-itself functionality. We therefore expect it to be more scalable
while providing new levels of user support. An early prototype has been
implemented and is presented in this demo.
","Context Spaces as the Cornerstone of a Near-Transparent &
  Self-Reorganizing Semantic Desktop"
"  Effective general-purpose search strategies are an important component in
Constraint Programming. We introduce a new idea, namely, using correlations
between variables to guide search. Variable correlations are measured and
maintained by using domain changes during constraint propagation. We propose
two variable heuristics based on the correlation matrix, crbs-sum and crbs-max.
We evaluate our correlation heuristics with well known heuristics, namely,
dom/wdeg, impact-based search and activity-based search. Experiments on a large
set of benchmarks show that our correlation heuristics are competitive with the
other heuristics, and can be the fastest on many series.
",Correlation Heuristics for Constraint Programming
"  Conceptual formalism supported by typical ontologies may not be sufficient to
represent uncertainty information which is caused due to the lack of clear cut
boundaries between concepts of a domain. Fuzzy ontologies are proposed to offer
a way to deal with this uncertainty. This paper describes the state of the art
in developing fuzzy ontologies. The survey is produced by studying about 35
works on developing fuzzy ontologies from a batch of 100 articles in the field
of fuzzy ontologies.
",The State of the Art in Developing Fuzzy Ontologies: A Survey
"  In many practical uses of reinforcement learning (RL) the set of actions
available at a given state is a random variable, with realizations governed by
an exogenous stochastic process. Somewhat surprisingly, the foundations for
such sequential decision processes have been unaddressed. In this work, we
formalize and investigate MDPs with stochastic action sets (SAS-MDPs) to
provide these foundations. We show that optimal policies and value functions in
this model have a structure that admits a compact representation. From an RL
perspective, we show that Q-learning with sampled action sets is sound. In
model-based settings, we consider two important special cases: when individual
actions are available with independent probabilities; and a sampling-based
model for unknown distributions. We develop poly-time value and policy
iteration methods for both cases; and in the first, we offer a poly-time linear
programming solution.
",Planning and Learning with Stochastic Action Sets
"  We propose an algorithm for constructing efficient patrolling strategies in
the Internet environment, where the protected targets are nodes connected to
the network and the patrollers are software agents capable of
detecting/preventing undesirable activities on the nodes. The algorithm is
based on a novel compositional principle designed for a special class of
strategies, and it can quickly construct (sub)optimal solutions even if the
number of targets reaches hundreds of millions.
","Synthesizing Efficient Solutions for Patrolling Problems in the Internet
  Environment"
"  Advanced Driver Assistance Systems (ADAS) improve driving safety
significantly. They alert drivers from unsafe traffic conditions when a
dangerous maneuver appears. Traditional methods to predict driving maneuvers
are mostly based on data-driven models alone. However, existing methods to
understand the driver's intention remain an ongoing challenge due to a lack of
intersection of human cognition and data analysis. To overcome this challenge,
we propose a novel method that combines both the cognition-driven model and the
data-driven model. We introduce a model named Cognitive Fusion-RNN (CF-RNN)
which fuses the data inside the vehicle and the data outside the vehicle in a
cognitive way. The CF-RNN model consists of two Long Short-Term Memory (LSTM)
branches regulated by human reaction time. Experiments on the Brain4Cars
benchmark dataset demonstrate that the proposed method outperforms previous
methods and achieves state-of-the-art performance.
","Driving maneuvers prediction based on cognition-driven and data-driven
  method"
"  The system's ability to adapt and self-organize are two key factors when it
comes to how well the system can survive the changes to the environment and the
plant they work within. Intelligent control improves these two factors in
controllers. Considering the increasing complexity of dynamic systems along
with their need for feedback controls, using more complicated controls has
become necessary and intelligent control can be a suitable response to this
necessity. This paper briefly describes the structure of intelligent control
and provides a review on fuzzy logic and neural networks which are some of the
base methods for intelligent control. The different aspects of these two
methods are then compared together and an example of a combined method is
presented.
",A review of neuro-fuzzy systems based on intelligent control
"  In this paper we present a new Ant Colony Optimisation-based algorithm for
Sudoku, which out-performs existing methods on large instances. Our method
includes a novel anti-stagnation operator, which we call Best Value
Evaporation.
",Solving Sudoku with Ant Colony Optimisation
"  In this paper we present the Creative Invention Benchmark (CrIB), a
2000-problem benchmark for evaluating a particular facet of computational
creativity. Specifically, we address combinational p-creativity, the creativity
at play when someone combines existing knowledge to achieve a solution novel to
that individual. We present generation strategies for the five problem
categories of the benchmark and a set of initial baselines.
",Creative Invention Benchmark
"  Effective solving of constraint problems often requires choosing good or
specific search heuristics. However, choosing or designing a good search
heuristic is non-trivial and is often a manual process. In this paper, rather
than manually choosing/designing search heuristics, we propose the use of
bandit-based learning techniques to automatically select search heuristics. Our
approach is online where the solver learns and selects from a set of heuristics
during search. The goal is to obtain automatic search heuristics which give
robust performance. Preliminary experiments show that our adaptive technique is
more robust than the original search heuristics. It can also outperform the
original heuristics.
",Learning Robust Search Strategies Using a Bandit-Based Approach
"  Much research in computational argumentation assumes that arguments and
counterarguments can be obtained in some way. Yet, to improve and apply models
of argument, we need methods for acquiring them. Current approaches include
argument mining from text, hand coding of arguments by researchers, or
generating arguments from knowledge bases. In this paper, we propose a new
approach, which we call argument harvesting, that uses a chatbot to enter into
a dialogue with a participant to get arguments and counterarguments from him or
her. Because it is automated, the chatbot can be used repeatedly in many
dialogues, and thereby it can generate a large corpus. We describe the
architecture of the chatbot, provide methods for managing a corpus of arguments
and counterarguments, and an evaluation of our approach in a case study
concerning attitudes of women to participation in sport.
",Argument Harvesting Using Chatbots
"  In recent years, reinforcement learning has achieved many remarkable
successes due to the growing adoption of deep learning techniques and the rapid
growth in computing power. Nevertheless, it is well-known that flat
reinforcement learning algorithms are often not able to learn well and
data-efficient in tasks having hierarchical structures, e.g. consisting of
multiple subtasks. Hierarchical reinforcement learning is a principled approach
that is able to tackle these challenging tasks. On the other hand, many
real-world tasks usually have only partial observability in which state
measurements are often imperfect and partially observable. The problems of RL
in such settings can be formulated as a partially observable Markov decision
process (POMDP). In this paper, we study hierarchical RL in POMDP in which the
tasks have only partial observability and possess hierarchical properties. We
propose a hierarchical deep reinforcement learning approach for learning in
hierarchical POMDP. The deep hierarchical RL algorithm is proposed to apply to
both MDP and POMDP learning. We evaluate the proposed algorithm on various
challenging hierarchical POMDP.
","Deep Hierarchical Reinforcement Learning Algorithm in Partially
  Observable Markov Decision Processes"
"  Reinforcement learning has enjoyed multiple successes in recent years.
However, these successes typically require very large amounts of data before an
agent achieves acceptable performance. This paper introduces a novel way of
combating such requirements by leveraging existing (human or agent) knowledge.
In particular, this paper uses demonstrations from agents and humans, allowing
an untrained agent to quickly achieve high performance. We empirically compare
with, and highlight the weakness of, HAT and CHAT, methods of transferring
knowledge from a source agent/human to a target agent. This paper introduces an
effective transfer approach, DRoP, combining the offline knowledge
(demonstrations recorded before learning) with online confidence-based
performance analysis. DRoP dynamically involves the demonstrator's knowledge,
integrating it into the reinforcement learning agent's online learning loop to
achieve efficient and robust learning.
","Interactive Reinforcement Learning with Dynamic Reuse of Prior Knowledge
  from Human/Agent's Demonstration"
"  Ensuring flexible and efficient manufacturing of customized products in an
increasing dynamic and turbulent environment without sacrificing cost
effectiveness, product quality and on-time delivery has become a key issue for
most industrial enterprises. A promising approach to cope with this challenge
is the integration of cognitive capabilities in systems and processes with the
aim of expanding the knowledge base used to perform managerial and operational
tasks. In this work, a novel approach to real-time rescheduling is proposed in
order to achieve sustainable improvements in flexibility and adaptability of
production systems through the integration of artificial cognitive
capabilities, involving perception, reasoning/learning and planning skills.
Moreover, an industrial example is discussed where the SOAR cognitive
architecture capabilities are integrated in a software prototype, showing that
the approach enables the rescheduling system to respond to events in an
autonomic way, and to acquire experience through intensive simulation while
performing repair tasks.
",A Cognitive Approach to Real-time Rescheduling using SOAR-RL
"  In order to reach higher degrees of flexibility, adaptability and autonomy in
manufacturing systems, it is essential to develop new rescheduling
methodologies which resort to cognitive capabilities, similar to those found in
human beings. Artificial cognition is important for designing planning and
control systems that generate and represent knowledge about heuristics for
repair-based scheduling. Rescheduling knowledge in the form of decision rules
is used to deal with unforeseen events and disturbances reactively in real
time, and take advantage of the ability to act interactively with the user to
counteract the effects of disruptions. In this work, to achieve the
aforementioned goals, a novel approach to generate rescheduling knowledge in
the form of dynamic first-order logical rules is proposed. The proposed
approach is based on the integration of reinforcement learning with artificial
cognitive capabilities involving perception and reasoning/learning skills
embedded in the Soar cognitive architecture. An industrial example is discussed
showing that the approach enables the scheduling system to assess its
operational range in an autonomic way, and to acquire experience through
intensive simulation while performing repair tasks.
","Generating Rescheduling Knowledge using Reinforcement Learning in a
  Cognitive Architecture"
"  This paper describes the deployment and implementation of a blockchain to
improve the security, knowledge, intelligence and collaboration during the
inter-agent communication processes in restrict domains of the Internet
Infrastructure. It is a work that proposes the application of a blockchain,
platform independent, on a particular model of agents, but that can be used in
similar proposals, once the results on the specific model were satisfactory.
","Blockchain to Improve Security, Knowledge and Collaboration Inter-Agent
  Communication over Restrict Domains of the Internet Infrastructure"
"  In this paper, we introduce a novel algorithm to solve projected model
counting (PMC). PMC asks to count solutions of a Boolean formula with respect
to a given set of projected variables, where multiple solutions that are
identical when restricted to the projected variables count as only one
solution. Our algorithm exploits small treewidth of the primal graph of the
input instance. It runs in time $O({2^{2^{k+4}} n^2})$ where k is the treewidth
and n is the input size of the instance. In other words, we obtain that the
problem PMC is fixed-parameter tractable when parameterized by treewidth.
Further, we take the exponential time hypothesis (ETH) into consideration and
establish lower bounds of bounded treewidth algorithms for PMC, yielding
asymptotically tight runtime bounds of our algorithm.
",Exploiting Treewidth for Projected Model Counting and its Limits
"  There is an increasing demand for algorithms to explain their outcomes. So
far, there is no method that explains the rankings produced by a ranking
algorithm. To address this gap we propose LISTEN, a LISTwise ExplaiNer, to
explain rankings produced by a ranking algorithm. To efficiently use LISTEN in
production, we train a neural network to learn the underlying explanation space
created by LISTEN; we call this model Q-LISTEN. We show that LISTEN produces
faithful explanations and that Q-LISTEN is able to learn these explanations.
Moreover, we show that LISTEN is safe to use in a real world environment: users
of a news recommendation system do not behave significantly differently when
they are exposed to explanations generated by LISTEN instead of manually
generated explanations.
",Faithfully Explaining Rankings in a News Recommender System
"  The curse of dimensionality in the realm of association rules is twofold.
Firstly, we have the well known exponential increase in computational
complexity with increasing item set size. Secondly, there is a \emph{related
curse} concerned with the distribution of (spare) data itself in high
dimension. The former problem is often coped with by projection, i.e., feature
selection, whereas the best known strategy for the latter is avoidance. This
work summarizes the first attempt to provide a computationally feasible method
for measuring the extent of dimension curse present in a data set with respect
to a particular class machine of learning procedures. This recent development
enables the application of various other methods from geometric analysis to be
investigated and applied in machine learning procedures in the presence of high
dimension.
",Intrinsic dimension and its application to association rules
"  Inferring other agents' mental states such as their knowledge, beliefs and
intentions is thought to be essential for effective interactions with other
agents. Recently, multiagent systems trained via deep reinforcement learning
have been shown to succeed in solving different tasks, but it remains unclear
how each agent modeled or represented other agents in their environment. In
this work we test whether deep reinforcement learning agents explicitly
represent other agents' intentions (their specific aims or goals) during a task
in which the agents had to coordinate the covering of different spots in a 2D
environment. In particular, we tracked over time the performance of a linear
decoder trained to predict the final goal of all agents from the hidden state
of each agent's neural network controller. We observed that the hidden layers
of agents represented explicit information about other agents' goals, i.e. the
target landmark they ended up covering. We also performed a series of
experiments, in which some agents were replaced by others with fixed goals, to
test the level of generalization of the trained agents. We noticed that during
the training phase the agents developed a differential preference for each
goal, which hindered generalization. To alleviate the above problem, we propose
simple changes to the MADDPG training algorithm which leads to better
generalization against unseen agents. We believe that training protocols
promoting more active intention reading mechanisms, e.g. by preventing simple
symmetry-breaking solutions, is a promising direction towards achieving a more
robust generalization in different cooperative and competitive tasks.
",Do deep reinforcement learning agents model intentions?
"  A recent approach based on Bayesian inverse planning for the ""theory of mind""
has shown good performance in modeling human cognition. However, perfect
inverse planning differs from human cognition during one kind of complex tasks
due to human bounded rationality. One example is an environment in which there
are many available plans for achieving a specific goal. We propose a ""plan
predictability oriented model"" as a model of inferring other peoples' goals in
complex environments. This model adds the bias that people prefer predictable
plans. This bias is calculated with simple plan prediction. We tested this
model with a behavioral experiment in which humans observed the partial path of
goal-directed actions. Our model had a higher correlation with human inference.
We also confirmed the robustness of our model with complex tasks and determined
that it can be improved by taking account of individual differences in ""bounded
rationality"".
","Modeling Human Inference of Others' Intentions in Complex Situations
  with Plan Predictability Bias"
"  Dealing with memory and time constraints are current challenges when learning
from data streams with a massive amount of data. Many algorithms have been
proposed to handle these difficulties, among them, the Very Fast Decision Tree
(VFDT) algorithm. Although the VFDT has been widely used in data stream mining,
in the last years, several authors have suggested modifications to increase its
performance, putting aside memory concerns by proposing memory-costly
solutions. Besides, most data stream mining solutions have been centred around
ensembles, which combine the memory costs of their weak learners, usually
VFDTs. To reduce the memory cost, keeping the predictive performance, this
study proposes the Strict VFDT (SVFDT), a novel algorithm based on the VFDT.
The SVFDT algorithm minimises unnecessary tree growth, substantially reducing
memory usage and keeping competitive predictive performance. Moreover, since it
creates much more shallow trees than VFDT, SVFDT can achieve a shorter
processing time. Experiments were carried out comparing the SVFDT with the VFDT
in 11 benchmark data stream datasets. This comparison assessed the trade-off
between accuracy, memory, and processing time. Statistical analysis showed that
the proposed algorithm obtained similar predictive performance and
significantly reduced processing time and memory use. Thus, SVFDT is a suitable
option for data stream mining with memory and time limitations, recommended as
a weak learner in ensemble-based solutions.
","Strict Very Fast Decision Tree: a memory conservative algorithm for data
  stream mining"
"  Recursive self-improving (RSI) systems have been dreamed of since the early
days of computer science and artificial intelligence. However, many existing
studies on RSI systems remain philosophical, and lacks clear formulation and
results. In this paper, we provide a formal definition for one class of RSI
systems, and then demonstrate the existence of computable and efficient RSI
systems on a restricted version. We use simulation to empirically show that we
achieve logarithmic runtime complexity with respect to the size of the search
space, and these results suggest it is possible to achieve an efficient
recursive self-improvement.
",A Formulation of Recursive Self-Improvement and Its Possible Efficiency
"  Loading the containers on the ship from a yard, is an impor- tant part of
port operations. Finding the optimal sequence for the loading of containers, is
known to be computationally hard and is an example of combinatorial
optimization, which leads to the application of simple heuristics in practice.
In this paper, we propose an approach which uses a mix of Evolutionary
Strategies and Reinforcement Learning (RL) tech- niques to find an
approximation of the optimal solution. The RL based agent uses the Policy
Gradient method, an evolutionary reward strategy and a Pool of good
(not-optimal) solutions to find the approximation. We find that the RL agent
learns near-optimal solutions that outperforms the heuristic solutions. We also
observe that the RL agent assisted with a pool generalizes better for unseen
problems than an RL agent without a pool. We present our results on synthetic
data as well as on subsets of real-world problems taken from container
terminal. The results validate that our approach does comparatively better than
the heuristics solutions available, and adapts to unseen problems better.
",Evolutionary RL for Container Loading
"  Although there has been remarkable progress and impressive performance on
reinforcement learning (RL) on Atari games, there are many problems with
challenging characteristics that have not yet been explored in Deep Learning
for RL. These include reward sparsity, abrupt context-dependent reversals of
strategy and time-sensitive game play. In this paper, we present Space
Fortress, a game that incorporates all these characteristics and experimentally
show that the presence of any of these renders state of the art Deep RL
algorithms incapable of learning. Then, we present our enhancements to an
existing algorithm and show big performance increases through each enhancement
through an ablation study. We discuss how each of these enhancements was able
to help and also argue that appropriate transfer learning boosts performance.
",Learning Time-Sensitive Strategies in Space Fortress
"  We present ASP Modulo `Space-Time', a declarative representational and
computational framework to perform commonsense reasoning about regions with
both spatial and temporal components. Supported are capabilities for mixed
qualitative-quantitative reasoning, consistency checking, and inferring
compositions of space-time relations; these capabilities combine and synergise
for applications in a range of AI application areas where the processing and
interpretation of spatio-temporal data is crucial. The framework and resulting
system is the only general KR-based method for declaratively reasoning about
the dynamics of `space-time' regions as first-class objects. We present an
empirical evaluation (with scalability and robustness results), and include
diverse application examples involving interpretation and control tasks.
",Answer Set Programming Modulo `Space-Time'
"  Recent approaches to human concept learning have successfully combined the
power of symbolic, infinitely productive rule systems and statistical learning
to explain our ability to learn new concepts from just a few examples. The aim
of most of these studies is to reveal the underlying language structuring these
representations and providing a general substrate for thought. However,
describing a model of thought that is fixed once trained is against the
extensive literature that shows how experience shapes concept learning. Here,
we ask about the plasticity of these symbolic descriptive languages. We perform
a concept learning experiment that demonstrates that humans can change very
rapidly the repertoire of symbols they use to identify concepts, by compiling
expressions which are frequently used into new symbols of the language. The
pattern of concept learning times is accurately described by a Bayesian agent
that rationally updates the probability of compiling a new expression according
to how useful it has been to compress concepts so far. By portraying the
Language of Thought as a flexible system of rules, we also highlight the
difficulties to pin it down empirically.
","Towards a more flexible Language of Thought: Bayesian grammar updates
  after each concept exposure"
"  STV and ranked pairs (RP) are two well-studied voting rules for group
decision-making. They proceed in multiple rounds, and are affected by how ties
are broken in each round. However, the literature is surprisingly vague about
how ties should be broken. We propose the first algorithms for computing the
set of alternatives that are winners under some tiebreaking mechanism under STV
and RP, which is also known as parallel-universes tiebreaking (PUT).
Unfortunately, PUT-winners are NP-complete to compute under STV and RP, and
standard search algorithms from AI do not apply. We propose multiple DFS-based
algorithms along with pruning strategies and heuristics to prioritize search
direction to significantly improve the performance using machine learning. We
also propose novel ILP formulations for PUT-winners under STV and RP,
respectively. Experiments on synthetic and real-world data show that our
algorithms are overall significantly faster than ILP, while there are a few
cases where ILP is significantly faster for RP.
","Practical Algorithms for STV and Ranked Pairs with Parallel Universes
  Tiebreaking"
"  Deep hierarchical reinforcement learning has gained a lot of attention in
recent years due to its ability to produce state-of-the-art results in
challenging environments where non-hierarchical frameworks fail to learn useful
policies. However, as problem domains become more complex, deep hierarchical
reinforcement learning can become inefficient, leading to longer convergence
times and poor performance. We introduce the Deep Nested Agent framework, which
is a variant of deep hierarchical reinforcement learning where information from
the main agent is propagated to the low level $nested$ agent by incorporating
this information into the nested agent's state. We demonstrate the
effectiveness and performance of the Deep Nested Agent framework by applying it
to three scenarios in Minecraft with comparisons to a deep non-hierarchical
single agent framework, as well as, a deep hierarchical framework.
",Hierarchical Reinforcement Learning with Deep Nested Agents
"  A modern radar may be designed to perform multiple functions, such as
surveillance, tracking, and fire control. Each function requires the radar to
execute a number of transmit-receive tasks. A radar resource management (RRM)
module makes decisions on parameter selection, prioritization, and scheduling
of such tasks. RRM becomes especially challenging in overload situations, where
some tasks may need to be delayed or even dropped. In general, task scheduling
is an NP-hard problem. In this work, we develop the branch-and-bound (B&B)
method which obtains the optimal solution but at exponential computational
complexity. On the other hand, heuristic methods have low complexity but
provide relatively poor performance. We resort to machine learning-based
techniques to address this issue; specifically we propose an approximate
algorithm based on the Monte Carlo tree search method. Along with using bound
and dominance rules to eliminate nodes from the search tree, we use a policy
network to help to reduce the width of the search. Such a network can be
trained using solutions obtained by running the B&B method offline on problems
with feasible complexity. We show that the proposed method provides
near-optimal performance, but with computational complexity orders of magnitude
smaller than the B&B algorithm.
","Multifunction Cognitive Radar Task Scheduling Using Monte Carlo Tree
  Search and Policy Networks"
"  Model counting is the problem of computing the number of satisfying
assignments of a given propositional formula. Although exact model counters can
be naturally furnished by most of the knowledge compilation (KC) methods, in
practice, they fail to generate the compiled results for the exact counting of
models for certain formulas due to the explosion in sizes. Decision-DNNF is an
important KC language that captures most of the practical compilers. We propose
a generalized Decision-DNNF (referred to as partial Decision-DNNF) via
introducing a class of new leaf vertices (called unknown vertices), and then
propose an algorithm called PartialKC to generate randomly partial
Decision-DNNF formulas from the given formulas. An unbiased estimate of the
model number can be computed via a randomly partial Decision-DNNF formula. Each
calling of PartialKC consists of multiple callings of MicroKC, while each of
the latter callings is a process of importance sampling equipped with KC
technologies. The experimental results show that PartialKC is more accurate
than both SampleSearch and SearchTreeSampler, PartialKC scales better than
SearchTreeSampler, and the KC technologies can obviously accelerate sampling.
",Approximate Model Counting by Partial Knowledge Compilation
"  A generally intelligent agent must be able to teach itself how to solve
problems in complex domains with minimal human supervision. Recently, deep
reinforcement learning algorithms combined with self-play have achieved
superhuman proficiency in Go, Chess, and Shogi without human data or domain
knowledge. In these environments, a reward is always received at the end of the
game, however, for many combinatorial optimization environments, rewards are
sparse and episodes are not guaranteed to terminate. We introduce Autodidactic
Iteration: a novel reinforcement learning algorithm that is able to teach
itself how to solve the Rubik's Cube with no human assistance. Our algorithm is
able to solve 100% of randomly scrambled cubes while achieving a median solve
length of 30 moves -- less than or equal to solvers that employ human domain
knowledge.
",Solving the Rubik's Cube Without Human Knowledge
"  A parameterized skill is a mapping from multiple goals/task parameters to the
policy parameters to accomplish them. Existing works in the literature show how
a parameterized skill can be learned given a task space that defines all the
possible achievable goals. In this work, we focus on tasks defined in terms of
final states (goals), and we face on the challenge where the agent aims to
autonomously acquire a parameterized skill to manipulate an initially unknown
environment. In this case, the task space is not known a priori and the agent
has to autonomously discover it. The agent may posit as a task space its whole
sensory space (i.e. the space of all possible sensor readings) as the
achievable goals will certainly be a subset of this space. However, the space
of achievable goals may be a very tiny subspace in relation to the whole
sensory space, thus directly using the sensor space as task space exposes the
agent to the curse of dimensionality and makes existing autonomous skill
acquisition algorithms inefficient. In this work we present an algorithm that
actively discovers the manifold of the achievable goals within the sensor
space. We validate the algorithm by employing it in multiple different
simulated scenarios where the agent actions achieve different types of goals:
moving a redundant arm, pushing an object, and changing the color of an object.
",Autonomous discovery of the goal space to learn a parameterized skill
"  {Radio Frequency Identification technology has gained popularity for cheap
and easy deployment. In the realm of manufacturing shopfloor, it can be used to
track the location of manufacturing objects to achieve better efficiency. The
underlying challenge of localization lies in the non-stationary characteristics
of manufacturing shopfloor which calls for an adaptive life-long learning
strategy in order to arrive at accurate localization results. This paper
presents an evolving model based on a novel evolving intelligent system, namely
evolving Type-2 Quantum Fuzzy Neural Network (eT2QFNN), which features an
interval type-2 quantum fuzzy set with uncertain jump positions. The quantum
fuzzy set possesses a graded membership degree which enables better
identification of overlaps between classes. The eT2QFNN works fully in the
evolving mode where all parameters including the number of rules are
automatically adjusted and generated on the fly. The parameter adjustment
scenario relies on decoupled extended Kalman filter method. Our numerical study
shows that eT2QFNN is able to deliver comparable accuracy compared to
state-of-the-art algorithms.
",An Online RFID Localization in the Manufacturing Shopfloor
"  Given that there exist many different formal and precise treatments of
deontologi- cal and consequentialist ethics, we turn to virtue ethics and
consider what could be a formalization of virtue ethics that makes it amenable
to automation. We present an embroyonic formalization in a cognitive calculus
(which subsumes a quantified first-order logic) that has been previously used
to model robust ethical principles, in both the deontological and
consequentialist traditions.
",One Formalization of Virtue Ethics via Learning
"  We consider the problem of predicting power outages in an electrical power
grid due to hazards produced by convective storms. These storms produce extreme
weather phenomena such as intense wind, tornadoes and lightning over a small
area. In this paper, we discuss the application of state-of-the-art machine
learning techniques, such as random forest classifiers and deep neural
networks, to predict the amount of damage caused by storms. We cast this
application as a classification problem where the goal is to classify storm
cells into a finite number of classes, each corresponding to a certain amount
of expected damage. The classification method use as input features estimates
for storm cell location and movement which has to be extracted from the raw
data.
  A main challenge of this application is that the training data is heavily
imbalanced as the occurrence of extreme weather events is rare. In order to
address this issue, we applied SMOTE technique.
",Predicting Electricity Outages Caused by Convective Storms
"  The research area of real-time heuristics search has produced quite many
algorithms. In the landscape of real-time heuristics search research, it is not
rare to find that an algorithm X that appears to perform better than algorithm
Y on a group of problems, performed worse than Y for another group of problems.
If these published algorithms are combined to generate a more powerful space of
algorithms, then that novel space of algorithms may solve a distribution of
problems more efficiently. Based on this intuition, a recent work Bulitko 2016
has defined the task of finding a combination of heuristics search algorithms
as a survival task. In this evolutionary approach, a space of algorithms is
defined over a set of building blocks published algorithms and a simulated
evolution is used to recombine these building blocks to find out the best
algorithm from that space of algorithms.
  In this paper, we extend the set of building blocks by adding one published
algorithm, namely lookahead based A-star shaped local search space generation
method from LSSLRTA-star, plus an unpublished novel strategy to generate local
search space with Greedy Best First Search. Then we perform experiments in the
new space of algorithms, which show that the best algorithms selected by the
evolutionary process have the following property: the deeper is the lookahead
depth of an algorithm, the lower is its suboptimality and scrubbing complexity.
",Evolving Real-Time Heuristics Search Algorithms with Building Blocks
"  In programming by example, users ""write"" programs by generating a small
number of input-output examples and asking the computer to synthesize
consistent programs. We consider a challenging problem in this domain: learning
regular expressions (regexes) from positive and negative example strings. This
problem is challenging, as (1) user-generated examples may not be informative
enough to sufficiently constrain the hypothesis space, and (2) even if
user-generated examples are in principle informative, there is still a massive
search space to examine. We frame regex induction as the problem of inferring a
probabilistic regular grammar and propose an efficient inference approach that
uses a novel stochastic process recognition model. This model incrementally
""grows"" a grammar using positive examples as a scaffold. We show that this
approach is competitive with human ability to learn regexes from examples.
","Bayesian Inference of Regular Expressions from Human-Generated Example
  Strings"
"  This paper presents Unlimited Computable AI, or UCAI, that is a family of
computable variants of AIXI. UCAI is more powerful than AIXItl, that is a
conventional family of computable variants of AIXI, in the following ways: 1)
UCAI supports models of terminating computation, including typed lambda
calculus, while AIXItl only supports Turing machine with timeout t, which can
be simulated by typed lambda calculus for any t; 2) unlike UCAI, AIXItl limits
the program length to l.
",Computable Variants of AIXI which are More Powerful than AIXItl
"  The complexity of dynamics in AI techniques is already approaching that of
complex adaptive systems, thus curtailing the feasibility of formal
controllability and reachability analysis in the context of AI safety. It
follows that the envisioned instances of Artificial General Intelligence (AGI)
will also suffer from challenges of complexity. To tackle such issues, we
propose the modeling of deleterious behaviors in AI and AGI as psychological
disorders, thereby enabling the employment of psychopathological approaches to
analysis and control of misbehaviors. Accordingly, we present a discussion on
the feasibility of the psychopathological approaches to AI safety, and propose
general directions for research on modeling, diagnosis, and treatment of
psychological disorders in AGI.
",A Psychopathological Approach to Safety Engineering in AI and AGI
"  Mathematics has played a substantial role to revolutionize the medical
science. Intelligent systems based on mathematical theories have proved to be
efficient in diagnosing various diseases. In this paper, we used an expert
system based on soft set theory and fuzzy set theory named as a soft expert
system to diagnose tropical disease dengue. The objective to use soft expert
system is to predict the risk level of a patient having dengue fever by using
input variables like age, TLC, SGOT, platelets count and blood pressure. The
proposed method explicitly demonstrates the exact percentage of the risk level
of dengue fever automatically circumventing for all possible (medical)
imprecisions.
","A distinct approach to diagnose Dengue Fever with the help of Soft Set
  Theory"
"  This paper considers the learning of Boolean rules in either disjunctive
normal form (DNF, OR-of-ANDs, equivalent to decision rule sets) or conjunctive
normal form (CNF, AND-of-ORs) as an interpretable model for classification. An
integer program is formulated to optimally trade classification accuracy for
rule simplicity. Column generation (CG) is used to efficiently search over an
exponential number of candidate clauses (conjunctions or disjunctions) without
the need for heuristic rule mining. This approach also bounds the gap between
the selected rule set and the best possible rule set on the training data. To
handle large datasets, we propose an approximate CG algorithm using
randomization. Compared to three recently proposed alternatives, the CG
algorithm dominates the accuracy-simplicity trade-off in 7 out of 15 datasets.
When maximized for accuracy, CG is competitive with rule learners designed for
this purpose, sometimes finding significantly simpler solutions that are no
less accurate.
",Boolean Decision Rules via Column Generation
"  As people learn to navigate the world, autonomic nervous system (e.g., ""fight
or flight"") responses provide intrinsic feedback about the potential
consequence of action choices (e.g., becoming nervous when close to a cliff
edge or driving fast around a bend.) Physiological changes are correlated with
these biological preparations to protect one-self from danger. We present a
novel approach to reinforcement learning that leverages a task-independent
intrinsic reward function trained on peripheral pulse measurements that are
correlated with human autonomic nervous system responses. Our hypothesis is
that such reward functions can circumvent the challenges associated with sparse
and skewed rewards in reinforcement learning settings and can help improve
sample efficiency. We test this in a simulated driving environment and show
that it can increase the speed of learning and reduce the number of collisions
during the learning stage.
","Visceral Machines: Risk-Aversion in Reinforcement Learning with
  Intrinsic Physiological Rewards"
"  The Sensor, Observation, Sample, and Actuator (SOSA) ontology provides a
formal but lightweight general-purpose specification for modeling the
interaction between the entities involved in the acts of observation,
actuation, and sampling. SOSA is the result of rethinking the W3C-XG Semantic
Sensor Network (SSN) ontology based on changes in scope and target audience,
technical developments, and lessons learned over the past years. SOSA also acts
as a replacement of SSN's Stimulus Sensor Observation (SSO) core. It has been
developed by the first joint working group of the Open Geospatial Consortium
(OGC) and the World Wide Web Consortium (W3C) on \emph{Spatial Data on the
Web}. In this work, we motivate the need for SOSA, provide an overview of the
main classes and properties, and briefly discuss its integration with the new
release of the SSN ontology as well as various other alignments to
specifications such as OGC's Observations and Measurements (O\&M),
Dolce-Ultralite (DUL), and other prominent ontologies. We will also touch upon
common modeling problems and application areas related to publishing and
searching observation, sampling, and actuation data on the Web. The SOSA
ontology and standard can be accessed at
\url{https://www.w3.org/TR/vocab-ssn/}.
","SOSA: A Lightweight Ontology for Sensors, Observations, Samples, and
  Actuators"
"  Applying reinforcement learning in physical-world tasks is extremely
challenging. It is commonly infeasible to sample a large number of trials, as
required by current reinforcement learning methods, in a physical environment.
This paper reports our project on using reinforcement learning for better
commodity search in Taobao, one of the largest online retail platforms and
meanwhile a physical environment with a high sampling cost. Instead of training
reinforcement learning in Taobao directly, we present our approach: first we
build Virtual Taobao, a simulator learned from historical customer behavior
data through the proposed GAN-SD (GAN for Simulating Distributions) and MAIL
(multi-agent adversarial imitation learning), and then we train policies in
Virtual Taobao with no physical costs in which ANC (Action Norm Constraint)
strategy is proposed to reduce over-fitting. In experiments, Virtual Taobao is
trained from hundreds of millions of customers' records, and its properties are
compared with the real environment. The results disclose that Virtual Taobao
faithfully recovers important properties of the real environment. We also show
that the policies trained in Virtual Taobao can have significantly superior
online performance to the traditional supervised approaches. We hope our work
could shed some light on reinforcement learning applications in complex
physical environments.
","Virtual-Taobao: Virtualizing Real-world Online Retail Environment for
  Reinforcement Learning"
"  Recent years have witnessed the successful application of low-dimensional
vector space representations of knowledge graphs to predict missing facts or
find erroneous ones. However, it is not yet well-understood to what extent
ontological knowledge, e.g. given as a set of (existential) rules, can be
embedded in a principled way. To address this shortcoming, in this paper we
introduce a general framework based on a view of relations as regions, which
allows us to study the compatibility between ontological knowledge and
different types of vector space embeddings. Our technical contribution is
two-fold. First, we show that some of the most popular existing embedding
methods are not capable of modelling even very simple types of rules, which in
particular also means that they are not able to learn the type of dependencies
captured by such rules. Second, we study a model in which relations are
modelled as convex regions. We show particular that ontologies which are
expressed using so-called quasi-chained existential rules can be exactly
represented using convex regions, such that any set of facts which is induced
using that vector space embedding is logically consistent and deductively
closed with respect to the input ontology.
","From Knowledge Graph Embedding to Ontology Embedding? An Analysis of the
  Compatibility between Vector Space Representations and Rules"
"  The main objective of explanations is to transmit knowledge to humans. This
work proposes to construct informative explanations for predictions made from
machine learning models. Motivated by the observations from social sciences,
our approach selects data points from the training sample that exhibit special
characteristics crucial for explanation, for instance, ones contrastive to the
classification prediction and ones representative of the models. Subsequently,
semantic concepts are derived from the selected data points through the use of
domain ontologies. These concepts are filtered and ranked to produce
informative explanations that improves human understanding. The main features
of our approach are that (1) knowledge about explanations is captured in the
form of ontological concepts, (2) explanations include contrastive evidences in
addition to normal evidences, and (3) explanations are user relevant.
",Semantic Explanations of Predictions
"  Knowledge tracing (KT), a key component of an intelligent tutoring system, is
a machine learning technique that estimates the mastery level of a student
based on his/her past performance. The objective of KT is to predict a
student's response to the next question. Compared with traditional KT models,
deep learning-based KT (DLKT) models show better predictive performance because
of the representation power of deep neural networks. Various methods have been
proposed to improve the performance of DLKT, but few studies have been
conducted on the reliability of DLKT. In this work, we claim that the existing
DLKTs are not reliable in real education environments. To substantiate the
claim, we show limitations of DLKT from various perspectives such as knowledge
state update failure, catastrophic forgetting, and non-interpretability. We
then propose a novel regularization to address these problems. The proposed
method allows us to achieve trustworthy DLKT. In addition, the proposed model
which is trained on scenarios with forgetting can also be easily extended to
scenarios without forgetting.
",Deep Trustworthy Knowledge Tracing
"  The recent years have witnessed the rise of accurate but obscure decision
systems which hide the logic of their internal decision processes to the users.
The lack of explanations for the decisions of black box systems is a key
ethical issue, and a limitation to the adoption of machine learning components
in socially sensitive and safety-critical contexts. %Therefore, we need
explanations that reveals the reasons why a predictor takes a certain decision.
In this paper we focus on the problem of black box outcome explanation, i.e.,
explaining the reasons of the decision taken on a specific instance. We propose
LORE, an agnostic method able to provide interpretable and faithful
explanations. LORE first leans a local interpretable predictor on a synthetic
neighborhood generated by a genetic algorithm. Then it derives from the logic
of the local interpretable predictor a meaningful explanation consisting of: a
decision rule, which explains the reasons of the decision; and a set of
counterfactual rules, suggesting the changes in the instance's features that
lead to a different outcome. Wide experiments show that LORE outperforms
existing methods and baselines both in the quality of explanations and in the
accuracy in mimicking the black box.
",Local Rule-Based Explanations of Black Box Decision Systems
"  We introduce DeepProbLog, a probabilistic logic programming language that
incorporates deep learning by means of neural predicates. We show how existing
inference and learning techniques can be adapted for the new language. Our
experiments demonstrate that DeepProbLog supports both symbolic and subsymbolic
representations and inference, 1) program induction, 2) probabilistic (logic)
programming, and 3) (deep) learning from examples. To the best of our
knowledge, this work is the first to propose a framework where general-purpose
neural networks and expressive probabilistic-logical modeling and reasoning are
integrated in a way that exploits the full expressiveness and strengths of both
worlds and can be trained end-to-end based on examples.
",DeepProbLog: Neural Probabilistic Logic Programming
"  The reconstruction and analyzation of high energy particle physics data is
just as important as the analyzation of the structure in real world networks.
In a previous study it was explored how hierarchical clustering algorithms can
be combined with kt cluster algorithms to provide a more generic clusterization
method. Building on that, this paper explores the possibilities to involve deep
learning in the process of cluster computation, by applying reinforcement
learning techniques. The result is a model, that by learning on a modest
dataset of 10; 000 nodes during 70 epochs can reach 83; 77% precision in
predicting the appropriate clusters.
",Hierarchical clustering with deep Q-learning
"  Artificial autonomous agents and robots interacting in complex environments
are required to continually acquire and fine-tune knowledge over sustained
periods of time. The ability to learn from continuous streams of information is
referred to as lifelong learning and represents a long-standing challenge for
neural network models due to catastrophic forgetting. Computational models of
lifelong learning typically alleviate catastrophic forgetting in experimental
scenarios with given datasets of static images and limited complexity, thereby
differing significantly from the conditions artificial agents are exposed to.
In more natural settings, sequential information may become progressively
available over time and access to previous experience may be restricted. In
this paper, we propose a dual-memory self-organizing architecture for lifelong
learning scenarios. The architecture comprises two growing recurrent networks
with the complementary tasks of learning object instances (episodic memory) and
categories (semantic memory). Both growing networks can expand in response to
novel sensory experience: the episodic memory learns fine-grained
spatiotemporal representations of object instances in an unsupervised fashion
while the semantic memory uses task-relevant signals to regulate structural
plasticity levels and develop more compact representations from episodic
experience. For the consolidation of knowledge in the absence of external
sensory input, the episodic memory periodically replays trajectories of neural
reactivations. We evaluate the proposed model on the CORe50 benchmark dataset
for continuous object recognition, showing that we significantly outperform
current methods of lifelong learning in three different incremental learning
scenarios
","Lifelong Learning of Spatiotemporal Representations with Dual-Memory
  Recurrent Self-Organization"
"  Many problems in operations research require that constraints be specified in
the model. Determining the right constraints is a hard and laborsome task. We
propose an approach to automate this process using artificial intelligence and
machine learning principles. So far there has been only little work on learning
constraints within the operations research community. We focus on personnel
rostering and scheduling problems in which there are often past schedules
available and show that it is possible to automatically learn constraints from
such examples. To realize this, we adapted some techniques from the constraint
programming community and we have extended them in order to cope with
multidimensional examples. The method uses a tensor representation of the
example, which helps in capturing the dimensionality as well as the structure
of the example, and applies tensor operations to find the constraints that are
satisfied by the example. To evaluate the proposed algorithm, we used
constraints from the Nurse Rostering Competition and generated solutions that
satisfy these constraints; these solutions were then used as examples to learn
constraints. Experiments demonstrate that the proposed algorithm is capable of
producing human readable constraints that capture the underlying
characteristics of the examples.
",Automating Personnel Rostering by Learning Constraints Using Tensors
"  Off-policy reinforcement learning enables near-optimal policy from suboptimal
experience, thereby provisions opportunity for artificial intelligence
applications in healthcare. Previous works have mainly framed patient-clinician
interactions as Markov decision processes, while true physiological states are
not necessarily fully observable from clinical data. We capture this situation
with partially observable Markov decision process, in which an agent optimises
its actions in a belief represented as a distribution of patient states
inferred from individual history trajectories. A Gaussian mixture model is
fitted for the observed data. Moreover, we take into account the fact that
nuance in pharmaceutical dosage could presumably result in significantly
different effect by modelling a continuous policy through a Gaussian
approximator directly in the policy space, i.e. the actor. To address the
challenge of infinite number of possible belief states which renders exact
value iteration intractable, we evaluate and plan for only every encountered
belief, through heuristic search tree by tightly maintaining lower and upper
bounds of the true value of belief. We further resort to function
approximations to update value bounds estimation, i.e. the critic, so that the
tree search can be improved through more compact bounds at the fringe nodes
that will be back-propagated to the root. Both actor and critic parameters are
learned via gradient-based approaches. Our proposed policy trained from real
intensive care unit data is capable of dictating dosing on vasopressors and
intravenous fluids for sepsis patients that lead to the best patient outcomes.
","The Actor Search Tree Critic (ASTC) for Off-Policy POMDP Learning in
  Medical Decision Making"
"  Workforce Scheduling and Routing Problems (WSRP) are very common in many
practical domains, and usually, have a number of objectives. Illumination
algorithms such as Map-Elites (ME) have recently gained traction in application
to {\em design} problems, in providing multiple diverse solutions as well as
illuminating the solution space in terms of user-defined characteristics, but
typically require significant computational effort to produce the solution
archive. We investigate whether ME can provide an effective approach to solving
WSRP, a {\em repetitive} problem in which solutions have to be produced quickly
and often. The goals of the paper are two-fold. The first is to evaluate
whether ME can provide solutions of competitive quality to an Evolutionary
Algorithm (EA) in terms of a single objective function, and the second to
examine its ability to provide a repertoire of solutions that maximise user
choice. We find that very small computational budgets favour the EA in terms of
quality, but ME outperforms the EA at larger budgets, provides a more diverse
array of solutions, and lends insight to the end-user.
","Optimisation and Illumination of a Real-world Workforce Scheduling and
  Routing Application via Map-Elites"
"  The adoption of machine learning in high-stakes applications such as
healthcare and law has lagged in part because predictions are not accompanied
by explanations comprehensible to the domain user, who often holds the ultimate
responsibility for decisions and outcomes. In this paper, we propose an
approach to generate such explanations in which training data is augmented to
include, in addition to features and labels, explanations elicited from domain
users. A joint model is then learned to produce both labels and explanations
from the input features. This simple idea ensures that explanations are
tailored to the complexity expectations and domain knowledge of the consumer.
Evaluation spans multiple modeling techniques on a game dataset, a (visual)
aesthetics dataset, a chemical odor dataset and a Melanoma dataset showing that
our approach is generalizable across domains and algorithms. Results
demonstrate that meaningful explanations can be reliably taught to machine
learning algorithms, and in some cases, also improve modeling accuracy.
",Teaching Meaningful Explanations
"  We propose the problem of tutorial generation for games, i.e. to generate
tutorials which can teach players to play games, as an AI problem. This problem
can be approached in several ways, including generating natural language
descriptions of game rules, generating instructive game levels, and generating
demonstrations of how to play a game using agents that play in a human-like
manner. We further argue that the General Video Game AI framework provides a
useful testbed for addressing this problem.
","""Press Space to Fire"": Automatic Video Game Tutorial Generation"
"  Construct, Merge, Solve and Adapt (CMSA) is a general hybrid metaheuristic
for solving combinatorial optimization problems. At each iteration, CMSA (1)
constructs feasible solutions to the tackled problem instance in a
probabilistic way and (2) solves a reduced problem instance (if possible) to
optimality. The construction of feasible solutions is hereby problem-specific,
usually involving a fast greedy heuristic. The goal of this paper is to design
a problem-agnostic CMSA variant whose exclusive input is an integer linear
program (ILP). In order to reduce the complexity of this task, the current
study is restricted to binary ILPs. In addition to a basic problem-agnostic
CMSA variant, we also present an extended version that makes use of a
constraint propagation engine for constructing solutions. The results show that
our technique is able to match the upper bounds of the standalone application
of CPLEX in the context of rather easy-to-solve instances, while it generally
outperforms the standalone application of CPLEX in the context of hard
instances. Moreover, the results indicate that the support of the constraint
propagation engine is useful in the context of problems for which finding
feasible solutions is rather difficult.
",Generic CP-Supported CMSA for Binary Integer Linear Programs
"  We introduce the open-ended, modular, self-improving Omega AI unification
architecture which is a refinement of Solomonoff's Alpha architecture, as
considered from first principles. The architecture embodies several crucial
principles of general intelligence including diversity of representations,
diversity of data types, integrated memory, modularity, and higher-order
cognition. We retain the basic design of a fundamental algorithmic substrate
called an ""AI kernel"" for problem solving and basic cognitive functions like
memory, and a larger, modular architecture that re-uses the kernel in many
ways. Omega includes eight representation languages and six classes of neural
networks, which are briefly introduced. The architecture is intended to
initially address data science automation, hence it includes many problem
solving methods for statistical tasks. We review the broad software
architecture, higher-order cognition, self-improvement, modular neural
architectures, intelligent agents, the process and memory hierarchy, hardware
abstraction, peer-to-peer computing, and data abstraction facility.
",Omega: An Architecture for AI Unification
"  Large ontologies still pose serious challenges to state-of-the-art ontology
alignment systems. In the paper we present an approach that combines a lexical
index, a neural embedding model and locality modules to effectively divide an
input ontology matching task into smaller and more tractable matching
(sub)tasks. We have conducted a comprehensive evaluation using the datasets of
the Ontology Alignment Evaluation Initiative. The results are encouraging and
suggest that the proposed methods are adequate in practice and can be
integrated within the workflow of state-of-the-art systems.
","Breaking-down the Ontology Alignment Task with a Lexical Index and
  Neural Embeddings"
"  While there exist many methods in machine learning for comparison of letter
string data, most are better equipped to handle strings that represent natural
language, and their performance will not hold up when presented with strings
that correspond to mathematical expressions. Based on the graphical
representation of the expression tree, here we propose a simple method for
encoding such expressions that is only sensitive to their structural
properties, and invariant to the specifics which can vary between two seemingly
different, but semantically similar mathematical expressions.
",Invariant Representation of Mathematical Expressions
"  We introduce collapsed compilation, a novel approximate inference algorithm
for discrete probabilistic graphical models. It is a collapsed sampling
algorithm that incrementally selects which variable to sample next based on the
partial sample obtained so far. This online collapsing, together with knowledge
compilation inference on the remaining variables, naturally exploits local
structure and context- specific independence in the distribution. These
properties are naturally exploited in exact inference, but are difficult to
harness for approximate inference. More- over, by having a partially compiled
circuit available during sampling, collapsed compilation has access to a highly
effective proposal distribution for importance sampling. Our experimental
evaluation shows that collapsed compilation performs well on standard
benchmarks. In particular, when the amount of exact inference is equally
limited, collapsed compilation is competitive with the state of the art, and
outperforms it on several benchmarks.
","Approximate Knowledge Compilation by Online Collapsed Importance
  Sampling"
"  Answer Set Programming (ASP) is a well-known problem solving approach based
on nonmonotonic logic programs. HEX-programs extend ASP with external atoms for
accessing arbitrary external information, which can introduce values that do
not appear in the input program. In this work we consider inconsistent ASP- and
HEX-programs, i.e., programs without answer sets. We study characterizations of
inconsistency, introduce a novel notion for explaining inconsistencies in terms
of input facts, analyze the complexity of reasoning tasks in context of
inconsistency analysis, and present techniques for computing inconsistency
reasons. This theoretical work is motivated by two concrete applications, which
we also present. The first one is the new modeling technique of query answering
over subprograms as a convenient alternative to the well-known saturation
technique. The second application is a new evaluation algorithm for
HEX-programs based on conflict-driven learning for programs with multiple
components: while for certain program classes previous techniques suffer an
evaluation bottleneck, the new approach shows significant, potentially
exponential speedup in our experiments. Since well-known ASP extensions such as
constraint ASP and DL-programs correspond to special cases of HEX, all
presented results are interesting beyond the specific formalism.
",Technical Report: Inconsistency in Answer Set Programs and Extensions
"  Humans learn to play video games significantly faster than the
state-of-the-art reinforcement learning (RL) algorithms. People seem to build
simple models that are easy to learn to support planning and strategic
exploration. Inspired by this, we investigate two issues in leveraging
model-based RL for sample efficiency. First we investigate how to perform
strategic exploration when exact planning is not feasible and empirically show
that optimistic Monte Carlo Tree Search outperforms posterior sampling methods.
Second we show how to learn simple deterministic models to support fast
learning using object representation. We illustrate the benefit of these ideas
by introducing a novel algorithm, Strategic Object Oriented Reinforcement
Learning (SOORL), that outperforms state-of-the-art algorithms in the game of
Pitfall! in less than 50 episodes.
","Fast Exploration with Simplified Models and Approximately Optimistic
  Planning in Model Based Reinforcement Learning"
"  Current approaches to explaining the decisions of deep learning systems for
medical tasks have focused on visualising the elements that have contributed to
each decision. We argue that such approaches are not enough to ""open the black
box"" of medical decision making systems because they are missing a key
component that has been used as a standard communication tool between doctors
for centuries: language. We propose a model-agnostic interpretability method
that involves training a simple recurrent neural network model to produce
descriptive sentences to clarify the decision of deep learning classifiers.
  We test our method on the task of detecting hip fractures from frontal pelvic
x-rays. This process requires minimal additional labelling despite producing
text containing elements that the original deep learning classification model
was not specifically trained to detect.
  The experimental results show that: 1) the sentences produced by our method
consistently contain the desired information, 2) the generated sentences are
preferred by doctors compared to current tools that create saliency maps, and
3) the combination of visualisations and generated text is better than either
alone.
","Producing radiologist-quality reports for interpretable artificial
  intelligence"
"  Causally insufficient structures (models with latent or hidden variables, or
with confounding etc.) of joint probability distributions have been subject of
intense study not only in statistics, but also in various AI systems. In AI,
belief networks, being representations of joint probability distribution with
an underlying directed acyclic graph structure, are paid special attention due
to the fact that efficient reasoning (uncertainty propagation) methods have
been developed for belief network structures. Algorithms have been therefore
developed to acquire the belief network structure from data. As artifacts due
to variable hiding negatively influence the performance of derived belief
networks, models with latent variables have been studied and several algorithms
for learning belief network structure under causal insufficiency have also been
developed.
  Regrettably, some of them are known already to be erroneous (e.g. IC
algorithm of [Pearl:Verma:91]. This paper is devoted to another algorithm, the
Fast Causal Inference (FCI) Algorithm of [Spirtes:93]. It is proven by a
specially constructed example that this algorithm, as it stands in
[Spirtes:93], is also erroneous. Fundamental reason for failure of this
algorithm is the temporary introduction of non-real links between nodes of the
network with the intention of later removal. While for trivial dependency
structures these non-real links may be actually removed, this may not be the
case for complex ones, e.g. for the case described in this paper. A remedy of
this failure is proposed.
",Too Fast Causal Inference under Causal Insufficiency
"  Traditional exploration methods in RL require agents to perform random
actions to find rewards. But these approaches struggle on sparse-reward domains
like Montezuma's Revenge where the probability that any random action sequence
leads to reward is extremely low. Recent algorithms have performed well on such
tasks by encouraging agents to visit new states or perform new actions in
relation to all prior training episodes (which we call across-training
novelty). But such algorithms do not consider whether an agent exhibits
intra-life novelty: doing something new within the current episode, regardless
of whether those behaviors have been performed in previous episodes. We
hypothesize that across-training novelty might discourage agents from
revisiting initially non-rewarding states that could become important stepping
stones later in training. We introduce Deep Curiosity Search (DeepCS), which
encourages intra-life exploration by rewarding agents for visiting as many
different states as possible within each episode, and show that DeepCS matches
the performance of current state-of-the-art methods on Montezuma's Revenge. We
further show that DeepCS improves exploration on Amidar, Freeway, Gravitar, and
Tutankham (many of which are hard exploration games). Surprisingly, DeepCS
doubles A2C performance on Seaquest, a game we would not have expected to
benefit from intra-life exploration because the arena is small and already
easily navigated by naive exploration techniques. In one run, DeepCS achieves a
maximum training score of 80,000 points on Seaquest, higher than any methods
other than Ape-X. The strong performance of DeepCS on these sparse- and
dense-reward tasks suggests that encouraging intra-life novelty is an
interesting, new approach for improving performance in Deep RL and motivates
further research into hybridizing across-training and intra-life exploration
methods.
","Deep Curiosity Search: Intra-Life Exploration Can Improve Performance on
  Challenging Deep Reinforcement Learning Problems"
"  We analyze and reframe AI progress. In addition to the prevailing metrics of
performance, we highlight the usually neglected costs paid in the development
and deployment of a system, including: data, expert knowledge, human oversight,
software resources, computing cycles, hardware and network facilities,
development time, etc. These costs are paid throughout the life cycle of an AI
system, fall differentially on different individuals, and vary in magnitude
depending on the replicability and generality of the AI solution. The
multidimensional performance and cost space can be collapsed to a single
utility metric for a user with transitive and complete preferences. Even absent
a single utility function, AI advances can be generically assessed by whether
they expand the Pareto (optimal) surface. We explore a subset of these
neglected dimensions using the two case studies of Alpha* and ALE. This
broadened conception of progress in AI should lead to novel ways of measuring
success in AI, and can help set milestones for future progress.
",Accounting for the Neglected Dimensions of AI Progress
"  An almost-perfect chess playing agent has been a long standing challenge in
the field of Artificial Intelligence. Some of the recent advances demonstrate
we are approaching that goal. In this project, we provide methods for faster
training of self-play style algorithms, mathematical details of the algorithm
used, various potential future directions, and discuss most of the relevant
work in the area of computer chess. Deep Pepper uses embedded knowledge to
accelerate the training of the chess engine over a ""tabula rasa"" system such as
Alpha Zero. We also release our code to promote further research.
","Deep Pepper: Expert Iteration based Chess agent in the Reinforcement
  Learning Setting"
"  We extend the decomposition approach for learning Bayesian networks (BNs)
proposed by (Xie et. al.) to learning multivariate regression chain graphs (MVR
CGs), which include BNs as a special case. The same advantages of this
decomposition approach hold in the more general setting: reduced complexity and
increased power of computational independence tests. Moreover, latent (hidden)
variables can be represented in MVR CGs by using bidirected edges, and our
algorithm correctly recovers any independence structure that is faithful to an
MVR CG, thus greatly extending the range of applications of decomposition-based
model selection techniques. Simulations under a variety of settings demonstrate
the competitive performance of our method in comparison with the PC-like
algorithm (Sonntag and Pena). In fact, the decomposition-based algorithm
usually outperforms the PC-like algorithm except in running time. The
performance of both algorithms is much better when the underlying graph is
sparse.
","Structural Learning of Multivariate Regression Chain Graphs via
  Decomposition"
"  This paper draws a parallel between similarity-based categorisation models
developed in cognitive psychology and the nearest neighbour classifier (1-NN)
in machine learning. Conceived as a result of the historical rivalry between
prototype theories (abstraction) and exemplar theories (memorisation), recent
models of human categorisation seek a compromise in-between. Regarding the
stimuli (entities to be categorised) as points in a metric space, machine
learning offers a large collection of methods to select a small, representative
and discriminative point set. These methods are known under various names:
instance selection, data editing, prototype selection, prototype generation or
prototype replacement. The nearest neighbour classifier is used with the
selected reference set. Such a set can be interpreted as a data-driven
categorisation model. We juxtapose the models from the two fields to enable
cross-referencing. We believe that both machine learning and cognitive
psychology can draw inspiration from the comparison and enrich their repertoire
of similarity-based models.
","Learning from Exemplars and Prototypes in Machine Learning and
  Psychology"
"  The General Video Game AI competitions have been the testing ground for
several techniques for game playing, such as evolutionary computation
techniques, tree search algorithms, hyper heuristic based or knowledge based
algorithms. So far the metrics used to evaluate the performance of agents have
been win ratio, game score and length of games. In this paper we provide a
wider set of metrics and a comparison method for evaluating and comparing
agents. The metrics and the comparison method give shallow introspection into
the agent's decision making process and they can be applied to any agent
regardless of its algorithmic nature. In this work, the metrics and the
comparison method are used to measure the impact of the terms that compose a
tree policy of an MCTS based agent, comparing with several baseline agents. The
results clearly show how promising such general approach is and how it can be
useful to understand the behaviour of an AI agent, in particular, how the
comparison with baseline agents can help understanding the shape of the agent
decision landscape. The presented metrics and comparison method represent a
step toward to more descriptive ways of logging and analysing agent's
behaviours.
",Shallow decision-making analysis in General Video Game Playing
"  Creating Non-Player Characters (NPCs) that can react robustly to unforeseen
player behaviour or novel game content is difficult and time-consuming. This
hinders the design of believable characters, and the inclusion of NPCs in games
that rely heavily on procedural content generation. We have previously
addressed this challenge by means of empowerment, a model of intrinsic
motivation, and demonstrated how a coupled empowerment maximisation (CEM)
policy can yield generic, companion-like behaviour. In this paper, we extend
the CEM framework with a minimisation policy to give rise to adversarial
behaviour. We conduct a qualitative, exploratory study in a dungeon-crawler
game, demonstrating that CEM can exploit the affordances of different content
facets in adaptive adversarial behaviour without modifications to the policy.
Changes to the level design, underlying mechanics and our character's actions
do not threaten our NPC's robustness, but yield new and surprising ways to be
mean.
","New And Surprising Ways to Be Mean. Adversarial NPCs with Coupled
  Empowerment Minimisation"
"  Humans readily generalize, applying prior knowledge to novel situations and
stimuli. Advances in machine learning and artificial intelligence have begun to
approximate and even surpass human performance, but machine systems reliably
struggle to generalize information to untrained situations. We describe a
neural network model that is trained to play one video game (Breakout) and
demonstrates one-shot generalization to a new game (Pong). The model
generalizes by learning representations that are functionally and formally
symbolic from training data, without feedback, and without requiring that
structured representations be specified a priori. The model uses unsupervised
comparison to discover which characteristics of the input are invariant, and to
learn relational predicates; it then applies these predicates to arguments in a
symbolic fashion, using oscillatory regularities in network firing to
dynamically bind predicates to arguments. We argue that models of human
cognition must account for far-reaching and flexible generalization, and that
in order to do so, models must be able to discover symbolic representations
from unstructured data, a process we call predicate learning. Only then can
models begin to adequately explain where human-like representations come from,
why human cognition is the way it is, and why it continues to differ from
machine intelligence in crucial ways.
",Human-like generalization in a machine through predicate learning
"  Concepts are the foundation of human deep learning, understanding, and
knowledge integration and transfer. We propose concept-oriented deep learning
(CODL) which extends (machine) deep learning with concept representations and
conceptual understanding capability. CODL addresses some of the major
limitations of deep learning: interpretability, transferability, contextual
adaptation, and requirement for lots of labeled training data. We discuss the
major aspects of CODL including concept graph, concept representations, concept
exemplars, and concept representation learning systems supporting incremental
and continual learning.
",Concept-Oriented Deep Learning
"  Dyna is a fundamental approach to model-based reinforcement learning (MBRL)
that interleaves planning, acting, and learning in an online setting. In the
most typical application of Dyna, the dynamics model is used to generate
one-step transitions from selected start states from the agent's history, which
are used to update the agent's value function or policy as if they were real
experiences. In this work, one-step Dyna was applied to several games from the
Arcade Learning Environment (ALE). We found that the model-based updates
offered surprisingly little benefit over simply performing more updates with
the agent's existing experience, even when using a perfect model. We
hypothesize that to get the most from planning, the model must be used to
generate unfamiliar experience. To test this, we experimented with the ""shape""
of planning in multiple different concrete instantiations of Dyna, performing
fewer, longer rollouts, rather than many short rollouts. We found that planning
shape has a profound impact on the efficacy of Dyna for both perfect and
learned models. In addition to these findings regarding Dyna in general, our
results represent, to our knowledge, the first time that a learned dynamics
model has been successfully used for planning in the ALE, suggesting that Dyna
may be a viable approach to MBRL in the ALE and other high-dimensional
problems.
","The Effect of Planning Shape on Dyna-style Planning in High-dimensional
  State Spaces"
"  Can machines design? Can they come up with creative solutions to problems and
build tools and artifacts across a wide range of domains? Recent advances in
the field of computational creativity and formal Artificial General
Intelligence (AGI) provide frameworks for machines with the general ability to
design. In this paper we propose to integrate a formal computational creativity
framework into the G\""odel machine framework. We call the resulting framework
design G\""odel machine. Such a machine could solve a variety of design problems
by generating novel concepts. In addition, it could change the way these
concepts are generated by modifying itself. The design G\""odel machine is able
to improve its initial design program, once it has proven that a modification
would increase its return on the utility function. Finally, we sketch out a
specific version of the design G\""odel machine which specifically addresses the
design of complex software and hardware systems. Future work aims at the
development of a more formal version of the design G\""odel machine and a proof
of concept implementation.
",Can Machines Design? An Artificial General Intelligence Approach
"  Hierarchical Task Network (HTN) planning is a practical and efficient
approach to planning when the 'standard operating procedures' for a domain are
available. Like Belief-Desire-Intention (BDI) agent reasoning, HTN planning
performs hierarchical and context-based refinement of goals into subgoals and
basic actions. However, while HTN planners 'lookahead' over the consequences of
choosing one refinement over another, BDI agents interleave refinement with
acting. There has been renewed interest in making HTN planners behave more like
BDI agent systems, e.g. to have a unified representation for acting and
planning. However, past work on the subject has remained informal or
implementation-focused. This paper is a formal account of 'HTN acting', which
supports interleaved deliberation, acting, and failure recovery. We use the
syntax of the most general HTN planning formalism and build on its core
semantics, and we provide an algorithm which combines our new formalism with
the processing of exogenous events. We also study the properties of HTN acting
and its relation to HTN planning.
","Addendum to ""HTN Acting: A Formalism and an Algorithm"""
"  Knowledge tracing is one of the key research areas for empowering
personalized education. It is a task to model students' mastery level of a
knowledge component (KC) based on their historical learning trajectories. In
recent years, a recurrent neural network model called deep knowledge tracing
(DKT) has been proposed to handle the knowledge tracing task and literature has
shown that DKT generally outperforms traditional methods. However, through our
extensive experimentation, we have noticed two major problems in the DKT model.
The first problem is that the model fails to reconstruct the observed input. As
a result, even when a student performs well on a KC, the prediction of that
KC's mastery level decreases instead, and vice versa. Second, the predicted
performance for KCs across time-steps is not consistent. This is undesirable
and unreasonable because student's performance is expected to transit gradually
over time. To address these problems, we introduce regularization terms that
correspond to reconstruction and waviness to the loss function of the original
DKT model to enhance the consistency in prediction. Experiments show that the
regularized loss function effectively alleviates the two problems without
degrading the original task of DKT.
","Addressing Two Problems in Deep Knowledge Tracing via
  Prediction-Consistent Regularization"
"  During the 60s and 70s, AI researchers explored intuitions about intelligence
by writing programs that displayed intelligent behavior. Many good ideas came
out from this work but programs written by hand were not robust or general.
After the 80s, research increasingly shifted to the development of learners
capable of inferring behavior and functions from experience and data, and
solvers capable of tackling well-defined but intractable models like SAT,
classical planning, Bayesian networks, and POMDPs. The learning approach has
achieved considerable success but results in black boxes that do not have the
flexibility, transparency, and generality of their model-based counterparts.
Model-based approaches, on the other hand, require models and scalable
algorithms. Model-free learners and model-based solvers have close parallels
with Systems 1 and 2 in current theories of the human mind: the first, a fast,
opaque, and inflexible intuitive mind; the second, a slow, transparent, and
flexible analytical mind. In this paper, I review developments in AI and draw
on these theories to discuss the gap between model-free learners and
model-based solvers, a gap that needs to be bridged in order to have
intelligent systems that are robust and general.
","Model-free, Model-based, and General Intelligence"
"  Shenoy and Shafer {Shenoy:90} demonstrated that both for Dempster-Shafer
Theory and probability theory there exists a possibility to calculate
efficiently marginals of joint belief distributions (by so-called local
computations) provided that the joint distribution can be decomposed
(factorized) into a belief network. A number of algorithms exists for
decomposition of probabilistic joint belief distribution into a bayesian
(belief) network from data. For example
  Spirtes, Glymour and Schein{Spirtes:90b} formulated a Conjecture that a
direct dependence test and a head-to-head meeting test would suffice to
construe bayesian network from data in such a way that Pearl's concept of
d-separation {Geiger:90} applies.
  This paper is intended to transfer Spirtes, Glymour and Scheines
{Spirtes:90b} approach onto the ground of the Dempster-Shafer Theory (DST). For
this purpose, a frequentionistic interpretation of the DST developed in
{Klopotek:93b} is exploited. A special notion of conditionality for DST is
introduced and demonstrated to behave with respect to Pearl's d-separation
{Geiger:90} much the same way as conditional probability (though some
differences like non-uniqueness are evident). Based on this, an algorithm
analogous to that from {Spirtes:90b} is developed.
  The notion of a partially oriented graph (pog) is introduced and within this
graph the notion of p-d-separation is defined. If direct dependence test and
head-to-head meeting test are used to orient the pog then its p-d-separation is
shown to be equivalent to the Pearl's d-separation for any compatible dag.
",Dempsterian-Shaferian Belief Network From Data
"  Hybrid Bayesian Networks (HBNs), which contain both discrete and continuous
variables, arise naturally in many application areas (e.g., image
understanding, data fusion, medical diagnosis, fraud detection). This paper
concerns inference in an important subclass of HBNs, the conditional Gaussian
(CG) networks, in which all continuous random variables have Gaussian
distributions and all children of continuous random variables must be
continuous. Inference in CG networks can be NP-hard even for special-case
structures, such as poly-trees, where inference in discrete Bayesian networks
can be performed in polynomial time. Therefore, approximate inference is
required. In approximate inference, it is often necessary to trade off accuracy
against solution time. This paper presents an extension to the Hybrid Message
Passing inference algorithm for general CG networks and an algorithm for
optimizing its accuracy given a bound on computation time. The extended
algorithm uses Gaussian mixture reduction to prevent an exponential increase in
the number of Gaussian mixture components. The trade-off algorithm performs
pre-processing to find optimal run-time settings for the extended algorithm.
Experimental results for four CG networks compare performance of the extended
algorithm with existing algorithms and show the optimal settings for these CG
networks.
","Gaussian Mixture Reduction for Time-Constrained Approximate Inference in
  Hybrid Bayesian Networks"
"  During the past quarter-century, situation awareness (SAW) has become a
critical research theme, because of its importance. Since the concept of SAW
was first introduced during World War I, various versions of SAW have been
researched and introduced. Predictive Situation Awareness (PSAW) focuses on the
ability to predict aspects of a temporally evolving situation over time. PSAW
requires a formal representation and a reasoning method using such a
representation. A Multi-Entity Bayesian Network (MEBN) is a knowledge
representation formalism combining Bayesian Networks (BN) with First-Order
Logic (FOL). MEBN can be used to represent uncertain situations (supported by
BN) as well as complex situations (supported by FOL). Also, efficient reasoning
algorithms for MEBN have been developed. MEBN can be a formal representation to
support PSAW and has been used for several PSAW systems. Although several MEBN
applications for PSAW exist, very little work can be found in the literature
that attempts to generalize a MEBN model to support PSAW. In this research, we
define a reference model for MEBN in PSAW, called a PSAW-MEBN reference model.
The PSAW-MEBN reference model enables us to easily develop a MEBN model for
PSAW by supporting the design of a MEBN model for PSAW. In this research, we
introduce two example use cases using the PSAW-MEBN reference model to develop
MEBN models to support PSAW: a Smart Manufacturing System and a Maritime Domain
Awareness System.
","Reference Model of Multi-Entity Bayesian Networks for Predictive
  Situation Awareness"
"  Petri Nets is very interesting tool for studying and simulating different
behaviors of information systems. It can be used in different applications
based on the appropriate class of Petri Nets whereas it is classical, colored
or timed Petri Nets. In this paper we introduce a new approach of Petri Nets
called orbital Petri Nets (OPN) for studying the orbital rotating systems
within a specific domain. The study investigated and analyzed OPN with
highlighting the problem of space debris collision problem as a case study. The
mathematical investigation results of two OPN models proved that space debris
collision problem can be prevented based on the new method of firing sequence
in OPN. By this study, new smart algorithms can be implemented and simulated by
orbital Petri Nets for mitigating the space debris collision problem as a next
work.
",Orbital Petri Nets: A Novel Petri Net Approach
"  Current GP frameworks are highly effective on a range of real and simulated
benchmarks. However, due to the high dimensionality of the genotypes for GP,
the task of visualising the fitness landscape for GP search can be difficult.
This paper describes a new framework: Floating Point Grammatical Evolution
(FP-GE) which uses a single floating point genotype to encode an individual
program. This encoding permits easier visualisation of the fitness landscape
arbitrary problems by providing a way to map fitness against a single
dimension. The new framework also makes it trivially easy to apply continuous
search algorithms, such as Differential Evolution, to the search problem. In
this work, the FP-GE framework is tested against several regression problems,
visualising the search landscape for these and comparing different search
meta-heuristics.
",A Preliminary Exploration of Floating Point Grammatical Evolution
"  Transfer learning can greatly speed up reinforcement learning for a new task
by leveraging policies of relevant tasks.
  Existing works of policy reuse either focus on only selecting a single best
source policy for transfer without considering contexts, or cannot guarantee to
learn an optimal policy for a target task.
  To improve transfer efficiency and guarantee optimality, we develop a novel
policy reuse method, called Context-Aware Policy reuSe (CAPS), that enables
multi-policy transfer. Our method learns when and which source policy is best
for reuse, as well as when to terminate its reuse. CAPS provides theoretical
guarantees in convergence and optimality for both source policy selection and
target task learning. Empirical results on a grid-based navigation domain and
the Pygame Learning Environment demonstrate that CAPS significantly outperforms
other state-of-the-art policy reuse methods.
",Context-Aware Policy Reuse
"  Our goal is for AI systems to correctly identify and act according to their
human user's objectives. Cooperative Inverse Reinforcement Learning (CIRL)
formalizes this value alignment problem as a two-player game between a human
and robot, in which only the human knows the parameters of the reward function:
the robot needs to learn them as the interaction unfolds. Previous work showed
that CIRL can be solved as a POMDP, but with an action space size exponential
in the size of the reward parameter space. In this work, we exploit a specific
property of CIRL---the human is a full information agent---to derive an
optimality-preserving modification to the standard Bellman update; this reduces
the complexity of the problem by an exponential factor and allows us to relax
CIRL's assumption of human rationality. We apply this update to a variety of
POMDP solvers and find that it enables us to scale CIRL to non-trivial
problems, with larger reward parameter spaces, and larger action spaces for
both robot and human. In solutions to these larger problems, the human exhibits
pedagogic (teaching) behavior, while the robot interprets it as such and
attains higher value for the human.
","An Efficient, Generalized Bellman Update For Cooperative Inverse
  Reinforcement Learning"
"  Stream constraint programming is a recent addition to the family of
constraint programming frameworks, where variable domains are sets of infinite
streams over finite alphabets. Previous works showed promising results for its
applicability to real-world planning and control problems. In this paper,
motivated by the modelling of planning applications, we improve the
expressiveness of the framework by introducing 1) the ""until"" constraint, a new
construct that is adapted from Linear Temporal Logic and 2) the @ operator on
streams, a syntactic sugar for which we provide a more efficient solving
algorithm over simple desugaring. For both constructs, we propose corresponding
novel solving algorithms and prove their correctness. We present competitive
experimental results on the Missionaries and Cannibals logic puzzle and a
standard path planning application on the grid, by comparing with Apt and
Brand's method for verifying eventuality conditions using a CP approach.
",Augmenting Stream Constraint Programming with Eventuality Conditions
"  Model-based strategies for control are critical to obtain sample efficient
learning. Dyna is a planning paradigm that naturally interleaves learning and
planning, by simulating one-step experience to update the action-value
function. This elegant planning strategy has been mostly explored in the
tabular setting. The aim of this paper is to revisit sample-based planning, in
stochastic and continuous domains with learned models. We first highlight the
flexibility afforded by a model over Experience Replay (ER). Replay-based
methods can be seen as stochastic planning methods that repeatedly sample from
a buffer of recent agent-environment interactions and perform updates to
improve data efficiency. We show that a model, as opposed to a replay buffer,
is particularly useful for specifying which states to sample from during
planning, such as predecessor states that propagate information in reverse from
a state more quickly. We introduce a semi-parametric model learning approach,
called Reweighted Experience Models (REMs), that makes it simple to sample next
states or predecessors. We demonstrate that REM-Dyna exhibits similar
advantages over replay-based methods in learning in continuous state problems,
and that the performance gap grows when moving to stochastic domains, of
increasing size.
","Organizing Experience: A Deeper Look at Replay Mechanisms for
  Sample-based Planning in Continuous State Domains"
"  We describe a search-based approach to generating new levels for bullet hell
games, which are action games characterized by and requiring avoidance of a
very large amount of projectiles. Levels are represented using a
domain-specific description language, and search in the space defined by this
language is performed by a novel variant of the Map-Elites algorithm which
incorporates a feasible- infeasible approach to constraint satisfaction.
Simulation-based evaluation is used to gauge the fitness of levels, using an
agent based on best-first search. The performance of the agent can be tuned
according to the two dimensions of strategy and dexterity, making it possible
to search for level configurations that require a specific combination of both.
As far as we know, this paper describes the first generator for this game
genre, and includes several algorithmic innovations.
",Talakat: Bullet Hell Generation through Constrained Map-Elites
"  All it takes to identify the computer programs which are Artificial
Intelligence is to give them a test and award AI to those that pass the test.
Let us say that the scores they earn at the test will be called IQ. We cannot
pinpoint a minimum IQ threshold that a program has to cover in order to be AI,
however, we will choose a certain value. Thus, our definition for AI will be
any program the IQ of which is above the chosen value. While this idea has
already been implemented in [3], here we will revisit this construct in order
to introduce certain improvements.
",The IQ of Artificial Intelligence
"  We draw attention to an important, yet largely overlooked aspect of
evaluating fairness for automated decision making systems---namely risk and
welfare considerations. Our proposed family of measures corresponds to the
long-established formulations of cardinal social welfare in economics, and is
justified by the Rawlsian conception of fairness behind a veil of ignorance.
The convex formulation of our welfare-based measures of fairness allows us to
integrate them as a constraint into any convex loss minimization pipeline. Our
empirical analysis reveals interesting trade-offs between our proposal and (a)
prediction accuracy, (b) group discrimination, and (c) Dwork et al.'s notion of
individual fairness. Furthermore and perhaps most importantly, our work
provides both heuristic justification and empirical evidence suggesting that a
lower-bound on our measures often leads to bounded inequality in algorithmic
outcomes; hence presenting the first computationally feasible mechanism for
bounding individual-level inequality.
","Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated
  Decision Making"
"  This paper describes an architecture for controlling non-player characters
(NPC) in the First Person Shooter (FPS) game Unreal Tournament 2004.
Specifically, the DRE-Bot architecture is made up of three reinforcement
learners, Danger, Replenish and Explore, which use the tabular Sarsa({\lambda})
algorithm. This algorithm enables the NPC to learn through trial and error
building up experience over time in an approach inspired by human learning.
Experimentation is carried to measure the performance of DRE-Bot when competing
against fixed strategy bots that ship with the game. The discount parameter,
{\gamma}, and the trace parameter, {\lambda}, are also varied to see if their
values have an effect on the performance.
","DRE-Bot: A Hierarchical First Person Shooter Bot Using Multiple
  Sarsa({\lambda}) Reinforcement Learners"
"  We prove the existence of a semilinear representation of Cellular Automata
(CA) with the introduction of multiple convolution kernels. Examples of the
technique are presented for rules akin to the ""edge-of-chaos"" including the
Turing universal rule 110 for further utilization in the area of reservoir
computing. We also examine the significance of their dual representation on a
frequency or wavelength domain as a superposition of plane waves for
distributed computing applications including a new proposal for a ""Hologrid""
that could be realized with present Wi-Fi,Li-Fi technologies.
",Holographic Automata for Ambient Immersive A. I. via Reservoir Computing
"  While reinforcement learning (RL) has been applied to turn-based board games
for many years, more complex games involving decision-making in real-time are
beginning to receive more attention. A challenge in such environments is that
the time that elapses between deciding to take an action and receiving a reward
based on its outcome can be longer than the interval between successive
decisions. We explore this in the context of a non-player character (NPC) in a
modern first-person shooter game. Such games take place in 3D environments
where players, both human and computer-controlled, compete by engaging in
combat and completing task objectives. We investigate the use of RL to enable
NPCs to gather experience from game-play and improve their shooting skill over
time from a reward signal based on the damage caused to opponents. We propose a
new method for RL updates and reward calculations, in which the updates are
carried out periodically, after each shooting encounter has ended, and a new
weighted-reward mechanism is used which increases the reward applied to actions
that lead to damaging the opponent in successive hits in what we term ""hit
clusters"".
","Learning to Shoot in First Person Shooter Games by Stabilizing Actions
  and Clustering Rewards for Reinforcement Learning"
"  Can we train a machine to detect if another machine has understood a concept?
In principle, this is possible by conducting tests on the subject of that
concept. However we want this procedure to be done by avoiding direct
questions. In other words, we would like to isolate the absolute meaning of an
abstract idea by putting it into a class of equivalence, hence without adopting
straight definitions or showing how this idea ""works"" in practice. We discuss
the metaphysical implications hidden in the above question, with the aim of
providing a plausible reference framework.
",Understanding the Meaning of Understanding
"  We introduce a new approach to hierarchy formation and task decomposition in
hierarchical reinforcement learning. Our method is based on the Hierarchy Of
Abstract Machines (HAM) framework because HAM approach is able to design
efficient controllers that will realize specific behaviors in real robots. The
key to our algorithm is the introduction of the internal or ""mental""
environment in which the state represents the structure of the HAM hierarchy.
The internal action in this environment leads to changes the hierarchy of HAMs.
We propose the classical Q-learning procedure in the internal environment which
allows the agent to obtain an optimal hierarchy. We extends the HAM framework
by adding on-model approach to select the appropriate sub-machine to execute
action sequences for certain class of external environment states. Preliminary
experiments demonstrated the prospects of the method.
","Automatic formation of the structure of abstract machines in
  hierarchical reinforcement learning with state clustering"
"  In many real-world problems, there is the possibility to configure, to a
limited extent, some environmental parameters to improve the performance of a
learning agent. In this paper, we propose a novel framework, Configurable
Markov Decision Processes (Conf-MDPs), to model this new type of interaction
with the environment. Furthermore, we provide a new learning algorithm, Safe
Policy-Model Iteration (SPMI), to jointly and adaptively optimize the policy
and the environment configuration. After having introduced our approach and
derived some theoretical results, we present the experimental evaluation in two
explicative problems to show the benefits of the environment configurability on
the performance of the learned policy.
",Configurable Markov Decision Processes
"  In current state-of-the-art commercial first person shooter games, computer
controlled bots, also known as non player characters, can often be easily
distinguishable from those controlled by humans. Tell-tale signs such as failed
navigation, ""sixth sense"" knowledge of human players' whereabouts and
deterministic, scripted behaviors are some of the causes of this. We propose,
however, that one of the biggest indicators of non humanlike behavior in these
games can be found in the weapon shooting capability of the bot. Consistently
perfect accuracy and ""locking on"" to opponents in their visual field from any
distance are indicative capabilities of bots that are not found in human
players. Traditionally, the bot is handicapped in some way with either a timed
reaction delay or a random perturbation to its aim, which doesn't adapt or
improve its technique over time. We hypothesize that enabling the bot to learn
the skill of shooting through trial and error, in the same way a human player
learns, will lead to greater variation in game-play and produce less
predictable non player characters. This paper describes a reinforcement
learning shooting mechanism for adapting shooting over time based on a dynamic
reward signal from the amount of damage caused to opponents.
","Adaptive Shooting for Bots in First Person Shooter Games Using
  Reinforcement Learning"
"  Optimal action selection in decision problems characterized by sparse,
delayed rewards is still an open challenge. For these problems, current deep
reinforcement learning methods require enormous amounts of data to learn
controllers that reach human-level performance. In this work, we propose a
method that interleaves planning and learning to address this issue. The
planning step hinges on the Iterated-Width (IW) planner, a state of the art
planner that makes explicit use of the state representation to perform
structured exploration. IW is able to scale up to problems independently of the
size of the state space. From the state-actions visited by IW, the learning
step estimates a compact policy, which in turn is used to guide the planning
step. The type of exploration used by our method is radically different than
the standard random exploration used in RL. We evaluate our method in simple
problems where we show it to have superior performance than the
state-of-the-art reinforcement learning algorithms A2C and Alpha Zero. Finally,
we present preliminary results in a subset of the Atari games suite.
",Improving width-based planning with compact policies
"  Although there are many approaches to implement intrinsically motivated
artificial agents, the combined usage of multiple intrinsic drives remains
still a relatively unexplored research area. Specifically, we hypothesize that
a mechanism capable of quantifying and controlling the evolution of the
information flow between the agent and the environment could be the fundamental
component for implementing a higher degree of autonomy into artificial
intelligent agents. This paper propose a unified strategy for implementing two
semantically orthogonal intrinsic motivations: curiosity and empowerment.
Curiosity reward informs the agent about the relevance of a recent agent
action, whereas empowerment is implemented as the opposite information flow
from the agent to the environment that quantifies the agent's potential of
controlling its own future. We show that an additional homeostatic drive is
derived from the curiosity reward, which generalizes and enhances the
information gain of a classical curious/heterostatic reinforcement learning
agent. We show how a shared internal model by curiosity and empowerment
facilitates a more efficient training of the empowerment function. Finally, we
discuss future directions for further leveraging the interplay between these
two intrinsic rewards.
","A unified strategy for implementing curiosity and empowerment driven
  reinforcement learning"
"  The Steiner Tree Problem (STP) in graphs is an important problem with various
applications in many areas such as design of integrated circuits, evolution
theory, networking, etc. In this paper, we propose an algorithm to solve the
STP. The algorithm includes a reducer and a solver using Variable Neighborhood
Descent (VND), interacting with each other during the search. New constructive
heuristics and a vertex score system for intensification purpose are proposed.
The algorithm is tested on a set of benchmarks which shows encouraging results.
","Solving the Steiner Tree Problem in graphs with Variable Neighborhood
  Descent"
"  This paper proposes a method to translate multilevel flow modeling (MFM) into
a first-order language (FOL), which enables the utilisation of logical
techniques, such as inference engines and abductive reasoners. An example of
this is a planning task for a toy plant that can be solved in FOL using
abduction. In addition, owing to the expressivity of FOL, the language is
capable of describing actions and their preconditions. This allows the
derivation of procedures consisting of multiple actions.
",Translating MFM into FOL: towards plant operation planning
"  Smart factories are on the verge of becoming the new industrial paradigm,
wherein optimization permeates all aspects of production, from concept
generation to sales. To fully pursue this paradigm, flexibility in the
production means as well as in their timely organization is of paramount
importance. AI is planning a major role in this transition, but the scenarios
encountered in practice might be challenging for current tools. Task planning
is one example where AI enables more efficient and flexible operation through
an online automated adaptation and rescheduling of the activities to cope with
new operational constraints and demands.
  In this paper we present SMarTplan, a task planner specifically conceived to
deal with real-world scenarios in the emerging smart factory paradigm.
Including both special-purpose and general-purpose algorithms, SMarTplan is
based on current automated reasoning technology and it is designed to tackle
complex application domains. In particular, we show its effectiveness on a
logistic scenario, by comparing its specialized version with the general
purpose one, and extending the comparison to other state-of-the-art task
planners.
",SMarTplan: a Task Planner for Smart Factories
"  The robotic systems continuously interact with complex dynamical systems in
the physical world. Reliable predictions of spatiotemporal evolution of these
dynamical systems, with limited knowledge of system dynamics, are crucial for
autonomous operation. In this paper, we present HybridNet, a framework that
integrates data-driven deep learning and model-driven computation to reliably
predict spatiotemporal evolution of a dynamical systems even with in-exact
knowledge of their parameters. A data-driven deep neural network (DNN) with
Convolutional LSTM (ConvLSTM) as the backbone is employed to predict the
time-varying evolution of the external forces/perturbations. On the other hand,
the model-driven computation is performed using Cellular Neural Network (CeNN),
a neuro-inspired algorithm to model dynamical systems defined by coupled
partial differential equations (PDEs). CeNN converts the intricate numerical
computation into a series of convolution operations, enabling a trainable PDE
solver. With a feedback control loop, HybridNet can learn the physical
parameters governing the system's dynamics in real-time, and accordingly adapt
the computation models to enhance prediction accuracy for time-evolving
dynamical systems. The experimental results on two dynamical systems, namely,
heat convection-diffusion system, and fluid dynamical system, demonstrate that
the HybridNet produces higher accuracy than the state-of-the-art deep learning
based approach.
","HybridNet: Integrating Model-based and Data-driven Learning to Predict
  Evolution of Dynamical Systems"
"  Several researchers have argued that a machine learning system's
interpretability should be defined in relation to a specific agent or task: we
should not ask if the system is interpretable, but to whom is it interpretable.
We describe a model intended to help answer this question, by identifying
different roles that agents can fulfill in relation to the machine learning
system. We illustrate the use of our model in a variety of scenarios, exploring
how an agent's role influences its goals, and the implications for defining
interpretability. Finally, we make suggestions for how our model could be
useful to interpretability researchers, system developers, and regulatory
bodies auditing machine learning systems.
","Interpretable to Whom? A Role-based Model for Analyzing Interpretable
  Machine Learning Systems"
"  In this paper, we introduce a skill-balancing mechanism for adversarial
non-player characters (NPCs), called Skilled Experience Catalogue (SEC). The
objective of this mechanism is to approximately match the skill level of an NPC
to an opponent in real-time. We test the technique in the context of a
First-Person Shooter (FPS) game. Specifically, the technique adjusts a
reinforcement learning NPC's proficiency with a weapon based on its current
performance against an opponent. Firstly, a catalogue of experience, in the
form of stored learning policies, is built up by playing a series of training
games. Once the NPC has been sufficiently trained, the catalogue acts as a
timeline of experience with incremental knowledge milestones in the form of
stored learning policies. If the NPC is performing poorly, it can jump to a
later stage in the learning timeline to be equipped with more informed
decision-making. Likewise, if it is performing significantly better than the
opponent, it will jump to an earlier stage. The NPC continues to learn in
real-time using reinforcement learning but its policy is adjusted, as required,
by loading the most suitable milestones for the current circumstances.
","Skilled Experience Catalogue: A Skill-Balancing Mechanism for Non-Player
  Characters using Reinforcement Learning"
"  In this exploratory article, we draw attention to the common formal ground
among various estimators such as the belief functions of evidence theory and
their relatives, approximation quality of rough set theory, and contextual
probability. The unifying concept will be a general filter function composed of
a basic probability and a weighting which varies according to the problem at
hand. To compare the various filter functions we conclude with a simulation
study with an example from the area of item response theory.
",Approximation by filter functions
"  This note reviews Section 2 of Dung's seminal 1995 paper on abstract
argumentation theory. In particular, we clarify and make explicit all of the
proofs mentioned therein, and provide more examples to illustrate the
definitions, with the aim to help readers approaching abstract argumentation
theory for the first time. However, we provide minimal commentary and will
refer the reader to Dung's paper for the intuitions behind various concepts.
The appropriate mathematical prerequisites are provided in the appendices.
",Notes on Abstract Argumentation Theory
"  Abstract Dialectical Frameworks (ADFs) generalize Dung's argumentation
frameworks allowing various relationships among arguments to be expressed in a
systematic way. We further generalize ADFs so as to accommodate arbitrary
acceptance degrees for the arguments. This makes ADFs applicable in domains
where both the initial status of arguments and their relationship are only
insufficiently specified by Boolean functions. We define all standard ADF
semantics for the weighted case, including grounded, preferred and stable
semantics. We illustrate our approach using acceptance degrees from the unit
interval and show how other valuation structures can be integrated. In each
case it is sufficient to specify how the generalized acceptance conditions are
represented by formulas, and to specify the information ordering underlying the
characteristic ADF operator. We also present complexity results for problems
related to weighted ADFs.
",Weighted Abstract Dialectical Frameworks: Extended and Revised Report
"  To generate trust with their users, Explainable Artificial Intelligence (XAI)
systems need to include an explanation model that can communicate the internal
decisions, behaviours and actions to the interacting humans. Successful
explanation involves both cognitive and social processes. In this paper we
focus on the challenge of meaningful interaction between an explainer and an
explainee and investigate the structural aspects of an explanation in order to
propose a human explanation dialog model. We follow a bottom-up approach to
derive the model by analysing transcripts of 398 different explanation dialog
types. We use grounded theory to code and identify key components of which an
explanation dialog consists. We carry out further analysis to identify the
relationships between components and sequences and cycles that occur in a
dialog. We present a generalized state model obtained by the analysis and
compare it with an existing conceptual dialog model of explanation.
",Towards a Grounded Dialog Model for Explainable Artificial Intelligence
"  To test the effectiveness of process discovery algorithms, a Process
Discovery Contest (PDC) has been set up. This PDC uses a classification
approach to measure this effectiveness: The better the discovered model can
classify whether or not a new trace conforms to the event log, the better the
discovery algorithm is supposed to be. Unfortunately, even the state-of-the-art
fully-automated discovery algorithms score poorly on this classification. Even
the best of these algorithms, the Inductive Miner, scored only 147 correct
classified traces out of 200 traces on the PDC of 2017. This paper introduces
the rule-based log skeleton model, which is closely related to the Declare
constraint model, together with a way to classify traces using this model. This
classification using log skeletons is shown to score better on the PDC of 2017
than state-of-the-art discovery algorithms: 194 out of 200. As a result, one
can argue that the fully-automated algorithm to construct (or: discover) a log
skeleton from an event log outperforms existing state-of-the-art
fully-automated discovery algorithms.
",Log Skeletons: A Classification Approach to Process Discovery
"  This paper describes a new implementation of Planet Wars, designed from the
outset for Game AI research. The skill-depth of the game makes it a challenge
for game-playing agents, and the speed of more than 1 million game ticks per
second enables rapid experimentation and prototyping. The parameterised nature
of the game together with an interchangeable actuator model make it well suited
to automated game tuning. The game is designed to be fun to play for humans,
and is directly playable by General Video Game AI agents.
",Game AI Research with Fast Planet Wars Variants
"  Almost all the knowledge empowered applications rely upon accurate knowledge,
which has to be either collected manually with high cost, or extracted
automatically with unignorable errors. In this paper, we study 20 Questions, an
online interactive game where each question-response pair corresponds to a fact
of the target entity, to acquire highly accurate knowledge effectively with
nearly zero labor cost. Knowledge acquisition via 20 Questions predominantly
presents two challenges to the intelligent agent playing games with human
players. The first one is to seek enough information and identify the target
entity with as few questions as possible, while the second one is to leverage
the remaining questioning opportunities to acquire valuable knowledge
effectively, both of which count on good questioning strategies. To address
these challenges, we propose the Learning-to-Ask (LA) framework, within which
the agent learns smart questioning strategies for information seeking and
knowledge acquisition by means of deep reinforcement learning and generalized
matrix factorization respectively. In addition, a Bayesian approach to
represent knowledge is adopted to ensure robustness to noisy user responses.
Simulating experiments on real data show that LA is able to equip the agent
with effective questioning strategies, which result in high winning rates and
rapid knowledge acquisition. Moreover, the questioning strategies for
information seeking and knowledge acquisition boost the performance of each
other, allowing the agent to start with a relatively small knowledge set and
quickly improve its knowledge base in the absence of constant human
supervision.
",Learning-to-Ask: Knowledge Acquisition via 20 Questions
"  Like any field of empirical science, AI may be approached axiomatically. We
formulate requirements for a general-purpose, human-level AI system in terms of
postulates. We review the methodology of deep learning, examining the explicit
and tacit assumptions in deep learning research. Deep Learning methodology
seeks to overcome limitations in traditional machine learning research as it
combines facets of model richness, generality, and practical applicability. The
methodology so far has produced outstanding results due to a productive synergy
of function approximation, under plausible assumptions of irreducibility and
the efficiency of back-propagation family of algorithms. We examine these
winning traits of deep learning, and also observe the various known failure
modes of deep learning. We conclude by giving recommendations on how to extend
deep learning methodology to cover the postulates of general-purpose AI
including modularity, and cognitive architecture. We also relate deep learning
to advances in theoretical neuroscience research.
","The Foundations of Deep Learning with a Path Towards General
  Intelligence"
"  We explore the relations between the zeta distribution and algorithmic
information theory via a new model of the transfer learning problem. The
program distribution is approximated by a zeta distribution with parameter near
$1$. We model the training sequence as a stochastic process. We analyze the
upper temporal bound for learning a training sequence and its entropy rates,
assuming an oracle for the transfer learning problem. We argue from empirical
evidence that power-law models are suitable for natural processes. Four
sequence models are proposed. Random typing model is like no-free lunch where
transfer learning does not work. Zeta process independently samples programs
from the zeta distribution. A model of common sub-programs inspired by genetics
uses a database of sub-programs. An evolutionary zeta process samples mutations
from Zeta distribution. The analysis of stochastic processes inspired by
evolution suggest that AI may be feasible in nature, countering no-free lunch
sort of arguments.
",Zeta Distribution and Transfer Learning Problem
"  Formalizing self reproduction in dynamical hierarchies is one of the
important problems in Artificial Life (AL) studies. We study, in this paper, an
inductively defined algebraic framework for self reproduction on macroscopic
organizational levels under dynamical system setting for simulated AL models
and explore some existential results. Starting with defining self reproduction
for atomic entities we define self reproduction with possible mutations on
higher organizational levels in terms of hierarchical sets and the
corresponding inductively defined `meta' - reactions. We introduce constraints
to distinguish a collection of entities from genuine cases of emergent
organizational structures.
",An Inductive Formalization of Self Reproduction in Dynamical Hierarchies
"  The well-known Late Acceptance Hill Climbing (LAHC) search aims to overcome
the main downside of traditional Hill Climbing (HC) search, which is often
quickly trapped in a local optimum due to strictly accepting only non-worsening
moves within each iteration. In contrast, LAHC also accepts worsening moves, by
keeping a circular array of fitness values of previously visited solutions and
comparing the fitness values of candidate solutions against the least recent
element in the array. While this straightforward strategy has proven effective,
there are nevertheless situations where LAHC can unfortunately behave in a
similar manner to HC. For example, when a new local optimum is found, often the
same fitness value is stored many times in the array. To address this
shortcoming, we propose new acceptance and replacement strategies to take into
account worsening, improving, and sideways movement scenarios with the aim to
improve the diversity of values in the array. Compared to LAHC, the proposed
Diversified Late Acceptance Search approach is shown to lead to better quality
solutions that are obtained with a lower number of iterations on benchmark
Travelling Salesman Problems and Quadratic Assignment Problems.
",Diversified Late Acceptance Search
"  Fully observable non-deterministic (FOND) planning is becoming increasingly
important as an approach for computing proper policies in probabilistic
planning, extended temporal plans in LTL planning, and general plans in
generalized planning. In this work, we introduce a SAT encoding for FOND
planning that is compact and can produce compact strong cyclic policies. Simple
variations of the encodings are also introduced for strong planning and for
what we call, dual FOND planning, where some non-deterministic actions are
assumed to be fair (e.g., probabilistic) and others unfair (e.g., adversarial).
The resulting FOND planners are compared empirically with existing planners
over existing and new benchmarks. The notion of ""probabilistic interesting
problems"" is also revisited to yield a more comprehensive picture of the
strengths and limitations of current FOND planners and the proposed SAT
approach.
",Compact Policies for Fully-Observable Non-Deterministic Planning as SAT
"  We study practical approaches to solving the token swapping (TSWAP) problem
optimally in this short paper. In TSWAP, we are given an undirected graph with
colored vertices. A colored token is placed in each vertex. A pair of tokens
can be swapped between adjacent vertices. The goal is to perform a sequence of
swaps so that token and vertex colors agree across the graph. The minimum
number of swaps is required in the optimization variant of the problem. We
observed similarities between the TSWAP problem and multi-agent path finding
(MAPF) where instead of tokens we have multiple agents that need to be moved
from their current vertices to given unique target vertices. The difference
between both problems consists in local conditions that state transitions
(swaps/moves) must satisfy. We developed two algorithms for solving TSWAP
optimally by adapting two different approaches to MAPF - CBS and MDD- SAT. This
constitutes the first attempt to design optimal solving algorithms for TSWAP.
Experimental evaluation on various types of graphs shows that the reduction to
SAT scales better than CBS in optimal TSWAP solving.
","Finding Optimal Solutions to Token Swapping by Conflict-based Search and
  Reduction to SAT"
"  P. Kabamba developed generation theory as a tool for studying
self-reproducing systems. We provide an alternative definition of a generation
system and give a complete solution to the problem of finding optimal seeds for
a finite self-replicating system. We also exhibit examples illustrating a
connection between self-replication and fixed-point theory.
",Optimal Seeding and Self-Reproduction from a Mathematical Point of View
"  Connected vehicle fleets are deployed worldwide in several industrial IoT
scenarios. With the gradual increase of machines being controlled and managed
through networked smart devices, the predictive maintenance potential grows
rapidly. Predictive maintenance has the potential of optimizing uptime as well
as performance such that time and labor associated with inspections and
preventive maintenance are reduced. In order to understand the trends of
vehicle faults with respect to important vehicle attributes viz mileage, age,
vehicle type etc this problem is addressed through hierarchical modified fuzzy
support vector machine (HMFSVM). The proposed method is compared with other
commonly used approaches like logistic regression, random forests and support
vector machines. This helps better implementation of telematics data to ensure
preventative management as part of the desired solution. The superiority of the
proposed method is highlighted through several experimental results.
","Predictive Maintenance for Industrial IoT of Vehicle Fleets using
  Hierarchical Modified Fuzzy Support Vector Machine"
"  Deck building is a crucial component in playing Collectible Card Games
(CCGs). The goal of deck building is to choose a fixed-sized subset of cards
from a large card pool, so that they work well together in-game against
specific opponents. Existing methods either lack flexibility to adapt to
different opponents or require large computational resources, still making them
unsuitable for any real-time or large-scale application. We propose a new deck
recommendation system, named Q-DeckRec, which learns a deck search policy
during a training phase and uses it to solve deck building problem instances.
Our experimental results demonstrate Q-DeckRec requires less computational
resources to build winning-effective decks after a training phase compared to
several baseline methods.
",Q-DeckRec: A Fast Deck Recommendation System for Collectible Card Games
"  We propose a simplification of the Theory-of-Mind Network architecture, which
focuses on modeling complex, deterministic machines as a proxy for modeling
nondeterministic, conscious entities. We then validate this architecture in the
context of understanding engines, which, we argue, meet the required internal
and external complexity to yield meaningful abstractions.
",Theory of Machine Networks: A Case Study
"  We present a general constraint-based encoding for domain-independent task
planning. Task planning is characterized by causal relationships expressed as
conditions and effects of optional actions. Possible actions are typically
represented by templates, where each template can be instantiated into a number
of primitive actions. While most previous work for domain-independent task
planning has focused on primitive actions in a state-oriented view, our
encoding uses a fully lifted representation at the level of action templates.
It follows a time-oriented view in the spirit of previous work in
constraint-based scheduling. As a result, the proposed encoding is simple and
compact as it grows with the number of actions in a solution plan rather than
the number of possible primitive actions. When solved with an SMT solver, we
show that the proposed encoding is slightly more efficient than
state-of-the-art methods on temporally constrained planning benchmarks while
clearly outperforming other fully constraint-based approaches.
",A Constraint-based Encoding for Domain-Independent Temporal Planning
"  Modern AI and robotic systems are characterized by a high and ever-increasing
level of autonomy. At the same time, their applications in fields such as
autonomous driving, service robotics and digital personal assistants move
closer to humans. From the combination of both developments emerges the field
of AI ethics which recognizes that the actions of autonomous machines entail
moral dimensions and tries to answer the question of how we can build moral
machines. In this paper we argue for taking inspiration from Aristotelian
virtue ethics by showing that it forms a suitable combination with modern AI
due to its focus on learning from experience. We furthermore propose that
imitation learning from moral exemplars, a central concept in virtue ethics,
can solve the value alignment problem. Finally, we show that an intelligent
system endowed with the virtues of temperance and friendship to humans would
not pose a control problem as it would not have the desire for limitless
self-improvement.
",The Virtuous Machine - Old Ethics for New Technology?
"  We provide a proof of the the Front-Door adjustment formula using the
do-calculus.
",A Proof of the Front-Door Adjustment Formula
"  Epistemic logics are a primary formalism for multi-agent systems but major
reasoning tasks in such epistemic logics are intractable, which impedes
applications of multi-agent epistemic logics in automatic planning. Knowledge
compilation provides a promising way of resolving the intractability by
identifying expressive fragments of epistemic logics that are tractable for
important reasoning tasks such as satisfiability and forgetting. The property
of logical separability allows to decompose a formula into some of its
subformulas and thus modular algorithms for various reasoning tasks can be
developed. In this paper, by employing logical separability, we propose an
approach to knowledge compilation for the logic Kn by defining a normal form
SDNF. Among several novel results, we show that every epistemic formula can be
equivalently compiled into a formula in SDNF, major reasoning tasks in SDNF are
tractable, and formulas in SDNF enjoy the logical separability. Our results
shed some lights on modular approaches to knowledge compilation. Furthermore,
we apply our results in the multi-agent epistemic planning. Finally, we extend
the above result to the logic K45n that is Kn extended by introspection axioms
4 and 5.
",Knowledge Compilation in Multi-Agent Epistemic Logics
"  Semantic vectors are learned from data to express semantic relationships
between elements of information, for the purpose of solving and informing
downstream tasks. Other models exist that learn to map and classify supervised
data. However, the two worlds of learning rarely interact to inform one another
dynamically, whether across types of data or levels of semantics, in order to
form a unified model. We explore the research problem of learning these vectors
and propose a framework for learning the semantics of knowledge incrementally
and online, across multiple mediums of data, via binary vectors. We discuss the
aspects of this framework to spur future research on this approach and problem.
",A Computational Theory for Life-Long Learning of Semantics
"  We propose an alternative proof concerning necessary and sufficient
conditions to split the problem of searching for d-separators and building the
skeleton of a DAG into small problems for every node of a separation tree T.
The proof is simpler than the original [1]. The same proof structure has been
used in [2] for learning the structure of multivariate regression chain graphs
(MVR CGs).
","Comment on: Decomposition of structural learning about directed acyclic
  graphs [1]"
"  In recent years, multi-agent epistemic planning has received attention from
both dynamic logic and planning communities. Existing implementations of
multi-agent epistemic planning are based on compilation into classical planning
and suffer from various limitations, such as generating only linear plans,
restriction to public actions, and incapability to handle disjunctive beliefs.
In this paper, we propose a general representation language for multi-agent
epistemic planning where the initial KB and the goal, the preconditions and
effects of actions can be arbitrary multi-agent epistemic formulas, and the
solution is an action tree branching on sensing results. To support efficient
reasoning in the multi-agent KD45 logic, we make use of a normal form called
alternating cover disjunctive formulas (ACDFs). We propose basic revision and
update algorithms for ACDFs. We also handle static propositional common
knowledge, which we call constraints. Based on our reasoning, revision and
update algorithms, adapting the PrAO algorithm for contingent planning from the
literature, we implemented a multi-agent epistemic planner called MEPK. Our
experimental results show the viability of our approach.
","A General Multi-agent Epistemic Planner Based on Higher-order Belief
  Change"
"  Dependence is an important concept for many tasks in artificial intelligence.
A task can be executed more efficiently by discarding something independent
from the task. In this paper, we propose two novel notions of dependence in
propositional logic: formula-formula dependence and formula forgetting. The
first is a relation between formulas capturing whether a formula depends on
another one, while the second is an operation that returns the strongest
consequence independent of a formula. We also apply these two notions in two
well-known issues: belief update and conservative extension. Firstly, we define
a new update operator based on formula-formula dependence. Furthermore, we
reduce conservative extension to formula forgetting.
","Dependence in Propositional Logic: Formula-Formula Dependence and
  Formula Forgetting -- Application to Belief Update and Conservative Extension"
"  Human cognition is a complex process facilitated by the intricate
architecture of human brain. However, human cognition is often reduced to
quantum theory based events in principle because of their correlative
conjectures for the purpose of analysis for reciprocal understanding. In this
paper, we begin our analysis of human cognition via formal methods and proceed
towards quantum theories. Human cognition often violate classic probabilities
on which formal representation of conceptual spaces are built. Further,
geometric representation of conceptual spaces proposed by Gardenfors discusses
the underlying content but lacks a systematic approach (Gardenfors, 2000; Kitto
et. al, 2012). However, the aforementioned views are not contradictory but
different perspective with a gap towards sufficient understanding of human
cognitive process. A comprehensive and systematic approach to model a
relatively complex scenario can be addressed by vector space approach of
conceptual spaces as discussed in literature. In this research, we have
proposed an approach that uses both formal representation and Gardenfors
geometric approach. The proposed model of high dimensional formal
representation of conceptual space is mathematically analysed and inferred to
exhibit quantum aspects. Also, the proposed model achieves cognition, in
particular, consciousness. We have demonstrated this process of achieving
consciousness with a constructive learning scenario. We have also proposed an
algorithm for conceptual scaling of a real world scenario under different
quality dimensions to obtain a conceptual scale.
","Quantum aspects of high dimensional formal representation of conceptual
  spaces"
"  Neuroethology has been an active field of study for more than a century now.
Out of some of the most interesting species that has been studied so far,
weakly electric fish is a fascinating one. It performs communication,
echo-location and inter-species detection efficiently with an interesting
configuration of sensors, neu-rons and a simple brain. In this paper we propose
a cognitive architecture inspired by the way these fishes handle and process
information. We believe that it is eas-ier to understand and mimic the neural
architectures of a simpler species than that of human. Hence, the proposed
architecture is expected to both help research in cognitive robotics and also
help understand more complicated brains like that of human beings.
",WEBCA: Weakly-Electric-Fish Bioinspired Cognitive Architecture
"  Artificial Intelligence is becoming instrumental in a variety of
applications. Games serve as a good breeding ground for trying and testing
these algorithms in a sandbox with simpler constraints in comparison to real
life. In this project, we aim to develop an AI agent that can solve the
classical Japanese game of Sokoban using various algorithms and heuristics and
compare their performances through standard metrics.
",AI in Game Playing: Sokoban Solver
"  How can one detect friendly and adversarial behavior from raw data? Detecting
whether an environment is a friend, a foe, or anything in between, remains a
poorly understood yet desirable ability for safe and robust agents. This paper
proposes a definition of these environmental ""attitudes"" based on an
characterization of the environment's ability to react to the agent's private
strategy. We define an objective function for a one-shot game that allows
deriving the environment's probability distribution under friendly and
adversarial assumptions alongside the agent's optimal strategy. Furthermore, we
present an algorithm to compute these equilibrium strategies, and show
experimentally that both friendly and adversarial environments possess
non-trivial optimal strategies.
",Modeling Friends and Foes
"  ML 2.0: In this paper, we propose a paradigm shift from the current practice
of creating machine learning models - which requires months-long discovery,
exploration and ""feasibility report"" generation, followed by re-engineering for
deployment - in favor of a rapid, 8-week process of development, understanding,
validation and deployment that can executed by developers or subject matter
experts (non-ML experts) using reusable APIs. This accomplishes what we call a
""minimum viable data-driven model,"" delivering a ready-to-use machine learning
model for problems that haven't been solved before using machine learning. We
provide provisions for the refinement and adaptation of the ""model,"" with
strict enforcement and adherence to both the scaffolding/abstractions and the
process. We imagine that this will bring forth the second phase in machine
learning, in which discovery is subsumed by more targeted goals of delivery and
impact.
",Machine learning 2.0 : Engineering Data Driven AI Products
"  A subtle difference between propositional and relational data is that in many
relational models, marginal probabilities depend on the population or domain
size. This paper connects the dependence on population size to the classic
notion of projectivity from statistical theory: Projectivity implies that
relational predictions are robust with respect to changes in domain size. We
discuss projectivity for a number of common SRL systems, and identify syntactic
fragments that are guaranteed to yield projective models. The syntactic
conditions are restrictive, which suggests that projectivity is difficult to
achieve in SRL, and care must be taken when working with different domain
sizes.
","Inference, Learning, and Population Size: Projectivity for SRL Models"
"  Lifted inference reduces the complexity of inference in relational
probabilistic models by identifying groups of constants (or atoms) which behave
symmetric to each other. A number of techniques have been proposed in the
literature for lifting marginal as well MAP inference. We present the first
application of lifting rules for marginal-MAP (MMAP), an important inference
problem in models having latent (random) variables. Our main contribution is
two fold: (1) we define a new equivalence class of (logical) variables, called
Single Occurrence for MAX (SOM), and show that solution lies at extreme with
respect to the SOM variables, i.e., predicate groundings differing only in the
instantiation of the SOM variables take the same truth value (2) we define a
sub-class {\em SOM-R} (SOM Reduce) and exploit properties of extreme
assignments to show that MMAP inference can be performed by reducing the domain
of SOM-R variables to a single constant.We refer to our lifting technique as
the {\em SOM-R} rule for lifted MMAP. Combined with existing rules such as
decomposer and binomial, this results in a powerful framework for lifted MMAP.
Experiments on three benchmark domains show significant gains in both time and
memory compared to ground inference as well as lifted approaches not using
SOM-R.
",Lifted Marginal MAP Inference
"  One popular way for lifted inference in probabilistic graphical models is to
first merge symmetric states into a single cluster (orbit) and then use these
for downstream inference, via variations of orbital MCMC [Niepert, 2012]. These
orbits are represented compactly using permutations over variables, and
variable-value (VV) pairs, but they can miss several state symmetries in a
domain.
  We define the notion of permutations over block-value (BV) pairs, where a
block is a set of variables. BV strictly generalizes VV symmetries, and can
compute many more symmetries for increasing block sizes. To operationalize use
of BV permutations in lifted inference, we describe 1) an algorithm to compute
BV permutations given a block partition of the variables, 2) BV-MCMC, an
extension of orbital MCMC that can sample from BV orbits, and 3) a heuristic to
suggest good block partitions. Our experiments show that BV-MCMC can mix much
faster compared to vanilla MCMC and orbital MCMC.
",Block-Value Symmetries in Probabilistic Graphical Models
"  Standard approaches for inference in probabilistic formalisms with
first-order constructs include lifted variable elimination (LVE) for single
queries as well as first-order knowledge compilation (FOKC) based on weighted
model counting. To handle multiple queries efficiently, the lifted junction
tree algorithm (LJT) uses a first-order cluster representation of a model and
LVE as a subroutine in its computations. For certain inputs, the
implementations of LVE and, as a result, LJT ground parts of a model where FOKC
has a lifted run. The purpose of this paper is to prepare LJT as a backbone for
lifted inference and to use any exact inference algorithm as subroutine. Using
FOKC in LJT allows us to compute answers faster than LJT, LVE, and FOKC for
certain inputs.
","Fusing First-order Knowledge Compilation and the Lifted Junction Tree
  Algorithm"
"  The lifted dynamic junction tree algorithm (LDJT) efficiently answers
filtering and prediction queries for probabilistic relational temporal models
by building and then reusing a first-order cluster representation of a
knowledge base for multiple queries and time steps. Unfortunately, a non-ideal
elimination order can lead to groundings even though a lifted run is possible
for a model. We extend LDJT (i) to identify unnecessary groundings while
proceeding in time and (ii) to prevent groundings by delaying eliminations
through changes in a temporal first-order cluster representation. The extended
version of LDJT answers multiple temporal queries orders of magnitude faster
than the original version.
","Preventing Unnecessary Groundings in the Lifted Dynamic Junction Tree
  Algorithm"
"  We revisit the classical problem of exact inference on probabilistic
graphical models (PGMs). Our algorithm is based on recent \emph{worst-case
optimal database join} algorithms, which can be asymptotically faster than
traditional data processing methods. We present the first empirical evaluation
of these algorithms via JoinInfer -- a new exact inference engine. We
empirically explore the properties of the data for which our engine can be
expected to outperform traditional inference engines, refining current
theoretical notions. Further, JoinInfer outperforms existing state-of-the-art
inference engines (ACE, IJGP and libDAI) on some standard benchmark datasets by
up to a factor of 630x. Finally, we propose a promising data-driven heuristic
that extends JoinInfer to automatically tailor its parameters and/or switch to
the traditional inference algorithms.
",Hypertree Decompositions Revisited for PGMs
"  Recently a strong poker-playing algorithm called DeepStack was published,
which is able to find an approximate Nash equilibrium during gameplay by using
heuristic values of future states predicted by deep neural networks. This paper
analyzes new ways of encoding the inputs and outputs of DeepStack's deep
counterfactual value networks based on traditional abstraction techniques, as
well as an unabstracted encoding, which was able to increase the network's
accuracy.
",Analysis and Optimization of Deep Counterfactual Value Networks
"  A number of problems in relational Artificial Intelligence can be viewed as
Stochastic Constraint Optimization Problems (SCOPs). These are constraint
optimization problems that involve objectives or constraints with a stochastic
component. Building on the recently proposed language SC-ProbLog for modeling
SCOPs, we propose a new method for solving these problems. Earlier methods used
Probabilistic Logic Programming (PLP) techniques to create Ordered Binary
Decision Diagrams (OBDDs), which were decomposed into smaller constraints in
order to exploit existing constraint programming (CP) solvers. We argue that
this approach has as drawback that a decomposed representation of an OBDD does
not guarantee domain consistency during search, and hence limits the efficiency
of the solver. For the specific case of monotonic distributions, we suggest an
alternative method for using CP in SCOP, based on the development of a new
propagator; we show that this propagator is linear in the size of the OBDD, and
has the potential to be more efficient than the decomposition method, as it
maintains domain consistency.
","Stochastic Constraint Optimization using Propagation on Ordered Binary
  Decision Diagrams"
"  In this paper, we introduce a novel MCTS based approach that is derived from
the laws of the thermodynamics. The algorithm coined Fractal Monte Carlo (FMC),
allows us to create an agent that takes intelligent actions in both continuous
and discrete environments while providing control over every aspect of the
agent behavior. Results show that FMC is several orders of magnitude more
efficient than similar techniques, such as MCTS, in the Atari games tested.
",Solving Atari Games Using Fractals And Entropy
"  We consider decision problems under uncertainty where the options available
to a decision maker and the resulting outcome are related through a causal
mechanism which is unknown to the decision maker. We ask how a decision maker
can learn about this causal mechanism through sequential decision making as
well as using current causal knowledge inside each round in order to make
better choices had she not considered causal knowledge and propose a decision
making procedure in which an agent holds \textit{beliefs} about her environment
which are used to make a choice and are updated using the observed outcome. As
proof of concept, we present an implementation of this causal decision making
model and apply it in a simple scenario. We show that the model achieves a
performance similar to the classic Q-learning while it also acquires a causal
model of the environment.
","Playing against Nature: causal discovery for decision making under
  uncertainty"
"  Learning a policy capable of moving an agent between any two states in the
environment is important for many robotics problems involving navigation and
manipulation. Due to the sparsity of rewards in such tasks, applying
reinforcement learning in these scenarios can be challenging. Common approaches
for tackling this problem include reward engineering with auxiliary rewards,
requiring domain-specific knowledge or changing the objective.
  In this work, we introduce a method based on region-growing that allows
learning in an environment with any pair of initial and goal states. Our
algorithm first learns how to move between nearby states and then increases the
difficulty of the start-goal transitions as the agent's performance improves.
This approach creates an efficient curriculum for learning the objective
behavior of reaching any goal from any initial state. In addition, we describe
a method to adaptively adjust expansion of the growing region that allows
automatic adjustment of the key exploration hyperparameter to environments with
different requirements. We evaluate our approach on a set of simulated
navigation and manipulation tasks, where we demonstrate that our algorithm can
efficiently learn a policy in the presence of sparse rewards.
",Region Growing Curriculum Generation for Reinforcement Learning
"  The lifted dynamic junction tree algorithm (LDJT) efficiently answers
filtering and prediction queries for probabilistic relational temporal models
by building and then reusing a first-order cluster representation of a
knowledge base for multiple queries and time steps. We extend LDJT to (i) solve
the smoothing inference problem to answer hindsight queries by introducing an
efficient backward pass and (ii) discuss different options to instantiate a
first-order cluster representation during a backward pass. Further, our
relational forward backward algorithm makes hindsight queries to the very
beginning feasible. LDJT answers multiple temporal queries faster than the
static lifted junction tree algorithm on an unrolled model, which performs
smoothing during message passing.
",Answering Hindsight Queries with Lifted Dynamic Junction Trees
"  Knowledge graphs encode uniquely identifiable entities to other entities or
literal values by means of relationships, thus enabling semantically rich
querying over the stored data. Typically, the semantics of such queries are
often crisp thereby resulting in crisp answers. Query log statistics show that
a majority of the queries issued to knowledge graphs are often entity centric
queries. When a user needs additional answers the state-of-the-art in assisting
users is to rewrite the original query resulting in a set of approximations.
Several strategies have been proposed in past to address this. They typically
move up the taxonomy to relax a specific element to a more generic element.
Entities don't have a taxonomy and they end up being generalized. To address
this issue, in this paper, we propose an entity centric reformulation strategy
that utilizes schema information and entity features present in the graph to
suggest rewrites. Once the features are identified, the entity in concern is
reformulated as a set of features. Since entities can have a large number of
features, we introduce strategies that select the top-k most relevant and
{informative ranked features and augment them to the original query to create a
valid reformulation. We then evaluate our approach by showing that our
reformulation strategy produces results that are more informative when compared
with state-of-the-art
",Feature-based reformulation of entities in triple pattern queries
"  Upper limb Prosthetic can be viewed as an independent cognitive system in
order to develop a conceptual space. In this paper, we provide a detailed
analogical reasoning of prosthetic arm to build the conceptual spaces with the
help of the theory called geometric framework of conceptual spaces proposed by
Gardenfors. Terminologies of conceptual spaces such as concepts, similarities,
properties, quality dimensions and prototype are applied for a specific
prosthetic system and conceptual space is built for prosthetic arm. Concept
lattice traversals are used on the lattice represented conceptual spaces.
Cognitive functionalities such as generalization (Similarities) and
specialization (Differences) are achieved in the lattice represented conceptual
space. This might well prove to design intelligent prosthetics to assist
challenged humans. Geometric framework of conceptual spaces holds similar
concepts closer in geometric structures in a way similar to concept lattices.
Hence, we also propose to use concept lattice to represent concepts of
geometric framework of conceptual spaces. Also, we extend our discussion with
our insights on conceptual spaces of bidirectional hand prosthetics.
","Lattice based Conceptual Spaces to Explore Cognitive Functionalities for
  Prosthetic Arm"
"  In the following writing we discuss a conceptual framework for representing
events and scenarios from the perspective of a novel form of causal analysis.
This causal analysis is applied to the events and scenarios so as to determine
measures that could be used to manage the development of the processes that
they are a part of in real time. An overall terminological framework and
entity-relationship model are suggested along with a specification of the
functional sets involved in both reasoning and analytics. The model is
considered to be a specific case of the generic problem of finding sequential
series in disparate data. The specific inference and reasoning processes are
identified for future implementation.
",Representing scenarios for process evolution management
"  Dial-a-ride problem (DARP) deals with the transportation of users between
pickup and drop-off locations associated with specified time windows. This
paper proposes a novel algorithm called multi-atomic annealing (MATA) to solve
static dial-a-ride problem. Two new local search operators (burn and reform), a
new construction heuristic and two request sequencing mechanisms (Sorted List
and Random List) are developed. Computational experiments conducted on various
standard DARP test instances prove that MATA is an expeditious meta-heuristic
in contrast to other existing methods. In all experiments, MATA demonstrates
the capability to obtain high quality solutions, faster convergence, and
quicker attainment of a first feasible solution. It is observed that MATA
attains a first feasible solution 29.8 to 65.1% faster, and obtains a final
solution that is 3.9 to 5.2% better, when compared to other algorithms within
60 sec.
",Multi-atomic Annealing Heuristic for Static Dial-a-ride Problem
"  Today's software industry requires individuals who are proficient in as many
programming languages as possible. Structured query language (SQL), as an
adopted standard, is no exception, as it is the most widely used query language
to retrieve and manipulate data. However, the process of learning SQL turns out
to be challenging. The need for a computer-aided solution to help users learn
SQL and improve their proficiency is vital. In this study, we present a new
approach to help users conceptualize basic building blocks of the language
faster and more efficiently. The adaptive design of the proposed approach aids
users in learning SQL by supporting their own path to the solution and
employing successful previous attempts, while not enforcing the ideal solution
provided by the instructor. Furthermore, we perform an empirical evaluation
with 93 participants and demonstrate that the employment of hints is
successful, being especially beneficial for users with lower prior knowledge.
",Recommender system for learning SQL using hints
"  Reasoning about exceptions in ontologies is nowadays one of the challenges
the description logics community is facing. The paper describes a preferential
approach for dealing with exceptions in Description Logics, based on the
rational closure. The rational closure has the merit of providing a simple and
efficient approach for reasoning with exceptions, but it does not allow
independent handling of the inheritance of different defeasible properties of
concepts. In this work we outline a possible solution to this problem by
introducing a variant of the lexicographical closure, that we call skeptical
closure, which requires to construct a single base. We develop a bi-preference
semantics semantics for defining a characterization of the skeptical closure.
","Reasoning about exceptions in ontologies: from the lexicographic closure
  to the skeptical closure"
"  Given a malfunctioning system, sequential diagnosis aims at identifying the
root cause of the failure in terms of abnormally behaving system components. As
initial system observations usually do not suffice to deterministically pin
down just one explanation of the system's misbehavior, additional system
measurements can help to differentiate between possible explanations. The goal
is to restrict the space of explanations until there is only one (highly
probable) explanation left. To achieve this with a minimal-cost set of
measurements, various (active learning) heuristics for selecting the best next
measurement have been proposed.
  We report preliminary results of extensive ongoing experiments with a set of
selection heuristics on real-world diagnosis cases. In particular, we try to
answer questions such as ""Is some heuristic always superior to all others?"",
""On which factors does the (relative) performance of the particular heuristics
depend?"" or ""Under which circumstances should I use which heuristic?""
",Evaluating Active Learning Heuristics for Sequential Diagnosis
"  We propose a Multi-vAlue Rule Set (MRS) model for in-hospital predicting
patient mortality. Compared to rule sets built from single-valued rules, MRS
adopts a more generalized form of association rules that allows multiple values
in a condition. Rules of this form are more concise than classical
single-valued rules in capturing and describing patterns in data. Our
formulation also pursues a higher efficiency of feature utilization, which
reduces possible cost in data collection and storage. We propose a Bayesian
framework for formulating a MRS model and propose an efficient inference method
for learning a maximum \emph{a posteriori}, incorporating theoretically
grounded bounds to iteratively reduce the search space and improve the search
efficiency. Experiments show that our model was able to achieve better
performance than baseline method including the current system used by the
hospital.
",Interpretable Patient Mortality Prediction with Multi-value Rule Sets
"  Human moving path is an important feature in architecture design. By studying
the path, architects know where to arrange the basic elements (e.g. structures,
glasses, furniture, etc.) in the space. This paper presents SimArch, a
multi-agent system for human moving path simulation. It involves a behavior
model built by using a Markov Decision Process. The model simulates human
mental states, target range detection, and collision prediction when agents are
on the floor, in a particular small gallery, looking at an exhibit, or leaving
the floor. It also models different kinds of human characteristics by assigning
different transition probabilities. A modified weighted A* search algorithm
quickly plans the sub-optimal path of the agents. In an experiment, SimArch
takes a series of preprocessed floorplans as inputs, simulates the moving path,
and outputs a density map for evaluation. The density map provides the
prediction that how likely a person will occur in a location. A following
discussion illustrates how architects can use the density map to improve their
floorplan design.
","SimArch: A Multi-agent System For Human Path Simulation In Architecture
  Design"
"  This paper introduces a fully automatic method for generating video game
tutorials. The AtDELFI system (AuTomatically DEsigning Legible, Full
Instructions for games) was created to investigate procedural generation of
instructions that teach players how to play video games. We present a
representation of game rules and mechanics using a graph system as well as a
tutorial generation method that uses said graph representation. We demonstrate
the concept by testing it on games within the General Video Game Artificial
Intelligence (GVG-AI) framework; the paper discusses tutorials generated for
eight different games. Our findings suggest that a graph representation scheme
works well for simple arcade style games such as Space Invaders and Pacman, but
it appears that tutorials for more complex games might require higher-level
understanding of the game than just single mechanics.
","AtDelfi: Automatically Designing Legible, Full Instructions For Games"
"  Kingdomino is introduced as an interesting game for studying game playing:
the game is multiplayer (4 independent players per game); it has a limited game
depth (13 moves per player); and it has limited but not insignificant
interaction among players.
  Several strategies based on locally greedy players, Monte Carlo Evaluation
(MCE), and Monte Carlo Tree Search (MCTS) are presented with variants. We
examine a variation of UCT called progressive win bias and a playout policy
(Player-greedy) focused on selecting good moves for the player. A thorough
evaluation is done showing how the strategies perform and how to choose
parameters given specific time constraints. The evaluation shows that
surprisingly MCE is stronger than MCTS for a game like Kingdomino.
  All experiments use a cloud-native design, with a game server in a Docker
container, and agents communicating using a REST-style JSON protocol. This
enables a multi-language approach to separating the game state, the strategy
implementations, and the coordination layer.
",Monte Carlo Methods for the Game Kingdomino
"  Manufacturing is transitioning from a mass production model to a
manufacturing as a service model in which manufacturing facilities 'bid' to
produce products. To decide whether to bid for a complex, previously unseen
product, a manufacturing facility must be able to synthesize, 'on the fly', a
process plan controller that delegates abstract manufacturing tasks in the
supplied process recipe to the appropriate manufacturing resources, e.g., CNC
machines, robots etc. Previous work in applying AI behaviour composition to
synthesize process plan controllers has considered only finite state ad-hoc
representations. Here, we study the problem in the relational setting of the
Situation Calculus. By taking advantage of recent work on abstraction in the
Situation Calculus, process recipes and available resources are represented by
ConGolog programs over, respectively, an abstract and a concrete action theory.
This allows us to capture the problem in a formal, general framework, and show
decidability for the case of bounded action theories. We also provide
techniques for actually synthesizing the controller.
",Situation Calculus for Synthesis of Manufacturing Controllers
"  The ability to model continuous change in Reiter's temporal situation
calculus action theories has attracted a lot of interest. In this paper, we
propose a new development of his approach, which is directly inspired by hybrid
systems in control theory. Specifically, while keeping the foundations of
Reiter's axiomatization, we propose an elegant extension of his approach by
adding a time argument to all fluents that represent continuous change.
Thereby, we insure that change can happen not only because of actions, but also
due to the passage of time. We present a systematic methodology to derive, from
simple premises, a new group of axioms which specify how continuous fluents
change over time within a situation. We study regression for our new temporal
basic action theories and demonstrate what reasoning problems can be solved.
Finally, we formally show that our temporal basic action theories indeed
capture hybrid automata.
",Hybrid Temporal Situation Calculus
"  In the past few years, the area of Machine Learning (ML) has witnessed
tremendous advancements, becoming a pervasive technology in a wide range of
applications. One area that can significantly benefit from the use of ML is
Combinatorial Optimization. The three pillars of constraint satisfaction and
optimization problem solving, i.e., modeling, search, and optimization, can
exploit ML techniques to boost their accuracy, efficiency and effectiveness. In
this survey we focus on the modeling component, whose effectiveness is crucial
for solving the problem. The modeling activity has been traditionally shaped by
optimization and domain experts, interacting to provide realistic results.
Machine Learning techniques can tremendously ease the process, and exploit the
available data to either create models or refine expert-designed ones. In this
survey we cover approaches that have been recently proposed to enhance the
modeling process by learning either single constraints, objective functions, or
the whole model. We highlight common themes to multiple approaches and draw
connections with related fields of research.
",Boosting Combinatorial Problem Modeling with Machine Learning
"  Evidence in probabilistic reasoning may be 'hard' or 'soft', that is, it may
be of yes/no form, or it may involve a strength of belief, in the unit interval
[0, 1]. Reasoning with soft, [0, 1]-valued evidence is important in many
situations but may lead to different, confusing interpretations. This paper
intends to bring more mathematical and conceptual clarity to the field by
shifting the existing focus from specification of soft evidence to accomodation
of soft evidence. There are two main approaches, known as Jeffrey's rule and
Pearl's method; they give different outcomes on soft evidence. This paper
argues that they can be understood as correction and as improvement. It
describes these two approaches as different ways of updating with soft
evidence, highlighting their differences, similarities and applications. This
account is based on a novel channel-based approach to Bayesian probability.
Proper understanding of these two update mechanisms is highly relevant for
inference, decision tools and probabilistic programming languages.
","The Mathematics of Changing one's Mind, via Jeffrey's or via Pearl's
  update rule"
"  This paper targets the efficient construction of a safety shield for decision
making in scenarios that incorporate uncertainty. Markov decision processes
(MDPs) are prominent models to capture such planning problems. Reinforcement
learning (RL) is a machine learning technique to determine near-optimal
policies in MDPs that may be unknown prior to exploring the model. However,
during exploration, RL is prone to induce behavior that is undesirable or not
allowed in safety- or mission-critical contexts. We introduce the concept of a
probabilistic shield that enables decision-making to adhere to safety
constraints with high probability. In a separation of concerns, we employ
formal verification to efficiently compute the probabilities of critical
decisions within a safety-relevant fragment of the MDP. We use these results to
realize a shield that is applied to an RL algorithm which then optimizes the
actual performance objective. We discuss tradeoffs between sufficient progress
in exploration of the environment and ensuring safety. In our experiments, we
demonstrate on the arcade game PAC-MAN and on a case study involving service
robots that the learning efficiency increases as the learning needs orders of
magnitude fewer episodes.
",Safe Reinforcement Learning via Probabilistic Shields
"  It is the focus of this work to extend and study the previously proposed
quantum-like Bayesian networks to deal with decision-making scenarios by
incorporating the notion of maximum expected utility in influence diagrams. The
general idea is to take advantage of the quantum interference terms produced in
the quantum-like Bayesian Network to influence the probabilities used to
compute the expected utility of some action. This way, we are not proposing a
new type of expected utility hypothesis. On the contrary, we are keeping it
under its classical definition. We are only incorporating it as an extension of
a probabilistic graphical model in a compact graphical representation called an
influence diagram in which the utility function depends on the probabilistic
influences of the quantum-like Bayesian network.
  Our findings suggest that the proposed quantum-like influence digram can
indeed take advantage of the quantum interference effects of quantum-like
Bayesian Networks to maximise the utility of a cooperative behaviour in
detriment of a fully rational defect behaviour under the prisoner's dilemma
game.
","Introducing Quantum-Like Influence Diagrams for Violations of the Sure
  Thing Principle"
"  Monte Carlo tree search (MCTS) is a popular choice for solving sequential
anytime problems. However, it depends on a numeric feedback signal, which can
be difficult to define. Real-time MCTS is a variant which may only rarely
encounter states with an explicit, extrinsic reward. To deal with such cases,
the experimenter has to supply an additional numeric feedback signal in the
form of a heuristic, which intrinsically guides the agent. Recent work has
shown evidence that in different areas the underlying structure is ordinal and
not numerical. Hence erroneous and biased heuristics are inevitable, especially
in such domains. In this paper, we propose a MCTS variant which only depends on
qualitative feedback, and therefore opens up new applications for MCTS. We also
find indications that translating absolute into ordinal feedback may be
beneficial. Using a puzzle domain, we show that our preference-based MCTS
variant, wich only receives qualitative feedback, is able to reach a
performance level comparable to a regular MCTS baseline, which obtains
quantitative feedback.
",Preference-Based Monte Carlo Tree Search
"  Mathematically, ternary coding is more efficient than binary coding. It is
little used in computation because technology for binary processing is already
established and the implementation of ternary coding is more complicated, but
remains relevant in algorithms that use decision trees and in communications.
In this paper we present a new comparison of binary and ternary coding and
their relative efficiencies are computed both for number representation and
decision trees. The implications of our inability to use optimal representation
through mathematics or logic are examined. Apart from considerations of
representation efficiency, ternary coding appears preferable to binary coding
in classification of many real-world problems of artificial intelligence (AI)
and medicine. We examine the problem of identifying appropriate three classes
for domain-specific applications.
",On Ternary Coding and Three-Valued Logic
"  The automatic generation of game tutorials is a challenging AI problem. While
it is possible to generate annotations and instructions that explain to the
player how the game is played, this paper focuses on generating a gameplay
experience that introduces the player to a game mechanic. It evolves small
levels for the Mario AI Framework that can only be beaten by an agent that
knows how to perform specific actions in the game. It uses variations of a
perfect A* agent that are limited in various ways, such as not being able to
jump high or see enemies, to test how failing to do certain actions can stop
the player from beating the level.
",Generating Levels That Teach Mechanics
"  We present the design of a competitive artificial intelligence for Scopone, a
popular Italian card game. We compare rule-based players using the most
established strategies (one for beginners and two for advanced players) against
players using Monte Carlo Tree Search (MCTS) and Information Set Monte Carlo
Tree Search (ISMCTS) with different reward functions and simulation strategies.
MCTS requires complete information about the game state and thus implements a
cheating player while ISMCTS can deal with incomplete information and thus
implements a fair player. Our results show that, as expected, the cheating MCTS
outperforms all the other strategies; ISMCTS is stronger than all the
rule-based players implementing well-known and most advanced strategies and it
also turns out to be a challenging opponent for human players.
","Traditional Wisdom and Monte Carlo Tree Search Face-to-Face in the Card
  Game Scopone"
"  The importance of hierarchically structured representations for tractable
planning has long been acknowledged. However, the questions of how people
discover such abstractions and how to define a set of optimal abstractions
remain open. This problem has been explored in cognitive science in the problem
solving literature and in computer science in hierarchical reinforcement
learning. Here, we emphasize an algorithmic perspective on learning
hierarchical representations in which the objective is to efficiently encode
the structure of the problem, or, equivalently, to learn an algorithm with
minimal length. We introduce a novel problem-solving paradigm that links
problem solving and program induction under the Markov Decision Process (MDP)
framework. Using this task, we target the question of whether humans discover
hierarchical solutions by maximizing efficiency in number of actions they
generate or by minimizing the complexity of the resulting representation and
find evidence for the primacy of representational efficiency.
","Representational efficiency outweighs action efficiency in human program
  induction"
"  Fuzzy quantification is a subtopic of fuzzy logic which deals with the
modelling of the quantified expressions we can find in natural language. Fuzzy
quantifiers have been successfully applied in several fields like fuzzy,
control, fuzzy databases, information retrieval, natural language generation,
etc. Their ability to model and evaluate linguistic expressions in a
mathematical way, makes fuzzy quantifiers very powerful for data analytics and
data mining applications. In this paper we will give a general overview of the
main applications of fuzzy quantifiers in this field as well as some ideas to
use them in new application contexts.
",Fuzzy quantification for linguistic data analysis and data mining
"  With the rapid advancements in cancer research, the information that is
useful for characterizing disease, staging tumors, and creating treatment and
survivorship plans has been changing at a pace that creates challenges when
physicians try to remain current. One example involves increasing usage of
biomarkers when characterizing the pathologic prognostic stage of a breast
tumor. We present our semantic technology approach to support cancer
characterization and demonstrate it in our end-to-end prototype system that
collects the newest breast cancer staging criteria from authoritative oncology
manuals to construct an ontology for breast cancer. Using a tool we developed
that utilizes this ontology, physician-facing applications can be used to
quickly stage a new patient to support identifying risks, treatment options,
and monitoring plans based on authoritative and best practice guidelines.
Physicians can also re-stage existing patients or patient populations, allowing
them to find patients whose stage has changed in a given patient cohort. As new
guidelines emerge, using our proposed mechanism, which is grounded by semantic
technologies for ingesting new data from staging manuals, we have created an
enriched cancer staging ontology that integrates relevant data from several
sources with very little human intervention.
","Knowledge Integration for Disease Characterization: A Breast Cancer
  Example"
"  Designing hierarchical reinforcement learning algorithms that exhibit safe
behaviour is not only vital for practical applications but also, facilitates a
better understanding of an agent's decisions. We tackle this problem in the
options framework, a particular way to specify temporally abstract actions
which allow an agent to use sub-policies with start and end conditions. We
consider a behaviour as safe that avoids regions of state-space with high
uncertainty in the outcomes of actions. We propose an optimization objective
that learns safe options by encouraging the agent to visit states with higher
behavioural consistency. The proposed objective results in a trade-off between
maximizing the standard expected return and minimizing the effect of model
uncertainty in the return. We propose a policy gradient algorithm to optimize
the constrained objective function. We examine the quantitative and qualitative
behaviour of the proposed approach in a tabular grid-world, continuous-state
puddle-world, and three games from the Arcade Learning Environment: Ms.Pacman,
Amidar, and Q*Bert. Our approach achieves a reduction in the variance of
return, boosts performance in environments with intrinsic variability in the
reward structure, and compares favorably both with primitive actions as well as
with risk-neutral options.
",Safe Option-Critic: Learning Safety in the Option-Critic Architecture
"  In this paper, we study how to model taxi drivers' behaviour and geographical
information for an interesting and challenging task: the next destination
prediction in a taxi journey. Predicting the next location is a well studied
problem in human mobility, which finds several applications in real-world
scenarios, from optimizing the efficiency of electronic dispatching systems to
predicting and reducing the traffic jam. This task is normally modeled as a
multiclass classification problem, where the goal is to select, among a set of
already known locations, the next taxi destination. We present a Recurrent
Neural Network (RNN) approach that models the taxi drivers' behaviour and
encodes the semantics of visited locations by using geographical information
from Location-Based Social Networks (LBSNs). In particular, RNNs are trained to
predict the exact coordinates of the next destination, overcoming the problem
of producing, in output, a limited set of locations, seen during the training
phase. The proposed approach was tested on the ECML/PKDD Discovery Challenge
2015 dataset - based on the city of Porto -, obtaining better results with
respect to the competition winner, whilst using less information, and on
Manhattan and San Francisco datasets.
",Modeling Taxi Drivers' Behaviour for the Next Destination Prediction
"  Deep reinforcement learning, and especially the Asynchronous Advantage
Actor-Critic algorithm, has been successfully used to achieve super-human
performance in a variety of video games. Starcraft II is a new challenge for
the reinforcement learning community with the release of pysc2 learning
environment proposed by Google Deepmind and Blizzard Entertainment. Despite
being a target for several AI developers, few have achieved human level
performance. In this project we explain the complexities of this environment
and discuss the results from our experiments on the environment. We have
compared various architectures and have proved that transfer learning can be an
effective paradigm in reinforcement learning research for complex scenarios
requiring skill transfer.
",Asynchronous Advantage Actor-Critic Agent for Starcraft II
"  Multi-view datasets are frequently encountered in learning tasks, such as web
data mining and multimedia information analysis. Given a multi-view dataset,
traditional learning algorithms usually decompose it into several single-view
datasets, from each of which a single-view model is learned. In contrast, a
multi-view learning algorithm can achieve better performance by cooperative
learning on the multi-view data. However, existing multi-view approaches mainly
focus on the views that are visible and ignore the hidden information behind
the visible views, which usually contains some intrinsic information of the
multi-view data, or vice versa. To address this problem, this paper proposes a
multi-view fuzzy logic system, which utilizes both the hidden information
shared by the multiple visible views and the information of each visible view.
Extensive experiments were conducted to validate its effectiveness.
","Multi-View Fuzzy Logic System with the Cooperation between Visible and
  Hidden Views"
"  Today's automated vehicles lack the ability to cooperate implicitly with
others. This work presents a Monte Carlo Tree Search (MCTS) based approach for
decentralized cooperative planning using macro-actions for automated vehicles
in heterogeneous environments. Based on cooperative modeling of other agents
and Decoupled-UCT (a variant of MCTS), the algorithm evaluates the
state-action-values of each agent in a cooperative and decentralized manner,
explicitly modeling the interdependence of actions between traffic
participants. Macro-actions allow for temporal extension over multiple time
steps and increase the effective search depth requiring fewer iterations to
plan over longer horizons. Without predefined policies for macro-actions, the
algorithm simultaneously learns policies over and within macro-actions. The
proposed method is evaluated under several conflict scenarios, showing that the
algorithm can achieve effective cooperative planning with learned macro-actions
in heterogeneous environments.
","Decentralized Cooperative Planning for Automated Vehicles with
  Hierarchical Monte Carlo Tree Search"
"  The AGI alignment problem has a bimodal distribution of outcomes with most
outcomes clustering around the poles of total success and existential,
catastrophic failure. Consequently, attempts to solve AGI alignment should, all
else equal, prefer false negatives (ignoring research programs that would have
been successful) to false positives (pursuing research programs that will
unexpectedly fail). Thus, we propose adopting a policy of responding to points
of philosophical and practical uncertainty associated with the alignment
problem by limiting and choosing necessary assumptions to reduce the risk of
false positives. Herein we explore in detail two relevant points of uncertainty
that AGI alignment research hinges on---meta-ethical uncertainty and
uncertainty about mental phenomena---and show how to reduce false positives in
response to them.
",Robustness to fundamental uncertainty in AGI alignment
"  Darwiche and Pearl's seminal 1997 article outlined a number of baseline
principles for a logic of iterated belief revision. These principles, the DP
postulates, have been supplemented in a number of alternative ways. Most of the
suggestions made have resulted in a form of `reductionism' that identifies
belief states with orderings of worlds. However, this position has recently
been criticised as being unacceptably strong. Other proposals, such as the
popular principle (P), aka `Independence', characteristic of `admissible'
revision operators, remain commendably more modest. In this paper, we
supplement both the DP postulates and (P) with a number of novel conditions.
While the DP postulates constrain the relation between a prior and a posterior
conditional belief set, our new principles notably govern the relation between
two posterior conditional belief sets obtained from a common prior by different
revisions. We show that operators from the resulting family, which subsumes
both lexicographic and restrained revision, can be represented as relating
belief states that are associated with a `proper ordinal interval' (POI)
assignment, a structure more fine-grained than a simple ordering of worlds. We
close the paper by noting that these operators satisfy iterated versions of a
large number of AGM era postulates, including Superexpansion, that are not
sound for admissible operators in general.
","On Strengthening the Logic of Iterated Belief Revision: Proper Ordinal
  Interval Operators"
"  We explore methods for option discovery based on variational inference and
make two algorithmic contributions. First: we highlight a tight connection
between variational option discovery methods and variational autoencoders, and
introduce Variational Autoencoding Learning of Options by Reinforcement
(VALOR), a new method derived from the connection. In VALOR, the policy encodes
contexts from a noise distribution into trajectories, and the decoder recovers
the contexts from the complete trajectories. Second: we propose a curriculum
learning approach where the number of contexts seen by the agent increases
whenever the agent's performance is strong enough (as measured by the decoder)
on the current set of contexts. We show that this simple trick stabilizes
training for VALOR and prior variational option discovery methods, allowing a
single agent to learn many more modes of behavior than it could with a fixed
context distribution. Finally, we investigate other topics related to
variational option discovery, including fundamental limitations of the general
approach and the applicability of learned options to downstream tasks.
",Variational Option Discovery Algorithms
"  We present a method of endowing agents in an agent-based model (ABM) with
sophisticated cognitive capabilities and a naturally tunable level of
intelligence. Often, ABMs use random behavior or greedy algorithms for
maximizing objectives (such as a predator always chasing after the closest
prey). However, random behavior is too simplistic in many circumstances and
greedy algorithms, as well as classic AI planning techniques, can be brittle in
the context of the unpredictable and emergent situations in which agents may
find themselves. Our method, called agent-centric Monte Carlo cognition
(ACMCC), centers around using a separate agent-based model to represent the
agents' cognition. This model is then used by the agents in the primary model
to predict the outcomes of their actions, and thus guide their behavior. To
that end, we have implemented our method in the NetLogo agent-based modeling
platform, using the recently released LevelSpace extension, which we developed
to allow NetLogo models to interact with other NetLogo models. As an
illustrative example, we extend the Wolf Sheep Predation model (included with
NetLogo) by using ACMCC to guide animal behavior, and analyze the impact on
agent performance and model dynamics. We find that ACMCC provides a reliable
and understandable method of controlling agent intelligence, and has a large
impact on agent performance and model dynamics even at low settings.
","Agent cognition through micro-simulations: Adaptive and tunable
  intelligence with NetLogo LevelSpace"
"  The capability of making explainable inferences regarding physical processes
has long been desired. One fundamental physical process is object motion.
Inferring what causes the motion of a group of objects can even be a
challenging task for experts, e.g., in forensics science. Most of the work in
the literature relies on physics simulation to draw such infer- ences. The
simulation requires a precise model of the under- lying domain to work well and
is essentially a black-box from which one can hardly obtain any useful
explanation. By contrast, qualitative reasoning methods have the advan- tage in
making transparent inferences with ambiguous infor- mation, which makes it
suitable for this task. However, there has been no suitable qualitative theory
proposed for object motion in three-dimensional space. In this paper, we take
this challenge and develop a qualitative theory for the motion of rigid
objects. Based on this theory, we develop a reasoning method to solve a very
interesting problem: Assuming there are several objects that were initially at
rest and now have started to move. We want to infer what action causes the
movement of these objects.
","Towards Explainable Inference about Object Motion using Qualitative
  Reasoning"
"  The Decision Model and Notation (DMN) is a recent OMG standard for the
elicitation and representation of decision models, and for managing their
interconnection with business processes. DMN builds on the notion of decision
tables, and their combination into more complex decision requirements graphs
(DRGs), which bridge between business process models and decision logic models.
DRGs may rely on additional, external business knowledge models, whose
functioning is not part of the standard. In this work, we consider one of the
most important types of business knowledge, namely background knowledge that
conceptually accounts for the structural aspects of the domain of interest, and
propose decision knowledge bases (DKBs), which semantically combine DRGs
modeled in DMN, and domain knowledge captured by means of first-order logic
with datatypes. We provide a logic-based semantics for such an integration, and
formalize different DMN reasoning tasks for DKBs. We then consider background
knowledge formulated as a description logic ontology with datatypes, and show
how the main verification tasks for DMN in this enriched setting can be
formalized as standard DL reasoning services, and actually carried out in
ExpTime. We discuss the effectiveness of our framework on a case study in
maritime security.
","Semantic DMN: Formalizing and Reasoning About Decisions in the Presence
  of Background Knowledge"
"  A new wave of decision-support systems are being built today using AI
services that draw insights from data (like text and video) and incorporate
them in human-in-the-loop assistance. However, just as we expect humans to be
ethical, the same expectation needs to be met by automated systems that
increasingly get delegated to act on their behalf. A very important aspect of
an ethical behavior is to avoid (intended, perceived, or accidental) bias. Bias
occurs when the data distribution is not representative enough of the natural
phenomenon one wants to model and reason about. The possibly biased behavior of
a service is hard to detect and handle if the AI service is merely being used
and not developed from scratch, since the training data set is not available.
In this situation, we envisage a 3rd party rating agency that is independent of
the API producer or consumer and has its own set of biased and unbiased data,
with customizable distributions. We propose a 2-step rating approach that
generates bias ratings signifying whether the AI service is unbiased
compensating, data-sensitive biased, or biased. The approach also works on
composite services. We implement it in the context of text translation and
report interesting results.
",Towards Composable Bias Rating of AI Services
"  Artificial intelligence recently had a great advancements caused by the
emergence of new processing power and machine learning methods. Having said
that, the learning capability of artificial intelligence is still at its
infancy comparing to the learning capability of human and many animals. Many of
the current artificial intelligence applications can only operate in a very
orchestrated, specific environments with an extensive training set that exactly
describes the conditions that will occur during execution time. Having that in
mind, and considering the several existing machine learning methods this
question rises that 'What are some of the best ways for a machine to learn?'
Regarding the learning methods of human, Confucius' point of view is that they
are by experience, imitation and reflection. This paper tries to explore and
discuss regarding these three ways of learning and their implementations in
machines by having a look at how they happen in minds.
","Experience, Imitation and Reflection; Confucius' Conjecture and Machine
  Learning"
"  Answer Set Programming (ASP) is one of the major declarative programming
paradigms in the area of logic programming and non-monotonic reasoning. Despite
that ASP features a simple syntax and an intuitive semantics, errors are common
during the development of ASP programs. In this paper we propose a novel
debugging approach allowing for interactive localization of bugs in non-ground
programs. The new approach points the user directly to a set of non-ground
rules involved in the bug, which might be refined (up to the point in which the
bug is easily identified) by asking the programmer a sequence of questions on
an expected answer set. The approach has been implemented on top of the ASP
solver WASP. The resulting debugger has been complemented by a user-friendly
graphical interface, and integrated in ASPIDE, a rich IDE for answer set
programs. In addition, an empirical analysis shows that the new debugger is not
affected by the grounding blowup limiting the application of previous
approaches based on meta-programming. Under consideration in Theory and
Practice of Logic Programming (TPLP).
",Debugging Non-Ground ASP Programs: Technique and Graphical Tools
"  Industry is evolving towards Industry 4.0, which holds the promise of
increased flexibility in manufacturing, better quality and improved
productivity. A core actor of this growth is using sensors, which must capture
data that can used in unforeseen ways to achieve a performance not achievable
without them. However, the complexity of this improved setting is much greater
than what is currently used in practice. Hence, it is imperative that the
management cannot only be performed by human labor force, but part of that will
be done by automated algorithms instead. A natural way to represent the data
generated by this large amount of sensors, which are not acting measuring
independent variables, and the interaction of the different devices is by using
a graph data model. Then, machine learning could be used to aid the Industry
4.0 system to, for example, perform predictive maintenance. However, machine
learning directly on graphs, needs feature engineering and has scalability
issues. In this paper we discuss methods to convert (embed) the graph in a
vector space, such that it becomes feasible to use traditional machine learning
methods for Industry 4.0 settings.
","Leveraging Knowledge Graph Embedding Techniques for Industry 4.0 Use
  Cases"
"  In 2016, 2017, and 2018 at the IEEE Conference on Computational Intelligence
in Games, the authors of this paper ran a competition for agents that can play
classic text-based adventure games. This competition fills a gap in existing
game AI competitions that have typically focussed on traditional card/board
games or modern video games with graphical interfaces. By providing a platform
for evaluating agents in text-based adventures, the competition provides a
novel benchmark for game AI with unique challenges for natural language
understanding and generation. This paper summarises the three competitions ran
in 2016, 2017, and 2018 (including details of open source implementations of
both the competition framework and our competitors) and presents the results of
an improved evaluation of these competitors across 20 games.
",The Text-Based Adventure AI Competition
"  Knowledge base (KB) is an important aspect in artificial intelligence. One
significant challenge faced by KB construction is that it contains many noises,
which prevents its effective usage. Even though some KB cleansing algorithms
have been proposed, they focus on the structure of the knowledge graph and
neglect the relation between the concepts, which could be helpful to discover
wrong relations in KB. Motived by this, we measure the relation of two concepts
by the distance between their corresponding instances and detect errors within
the intersection of the conflicting concept sets. For efficient and effective
knowledge base cleansing, we first apply a distance-based Model to determine
the conflicting concept sets using two different methods. Then, we propose and
analyze several algorithms on how to detect and repairing the errors based on
our model, where we use hash method for an efficient way to calculate distance.
Experimental results demonstrate that the proposed approaches could cleanse the
knowledge bases efficiently and effectively.
",Error Detection in a Large-Scale Lexical Taxonomy
"  We study the problem of finite ontology mediated query answering (FOMQA), the
variant of OMQA where the represented world is assumed to be finite, and thus
only finite models of the ontology are considered. We adopt the most typical
setting with unions of conjunctive queries and ontologies expressed in
description logics (DLs). The study of FOMQA is relevant in settings that are
not finitely controllable. This is the case not only for DLs without the finite
model property, but also for those allowing transitive role declarations. When
transitive roles are allowed, evaluating queries is challenging: FOMQA is
undecidable for SHOIF and only known to be decidable for the Horn fragment of
ALCIF. We show decidability of FOMQA for three proper fragments of SOIF: SOI,
SOF, and SIF. Our approach is to characterise models relevant for deciding
finite query entailment. Relying on a certain regularity of these models, we
develop automata-based decision procedures with optimal complexity bounds.
","Finite Query Answering in Expressive Description Logics with Transitive
  Roles"
"  Focusing on Business AI, this article introduces the AIQ quadrant that
enables us to measure AI for business applications in a relative comparative
manner, i.e. to judge that software A has more or less intelligence than
software B. Recognizing that the goal of Business software is to maximize value
in terms of business results, the dimensions of the quadrant are the key
factors that determine the business value of AI software: Level of Output
Quality (Smartness) and Level of Automation. The use of the quadrant is
illustrated by several software solutions to support the real life business
challenge of field service scheduling. The role of machine learning and
conversational digital assistants in increasing the business value are also
discussed and illustrated with a recent integration of existing intelligent
digital assistants for factory floor decision making with the new version of
Google Glass. Such hands free AI solutions elevate the AIQ level to its
ultimate position.
",AIQ: Measuring Intelligence of Business AI Software
"  With the intensified use of intelligent things, the demands on the
technological systems are increasing permanently. A possible approach to meet
the continuously changing challenges is to shift the system integration from
design to run-time by using adaptive systems. Diverse adaptivity properties,
so-called self-* properties, form the basis of these systems and one of the
properties is self-improvement. It describes the ability of a system not only
to adapt to a changing environment according to a predefined model, but also
the capability to adapt the adaptation logic of the whole system. In this
paper, a closer look is taken at the structure of self-adaptive systems.
Additionally, the systems' ability to improve themselves during run-time is
described from the perspective of Organic Computing. Furthermore, four
different strategies for self-improvement are presented, following the taxonomy
of self-adaptation suggested by Christian Krupitzer et al.
","Self-Adaptive Systems in Organic Computing: Strategies for
  Self-Improvement"
"  Although Path-Relinking is an effective local search method for many
combinatorial optimization problems, its application is not straightforward in
solving the MAX-SAT, an optimization variant of the satisfiability problem
(SAT) that has many real-world applications and has gained more and more
attention in academy and industry. Indeed, it was not used in any recent
competitive MAX-SAT algorithms in our knowledge. In this paper, we propose a
new local search algorithm called IPBMR for the MAX-SAT, that remedies the
drawbacks of the Path-Relinking method by using a careful combination of three
components: a new strategy named Path-Breaking to avoid unpromising regions of
the search space when generating trajectories between two elite solutions; a
weak and a strong mutation strategies, together with restarts, to diversify the
search; and stochastic path generating steps to avoid premature local optimum
solutions. We then present experimental results to show that IPBMR outperforms
two of the best state-of-the-art MAX-SAT solvers, and an empirical
investigation to identify and explain the effect of the three components in
IPBMR.
","An Iterative Path-Breaking Approach with Mutation and Restart Strategies
  for the MAX-SAT Problem"
"  Artificial Intelligence (AI) achieved super-human performance in a broad
variety of domains. We say that an AI is made Artificially Stupid on a task
when some limitations are deliberately introduced to match a human's ability to
do the task. An Artificial General Intelligence (AGI) can be made safer by
limiting its computing power and memory, or by introducing Artificial Stupidity
on certain tasks. We survey human intellectual limits and give recommendations
for which limits to implement in order to build a safe AGI.
",Building Safer AGI by introducing Artificial Stupidity
"  Abstract argumentation frameworks are formal systems that facilitate
obtaining conclusions from non-monotonic knowledge systems. Within such a
system, an argumentation semantics is defined as a set of arguments with some
desired qualities, for example, that the elements are not in conflict with each
other. Splitting an argumentation framework can efficiently speed up the
computation of argumentation semantics. With respect to stable semantics, two
methods have been proposed to split an argumentation framework either in a
unidirectional or bidirectional fashion. The advantage of bidirectional
splitting is that it is not structure-dependent and, unlike unidirectional
splitting, it can be used for frameworks consisting of a single strongly
connected component. Bidirectional splitting makes use of a minimum cut. In
this paper, we implement and test the performance of the bidirectional
splitting method, along with two types of graph cut algorithms. Experimental
data suggest that using a minimum cut will not improve the performance of
computing stable semantics in most cases. Hence, instead of a minimum cut, we
propose to use a balanced cut, where the framework is split into two
sub-frameworks of equal size. Experimental results conducted on bidirectional
splitting using the balanced cut show a significant improvement in the
performance of computing semantics.
","An Implementation, Empirical Evaluation and Proposed Improvement for
  Bidirectional Splitting Method for Argumentation Frameworks under Stable
  Semantics"
"  In this book we introduce the plithogenic set (as generalization of crisp,
fuzzy, intuitionistic fuzzy, and neutrosophic sets), plithogenic logic (as
generalization of classical, fuzzy, intuitionistic fuzzy, and neutrosophic
logics), plithogenic probability (as generalization of classical, imprecise,
and neutrosophic probabilities), and plithogenic statistics (as generalization
of classical, and neutrosophic statistics). Plithogenic Set is a set whose
elements are characterized by one or more attributes, and each attribute may
have many values. An attribute value v has a corresponding (fuzzy,
intuitionistic fuzzy, or neutrosophic) degree of appurtenance d(x,v) of the
element x, to the set P, with respect to some given criteria. In order to
obtain a better accuracy for the plithogenic aggregation operators in the
plithogenic set, logic, probability and for a more exact inclusion (partial
order), a (fuzzy, intuitionistic fuzzy, or neutrosophic) contradiction
(dissimilarity) degree is defined between each attribute value and the dominant
(most important) attribute value. The plithogenic intersection and union are
linear combinations of the fuzzy operators tnorm and tconorm, while the
plithogenic complement, inclusion, equality are influenced by the attribute
values contradiction (dissimilarity) degrees. Formal definitions of plithogenic
set, logic, probability, statistics are presented into the book, followed by
plithogenic aggregation operators, various theorems related to them, and
afterwards examples and applications of these new concepts in our everyday
life.
","Plithogeny, Plithogenic Set, Logic, Probability, and Statistics"
"  We are interested in the problem of finding $k$ nearest neighbours in the
plane and in the presence of polygonal obstacles ($\textit{OkNN}$). Widely used
algorithms for OkNN are based on incremental visibility graphs, which means
they require costly and online visibility checking and have worst-case
quadratic running time. Recently $\mathbf{Polyanya}$, a fast point-to-point
pathfinding algorithm was proposed which avoids the disadvantages of visibility
graphs by searching over an alternative data structure known as a navigation
mesh. Previously, we adapted $\mathbf{Polyanya}$ to multi-target scenarios by
developing two specialised heuristic functions: the $\mathbf{Interval
heuristic}$ $h_v$ and the $\mathbf{Target heuristic}$ $h_t$. Though these
methods outperform visibility graph algorithms by orders of magnitude in all
our experiments they are not robust: $h_v$ expands many redundant nodes when
the set of neighbours is small while $h_t$ performs poorly when the set of
neighbours is large. In this paper, we propose new algorithms and heuristics
for OkNN which perform well regardless of neighbour density.
","Faster and More Robust Mesh-based Algorithms for Obstacle k-Nearest
  Neighbour"
"  Neural networks excel in detecting regular patterns but are less successful
in representing and manipulating complex data structures, possibly due to the
lack of an external memory. This has led to the recent development of a new
line of architectures known as Memory-Augmented Neural Networks (MANNs), each
of which consists of a neural network that interacts with an external memory
matrix. However, this RAM-like memory matrix is unstructured and thus does not
naturally encode structured objects. Here we design a new MANN dubbed
Relational Dynamic Memory Network (RMDN) to bridge the gap. Like existing
MANNs, RMDN has a neural controller but its memory is structured as
multi-relational graphs. RMDN uses the memory to represent and manipulate
graph-structured data in response to query; and as a neural network, RMDN is
trainable from labeled data. Thus RMDN learns to answer queries about a set of
graph-structured objects without explicit programming. We evaluate the
capability of RMDN on several important prediction problems, including software
vulnerability, molecular bioactivity and chemical-chemical interaction. Results
demonstrate the efficacy of the proposed model.
",Relational dynamic memory networks
"  Motion plans are often randomly generated for minor game NPCs. Repetitive or
regular movements, however, require non-trivial programming effort and/or
integration with a pathing system. We here describe an example-based approach
to path generation that requires little or no additional programming effort.
Our work modifies the Wave Function Collapse (WFC) algorithm, adapting it to
produce pathing plans similar to an input sketch. We show how simple sketch
modifications control path characteristics, and demonstrate feasibility through
a usable Unity implementation.
",Generating Paths with WFC
"  LPMLN is a probabilistic extension of answer set programs with the weight
scheme derived from that of Markov Logic. Previous work has shown how inference
in LPMLN can be achieved. In this paper, we present the concept of weight
learning in LPMLN and learning algorithms for LPMLN derived from those for
Markov Logic. We also present a prototype implementation that uses answer set
solvers for learning as well as some example domains that illustrate distinct
features of LPMLN learning. Learning in LPMLN is in accordance with the stable
model semantics, thereby it learns parameters for probabilistic extensions of
knowledge-rich domains where answer set programming has shown to be useful but
limited to the deterministic case, such as reachability analysis and reasoning
about actions in dynamic domains. We also apply the method to learn the
parameters for probabilistic abductive reasoning about actions.
",Weight Learning in a Probabilistic Extension of Answer Set Programs
"  There are many examples of human decision making which cannot be modeled by
classical probabilistic and logic models, on which the current AI systems are
based. Hence the need for a modeling framework which can enable intelligent
systems to detect and predict cognitive biases in human decisions to facilitate
better human-agent interaction. We give a few examples of irrational behavior
and use a generalized probabilistic model inspired by the mathematical
framework of Quantum Theory to model and explain such behavior.
","Reconciling Irrational Human Behavior with AI based Decision Making: A
  Quantum Probabilistic Approach"
"  Most commonly, the Open World Assumption is adopted as a standard strategy
for the design, construction and use of ontologies. This strategy limits the
inferencing capabilities of any system because non-asserted statements (missing
knowledge) could be assumed to be alternatively true or false. As we will
demonstrate, this is especially the case of first-order logic (FOL) ontologies
where non-asserted statements is nowadays one of the main obstacles to its
practical application in automated commonsense reasoning tasks. In this paper,
we investigate the application of the Closed World Assumption (CWA) to enable a
better exploitation of FOL ontologies by using state-of-the-art automated
theorem provers. To that end, we explore different CWA formulations for the
structural knowledge encoded in a FOL translation of the SUMO ontology,
discovering that almost 30 % of the structural knowledge is missing. We
evaluate these formulations on a practical experimentation using a very large
commonsense benchmark obtained from WordNet through its mapping to SUMO. The
results show that the competency of the ontology improves more than 50 % when
reasoning under the CWA. Thus, applying the CWA automatically to FOL ontologies
reduces their ambiguity and more commonsense questions can be answered
","Applying the Closed World Assumption to SUMO-based FOL Ontologies for
  Effective Commonsense Reasoning"
"  Given (1) a set of clauses $T$ in some first-order language $\cal L$ and (2)
a cost function $c : B_{{\cal L}} \rightarrow \mathbb{R}_{+}$, mapping each
ground atom in the Herbrand base $B_{{\cal L}}$ to a non-negative real, then
the problem of finding a minimal cost Herbrand model is to either find a
Herbrand model $\cal I$ of $T$ which is guaranteed to minimise the sum of the
costs of true ground atoms, or establish that there is no Herbrand model for
$T$. A branch-cut-and-price integer programming (IP) approach to solving this
problem is presented. Since the number of ground instantiations of clauses and
the size of the Herbrand base are both infinite in general, we add the
corresponding IP constraints and IP variables `on the fly' via `cutting' and
`pricing' respectively. In the special case of a finite Herbrand base we show
that adding all IP variables and constraints from the outset can be
advantageous, showing that a challenging Markov logic network MAP problem can
be solved in this way if encoded appropriately.
",Finding Minimal Cost Herbrand Models with Branch-Cut-and-Price
"  This paper presents an artificial intelligence algorithm that can be used to
derive formulas from various scientific disciplines called automatic derivation
machine. First, the formula is abstractly expressed as a multiway tree model,
and then each step of the formula derivation transformation is abstracted as a
mapping of multiway trees. Derivation steps similar can be expressed as a
reusable formula template by a multiway tree map. After that, the formula
multiway tree is eigen-encoded to feature vectors construct the feature space
of formulas, the Q-learning model using in this feature space can achieve the
derivation by making training data from derivation process. Finally, an
automatic formula derivation machine is made to choose the next derivation step
based on the current state and object. We also make an example about the
nuclear reactor physics problem to show how the automatic derivation machine
works.
",Automatic Derivation Of Formulas Using Reforcement Learning
"  Approaches to goal recognition have progressively relaxed the requirements
about the amount of domain knowledge and available observations, yielding
accurate and efficient algorithms capable of recognizing goals. However, to
recognize goals in raw data, recent approaches require either human engineered
domain knowledge, or samples of behavior that account for almost all actions
being observed to infer possible goals. This is clearly too strong a
requirement for real-world applications of goal recognition, and we develop an
approach that leverages advances in recurrent neural networks to perform goal
recognition as a classification task, using encoded plan traces for training.
We empirically evaluate our approach against the state-of-the-art in goal
recognition with image-based domains, and discuss under which conditions our
approach is superior to previous ones.
",LSTM-Based Goal Recognition in Latent Space
"  Approaches to decision-making under uncertainty in the belief function
framework are reviewed. Most methods are shown to blend criteria for decision
under ignorance with the maximum expected utility principle of Bayesian
decision theory. A distinction is made between methods that construct a
complete preference relation among acts, and those that allow incomparability
of some acts due to lack of information. Methods developed in the imprecise
probability framework are applicable in the Dempster-Shafer context and are
also reviewed. Shafer's constructive decision theory, which substitutes the
notion of goal for that of utility, is described and contrasted with other
approaches. The paper ends by pointing out the need to carry out deeper
investigation of fundamental issues related to decision-making with belief
functions and to assess the descriptive, normative and prescriptive values of
the different approaches.
",Decision-Making with Belief Functions: a Review
"  This work proposes an engine for the Creation Of Novel Adventure Narrative
(CONAN), which is a procedural quest generator. It uses a planning approach to
story generation. The engine is tested on its ability to create quests, which
are sets of actions that must be performed in order to achieve a certain goal,
usually for a reward. The engine takes in a world description represented as a
set of facts, including characters, locations, and items, and generates quests
according to the state of the world and the preferences of the characters. We
evaluate quests through the classification of the motivations behind the
quests, based on the sequences of actions required to complete the quests. We
also compare different world descriptions and analyze the difference in
motivations for the quests produced by the engine. Compared against human
structural quest analysis, the current engine was found to be able to replicate
the quest structures found in commercial video game quests.
",Let CONAN tell you a story: Procedural quest generation
"  This paper describes a novel perspective on the foundations of mathematics:
how mathematics may be seen to be largely about 'information compression via
the matching and unification of patterns' (ICMUP). ICMUP is itself a novel
approach to information compression, couched in terms of non-mathematical
primitives, as is necessary in any investigation of the foundations of
mathematics. This new perspective on the foundations of mathematics has grown
out of an extensive programme of research developing the ""SP Theory of
Intelligence"" and its realisation in the ""SP Computer Model"", a system in which
a generalised version of ICMUP -- the powerful concept of SP-multiple-alignment
-- plays a central role. These ideas may be seen to be part of a ""Big Picture""
comprising six areas of interest, with information compression as a unifying
theme. The paper describes the close relation between mathematics and
information compression, and describes examples showing how variants of ICMUP
may be seen in widely-used structures and operations in mathematics. Examples
are also given to show how the mathematics-related disciplines of logic and
computing may be understood as ICMUP. There are many potential benefits and
applications of these ideas.
","Mathematics as information compression via the matching and unification
  of patterns"
"  The paper presents a knowledge representation language $\mathcal{A}log$ which
extends ASP with aggregates. The goal is to have a language based on simple
syntax and clear intuitive and mathematical semantics. We give some properties
of $\mathcal{A}log$, an algorithm for computing its answer sets, and comparison
with other approaches.
",Vicious Circle Principle and Logic Programs with Aggregates
"  Detecting small sets of relevant patterns from a given dataset is a central
challenge in data mining. The relevance of a pattern is based on user-provided
criteria; typically, all patterns that satisfy certain criteria are considered
relevant. Rule-based languages like Answer Set Programming (ASP) seem
well-suited for specifying such criteria in a form of constraints. Although
progress has been made, on the one hand, on solving individual mining problems
and, on the other hand, developing generic mining systems, the existing methods
either focus on scalability or on generality. In this paper we make steps
towards combining local (frequency, size, cost) and global (various condensed
representations like maximal, closed, skyline) constraints in a generic and
efficient way. We present a hybrid approach for itemset, sequence and graph
mining which exploits dedicated highly optimized mining systems to detect
frequent patterns and then filters the results using declarative ASP. To
further demonstrate the generic nature of our hybrid framework we apply it to a
problem of approximately tiling a database. Experiments on real-world datasets
show the effectiveness of the proposed method and computational gains for
itemset, sequence and graph mining, as well as approximate tiling.
  Under consideration in Theory and Practice of Logic Programming (TPLP).
",Hybrid ASP-based Approach to Pattern Mining
"  Internet market makers are always facing intense competitive environment,
where personalized price reductions or discounted coupons are provided for
attracting more customers. Participants in such a price war scenario have to
invest a lot to catch up with other competitors. However, such a huge cost of
money may not always lead to an improvement of market share. This is mainly due
to a lack of information about others' strategies or customers' willingness
when participants develop their strategies.
  In order to obtain this hidden information through observable data, we study
the relationship between companies and customers in the Internet price war.
Theoretically, we provide a formalization of the problem as a stochastic game
with imperfect and incomplete information. Then we develop a variant of Latent
Dirichlet Allocation (LDA) to infer latent variables under the current market
environment, which represents the preferences of customers and strategies of
competitors. To our best knowledge, it is the first time that LDA is applied to
game scenario.
  We conduct simulated experiments where our LDA model exhibits a significant
improvement on finding strategies in the Internet price war by including all
available market information of the market maker's competitors. And the model
is applied to an open dataset for real business. Through comparisons on the
likelihood of prediction for users' behavior and distribution distance between
inferred opponent's strategy and the real one, our model is shown to be able to
provide a better understanding for the market environment.
  Our work marks a successful learning method to infer latent information in
the environment of price war by the LDA modeling, and sets an example for
related competitive applications to follow.
",Latent Dirichlet Allocation for Internet Price War
"  The ability to conduct logical reasoning is a fundamental aspect of
intelligent human behavior, and thus an important problem along the way to
human-level artificial intelligence. Traditionally, logic-based symbolic
methods from the field of knowledge representation and reasoning have been used
to equip agents with capabilities that resemble human logical reasoning
qualities. More recently, however, there has been an increasing interest in
using machine learning rather than logic-based symbolic formalisms to tackle
these tasks. In this paper, we employ state-of-the-art methods for training
deep neural networks to devise a novel model that is able to learn how to
effectively perform logical reasoning in the form of basic ontology reasoning.
This is an important and at the same time very natural logical reasoning task,
which is why the presented approach is applicable to a plethora of important
real-world problems. We present the outcomes of several experiments, which show
that our model is able to learn to perform highly accurate ontology reasoning
on very large, diverse, and challenging benchmarks. Furthermore, it turned out
that the suggested approach suffers much less from different obstacles that
prohibit logic-based symbolic reasoning, and, at the same time, is surprisingly
plausible from a biological point of view.
",Ontology Reasoning with Deep Neural Networks
"  The digitalization of automation engineering generates large quantities of
engineering data that is interlinked in knowledge graphs. Classifying and
clustering subgraphs according to their functionality is useful to discover
functionally equivalent engineering artifacts that exhibit different graph
structures. This paper presents a new graph learning algorithm designed to
classify engineering data artifacts -- represented in the form of graphs --
according to their structure and neighborhood features. Our Structural Graph
Convolutional Neural Network (SGCNN) is capable of learning graphs and
subgraphs with a novel graph invariant convolution kernel and
downsampling/pooling algorithm. On a realistic engineering-related dataset, we
show that SGCNN is capable of achieving ~91% classification accuracy.
","Future Automation Engineering using Structural Graph Convolutional
  Neural Networks"
"  We provide a detailed example for modular ontology modeling based on ontology
design patterns.
","A Tutorial on Modular Ontology Modeling with Ontology Design Patterns:
  The Cooking Recipes Ontology"
"  In recent years, non-monotonic Inductive Logic Programming has received
growing interest. Specifically, several new learning frameworks and algorithms
have been introduced for learning under the answer set semantics, allowing the
learning of common-sense knowledge involving defaults and exceptions, which are
essential aspects of human reasoning. In this paper, we present a
noise-tolerant generalisation of the learning from answer sets framework. We
evaluate our ILASP3 system, both on synthetic and on real datasets, represented
in the new framework. In particular, we show that on many of the datasets
ILASP3 achieves a higher accuracy than other ILP systems that have previously
been applied to the datasets, including a recently proposed differentiable
learning framework.
",Inductive Learning of Answer Set Programs from Noisy Examples
"  Artificial intelligence (AI) is the core technology of technological
revolution and industrial transformation. As one of the new intelligent needs
in the AI 2.0 era, financial intelligence has elicited much attention from the
academia and industry. In our current dynamic capital market, financial
intelligence demonstrates a fast and accurate machine learning capability to
handle complex data and has gradually acquired the potential to become a
""financial brain"". In this work, we survey existing studies on financial
intelligence. First, we describe the concept of financial intelligence and
elaborate on its position in the financial technology field. Second, we
introduce the development of financial intelligence and review state-of-the-art
techniques in wealth management, risk management, financial security, financial
consulting, and blockchain. Finally, we propose a research framework called
FinBrain and summarize four open issues, namely, explainable financial agents
and causality, perception and prediction under uncertainty, risk-sensitive and
robust decision making, and multi-agent game and mechanism design. We believe
that these research directions can lay the foundation for the development of AI
2.0 in the finance field.
",FinBrain: When Finance Meets AI 2.0
"  This article identifies and discusses the theoretical foundations that were
considered in the design of the A2RD model. In addition to the points
considered, references are made to the studies available and considered in the
approach.
",Theoretical Foundations of the A2RD Project: Part I
"  This paper describes the Autonomous Architecture Over Restricted Domains
project. It begins with the description of the context upon which the project
is focused, and in the sequence describes the project and implementation
models. It finish by presenting the environment conceptual model, showing where
stand the components, inputs and facilities required to interact among the
intelligent agents of the various implementations in their respective and
restricted, routing domains (Autonomous Systems) which together make the
Internet work.
",A Summary Description of the A2RD Project
"  The performance of enumerating all solutions to an instance of Langford's
Problem is sensitive to the model and the search strategy. In this paper we
compare the performance of a large variety of models, all derived from two base
viewpoints. We empirically show that a channelled model with a static branching
order on one of the viewpoints offers the best performance out of all the
options we consider. Surprisingly, one of the base models proves very effective
for propagation, while the other provides an effective means of stating a
static search order.
",Modelling Langford's Problem: A Viewpoint for Search
"  Comprehending procedural text, e.g., a paragraph describing photosynthesis,
requires modeling actions and the state changes they produce, so that questions
about entities at different timepoints can be answered. Although several recent
systems have shown impressive progress in this task, their predictions can be
globally inconsistent or highly improbable. In this paper, we show how the
predicted effects of actions in the context of a paragraph can be improved in
two ways: (1) by incorporating global, commonsense constraints (e.g., a
non-existent entity cannot be destroyed), and (2) by biasing reading with
preferences from large-scale corpora (e.g., trees rarely move). Unlike earlier
methods, we treat the problem as a neural structured prediction task, allowing
hard and soft constraints to steer the model away from unlikely predictions. We
show that the new model significantly outperforms earlier systems on a
benchmark dataset for procedural text comprehension (+8% relative gain), and
that it also avoids some of the nonsensical predictions that earlier systems
make.
","Reasoning about Actions and State Changes by Injecting Commonsense
  Knowledge"
"  In our experience, some ontology users find it much easier to convey logical
statements using rules rather than OWL (or description logic) axioms. Based on
recent theoretical developments on transformations between rules and
description logics, we develop ROWL, a Protege plugin that allows users to
enter OWL axioms by way of rules; the plugin then automatically converts these
rules into OWL DL axioms if possible, and prompts the user in case such a
conversion is not possible without weakening the semantics of the rule.
",Modeling OWL with Rules: The ROWL Protege Plugin
"  We demonstrate how to efficiently compute the probability of victory in Fire
Emblem arena battles. The probability can be expressed in terms of a
multivariate recurrence relation which lends itself to a straightforward
dynamic programming solution. Some implementation issues are addressed, and a
full implementation is provided in code.
",Victory Probability in the Fire Emblem Arena
"  Argumentation is a non-monotonic process. This reflects the fact that
argumentation involves uncertain information, and so new information can cause
a change in the conclusions drawn. However, the base logic does not need to be
non-monotonic. Indeed, most proposals for structured argumentation use a
monotonic base logic (e.g. some form of modus ponens with a rule-based
language, or classical logic). Nonetheless, there are issues in capturing
defeasible reasoning in argumentation including choice of base logic and
modelling of defeasible knowledge. And there are insights and tools to be
harnessed for research in non-monontonic logics. We consider some of these
issues in this paper.
",Non-monotonic Reasoning in Deductive Argumentation
"  This paper discussed the {\it robust alignment} problem, that is, the problem
of aligning the goals of algorithms with human preferences. It presented a
general roadmap to tackle this issue. Interestingly, this roadmap identifies 5
critical steps, as well as many relevant aspects of these 5 steps. In other
words, we have presented a large number of hopefully more tractable subproblems
that readers are highly encouraged to tackle. Hopefully, this combination
allows to better highlight the most pressing problems, how every expertise can
be best used to, and how combining the solutions to subproblems might add up to
solve robust alignment.
",A Roadmap for Robust End-to-End Alignment
"  Chance Constrained Markov Decision Processes maximize reward subject to a
bounded probability of failure, and have been frequently applied for planning
with potentially dangerous outcomes or unknown environments. Solution
algorithms have required strong heuristics or have been limited to relatively
small problems with up to millions of states, because the optimal action to
take from a given state depends on the probability of failure in the rest of
the policy, leading to a coupled problem that is difficult to solve. In this
paper we examine a generalization of a CCMDP that trades off probability of
failure against reward through a functional relationship. We derive a
constraint that can be applied to each state history in a policy individually,
and which guarantees that the chance constraint will be satisfied. The approach
decouples states in the CCMDP, so that large problems can be solved
efficiently. We then introduce Vulcan, which uses our constraint in order to
apply Monte Carlo Tree Search to CCMDPs. Vulcan can be applied to problems
where it is unfeasible to generate the entire state space, and policies must be
returned in an anytime manner. We show that Vulcan and its variants run tens to
hundreds of times faster than linear programming methods, and over ten times
faster than heuristic based methods, all without the need for a heuristic, and
returning solutions with a mean suboptimality on the order of a few percent.
Finally, we use Vulcan to solve for a chance constrained policy in a CCMDP with
over $10^{13}$ states in 3 minutes.
","Vulcan: A Monte Carlo Algorithm for Large Chance Constrained MDPs with
  Risk Bounding Functions"
"  A desirable property of an intelligent agent is its ability to understand its
environment to quickly generalize to novel tasks and compose simpler tasks into
more complex ones. If the environment has geometric or arithmetic structure,
the agent should exploit these for faster generalization. Building on recent
work that augments the environment with user-specified attributes, we show that
further equipping these attributes with the appropriate geometric and
arithmetic structure brings substantial gains in sample complexity.
",Planning with Arithmetic and Geometric Attributes
"  A hallmark of human cognition is the ability to continually acquire and
distill observations of the world into meaningful, predictive theories. In this
paper we present a new mechanism for logical theory acquisition which takes a
set of observed facts and learns to extract from them a set of logical rules
and a small set of core facts which together entail the observations. Our
approach is neuro-symbolic in the sense that the rule pred- icates and core
facts are given dense vector representations. The rules are applied to the core
facts using a soft unification procedure to infer additional facts. After k
steps of forward inference, the consequences are compared to the initial
observations and the rules and core facts are then encouraged towards
representations that more faithfully generate the observations through
inference. Our approach is based on a novel neural forward-chaining
differentiable rule induction network. The rules are interpretable and learned
compositionally from their predicates, which may be invented. We demonstrate
the efficacy of our approach on a variety of ILP rule induction and domain
theory learning datasets.
",Logical Rule Induction and Theory Learning Using Neural Theorem Proving
"  Automated game design has remained a key challenge within the field of Game
AI. In this paper, we introduce a method for recombining existing games to
create new games through a process called conceptual expansion. Prior automated
game design approaches have relied on hand-authored or crowd-sourced knowledge,
which limits the scope and applications of such systems. Our approach instead
relies on machine learning to learn approximate representations of games. Our
approach recombines knowledge from these learned representations to create new
games via conceptual expansion. We evaluate this approach by demonstrating the
ability for the system to recreate existing games. To the best of our
knowledge, this represents the first machine learning-based automated game
design system.
",Automated Game Design via Conceptual Expansion
"  Jakob Bernoulli, working in the late 17th century, identified a gap in
contemporary probability theory. He cautioned that it was inadequate to specify
force of proof (probability of provability) for some kinds of uncertain
arguments. After 300 years, this gap remains in present-day probability theory.
We present axioms analogous to Kolmogorov's axioms for probability, specifying
uncertainty that lies in an argument's inference/implication itself rather than
in its premise and conclusion. The axioms focus on arguments spanning two
Boolean algebras, but generalize the obligatory: ""force of proof of A implies B
is the probability of B or not A"" in the case that the Boolean algebras are
identical. We propose a categorical framework that relies on generalized
probabilities (objects) to express uncertainty in premises, to mix with
arguments (morphisms) to express uncertainty embedded directly in
inference/implication. There is a direct application to Shafer's evidence
theory (Dempster-Shafer theory), greatly expanding its scope for applications.
Therefore, we can offer this framework not only as an optimal solution to a
difficult historical puzzle, but also to advance the frontiers of contemporary
artificial intelligence.
  Keywords: force of proof, probability of provability, Ars Conjectandi, non
additive probabilities, evidence theory.
",The Force of Proof by Which Any Argument Prevails
"  With an increasing number of web services, providing an end-to-end Quality of
Service (QoS) guarantee in responding to user queries is becoming an important
concern. Multiple QoS parameters (e.g., response time, latency, throughput,
reliability, availability, success rate) are associated with a service,
thereby, service composition with a large number of candidate services is a
challenging multi-objective optimization problem. In this paper, we study the
multi-constrained multi-objective QoS aware web service composition problem and
propose three different approaches to solve the same, one optimal, based on
Pareto front construction and two other based on heuristically traversing the
solution space. We compare the performance of the heuristics against the
optimal, and show the effectiveness of our proposals over other classical
approaches for the same problem setting, with experiments on WSC-2009 and
ICEBE-2005 datasets.
",QoS aware Automatic Web Service Composition with Multiple objectives
"  Monte Carlo Tree Search (MCTS) is particularly adapted to domains where the
potential actions can be represented as a tree of sequential decisions. For an
effective action selection, MCTS performs many simulations to build a reliable
tree representation of the decision space. As such, a bottleneck to MCTS
appears when enough simulations cannot be performed between action selections.
This is particularly highlighted in continuously running tasks, for which the
time available to perform simulations between actions tends to be limited due
to the environment's state constantly changing. In this paper, we present an
approach that takes advantage of the anytime characteristic of MCTS to increase
the simulation time when allowed. Our approach is to effectively balance the
prospect of selecting an action with the time that can be spared to perform
MCTS simulations before the next action selection. For that, we considered the
simulation time as a decision variable to be selected alongside an action. We
extended the Hierarchical Optimistic Optimization applied to Tree (HOOT) method
to adapt our approach to environments with a continuous decision space. We
evaluated our approach for environments with a continuous decision space
through OpenAI gym's Pendulum and Continuous Mountain Car environments and for
environments with discrete action space through the arcade learning environment
(ALE) platform. The evaluation results show that, with variable simulation
times, the proposed approach outperforms the conventional MCTS in the evaluated
continuous decision space tasks and improves the performance of MCTS in most of
the ALE tasks.
","Monte Carlo Tree Search with Scalable Simulation Periods for
  Continuously Running Tasks"
"  This paper introduces an information-theoretic method for selecting a subset
of problems which gives the most information about a group of problem-solving
algorithms. This method was tested on the games in the General Video Game AI
(GVGAI) framework, allowing us to identify a smaller set of games that still
gives a large amount of information about the abilities of different
game-playing agents. This approach can be used to make agent testing more
efficient. We can achieve almost as good discriminatory accuracy when testing
on only a handful of games as when testing on more than a hundred games,
something which is often computationally infeasible. Furthermore, this method
can be extended to study the dimensions of the effective variance in game
design between these games, allowing us to identify which games differentiate
between agents in the most complementary ways.
","A Continuous Information Gain Measure to Find the Most Discriminatory
  Problems for AI Benchmarking"
"  The ordered weighted averaging (OWA) operators play a crucial role in
aggregating multiple criteria evaluations into an overall assessment supporting
the decision makers' choice. One key point steps is to determine the associated
weights. In this paper, we first briefly review some main methods for
determining the weights by using distribution functions. Then we propose a new
approach for determining OWA weights by using the RIM quantifier. Motivated by
the idea of normal distribution-based method to determine the OWA weights, we
develop a method based on elliptical distributions for determining the OWA
weights, and some of its desirable properties have been investigated.
","Elliptical Distributions-Based Weights-Determining Method for OWA
  Operators"
"  Dependability on AI models is of utmost importance to ensure full acceptance
of the AI systems. One of the key aspects of the dependable AI system is to
ensure that all its decisions are fair and not biased towards any individual.
In this paper, we address the problem of detecting whether a model has an
individual discrimination. Such a discrimination exists when two individuals
who differ only in the values of their protected attributes (such as,
gender/race) while the values of their non-protected ones are exactly the same,
get different decisions. Measuring individual discrimination requires an
exhaustive testing, which is infeasible for a non-trivial system. In this
paper, we present an automated technique to generate test inputs, which is
geared towards finding individual discrimination. Our technique combines the
well-known technique called symbolic execution along with the local
explainability for generation of effective test cases. Our experimental results
clearly demonstrate that our technique produces 3.72 times more successful test
cases than the existing state-of-the-art across all our chosen benchmarks.
","Automated Test Generation to Detect Individual Discrimination in AI
  Models"
"  Finding tight bounds on the optimal solution is a critical element of
practical solution methods for discrete optimization problems. In the last
decade, decision diagrams (DDs) have brought a new perspective on obtaining
upper and lower bounds that can be significantly better than classical bounding
mechanisms, such as linear relaxations. It is well known that the quality of
the bounds achieved through this flexible bounding method is highly reliant on
the ordering of variables chosen for building the diagram, and finding an
ordering that optimizes standard metrics is an NP-hard problem. In this paper,
we propose an innovative and generic approach based on deep reinforcement
learning for obtaining an ordering for tightening the bounds obtained with
relaxed and restricted DDs. We apply the approach to both the Maximum
Independent Set Problem and the Maximum Cut Problem. Experimental results on
synthetic instances show that the deep reinforcement learning approach, by
achieving tighter objective function bounds, generally outperforms ordering
methods commonly used in the literature when the distribution of instances is
known. To the best knowledge of the authors, this is the first paper to apply
machine learning to directly improve relaxation bounds obtained by
general-purpose bounding mechanisms for combinatorial optimization problems.
","Improving Optimization Bounds using Machine Learning: Decision Diagrams
  meet Deep Reinforcement Learning"
"  Deep reinforcement learning can match or exceed human performance in stable
contexts, but with minor changes to the environment artificial networks, unlike
humans, often cannot adapt. Humans rely on a combination of heuristics to
simplify computational load and imagination to extend experiential learning to
new and more challenging environments. Motivated by theories of the
hierarchical organization of the human prefrontal networks, we have developed a
model of hierarchical reinforcement learning that combines both heuristics and
imagination into a stumbler-strategist network. We test performance of this
network using Wythoff's game, a gridworld environment with a known optimal
strategy. We show that a heuristic labeling of each position as hot or cold,
combined with imagined play, both accelerates learning and promotes transfer to
novel games, while also improving model interpretability.
",Combining imagination and heuristics to learn strategies that generalize
"  Vulnerable road users (VRUs, i.e. cyclists and pedestrians) will play an
important role in future traffic. To avoid accidents and achieve a highly
efficient traffic flow, it is important to detect VRUs and to predict their
intentions. In this article a holistic approach for detecting intentions of
VRUs by cooperative methods is presented. The intention detection consists of
basic movement primitive prediction, e.g. standing, moving, turning, and a
forecast of the future trajectory. Vehicles equipped with sensors, data
processing systems and communication abilities, referred to as intelligent
vehicles, acquire and maintain a local model of their surrounding traffic
environment, e.g. crossing cyclists. Heterogeneous, open sets of agents
(cooperating and interacting vehicles, infrastructure, e.g. cameras and laser
scanners, and VRUs equipped with smart devices and body-worn sensors) exchange
information forming a multi-modal sensor system with the goal to reliably and
robustly detect VRUs and their intentions under consideration of real time
requirements and uncertainties. The resulting model allows to extend the
perceptual horizon of the individual agent beyond their own sensory
capabilities, enabling a longer forecast horizon. Concealments,
implausibilities and inconsistencies are resolved by the collective
intelligence of cooperating agents. Novel techniques of signal processing and
modelling in combination with analytical and learning based approaches of
pattern and activity recognition are used for detection, as well as intention
prediction of VRUs. Cooperation, by means of probabilistic sensor and knowledge
fusion, takes place on the level of perception and intention recognition. Based
on the requirements of the cooperative approach for the communication a new
strategy for an ad hoc network is proposed.
","Detecting Intentions of Vulnerable Road Users Based on Collective
  Intelligence"
"  We propose a multiple-komi modification of the AlphaGo Zero/Leela Zero
paradigm. The winrate as a function of the komi is modeled with a
two-parameters sigmoid function, so that the neural network must predict just
one more variable to assess the winrate for all komi values. A second novel
feature is that training is based on self-play games that occasionally branch
-- with changed komi -- when the position is uneven. With this setting,
reinforcement learning is showed to work on 7x7 Go, obtaining very strong
playing agents. As a useful byproduct, the sigmoid parameters given by the
network allow to estimate the score difference on the board, and to evaluate
how much the game is decided.
","SAI, a Sensible Artificial Intelligence that plays Go"
"  The ACM RecSys'18 Late-Breaking Results track (previously known as the Poster
track) is part of the main program of the 2018 ACM Conference on Recommender
Systems in Vancouver, Canada. The track attracted 48 submissions this year out
of which 18 papers could be accepted resulting in an acceptance rated of 37.5%.
",ACM RecSys 2018 Late-Breaking Results Proceedings
"  In this work, we attempt to answer a critical question: whether there exists
some input sequence that will cause a well-trained discrete-space neural
network sequence-to-sequence (seq2seq) model to generate egregious outputs
(aggressive, malicious, attacking, etc.). And if such inputs exist, how to find
them efficiently. We adopt an empirical methodology, in which we first create
lists of egregious output sequences, and then design a discrete optimization
algorithm to find input sequences that will cause the model to generate them.
Moreover, the optimization algorithm is enhanced for large vocabulary search
and constrained to search for input sequences that are likely to be input by
real-world users. In our experiments, we apply this approach to dialogue
response generation models trained on three real-world dialogue data-sets:
Ubuntu, Switchboard and OpenSubtitles, testing whether the model can generate
malicious responses. We demonstrate that given the trigger inputs our algorithm
finds, a significant number of malicious sentences are assigned large
probability by the model, which reveals an undesirable consequence of standard
seq2seq training.
",Detecting egregious responses in neural sequence-to-sequence models
"  In many real-world applications (e.g., planetary exploration, robot
navigation), an autonomous agent must be able to explore a space with
guaranteed safety. Most safe exploration algorithms in the field of
reinforcement learning and robotics have been based on the assumption that the
safety features are a priori known and time-invariant. This paper presents a
learning algorithm called ST-SafeMDP for exploring Markov decision processes
(MDPs) that is based on the assumption that the safety features are a priori
unknown and time-variant. In this setting, the agent explores MDPs while
constraining the probability of entering unsafe states defined by a safety
function being below a threshold. The unknown and time-variant safety values
are modeled using a spatio-temporal Gaussian process. However, there remains an
issue that an agent may have no viable action in a shrinking true safe space.
To address this issue, we formulate a problem maximizing the cumulative number
of safe states in the worst case scenario with respect to future observations.
The effectiveness of this approach was demonstrated in two simulation settings,
including one using real lunar terrain data.
","Safe Exploration in Markov Decision Processes with Time-Variant Safety
  using Spatio-Temporal Gaussian Process"
"  In graphs with rich texts, incorporating textual information with structural
information would benefit constructing expressive graph embeddings. Among
various graph embedding models, random walk (RW)-based is one of the most
popular and successful groups. However, it is challenged by two issues when
applied on graphs with rich texts: (i) sampling efficiency: deriving from the
training objective of RW-based models (e.g., DeepWalk and node2vec), we show
that RW-based models are likely to generate large amounts of redundant training
samples due to three main drawbacks. (ii) text utilization: these models have
difficulty in dealing with zero-shot scenarios where graph embedding models
have to infer graph structures directly from texts. To solve these problems, we
propose a novel framework, namely Text-driven Graph Embedding with Pairs
Sampling (TGE-PS). TGE-PS uses Pairs Sampling (PS) to improve the sampling
strategy of RW, being able to reduce ~99% training samples while preserving
competitive performance. TGE-PS uses Text-driven Graph Embedding (TGE), an
inductive graph embedding approach, to generate node embeddings from texts.
Since each node contains rich texts, TGE is able to generate high-quality
embeddings and provide reasonable predictions on existence of links to unseen
nodes. We evaluate TGE-PS on several real-world datasets, and experiment
results demonstrate that TGE-PS produces state-of-the-art results on both
traditional and zero-shot link prediction tasks.
",Sampled in Pairs and Driven by Text: A New Graph Embedding Framework
"  In this work, an ontology-based model for AI-assisted medicine side-effect
(SE) prediction is developed, where three main components, including the drug
model, the treatment model, and the AI-assisted prediction model, of proposed
model are presented. To validate the proposed model, an ANN structure is
established and trained by two hundred and forty-two TCM prescriptions. These
data are gathered and classified from the most famous ancient TCM book and more
than one thousand SE reports, in which two ontology-based attributions, hot and
cold, are introduced to evaluate whether the prescription will cause SE or not.
The results preliminarily reveal that it is a relationship between the
ontology-based attributions and the corresponding predicted indicator that can
be learnt by AI for predicting the SE, which suggests the proposed model has a
potential in AI-assisted SE prediction. However, it should be noted that, the
proposed model highly depends on the sufficient clinic data, and hereby, much
deeper exploration is important for enhancing the accuracy of the prediction.
","An Ontology-Based Artificial Intelligence Model for Medicine Side-Effect
  Prediction: Taking Traditional Chinese Medicine as An Example"
"  Compact optimization algorithms are a class of Estimation of Distribution
Algorithms (EDAs) characterized by extremely limited memory requirements (hence
they are called ""compact""). As all EDAs, compact algorithms build and update a
probabilistic model of the distribution of solutions within the search space,
as opposed to population-based algorithms that instead make use of an explicit
population of solutions. In addition to that, to keep their memory consumption
low, compact algorithms purposely employ simple probabilistic models that can
be described with a small number of parameters. Despite their simplicity,
compact algorithms have shown good performances on a broad range of benchmark
functions and real-world problems. However, compact algorithms also come with
some drawbacks, i.e. they tend to premature convergence and show poorer
performance on non-separable problems. To overcome these limitations, here we
investigate a possible algorithmic scheme obtained by combining compact
algorithms with a non-disruptive restart mechanism taken from the literature,
named Re-Sampled Inheritance (RI). The resulting compact algorithms with RI are
tested on the CEC 2014 benchmark functions. The numerical results show on the
one hand that the use of RI consistently enhances the performances of compact
algorithms, still keeping a limited usage of memory. On the other hand, our
experiments show that among the tested algorithms, the best performance is
obtained by compact Differential Evolution with RI.
",Compact Optimization Algorithms with Re-sampled Inheritance
"  In this paper, we study liquid democracy, a collective decision making
paradigm which lies between direct and representative democracy. One main
feature of liquid democracy is that voters can delegate their votes in a
transitive manner so that: A delegates to B and B delegates to C leads to A
delegates to C. Unfortunately, this process may not converge as there may not
even exist a stable state (also called equilibrium). In this paper, we
investigate the stability of the delegation process in liquid democracy when
voters have restricted types of preference on the agent representing them
(e.g., single-peaked preferences). We show that various natural structures of
preferences guarantee the existence of an equilibrium and we obtain both
tractability and hardness results for the problem of computing several
equilibria with some desirable properties.
",Iterative Delegations in Liquid Democracy with Restricted Preferences
"  We study properties related to relevance in non-monotonic consequence
relations obtained by systems of structured argumentation. Relevance desiderata
concern the robustness of a consequence relation under the addition of
irrelevant information. For an account of what (ir)relevance amounts to we use
syntactic and semantic considerations. Syntactic criteria have been proposed in
the domain of relevance logic and were recently used in argumentation theory
under the names of non-interference and crash-resistance. The basic idea is
that the conclusions of a given argumentative theory should be robust under
adding information that shares no propositional variables with the original
database. Some semantic relevance criteria are known from non-monotonic logic.
For instance, cautious monotony states that if we obtain certain conclusions
from an argumentation theory, we may expect to still obtain the same
conclusions if we add some of them to the given database. In this paper we
investigate properties of structured argumentation systems that warrant
relevance desiderata.
",Relevance in Structured Argumentation
"  Firstly in this paper we propose a new criterion function for evaluation of
the reductive property about the fuzzy reasoning result for fuzzy modus ponens
and fuzzy modus tollens. Secondly unlike fuzzy reasoning methods based on the
similarity measure, we propose a new fuzzy reasoning method based on distance
measure. Thirdly the reductive property for 5 fuzzy reasoning methods are
checked with respect to fuzzy modus ponens and fuzzy modus tollens. Through the
experiment, we show that proposed method is better than the previous methods in
accordance with human thinking.
","Reductive property of new fuzzy reasoning method based on distance
  measure"
"  While deep reinforcement learning (DRL) has led to numerous successes in
recent years, reproducing these successes can be extremely challenging. One
reproducibility challenge particularly relevant to DRL is nondeterminism in the
training process, which can substantially affect the results. Motivated by this
challenge, we study the positive impacts of deterministic implementations in
eliminating nondeterminism in training. To do so, we consider the particular
case of the deep Q-learning algorithm, for which we produce a deterministic
implementation by identifying and controlling all sources of nondeterminism in
the training process. One by one, we then allow individual sources of
nondeterminism to affect our otherwise deterministic implementation, and
measure the impact of each source on the variance in performance. We find that
individual sources of nondeterminism can substantially impact the performance
of agent, illustrating the benefits of deterministic implementations. In
addition, we also discuss the important role of deterministic implementations
in achieving exact replicability of results.
","Deterministic Implementations for Reproducibility in Deep Reinforcement
  Learning"
"  The General Data Protection Regulation (GDPR) is a European Union regulation
that will replace the existing Data Protection Directive on 25 May 2018. The
most significant change is a huge increase in the maximum fine that can be
levied for breaches of the regulation. Yet fewer than half of UK companies are
fully aware of GDPR - and a number of those who were preparing for it stopped
doing so when the Brexit vote was announced. A last-minute rush to become
compliant is therefore expected, and numerous companies are starting to offer
advice, checklists and consultancy on how to comply with GDPR. In such an
environment, artificial intelligence technologies ought to be able to assist by
providing best advice; asking all and only the relevant questions; monitoring
activities; and carrying out assessments. The paper considers four areas of
GDPR compliance where rule based technologies and/or machine learning
techniques may be relevant: * Following compliance checklists and codes of
conduct; * Supporting risk assessments; * Complying with the new regulations
regarding technologies that perform automatic profiling; * Complying with the
new regulations concerning recognising and reporting breaches of security. It
concludes that AI technology can support each of these four areas. The
requirements that GDPR (or organisations that need to comply with GDPR) state
for explanation and justification of reasoning imply that rule-based approaches
are likely to be more helpful than machine learning approaches. However, there
may be good business reasons to take a different approach in some
circumstances.
","Using Artificial Intelligence to Support Compliance with the General
  Data Protection Regulation"
"  In this paper, a new offline actor-critic learning algorithm is introduced:
Sampled Policy Gradient (SPG). SPG samples in the action space to calculate an
approximated policy gradient by using the critic to evaluate the samples. This
sampling allows SPG to search the action-Q-value space more globally than
deterministic policy gradient (DPG), enabling it to theoretically avoid more
local optima. SPG is compared to Q-learning and the actor-critic algorithms
CACLA and DPG in a pellet collection task and a self play environment in the
game Agar.io. The online game Agar.io has become massively popular on the
internet due to intuitive game design and the ability to instantly compete
against players around the world. From the point of view of artificial
intelligence this game is also very intriguing: The game has a continuous input
and action space and allows to have diverse agents with complex strategies
compete against each other. The experimental results show that Q-Learning and
CACLA outperform a pre-programmed greedy bot in the pellet collection task, but
all algorithms fail to outperform this bot in a fighting scenario. The SPG
algorithm is analyzed to have great extendability through offline exploration
and it matches DPG in performance even in its basic form without extensive
sampling.
",Sampled Policy Gradient for Learning to Play the Game Agar.io
"  We address item relocation problems in graphs in this paper. We assume items
placed in vertices of an undirected graph with at most one item per vertex.
Items can be moved across edges while various constraints depending on the type
of relocation problem must be satisfied. We introduce a general problem
formulation that encompasses known types of item relocation problems such as
multi-agent path finding (MAPF) and token swapping (TSWAP). In this formulation
we express two new types of relocation problems derived from token swapping
that we call token rotation (TROT) and token permutation (TPERM). Our solving
approach for item relocation combines satisfiability modulo theory (SMT) with
conflict-based search (CBS). We interpret CBS in the SMT framework where we
start with the basic model and refine the model with a collision resolution
constraint whenever a collision between items occurs in the current solution.
The key difference between the standard CBS and our SMT-based modification of
CBS (SMT-CBS) is that the standard CBS branches the search to resolve the
collision while in SMT-CBS we iteratively add a single disjunctive collision
resolution constraint. Experimental evaluation on several benchmarks shows that
the SMT-CBS algorithm significantly outperforms the standard CBS. We also
compared SMT-CBS with a modification of the SAT-based MDD-SAT solver that uses
an eager modeling of item relocation in which all potential collisions are
eliminated by constrains in advance. Experiments show that lazy approach in
SMT-CBS produce fewer constraint than MDD-SAT and also achieves faster solving
run-times.
","Lazy Modeling of Variants of Token Swapping Problem and Multi-agent Path
  Finding through Combination of Satisfiability Modulo Theories and
  Conflict-based Search"
"  When modeling real world domains we have to deal with information that is
incomplete or that comes from sources with different trust levels. This
motivates the need for managing uncertainty in the Semantic Web. To this
purpose, we introduced a probabilistic semantics, named DISPONTE, in order to
combine description logics with probability theory. The probability of a query
can be then computed from the set of its explanations by building a Binary
Decision Diagram (BDD). The set of explanations can be found using the tableau
algorithm, which has to handle non-determinism. Prolog, with its efficient
handling of non-determinism, is suitable for implementing the tableau
algorithm. TRILL and TRILLP are systems offering a Prolog implementation of the
tableau algorithm. TRILLP builds a pinpointing formula, that compactly
represents the set of explanations and can be directly translated into a BDD.
Both reasoners were shown to outperform state-of-the-art DL reasoners. In this
paper, we present an improvement of TRILLP, named TORNADO, in which the BDD is
directly built during the construction of the tableau, further speeding up the
overall inference process. An experimental comparison shows the effectiveness
of TORNADO. All systems can be tried online in the TRILL on SWISH web
application at http://trill.ml.unife.it/.
","Probabilistic DL Reasoning with Pinpointing Formulas: A Prolog-based
  Approach"
"  Sequence-to-sequence (encoder-decoder) models with attention constitute a
cornerstone of deep learning research, as they have enabled unprecedented
sequential data modeling capabilities. This effectiveness largely stems from
the capacity of these models to infer salient temporal dynamics over long
horizons; these are encoded into the obtained neural attention (NA)
distributions. However, existing NA formulations essentially constitute
point-wise selection mechanisms over the observed source sequences; that is,
attention weights computation relies on the assumption that each source
sequence element is independent of the rest. Unfortunately, although
convenient, this assumption fails to account for higher-order dependencies
which might be prevalent in real-world data. This paper addresses these
limitations by leveraging Quantum-Statistical modeling arguments. Specifically,
our work broadens the notion of NA, by attempting to account for the case that
the NA model becomes inherently incapable of discerning between individual
source elements; this is assumed to be the case due to higher-order temporal
dynamics. On the contrary, we postulate that in some cases selection may be
feasible only at the level of pairs of source sequence elements. To this end,
we cast NA into inference of an attention density matrix (ADM) approximation.
We derive effective training and inference algorithms, and evaluate our
approach in the context of a machine translation (MT) application. We perform
experiments with challenging benchmark datasets. As we show, our approach
yields favorable outcomes in terms of several evaluation metrics.
",Quantum Statistics-Inspired Neural Attention
"  Ranking is a fundamental and widely studied problem in scenarios such as
search, advertising, and recommendation. However, joint optimization for
multi-scenario ranking, which aims to improve the overall performance of
several ranking strategies in different scenarios, is rather untouched.
Separately optimizing each individual strategy has two limitations. The first
one is lack of collaboration between scenarios meaning that each strategy
maximizes its own objective but ignores the goals of other strategies, leading
to a sub-optimal overall performance. The second limitation is the inability of
modeling the correlation between scenarios meaning that independent
optimization in one scenario only uses its own user data but ignores the
context in other scenarios.
  In this paper, we formulate multi-scenario ranking as a fully cooperative,
partially observable, multi-agent sequential decision problem. We propose a
novel model named Multi-Agent Recurrent Deterministic Policy Gradient (MA-RDPG)
which has a communication component for passing messages, several private
actors (agents) for making actions for ranking, and a centralized critic for
evaluating the overall performance of the co-working actors. Each scenario is
treated as an agent (actor). Agents collaborate with each other by sharing a
global action-value function (the critic) and passing messages that encodes
historical information across scenarios. The model is evaluated with online
settings on a large E-commerce platform. Results show that the proposed model
exhibits significant improvements against baselines in terms of the overall
performance.
","Learning to Collaborate: Multi-Scenario Ranking via Multi-Agent
  Reinforcement Learning"
"  Tasks with complex temporal structures and long horizons pose a challenge for
reinforcement learning agents due to the difficulty in specifying the tasks in
terms of reward functions as well as large variances in the learning signals.
We propose to address these problems by combining temporal logic (TL) with
reinforcement learning from demonstrations. Our method automatically generates
intrinsic rewards that align with the overall task goal given a TL task
specification. The policy resulting from our framework has an interpretable and
hierarchical structure. We validate the proposed method experimentally on a set
of robotic manipulation tasks.
",Automata Guided Reinforcement Learning With Demonstrations
"  LinkedIn Talent Solutions business contributes to around 65% of LinkedIn's
annual revenue, and provides tools for job providers to reach out to potential
candidates and for job seekers to find suitable career opportunities.
LinkedIn's job ecosystem has been designed as a platform to connect job
providers and job seekers, and to serve as a marketplace for efficient matching
between potential candidates and job openings. A key mechanism to help achieve
these goals is the LinkedIn Recruiter product, which enables recruiters to
search for relevant candidates and obtain candidate recommendations for their
job postings. In this work, we highlight a set of unique information retrieval,
system, and modeling challenges associated with talent search and
recommendation systems.
","Talent Search and Recommendation Systems at LinkedIn: Practical
  Challenges and Lessons Learned"
"  Previous efforts in recommendation of candidates for talent search followed
the general pattern of receiving an initial search criteria and generating a
set of candidates utilizing a pre-trained model. Traditionally, the generated
recommendations are final, that is, the list of potential candidates is not
modified unless the user explicitly changes his/her search criteria. In this
paper, we are proposing a candidate recommendation model which takes into
account the immediate feedback of the user, and updates the candidate
recommendations at each step. This setting also allows for very uninformative
initial search queries, since we pinpoint the user's intent due to the feedback
during the search session. To achieve our goal, we employ an intent clustering
method based on topic modeling which separates the candidate space into
meaningful, possibly overlapping, subsets (which we call intent clusters) for
each position. On top of the candidate segments, we apply a multi-armed bandit
approach to choose which intent cluster is more appropriate for the current
session. We also present an online learning scheme which updates the intent
clusters within the session, due to user feedback, to achieve further
personalization. Our offline experiments as well as the results from the online
deployment of our solution demonstrate the benefits of our proposed
methodology.
",In-Session Personalization for Talent Search
"  Although many reinforcement learning methods have been proposed for learning
the optimal solutions in single-agent continuous-action domains, multiagent
coordination domains with continuous actions have received relatively few
investigations. In this paper, we propose an independent learner hierarchical
method, named Sample Continuous Coordination with recursive Frequency Maximum
Q-Value (SCC-rFMQ), which divides the cooperative problem with continuous
actions into two layers. The first layer samples a finite set of actions from
the continuous action spaces by a re-sampling mechanism with variable
exploratory rates, and the second layer evaluates the actions in the sampled
action set and updates the policy using a reinforcement learning cooperative
method. By constructing cooperative mechanisms at both levels, SCC-rFMQ can
handle cooperative problems in continuous action cooperative Markov games
effectively. The effectiveness of SCC-rFMQ is experimentally demonstrated on
two well-designed games, i.e., a continuous version of the climbing game and a
cooperative version of the boat problem. Experimental results show that
SCC-rFMQ outperforms other reinforcement learning algorithms.
",SCC-rFMQ Learning in Cooperative Markov Games with Continuous Actions
"  Open data refers to data that is freely available for reuse. Although there
has been rapid increase in availability of open data to public in the last
decade, this has not translated into better decision-support tools for them. We
propose intelligent conversation generators as a grand challenge that would
automatically create data-driven conversation interfaces (CIs), also known as
chatbots or dialog systems, from open data and deliver personalized analytical
insights to users based on their contextual needs. Such generators will not
only help bring Artificial Intelligence (AI)-based solutions for important
societal problems to the masses but also advance AI by providing an integrative
testbed for human-centric AI and filling gaps in the state-of-art towards this
aim.
",Decision-support for the Masses by Enabling Conversations with Open Data
"  We propose the genetic algorithm for time window optimization, which is an
embedded genetic algorithm (GA), to optimize the time window (TW) of the
attributes using feature selection and support vector machine. This GA is
evolved using the results of a trading simulation, and it determines the best
TW for each technical indicator. An appropriate evaluation was conducted using
a walk-forward trading simulation, and the trained model was verified to be
generalizable for forecasting other stock data. The results show that using the
GA to determine the TW can improve the rate of return, leading to better
prediction models than those resulting from using the default TW.
","A generalized financial time series forecasting model based on automatic
  feature engineering using genetic algorithms and support vector machine"
"  The growing influence and decision-making capacities of Autonomous systems
and Artificial Intelligence in our lives force us to consider the values
embedded in these systems. But how ethics should be implemented into these
systems? In this study, the solution is seen on philosophical conceptualization
as a framework to form practical implementation model for ethics of AI. To take
the first steps on conceptualization main concepts used on the field needs to
be identified. A keyword based Systematic Mapping Study (SMS) on the keywords
used in AI and ethics was conducted to help in identifying, defying and
comparing main concepts used in current AI ethics discourse. Out of 1062 papers
retrieved SMS discovered 37 re-occurring keywords in 83 academic papers. We
suggest that the focus on finding keywords is the first step in guiding and
providing direction for future research in the AI ethics field.
","The Key Concepts of Ethics of Artificial Intelligence - A Keyword based
  Systematic Mapping Study"
"  The semantic information regulates the expressiveness of a web service.
State-of-the-art approaches in web services research have used the semantics of
a web service for different purposes, mainly for service discovery,
composition, execution etc. In this paper, our main focus is on semantic driven
Quality of Service (QoS) aware service composition. Most of the contemporary
approaches on service composition have used the semantic information to combine
the services appropriately to generate the composition solution. However, in
this paper, our intention is to use the semantic information to expedite the
service composition algorithm. Here, we present a service composition framework
that uses semantic information of a web service to generate different clusters,
where the services are semantically related within a cluster. Our final aim is
to construct a composition solution using these clusters that can efficiently
scale to large service spaces, while ensuring solution quality. Experimental
results show the efficiency of our proposed method.
","A Methodology for Search Space Reduction in QoS Aware Semantic Web
  Service Composition"
"  Weighted bipolar argumentation frameworks offer a tool for decision support
and social media analysis. Arguments are evaluated by an iterative procedure
that takes initial weights and attack and support relations into account. Until
recently, convergence of these iterative procedures was not very well
understood in cyclic graphs. Mossakowski and Neuhaus recently introduced a
unification of different approaches and proved first convergence and divergence
results. We build up on this work, simplify and generalize convergence results
and complement them with runtime guarantees. As it turns out, there is a
tradeoff between semantics' convergence guarantees and their ability to move
strength values away from the initial weights. We demonstrate that divergence
problems can be avoided without this tradeoff by continuizing semantics.
Semantically, we extend the framework with a Duality property that assures a
symmetric impact of attack and support relations. We also present a Java
implementation of modular semantics and explain the practical usefulness of the
theoretical ideas.
","Extending Modular Semantics for Bipolar Weighted Argumentation
  (Technical Report)"
"  Recent research in extensions of Answer Set Programming has included a
renewed interest in the language of Epistemic Specifications, which adds modal
operators K (""known"") and M (""may be true"") to provide for more powerful
introspective reasoning and enhanced capability, particularly when reasoning
with incomplete information. An epistemic logic program is a set of rules in
this language. Infused with the research has been the desire for an efficient
solver to enable the practical use of such programs for problem solving. In
this paper, we report on the current state of development of epistemic logic
program solvers.
",A survey of advances in epistemic logic program solvers
"  Starcraft II (SC2) is widely considered as the most challenging Real Time
Strategy (RTS) game. The underlying challenges include a large observation
space, a huge (continuous and infinite) action space, partial observations,
simultaneous move for all players, and long horizon delayed rewards for local
decisions. To push the frontier of AI research, Deepmind and Blizzard jointly
developed the StarCraft II Learning Environment (SC2LE) as a testbench of
complex decision making systems. SC2LE provides a few mini games such as
MoveToBeacon, CollectMineralShards, and DefeatRoaches, where some AI agents
have achieved the performance level of human professional players. However, for
full games, the current AI agents are still far from achieving human
professional level performance. To bridge this gap, we present two full game AI
agents in this paper - the AI agent TStarBot1 is based on deep reinforcement
learning over a flat action structure, and the AI agent TStarBot2 is based on
hard-coded rules over a hierarchical action structure. Both TStarBot1 and
TStarBot2 are able to defeat the built-in AI agents from level 1 to level 10 in
a full game (1v1 Zerg-vs-Zerg game on the AbyssalReef map), noting that level
8, level 9, and level 10 are cheating agents with unfair advantages such as
full vision on the whole map and resource harvest boosting. To the best of our
knowledge, this is the first public work to investigate AI agents that can
defeat the built-in AI in the StarCraft II full game.
","TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in
  the Full Game"
"  A route planning query has many real-world applications and has been studied
extensively in outdoor spaces such as road networks or Euclidean space. Despite
its many applications in indoor venues (e.g., shopping centres, libraries,
airports), almost all existing studies are specifically designed for outdoor
spaces and do not take into account unique properties of the indoor spaces such
as hallways, stairs, escalators, rooms etc. We identify this research gap and
formally define the problem of category aware multi-criteria route planning
query, denoted by CAM, which returns the optimal route from an indoor source
point to an indoor target point that passes through at least one indoor point
from each given category while minimizing the total cost of the route in terms
of travel distance and other relevant attributes. We show that CAM query is
NP-hard. Based on a novel dominance-based pruning, we propose an efficient
algorithm which generates high-quality results. We provide an extensive
experimental study conducted on the largest shopping centre in Australia and
compare our algorithm with alternative approaches. The experiments demonstrate
that our algorithm is highly efficient and produces quality results.
","An Efficient Approximation Algorithm for Multi-criteria Indoor Route
  Planning Queries"
"  As Artificial Intelligence (AI) technologies proliferate, concern has
centered around the long-term dangers of job loss or threats of machines
causing harm to humans. All of this concern, however, detracts from the more
pertinent and already existing threats posed by AI today: its ability to
amplify bias found in training datasets, and swiftly impact marginalized
populations at scale. Government and public sector institutions have a
responsibility to citizens to establish a dialogue with technology developers
and release thoughtful policy around data standards to ensure diverse
representation in datasets to prevent bias amplification and ensure that AI
systems are built with inclusion in mind.
",Bias Amplification in Artificial Intelligence Systems
"  This paper argues the need for research to realize uncertainty-aware
artificial intelligence and machine learning (AI\&ML) systems for decision
support by describing a number of motivating scenarios. Furthermore, the paper
defines uncertainty-awareness and lays out the challenges along with surveying
some promising research directions. A theoretical demonstration illustrates how
two emerging uncertainty-aware ML and AI technologies could be integrated and
be of value for a route planning operation.
",Uncertainty Aware AI ML: Why and How
"  We enable aProbLog---a probabilistic logical programming approach---to reason
in presence of uncertain probabilities represented as Beta-distributed random
variables. We achieve the same performance of state-of-the-art algorithms for
highly specified and engineered domains, while simultaneously we maintain the
flexibility offered by aProbLog in handling complex relational domains. Our
motivation is that faithfully capturing the distribution of probabilities is
necessary to compute an expected utility for effective decision making under
uncertainty: unfortunately, these probability distributions can be highly
uncertain due to sparse data. To understand and accurately manipulate such
probability distributions we need a well-defined theoretical framework that is
provided by the Beta distribution, which specifies a distribution of
probabilities representing all the possible values of a probability when the
exact value is unknown.
",Probabilistic Logic Programming with Beta-Distributed Random Variables
"  Artificial Intelligence (AI) approaches to problem-solving and
decision-making are becoming more and more complex, leading to a decrease in
the understandability of solutions. The European Union's new General Data
Protection Regulation tries to tackle this problem by stipulating a ""right to
explanation"" for decisions made by AI systems. One of the AI paradigms that may
be affected by this new regulation is Answer Set Programming (ASP). Thanks to
the emergence of efficient solvers, ASP has recently been used for
problem-solving in a variety of domains, including medicine, cryptography, and
biology. To ensure the successful application of ASP as a problem-solving
paradigm in the future, explanations of ASP solutions are crucial. In this
survey, we give an overview of approaches that provide an answer to the
question of why an answer set is a solution to a given problem, notably
off-line justifications, causal graphs, argumentative explanations and why-not
provenance, and highlight their similarities and differences. Moreover, we
review methods explaining why a set of literals is not an answer set or why no
solution exists at all.
","Answering the ""why"" in Answer Set Programming - A Survey of Explanation
  Approaches"
"  This paper describes how to carry out a feasibility study for a potential
knowledge based system application. It discusses factors to be considered under
three headings: the business case, the technical feasibility, and stakeholder
issues. It concludes with a case study of a feasibility study for a KBS to
guide surgeons in diagnosis and treatment of thyroid conditions.
",Conducting Feasibility Studies for Knowledge Based Systems
"  Aging population ratios are rising significantly. Meanwhile, smart home based
health monitoring services are evolving rapidly to become a viable alternative
to traditional healthcare solutions. Such services can augment qualitative
analyses done by gerontologists with quantitative data. Hence, the recognition
of Activities of Daily Living (ADL) has become an active domain of research in
recent times. For a system to perform human activity recognition in a
real-world environment, multiple requirements exist, such as scalability,
robustness, ability to deal with uncertainty (e.g., missing sensor data), to
operate with multi-occupants and to take into account their privacy and
security. This paper attempts to address the requirements of scalability and
robustness, by describing a reasoning mechanism based on modular spatial and/or
temporal context models as a network of ontologies. The reasoning mechanism has
been implemented in a smart home system referred to as Arianna+. The paper
presents and discusses a use case, and experiments are performed on a simulated
dataset, to showcase Arianna+'s modularity feature, internal working, and
computational performance. Results indicate scalability and robustness for
human activity recognition processes.
","Arianna+: Scalable Human Activity Recognition by Reasoning with a
  Network of Ontologies"
"  Recent progress in logic programming (e.g., the development of the Answer Set
Programming paradigm) has made it possible to teach it to general undergraduate
and even middle/high school students. Given the limited exposure of these
students to computer science, the complexity of downloading, installing and
using tools for writing logic programs could be a major barrier for logic
programming to reach a much wider audience. We developed onlineSPARC, an online
answer set programming environment with a self contained file system and a
simple interface. It allows users to type/edit logic programs and perform
several tasks over programs, including asking a query to a program, getting the
answer sets of a program, and producing a drawing/animation based on the answer
sets of a program.
",onlineSPARC: a Programming Environment for Answer Set Programming
"  The representation of knowledge based on first-order logic captures the
richness of natural language and supports multiple probabilistic inference
models. Although symbolic representation enables quantitative reasoning with
statistical probability, it is difficult to utilize with machine learning
models as they perform numerical operations. In contrast, knowledge embedding
(i.e., high-dimensional and continuous vectors) is a feasible approach to
complex reasoning that can not only retain the semantic information of
knowledge but also establish the quantifiable relationship among them. In this
paper, we propose recursive neural knowledge network (RNKN), which combines
medical knowledge based on first-order logic with recursive neural network for
multi-disease diagnosis. After RNKN is efficiently trained from manually
annotated Chinese Electronic Medical Records (CEMRs), diagnosis-oriented
knowledge embeddings and weight matrixes are learned. Experimental results
verify that the diagnostic accuracy of RNKN is superior to that of some
classical machine learning models and Markov logic network (MLN). The results
also demonstrate that the more explicit the evidence extracted from CEMRs is,
the better is the performance achieved. RNKN gradually exhibits the
interpretation of knowledge embeddings as the number of training epochs
increases.
","Medical Knowledge Embedding Based on Recursive Neural Network for
  Multi-Disease Diagnosis"
"  Trains are part-and-parcel of every day lives in countries with large,
diverse, multi-lingual population like India. Consequently, an assistant which
can accurately predict and explain train delays will help people and businesses
alike. We present a novel conversation agent which can engage with people about
train status and inform them about its delay at in-line stations. It is trained
on past delay data from a subset of trains and generalizes to others.
",A Train Status Assistant for Indian Railways
"  In Intelligent Tutoring System (ITS), tracing the student's knowledge state
during learning has been studied for several decades in order to provide more
supportive learning instructions. In this paper, we propose a novel model for
knowledge tracing that i) captures students' learning ability and dynamically
assigns students into distinct groups with similar ability at regular time
intervals, and ii) combines this information with a Recurrent Neural Network
architecture known as Deep Knowledge Tracing. Experimental results confirm that
the proposed model is significantly better at predicting student performance
than well known state-of-the-art techniques for student modelling.
","Deep Knowledge Tracing and Dynamic Student Classification for Knowledge
  Tracing"
"  Multi-agent models are a suitable starting point to model complex social
interactions. However, as the complexity of the systems increase, we argue that
novel modeling approaches are needed that can deal with inter-dependencies at
different levels of society, where many heterogeneous parties (software agents,
robots, humans) are interacting and reacting to each other. In this paper, we
present a formalization of a social framework for agents based in the concept
of Social Practices as high level specifications of normal (expected) behavior
in a given social context. We argue that social practices facilitate the
practical reasoning of agents in standard social interactions.
",Interactions as Social Practices: towards a formalization
"  Representing meaning in the form of high dimensional vectors is a common and
powerful tool in biologically inspired architectures. While the meaning of a
set of concepts can be summarized by taking a (possibly weighted) sum of their
associated vectors, this has generally been treated as a one-way operation. In
this paper we show how a technique built to aid sparse vector decomposition
allows in many cases the exact recovery of the inputs and weights to such a
sum, allowing a single vector to represent an entire set of vectors from a
dictionary. We characterize the number of vectors that can be recovered under
various conditions, and explore several ways such a tool can be used for
vector-based reasoning.
",Representing Sets as Summed Semantic Vectors
"  The Knowledge graph (KG) uses the triples to describe the facts in the real
world. It has been widely used in intelligent analysis and applications.
However, possible noises and conflicts are inevitably introduced in the process
of constructing. And the KG based tasks or applications assume that the
knowledge in the KG is completely correct and inevitably bring about potential
deviations. In this paper, we establish a knowledge graph triple
trustworthiness measurement model that quantify their semantic correctness and
the true degree of the facts expressed. The model is a crisscrossing neural
network structure. It synthesizes the internal semantic information in the
triples and the global inference information of the KG to achieve the
trustworthiness measurement and fusion in the three levels of entity level,
relationship level, and KG global level. We analyzed the validity of the model
output confidence values, and conducted experiments in the real-world dataset
FB15K (from Freebase) for the knowledge graph error detection task. The
experimental results showed that compared with other models, our model achieved
significant and consistent improvements.
",Triple Trustworthiness Measurement for Knowledge Graph
"  Procedural content generation via Machine Learning (PCGML) is the umbrella
term for approaches that generate content for games via machine learning. One
of the benefits of PCGML is that, unlike search or grammar-based PCG, it does
not require hand authoring of initial content or rules. Instead, PCGML relies
on existing content and black box models, which can be difficult to tune or
tweak without expert knowledge. This is especially problematic when a human
designer needs to understand how to manipulate their data or models to achieve
desired results. We present an approach to Explainable PCGML via Design
Patterns in which the design patterns act as a vocabulary and mode of
interaction between user and model. We demonstrate that our technique
outperforms non-explainable versions of our system in interactions with five
expert designers, four of whom lack any machine learning expertise.
",Explainable PCGML via Game Design Patterns
"  We introduce the problem of generating Let's Play-style commentary of
gameplay video via machine learning. We propose an analysis of Let's Play
commentary and a framework for building such a system. To test this framework
we build an initial, naive implementation, which we use to interrogate the
assumptions of the framework. We demonstrate promising results towards future
Let's Play commentary generation.
",Towards Automated Let's Play Commentary
"  We propose the following question: what game-like interactive system would
provide a good environment for measuring the impact and success of a
co-creative, cooperative agent? Creativity is often formulated in terms of
novelty, value, surprise and interestingness. We review how these concepts are
measured in current computational intelligence research and provide a mapping
from modern electronic and tabletop games to open research problems in
mixed-initiative systems and computational co-creativity. We propose
application scenarios for future research, and a number of metrics under which
the performance of cooperative agents in these environments will be evaluated.
",Towards Game-based Metrics for Computational Co-creativity
"  Hanabi is a cooperative card game with hidden information that has won
important awards in the industry and received some recent academic attention. A
two-track competition of agents for the game will take place in the 2018 CIG
conference. In this paper, we develop a genetic algorithm that builds
rule-based agents by determining the best sequence of rules from a fixed rule
set to use as strategy. In three separate experiments, we remove human
assumptions regarding the ordering of rules, add new, more expressive rules to
the rule set and independently evolve agents specialized at specific game
sizes. As result, we achieve scores superior to previously published research
for the mirror and mixed evaluation of agents.
",Evolving Agents for the Hanabi 2018 CIG Competition
"  We present a second-order language that can be used to succinctly specify
ontologies in a consistent and transparent manner. This language is based on
ontology templates (OTTR), a framework for capturing recurring patterns of
axioms in ontological modelling. The language and our results are independent
of any specific DL. We define the language and its semantics, including the
case of negation-as-failure, investigate reasoning over ontologies specified
using our language, and show results about the decidability of useful reasoning
tasks about the language itself. We also state and discuss some open problems
that we believe to be of interest.
","Generating Ontologies from Templates: A Rule-Based Approach for
  Capturing Regularity"
"  A mixed group of manned and unmanned aerial vehicles is considered as a
distributed system. A lattice of tasks which may be fulfilled by the system
matches to it. An external multiplication operation is defined at the lattice,
which defines correspondingly linear logic operations. Linear implication and
tensor product are used to choose a system reconfiguration variant, i.e., to
determine a new task executor choice. The task lattice structure (i.e., the
system purpose) and the operation definitions largely define the choice. Thus,
the choice is mainly the system purpose consequence. Such a method of the
behavior variant choice facilitates the decision making by the pilot
controlling the group. The suggested approach is illustrated using an example
of a mixed group control at forest fire compression.
","A Way to Facilitate Decision Making in a Mixed Group of Manned and
  Unmanned Aerial Vehicles"
"  In this project, we combine AlphaGo algorithm with Curriculum Learning to
crack the game of Gomoku. Modifications like Double Networks Mechanism and
Winning Value Decay are implemented to solve the intrinsic asymmetry and
short-sight of Gomoku. Our final AI AlphaGomoku, through two days' training on
a single GPU, has reached humans' playing level.
","AlphaGomoku: An AlphaGo-based Gomoku Artificial Intelligence using
  Curriculum Learning"
"  Reinforcement learning (RL) agents aim at learning by interacting with an
environment, and are not designed for representing or reasoning with
declarative knowledge. Knowledge representation and reasoning (KRR) paradigms
are strong in declarative KRR tasks, but are ill-equipped to learn from such
experiences. In this work, we integrate logical-probabilistic KRR with
model-based RL, enabling agents to simultaneously reason with declarative
knowledge and learn from interaction experiences. The knowledge from humans and
RL is unified and used for dynamically computing task-specific planning models
under potentially new environments. Experiments were conducted using a mobile
robot working on dialog, navigation, and delivery tasks. Results show
significant improvements, in comparison to existing model-based RL methods.
","Robot Representation and Reasoning with Knowledge from Reinforcement
  Learning"
"  The ability to exploit the opportunities offered by AI within UK Defence
calls for an understanding of systemic issues required to achieve an effective
operational capability. This paper provides the authors' views of issues which
currently block UK Defence from fully benefitting from AI technology. These are
situated within a reference model for the AI Value Train, so enabling the
community to address the exploitation of such data and software intensive
systems in a systematic, end to end manner. The paper sets out the conditions
for success including: Researching future solutions to known problems and
clearly defined use cases; Addressing achievable use cases to show benefit;
Enhancing the availability of Defence-relevant data; Enhancing Defence 'know
how' in AI; Operating Software Intensive supply chain eco-systems at required
breadth and pace; Governance and, the integration of software and platform
supply chains and operating models.
","A Systems Approach to Achieving the Benefits of Artificial Intelligence
  in UK Defence"
"  Hierarchical planners that produce interpretable and appropriate plans are
desired, especially in its application to supporting human decision making. In
the typical development of the hierarchical planners, higher-level planners and
symbol grounding functions are manually created, and this manual creation
requires much human effort. In this paper, we propose a framework that can
automatically refine symbol grounding functions and a high-level planner to
reduce human effort for designing these modules. In our framework, symbol
grounding and high-level planning, which are based on manually-designed
knowledge bases, are modeled with semi-Markov decision processes. A policy
gradient method is then applied to refine the modules, in which two terms for
updating the modules are considered. The first term, called a reinforcement
term, contributes to updating the modules to improve the overall performance of
a hierarchical planner to produce appropriate plans. The second term, called a
penalty term, contributes to keeping refined modules consistent with the
manually-designed original modules. Namely, it keeps the planner, which uses
the refined modules, producing interpretable plans. We perform preliminary
experiments to solve the Mountain car problem, and its results show that a
manually-designed high-level planner and symbol grounding function were
successfully refined by our framework.
","Refining Manually-Designed Symbol Grounding and High-Level Planning by
  Policy Gradients"
"  There is general consensus that it is important for artificial intelligence
(AI) and machine learning systems to be explainable and/or interpretable.
However, there is no general consensus over what is meant by 'explainable' and
'interpretable'. In this paper, we argue that this lack of consensus is due to
there being several distinct stakeholder communities. We note that, while the
concerns of the individual communities are broadly compatible, they are not
identical, which gives rise to different intents and requirements for
explainability/interpretability. We use the software engineering distinction
between validation and verification, and the epistemological distinctions
between knowns/unknowns, to tease apart the concerns of the stakeholder
communities and highlight the areas where their foci overlap or diverge. It is
not the purpose of the authors of this paper to 'take sides' - we count
ourselves as members, to varying degrees, of multiple communities - but rather
to help disambiguate what stakeholders mean when they ask 'Why?' of an AI.
",Stakeholders in Explainable AI
"  This paper presents a practical application of Answer Set Programming to the
understanding of narratives about restaurants. While this task was investigated
in depth by Erik Mueller, exceptional scenarios remained a serious challenge
for his script-based story comprehension system. We present a methodology that
remedies this issue by modeling characters in a restaurant episode as
intentional agents. We focus especially on the refinement of certain components
of this methodology in order to increase coverage and performance. We present a
restaurant story corpus that we created to design and evaluate our methodology.
Under consideration in Theory and Practice of Logic Programming (TPLP).
","An Application of ASP Theories of Intentions to Understanding Restaurant
  Scenarios: Insights and Narrative Corpus"
"  The theory of belief functions is widely used for data from multiple sources.
Different evidence combination rules have been proposed in this framework
according to the properties of the sources to combine. However, most of these
combination rules are not efficient when there are a large number of sources.
This is due to either the complexity or the existence of an absorbing element
such as the total conflict mass function for the conjunctive based rules when
applied on unreliable evidence. In this paper, based on the assumption that the
majority of sources are reliable, a combination rule for a large number of
sources is proposed using a simple idea: the more common ideas the sources
share, the more reliable these sources are supposed to be. This rule is
adaptable for aggregating a large number of sources which may not all be
reliable. It will keep the spirit of the conjunctive rule to reinforce the
belief on the focal elements with which the sources are in agreement. The mass
on the emptyset will be kept as an indicator of the conflict. The proposed
rule, called LNS-CR (Conjunctive combinationRule for a Large Number of
Sources), is evaluated on synthetic mass functions. The experimental results
verify that the rule can be effectively used to combine a large number of mass
functions and to elicit the major opinion.
",A belief combination rule for a large number of sources
"  In this paper we study the problem of making predictions using multiple
structural casual models defined by different agents, under the constraint that
the prediction satisfies the criterion of counterfactual fairness. Relying on
the frameworks of causality, fairness and opinion pooling, we build upon and
extend previous work focusing on the qualitative aggregation of causal Bayesian
networks and causal models. In order to complement previous qualitative
results, we devise a method based on Monte Carlo simulations. This method
enables a decision-maker to aggregate the outputs of the causal models provided
by different experts while guaranteeing the counterfactual fairness of the
result. We demonstrate our approach on a simple, yet illustrative, toy case
study.
",Counterfactually Fair Prediction Using Multiple Causal Models
"  The paper presents an extension of Shannon entropy for neutrosophic
information. This extension uses a new formula for distance between two
neutrosophic triplets. In addition, the obtained results are particularized for
bifuzzy, intuitionistic and paraconsistent fuzzy information.
",Shannon Entropy for Neutrosophic Information
"  This paper presents a novel SHOI tableau calculus which incorporates
algebraic reasoning for deciding ontology consistency. Numerical restrictions
imposed by nominals, existential and universal restrictions are encoded into a
set of linear inequalities. Column generation and branch-and-price algorithms
are used to solve these inequalities. Our preliminary experiments indicate that
this calculus performs better on SHOI ontologies than standard tableau methods.
",Handling Nominals and Inverse Roles using Algebraic Reasoning
"  Humans learn complex latent structures from their environments (e.g., natural
language, mathematics, music, social hierarchies). In cognitive science and
cognitive neuroscience, models that infer higher-order structures from sensory
or first-order representations have been proposed to account for the complexity
and flexibility of human behavior. But how do the structures that these models
invoke arise in neural systems in the first place? To answer this question, we
explain how a system can learn latent representational structures (i.e.,
predicates) from experience with wholly unstructured data. During the process
of predicate learning, an artificial neural network exploits the naturally
occurring dynamic properties of distributed computing across neuronal
assemblies in order to learn predicates, but also to combine them
compositionally, two computational aspects which appear to be necessary for
human behavior as per formal theories in multiple domains. We describe how
predicates can be combined generatively using neural oscillations to achieve
human-like extrapolation and compositionality in an artificial neural network.
The ability to learn predicates from experience, to represent structures
compositionally, and to extrapolate to unseen data offers an inroads to
understanding and modeling the most complex human behaviors.
","Predicate learning in neural systems: Discovering latent generative
  structures"
"  We study the problem of representation learning in goal-conditioned
hierarchical reinforcement learning. In such hierarchical structures, a
higher-level controller solves tasks by iteratively communicating goals which a
lower-level policy is trained to reach. Accordingly, the choice of
representation -- the mapping of observation space to goal space -- is crucial.
To study this problem, we develop a notion of sub-optimality of a
representation, defined in terms of expected reward of the optimal hierarchical
policy using this representation. We derive expressions which bound the
sub-optimality and show how these expressions can be translated to
representation learning objectives which may be optimized in practice. Results
on a number of difficult continuous-control tasks show that our approach to
representation learning yields qualitatively better representations as well as
quantitatively better hierarchical policies, compared to existing methods (see
videos at https://sites.google.com/view/representation-hrl).
","Near-Optimal Representation Learning for Hierarchical Reinforcement
  Learning"
"  This paper presents Co-Arg, a new type of cognitive assistant to an
intelligence analyst that enables the synergistic integration of analyst
imagination and expertise, computer knowledge and critical reasoning, and crowd
wisdom, to draw defensible and persuasive conclusions from masses of evidence
of all types, in a world that is changing all the time. Co-Arg's goal is to
improve the quality of the analytic results and enhance their understandability
for both experts and novices. The performed analysis is based on a sound and
transparent argumentation that links evidence to conclusions in a way that
shows very clearly how the conclusions have been reached, what evidence was
used and how, what is not known, and what assumptions have been made. The
analytic results are presented in a report describes the analytic conclusion
and its probability, the main favoring and disfavoring arguments, the
justification of the key judgments and assumptions, and the missing information
that might increase the accuracy of the solution.
",Co-Arg: Cogent Argumentation with Crowd Elicitation
"  The aim of medical knowledge representation is to capture the detailed domain
knowledge in a clinically efficient manner and to offer a reliable resolution
with the acquired knowledge. The knowledge base to be used by a medical expert
system should allow incremental growth with inclusion of updated knowledge over
the time. As knowledge are gathered from a variety of knowledge sources by
different knowledge engineers, the problem of redundancy is an important
concern here due to increased processing time of knowledge and occupancy of
large computational storage to accommodate all the gathered knowledge. Also
there may exist many inconsistent knowledge in the knowledge base. In this
paper, we have proposed a rough set based lattice structure for knowledge
representation in medical expert systems which overcomes the problem of
redundancy and inconsistency in knowledge and offers computational efficiency
with respect to both time and space. We have also generated an optimal set of
decision rules that would be used directly by the inference engine. The
reliability of each rule has been measured using a new metric called
credibility factor, and the certainty and coverage factors of a decision rule
have been re-defined. With a set of decisions rules arranged in descending
order according to their reliability measures, the medical expert system will
consider the highly reliable and certain rules at first, then it would search
for the possible and uncertain rules at later stage, if recommended by
physicians. The proposed knowledge representation technique has been
illustrated using an example from the domain of low back pain. The proposed
scheme ensures completeness, consistency, integrity, non-redundancy, and ease
of access.
","Rough set based lattice structure for knowledge representation in
  medical expert systems: low back pain management case study"
"  Mining dense quasi-cliques is a well-known clustering task with applications
ranging from social networks over collaboration graphs to document analysis.
Recent work has extended this task to multiple graphs; i.e. the goal is to find
groups of vertices highly dense among multiple graphs. In this paper, we argue
that in a multi-graph scenario the sparsity is valuable for knowledge
extraction as well. We introduce the concept of contrasting quasi-clique
patterns: a collection of vertices highly dense in one graph but highly sparse
(i.e. less connected) in a second graph. Thus, these patterns specifically
highlight the difference/contrast between the considered graphs. Based on our
novel model, we propose an algorithm that enables fast computation of
contrasting patterns by exploiting intelligent traversal and pruning
techniques. We showcase the potential of contrasting patterns on a variety of
synthetic and real-world datasets.
",Mining Contrasting Quasi-Clique Patterns
"  Fairness is an increasingly important concern as machine learning models are
used to support decision making in high-stakes applications such as mortgage
lending, hiring, and prison sentencing. This paper introduces a new open source
Python toolkit for algorithmic fairness, AI Fairness 360 (AIF360), released
under an Apache v2.0 license {https://github.com/ibm/aif360). The main
objectives of this toolkit are to help facilitate the transition of fairness
research algorithms to use in an industrial setting and to provide a common
framework for fairness researchers to share and evaluate algorithms.
  The package includes a comprehensive set of fairness metrics for datasets and
models, explanations for these metrics, and algorithms to mitigate bias in
datasets and models. It also includes an interactive Web experience
(https://aif360.mybluemix.net) that provides a gentle introduction to the
concepts and capabilities for line-of-business users, as well as extensive
documentation, usage guidance, and industry-specific tutorials to enable data
scientists and practitioners to incorporate the most appropriate tool for their
problem into their work products. The architecture of the package has been
engineered to conform to a standard paradigm used in data science, thereby
further improving usability for practitioners. Such architectural design and
abstractions enable researchers and developers to extend the toolkit with their
new algorithms and improvements, and to use it for performance benchmarking. A
built-in testing infrastructure maintains code quality.
","AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and
  Mitigating Unwanted Algorithmic Bias"
"  While E-commerce has been growing explosively and online shopping has become
popular and even dominant in the present era, online transaction fraud control
has drawn considerable attention in business practice and academic research.
Conventional fraud control considers mainly the interactions of two major
involved decision parties, i.e. merchants and fraudsters, to make fraud
classification decision without paying much attention to dynamic looping effect
arose from the decisions made by other profit-related parties. This paper
proposes a novel fraud control framework that can quantify interactive effects
of decisions made by different parties and can adjust fraud control strategies
using data analytics, artificial intelligence, and dynamic optimization
techniques. Three control models, Naive, Myopic and Prospective Controls, were
developed based on the availability of data attributes and levels of label
maturity. The proposed models are purely data-driven and self-adaptive in a
real-time manner. The field test on Microsoft real online transaction data
suggested that new systems could sizably improve the company's profit.
","Discriminative Data-driven Self-adaptive Fraud Control Decision System
  with Incomplete Information"
"  Linear temporal logic and automaton-based run-time verification provide a
powerful framework for designing task and motion planning algorithms for
autonomous agents. The drawback to this approach is the computational cost of
operating on high resolution discrete abstractions of continuous dynamical
systems. In particular, the computational bottleneck that arises is converting
perceived environment variables into a labeling function on the states of a
Kripke structure or analogously the transitions of a labeled transition system.
This paper presents the design and empirical evaluation of an approach to
constructing the labeling function that exposes a large degree of parallelism
in the operation as well as efficient memory access patterns. The approach is
implemented on a commodity GPU and empirical results demonstrate the efficacy
of the labeling technique for real-time planning and decision-making.
","Accelerated Labeling of Discrete Abstractions for Autonomous Driving
  Subject to LTL Specifications"
"  This work is done as part of a master's thesis project. The goal is to
integrate two or more ontologies (of the same or close domains) in a new
consistent and coherent OWL ontology to insure semantic interoperability
between them. To do this, we have chosen to create a bridge ontology that
includes all source ontologies and their bridging axioms in a customized way.
In addition, we introduced a new criterion for obtaining an ontology of better
quality (having the minimum of semantic/logical conflicts). We have also
proposed new terminology and definitions that clarify the unclear and misplaced
""integration"" and ""merging"" notions that are randomly used in state-of-the-art
works. Finally, we tested and evaluated our OIA2R tool using ontologies and
reference alignments of the OAEI campaign. It turned out that it is generic,
efficient and powerful enough.
","A New Method for the Semantic Integration of Multiple OWL Ontologies
  using Alignments"
"  Minesweeper as a puzzle video game and is proved that it is an NPC problem.
We use CSP, Logic Inference and Sampling to make a minesweeper solver and we
limit us each select in 5 seconds.
","A Minesweeper Solver Using Logic Inference, CSP and Sampling"
"  The Clustered Traveling Salesman Problem with a Prespecified Order on the
Clusters, a variant of the well-known traveling salesman problem is studied in
literature. In this problem, delivery locations are divided into clusters with
different urgency levels and more urgent locations must be visited before less
urgent ones. However, this could lead to an inefficient route in terms of
traveling cost. This priority-oriented constraint can be relaxed by a rule
called d-relaxed priority that provides a trade-off between transportation cost
and emergency level. Our research proposes two approaches to solve the problem
with d-relaxed priority rule. We improve the mathematical formulation proposed
in the literature to construct an exact solution method. A meta-heuristic
method based on the framework of Iterated Local Search with problem-tailored
operators is also introduced to find approximate solutions. Experimental
results show the effectiveness of our methods.
","Solving the clustered traveling salesman problem with d-relaxed priority
  rule"
"  In the last couple of years, the rise of Artificial Intelligence and the
successes of academic breakthroughs in the field have been inescapable. Vast
sums of money have been thrown at AI start-ups. Many existing tech companies --
including the giants like Google, Amazon, Facebook, and Microsoft -- have
opened new research labs. The rapid changes in these everyday work and
entertainment tools have fueled a rising interest in the underlying technology
itself; journalists write about AI tirelessly, and companies -- of tech nature
or not -- brand themselves with AI, Machine Learning or Deep Learning whenever
they get a chance. Confronting squarely this media coverage, several analysts
are starting to voice concerns about over-interpretation of AI's blazing
successes and the sometimes poor public reporting on the topic. This paper
reviews briefly the track-record in AI and Machine Learning and finds this
pattern of early dramatic successes, followed by philosophical critique and
unexpected difficulties, if not downright stagnation, returning almost to the
clock in 30-year cycles since 1958.
",The 30-Year Cycle In The AI Debate
"  In situations where explicit communication is limited, human collaborators
act by learning to: (i) infer meaning behind their partner's actions, and (ii)
convey private information about the state to their partner implicitly through
actions. The first component of this learning process has been well-studied in
multi-agent systems, whereas the second --- which is equally crucial for
successful collaboration --- has not. To mimic both components mentioned above,
thereby completing the learning process, we introduce a novel algorithm: Policy
Belief Learning (PBL). PBL uses a belief module to model the other agent's
private information and a policy module to form a distribution over actions
informed by the belief module. Furthermore, to encourage communication by
actions, we propose a novel auxiliary reward which incentivizes one agent to
help its partner to make correct inferences about its private information. The
auxiliary reward for communication is integrated into the learning of the
policy module. We evaluate our approach on a set of environments including a
matrix game, particle environment and the non-competitive bidding problem from
contract bridge. We show empirically that this auxiliary reward is effective
and easy to generalize. These results demonstrate that our PBL algorithm can
produce strong pairs of agents in collaborative games where explicit
communication is disabled.
",Learning to Communicate Implicitly By Actions
"  Automatic charge prediction aims to predict appropriate final charges
according to the fact descriptions for a given criminal case. Automatic charge
prediction plays a critical role in assisting judges and lawyers to improve the
efficiency of legal decisions, and thus has received much attention.
Nevertheless, most existing works on automatic charge prediction perform
adequately on high-frequency charges but are not yet capable of predicting
few-shot charges with limited cases. In this paper, we propose a Sequence
Enhanced Capsule model, dubbed as SECaps model, to relieve this problem.
Specifically, following the work of capsule networks, we propose the seq-caps
layer, which considers sequence information and spatial information of legal
texts simultaneously. Then we design a attention residual unit, which provides
auxiliary information for charge prediction. In addition, our SECaps model
introduces focal loss, which relieves the problem of imbalanced charges.
Comparing the state-of-the-art methods, our SECaps model obtains 4.5% and 6.4%
absolutely considerable improvements under Macro F1 in Criminal-S and
Criminal-L respectively. The experimental results consistently demonstrate the
superiorities and competitiveness of our proposed model.
",SECaps: A Sequence Enhanced Capsule Model for Charge Prediction
"  In 'Winograd Schema' (WS) sentences like ""The city councilmen refused the
demonstrators a permit because they feared violence"" and ""The city councilmen
refused the demonstrators a permit because they advocated revolution"", it is
easy for adults to understand what ""they"" refers to but can be difficult for AI
systems. This paper describes how the SP System -- outlined in an appendix --
may solve this kind of problem of interpretation. The central idea is that a
knowledge of discontinuous associations amongst linguistic features, and an
ability to recognise such patterns of associations, provides a robust means of
determining what a pronoun like ""they"" refers to. For any AI system to solve
this kind of problem, it needs appropriate knowledge of relevant syntax and
semantics which, ideally, it should learn for itself. Although the SP System
has some strengths in unsupervised learning, its capabilities in this area are
not yet good enough to learn the kind of knowledge needed to interpret WS
examples, so it must be supplied with such knowledge at the outset. However,
its existing strengths in unsupervised learning suggest that it has potential
to learn the kind of knowledge needed for the interpretation of WS examples. In
particular, it has potential to learn the kind of discontinuous association of
linguistic features mentioned earlier.
","Interpreting Winograd Schemas Via the SP Theory of Intelligence and Its
  Realisation in the SP Computer Model"
"  We classify .NET files as either benign or malicious by examining directed
graphs derived from the set of functions comprising the given file. Each graph
is viewed probabilistically as a Markov chain where each node represents a code
block of the corresponding function, and by computing the PageRank vector
(Perron vector with transport), a probability measure can be defined over the
nodes of the given graph. Each graph is vectorized by computing Lebesgue
antiderivatives of hand-engineered functions defined on the vertex set of the
given graph against the PageRank measure. Files are subsequently vectorized by
aggregating the set of vectors corresponding to the set of graphs resulting
from decompiling the given file. The result is a fast, intuitive, and
easy-to-compute glass-box vectorization scheme, which can be leveraged for
training a standalone classifier or to augment an existing feature space. We
refer to this vectorization technique as PageRank Measure Integration
Vectorization (PMIV). We demonstrate the efficacy of PMIV by training a vanilla
random forest on 2.5 million samples of decompiled .NET, evenly split between
benign and malicious, from our in-house corpus and compare this model to a
baseline model which leverages a text-only feature space. The median time
needed for decompilation and scoring was 24ms.
","Applications of Graph Integration to Function Comparison and Malware
  Classification"
"  The issue of representing attacks to attacks in argumentation is receiving an
increasing attention as a useful conceptual modelling tool in several contexts.
In this paper we present AFRA, a formalism encompassing unlimited recursive
attacks within argumentation frameworks. AFRA satisfies the basic requirements
of definition simplicity and rigorous compatibility with Dung's theory of
argumentation. This paper provides a complete development of the AFRA formalism
complemented by illustrative examples and a detailed comparison with other
recursive attack formalizations.
",AFRA: Argumentation framework with recursive attacks
"  The theory of abstract argumentation frameworks (afs) has, in the main,
focused on finite structures, though there are many significant contexts where
argumentation can be regarded as a process involving infinite objects. To
address this limitation, in this paper we propose a novel approach for
describing infinite afs using tools from formal language theory. In particular,
the possibly infinite set of arguments is specified through the language
recognized by a deterministic finite automaton while a suitable formalism,
called attack expression, is introduced to describe the relation of attack
between arguments. The proposed approach is shown to satisfy some desirable
properties which can not be achieved through other ""naive"" uses of formal
languages. In particular, the approach is shown to be expressive enough to
capture (besides any arbitrary finite structure) a large variety of infinite
afs including two major examples from previous literature and two sample cases
from the domains of multi-agent negotiation and ambient intelligence. On the
computational side, we show that several decision and construction problems
which are known to be polynomial time solvable in finite afs are decidable in
the context of the proposed formalism and we provide the relevant algorithms.
Moreover we obtain additional results concerning the case of finitary afs.
",Automata for Infinite Argumentation Structures
"  In fuzzy decision-making processes based on linguistic information,
operations on discrete fuzzy numbers are commonly performed. Aggregation and
defuzzification operations are some of these often used operations. Many
aggregation and defuzzification operators produce results independent to the
decision makers strategy. On the other hand, the Weighted Average Based on
Levels (WABL) approach can take into account the level weights and the decision
makers optimism strategy. This gives flexibility to the WABL operator and,
through machine learning, can be trained in the direction of the decision
makers strategy, producing more satisfactory results for the decision maker.
However, in order to determine the WABL value, it is necessary to calculate
some integrals. In this study, the concept of WABL for discrete trapezoidal
fuzzy numbers is investigated, and analytical formulas have been proven to
facilitate the calculation of WABL value for these fuzzy numbers. Trapezoidal
and their special form, triangular fuzzy numbers, are the most commonly used
fuzzy number types in fuzzy modeling, so in this study, such numbers have been
studied. Computational examples explaining the theoretical results have been
performed.
","Analytical Formulations for the Level Based Weighted Average Value of
  Discrete Trapezoidal Fuzzy Numbers"
"  Automated theorem proving has long been a key task of artificial
intelligence. Proofs form the bedrock of rigorous scientific inquiry. Many
tools for both partially and fully automating their derivations have been
developed over the last half a century. Some examples of state-of-the-art
provers are E (Schulz, 2013), VAMPIRE (Kov\'acs & Voronkov, 2013), and Prover9
(McCune, 2005-2010). Newer theorem provers, such as E, use superposition
calculus in place of more traditional resolution and tableau based methods.
There have also been a number of past attempts to apply machine learning
methods to guiding proof search. Suttner & Ertel proposed a
multilayer-perceptron based method using hand-engineered features as far back
as 1990; Urban et al (2011) apply machine learning to tableau calculus; and
Loos et al (2017) recently proposed a method for guiding the E theorem prover
using deep nerual networks. All of this prior work, however, has one common
limitation: they all rely on the axioms of classical first-order logic. Very
little attention has been paid to automated theorem proving for non-classical
logics. One of the only recent examples is McLaughlin & Pfenning (2008) who
applied the polarized inverse method to intuitionistic propositional logic. The
literature is otherwise mostly silent. This is truly unfortunate, as there are
many reasons to desire non-classical proofs over classical.
Constructive/intuitionistic proofs should be of particular interest to computer
scientists thanks to the well-known Curry-Howard correspondence (Howard, 1980)
which tells us that all terminating programs correspond to a proof in
intuitionistic logic and vice versa. This work explores using Q-learning
(Watkins, 1989) to inform proof search for a specific system called
non-classical logic called Core Logic (Tennant, 2017).
",Learning to Reason
"  In a robotic mobile fulfillment system, robots bring shelves, called pods,
with storage items from the storage area to pick stations. At every pick
station there is a person -- the picker -- who takes parts from the pod and
packs them into boxes according to orders. Usually there are multiple shelves
at the pick station. In this case, they build a queue with the picker at its
head. When the picker does not need the pod any more, a robot transports the
pod back to the storage area. At that time, we need to answer a question:
""Where is the optimal place in the inventory to put this pod back?"". It is a
tough question, because there are many uncertainties to consider before
answering it. Moreover, each decision made to answer the question influences
the subsequent ones. The goal of this paper is to answer the question properly.
We call this problem the Pod Repositioning Problem and formulate a
deterministic model. This model is tested with different algorithms, including
binary integer programming, cheapest place, fixed place, random place, genetic
algorithms, and a novel algorithm called tetris.
","Deterministic Pod Repositioning Problem in Robotic Mobile Fulfillment
  Systems"
"  Complex industrial systems are continuously monitored by a large number of
heterogeneous sensors. The diversity of their operating conditions and the
possible fault types make it impossible to collect enough data for learning all
the possible fault patterns. The paper proposes an integrated automatic
unsupervised feature learning and one-class classification for fault detection
that uses data on healthy conditions only for its training. The approach is
based on stacked Extreme Learning Machines (namely Hierarchical, or HELM) and
comprises an autoencoder, performing unsupervised feature learning, stacked
with a one-class classifier monitoring the distance of the test data to the
training healthy class, thereby assessing the health of the system.
  This study provides a comprehensive evaluation of HELM fault detection
capability compared to other machine learning approaches, such as stand-alone
one-class classifiers (ELM and SVM), these same one-class classifiers combined
with traditional dimensionality reduction methods (PCA) and a Deep Belief
Network. The performance is first evaluated on a synthetic dataset that
encompasses typical characteristics of condition monitoring data. Subsequently,
the approach is evaluated on a real case study of a power plant fault. The
proposed algorithm for fault detection, combining feature learning with the
one-class classifier, demonstrates a better performance, particularly in cases
where condition monitoring data contain several non-informative signals.
","Feature Learning for Fault Detection in High-Dimensional
  Condition-Monitoring Signals"
"  Most of agents that learn policy for tasks with reinforcement learning (RL)
lack the ability to communicate with people, which makes human-agent
collaboration challenging. We believe that, in order for RL agents to
comprehend utterances from human colleagues, RL agents must infer the mental
states that people attribute to them because people sometimes infer an
interlocutor's mental states and communicate on the basis of this mental
inference. This paper proposes PublicSelf model, which is a model of a person
who infers how the person's own behavior appears to their colleagues. We
implemented the PublicSelf model for an RL agent in a simulated environment and
examined the inference of the model by comparing it with people's judgment. The
results showed that the agent's intention that people attributed to the agent's
movement was correctly inferred by the model in scenes where people could find
certain intentionality from the agent's behavior.
",Bayesian Inference of Self-intention Attributed by Observer
"  The Universal Turing Machine (TM) is a model for VonNeumann computers ---
general-purpose computers. A human brain can inside-skull-automatically learn a
universal TM so that he acts as a general-purpose computer and writes a
computer program for any practical purposes. It is unknown whether a machine
can accomplish the same. This theoretical work shows how the Developmental
Network (DN) can accomplish this. Unlike a traditional TM, the TM learned by DN
is a super TM --- Grounded, Emergent, Natural, Incremental, Skulled, Attentive,
Motivated, and Abstractive (GENISAMA). A DN is free of any central controller
(e.g., Master Map, convolution, or error back-propagation). Its learning from a
teacher TM is one transition observation at a time, immediate, and error-free
until all its neurons have been initialized by early observed teacher
transitions. From that point on, the DN is no longer error-free but is always
optimal at every time instance in the sense of maximal likelihood, conditioned
on its limited computational resources and the learning experience. This letter
also extends the Church-Turing thesis to automatic programming for general
purposes and sketchily proved it.
",A Model for Auto-Programming for General Purposes
"  Parameter learning is the technique for obtaining the probabilistic
parameters in conditional probability tables in Bayesian networks from tables
with (observed) data --- where it is assumed that the underlying graphical
structure is known. There are basically two ways of doing so, referred to as
maximal likelihood estimation (MLE) and as Bayesian learning. This paper
provides a categorical analysis of these two techniques and describes them in
terms of basic properties of the multiset monad M, the distribution monad D and
the Giry monad G. In essence, learning is about the reltionships between
multisets (used for counting) on the one hand and probability distributions on
the other. These relationsips will be described as suitable natural
transformations.
",Categorical Aspects of Parameter Learning
"  In this paper, we give an overview of the Legal Judgment Prediction (LJP)
competition at Chinese AI and Law challenge (CAIL2018). This competition
focuses on LJP which aims to predict the judgment results according to the
given facts. Specifically, in CAIL2018 , we proposed three subtasks of LJP for
the contestants, i.e., predicting relevant law articles, charges and prison
terms given the fact descriptions. CAIL2018 has attracted several hundreds
participants (601 teams, 1, 144 contestants from 269 organizations). In this
paper, we provide a detailed overview of the task definition, related works,
outstanding methods and competition results in CAIL2018.
",Overview of CAIL2018: Legal Judgment Prediction Competition
"  Proceedings of the AAAI Fall Symposium on Artificial Intelligence in
Government and Public Sector, Arlington, Virginia, USA, October 18-20, 2018
","AAAI FSS-18: Artificial Intelligence in Government and Public Sector
  Proceedings"
"  After the recent groundbreaking results of AlphaGo and AlphaZero, we have
seen strong interests in deep reinforcement learning and artificial general
intelligence (AGI) in game playing. However, deep learning is
resource-intensive and the theory is not yet well developed. For small games,
simple classical table-based Q-learning might still be the algorithm of choice.
General Game Playing (GGP) provides a good testbed for reinforcement learning
to research AGI. Q-learning is one of the canonical reinforcement learning
methods, and has been used by (Banerjee $\&$ Stone, IJCAI 2007) in GGP. In this
paper we implement Q-learning in GGP for three small-board games (Tic-Tac-Toe,
Connect Four, Hex)\footnote{source code: https://github.com/wh1992v/ggp-rl}, to
allow comparison to Banerjee et al.. We find that Q-learning converges to a
high win rate in GGP. For the $\epsilon$-greedy strategy, we propose a first
enhancement, the dynamic $\epsilon$ algorithm. In addition, inspired by (Gelly
$\&$ Silver, ICML 2007) we combine online search (Monte Carlo Search) to
enhance offline learning, and propose QM-learning for GGP. Both enhancements
improve the performance of classical Q-learning. In this work, GGP allows us to
show, if augmented by appropriate enhancements, that classical table-based
Q-learning can perform well in small games.
",Assessing the Potential of Classical Q-learning in General Game Playing
"  In open-ended environments, autonomous learning agents must set their own
goals and build their own curriculum through an intrinsically motivated
exploration. They may consider a large diversity of goals, aiming to discover
what is controllable in their environments, and what is not. Because some goals
might prove easy and some impossible, agents must actively select which goal to
practice at any moment, to maximize their overall mastery on the set of
learnable goals. This paper proposes CURIOUS, an algorithm that leverages 1) a
modular Universal Value Function Approximator with hindsight learning to
achieve a diversity of goals of different kinds within a unique policy and 2)
an automated curriculum learning mechanism that biases the attention of the
agent towards goals maximizing the absolute learning progress. Agents focus
sequentially on goals of increasing complexity, and focus back on goals that
are being forgotten. Experiments conducted in a new modular-goal robotic
environment show the resulting developmental self-organization of a learning
curriculum, and demonstrate properties of robustness to distracting goals,
forgetting and changes in body properties.
","CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement
  Learning"
"  In order to engender trust in AI, humans must understand what an AI system is
trying to achieve, and why. To overcome this problem, the underlying AI process
must produce justifications and explanations that are both transparent and
comprehensible to the user. AI Planning is well placed to be able to address
this challenge. In this paper we present a methodology to provide initial
explanations for the decisions made by the planner. Explanations are created by
allowing the user to suggest alternative actions in plans and then compare the
resulting plans with the one found by the planner. The methodology is
implemented in the new XAI-Plan framework.
",Towards Providing Explanations for AI Planner Decisions
"  The research activity outlined in this PhD thesis is devoted to define a
general approach, a concrete architecture and a prototype Process Management
System (PMS) for the automated adaptation of dynamic processes at run-time, on
the basis of a declarative specification of process tasks and relying on
well-established reasoning about actions and planning techniques. The purpose
is to demonstrate that the combination of procedural and imperative models with
declarative elements, along with the exploitation of techniques from the field
of artificial intelligence (AI), such as Situation Calculus, IndiGolog and
automated planning, can increase the ability of existing PMSs of supporting
dynamic processes. To this end, a prototype PMS named SmartPM, which is
specifically tailored for supporting collaborative work of process participants
during pervasive scenarios, has been developed. The adaptation mechanism
deployed on SmartPM is based on execution monitoring for detecting failures at
run-time, which does not require the definition of the adaptation strategy in
the process itself (as most of the current approaches do), and on automatic
planning techniques for the synthesis of the recovery procedure.
",SmartPM: Automatic Adaptation of Dynamic Processes at Run-Time
"  Optimization techniques play a significant role in improving description
logic reasoners covering the Web Ontology Language (OWL). These techniques are
essential to speed up these reasoners. Many of the optimization techniques are
based on heuristic choices. Optimal heuristic selection makes these techniques
more effective. The FaCT++ OWL reasoner and its Java version JFact implement an
optimization technique called ToDo list which is a substitute for a traditional
top-down approach in tableau-based reasoners. The ToDo list mechanism allows
one to arrange the order of applying different rules by giving each a priority.
Compared to a top-down approach, the ToDo list technique has a better control
over the application of expansion rules. Learning the proper heuristic order
for applying rules in ToDo lis} will have a great impact on reasoning speed. We
use a binary SVM technique to build our learning model. The model can help to
choose ontology-specific order sets to speed up OWL reasoning. On average, our
learning approach tested with 40 selected ontologies achieves a speedup of two
orders of magnitude when compared to the worst rule ordering choice.
",Optimizing Heuristics for Tableau-based OWL Reasoners
"  We briefly introduce herein a new form of distributed, multi-agent artificial
intelligence, which we refer to as ""tentacular."" Tentacular AI is distinguished
by six attributes, which among other things entail a capacity for reasoning and
planning based in highly expressive calculi (logics), and which enlists
subsidiary agents across distances circumscribed only by the reach of one or
more given networks.
","Tentacular Artificial Intelligence, and the Architecture Thereof,
  Introduced"
"  Most of the works on planning and learning, e.g., planning by (model based)
reinforcement learning, are based on two main assumptions: (i) the set of
states of the planning domain is fixed; (ii) the mapping between the
observations from the real word and the states is implicitly assumed or learned
offline, and it is not part of the planning domain. Consequently, the focus is
on learning the transitions between states. In this paper, we drop such
assumptions. We provide a formal framework in which (i) the agent can learn
dynamically new states of the planning domain; (ii) the mapping between
abstract states and the perception from the real world, represented by
continuous variables, is part of the planning domain; (iii) such mapping is
learned and updated along the ""life"" of the agent. We define an algorithm that
interleaves planning, acting, and learning, and allows the agent to update the
planning domain depending on how much it trusts the model w.r.t. the new
experiences learned by executing actions. We define a measure of coherence
between the planning domain and the real world as perceived by the agent. We
test our approach showing that the agent learns increasingly coherent models,
and that the system can scale to deal with models with an order of $10^6$
states.
","Incremental learning abstract discrete planning domains and mappings to
  continuous perceptions"
"  In this chapter tools and techniques from the mathematical theory of formal
concept analysis are applied to hypertext systems in general, and the World
Wide Web in particular. Various processes for the conceptual structuring of
hypertext are discussed: summarization, conceptual scaling, and the creation of
conceptual links. Well-known interchange formats for summarizing networked
information resources as resource meta-information are reviewed, and two new
interchange formats originating from formal concept analysis are advocated.
Also reviewed is conceptual scaling, which provides a principled approach to
the faceted analysis techniques in library science classification. The
important notion of conceptual linkage is introduced as a generalization of a
hyperlink. The automatic hyperization of the content of legacy data is
described, and the composite conceptual structuring with hypertext linkage is
defined. For the conceptual empowerment of the Web user, a new technique called
conceptual browsing is advocated. Conceptual browsing, which browses over
conceptual links, is dual mode (extensional versus intensional) and dual scope
(global versus local).
",Conceptual Analysis of Hypertext
"  We formalize the problem of selecting the optimal set of options for planning
as that of computing the smallest set of options so that planning converges in
less than a given maximum of value-iteration passes. We first show that the
problem is NP-hard, even if the task is constrained to be deterministic---the
first such complexity result for option discovery. We then present the first
polynomial-time boundedly suboptimal approximation algorithm for this setting,
and empirically evaluate it against both the optimal options and a
representative collection of heuristic approaches in simple grid-based domains
including the classic four-rooms problem.
",Finding Options that Minimize Planning Time
"  This paper summarizes some of the technical background, research ideas, and
possible development strategies for achieving machine common sense. Machine
common sense has long been a critical-but-missing component of Artificial
Intelligence (AI). Recent advances in machine learning have resulted in new AI
capabilities, but in all of these applications, machine reasoning is narrow and
highly specialized. Developers must carefully train or program systems for
every situation. General commonsense reasoning remains elusive. The absence of
common sense prevents intelligent systems from understanding their world,
behaving reasonably in unforeseen situations, communicating naturally with
people, and learning from new experiences. Its absence is perhaps the most
significant barrier between the narrowly focused AI applications we have today
and the more general, human-like AI systems we would like to build in the
future. Machine common sense remains a broad, potentially unbounded problem in
AI. There are a wide range of strategies that could be employed to make
progress on this difficult challenge. This paper discusses two diverse
strategies for focusing development on two different machine commonsense
services: (1) a service that learns from experience, like a child, to construct
computational models that mimic the core domains of child cognition for objects
(intuitive physics), agents (intentional actors), and places (spatial
navigation); and (2) service that learns from reading the Web, like a research
librarian, to construct a commonsense knowledge repository capable of answering
natural language and image-based questions about commonsense phenomena.
",Machine Common Sense Concept Paper
"  In this paper we present an extension of existing Nearest-Neighbor heuristics
to an algorithm called k-Repetitive-Nearest-Neighbor. The idea is to start with
a tour of k nodes and then perform a Nearest-Neighbor search from there on.
After doing this for all permutations of k nodes the result gets selected as
the shortest tour found. Experimental results show that for 2-RNN the solutions
quality remains relatively stable between about 10% to 40% above the optimum.
",k-RNN: Extending NN-heuristics for the TSP
"  We present the Goal Uncertain Stochastic Shortest Path (GUSSP) problem -- a
general framework to model path planning and decision making in stochastic
environments with goal uncertainty. The framework extends the stochastic
shortest path (SSP) model to dynamic environments in which it is impossible to
determine the exact goal states ahead of plan execution. GUSSPs introduce
flexibility in goal specification by allowing a belief over possible goal
configurations. The unique observations at potential goals helps the agent
identify the true goal during plan execution. The partial observability is
restricted to goals, facilitating the reduction to an SSP with a modified state
space. We formally define a GUSSP and discuss its theoretical properties. We
then propose an admissible heuristic that reduces the planning time using
FLARES -- a start-of-the-art probabilistic planner. We also propose a
determinization approach for solving this class of problems. Finally, we
present empirical results on a search and rescue mobile robot and three other
problem domains in simulation.
",Planning in Stochastic Environments with Goal Uncertainty
"  The purpose of the paper is to introduce a new approach of planning called
Assumption-Based Planning. This approach is a very interesting way to devise a
planner based on a multi-agent system in which the production of a global
shared plan is obtained by conjecture/refutation cycles. Contrary to classical
approaches, our contribution relies on the agents reasoning that leads to the
production of a plan from planning domains. To take into account complex
environments and the partial agents knowledge, we propose to consider the
planning problem as a defeasible reasoning where the agents exchange proposals
and counter-proposals and are able to reason about uncertainty. The
argumentation dialogue between agents must not be viewed as a negotiation
process but as an investigation process in order to build a plan. In this
paper, we focus on the mechanisms that allow an agent to produce `reasonable'
proposals according to its knowledge.
",Assumption-Based Planning
"  In this paper, we introduce a generic and fresh model for distributed
planning called ""Distributed Planning Through Graph Merging"" ({\sf DPGM}). This
model unifies the different steps of the distributed planning process into a
single step. Our approach is based on a planning graph structure for the agent
reasoning and a CSP mechanism for the individual plan extraction and the
coordination. We assume that no agent can reach the global goal alone.
Therefore the agents must cooperate, {\it i.e.,} take in into account potential
positive interactions between their activities to reach their common shared
goal. The originality of our model consists in considering as soon as possible,
{\it i.e.,} in the individual planning process, the positive and the negative
interactions between agents activities in order to reduce the search cost of a
global coordinated solution plan.
",Planification par fusions incr\'ementales de graphes
"  In cooperation, the workers must know how co-workers behave. However, an
agent's policy, which is embedded in a statistical machine learning model, is
hard to understand, and requires much time and knowledge to comprehend.
Therefore, it is difficult for people to predict the behavior of machine
learning robots, which makes Human Robot Cooperation challenging. In this
paper, we propose Instruction-based Behavior Explanation (IBE), a method to
explain an autonomous agent's future behavior. In IBE, an agent can
autonomously acquire the expressions to explain its own behavior by reusing the
instructions given by a human expert to accelerate the learning of the agent's
policy. IBE also enables a developmental agent, whose policy may change during
the cooperation, to explain its own behavior with sufficient time granularity.
","Autonomous Self-Explanation of Behavior for Interactive Reinforcement
  Learning Agents"
"  The monotonic ordinal classification has increased the interest of
researchers and practitioners within machine learning community in the last
years. In real applications, the problems with monotonicity constraints are
very frequent. To construct predictive monotone models from those problems,
many classifiers require as input a data set satisfying the monotonicity
relationships among all samples. Changing the class labels of the data set
(relabelling) is useful for this. Relabelling is assumed to be an important
building block for the construction of monotone classifiers and it is proved
that it can improve the predictive performance.
  In this paper, we will address the construction of monotone datasets
considering as noise the cases that do not meet the monotonicity restrictions.
For the first time in the specialized literature, we propose the use of noise
filtering algorithms in a preprocessing stage with a double goal: to increase
both the monotonicity index of the models and the accuracy of the predictions
for different monotonic classifiers. The experiments are performed over 12
datasets coming from classification and regression problems and show that our
scheme improves the prediction capabilities of the monotonic classifiers
instead of being applied to original and relabeled datasets. In addition, we
have included the analysis of noise filtering process in the particular case of
wine quality classification to understand its effect in the predictive models
generated.
",Label Noise Filtering Techniques to Improve Monotonic Classification
"  Artificial Intelligence (AI) has burrowed into our lives in various aspects;
however, without appropriate testing, deployed AI systems are often being
criticized to fail in critical and embarrassing cases. Existing testing
approaches mainly depend on fixed and pre-defined datasets, providing a limited
testing coverage. In this paper, we propose the concept of proactive testing to
dynamically generate testing data and evaluate the performance of AI systems.
We further introduce Challenge.AI, a new crowd system that features the
integration of crowdsourcing and machine learning techniques in the process of
error generation, error validation, error categorization, and error analysis.
We present experiences and insights into a participatory design with AI
developers. The evaluation shows that the crowd workflow is more effective with
the help of machine learning techniques. AI developers found that our system
can help them discover unknown errors made by the AI models, and engage in the
process of proactive testing.
",Challenge AI Mind: A Crowd System for Proactive AI Testing
"  In this chapter we discuss soft concept analysis, a study which identifies an
enriched notion of ""conceptual scale"" as developed in formal concept analysis
with an enriched notion of ""linguistic variable"" as discussed in fuzzy logic.
The identification ""enriched conceptual scale"" = ""enriched linguistic variable""
was made in a previous paper (Enriched interpretation, Robert E. Kent). In this
chapter we offer further arguments for the importance of this identification by
discussing the philosophy, spirit, and practical application of conceptual
scaling to the discovery, conceptual analysis, interpretation, and
categorization of networked information resources. We argue that a linguistic
variable, which has been defined at just the right generalization of valuated
categories, provides a natural definition for the process of soft conceptual
scaling. This enrichment using valuated categories models the relation of
indiscernability, a notion of central importance in rough set theory. At a more
fundamental level for soft concept analysis, it also models the derivation of
formal concepts, a process of central importance in formal concept analysis.
Soft concept analysis is synonymous with enriched concept analysis. From one
viewpoint, the study of soft concept analysis that is initiated here extends
formal concept analysis to soft computational structures. From another
viewpoint, soft concept analysis provides a natural foundation for soft
computation by unifying and explaining notions from soft computation in terms
of suitably generalized notions from formal concept analysis, rough set theory
and fuzzy set theory.
",Soft Concept Analysis
"  Planning has achieved significant progress in recent years. Among the various
approaches to scale up plan synthesis, the use of macro-actions has been widely
explored. As a first stage towards the development of a solution to learn
on-line macro-actions, we propose an algorithm to identify useful macro-actions
based on data mining techniques. The integration in the planning search of
these learned macro-actions shows significant improvements over six classical
planning benchmarks.
",Mining useful Macro-actions in Planning
"  In this paper, we introduce a new heuristic search algorithm based on mean
values for real-time planning, called MHSP. It consists in associating the
principles of UCT, a bandit-based algorithm which gave very good results in
computer games, and especially in Computer Go, with heuristic search in order
to obtain a real-time planner in the context of classical planning. MHSP is
evaluated on different planning problems and compared to existing algorithms
performing on-line search and learning. Besides, our results highlight the
capacity of MHSP to return plans in a real-time manner which tend to an optimal
plan over the time which is faster and of better quality compared to existing
algorithms in the literature.
",Mean-based Heuristic Search for Real-Time Planning
"  In the knowledge engineering community ""ontology"" is usually defined in the
tradition of Gruber as an ""explicit specification of a conceptualization"".
Several variations of this definition exist. In the paper we argue that (with
one notable exception) these definitions are of no explanatory value, because
they violate one of the basic rules for good definitions: The defining
statement (the definiens) should be clearer than the term that is defined (the
definiendum). In the paper we propose a different definition of ""ontology"" and
discuss how it helps to explain various phenomena: the ability of ontologies to
change, the role of the choice of vocabulary, the significance of annotations,
the possibility of collaborative ontology development, and the relationship
between ontological conceptualism and ontological realism.
",What is an Ontology?
"  For social robots to be brought more into widespread use in the fields of
companionship, care taking and domestic help, they must be capable of
demonstrating social intelligence. In order to be acceptable, they must exhibit
socio-communicative skills. Classic approaches to program HRI from observed
human-human interactions fails to capture the subtlety of multimodal
interactions as well as the key structural differences between robots and
humans. The former arises due to a difficulty in quantifying and coding
multimodal behaviours, while the latter due to a difference of the degrees of
liberty between a robot and a human. However, the notion of reverse engineering
from multimodal HRI traces to learn the underlying behavioral blueprint of the
robot given multimodal traces seems an option worth exploring. With this
spirit, the entire HRI can be seen as a sequence of exchanges of speech acts
between the robot and human, each act treated as an action, bearing in mind
that the entire sequence is goal-driven. Thus, this entire interaction can be
treated as a sequence of actions propelling the interaction from its initial to
goal state, also known as a plan in the domain of AI planning. In the same
domain, this action sequence that stems from plan execution can be represented
as a trace. AI techniques, such as machine learning, can be used to learn
behavioral models (also known as symbolic action models in AI), intended to be
reusable for AI planning, from the aforementioned multimodal traces. This
article reviews recent machine learning techniques for learning planning action
models which can be applied to the field of HRI with the intent of rendering
robots as socio-communicative.
",A Review on Learning Planning Action Models for Socio-Communicative HRI
"  This paper introduces the ""grasp-ability test"" as a ""goodness"" criteria by
which to compare which explanation is more or less meaningful than others for
users to understand the automated algorithmic data processing.
","Explainable artificial intelligence (XAI), the goodness criteria and the
  grasp-ability test"
"  Machine learning is an important tool for decision making, but its ethical
and responsible application requires rigorous vetting of its interpretability
and utility: an understudied problem, particularly for natural language
processing models. We propose an evaluation of interpretation on a real task
with real human users, where the effectiveness of interpretation is measured by
how much it improves human performance. We design a grounded, realistic
human-computer cooperative setting using a question answering task, Quizbowl.
We recruit both trivia experts and novices to play this game with computer as
their teammate, who communicates its prediction via three different
interpretations. We also provide design guidance for natural language
processing human-in-the-loop settings.
","What can AI do for me: Evaluating Machine Learning Interpretations in
  Cooperative Play"
"  A dialogue act (DA) represents the meaning of an utterance at the
illocutionary force level (Austin 1962) such as a question, a request, and a
greeting. Since DAs take charge of the most fundamental part of communication,
we believe that the elucidation of DA learning mechanism is important for
cognitive science and artificial intelligence. The purpose of this study is to
verify that scaffolding takes place when a human teaches a robot, and to let a
robot learn to estimate DAs and to make a response based on them step by step
utilizing scaffolding provided by a human. To realize that, it is necessary for
the robot to detect changes in utterance and rewards given by the partner and
continue learning accordingly. Experimental results demonstrated that
participants who continued interaction for a sufficiently long time often gave
scaffolding for the robot. Although the number of experiments is still
insufficient to obtain a definite conclusion, we observed that 1) the robot
quickly learned to respond to DAs in most cases if the participants only spoke
utterances that match the situation, 2) in the case of participants who builds
scaffolding differently from what we assumed, learning did not proceed quickly,
and 3) the robot could learn to estimate DAs almost exactly if the participants
kept interaction for a sufficiently long time even if the scaffolding was
unexpected.
",Stepwise Acquisition of Dialogue Act Through Human-Robot Interaction
"  Systems of deontic logic suffer either from being too expressive and
therefore hard to mechanize, or from being too simple to capture relevant
aspects of normative reasoning. In this article we look for a suitable way in
between: the automation of a simple logic of normative ideality and
sub-ideality that is not affected by many deontic paradoxes and that is
expressive enough to capture contrary-to-duty reason- ing. We show that this
logic is very useful to reason on normative scenarios from which one can
extract a certain kind of argumentative structure, called a Normative
Detachment Structure with Ideal Conditions. The theoretical analysis of the
logic is accompanied by examples of automated reasoning on a concrete legal
text.
","Automated Reasoning in Normative Detachment Structures with Ideal
  Conditions"
"  Disseminating accurate travel time information to road users helps achieve
traffic equilibrium and reduce traffic congestion. The deployment of Connected
Vehicles technology will provide unique opportunities for the implementation of
travel time prediction models. The aim of this study is twofold: (1) estimate
travel times in the freeway network at five-minute intervals using Basic Safety
Messages (BSM); (2) develop an eXtreme Gradient Boosting (XGB) model for
short-term travel time prediction on freeways. The XGB tree-based ensemble
prediction model is evaluated against common tree-based ensemble algorithms and
the evaluations are performed at five-minute intervals over a 30-minute
horizon. BSMs generated by the Safety Pilot Model Deployment conducted in Ann
Arbor, Michigan, were used. Nearly two billion messages were processed for
providing travel time estimates for the entire freeway network. A Combination
of grid search and five-fold cross-validation techniques using the travel time
estimates were used for developing the prediction models and tuning their
parameters. About 9.6 km freeway stretch was used for evaluating the XGB
together with the most common tree-based ensemble algorithms. The results show
that XGB is superior to all other algorithms, followed by the Gradient
Boosting. XGB travel time predictions were accurate and consistent with
variations during peak periods, with mean absolute percentage error in
prediction about 5.9% and 7.8% for 5-minute and 30-minute horizons,
respectively. Additionally, through applying the developed models to another
4.7 km stretch along the eastbound segment of M-14, the XGB demonstrated its
considerable advantages in travel time prediction during congested and
uncongested conditions.
","Comparative Evaluation of Tree-Based Ensemble Algorithms for Short-Term
  Travel Time Prediction"
"  In the era of big data, logistic planning can be made data-driven to take
advantage of accumulated knowledge in the past. While in the movie industry,
movie planning can also exploit the existing online movie knowledge library to
achieve better results. However, it is ineffective to solely rely on
conventional heuristics for movie planning, due to a large number of existing
movies and various real-world factors that contribute to the success of each
movie, such as the movie genre, available budget, production team (involving
actor, actress, director, and writer), etc. In this paper, we study a
""Blockbuster Planning"" (BP) problem to learn from previous movies and plan for
low budget yet high return new movies in a totally data-driven fashion. After a
thorough investigation of an online movie knowledge library, a novel movie
planning framework ""Blockbuster Planning with Maximized Movie Configuration
Acquaintance"" (BigMovie) is introduced in this paper. From the investment
perspective, BigMovie maximizes the estimated gross of the planned movies with
a given budget. It is able to accurately estimate the movie gross with a 0.26
mean absolute percentage error (and 0.16 for budget). Meanwhile, from the
production team's perspective, BigMovie is able to formulate an optimized team
with people/movie genres that team members are acquainted with. Historical
collaboration records are utilized to estimate acquaintance scores of movie
configuration factors via an acquaintance tensor. We formulate the BP problem
as a non-linear binary programming problem and prove its NP-hardness. To solve
it in polynomial time, BigMovie relaxes the hard binary constraints and
addresses the BP problem as a cubic programming problem. Extensive experiments
conducted on IMDB movie database demonstrate the capability of BigMovie for an
effective data-driven blockbuster planning.
",Data-driven Blockbuster Planning on Online Movie Knowledge Library
"  In the context of real-time planning, this paper investigates the
contributions of two enhancements for selecting actions. First, the
agenda-driven planning enhancement ranks relevant atomic goals and solves them
incrementally in a best-first manner. Second, the committed jump enhancement
commits a sequence of actions to be executed at the following time steps. To
assess these two enhancements, we developed a real-time planning algorithm in
which action selection can be driven by a goal-agenda, and committed jumps can
be done. Experimental results, performed on classical planning problems, show
that agenda-planning and committed jumps are clear advantages in the real-time
context. Used simultaneously, they enable the planner to be several orders of
magnitude faster and solution plans to be shorter.
",Planification en temps r\'eel avec agenda de buts et sauts
"  Devising intelligent robots or agents that interact with humans is a major
challenge for artificial intelligence. In such contexts, agents must constantly
adapt their decisions according to human activities and modify their goals. In
this paper, we tackle this problem by introducing a novel planning approach,
called Moving Goal Planning (MGP), to adapt plans to goal evolutions. This
planning algorithm draws inspiration from Moving Target Search (MTS)
algorithms. In order to limit the number of search iterations and to improve
its efficiency, MGP delays as much as possible triggering new searches when the
goal changes over time. To this purpose, MGP uses two strategies: Open Check
(OC) that checks if the new goal is still in the current search tree and Plan
Follow (PF) that estimates whether executing actions of the current plan brings
MGP closer to the new goal. Moreover, MGP uses a parsimonious strategy to
update incrementally the search tree at each new search that reduces the number
of calls to the heuristic function and speeds up the search. Finally, we show
evaluation results that demonstrate the effectiveness of our approach.
","MGP: Un algorithme de planification temps r\'eel prenant en compte
  l'\'evolution dynamique du but"
"  Many planning techniques have been developed to allow autonomous systems to
act and make decisions based on their perceptions of the environment. Among
these techniques, HTN ({\it Hierarchical Task Network}) planning is one of the
most used in practice. Unlike classical approaches of planning. HTN operates by
decomposing task into sub-tasks until each of these sub-tasks can be achieved
an action. This hierarchical representation provide a richer representation of
planning problems and allows to better guide the plan search and provides more
knowledge to the underlying algorithms. In this paper, we propose a new
approach of HTN planning in which, as in conventional planning, we instantiate
all planning operators before starting the search process. This approach has
proven its effectiveness in classical planning and is necessary for the
development of effective heuristics and encoding planning problems in other
formalism such as CSP or SAT. The instantiation is actually used by most modern
planners but has never been applied in an HTN based planning framework. We
present in this article a generic instantiation algorithm which implements many
simplification techniques to reduce the process complexity inspired from those
used in classical planning. Finally we present some results obtained from an
experimentation on a range of problems used in the international planning
competitions with a modified version of SHOP planner using fully instantiated
problems.
",Une approche totalement instanci\'ee pour la planification HTN
"  Multi-Criteria Decision Analysis (MCDA) methods are widely used in various
fields and disciplines. While most of the research has been focused on the
development and improvement of new MCDA methods, relatively limited attention
has been paid to their appropriate selection for the given decision problem.
Their improper application decreases the quality of recommendations, as
different MCDA methods deliver inconsistent results. The current paper presents
a methodological and practical framework for selecting suitable MCDA methods
for a particular decision situation. A set of 56 available MCDA methods was
analyzed and, based on that, a hierarchical set of methods characteristics and
the rule base were obtained. This analysis, rules and modelling of the
uncertainty in the decision problem description allowed to build a framework
supporting the selection of a MCDA method for a given decision-making
situation. The practical studies indicate consistency between the methods
recommended with the proposed approach and those used by the experts in
reference cases. The results of the research also showed that the proposed
approach can be used as a general framework for selecting an appropriate MCDA
method for a given area of decision support, even in cases of data gaps in the
decision-making problem description. The proposed framework was implemented
within a web platform available for public use at www.mcda.it.
",Generalised framework for multi-criteria method selection
"  ""Value alignment"" (VA) is considered as one of the top priorities in AI
research. Much of the existing research focuses on the ""A"" part and not the ""V""
part of ""value alignment."" This paper corrects that neglect by emphasizing the
""value"" side of VA and analyzes VA from the vantage point of requirements in
value theory, in particular, of avoiding the ""naturalistic fallacy""--a major
epistemic caveat. The paper begins by isolating two distinct forms of VA:
""mimetic"" and ""anchored."" Then it discusses which VA approach better avoids the
naturalistic fallacy. The discussion reveals stumbling blocks for VA approaches
that neglect implications of the naturalistic fallacy. Such problems are more
serious in mimetic VA since the mimetic process imitates human behavior that
may or may not rise to the level of correct ethical behavior. Anchored VA,
including hybrid VA, in contrast, holds more promise for future VA since it
anchors alignment by normative concepts of intrinsic value.
",Mimetic vs Anchored Value Alignment in Artificial Intelligence
"  We describe a solution to the student-project allocation problem using
simulated annealing. The problem involves assigning students to projects, where
each student has ranked a fixed number of projects in order of preference. Each
project is offered by a specific supervisor (or supervisors), and the goal is
to find an optimal matching of students to projects taking into account the
students' preferences, the constraint that only one student can be assigned to
a given project, and the constraint that supervisors have a maximum workload.
We show that when applied to a real dataset from a university physics
department, simulated annealing allows the rapid determination of high quality
solutions to this allocation problem. The quality of the solution is quantified
by a satisfaction metric derived from empirical student survey data. Our
approach provides high quality allocations in a matter of minutes that are as
good as those found previously by the course organizer using a laborious
trial-and-error approach. We investigate how the quality of the allocation is
affected by the ratio of the number of projects offered to the number of
students and the number of projects ranked by each student. We briefly discuss
how our approach can be generalized to include other types of constraints and
discuss its potential applicability to wider allocation problems.
",A simulated annealing approach to the student-project allocation problem
"  Domain-independent probabilistic planners input an MDP description in a
factored representation language such as PPDDL or RDDL, and exploit the
specifics of the representation for faster planning. Traditional algorithms
operate on each problem instance independently, and good methods for
transferring experience from policies of other instances of a domain to a new
instance do not exist. Recently, researchers have begun exploring the use of
deep reactive policies, trained via deep reinforcement learning (RL), for MDP
planning domains. One advantage of deep reactive policies is that they are more
amenable to transfer learning.
  In this paper, we present the first domain-independent transfer algorithm for
MDP planning domains expressed in an RDDL representation. Our architecture
exploits the symbolic state configuration and transition function of the domain
(available via RDDL) to learn a shared embedding space for states and
state-action pairs for all problem instances of a domain. We then learn an RL
agent in the embedding space, making a near zero-shot transfer possible, i.e.,
without much training on the new instance, and without using the domain
simulator at all. Experiments on three different benchmark domains underscore
the value of our transfer algorithm. Compared against planning from scratch,
and a state-of-the-art RL transfer algorithm, our transfer solution has
significantly superior learning curves.
",Transfer of Deep Reactive Policies for MDP Planning
"  Cloud infrastructures are being increasingly utilized in critical
infrastructures such as banking/finance, transportation and utility management.
Sophistication and resources used in recent security breaches including those
on critical infrastructures show that attackers are no longer limited by
monetary/computational constraints. In fact, they may be aided by entities with
large financial and human resources. Hence there is urgent need to develop
predictive approaches for cyber defense to strengthen cloud infrastructures
specifically utilized by critical infrastructures. Extensive research has been
done in the past on applying techniques such as Game Theory, Machine Learning
and Bayesian Networks among others for the predictive defense of critical
infrastructures. However a major drawback of these approaches is that they do
not incorporate probabilistic human behavior which limits their predictive
ability. In this paper, a stochastic approach is proposed to predict less
secure states in critical cloud systems which might lead to potential security
breaches. These less-secure states are deemed as `risky' states in our
approach. Markov Decision Process (MDP) is used to accurately incorporate user
behavior(s) as well as operational behavior of the cloud infrastructure through
a set of features. The developed reward/cost mechanism is then used to select
appropriate `actions' to identify risky states at future time steps by learning
an optimal policy. Experimental results show that the proposed framework
performs well in identifying future `risky' states. Through this work we
demonstrate the effectiveness of using probabilistic modeling (MDP) to
predictively secure critical cloud infrastructures.
","An approach to predictively securing critical cloud infrastructures
  through probabilistic modeling"
"  Recent studies have shown that the labels collected from crowdworkers can be
discriminatory with respect to sensitive attributes such as gender and race.
This raises questions about the suitability of using crowdsourced data for
further use, such as for training machine learning algorithms. In this work, we
address the problem of fair and diverse data collection from a crowd under
budget constraints. We propose a novel algorithm which maximizes the expected
accuracy of the collected data, while ensuring that the errors satisfy desired
notions of fairness. We provide guarantees on the performance of our algorithm
and show that the algorithm performs well in practice through experiments on a
real dataset.
","Crowdsourcing with Fairness, Diversity and Budget Constraints"
"  In privacy-preserving multi-agent planning, a group of agents attempt to
cooperatively solve a multi-agent planning problem while maintaining private
their data and actions. Although much work was carried out in this area in past
years, its theoretical foundations have not been fully worked out.
Specifically, although algorithms with precise privacy guarantees exist, even
their most efficient implementations are not fast enough on realistic
instances, whereas for practical algorithms no meaningful privacy guarantees
exist. Secure-MAFS, a variant of the multi-agent forward search algorithm
(MAFS) is the only practical algorithm to attempt to offer more precise
guarantees, but only in very limited settings and with proof sketches only. In
this paper we formulate a precise notion of secure computation for search-based
algorithms and prove that Secure MAFS has this property in all domains.
",Privacy Preserving Multi-Agent Planning with Provable Guarantees
"  Deep reinforcement learning (DRL) has gained great success by learning
directly from high-dimensional sensory inputs, yet is notorious for the lack of
interpretability. Interpretability of the subtasks is critical in hierarchical
decision-making as it increases the transparency of black-box-style DRL
approach and helps the RL practitioners to understand the high-level behavior
of the system better. In this paper, we introduce symbolic planning into DRL
and propose a framework of Symbolic Deep Reinforcement Learning (SDRL) that can
handle both high-dimensional sensory inputs and symbolic planning. The
task-level interpretability is enabled by relating symbolic actions to
options.This framework features a planner -- controller -- meta-controller
architecture, which takes charge of subtask scheduling, data-driven subtask
learning, and subtask evaluation, respectively. The three components
cross-fertilize each other and eventually converge to an optimal symbolic plan
along with the learned subtasks, bringing together the advantages of long-term
planning capability with symbolic knowledge and end-to-end reinforcement
learning directly from a high-dimensional sensory input. Experimental results
validate the interpretability of subtasks, along with improved data efficiency
compared with state-of-the-art approaches.
","SDRL: Interpretable and Data-efficient Deep Reinforcement Learning
  Leveraging Symbolic Planning"
"  Topic models are widely used for thematic structure discovery in text. But
traditional topic models often require dedicated inference procedures for
specific tasks at hand. Also, they are not designed to generate word-level
semantic representations. To address these limitations, we propose a topic
modeling approach based on Generative Adversarial Nets (GANs), called
Adversarial-neural Topic Model (ATM). The proposed ATM models topics with
Dirichlet prior and employs a generator network to capture the semantic
patterns among latent topics. Meanwhile, the generator could also produce
word-level semantic representations. To illustrate the feasibility of porting
ATM to tasks other than topic modeling, we apply ATM for open domain event
extraction. Our experimental results on the two public corpora show that ATM
generates more coherence topics, outperforming a number of competitive
baselines. Moreover, ATM is able to extract meaningful events from news
articles.
",ATM:Adversarial-neural Topic Model
"  Problem of finding 2D paths of special shape, e.g. paths comprised of line
segments having the property that the angle between any two consecutive
segments does not exceed the predefined threshold, is considered in the paper.
This problem is harder to solve than the one when shortest paths of any shape
are sought, since the planer's search space is substantially bigger as multiple
search nodes corresponding to the same location need to be considered. One way
to reduce the search effort is to fix the length of the path's segment and to
prune the nodes that violate the imposed constraint. This leads to
incompleteness and to the sensitivity of the 's performance to chosen parameter
value. In this work we introduce a novel technique that reduces this
sensitivity by automatically adjusting the length of the path's segment
on-the-fly, e.g. during the search. Embedding this technique into the known
grid-based angle-constrained path finding algorithm - LIAN, leads to notable
increase of the planner's effectiveness, e.g. success rate, while keeping
efficiency, e.g. runtime, overhead at reasonable level. Experimental evaluation
shows that LIAN with the suggested enhancements, dubbed eLIAN, solves up to
20\% of tasks more compared to the predecessor. Meanwhile, the solution quality
of eLIAN is nearly the same as the one of LIAN.
",eLIAN: Enhanced Algorithm for Angle-constrained Path Finding
"  Recent studies show that 85% of women have changed their traveled route to
avoid harassment and assault. Despite this, current mapping tools do not
empower users with information to take charge of their personal safety. We
propose SafeRoute, a novel solution to the problem of navigating cities and
avoiding street harassment and crime. Unlike other street navigation
applications, SafeRoute introduces a new type of path generation via deep
reinforcement learning. This enables us to successfully optimize for
multi-criteria path-finding and incorporate representation learning within our
framework. Our agent learns to pick favorable streets to create a safe and
short path with a reward function that incorporates safety and efficiency.
Given access to recent crime reports in many urban cities, we train our model
for experiments in Boston, New York, and San Francisco. We test our model on
areas of these cities, specifically the populated downtown regions where
tourists and those unfamiliar with the streets walk. We evaluate SafeRoute and
successfully improve over state-of-the-art methods by up to 17% in local
average distance from crimes while decreasing path length by up to 7%.
",SafeRoute: Learning to Navigate Streets Safely in an Urban Environment
"  Recent work on interpretability in machine learning and AI has focused on the
building of simplified models that approximate the true criteria used to make
decisions. These models are a useful pedagogical device for teaching trained
professionals how to predict what decisions will be made by the complex system,
and most importantly how the system might break. However, when considering any
such model it's important to remember Box's maxim that ""All models are wrong
but some are useful."" We focus on the distinction between these models and
explanations in philosophy and sociology. These models can be understood as a
""do it yourself kit"" for explanations, allowing a practitioner to directly
answer ""what if questions"" or generate contrastive explanations without
external assistance. Although a valuable ability, giving these models as
explanations appears more difficult than necessary, and other forms of
explanation may not have the same trade-offs. We contrast the different schools
of thought on what makes an explanation, and suggest that machine learning
might benefit from viewing the problem more broadly.
",Explaining Explanations in AI
"  Humans have consciousness as the ability to perceive events and objects: a
mental model of the world developed from the most impoverished of visual
stimuli, enabling humans to make rapid decisions and take actions. Although
spatial and temporal aspects of different scenes are generally diverse, the
underlying physics among environments still work the same way, thus learning an
abstract description of shared physical dynamics helps human to understand the
world. In this paper, we explore building this mental world with neural network
models through multi-task learning, namely the meta-world model. We show
through extensive experiments that our proposed meta-world models successfully
capture the common dynamics over the compact representations of visually
different environments from Atari Games. We also demonstrate that agents
equipped with our meta-world model possess the ability of visual
self-recognition, i.e., recognize themselves from the reflected mirrored
environment derived from the classic mirror self-recognition test (MSR).
",Learning Shared Dynamics with Meta-World Models
"  Ordered binary decision diagrams (OBDDs) are an efficient data structure for
representing and manipulating Boolean formulas. With respect to different
variable orders, the OBDDs' sizes may vary from linear to exponential in the
number of the Boolean variables. Finding the optimal variable order has been
proved a NP-complete problem. Many heuristics have been proposed to find a
near-optimal solution of this problem. In this paper, we propose a neural
network-based method to predict near-optimal variable orders for unknown
formulas. Viewing these formulas as hypergraphs, and lifting the message
passing neural network into 3-hypergraph (MPNN3), we are able to learn the
patterns of Boolean formula. Compared to the traditional methods, our method
can find a near-the-best solution with an extremely shorter time, even for some
hard examples.To the best of our knowledge, this is the first work on applying
neural network to OBDD reordering.
",Fast OBDD Reordering using Neural Message Passing on Hypergraph
"  Finding the most likely path to a set of failure states is important to the
analysis of safety-critical systems that operate over a sequence of time steps,
such as aircraft collision avoidance systems and autonomous cars. In many
applications such as autonomous driving, failures cannot be completely
eliminated due to the complex stochastic environment in which the system
operates. As a result, safety validation is not only concerned about whether a
failure can occur, but also discovering which failures are most likely to
occur. This article presents adaptive stress testing (AST), a framework for
finding the most likely path to a failure event in simulation. We consider a
general black box setting for partially observable and continuous-valued
systems operating in an environment with stochastic disturbances. We formulate
the problem as a Markov decision process and use reinforcement learning to
optimize it. The approach is simulation-based and does not require internal
knowledge of the system, making it suitable for black-box testing of large
systems. We present formulations for fully observable and partially observable
systems. In the latter case, we present a modified Monte Carlo tree search
algorithm that only requires access to the pseudorandom number generator of the
simulator to overcome partial observability. We also present an extension of
the framework, called differential adaptive stress testing (DAST), that can
find failures that occur in one system but not in another. This type of
differential analysis is useful in applications such as regression testing,
where we are concerned with finding areas of relative weakness compared to a
baseline. We demonstrate the effectiveness of the approach on an aircraft
collision avoidance application, where a prototype aircraft collision avoidance
system is stress tested to find the most likely scenarios of near mid-air
collision.
","Adaptive Stress Testing: Finding Likely Failure Events with
  Reinforcement Learning"
"  A group of intelligent agents which fulfill a set of tasks in parallel is
represented first by the tensor multiplication of corresponding processes in a
linear logic game category. An optimal itinerary in the configuration space of
the group states is defined as a play with maximal total reward in the
category. New moments also are: the reward is represented as a degree of
certainty (visibility) of an agent goal, and the system goals are chosen by the
greatest value corresponding to these processes in the system goal lattice.
","An Optimal Itinerary Generation in a Configuration Space of Large
  Intellectual Agent Groups with Linear Logic"
"  We propose a nonmonotonic Description Logic of typicality able to account for
the phenomenon of concept combination of prototypical concepts. The proposed
logic relies on the logic of typicality ALC TR, whose semantics is based on the
notion of rational closure, as well as on the distributed semantics of
probabilistic Description Logics, and is equipped with a cognitive heuristic
used by humans for concept composition. We first extend the logic of typicality
ALC TR by typicality inclusions whose intuitive meaning is that ""there is
probability p about the fact that typical Cs are Ds"". As in the distributed
semantics, we define different scenarios containing only some typicality
inclusions, each one having a suitable probability. We then focus on those
scenarios whose probabilities belong to a given and fixed range, and we exploit
such scenarios in order to ascribe typical properties to a concept C obtained
as the combination of two prototypical concepts. We also show that reasoning in
the proposed Description Logic is EXPTIME-complete as for the underlying ALC.
","A Description Logic Framework for Commonsense Conceptual Combination
  Integrating Typicality, Probabilities and Cognitive Heuristics"
"  The overarching problem in artificial intelligence (AI) is that we do not
understand the intelligence process well enough to enable the development of
adequate computational models. Much work has been done in AI over the years at
lower levels, but a big part of what has been missing involves the high level,
abstract, general nature of intelligence. We address this gap by developing a
model for general intelligence. To accomplish this, we focus on three basic
aspects of intelligence. First, we must realize the general order and nature of
intelligence at a high level. Second, we must come to know what these
realizations mean with respect to the overall intelligence process. Third, we
must describe these realizations as clearly as possible. We propose a
hierarchical model to help capture and exploit the order within intelligence.
The underlying order involves patterns of signals that become organized, stored
and activated in space and time. These patterns can be described using a
simple, general hierarchy, with physical signals at the lowest level,
information in the middle, and abstract signal representations at the top. This
high level perspective provides a big picture that literally helps us see the
intelligence process, thereby enabling fundamental realizations, a better
understanding and clear descriptions of the intelligence process. The resulting
model can be used to support all kinds of information processing across
multiple levels of abstraction. As computer technology improves, and as
cooperation increases between humans and computers, people will become more
efficient and more productive in performing their information processing tasks.
",A Model for General Intelligence
"  The ability to learn optimal control policies in systems where action space
is defined by sentences in natural language would allow many interesting
real-world applications such as automatic optimisation of dialogue systems.
Text-based games with multiple endings and rewards are a promising platform for
this task, since their feedback allows us to employ reinforcement learning
techniques to jointly learn text representations and control policies. We argue
that the key property of AI agents, especially in the text-games context, is
their ability to generalise to previously unseen games. We present a
minimalistic text-game playing agent, testing its generalisation and transfer
learning performance and showing its ability to play multiple games at once. We
also present pyfiction, an open-source library for universal access to
different text games that could, together with our agent that implements its
interface, serve as a baseline for future research.
",Baselines for Reinforcement Learning in Text Games
"  An intelligent agent performs actions in order to achieve its goals. Such
actions can either be externally directed, such as opening a door, or
internally directed, such as writing data to a memory location or strengthening
a synaptic connection. Some internal actions, to which we refer as
computations, potentially help the agent choose better actions. Considering
that (external) actions and computations might draw upon the same resources,
such as time and energy, deciding when to act or compute, as well as what to
compute, are detrimental to the performance of an agent.
  In an environment that provides rewards depending on an agent's behavior, an
action's value is typically defined as the sum of expected long-term rewards
succeeding the action (itself a complex quantity that depends on what the agent
goes on to do after the action in question). However, defining the value of a
computation is not as straightforward, as computations are only valuable in a
higher order way, through the alteration of actions.
  This thesis offers a principled way of computing the value of a computation
in a planning setting formalized as a Markov decision process. We present two
different definitions of computation values: static and dynamic. They address
two extreme cases of the computation budget: affording calculation of zero or
infinitely many steps in the future. We show that these values have desirable
properties, such as temporal consistency and asymptotic convergence.
  Furthermore, we propose methods for efficiently computing and approximating
the static and dynamic computation values. We describe a sense in which the
policies that greedily maximize these values can be optimal. We utilize these
principles to construct Monte Carlo tree search algorithms that outperform most
of the state-of-the-art in terms of finding higher quality actions given the
same simulation resources.
",Computing the Value of Computation for Planning
"  This paper provides a complexity analysis for the game of reconnaissance
blind chess (RBC), a recently-introduced variant of chess where each player
does not know the positions of the opponent's pieces a priori but may reveal a
subset of them through chosen, private sensing actions. In contrast to many
commonly studied imperfect information games like poker, an RBC player does not
know what the opponent knows or has chosen to learn, exponentially expanding
the size of the game's information sets (i.e., the number of possible game
states that are consistent with what a player has observed). Effective RBC
sensing and moving strategies must account for the uncertainty of both players,
an essential element of many real-world decision-making problems. Here we
evaluate RBC from a game theoretic perspective, tracking the proliferation of
information sets from the perspective of selected canonical bot players in
tournament play. We show that, even for effective sensing strategies, the game
sizes of RBC compare to those of Go while the average size of a player's
information set throughout an RBC game is much greater than that of a player in
Heads-up Limit Hold 'Em. We compare these measures of complexity among
different playing algorithms and provide cursory assessments of the various
sensing and moving strategies.
",On the Complexity of Reconnaissance Blind Chess
"  This paper presents a model of contrastive explanation using structural
casual models. The topic of causal explanation in artificial intelligence has
gathered interest in recent years as researchers and practitioners aim to
increase trust and understanding of intelligent decision-making. While
different sub-fields of artificial intelligence have looked into this problem
with a sub-field-specific view, there are few models that aim to capture
explanation more generally. One general model is based on structural causal
models. It defines an explanation as a fact that, if found to be true, would
constitute an actual cause of a specific event. However, research in philosophy
and social sciences shows that explanations are contrastive: that is, when
people ask for an explanation of an event -- the fact -- they (sometimes
implicitly) are asking for an explanation relative to some contrast case; that
is, ""Why P rather than Q?"". In this paper, we extend the structural causal
model approach to define two complementary notions of contrastive explanation,
and demonstrate them on two classical problems in artificial intelligence:
classification and planning. We believe that this model can help researchers in
subfields of artificial intelligence to better understand contrastive
explanation.
",Contrastive Explanation: A Structural-Model Approach
"  The paper develops a formal theory of the degree of justification of
arguments, which relies solely on the structure of an argumentation framework,
and which can be successfully interfaced with approaches to instantiated
argumentation. The theory is developed in three steps. First, the paper
introduces a graded generalization of the two key notions underpinning Dung's
semantics: self-defense and conflict-freeness. This leads to a natural
generalization of Dung's semantics, whereby standard extensions are weakened or
strengthened depending on the level of self-defense and conflict-freeness they
meet. The paper investigates the fixpoint theory of these semantics,
establishing existence results for them. Second, the paper shows how graded
semantics readily provide an approach to argument rankings, offering a novel
contribution to the recently growing research programme on ranking-based
semantics. Third, this novel approach to argument ranking is applied and
studied in the context of instantiated argumentation frameworks, and in so
doing is shown to account for a simple form of accrual of arguments within the
Dung paradigm. Finally, the theory is compared in detail with existing
approaches.
","On the Graded Acceptability of Arguments in Abstract and Instantiated
  Argumentation"
"  Multi-objective reinforcement learning (MORL) is the generalization of
standard reinforcement learning (RL) approaches to solve sequential decision
making problems that consist of several, possibly conflicting, objectives.
Generally, in such formulations, there is no single optimal policy which
optimizes all the objectives simultaneously, and instead, a number of policies
has to be found each optimizing a preference of the objectives. In other words,
the MORL is framed as a meta-learning problem, with the task distribution given
by a distribution over the preferences. We demonstrate that such a formulation
results in a better approximation of the Pareto optimal solutions in terms of
both the optimality and the computational efficiency. We evaluated our method
on obtaining Pareto optimal policies using a number of continuous control
problems with high degrees of freedom.
",Meta-Learning for Multi-objective Reinforcement Learning
"  Multi-cycle assignment problems address scenarios where a series of general
assignment problems has to be solved sequentially. Subsequent cycles can differ
from previous ones due to changing availability or creation of tasks and
agents, which makes an upfront static schedule infeasible and introduces
uncertainty in the task-agent assignment process. We consider the setting
where, besides profit maximization, it is also desired to maintain diverse
assignments for tasks and agents, such that all tasks have been assigned to all
agents over subsequent cycles. This problem of multi-cycle assignment with
rotational diversity is approached in two sub-problems: The outer problem which
augments the original profit maximization objective with additional information
about the state of rotational diversity while the inner problem solves the
adjusted general assignment problem in a single execution of the model. We
discuss strategies to augment the profit values and evaluate them
experimentally. The method's efficacy is shown in three case studies:
multi-cycle variants of the multiple knapsack and the multiple subset sum
problems, and a real-world case study on the test case selection and assignment
problem from the software engineering domain.
",Multi-Cycle Assignment Problems with Rotational Diversity
"  In barter exchanges, participants directly trade their endowed goods in a
constrained economic setting without money. Transactions in barter exchanges
are often facilitated via a central clearinghouse that must match participants
even in the face of uncertainty---over participants, existence and quality of
potential trades, and so on. Leveraging robust combinatorial optimization
techniques, we address uncertainty in kidney exchange, a real-world barter
market where patients swap (in)compatible paired donors. We provide two
scalable robust methods to handle two distinct types of uncertainty in kidney
exchange---over the quality and the existence of a potential match. The latter
case directly addresses a weakness in all stochastic-optimization-based methods
to the kidney exchange clearing problem, which all necessarily require explicit
estimates of the probability of a transaction existing---a still-unsolved
problem in this nascent market. We also propose a novel, scalable kidney
exchange formulation that eliminates the need for an exponential-time
constraint generation process in competing formulations, maintains provable
optimality, and serves as a subsolver for our robust approach. For each type of
uncertainty we demonstrate the benefits of robustness on real data from a
large, fielded kidney exchange in the United States. We conclude by drawing
parallels between robustness and notions of fairness in the kidney exchange
setting.
",Scalable Robust Kidney Exchange
"  We present a novel modular architecture for StarCraft II AI. The architecture
splits responsibilities between multiple modules that each control one aspect
of the game, such as build-order selection or tactics. A centralized scheduler
reviews macros suggested by all modules and decides their order of execution.
An updater keeps track of environment changes and instantiates macros into
series of executable actions. Modules in this framework can be optimized
independently or jointly via human design, planning, or reinforcement learning.
We apply deep reinforcement learning techniques to training two out of six
modules of a modular agent with self-play, achieving 94% or 87% win rates
against the ""Harder"" (level 5) built-in Blizzard bot in Zerg vs. Zerg matches,
with or without fog-of-war.
",Modular Architecture for StarCraft II with Deep Reinforcement Learning
"  Awareness of the possible impacts associated with artificial intelligence has
risen in proportion to progress in the field. While there are tremendous
benefits to society, many argue that there are just as many, if not more,
concerns related to advanced forms of artificial intelligence. Accordingly,
research into methods to develop artificial intelligence safely is increasingly
important. In this paper, we provide an overview of one such safety paradigm:
containment with a critical lens aimed toward generative adversarial networks
and potentially malicious artificial intelligence. Additionally, we illuminate
the potential for a developmental blindspot in the stovepiping of containment
mechanisms.
",Stovepiping and Malicious Software: A Critical Review of AGI Containment
"  Because combat environments change over time and technology upgrades are
widespread for ground vehicles, a large number of vehicles and equipment become
quickly obsolete. A possible solution for the U.S. Army is to develop fleets of
modular military vehicles, which are built by interchangeable substantial
components also known as modules. One of the typical characteristics of module
is their ease of assembly and disassembly through simple means such as
plug-in/pull-out actions, which allows for real-time fleet reconfiguration to
meet dynamic demands. Moreover, military demands are time-varying and highly
stochastic because commanders keep reacting to enemy's actions. To capture
these characteristics, we formulated an intelligent agent-based model to
imitate decision making process during fleet operation, which combines
real-time optimization with artificial intelligence. The agents are capable of
inferring enemy's future move based on historical data and optimize
dispatch/operation decisions accordingly. We implement our model to simulate an
attacker-defender game between two adversarial and intelligent players,
representing the commanders from modularized fleet and conventional fleet
respectively. Given the same level of combat resources and intelligence, we
highlight the tactical advantages of fleet modularity in terms of win rate,
unpredictability and suffered damage.
","Analysis of Fleet Modularity in an Artificial Intelligence-Based
  Attacker-Defender Game"
"  This contribution presents a very brief and critical discussion on automated
machine learning (AutoML), which is categorized here into two classes, referred
to as narrow AutoML and generalized AutoML, respectively. The conclusions
yielded from this discussion can be summarized as follows: (1) most existent
research on AutoML belongs to the class of narrow AutoML; (2) advances in
narrow AutoML are mainly motivated by commercial needs, while any possible
benefit obtained is definitely at a cost of increase in computing burdens;
(3)the concept of generalized AutoML has a strong tie in spirit with artificial
general intelligence (AGI), also called ""strong AI"", for which obstacles abound
for obtaining pivotal progresses.
",A Very Brief and Critical Discussion on AutoML
"  Cooking typically involves a plethora of decisions about ingredients and
tools that need to be chosen in order to write a good cooking recipe. Cooking
can be modelled in an optimization framework, as it involves a search space of
ingredients, kitchen tools, cooking times or temperatures. If we model as an
objective function the quality of the recipe, several problems arise. No
analytical expression can model all the recipes, so no gradients are available.
The objective function is subjective, in other words, it contains noise.
Moreover, evaluations are expensive both in time and human resources. Bayesian
Optimization (BO) emerges as an ideal methodology to tackle problems with these
characteristics. In this paper, we propose a methodology to suggest recipe
recommendations based on a Machine Learning (ML) model that fits real and
simulated data and BO. We provide empirical evidence with two experiments that
support the adequacy of the methodology.
",Suggesting Cooking Recipes Through Simulation and Bayesian Optimization
"  Constraint Programming (CP) is a powerful declarative programming paradigm
where inference and search are interleaved to find feasible and optimal
solutions to various type of constraint systems. However, handling logical
connectors with constructive information in CP is notoriously difficult. This
paper presents If Then Else (ITE), a lightweight implementation of stratified
constructive reasoning for logical connectives. Stratification is introduced to
cope with the risk of combinatorial explosion of constructing information from
nested and combined logical operators. ITE is an open-source library built on
top of SICStus Prolog clp(fd), which proposes various operators, including
constructive disjunction and negation, constructive implication and
conditional. These operators can be used to express global constraints and to
benefit from constructive reasoning for more domain pruning during constraint
filtering. Even though ITE is not competitive with specialized filtering
algorithms available in some global constraints implementations, its
expressiveness allows users to easily define well-tuned constraints with
powerful deduction capabilities. Our extended experimental results show that
ITE is more efficient than available generic approaches that handle logical
constraint systems over finite domains.
","ITE: A Lightweight Implementation of Stratified Reasoning for
  Constructive Logical Operators"
"  In this paper, we propose a new fuzzy reasoning principle, so called Movement
and Transformation Principle(MTP). This Principle is to obtain a new fuzzy
reasoning result by Movement and Transformation the consequent fuzzy set in
response to the Movement, Transformation, and Movement-Transformation
operations between the antecedent fuzzy set and fuzzificated observation
information. And then we presented fuzzy modus ponens and fuzzy modus tollens
based on MTP. We compare proposed method with Mamdani fuzzy system, Sugeno
fuzzy system, Wang distance type fuzzy reasoning method and Hellendoorn
functional type method. And then we applied to the learning experiments of the
fuzzy neural network based on MTP and compared it with the Sugeno method.
Through prediction experiments of fuzzy neural network on the precipitation
data and security situation data, learning accuracy and time performance are
clearly improved. Consequently we show that our method based on MTP is
computationally simple and does not involve nonlinear operations, so it is easy
to handle mathematically.
","New Movement and Transformation Principle of Fuzzy Reasoning and Its
  Application to Fuzzy Neural Network"
"  The article describes a special time-interval balancing in multi-processor
scheduling of composite modular jobs. This scheduling problem is close to
just-in-time planning approach. First, brief literature surveys are presented
on just-in-time scheduling and due-data/due-window scheduling problems.
Further, the problem and its formulation are proposed for the time-interval
balanced scheduling of composite modular jobs. The illustrative real world
planning example for modular home-building is described. Here, the main
objective function consists in a balance between production of the typical
building modules (details) and the assembly processes of the building(s) (by
several teams). The assembly plan has to be modified to satisfy the balance
requirements. The solving framework is based on the following: (i) clustering
of initial set of modular detail types to obtain about ten basic detail types
that correspond to main manufacturing conveyors; (ii) designing a preliminary
plan of assembly for buildings; (iii) detection of unbalanced time periods,
(iv) modification of the planning solution to improve the schedule balance. The
framework implements a metaheuristic based on local optimization approach. Two
other applications (supply chain management, information transmission systems)
are briefly described.
","Time-interval balancing in multi-processor scheduling of composite
  modular jobs (preliminary description)"
"  The use of language models for generating lyrics and poetry has received an
increased interest in the last few years. They pose a unique challenge relative
to standard natural language problems, as their ultimate purpose is reative,
notions of accuracy and reproducibility are secondary to notions of lyricism,
structure, and diversity. In this creative setting, traditional quantitative
measures for natural language problems, such as BLEU scores, prove inadequate:
a high-scoring model may either fail to produce output respecting the desired
structure (e.g. song verses), be a terribly boring creative companion, or both.
In this work we propose a mechanism for combining two separately trained
language models into a framework that is able to produce output respecting the
desired song structure, while providing a richness and diversity of vocabulary
that renders it more creatively appealing.
","Combining Learned Lyrical Structures and Vocabulary for Improved Lyric
  Generation"
"  We present the reinterpreted and reimplemented Copycat project, an
architecture solving letter analogy domain problems. To support a flexible
implementation change and rigor testing process, we propose a implementation
method in DrRacket by using functional abstraction, naming system,
initialization, and structural reference. Finally, benefits and limitations are
analyzed for cognitive architectures along the lines of Copycat.
",Reimplementation and Reinterpretation of the Copycat Project
"  This paper is concerned with the apparent greatest weakness of the
Mathematical Theory of Evidence (MTE) of Shafer \cite{Shafer:76}, which has
been strongly criticized by Wasserman \cite{Wasserman:92ijar}.
  Weaknesses of Shafer's proposal \cite{Shafer:90b} of probabilistic
interpretation of MTE belief functions is demonstrated. Thereafter a new
probabilistic interpretation of MTE conforming both to definition of belief
function and to Dempster's rule of combination of independent evidence. It is
shown that shaferian conditioning of belief functions on observations
\cite{Shafer:90b} may be treated as selection combined with modification of
data, that is data is not viewed as it is but it is casted into one's beliefs
in what it should be like.
",Mathematical Theory of Evidence Versus Evidence
"  Mathematical Theory of Evidence (MTE) is known as a foundation for reasoning
when knowledge is expressed at various levels of detail. Though much research
effort has been committed to this theory since its foundation, many questions
remain open. One of the most important open questions seems to be the
relationship between frequencies and the Mathematical Theory of Evidence. The
theory is blamed to leave frequencies outside (or aside of) its framework. The
seriousness of this accusation is obvious: no experiment may be run to compare
the performance of MTE-based models of real world processes against real world
data.
  In this paper we develop a frequentist model of the MTE bringing to fall the
above argument against MTE. We describe, how to interpret data in terms of MTE
belief functions, how to reason from data about conditional belief functions,
how to generate a random sample out of a MTE model, how to derive MTE model
from data and how to compare results of reasoning in MTE model and reasoning
from data.
  It is claimed in this paper that MTE is suitable to model some types of
destructive processes
",Reasoning From Data in the Mathematical Theory of Evidence
"  Artificial intelligence and medicine have a longstanding and proficuous
relationship. In the present work we develop a brief assessment of this
relationship with specific focus on machine learning, in which we highlight
some critical points which may hinder the use of machine learning techniques
for clinical diagnosis and therapy advice in practice. We then suggest a
conceptual framework to build successful systems to aid clinical diagnosis and
therapy advice, grounded on a novel concept we have coined drifting domains. We
focus on oncology to build our arguments, as this area of medicine furnishes
strong evidence for the critical points we take into account here.
","On the practice of classification learning for clinical diagnosis and
  therapy advice in oncology"
"  Artificial intelligence systems are being increasingly deployed due to their
potential to increase the efficiency, scale, consistency, fairness, and
accuracy of decisions. However, as many of these systems are opaque in their
operation, there is a growing demand for such systems to provide explanations
for their decisions. Conventional approaches to this problem attempt to expose
or discover the inner workings of a machine learning model with the hope that
the resulting explanations will be meaningful to the consumer. In contrast,
this paper suggests a new approach to this problem. It introduces a simple,
practical framework, called Teaching Explanations for Decisions (TED), that
provides meaningful explanations that match the mental model of the consumer.
We illustrate the generality and effectiveness of this approach with two
different examples, resulting in highly accurate explanations with no loss of
prediction accuracy for these two examples.
",TED: Teaching AI to Explain its Decisions
"  We predict credit applications with off-the-shelf, interchangeable black-box
classifiers and we explain single predictions with counterfactual explanations.
Counterfactual explanations expose the minimal changes required on the input
data to obtain a different result e.g., approved vs rejected application.
Despite their effectiveness, counterfactuals are mainly designed for changing
an undesired outcome of a prediction i.e. loan rejected. Counterfactuals,
however, can be difficult to interpret, especially when a high number of
features are involved in the explanation. Our contribution is two-fold: i) we
propose positive counterfactuals, i.e. we adapt counterfactual explanations to
also explain accepted loan applications, and ii) we propose two weighting
strategies to generate more interpretable counterfactuals. Experiments on the
HELOC loan applications dataset show that our contribution outperforms the
baseline counterfactual generation strategy, by leading to smaller and hence
more interpretable counterfactuals.
","Interpretable Credit Application Predictions With Counterfactual
  Explanations"
"  The problem to deal with in this project is the problem of routing electric
vehicles, which consists of finding the best routes for this type of vehicle,
so that they reach their destination, without running out of power and
optimizing to the maximum transportation costs. The importance of this problem
is mainly in the sector of shipments in the recent future, when obsolete energy
sources are replaced with renewable sources, where each vehicle contains a
number of packages that must be delivered at specific points in the city , but,
being electric, they do not have an optimal battery life, so having the ideal
routes traced is a vital aspect for the proper functioning of these. Now days
you can see applications of this problem in the cleaning sector, specifically
with the trucks responsible for collecting garbage, which aims to travel the
entire city in the most efficient way, without letting excessive garbage
accumulate.
",Genetic algorithm for optimal distribution in cities
"  Abductive reasoning generates explanatory hypotheses for new observations
using prior knowledge. This paper investigates the use of forgetting, also
known as uniform interpolation, to perform ABox abduction in description logic
(ALC) ontologies. Non-abducibles are specified by a forgetting signature which
can contain concept, but not role, symbols. The resulting hypotheses are
semantically minimal and each consist of a set of disjuncts. These disjuncts
are each independent explanations, and are not redundant with respect to the
background ontology or the other disjuncts, representing a form of hypothesis
space. The observations and hypotheses handled by the method can contain both
atomic or complex ALC concepts, excluding role assertions, and are not
restricted to Horn clauses. Two approaches to redundancy elimination are
explored for practical use: full and approximate. Using a prototype
implementation, experiments were performed over a corpus of real world
ontologies to investigate the practicality of both approaches across several
settings.
",ABox Abduction via Forgetting in ALC (Long Version)
"  Mathematical optimization offers highly-effective tools for finding solutions
for problems with well-defined goals, notably scheduling. However, optimization
solvers are often unexplainable black boxes whose solutions are inaccessible to
users and which users cannot interact with. We define a novel paradigm using
argumentation to empower the interaction between optimization solvers and
users, supported by tractable explanations which certify or refute solutions. A
solution can be from a solver or of interest to a user (in the context of
'what-if' scenarios). Specifically, we define argumentative and natural
language explanations for why a schedule is (not) feasible, (not) efficient or
(not) satisfying fixed user decisions, based on models of the fundamental
makespan scheduling problem in terms of abstract argumentation frameworks
(AFs). We define three types of AFs, whose stable extensions are in one-to-one
correspondence with schedules that are feasible, efficient and satisfying fixed
decisions, respectively. We extract the argumentative explanations from these
AFs and the natural language explanations from the argumentative ones.
",Argumentation for Explainable Scheduling (Full Paper with Proofs)
"  Bayesian approaches provide a principled solution to the
exploration-exploitation trade-off in Reinforcement Learning. Typical
approaches, however, either assume a fully observable environment or scale
poorly. This work introduces the Factored Bayes-Adaptive POMDP model, a
framework that is able to exploit the underlying structure while learning the
dynamics in partially observable systems. We also present a belief tracking
method to approximate the joint posterior over state and model variables, and
an adaptation of the Monte-Carlo Tree Search solution method, which together
are capable of solving the underlying problem near-optimally. Our method is
able to learn efficiently given a known factorization or also learn the
factorization and the model parameters at the same time. We demonstrate that
this approach is able to outperform current methods and tackle problems that
were previously infeasible.
",Bayesian Reinforcement Learning in Factored POMDPs
"  With the rapid growth of the express industry, intelligent warehouses that
employ autonomous robots for carrying parcels have been widely used to handle
the vast express volume. For such warehouses, the warehouse layout design plays
a key role in improving the transportation efficiency. However, this work is
still done by human experts, which is expensive and leads to suboptimal
results. In this paper, we aim to automate the warehouse layout designing
process. We propose a two-layer evolutionary algorithm to efficiently explore
the warehouse layout space, where an auxiliary objective fitness approximation
model is introduced to predict the outcome of the designed warehouse layout and
a two-layer population structure is proposed to incorporate the approximation
model into the ordinary evolution framework. Empirical experiments show that
our method can efficiently design effective warehouse layouts that outperform
both heuristic-designed and vanilla evolution-designed warehouse layouts.
","Layout Design for Intelligent Warehouse by Evolution with Fitness
  Approximation"
"  We present the state of the art in representing and reasoning with fuzzy
knowledge in Semantic Web Languages such as triple languages RDF/RDFS,
conceptual languages of the OWL 2 family and rule languages. We further show
how one may generalise them to so-called annotation domains, that cover also
e.g. temporal and provenance extensions.
",An Introduction to Fuzzy & Annotated Semantic Web Languages
"  We study the emergence of communication in multiagent adversarial settings
inspired by the classic Imitation game. A class of three player games is used
to explore how agents based on sequence to sequence (Seq2Seq) models can learn
to communicate information in adversarial settings. We propose a modeling
approach, an initial set of experiments and use signaling theory to support our
analysis. In addition, we describe how we operationalize the learning process
of actor-critic Seq2Seq based agents in these communicational games.
",Seq2Seq Mimic Games: A Signaling Perspective
"  Generalized planning is concerned with the computation of plans that solve
not one but multiple instances of a planning domain. Recently, it has been
shown that generalized plans can be expressed as mappings of feature values
into actions, and that they can often be computed with fully observable
non-deterministic (FOND) planners. The actions in such plans, however, are not
the actions in the instances themselves, which are not necessarily common to
other instances, but abstract actions that are defined on a set of common
features. The formulation assumes that the features and the abstract actions
are given. In this work, we address this limitation by showing how to learn
them automatically. The resulting account of generalized planning combines
learning and planning in a novel way: a learner, based on a Max SAT
formulation, yields the features and abstract actions from sampled state
transitions, and a FOND planner uses this information, suitably transformed, to
produce the general plans. Correctness guarantees are given and experimental
results on several domains are reported.
",Learning Features and Abstract Actions for Computing Generalized Plans
"  In this paper, we propose a cognitive system that acquires knowledge on
elderly daily activities to ensure their wellness in a smart home using a
Knowledge-Information-Data (KID) model. The novel cognitive framework called
high dimensional conceptual space is proposed and used as KID model. This KID
model is built using geometrical framework of conceptual spaces and formal
concept analysis (FCA) to overcome imprecise concept notation of conceptual
space with the help of topology based FCA. By doing so, conceptual space can be
represented using Hilbert space. This high dimensional conceptual space is
quantum inspired in terms of its concept representation. The knowledge learnt
by the KID model recognizes the daily activities of the elderly. Consequently,
the model identifies the scenario on which the wellness of the elderly has to
be ensured.
","Quantum Inspired High Dimensional Conceptual Space as KID Model for
  Elderly Assistance"
"  In the field of Autonomous Driving, the system controlling the vehicle can be
seen as an agent acting in a complex environment and thus naturally fits into
the modern framework of Reinforcement Learning. However, learning to drive can
be a challenging task and current results are often restricted to simplified
driving environments. To advance the field, we present a method to adaptively
restrict the action space of the agent according to its current driving
situation and show that it can be used to swiftly learn to drive in a realistic
environment based on the Deep Q-Network algorithm.
","Simulated Autonomous Driving in a Realistic Driving Environment using
  Deep Reinforcement Learning and a Deterministic Finite State Machine"
"  Item response theory (IRT) can be applied to the analysis of the evaluation
of results from AI benchmarks. The two-parameter IRT model provides two
indicators (difficulty and discrimination) on the side of the item (or AI
problem) while only one indicator (ability) on the side of the respondent (or
AI agent). In this paper we analyse how to make this set of indicators dual, by
adding a fourth indicator, generality, on the side of the respondent.
Generality is meant to be dual to discrimination, and it is based on
difficulty. Namely, generality is defined as a new metric that evaluates
whether an agent is consistently good at easy problems and bad at difficult
ones. With the addition of generality, we see that this set of four key
indicators can give us more insight on the results of AI benchmarks. In
particular, we explore two popular benchmarks in AI, the Arcade Learning
Environment (Atari 2600 games) and the General Video Game AI competition. We
provide some guidelines to estimate and interpret these indicators for other AI
benchmarks and competitions.
","Analysing Results from AI Benchmarks: Key Indicators and How to Obtain
  Them"
"  Reinforcement learning (RL) techniques, while often powerful, can suffer from
slow learning speeds, particularly in high dimensional spaces. Decomposition of
tasks into a hierarchical structure holds the potential to significantly speed
up learning, generalization, and transfer learning. However, the current task
decomposition techniques often rely on high-level knowledge provided by an
expert (e.g. using dynamic Bayesian networks) to extract a hierarchical task
structure; which is not necessarily available in autonomous systems. In this
paper, we propose a novel method based on Sequential Association Rule Mining
that can extract Hierarchical Structure of Tasks in Reinforcement Learning
(SARM-HSTRL) in an autonomous manner for both Markov decision processes (MDPs)
and factored MDPs. The proposed method leverages association rule mining to
discover the causal and temporal relationships among states in different
trajectories, and extracts a task hierarchy that captures these relationships
among sub-goals as termination conditions of different sub-tasks. We prove that
the extracted hierarchical policy offers a hierarchically optimal policy in
MDPs and factored MDPs. It should be noted that SARM-HSTRL extracts this
hierarchical optimal policy without having dynamic Bayesian networks in
scenarios with a single task trajectory and also with multiple tasks'
trajectories. Furthermore, it has been theoretically and empirically shown that
the extracted hierarchical task structure is consistent with trajectories and
provides the most efficient, reliable, and compact structure under appropriate
assumptions. The numerical results compare the performance of the proposed
SARM-HSTRL method with conventional HRL algorithms in terms of the accuracy in
detecting the sub-goals, the validity of the extracted hierarchies, and the
speed of learning in several testbeds.
","Autonomous Extraction of a Hierarchical Structure of Tasks in
  Reinforcement Learning, A Sequential Associate Rule Mining Approach"
"  The idea of reusing or transferring information from previously learned tasks
(source tasks) for the learning of new tasks (target tasks) has the potential
to significantly improve the sample efficiency of a reinforcement learning
agent. In this work, we describe a novel approach for reusing previously
acquired knowledge by using it to guide the exploration of an agent while it
learns new tasks. In order to do so, we employ a variant of the growing
self-organizing map algorithm, which is trained using a measure of similarity
that is defined directly in the space of the vectorized representations of the
value functions. In addition to enabling transfer across tasks, the resulting
map is simultaneously used to enable the efficient storage of previously
acquired task knowledge in an adaptive and scalable manner. We empirically
validate our approach in a simulated navigation environment, and also
demonstrate its utility through simple experiments using a mobile
micro-robotics platform. In addition, we demonstrate the scalability of this
approach, and analytically examine its relation to the proposed network growth
mechanism. Further, we briefly discuss some of the possible improvements and
extensions to this approach, as well as its relevance to real world scenarios
in the context of continual learning.
","Self-Organizing Maps for Storage and Transfer of Knowledge in
  Reinforcement Learning"
"  Jewelry has been an integral part of human culture since ages. One of the
most popular styles of jewelry is created by putting together precious and
semi-precious stones in diverse patterns. While technology is finding its way
in the production process of such jewelry, designing it remains a
time-consuming and involved task. In this paper, we propose a unique approach
using optimization methods coupled with machine learning techniques to generate
novel stone jewelry designs at scale. Our evaluation shows that designs
generated by our approach are highly likeable and visually appealing.
",Using AI to Design Stone Jewelry
"  The verification of planning domain models is crucial to ensure the safety,
integrity and correctness of planning-based automated systems. This task is
usually performed using model checking techniques. However, unconstrained
application of model checkers to verify planning domain models can result in
false positives, i.e.counterexamples that are unreachable by a sound planner
when using the domain under verification during a planning task. In this paper,
we discuss the downside of unconstrained planning domain model verification. We
then introduce the notion of a valid planning counterexample, and demonstrate
how model checkers, as well as state trajectory constraints planning
techniques, should be used to verify planning domain models so that invalid
planning counterexamples are not returned.
",Goal-constrained Planning Domain Model Verification of Safety Properties
"  This paper reviews the reasons that Human-in-the-Loop is both critical for
preventing widely-understood failure modes for machine learning, and not a
practical solution. Following this, we review two current heuristic methods for
addressing this. The first is provable safety envelopes, which are possible
only when the dynamics of the system are fully known, but can be useful safety
guarantees when optimal behavior is based on machine learning with
poorly-understood safety characteristics. The second is the simpler circuit
breaker model, which can forestall or prevent catastrophic outcomes by stopping
the system, without any specific model of the system. This paper proposes using
heuristic, dynamic safety envelopes, which are a plausible halfway point
between these approaches that allows human oversight without some of the more
difficult problems faced by Human-in-the-Loop systems. Finally, the paper
concludes with how this approach can be used for governance of systems where
otherwise unsafe systems are deployed.
",Oversight of Unsafe Systems via Dynamic Safety Envelopes
"  There has been significant interest of late in generating behavior of agents
that is interpretable to the human (observer) in the loop. However, the work in
this area has typically lacked coherence on the topic, with proposed solutions
for ""explicable"", ""legible"", ""predictable"" and ""transparent"" planning with
overlapping, and sometimes conflicting, semantics all aimed at some notion of
understanding what intentions the observer will ascribe to an agent by
observing its behavior. This is also true for the recent works on ""security""
and ""privacy"" of plans which are also trying to answer the same question, but
from the opposite point of view -- i.e. when the agent is trying to hide
instead of revealing its intentions. This paper attempts to provide a workable
taxonomy of relevant concepts in this exciting and emerging field of inquiry.
","Explicability? Legibility? Predictability? Transparency? Privacy?
  Security? The Emerging Landscape of Interpretable Agent Behavior"
"  Existing work for plan trace visualization in automated planning uses
pipeline-style visualizations, similar to plans in Gantt charts. Such
visualization do not capture the domain structure or dependencies between the
various fluents and actions. Additionally, plan traces in such visualizations
cannot be easily compared with one another without parsing the details of
individual actions, which imposes a higher cognitive load. We introduce
TGE-viz, a technique to visualize plan traces within an embedding of the entire
transition graph of a domain in low dimensional space. TGE-viz allows users to
visualize and criticize plans more intuitively for mixed-initiative planning.
It also allows users to visually appraise the structure of domains and the
dependencies in it.
","TGE-viz : Transition Graph Embedding for Visualization of Plan Traces
  and Domains"
"  Motivated by Shannon's model and recent rehabilitation of self-supervised
artificial intelligence having a ""World Model"", this paper propose an unified
intelligence-communication (UIC) model for describing a single agent and any
multi-agent system.
  Firstly, the environment is modelled as the generic communication channel
between agents. Secondly, the UIC model adopts a learning-agent model for
unifying several well-adopted agent architecture, e.g. rule-based agent model
in complex adaptive systems, layered model for describing human-level
intelligence, world-model based agent model. The model may also provide an
unified approach to investigate a multi-agent system (MAS) having multiple
action-perception modalities, e.g. explicitly information transfer and implicit
information transfer.
  This treatise would be divided into three parts, and this first part provides
an overview of the UIC model without introducing cumbersome mathematical
analysis and optimizations. In the second part of this treatise, case studies
with quantitative analysis driven by the UIC model would be provided,
exemplifying the adoption of the UIC model in multi-agent system. Specifically,
two representative cases would be studied, namely the analysis of a natural
multi-agent system, as well as the co-design of communication, perception and
action in an artificial multi-agent system. In the third part of this treatise,
the paper provides further insights and future research directions motivated by
the UIC model, such as unification of single intelligence and collective
intelligence, a possible explanation of intelligence emergence and a dual model
for agent-environment intelligence hypothesis.
  Notes: This paper is a Previewed Version, the extended full-version would be
released after being accepted.
","An Unified Intelligence-Communication Model for Multi-Agent System
  Part-I: Overview"
"  In this paper, we leverage the efficiency of Binarized Neural Networks (BNNs)
to learn complex state transition models of planning domains with discretized
factored state and action spaces. In order to directly exploit this transition
structure for planning, we present two novel compilations of the learned
factored planning problem with BNNs based on reductions to Weighted Partial
Maximum Boolean Satisfiability (FD-SAT-Plan+) as well as Binary Linear
Programming (FD-BLP-Plan+). Theoretically, we show that our SAT-based
Bi-Directional Neuron Activation Encoding is asymptotically the most compact
encoding relative to the current literature and supports Unit Propagation (UP)
-- an important property that facilitates efficiency in SAT solvers.
Experimentally, we validate the computational efficiency of our Bi-Directional
Neuron Activation Encoding in comparison to an existing neuron activation
encoding and demonstrate the ability to learn complex transition models with
BNNs. We test the runtime efficiency of both FD-SAT-Plan+ and FD-BLP-Plan+ on
the learned factored planning problem showing that FD-SAT-Plan+ scales better
with increasing BNN size and complexity. Finally, we present a finite-time
incremental constraint generation algorithm based on generalized landmark
constraints to improve the planning accuracy of our encodings through simulated
or real-world interaction.
","Compact and Efficient Encodings for Planning in Factored State and
  Action Spaces with Learned Binarized Neural Network Transition Models"
"  The growing range of applications of Machine Learning (ML) in a multitude of
settings motivates the ability of computing small explanations for predictions
made. Small explanations are generally accepted as easier for human decision
makers to understand. Most earlier work on computing explanations is based on
heuristic approaches, providing no guarantees of quality, in terms of how close
such solutions are from cardinality- or subset-minimal explanations. This paper
develops a constraint-agnostic solution for computing explanations for any ML
model. The proposed solution exploits abductive reasoning, and imposes the
requirement that the ML model can be represented as sets of constraints using
some target constraint reasoning system for which the decision problem can be
answered with some oracle. The experimental results, obtained on well-known
datasets, validate the scalability of the proposed approach as well as the
quality of the computed solutions.
",Abduction-Based Explanations for Machine Learning Models
"  Argumentation-based dialogue systems, which can handle and exchange arguments
through dialogue, have been widely researched. It is required that these
systems have sufficient supporting information to argue their claims
rationally; however, the systems often do not have enough of such information
in realistic situations. One way to fill in the gap is acquiring such missing
information from dialogue partners (information-seeking dialogue). Existing
information-seeking dialogue systems are based on handcrafted dialogue
strategies that exhaustively examine missing information. However, the proposed
strategies are not specialized in collecting information for constructing
rational arguments. Moreover, the number of system's inquiry candidates grows
in accordance with the size of the argument set that the system deal with. In
this paper, we formalize the process of information-seeking dialogue as Markov
decision processes (MDPs) and apply deep reinforcement learning (DRL) for
automatically optimizing a dialogue strategy. By utilizing DRL, our dialogue
strategy can successfully minimize objective functions, the number of turns it
takes for our system to collect necessary information in a dialogue. We
conducted dialogue experiments using two datasets from different domains of
argumentative dialogue. Experimental results show that the proposed
formalization based on MDP works well, and the policy optimized by DRL
outperformed existing heuristic dialogue strategies.
","Optimization of Information-Seeking Dialogue Strategy for
  Argumentation-Based Dialogue System"
"  We introduce two novel tree search algorithms that use a policy to guide
search. The first algorithm is a best-first enumeration that uses a cost
function that allows us to prove an upper bound on the number of nodes to be
expanded before reaching a goal state. We show that this best-first algorithm
is particularly well suited for `needle-in-a-haystack' problems. The second
algorithm is based on sampling and we prove an upper bound on the expected
number of nodes it expands before reaching a set of goal states. We show that
this algorithm is better suited for problems where many paths lead to a goal.
We validate these tree search algorithms on 1,000 computer-generated levels of
Sokoban, where the policy used to guide the search comes from a neural network
trained using A3C. Our results show that the policy tree search algorithms we
introduce are competitive with a state-of-the-art domain-independent planner
that uses heuristic search.
",Single-Agent Policy Tree Search With Guarantees
"  Many modern machine learning approaches require vast amounts of training data
to learn new concepts; conversely, human learning often requires few
examples--sometimes only one--from which the learner can abstract structural
concepts. We present a novel approach to introducing new spatial structures to
an AI agent, combining deep learning over qualitative spatial relations with
various heuristic search algorithms. The agent extracts spatial relations from
a sparse set of noisy examples of block-based structures, and trains
convolutional and sequential models of those relation sets. To create novel
examples of similar structures, the agent begins placing blocks on a virtual
table, uses a CNN to predict the most similar complete example structure after
each placement, an LSTM to predict the most likely set of remaining moves
needed to complete it, and recommends one using heuristic search. We verify
that the agent learned the concept by observing its virtual block-building
activities, wherein it ranks each potential subsequent action toward building
its learned concept. We empirically assess this approach with human
participants' ratings of the block structures. Initial results and qualitative
evaluations of structures generated by the trained agent show where it has
generalized concepts from the training data, which heuristics perform best
within the search space, and how we might improve learning and execution.
","Combining Deep Learning and Qualitative Spatial Reasoning to Learn
  Complex Structures from Sparse Examples with Noise"
"  This work examines the implications of uncoupled intersections with local
real-world topology and sensor setup on traffic light control approaches.
Control approaches are evaluated with respect to: Traffic flow, fuel
consumption and noise emission at intersections.
  The real-world road network of Friedrichshafen is depicted, preprocessed and
the present traffic light controlled intersections are modeled with respect to
state space and action space.
  Different strategies, containing fixed-time, gap-based and time-based control
approaches as well as our deep reinforcement learning based control approach,
are implemented and assessed. Our novel DRL approach allows for modeling the
TLC action space, with respect to phase selection as well as selection of
transition timings. It was found that real-world topologies, and thus
irregularly arranged intersections have an influence on the performance of
traffic light control approaches. This is even to be observed within the same
intersection types (n-arm, m-phases). Moreover we could show, that these
influences can be efficiently dealt with by our deep reinforcement learning
based control approach.
","Distributed traffic light control at uncoupled intersections with
  real-world topology by deep reinforcement learning"
"  We present a method for encoding game logs as numeric features in the card
game Dominion. We then run the manifold learning algorithm t-SNE on these
encodings to visualize the landscape of player strategies. By quantifying game
states as the relative prevalence of cards in a player's deck, we create
visualizations that capture qualitative differences in player strategies.
Different ways of deviating from the starting game state appear as different
rays in the visualization, giving it an intuitive explanation. This is a
promising new direction for understanding player strategies across games that
vary in length.
",Clustering Player Strategies from Variable-Length Game Logs in Dominion
"  In this paper, we introduce methods of encoding propositional logic programs
in vector spaces. Interpretations are represented by vectors and programs are
represented by matrices. The least model of a definite program is computed by
multiplying an interpretation vector and a program matrix. To optimize
computation in vector spaces, we provide a method of partial evaluation of
programs using linear algebra. Partial evaluation is done by unfolding rules in
a program, and it is realized in a vector space by multiplying program
matrices. We perform experiments using randomly generated programs and show
that partial evaluation has potential for realizing efficient computation in
huge scale of programs.
",Partial Evaluation of Logic Programs in Vector Spaces
"  Probabilistic argumentation allows reasoning about argumentation problems in
a way that is well-founded by probability theory. However, in practice, this
approach can be severely limited by the fact that probabilities are defined by
adding an exponential number of terms. We show that this exponential blowup can
be avoided in an interesting fragment of epistemic probabilistic argumentation
and that some computational problems that have been considered intractable can
be solved in polynomial time. We give efficient convex programming formulations
for these problems and explore how far our fragment can be extended without
loosing tractability.
","A Polynomial-time Fragment of Epistemic Probabilistic Argumentation
  (Technical Report)"
"  In this paper, we make a review on the concepts of rationality across several
different fields, namely in economics, psychology and evolutionary biology and
behavioural ecology. We review how processes like natural selection can help us
understand the evolution of cognition and how cognitive biases might be a
consequence of this natural selection. In the end we argue that humans are not
irrational, but rather rationally bounded and we complement the discussion on
how quantum cognitive models can contribute for the modelling and prediction of
human paradoxical decisions.
","Unifying Decision-Making: a Review on Evolutionary Theories on
  Rationality and Cognitive Biases"
"  Weighted bipolar argumentation frameworks allow modeling decision problems
and online discussions by defining arguments and their relationships. The
strength of arguments can be computed based on an initial weight and the
strength of attacking and supporting arguments. While previous approaches
assumed an acyclic argumentation graph and successively set arguments' strength
based on the strength of their parents, recently continuous dynamical systems
have been proposed as an alternative. Continuous models update arguments'
strength simultaneously and continuously. While there are currently no
analytical guarantees for convergence in general graphs, experiments show that
continuous models can converge quickly in large cyclic graphs with thousands of
arguments. Here, we focus on the high-level ideas of this approach and explain
key results and applications. We also introduce Attractor, a Java library that
can be used to solve weighted bipolar argumentation problems. Attractor
contains implementations of several discrete and continuous models and
numerical algorithms to compute solutions. It also provides base classes that
can be used to implement, to evaluate and to compare continuous models easily.
","A Tutorial for Weighted Bipolar Argumentation with Continuous Dynamical
  Systems and the Java Library Attractor"
"  In many real-world planning problems, action's impact differs with a place,
time and the context in which the action is applied. The same action with the
same effects in a different context or states can cause a different change. In
actions with incomplete precondition list, that applicable in several states
and circumstances, ambiguity regarding the impact of the action is challenging
even in small domains. To estimate the real impact of actions, an evaluation of
the effect list will not be enough; a relative estimation is more informative
and suitable for estimation of action's real impact. Recent work on
Over-subscription Planning (OSP) defined the net utility of action as the net
change in the state's value caused by the action. The notion of net utility of
action allows for a broader perspective on value action impact and use for a
more accurate evaluation of achievements of the action, considering inter-state
and intra-state dependencies. To achieve value-rational decisions in complex
reality often requires strategic, high level, planning with a global
perspective and values, while many local tactical decisions require real-time
information to estimate the impact of actions. This paper proposes an offline
action-value structure analysis to exploit the compactly represented
informativeness of net utility of actions to extend the scope of planning to
value uncertainty scenarios and to provide a real-time value-rational decision
planning tool. The result of the offline pre-processing phase is a compact
decision planning model representation for flexible, local reasoning of net
utility of actions with (offline) value ambiguity. The obtained flexibility is
beneficial for the online planning phase and real-time execution of actions
with value ambiguity. Our empirical evaluation shows the effectiveness of this
approach in domains with value ambiguity in their action-value-structure.
","Automated Tactical Decision Planning Model with Strategic Values
  Guidance for Local Action-Value-Ambiguity"
"  In this work we propose a novel task framework under which a variety of
physical reasoning puzzles can be constructed using very simple rules. Under
sparse reward settings, most of these tasks can be very challenging for a
reinforcement learning agent to learn. We build several simple environments
with this task framework in Mujoco and OpenAI gym and attempt to solve them. We
are able to solve the environments by designing curricula to guide the agent in
learning and using imitation learning methods to transfer knowledge from a
simpler environment. This is only a first step for the task framework, and
further research on how to solve the harder tasks and transfer knowledge
between tasks is needed.
","BlockPuzzle - A Challenge in Physical Reasoning and Generalization for
  Robot Learning"
"  The rise of deep learning has brought artificial intelligence (AI) to the
forefront. The ultimate goal of AI is to realize machines with human mind and
consciousness, but existing achievements mainly simulate intelligent behavior
on computer platforms. These achievements all belong to weak AI rather than
strong AI. How to achieve strong AI is not known yet in the field of
intelligence science. Currently, this field is calling for a new paradigm,
especially Theory of Cognitive Relativity (TCR). The TCR aims to summarize a
simple and elegant set of first principles about the nature of intelligence, at
least including the Principle of World's Relativity and the Principle of
Symbol's Relativity. The Principle of World's Relativity states that the
subjective world an intelligent agent can observe is strongly constrained by
the way it perceives the objective world. The Principle of Symbol's Relativity
states that an intelligent agent can use any physical symbol system to express
what it observes in its subjective world. The two principles are derived from
scientific facts and life experience. Thought experiments show that they are
important to understand high-level intelligence and necessary to establish a
scientific theory of mind and consciousness. Rather than brain-like
intelligence, the TCR indeed advocates a promising change in direction to
realize true AI, i.e. artificial general intelligence or artificial
consciousness, particularly different from humans' and animals'. Furthermore, a
TCR creed has been presented and extended to reveal the secrets of
consciousness and to guide realization of conscious machines. In the sense that
true AI could be diversely implemented in a brain-different way, the TCR would
probably drive an intelligence revolution in combination with some additional
first principles.
",Theory of Cognitive Relativity: A Promising Paradigm for True AI
"  StarCraft (SC) is one of the most popular and successful Real Time Strategy
(RTS) games. In recent years, SC is also widely accepted as a challenging
testbed for AI research because of its enormous state space, partially observed
information, multi-agent collaboration, and so on. With the help of annual
AIIDE and CIG competitions, a growing number of SC bots are proposed and
continuously improved. However, a large gap remains between the top-level bot
and the professional human player. One vital reason is that current SC bots
mainly rely on predefined rules to select macro actions during their games.
These rules are not scalable and efficient enough to cope with the enormous yet
partially observed state space in the game. In this paper, we propose a deep
reinforcement learning (DRL) framework to improve the selection of macro
actions. Our framework is based on the combination of the Ape-X DQN and the
Long-Short-Term-Memory (LSTM). We use this framework to build our bot, named as
LastOrder. Our evaluation, based on training against all bots from the AIIDE
2017 StarCraft AI competition set, shows that LastOrder achieves an 83% winning
rate, outperforming 26 bots in total 28 entrants.
",Macro action selection with deep reinforcement learning in StarCraft
"  This short position paper is intended to provide a critique of current
approaches to dialogue, as well as a roadmap for collaborative dialogue
research. It is unapologetically opinionated, but informed by 40 years of
dialogue re-search. No attempt is made to be comprehensive. The paper will
discuss current research into building so-called ""chatbots"", slot-filling
dialogue systems, and plan-based dialogue systems. For further discussion of
some of these issues, please see (Allen et al., in press).
",Back to the Future for Dialogue Research: A Position Paper
"  Predicting the time to build software is a very complex task for software
engineering managers. There are complex factors that can directly interfere
with the productivity of the development team. Factors directly related to the
complexity of the system to be developed drastically change the time necessary
for the completion of the works with the software factories. This work proposes
the use of a hybrid system based on artificial neural networks and fuzzy
systems to assist in the construction of an expert system based on rules to
support in the prediction of hours destined to the development of software
according to the complexity of the elements present in the same. The set of
fuzzy rules obtained by the system helps the management and control of software
development by providing a base of interpretable estimates based on fuzzy
rules. The model was submitted to tests on a real database, and its results
were promissory in the construction of an aid mechanism in the predictability
of the software construction.
","Regularized Fuzzy Neural Networks to Aid Effort Forecasting in the
  Construction and Software Development"
"  As autonomous agents become more ubiquitous, they will eventually have to
reason about the plans of other agents, which is known as theory of mind
reasoning. We develop a planning-as-inference framework in which agents perform
nested simulation to reason about the behavior of other agents in an online
manner. As a concrete application of this framework, we use probabilistic
programs to model a high-uncertainty variant of pursuit-evasion games in which
an agent must make inferences about the other agents' plans to craft
counter-plans. Our probabilistic programs incorporate a variety of complex
primitives such as field-of-view calculations and path planners, which enable
us to model quasi-realistic scenarios in a computationally tractable manner. We
perform extensive experimental evaluations which establish a variety of
rational behaviors and quantify how allocating computation across levels of
nesting affects the variance of our estimators.
",Nested Reasoning About Autonomous Agents Using Probabilistic Programs
"  In this report, we introduce an artificial dataset generator for
Photo-realistic Blocksworld domain. Blocksworld is one of the oldest high-level
task planning domain that is well defined but contains sufficient complexity,
e.g., the conflicting subgoals and the decomposability into subproblems. We aim
to make this dataset a benchmark for Neural-Symbolic integrated systems and
accelerate the research in this area. The key advantage of such systems is the
ability to obtain a symbolic model from the real-world input and perform a
fast, systematic, complete algorithm for symbolic reasoning, without any
supervision and the reward signal from the environment.
",Photo-Realistic Blocksworld Dataset
"  Many reality tasks such as robot coordination can be naturally modelled as
multi-agent cooperative system where the rewards are sparse. This paper focuses
on learning decentralized policies for such tasks using sub-optimal
demonstration. To learn the multi-agent cooperation effectively and tackle the
sub-optimality of demonstration, a self-improving learning method is proposed:
On the one hand, the centralized state-action values are initialized by the
demonstration and updated by the learned decentralized policy to improve the
sub-optimality. On the other hand, the Nash Equilibrium are found by the
current state-action value and are used as a guide to learn the policy. The
proposed method is evaluated on the combat RTS games which requires a high
level of multi-agent cooperation. Extensive experimental results on various
combat scenarios demonstrate that the proposed method can learn multi-agent
cooperation effectively. It significantly outperforms many state-of-the-art
demonstration based approaches.
",Cooperative Multi-Agent Policy Gradients with Sub-optimal Demonstration
"  Urban Traffic Networks are characterized by high dynamics of traffic flow and
increased travel time, including waiting times. This leads to more complex road
traffic management. The present research paper suggests an innovative advanced
traffic management system based on Hierarchical Interval Type-2 Fuzzy Logic
model optimized by the Particle Swarm Optimization (PSO) method. The aim of
designing this system is to perform dynamic route assignment to relieve traffic
congestion and limit the unexpected fluctuation effects on traffic flow. The
suggested system is executed and simulated using SUMO, a well-known microscopic
traffic simulator. For the present study, we have tested four large and
heterogeneous metropolitan areas located in the cities of Sfax, Luxembourg,
Bologna and Cologne. The experimental results proved the effectiveness of
learning the Hierarchical Interval type-2 Fuzzy logic using real time particle
swarm optimization technique PSO to accomplish multiobjective optimality
regarding two criteria: number of vehicles that reach their destination and
average travel time. The obtained results are encouraging, confirming the
efficiency of the proposed system.
","An Evolutionary Hierarchical Interval Type-2 Fuzzy Knowledge
  Representation System (EHIT2FKRS) for Travel Route Assignment"
"  While many see the prospect of autonomous machines as threatening, autonomy
may be exactly what we want in a superintelligent machine. There is a sense of
autonomy, deeply rooted in the ethical literature, in which an autonomous
machine is necessarily an ethical one. Development of the theory underlying
this idea not only reveals the advantages of autonomy, but it sheds light on a
number of issues in the ethics of artificial intelligence. It helps us to
understand what sort of obligations we owe to machines, and what obligations
they owe to us. It clears up the issue of assigning responsibility to machines
or their creators. More generally, a concept of autonomy that is adequate to
both human and artificial intelligence can lead to a more adequate ethical
theory for both.
",Truly Autonomous Machines Are Ethical
"  For being potentially destructive, in practice incomprehensible and for the
most unintelligible, contemporary technology is setting high challenges on our
society. New conception methods are urgently required. Reorganizing ideas and
discussions presented in AI and related fields, this position paper aims to
highlight the importance of normware--that is, computational artifacts
specifying norms--with respect to these issues, and argues for its
irreducibility with respect to software by making explicit its neglected
ecological dimension in the decision-making cycle.
",The Role of Normware in Trustworthy and Explainable AI
"  In order to more accurately situate and fit the neutrosophic logic into the
framework of nonstandard analysis, we present the neutrosophic inequalities,
neutrosophic equality, neutrosophic infimum and supremum, neutrosophic standard
intervals, including the cases when the neutrosophic logic standard and
nonstandard components T, I, F get values outside of the classical real unit
interval [0, 1], and a brief evolution of neutrosophic operators. The paper
intends to answer Imamura criticism that we found benefic in better
understanding the nonstandard neutrosophic logic, although the nonstandard
neutrosophic logic was never used in practical applications.
","About Nonstandard Neutrosophic Logic (Answers to Imamura 'Note on the
  Definition of Neutrosophic Logic')"
"  The pictorial jigsaw (PJ) puzzle is a well-known leisure game for humans.
Usually, a PJ puzzle game is played by one or several human players
face-to-face in the physical space. In this paper, we focus on how to solve PJ
puzzles in the cyberspace by a group of physically distributed human players.
We propose an approach to solving PJ puzzle by stigmergy-inspired
Internet-based human collective intelligence. The core of the approach is a
continuously executing loop, named the EIF loop, which consists of three
activities: exploration, integration, and feedback. In exploration, each player
tries to solve the PJ puzzle alone, without direct interactions with other
players. At any time, the result of a player's exploration is a partial
solution to the PJ puzzle, and a set of rejected neighboring relation between
pieces. The results of all players' exploration are integrated in real time
through integration, with the output of a continuously updated collective
opinion graph (COG). And through feedback, each player is provided with
personalized feedback information based on the current COG and the player's
exploration result, in order to accelerate his/her puzzle-solving process.
Exploratory experiments show that: (1) supported by this approach, the time to
solve PJ puzzle is nearly linear to the reciprocal of the number of players,
and shows better scalability to puzzle size than that of face-to-face
collaboration for 10-player groups; (2) for groups with 2 to 10 players, the
puzzle-solving time decreases 31.36%-64.57% on average, compared with the best
single players in the experiments.
","Solving Pictorial Jigsaw Puzzle by Stigmergy-inspired Internet-based
  Human Collective Intelligence"
"  In this article, I discuss what AI can and cannot yet do, and the
implications for humanity.
",Can Artificial Intelligence Do Everything That We Can?
"  As machine learning systems are increasingly used to make real world legal
and financial decisions, it is of paramount importance that we develop
algorithms to verify that these systems do not discriminate against minorities.
We design a scalable algorithm for verifying fairness specifications. Our
algorithm obtains strong correctness guarantees based on adaptive concentration
inequalities; such inequalities enable our algorithm to adaptively take samples
until it has enough data to make a decision. We implement our algorithm in a
tool called VeriFair, and show that it scales to large machine learning models,
including a deep recurrent neural network that is more than five orders of
magnitude larger than the largest previously-verified neural network. While our
technique only gives probabilistic guarantees due to the use of random samples,
we show that we can choose the probability of error to be extremely small.
",Probabilistic Verification of Fairness Properties via Concentration
"  This paper looks at recent debates in the enactivist literature on
computation and consciousness in order to assess major obstacles to building
artificial conscious agents. We consider a proposal from Villalobos and
Dewhurst (2018) for enactive computation on the basis of organizational
closure. We attempt to improve the argument by reflecting on the closed paths
through state space taken by finite state automata. This motivates a defense
against Clark's recent criticisms of ""extended consciousness"", and perhaps a
new perspective on living with machines.
",Conscious enactive computation
"  In this article a few of the qualitative spatio-temporal knowledge
representation techniques developed by the constraint reasoning community
within artificial intelligence are reviewed. The objective is to provide a
broad exposure to any other interested group who may utilize these
representations. The author has a particular interest in applying these calculi
(in a broad sense) in topological data analysis, as these schemes are highly
qualitative in nature.
","Selected Qualitative Spatio-temporal Calculi Developed for Constraint
  Reasoning: A Review"
"  It is a widely accepted principle that software without tests has bugs.
Testing reinforcement learning agents is especially difficult because of the
stochastic nature of both agents and environments, the complexity of
state-of-the-art models, and the sequential nature of their predictions.
Recently, the Arcade Learning Environment (ALE) has become one of the most
widely used benchmark suites for deep learning research, and state-of-the-art
Reinforcement Learning (RL) agents have been shown to routinely equal or exceed
human performance on many ALE tasks. Since ALE is based on emulation of
original Atari games, the environment does not provide semantically meaningful
representations of internal game state. This means that ALE has limited utility
as an environment for supporting testing or model introspection. We propose
ToyBox, a collection of reimplementations of these games that solves this
critical problem and enables robust testing of RL agents.
","ToyBox: Better Atari Environments for Testing Reinforcement Learning
  Agents"
"  Mathematical Theory of Evidence (MTE), a foundation for reasoning under
partial ignorance, is blamed to leave frequencies outside (or aside of) its
framework. The seriousness of this accusation is obvious: no experiment may be
run to compare the performance of MTE-based models of real world processes
against real world data.
  In this paper we consider this problem from the point of view of conditioning
in the MTE. We describe the class of belief functions for which marginal
consistency with observed frequencies may be achieved and conditional belief
functions are proper belief functions,%\ and deal with implications for
(marginal) approximation of general belief functions by this class of belief
functions and for inference models in MTE.
","On Marginally Correct Approximations of Dempster-Shafer Belief Functions
  from Data"
"  As artificial intelligence (AI) systems become increasingly ubiquitous, the
topic of AI governance for ethical decision-making by AI has captured public
imagination. Within the AI research community, this topic remains less familiar
to many researchers. In this paper, we complement existing surveys, which
largely focused on the psychological, social and legal discussions of the
topic, with an analysis of recent advances in technical solutions for AI
governance. By reviewing publications in leading AI conferences including AAAI,
AAMAS, ECAI and IJCAI, we propose a taxonomy which divides the field into four
areas: 1) exploring ethical dilemmas; 2) individual ethical decision
frameworks; 3) collective ethical decision frameworks; and 4) ethics in
human-AI interactions. We highlight the intuitions and key techniques used in
each approach, and discuss promising future research directions towards
successful integration of ethical AI systems into human societies.
",Building Ethics into Artificial Intelligence
"  In this paper we proposed an ultimate theory to solve the multi-target
control problem through its introduction to the machine learning framework in
automatic driving, which explored the implementation of excellent drivers'
knowledge acquisition. Nowadays there exist some core problems that have not
been fully realized by the researchers in automatic driving, such as the
optimal way to control the multi-target objective functions of energy saving,
safe driving, headway distance control and comfort driving, as well as the
resolvability of the networks that automatic driving relied on and the
high-performance chips like GPU on the complex driving environments. According
to these problems, we developed a new theory to map multitarget objective
functions in different spaces into the same one and thus introduced a machine
learning framework of SDL(Super Deep Learning) for optimal multi-targetcontrol
based on knowledge acquisition. We will present in this paper the optimal
multi-target control by combining the fuzzy relationship of each multi-target
objective function and the implementation of excellent drivers' knowledge
acquired by machine learning. Theoretically, the impact of this method will
exceed that of the fuzzy control method used in automatic train.
",The Modeling of SDL Aiming at Knowledge Acquisition in Automatic Driving
"  A rigorous empirical comparison of two stochastic solvers is important when
one of the solvers is a prototype of a new algorithm such as multiwalk (MWA).
When searching for global minima in $\mathbb{R}^p$, the key data structures of
MWA include: $p$ rulers with each ruler assigned $m$ marks and a set of $p$
neighborhood matrices of size up to $m(m-2)$, where each entry represents
absolute values of pairwise differences between $m$ marks. Before taking the
next step, a controller links the tableau of neighborhood matrices and computes
new and improved positions for each of the $m$ marks. The number of columns in
each neighborhood matrix is denoted as the neighborhood radius $r_n \le m-2$.
Any variant of the DEA (differential evolution algorithm) has an effective
population neighborhood of radius not larger than 1. Uncensored
first-passage-time performance experiments that vary the neighborhood radius of
a MW-solver can thus be readily compared to existing variants of DE-solvers.
The paper considers seven test cases of increasing complexity and demonstrates,
under uncensored first-passage-time performance experiments: (1) significant
variability in convergence rate for seven DE-based solver configurations, and
(2) consistent, monotonic, and significantly faster rate of convergence for the
MW-solver prototype as we increase the neighborhood radius from 4 to its
maximum value.
","On Uncensored Mean First-Passage-Time Performance Experiments with
  Multiwalk in $\mathbb{R}^p$: a New Stochastic Optimization Algorithm"
"  Given different types of constraints on human life, people must make
decisions that satisfy social activity needs. Minimizing costs (i.e., distance,
time, or money) associated with travel plays an important role in perceived and
realized social quality of life. Identifying optimal interaction locations on
road networks when there are multiple moving objects (MMO) with space-time
constraints remains a challenge. In this research, we formalize the problem of
finding dynamic ideal interaction locations for MMO as a spatial optimization
model and introduce a context-based geoprocessing heuristic framework to
address this problem. As a proof of concept, a case study involving
identification of a meetup location for multiple people under traffic
conditions is used to validate the proposed geoprocessing framework. Five
heuristic methods with regard to efficient shortest-path search space have been
tested. We find that the R* tree-based algorithm performs the best with high
quality solutions and low computation time. This framework is implemented in a
GIS environment to facilitate integration with external geographic contextual
information, e.g., temporary road barriers, points of interest (POI), and
real-time traffic information, when dynamically searching for ideal meetup
sites. The proposed method can be applied in trip planning, carpooling
services, collaborative interaction, and logistics management.
","A context-based geoprocessing framework for optimizing meetup location
  of multiple moving objects along road networks"
"  We consider a sequence of successively more restrictive definitions of
abstraction for causal models, starting with a notion introduced by Rubenstein
et al. (2017) called exact transformation that applies to probabilistic causal
models, moving to a notion of uniform transformation that applies to
deterministic causal models and does not allow differences to be hidden by the
""right"" choice of distribution, and then to abstraction, where the
interventions of interest are determined by the map from low-level states to
high-level states, and strong abstraction, which takes more seriously all
potential interventions in a model, not just the allowed interventions. We show
that procedures for combining micro-variables into macro-variables are
instances of our notion of strong abstraction, as are all the examples
considered by Rubenstein et al.
",Abstracting Causal Models
"  While various traditions under the 'virtue ethics' umbrella have been studied
extensively and advocated by ethicists, it has not been clear that there exists
a version of virtue ethics rigorous enough to be a target for machine ethics
(which we take to include the engineering of an ethical sensibility in a
machine or robot itself, not only the study of ethics in the humans who might
create artificial agents). We begin to address this by presenting an embryonic
formalization of a key part of any virtue-ethics theory: namely, the learning
of virtue by a focus on exemplars of moral virtue. Our work is based in part on
a computational formal logic previously used to formally model other ethical
theories and principles therein, and to implement these models in artificial
agents.
",Toward the Engineering of Virtuous Machines
"  Robots are increasingly used to carry out critical missions in extreme
environments that are hazardous for humans. This requires a high degree of
operational autonomy under uncertain conditions, and poses new challenges for
assuring the robot's safety and reliability. In this paper, we develop a
framework for probabilistic model checking on a layered Markov model to verify
the safety and reliability requirements of such robots, both at pre-mission
stage and during runtime. Two novel estimators based on conservative Bayesian
inference and imprecise probability model with sets of priors are introduced to
learn the unknown transition parameters from operational data. We demonstrate
our approach using data from a real-world deployment of unmanned underwater
vehicles in extreme environments.
",Probabilistic Model Checking of Robots Deployed in Extreme Environments
"  The question addressed in this paper is: If we present to a user an AI system
that explains how it works, how do we know whether the explanation works and
the user has achieved a pragmatic understanding of the AI? In other words, how
do we know that an explanainable AI system (XAI) is any good? Our focus is on
the key concepts of measurement. We discuss specific methods for evaluating:
(1) the goodness of explanations, (2) whether users are satisfied by
explanations, (3) how well users understand the AI systems, (4) how curiosity
motivates the search for explanations, (5) whether the user's trust and
reliance on the AI are appropriate, and finally, (6) how the human-XAI work
system performs. The recommendations we present derive from our integration of
extensive research literatures and our own psychometric evaluations.
",Metrics for Explainable AI: Challenges and Prospects
"  An autonomous system is constructed by a manufacturer, operates in a society
subject to norms and laws, and is interacting with end-users. We address the
challenge of how the moral values and views of all stakeholders can be
integrated and reflected in the moral behaviour of the autonomous system. We
propose an artificial moral agent architecture that uses techniques from
normative systems and formal argumentation to reach moral agreements among
stakeholders. We show how our architecture can be used not only for ethical
practical reasoning and collaborative decision-making, but also for the
explanation of such moral behavior.
","Building Jiminy Cricket: An Architecture for Moral Agreements Among
  Stakeholders"
"  Hyper-heuristics are a novel tool. They deal with complex optimization
problems where standalone solvers exhibit varied performance. Among such a tool
reside selection hyper-heuristics. By combining the strengths of each solver,
this kind of hyper-heuristic offers a more robust tool. However, their
effectiveness is highly dependent on the 'features' used to link them with the
problem that is being solved. Aiming at enhancing selection hyper-heuristics,
in this paper we propose two types of transformation: explicit and implicit.
The first one directly changes the distribution of critical points within the
feature domain while using a Euclidean distance to measure proximity. The
second one operates indirectly by preserving the distribution of critical
points but changing the distance metric through a kernel function. We focus on
analyzing the effect of each kind of transformation, and of their combinations.
We test our ideas in the domain of constraint satisfaction problems because of
their popularity and many practical applications. In this work, we compare the
performance of our proposals against those of previously published data.
Furthermore, we expand on previous research by increasing the number of
analyzed features. We found that, by incorporating transformations into the
model of selection hyper-heuristics, overall performance can be improved,
yielding more stable results. However, combining implicit and explicit
transformations was not as fruitful. Additionally, we ran some confirmatory
tests on the domain of knapsack problems. Again, we observed improved
stability, leading to the generation of hyper-heuristics whose profit had a
standard deviation between 20% and 30% smaller.
",Enhancing Selection Hyper-heuristics via Feature Transformations
"  Ethical and explainable artificial intelligence is an interdisciplinary
research area involving computer science, philosophy, logic, the social
sciences, etc. For an ethical autonomous system, the ability to justify and
explain its decision making is a crucial aspect of transparency and
trustworthiness. This paper takes a Value Driven Agent (VDA) as an example,
explicitly representing implicit knowledge of a machine learning-based
autonomous agent and using this formalism to justify and explain the decisions
of the agent. For this purpose, we introduce a novel formalism to describe the
intrinsic knowledge and solutions of a VDA in each situation. Based on this
formalism, we formulate an approach to justify and explain the decision-making
process of a VDA, in terms of a typical argumentation formalism,
Assumption-based Argumentation (ABA). As a result, a VDA in a given situation
is mapped onto an argumentation framework in which arguments are defined by the
notion of deduction. Justified actions with respect to semantics from
argumentation correspond to solutions of the VDA. The acceptance (rejection) of
arguments and their premises in the framework provides an explanation for why
an action was selected (or not). Furthermore, we go beyond the existing version
of VDA, considering not only practical reasoning, but also epistemic reasoning,
such that the inconsistency of knowledge of the VDA can be identified, handled
and explained.
","Representation, Justification and Explanation in a Value Driven Agent:
  An Argumentation-Based Approach"
"  The recently released AlphaZero algorithm achieves superhuman performance in
the games of chess, shogi and Go, which raises two open questions. Firstly, as
there is a finite number of possibilities in the game, is there a quantifiable
intelligence measurement for evaluating intelligent systems, e.g. AlphaZero?
Secondly, AlphaZero introduces sophisticated reinforcement learning and
self-play to efficiently encode the possible states, is there a simple
information-theoretic model to represent the learning process and offer more
insights in fostering strong AI systems?
  This paper explores the above two questions by proposing a simple variance of
Shannon's communication model, the concept of intelligence entropy and the
Unified Intelligence-Communication Model is proposed, which provide an
information-theoretic metric for investigating the intelligence level and also
provide an bound for intelligent agents in the form of Shannon's capacity,
namely, the intelligence capacity. This paper then applies the concept and
model to AlphaZero as a case study and explains the learning process of
intelligent agent as turbo-like iterative decoding, so that the learning
performance of AlphaZero may be quantitatively evaluated. Finally, conclusions
are provided along with theoretical and practical remarks.
","The Entropy of Artificial Intelligence and a Case Study of AlphaZero
  from Shannon's Perspective"
"  As reinforcement learning algorithms are being applied to increasingly
complicated and realistic tasks, it is becoming increasingly difficult to solve
such problems within a practical time frame. Hence, we focus on a
\textit{satisficing} strategy that looks for an action whose value is above the
aspiration level (analogous to the break-even point), rather than the optimal
action. In this paper, we introduce a simple mathematical model called
risk-sensitive satisficing ($RS$) that implements a satisficing strategy by
integrating risk-averse and risk-prone attitudes under the greedy policy. We
apply the proposed model to the $K$-armed bandit problems, which constitute the
most basic class of reinforcement learning tasks, and prove two propositions.
The first is that $RS$ is guaranteed to find an action whose value is above the
aspiration level. The second is that the regret (expected loss) of $RS$ is
upper bounded by a finite value, given that the aspiration level is set to an
""optimal level"" so that satisficing implies optimizing. We confirm the results
through numerical simulations and compare the performance of $RS$ with that of
other representative algorithms for the $K$-armed bandit problems.
","Guaranteed satisficing and finite regret: Analysis of a cognitive
  satisficing value function"
"  Ontology authoring is a complex process, where commonly the automated
reasoner is invoked for verification of newly introduced changes, therewith
amounting to a time-consuming test-last approach. Test-Driven Development (TDD)
for ontology authoring is a recent {\em test-first} approach that aims to
reduce authoring time and increase authoring efficiency. Current TDD testing
falls short on coverage of OWL features and possible test outcomes, the
rigorous foundation thereof, and evaluations to ascertain its effectiveness.
  We aim to address these issues in one instantiation of TDD for ontology
authoring. We first propose a succinct, logic-based model of TDD testing and
present novel TDD algorithms so as to cover also any OWL 2 class expression for
the TBox and for the principal ABox assertions, and prove their correctness.
The algorithms use methods from the OWL API directly such that reclassification
is not necessary for test execution, therewith reducing ontology authoring
time. The algorithms were implemented in TDDonto2, a Prot\'eg\'e plugin.
TDDonto2 was evaluated on editing efficiency and by users. The editing
efficiency study demonstrated that it is faster than a typical ontology
authoring interface, especially for medium size and large ontologies. The user
evaluation demonstrated that modellers make significantly less errors with
TDDonto2 compared to the standard Prot\'eg\'e interface and complete their
tasks better using less time. Thus, the results indicate that Test-Driven
Development is a promising approach in an ontology development methodology.
",More Effective Ontology Authoring with Test-Driven Development
"  One important obstacle in applying Dempster-Shafer Theory (DST) is its
relationship to frequencies. In particular, there exist serious difficulties in
finding factorizations of belief functions from data.
  In probability theory factorizations are usually related to notion of
(conditional) independence and their possibility tested accordingly. However,
in DST conditional belief distributions prove to be non-proper belief functions
(that is ones connected with negative ""frequencies""). This makes statistical
testing of potential conditional independencies practically impossible, as no
coherent interpretation could be found so far for negative belief function
values.
  In this paper a novel attempt is made to overcome this difficulty. In the
proposal no conditional beliefs are calculated, but instead a new measure F is
introduced within the framework of DST, closely related to conditional
independence, allowing to apply conventional statistical tests for detection of
dependence/independence.
",Factorization of Dempster-Shafer Belief Functions Based on Data
"  This paper studies the question on whether machines can be rational. It
observes the existing reasons why humans are not rational which is due to
imperfect and limited information, limited and inconsistent processing power
through the brain and the inability to optimize decisions and achieve maximum
utility. It studies whether these limitations of humans are transferred to the
limitations of machines. The conclusion reached is that even though machines
are not rational advances in technological developments make these machines
more rational. It also concludes that machines can be more rational than
humans.
",The limit of artificial intelligence: Can machines be rational?
"  We propose a new deep learning architecture for the tasks of semantic
segmentation and depth prediction from RGB-D images. We revise the state of art
based on the RGB and depth feature fusion, where both modalities are assumed to
be available at train and test time. We propose a new architecture where the
feature fusion is replaced with a common deep representation. Combined with an
encoder-decoder type of the network, the architecture can jointly learn models
for semantic segmentation and depth estimation based on their common
representation. This representation, inspired by multi-view learning, offers
several important advantages, such as using one modality available at test time
to reconstruct the missing modality. In the RGB-D case, this enables the
cross-modality scenarios, such as using depth data for semantically
segmentation and the RGB images for depth estimation. We demonstrate the
effectiveness of the proposed network on two publicly available RGB-D datasets.
The experimental results show that the proposed method works well in both
semantic segmentation and depth estimation tasks.
",Learning Common Representation from RGB and Depth Images
"  Continual learning is the ability of agents to improve their capacities
throughout multiple tasks continually. While recent works in the literature of
continual learning mostly focused on developing either particular loss
functions or specialized structures of neural network explaining the episodic
memory or neural plasticity, we study continual learning from the perspective
of the training mechanism. Specifically, we propose a COnitnual Match BAsed
Training (COMBAT) framework for training a population of advantage-actor-critic
(A2C) agents in Pommerman, a partially observable multi-agent environment with
no communication. Following the COMBAT framework, we trained an agent, namely,
Navocado, that won the title of the top 1 learning agent in the NeurIPS 2018
Pommerman Competition. Two critical features of our agent are worth mentioning.
Firstly, our agent did not learn from any demonstrations. Secondly, our agent
is highly reproducible. As a technical report, we articulate the design of
state space, action space, reward, and most importantly, the COMBAT framework
for our Pommerman agent. We show in the experiments that Pommerman is a perfect
environment for studying continual learning, and the agent can improve its
performance by continually learning new skills without forgetting the old ones.
Finally, the result in the Pommerman Competition verifies the robustness of our
agent when competing with various opponents.
",Continual Match Based Training in Pommerman: Technical Report
"  Sequencing items in adaptive learning systems typically relies on a large
pool of interactive assessment items (questions) that are analyzed into a
hierarchy of skills or Knowledge Components (KCs). Educational data mining
techniques can be used to analyze students performance data in order to
optimize the mapping of items to KCs. Standard methods that map items into KCs
using item-similarity measures make the implicit assumption that students
performance on items that depend on the same skill should be similar. This
assumption holds if the latent trait (mastery of the underlying skill) is
relatively fixed during students activity, as in the context of testing, which
is the primary context in which these measures were developed and applied.
However, in adaptive learning systems that aim for learning, and address
subject matters such as K6 Math that consist of multiple sub-skills, this
assumption does not hold. In this paper we propose a new item-similarity
measure, termed Kappa Learning (KL), which aims to address this gap. KL
identifies similarity between items under the assumption of learning, namely,
that learners mastery of the underlying skills changes as they progress through
the items. We evaluate Kappa Learning on data from a computerized tutor that
teaches Fractions for 4th grade, with experts tagging as ground truth, and on
simulated data. Our results show that clustering that is based on Kappa
Learning outperforms clustering that is based on commonly used similarity
measures (Cohen Kappa, Yule, and Pearson).
","Kappa Learning: A New Method for Measuring Similarity Between
  Educational Items Using Performance Data"
"  In order to solve the limited buffer scheduling problems in flexible flow
shops with setup times, this paper proposes an improved whale optimization
algorithm (IWOA) as a global optimization algorithm. Firstly, this paper
presents a mathematic programming model for limited buffer in flexible flow
shops with setup times, and applies the IWOA algorithm as the global
optimization algorithm. Based on the whale optimization algorithm (WOA), the
improved algorithm uses Levy flight, opposition-based learning strategy and
simulated annealing to expand the search range, enhance the ability for jumping
out of local extremum, and improve the continuous evolution of the algorithm.
To verify the improvement of the proposed algorithm on the optimization ability
of the standard WOA algorithm, the IWOA algorithm is tested by verification
examples of small-scale and large-scale flexible flow shop scheduling problems,
and the imperialist competitive algorithm (ICA), bat algorithm (BA), and whale
optimization algorithm (WOA) are used for comparision. Based on the instance
data of bus manufacturer, simulation tests are made on the four algorithms
under variouis of practical evalucation scenarios. The simulation results show
that the IWOA algorithm can better solve this type of limited buffer scheduling
problem in flexible flow shops with setup times compared with the state of the
art algorithms.
","Research on Limited Buffer Scheduling Problems in Flexible Flow Shops
  with Setup Times"
"  As artificial intelligence (AI) systems become increasingly complex and
ubiquitous, these systems will be responsible for making decisions that
directly affect individuals and society as a whole. Such decisions will need to
be justified due to ethical concerns as well as trust, but achieving this has
become difficult due to the `black-box' nature many AI models have adopted.
Explainable AI (XAI) can potentially address this problem by explaining its
actions, decisions and behaviours of the system to users. However, much
research in XAI is done in a vacuum using only the researchers' intuition of
what constitutes a `good' explanation while ignoring the interaction and the
human aspect. This workshop invites researchers in the HCI community and
related fields to have a discourse about human-centred approaches to XAI rooted
in interaction and to shed light and spark discussion on interaction design
challenges in XAI.
",Interaction Design for Explainable AI: Workshop Proceedings
"  Artificial Intelligence (AI) technologies could be broadly categorised into
Analytics and Autonomy. Analytics focuses on algorithms offering perception,
comprehension, and projection of knowledge gleaned from sensorial data.
Autonomy revolves around decision making, and influencing and shaping the
environment through action production. A smart autonomous system (SAS) combines
analytics and autonomy to understand, learn, decide and act autonomously. To be
useful, SAS must be trusted and that requires testing. Lifelong learning of a
SAS compounds the testing process. In the remote chance that it is possible to
fully test and certify the system pre-release, which is theoretically an
undecidable problem, it is near impossible to predict the future behaviours
that these systems, alone or collectively, will exhibit. While it may be
feasible to severely restrict such systems\textquoteright \ learning abilities
to limit the potential unpredictability of their behaviours, an undesirable
consequence may be severely limiting their utility. In this paper, we propose
the architecture for a watchdog AI (WAI) agent dedicated to lifelong functional
testing of SAS. We further propose system specifications including a level of
abstraction whereby humans shepherd a swarm of WAI agents to oversee an
ecosystem made of humans and SAS. The discussion extends to the challenges,
pros, and cons of the proposed concept.
","Lifelong Testing of Smart Autonomous Systems by Shepherding a Swarm of
  Watchdog Artificial Intelligence Agents"
"  As machine learning models become more accurate, they typically become more
complex and uninterpretable by humans. The black-box character of these models
holds back its acceptance in practice, especially in high-risk domains where
the consequences of failure could be catastrophic such as health-care or
defense. Providing understandable and useful explanations behind ML models or
predictions can increase the trust of the user. Example-based reasoning, which
entails leveraging previous experience with analogous tasks to make a decision,
is a well known strategy for problem solving and justification. This work
presents a new explanation extraction method called LEAFAGE, for a prediction
made by any black-box ML model. The explanation consists of the visualization
of similar examples from the training set and the importance of each feature.
Moreover, these explanations are contrastive which aims to take the
expectations of the user into account. LEAFAGE is evaluated in terms of
fidelity to the underlying black-box model and usefulness to the user. The
results showed that LEAFAGE performs overall better than the current
state-of-the-art method LIME in terms of fidelity, on ML models with non-linear
decision boundary. A user-study was conducted which focused on revealing the
differences between example-based and feature importance-based explanations. It
showed that example-based explanations performed significantly better than
feature importance-based explanation, in terms of perceived transparency,
information sufficiency, competence and confidence. Counter-intuitively, when
the gained knowledge of the participants was tested, it showed that they
learned less about the black-box model after seeing a feature importance-based
explanation than seeing no explanation at all. The participants found feature
importance-based explanation vague and hard to generalize it to other
instances.
","LEAFAGE: Example-based and Feature importance-based Explanationsfor
  Black-box ML models"
"  In the literature, the optimization problem to identify a set of composite
hypotheses H, which will yield the $k$ largest $P(H|S_e)$ where a composite
hypothesis is an instantiation of all the nodes in the network except the
evidence nodes \cite{KSy:93} is of significant interest. This problem is called
""finding the $k$ Most Plausible Explanation (MPE) of a given evidence $S_e$ in
a Bayesian belief network"".
  The problem of finding $k$ most probable hypotheses is generally NP-hard
\cite{Cooper:90}. Therefore in the past various simplifications of the task by
restricting $k$ (to 1 or 2), restricting the structure (e.g. to singly
connected networks), or shifting the complexity to spatial domain have been
investigated.
  A genetic algorithm is proposed in this paper to overcome some of these
restrictions while stepping out from probabilistic domain onto the general
Valuation based System (VBS) framework is also proposed by generalizing the
genetic algorithm approach to the realm of Dempster-Shafer belief calculus.
",Reasoning and Facts Explanation in Valuation Based Systems
"  Constraint Satisfaction Problems (CSPs) typically have many solutions that
satisfy all constraints. Often though, some solutions are preferred over
others, that is, some solutions dominate other solutions. We present solution
dominance as a formal framework to reason about such settings. We define
Constraint Dominance Problems (CDPs) as CSPs with a dominance relation, that
is, a preorder over the solutions of the CSP. This framework captures many
well-known variants of constraint satisfaction, including optimization,
multi-objective optimization, Max-CSP, minimal models, minimum correction
subsets as well as optimization over CP-nets and arbitrary dominance relations.
We extend MiniZinc, a declarative language for modeling CSPs, to CDPs by
introducing dominance nogoods; these can be derived from dominance relations in
a principled way. A generic method for solving arbitrary CDPs incrementally
calls a CSP solver and is compatible with any existing solver that supports
MiniZinc. This encourages experimenting with different solution dominance
relations for a problem, as well as comparing different solvers without having
to modify their implementations.
",Solution Dominance over Constraint Satisfaction Problems
"  This paper addresses the Traveling Salesman Problem with Drone (TSP-D), in
which a truck and drone are used to deliver parcels to customers. The objective
of this problem is to either minimize the total operational cost (min-cost
TSP-D) or minimize the completion time for the truck and drone (min-time
TSP-D). This problem has gained a lot of attention in the last few years since
it is matched with the recent trends in a new delivery method among logistics
companies. To solve the TSP-D, we propose a hybrid genetic search with dynamic
population management and adaptive diversity control based on a split
algorithm, problem-tailored crossover and local search operators, a new restore
method to advance the convergence and an adaptive penalization mechanism to
dynamically balance the search between feasible/infeasible solutions. The
computational results show that the proposed algorithm outperforms existing
methods in terms of solution quality and improves best known solutions found in
the literature. Moreover, various analyses on the impacts of crossover choice
and heuristic components have been conducted to analysis further their
sensitivity to the performance of our method.
",A Hybrid Genetic Algorithm for the Traveling Salesman Problem with Drone
"  People frequently face challenging decision-making problems in which outcomes
are uncertain or unknown. Artificial intelligence (AI) algorithms exist that
can outperform humans at learning such tasks. Thus, there is an opportunity for
AI agents to assist people in learning these tasks more effectively. In this
work, we use a multi-armed bandit as a controlled setting in which to explore
this direction. We pair humans with a selection of agents and observe how well
each human-agent team performs. We find that team performance can beat both
human and agent performance in isolation. Interestingly, we also find that an
agent's performance in isolation does not necessarily correlate with the
human-agent team's performance. A drop in agent performance can lead to a
disproportionately large drop in team performance, or in some settings can even
improve team performance. Pairing a human with an agent that performs slightly
better than them can make them perform much better, while pairing them with an
agent that performs the same can make them them perform much worse. Further,
our results suggest that people have different exploration strategies and might
perform better with agents that match their strategy. Overall, optimizing
human-agent team performance requires going beyond optimizing agent
performance, to understanding how the agent's suggestions will influence human
decision-making.
",Human-AI Learning Performance in Multi-Armed Bandits
"  In this paper, we present a method using AI techniques to solve a case of
pure mathematics applications for finding narrow admissible tuples. The
original problem is formulated into a combinatorial optimization problem. In
particular, we show how to exploit the local search structure to formulate the
problem landscape for dramatic reductions in search space and for non-trivial
elimination in search barriers, and then to realize intelligent search
strategies for effectively escaping from local minima. Experimental results
demonstrate that the proposed method is able to efficiently find best known
solutions. This research sheds light on exploiting the local problem structure
for an efficient search in combinatorial landscapes as an application of AI to
a new problem domain.
","Exploiting Problem Structure in Combinatorial Landscapes: A Case Study
  on Pure Mathematics Application"
"  Recent successes in Reinforcement Learning have encouraged a fast-growing
network of RL researchers and a number of breakthroughs in RL research. As the
RL community and the body of RL work grows, so does the need for widely
applicable benchmarks that can fairly and effectively evaluate a variety of RL
algorithms.
  This need is particularly apparent in the realm of Hierarchical Reinforcement
Learning (HRL). While many existing test domains may exhibit hierarchical
action or state structures, modern RL algorithms still exhibit great difficulty
in solving domains that necessitate hierarchical modeling and action planning,
even when such domains are seemingly trivial. These difficulties highlight both
the need for more focus on HRL algorithms themselves, and the need for new
testbeds that will encourage and validate HRL research.
  Existing HRL testbeds exhibit a Goldilocks problem; they are often either too
simple (e.g. Taxi) or too complex (e.g. Montezuma's Revenge from the Arcade
Learning Environment). In this paper we present the Escape Room Domain (ERD), a
new flexible, scalable, and fully implemented testing domain for HRL that
bridges the ""moderate complexity"" gap left behind by existing alternatives. ERD
is open-source and freely available through GitHub, and conforms to widely-used
public testing interfaces for simple integration and testing with a variety of
public RL agent implementations. We show that the ERD presents a suite of
challenges with scalable difficulty to provide a smooth learning gradient from
Taxi to the Arcade Learning Environment.
","Escape Room: A Configurable Testbed for Hierarchical Reinforcement
  Learning"
"  We propose a novel approach for trip prediction by analyzing user's trip
histories. We augment users' (self-) trip histories by adding 'similar' trips
from other users, which could be informative and useful for predicting future
trips for a given user. This also helps to cope with noisy or sparse trip
histories, where the self-history by itself does not provide a reliable
prediction of future trips. We show empirical evidence that by enriching the
users' trip histories with additional trips, one can improve the prediction
error by 15%-40%, evaluated on multiple subsets of the Nancy2012 dataset. This
real-world dataset is collected from public transportation ticket validations
in the city of Nancy, France. Our prediction tool is a central component of a
trip simulator system designed to analyze the functionality of public
transportation in the city of Nancy.
",Trip Prediction by Leveraging Trip Histories from Neighboring Users
"  This paper studies whether rationality can be computed. Rationality is
defined as the use of complete information, which is processed with a perfect
biological or physical brain, in an optimized fashion. To compute rationality
one needs to quantify how complete is the information, how perfect is the
physical or biological brain and how optimized is the entire decision making
system. The rationality of a model (i.e. physical or biological brain) is
measured by the expected accuracy of the model. The rationality of the
optimization procedure is measured as the ratio of the achieved objective (i.e.
utility) to the global objective. The overall rationality of a decision is
measured as the product of the rationality of the model and the rationality of
the optimization procedure. The conclusion reached is that rationality can be
computed for convex optimization problems.
",Can rationality be measured?
"  Counterfactual Regret Minimization (CRF) is a fundamental and effective
technique for solving Imperfect Information Games (IIG). However, the original
CRF algorithm only works for discrete state and action spaces, and the
resulting strategy is maintained as a tabular representation. Such tabular
representation limits the method from being directly applied to large games and
continuing to improve from a poor strategy profile. In this paper, we propose a
double neural representation for the imperfect information games, where one
neural network represents the cumulative regret, and the other represents the
average strategy. Furthermore, we adopt the counterfactual regret minimization
algorithm to optimize this double neural representation. To make neural
learning efficient, we also developed several novel techniques including a
robust sampling method, mini-batch Monte Carlo Counterfactual Regret
Minimization (MCCFR) and Monte Carlo Counterfactual Regret Minimization Plus
(MCCFR+) which may be of independent interests. Experimentally, we demonstrate
that the proposed double neural algorithm converges significantly better than
the reinforcement learning counterpart.
",Double Neural Counterfactual Regret Minimization
"  In the multi-agent path finding problem (MAPF) we are given a set of agents
each with respective start and goal positions. The task is to find paths for
all agents while avoiding collisions aiming to minimize an objective function.
Two such common objective functions is the sum-of-costs and the makespan. Many
optimal solvers were introduced in the past decade - two prominent categories
of solvers can be distinguished: search-based solvers and compilation-based
solvers.
  Search-based solvers were developed and tested for the sum-of-costs objective
while the most prominent compilation-based solvers that are built around
Boolean satisfiability (SAT) were designed for the makespan objective. Very
little was known on the performance and relevance of the compilation-based
approach on the sum-of-costs objective. In this paper we show how to close the
gap between these cost functions in the compilation-based approach. Moreover we
study applicability of various techniques developed for search-based solvers in
the compilation-based approach.
  A part of this paper introduces a SAT-solver that is directly aimed to solve
the sum-of-costs objective function. Using both a lower bound on the
sum-of-costs and an upper bound on the makespan, we are able to have a
reasonable number of variables in our SAT encoding. We then further improve the
encoding by borrowing ideas from ICTS, a search-based solver. Experimental
evaluation on several domains show that there are many scenarios where our new
SAT-based methods outperforms the best variants of previous sum-of-costs search
solvers - the ICTS, CBS algorithms, and ICBS algorithms.
","A Summary of Adaptation of Techniques from Search-based Optimal
  Multi-Agent Path Finding Solvers to Compilation-based Approach"
"  Real-Time Strategy (RTS) games have recently become a popular testbed for
artificial intelligence research. They represent a complex adversarial domain
providing a number of interesting AI challenges. There exists a wide variety of
research-supporting software tools, libraries and frameworks for one RTS game
in particular -- StarCraft: Brood War. These tools are designed to address
various specific sub-problems, such as resource allocation or opponent
modelling so that researchers can focus exclusively on the tasks relevant to
them. We present one such tool -- a library called StarAlgo that produces plans
for the coordinated movement of squads (groups of combat units) within the game
world. StarAlgo library can solve the squad movement planning problem using one
of two algorithms: Monte Carlo Tree Search Considering Durations (MCTSCD) and a
slightly modified version of Negamax. We evaluate both the algorithms, compare
them, and demonstrate their usage. The library is implemented as a static C++
library that can be easily plugged into most StarCraft AI bots.
","StarAlgo: A Squad Movement Planning Library for StarCraft using Monte
  Carlo Tree Search and Negamax"
"  In this article, we provide and overview of what we consider to be some of
the most pressing research questions facing the fields of artificial
intelligence (AI) and computational intelligence (CI); with the latter focusing
on algorithms that are inspired by various natural phenomena. We demarcate
these questions using five unique Rs - namely, (i) rationalizability, (ii)
resilience, (iii) reproducibility, (iv) realism, and (v) responsibility.
Notably, just as air serves as the basic element of biological life, the term
AIR5 - cumulatively referring to the five aforementioned Rs - is introduced
herein to mark some of the basic elements of artificial life (supporting the
sustained growth of AI and CI). A brief summary of each of the Rs is presented,
highlighting their relevance as pillars of future research in this arena.
",AIR5: Five Pillars of Artificial Intelligence Research
"  Utility functions or their equivalents (value functions, objective functions,
loss functions, reward functions, preference orderings) are a central tool in
most current machine learning systems. These mechanisms for defining goals and
guiding optimization run into practical and conceptual difficulty when there
are independent, multi-dimensional objectives that need to be pursued
simultaneously and cannot be reduced to each other. Ethicists have proved
several impossibility theorems that stem from this origin; those results appear
to show that there is no way of formally specifying what it means for an
outcome to be good for a population without violating strong human ethical
intuitions (in such cases, the objective function is a social welfare
function). We argue that this is a practical problem for any machine learning
system (such as medical decision support systems or autonomous weapons) or
rigidly rule-based bureaucracy that will make high stakes decisions about human
lives: such systems should not use objective functions in the strict
mathematical sense.
  We explore the alternative of using uncertain objectives, represented for
instance as partially ordered preferences, or as probability distributions over
total orders. We show that previously known impossibility theorems can be
transformed into uncertainty theorems in both of those settings, and prove
lower bounds on how much uncertainty is implied by the impossibility results.
We close by proposing two conjectures about the relationship between
uncertainty in objectives and severe unintended consequences from AI systems.
","Impossibility and Uncertainty Theorems in AI Value Alignment (or why
  your AGI should not have a utility function)"
"  Controlling a high degrees of freedom humanoid robot is acknowledged as one
of the hardest problems in Robotics. Due to the lack of mathematical models, an
approach frequently employed is to rely on human intuition to design keyframe
movements by hand, usually aided by graphical tools. In this paper, we propose
a learning framework based on neural networks in order to mimic humanoid robot
movements. The developed technique does not make any assumption about the
underlying implementation of the movement, therefore both keyframe and
model-based motions may be learned. The framework was applied in the RoboCup 3D
Soccer Simulation domain and promising results were obtained using the same
network architecture for several motions, even when copying motions from
another teams.
",Learning Humanoid Robot Motions Through Deep Neural Networks
"  In artificial intelligence (AI) mediated workforce management systems (e.g.,
crowdsourcing), long-term success depends on workers accomplishing tasks
productively and resting well. This dual objective can be summarized by the
concept of productive laziness. Existing scheduling approaches mostly focus on
efficiency but overlook worker wellbeing through proper rest. In order to
enable workforce management systems to follow the IEEE Ethically Aligned Design
guidelines to prioritize worker wellbeing, we propose a distributed
Computational Productive Laziness (CPL) approach in this paper. It
intelligently recommends personalized work-rest schedules based on local data
concerning a worker's capabilities and situational factors to incorporate
opportunistic resting and achieve superlinear collective productivity without
the need for explicit coordination messages. Extensive experiments based on a
real-world dataset of over 5,000 workers demonstrate that CPL enables workers
to spend 70% of the effort to complete 90% of the tasks on average, providing
more ethically aligned scheduling than existing approaches.
",Ethically Aligned Opportunistic Scheduling for Productive Laziness
"  This is a short (and personal) introduction in German to the connections
between artificial intelligence, philosophy, and logic, and to the author's
work.
  Dies ist eine kurze (und persoenliche) Einfuehrung in die Zusammenhaenge
zwischen Kuenstlicher Intelligenz, Philosophie, und Logik, und in die Arbeiten
des Autors.
","KI, Philosophie, Logik"
"  This paper introduces a simple and fast variant of Planet Wars as a test-bed
for statistical planning based Game AI agents, and for noisy hyper-parameter
optimisation. Planet Wars is a real-time strategy game with simple rules but
complex game-play. The variant introduced in this paper is designed for speed
to enable efficient experimentation, and also for a fixed action space to
enable practical inter-operability with General Video Game AI agents. If we
treat the game as a win-loss game (which is standard), then this leads to
challenging noisy optimisation problems both in tuning agents to play the game,
and in tuning game parameters. Here we focus on the problem of tuning an agent,
and report results using the recently developed N-Tuple Bandit Evolutionary
Algorithm and a number of other optimisers, including Sequential Model-based
Algorithm Configuration (SMAC). Results indicate that the N-Tuple Bandit
Evolutionary offers competitive performance as well as insight into the effects
of combinations of parameter choices.
","Efficient Evolutionary Methods for Game Agent Optimisation: Model-Based
  is Best"
"  The solution convergence of Markov Decision Processes (MDPs) can be
accelerated by prioritized sweeping of states ranked by their potential impacts
to other states. In this paper, we present new heuristics to speed up the
solution convergence of MDPs. First, we quantify the level of reachability of
every state using the Mean First Passage Time (MFPT) and show that such
reachability characterization very well assesses the importance of states which
is used for effective state prioritization. Then, we introduce the notion of
backup differentials as an extension to the prioritized sweeping mechanism, in
order to evaluate the impacts of states at an even finer scale. Finally, we
extend the state prioritization to the temporal process, where only partial
sweeping can be performed during certain intermediate value iteration stages.
To validate our design, we have performed numerical evaluations by comparing
the proposed new heuristics with corresponding classic baseline mechanisms. The
evaluation results showed that our reachability based framework and its
differential variants have outperformed the state-of-the-art solutions in terms
of both practical runtime and number of iterations.
","Reachability and Differential based Heuristics for Solving Markov
  Decision Processes"
"  Decision-making problems can be modeled as combinatorial optimization
problems with Constraint Programming formalisms such as Constrained
Optimization Problems. However, few Constraint Programming formalisms can deal
with both optimization and uncertainty at the same time, and none of them are
convenient to model problems we tackle in this paper.
  Here, we propose a way to deal with combinatorial optimization problems under
uncertainty within the classical Constrained Optimization Problems formalism by
injecting the Rank Dependent Utility from decision theory. We also propose a
proof of concept of our method to show it is implementable and can solve
concrete decision-making problems using a regular constraint solver, and
propose a bot that won the partially observable track of the 2018 {\mu}RTS AI
competition.
  Our result shows it is possible to handle uncertainty with regular Constraint
Programming solvers, without having to define a new formalism neither to
develop dedicated solvers. This brings new perspective to tackle uncertainty in
Constraint Programming.
","Constrained optimization under uncertainty for decision-making problems:
  Application to Real-Time Strategy games"
"  The design of reward functions in reinforcement learning is a human skill
that comes with experience. Unfortunately, there is not any methodology in the
literature that could guide a human to design the reward function or to allow a
human to transfer the skills developed in designing reward functions to another
human and in a systematic manner. In this paper, we use Systematic
Instructional Design, an approach in human education, to engineer a machine
education methodology to design reward functions for reinforcement learning. We
demonstrate the methodology in designing a hierarchical genetic reinforcement
learner that adopts a neural network representation to evolve a swarm
controller for an agent shepherding a boids-based swarm. The results reveal
that the methodology is able to guide the design of hierarchical reinforcement
learners, with each model in the hierarchy learning incrementally through a
multi-part reward function. The hierarchy acts as a decision fusion function
that combines the individual behaviours and skills learnt by each instruction
to create a smart shepherd to control the swarm.
","Machine Teaching in Hierarchical Genetic Reinforcement Learning:
  Curriculum Design of Reward Functions for Swarm Shepherding"
"  This document represents the proceedings of the 2018 XCSP3 Competition. The
results of this competition of constraint solvers were presented at CP'18, the
24th International Conference on Principles and Practice of Constraint
Programming, held in Lille, France from 27th August 2018 to 31th August, 2018.
",Proceedings of the 2018 XCSP3 Competition
"  ""Ge She Zhi Zhi"" is a novel saying in Chinese, stated as ""To investigate
things from the underlying principle(s) and to acquire knowledge in the form of
mathematical representations"". The saying is adopted and modified based on the
ideas from the Eastern and Western philosophers. This position paper discusses
the saying in the background of artificial intelligence (AI). Some related
subjects, such as the ultimate goals of AI and two levels of knowledge
representations, are discussed from the perspective of machine learning. A case
study on objective evaluations over multi attributes, a typical problem in the
filed of social computing, is given to support the saying for wide
applications. A methodology of meta rules is proposed for examining the
objectiveness of the evaluations. The possible problems of the saying are also
presented.
","""Ge Shu Zhi Zhi"": Towards Deep Understanding about Worlds"
"  AI Safety researchers attempting to align values of highly capable
intelligent systems with those of humanity face a number of challenges
including personal value extraction, multi-agent value merger and finally
in-silico encoding. State-of-the-art research in value alignment shows
difficulties in every stage in this process, but merger of incompatible
preferences is a particularly difficult challenge to overcome. In this paper we
assume that the value extraction problem will be solved and propose a possible
way to implement an AI solution which optimally aligns with individual
preferences of each user. We conclude by analyzing benefits and limitations of
the proposed approach.
","Personal Universes: A Solution to the Multi-Agent Value Alignment
  Problem"
"  We propose a planning-based method to teach an agent to manage portfolio from
scratch. Our approach combines deep reinforcement learning techniques with
search techniques like AlphaGo. By uniting the advantages in A* search
algorithm with Monte Carlo tree search, we come up with a new algorithm named
A* tree search in which best information is returned to guide next search.
Also, the expansion mode of Monte Carlo tree is improved for a higher
utilization of the neural network. The suggested algorithm can also optimize
non-differentiable utility function by combinatorial search. This technique is
then used in our trading system. The major component is a neural network that
is trained by trading experiences from tree search and outputs prior
probability to guide search by pruning away branches in turn. Experimental
results on simulated and real financial data verify the robustness of the
proposed trading system and the trading system produces better strategies than
several approaches based on reinforcement learning.
",A* Tree Search for Portfolio Management
"  There have been numerous attempts in explaining the general learning
behaviours by various cognitive models. Multiple hypotheses have been put
further to qualitatively argue the best-fit model for motor skill acquisition
task and its variations. In this context, for a discrete sequence production
(DSP) task, one of the most insightful models is Verwey's Dual Processor Model
(DPM). It largely explains the learning and behavioural phenomenon of skilled
discrete key-press sequences without providing any concrete computational basis
of reinforcement. Therefore, we propose a quantitative explanation for Verwey's
DPM hypothesis by experimentally establishing a general computational framework
for motor skill learning. We attempt combining the qualitative and quantitative
theories based on a best-fit model of the experimental simulations of
variations of dual processor models. The fundamental premise of sequential
decision making for skill learning is based on interacting model-based (MB) and
model-free (MF) reinforcement learning (RL) processes. Our unifying framework
shows the proposed idea agrees well to Verwey's DPM and Fitts' three phases of
skill learning. The accuracy of our model can further be validated by its
statistical fit with the human-generated data on simple environment tasks like
the grid-world.
",A Computational Framework for Motor Skill Acquisition
"  We propose a generalized decision-theoretic system for a heterogeneous team
of autonomous agents who are tasked with online identification of
phenotypically expressed stress in crop fields.. This system employs four
distinct types of agents, specific to four available sensor modalities:
satellites (Layer 3), uninhabited aerial vehicles (L2), uninhabited ground
vehicles (L1), and static ground-level sensors (L0). Layers 3, 2, and 1 are
tasked with performing image processing at the available resolution of the
sensor modality and, along with data generated by layer 0 sensors, identify
erroneous differences that arise over time. Our goal is to limit the use of the
more computationally and temporally expensive subsequent layers. Therefore,
from layer 3 to 1, each layer only investigates areas that previous layers have
identified as potentially afflicted by stress. We introduce a reinforcement
learning technique based on Perkins' Monte Carlo Exploring Starts for a
generalized Markovian model for each layer's decision problem, and label the
system the Agricultural Distributed Decision Framework (ADDF). As our domain is
real-world and online, we illustrate implementations of the two major
components of our system: a clustering-based image processing methodology and a
two-layer POMDP implementation.
","Towards a Decentralized, Autonomous Multiagent Framework for Mitigating
  Crop Loss"
"  In temporal planning, many different temporal network formalisms are used to
model real world situations. Each of these formalisms has different features
which affect how easy it is to determine whether the underlying network of
temporal constraints is consistent. While many of the simpler models have been
well-studied from a computational complexity perspective, the algorithms
developed for advanced models which combine features have very loose complexity
bounds. In this paper, we provide tight completeness bounds for strong, weak,
and dynamic controllability checking of temporal networks that have conditions,
disjunctions, and temporal uncertainty. Our work exposes some of the subtle
differences between these different structures and, remarkably, establishes a
guarantee that all of these problems are computable in PSPACE.
","Complexity Bounds for the Controllability of Temporal Networks with
  Conditions, Disjunctions, and Uncertainty"
"  Orchestration of campaigns for online display advertising requires marketers
to forecast audience size at the granularity of specific attributes of web
traffic, characterized by the categorical nature of all attributes (e.g. {US,
Chrome, Mobile}). With each attribute taking many values, the very large
attribute combination set makes estimating audience size for any specific
attribute combination challenging. We modify Eclat, a frequent itemset mining
(FIM) algorithm, to accommodate categorical variables. For consequent frequent
and infrequent itemsets, we then provide forecasts using time series analysis
with conditional probabilities to aid approximation. An extensive simulation,
based on typical characteristics of audience data, is built to stress test our
modified-FIM approach. In two real datasets, comparison with baselines
including neural network models, shows that our method lowers computation time
of FIM for categorical data. On hold out samples we show that the proposed
forecasting method outperforms these baselines.
",Forecasting Granular Audience Size for Online Advertising
"  Machine learning systems regularly deal with structured data in real-world
applications. Unfortunately, such data has been difficult to faithfully
represent in a way that most machine learning techniques would expect, i.e. as
a real-valued vector of a fixed, pre-specified size. In this work, we introduce
a novel approach that compiles structured data into a satisfiability problem
which has in its set of solutions at least (and often only) the input data. The
satisfiability problem is constructed from constraints which are generated
automatically a priori from a given signature, thus trivially allowing for a
bag-of-words-esque vector representation of the input to be constructed. The
method is demonstrated in two areas, automated reasoning and natural language
processing, where it is shown to produce vector representations of
natural-language sentences and first-order logic clauses that can be precisely
translated back to their original, structured input forms.
",High-Fidelity Vector Space Models of Structured Data
"  Artificial intelligence (AI) research enjoyed an initial period of enthusiasm
in the 1970s and 80s. But this enthusiasm was tempered by a long interlude of
frustration when genuinely useful AI applications failed to be forthcoming.
Today, we are experiencing once again a period of enthusiasm, fired above all
by the successes of the technology of deep neural networks or deep machine
learning. In this paper we draw attention to what we take to be serious
problems underlying current views of artificial intelligence encouraged by
these successes, especially in the domain of language processing. We then show
an alternative approach to language-centric AI, in which we identify a role for
philosophy.
",Making AI meaningful again
"  This book contains the accepted papers at ProSocrates 2017 Symposium:
Problem-solving,Creativity and Spatial Reasoning in Cognitive Systems.
ProSocrates 2017 symposium was held at the Hansewissenschaftkolleg (HWK) of
Advanced Studies in Delmenhorst, 20-21July 2017. This was the second edition of
this symposium which aims to bring together researchers interested in spatial
reasoning, problem solving and creativity.
","Proceedings of the 2nd Symposium on Problem-solving, Creativity and
  Spatial Reasoning in Cognitive Systems, ProSocrates 2017"
"  In many problem settings, most notably in game playing, an agent receives a
possibly delayed reward for its actions. Often, those rewards are handcrafted
and not naturally given. Even simple terminal-only rewards, like winning equals
1 and losing equals -1, can not be seen as an unbiased statement, since these
values are chosen arbitrarily, and the behavior of the learner may change with
different encodings, such as setting the value of a loss to -0:5, which is
often done in practice to encourage learning. It is hard to argue about good
rewards and the performance of an agent often depends on the design of the
reward signal. In particular, in domains where states by nature only have an
ordinal ranking and where meaningful distance information between game state
values are not available, a numerical reward signal is necessarily biased. In
this paper, we take a look at Monte Carlo Tree Search (MCTS), a popular
algorithm to solve MDPs, highlight a reoccurring problem concerning its use of
rewards, and show that an ordinal treatment of the rewards overcomes this
problem. Using the General Video Game Playing framework we show a dominance of
our newly proposed ordinal MCTS algorithm over preference-based MCTS, vanilla
MCTS and various other MCTS variants.
",Ordinal Monte Carlo Tree Search
"  The paper reports on an experiment, in which a Knowledge-Based Reinforcement
Learning (KB-RL) method was compared to a Neural Network (NN) approach in
solving a classical Artificial Intelligence (AI) task. In contrast to NNs,
which require a substantial amount of data to learn a good policy, the KB-RL
method seeks to encode human knowledge into the solution, considerably reducing
the amount of data needed for a good policy. By means of Reinforcement Learning
(RL), KB-RL learns to optimize the model and improves the output of the system.
Furthermore, KB-RL offers the advantage of a clear explanation of the taken
decisions as well as transparent reasoning behind the solution.
  The goal of the reported experiment was to examine the performance of the
KB-RL method in contrast to the Neural Network and to explore the capabilities
of KB-RL to deliver a strong solution for the AI tasks. The results show that,
within the designed settings, KB-RL outperformed the NN, and was able to learn
a better policy from the available amount of data. These results support the
opinion that Artificial Intelligence can benefit from the discovery and study
of alternative approaches, potentially extending the frontiers of AI.
","Comparing Knowledge-based Reinforcement Learning to Neural Networks in a
  Strategy Game"
"  In this paper, We Apply Reinforcement learning (RL) techniques to train a
realistic biomechanical model to work with different people and on different
walking environments. We benchmarking 3 RL algorithms: Deep Deterministic
Policy Gradient (DDPG), Trust Region Policy Optimization (TRPO) and Proximal
Policy Optimization (PPO) in OpenSim environment, Also we apply imitation
learning to a prosthetics domain to reduce the training time needed to design
customized prosthetics. We use DDPG algorithm to train an original expert
agent. We then propose a modification to the Dataset Aggregation (DAgger)
algorithm to reuse the expert knowledge and train a new target agent to
replicate that behaviour in fewer than 5 iterations, compared to the 100
iterations taken by the expert agent which means reducing training time by 95%.
Our modifications to the DAgger algorithm improve the balance between
exploiting the expert policy and exploring the environment. We show empirically
that these improve convergence time of the target agent, particularly when
there is some degree of variation between expert and naive agent.
",Transfer Learning for Prosthetics Using Imitation Learning
"  Robots frequently face complex tasks that require more than one action, where
sequential decision-making (SDM) capabilities become necessary. The key
contribution of this work is a robot SDM framework, called LCORPP, that
supports the simultaneous capabilities of supervised learning for passive state
estimation, automated reasoning with declarative human knowledge, and planning
under uncertainty toward achieving long-term goals. In particular, we use a
hybrid reasoning paradigm to refine the state estimator, and provide
informative priors for the probabilistic planner. In experiments, a mobile
robot is tasked with estimating human intentions using their motion
trajectories, declarative contextual knowledge, and human-robot interaction
(dialog-based and motion-based). Results suggest that, in efficiency and
accuracy, our framework performs better than its no-learning and no-reasoning
counterparts in office environment.
","Learning and Reasoning for Robot Sequential Decision Making under
  Uncertainty"
"  In this paper we propose a new training loop for deep reinforcement learning
agents with an evolutionary generator. Evolutionary procedural content
generation has been used in the creation of maps and levels for games before.
Our system incorporates an evolutionary map generator to construct a training
curriculum that is evolved to maximize loss within the state-of-the-art Double
Dueling Deep Q Network architecture with prioritized replay. We present a
case-study in which we prove the efficacy of our new method on a game with a
discrete, large action space we made called Attackers and Defenders. Our
results demonstrate that training on an evolutionarily-curated curriculum
(directed sampling) of maps both expedites training and improves generalization
when compared to a network trained on an undirected sampling of maps.
","Evolutionarily-Curated Curriculum Learning for Deep Reinforcement
  Learning Agents"
"  We develop a likelihood free inference procedure for conditioning a
probabilistic model on a predicate. A predicate is a Boolean valued function
which expresses a yes/no question about a domain. Our contribution, which we
call predicate exchange, constructs a softened predicate which takes value in
the unit interval [0, 1] as opposed to a simply true or false. Intuitively, 1
corresponds to true, and a high value (such as 0.999) corresponds to ""nearly
true"" as determined by a distance metric. We define Boolean algebra for soft
predicates, such that they can be negated, conjoined and disjoined arbitrarily.
A softened predicate can serve as a tractable proxy to a likelihood function
for approximate posterior inference. However, to target exact inference, we
temper the relaxation by a temperature parameter, and add a accept/reject phase
use to replica exchange Markov Chain Mont Carlo, which exchanges states between
a sequence of models conditioned on predicates at varying temperatures. We
describe a lightweight implementation of predicate exchange that it provides a
language independent layer that can be implemented on top of existingn modeling
formalisms.
",Soft Constraints for Inference with Declarative Knowledge
"  Multi-Agent Pathfinding (MAPF) is the problem of finding paths for multiple
agents such that every agent reaches its goal and the agents do not collide.
Most prior work on MAPF was on grids, assumed agents' actions have uniform
duration, and that time is discretized into timesteps. We propose a MAPF
algorithm that does not rely on these assumptions, is complete, and provides
provably optimal solutions. This algorithm is based on a novel adaptation of
Safe interval path planning (SIPP), a continuous time single-agent planning
algorithm, and a modified version of Conflict-based search (CBS), a state of
the art multi-agent pathfinding algorithm. We analyze this algorithm, discuss
its pros and cons, and evaluate it experimentally on several standard
benchmarks.
",Multi-Agent Pathfinding with Continuous Time
"  Distributed computing which uses Web services as fundamental elements,
enables high-speed development of software applications through composing many
interoperating, distributed, re-usable, and autonomous services. As a
fundamental challenge for service developers, service composition must fulfil
functional requirements and optimise Quality of Service (QoS) attributes,
simultaneously. On the other hand, huge amounts of data have been created by
advances in technologies, which may be exchanged between services.
Data-intensive Web services are of great interest to implement data-intensive
processes. However, current approaches to Web service composition have omitted
either the effect of data, or the distribution of services. Evolutionary
Computing (EC) techniques allow for the creation of compositions that meet all
the above factors. In this paper, we will develop Genetic Algorithm (GA)-based
approach for solving the problem of distributed data-intensive Web service
composition (DWSC). In particular, we will introduce two new heuristics, i.e.
Longest Common Subsequence(LCS) distance of services, in designing crossover
operators. Additionally, a new local search technique incorporating distance of
services will be proposed.
","Distance-Guided GA-Based Approach to Distributed Data-Intensive Web
  Service Composition"
"  By achieving their purposes through interactions with the physical world,
Cyber-Physical Systems (CPS) pose new challenges in terms of dependability.
Indeed, the evolution of the physical systems they control with transducers can
be affected by surrounding physical processes over which they have no control
and which may potentially hamper the achievement of their purposes. While it is
illusory to hope for a comprehensive model of the physical environment at
design time to anticipate and remove faults that may occur once these systems
are deployed, it becomes necessary to evaluate their degree of effectiveness in
vivo. In this paper, the degree of effectiveness is formally defined and
generalized in the context of the measure theory. The measure is developed in
the context of the Transferable Belief Model (TBM), an elaboration on the
Dempster-Shafer Theory (DST) of evidence so as to handle epistemic and aleatory
uncertainties respectively pertaining the users' expectations and the natural
variability of the physical environment. The TBM is used in conjunction with
the Input/Output Hidden Markov Modeling framework (we denote by Ev-IOHMM) to
specify the expected evolution of the physical system controlled by the CPS and
the tolerances towards uncertainties. The measure of effectiveness is then
obtained from the forward algorithm, leveraging the conflict entailed by the
successive combinations of the beliefs obtained from observations of the
physical system and the beliefs corresponding to its expected evolution. The
proposed approach is applied to autonomous vehicles and show how the degree of
effectiveness can be used for bench-marking their controller relative to the
highway code speed limitations and passengers' well-being constraints, both
modeled through an Ev-IOHMM.
",Effectiveness Assessment of Cyber-Physical Systems
"  There is a disconnect between explanatory artificial intelligence (XAI)
methods and the types of explanations that are useful for and demanded by
society (policy makers, government officials, etc.) Questions that experts in
artificial intelligence (AI) ask opaque systems provide inside explanations,
focused on debugging, reliability, and validation. These are different from
those that society will ask of these systems to build trust and confidence in
their decisions. Although explanatory AI systems can answer many questions that
experts desire, they often don't explain why they made decisions in a way that
is precise (true to the model) and understandable to humans. These outside
explanations can be used to build trust, comply with regulatory and policy
changes, and act as external validation. In this paper, we focus on XAI methods
for deep neural networks (DNNs) because of DNNs' use in decision-making and
inherent opacity. We explore the types of questions that explanatory DNN
systems can answer and discuss challenges in building explanatory systems that
provide outside explanations for societal requirements and benefit.
",Explaining Explanations to Society
"  We address the problem of designing a conversational avatar capable of a
sequence of casual conversations with older adults. Users at risk of
loneliness, social anxiety or a sense of ennui may benefit from practicing such
conversations in private, at their convenience. We describe an automatic spoken
dialogue manager for LISSA, an on-screen virtual agent that can keep older
users involved in conversations over several sessions, each lasting 10-20
minutes. The idea behind LISSA is to improve users' communication skills by
providing feedback on their non-verbal behavior at certain points in the course
of the conversations. In this paper, we analyze the dialogues collected from
the first session between LISSA and each of 8 participants. We examine the
quality of the conversations by comparing the transcripts with those collected
in a WOZ setting. LISSA's contributions to the conversations were judged by
research assistants who rated the extent to which the contributions were
""natural"", ""on track"", ""encouraging"", ""understanding"", ""relevant"", and
""polite"". The results show that the automatic dialogue manager was able to
handle conversation with the users smoothly and naturally.
","Dialogue Design and Management for Multi-Session Casual Conversation
  with Older Adults"
"  This paper presents Mixed Formal Learning, a new architecture that learns
models based on formal mathematical representations of the domain of interest
and exposes latent variables. The second element in the architecture learns a
particular skill, typically by using traditional prediction or classification
mechanisms. Our key findings include that this architecture: (1) Facilitates
transparency by exposing key latent variables based on a learned mathematical
model; (2) Enables Low Shot and Zero Shot training of machine learning without
sacrificing accuracy or recall.
",Mixed Formal Learning: A Path to Transparent Machine Learning
"  With the development of graph convolutional networks (GCN), deep learning
methods have started to be used on graph data. In additional to convolutional
layers, pooling layers are another important components of deep learning.
However, no effective pooling methods have been developed for graphs currently.
In this work, we propose the graph pooling (gPool) layer, which employs a
trainable projection vector to measure the importance of nodes in graphs. By
selecting the k-most important nodes to form the new graph, gPool achieves the
same objective as regular max pooling layers operating on images. Another
limitation of GCN when used on graph-based text representation tasks is that,
GCNs do not consider the order information of nodes in graph. To address this
limitation, we propose the hybrid convolutional (hConv) layer that combines GCN
and regular convolutional operations. The hConv layer is capable of increasing
receptive fields quickly and computing features automatically. Based on the
proposed gPool and hConv layers, we develop new deep networks for text
categorization tasks. Our results show that the networks based on gPool and
hConv layers achieves new state-of-the-art performance as compared to baseline
methods.
","Learning Graph Pooling and Hybrid Convolutional Operations for Text
  Representations"
"  Word representations are created using analogy context-based statistics and
lexical relations on words. Word representations are inputs for the learning
models in Natural Language Understanding (NLU) tasks. However, to understand
language, knowing only the context is not sufficient. Reading between the lines
is a key component of NLU. Embedding deeper word relationships which are not
represented in the context enhances the word representation. This paper
presents a word embedding which combines an analogy, context-based statistics
using Word2Vec, and deeper word relationships using Conceptnet, to create an
expanded word representation. In order to fine-tune the word representation,
Self-Organizing Map is used to optimize it. The proposed word representation is
compared with semantic word representations using Simlex 999. Furthermore, the
use of 3D visual representations has shown to be capable of representing the
similarity and association between words. The proposed word representation
shows a Spearman correlation score of 0.886 and provided the best results when
compared to the current state-of-the-art methods, and exceed the human
performance of 0.78.
","Enhancing Semantic Word Representations by Embedding Deeper Word
  Relationships"
"  This paper applies a genetic algorithm and fuzzy markup language to construct
a human and smart machine cooperative learning system on game of Go. The
genetic fuzzy markup language (GFML)-based Robot Agent can work on various
kinds of robots, including Palro, Pepper, and TMUs robots. We use the
parameters of FAIR open source Darkforest and OpenGo AI bots to construct the
knowledge base of Open Go Darkforest (OGD) cloud platform for student learning
on the Internet. In addition, we adopt the data from AlphaGo Master sixty
online games as the training data to construct the knowledge base and rule base
of the co-learning system. First, the Darkforest predicts the win rate based on
various simulation numbers and matching rates for each game on OGD platform,
then the win rate of OpenGo is as the final desired output. The experimental
results show that the proposed approach can improve knowledge base and rule
base of the prediction ability based on Darkforest and OpenGo AI bot with
various simulation numbers.
","A GFML-based Robot Agent for Human and Machine Cooperative Learning on
  Game of Go"
"  Learning in multi-agent scenarios is a fruitful research direction, but
current approaches still show scalability problems in multiple games with
general reward settings and different opponent types. The Multi-Agent
Reinforcement Learning in Malm\""O (MARL\""O) competition is a new challenge that
proposes research in this domain using multiple 3D games. The goal of this
contest is to foster research in general agents that can learn across different
games and opponent types, proposing a challenge as a milestone in the direction
of Artificial General Intelligence.
","The Multi-Agent Reinforcement Learning in Malm\""O (MARL\""O) Competition"
"  There is much debate in machine ethics about the most appropriate way to
introduce ethical reasoning capabilities into intelligent autonomous machines.
Recent incidents involving autonomous vehicles in which humans have been killed
or injured have raised questions about how we ensure that such vehicles have an
ethical dimension to their behaviour and are therefore trustworthy. The main
problem is that hardwiring such machines with rules not to cause harm or damage
is not consistent with the notion of autonomy and intelligence. Also, such
ethical hardwiring does not leave intelligent autonomous machines with any
course of action if they encounter situations or dilemmas for which they are
not programmed or where some harm is caused no matter what course of action is
taken. Teaching machines so that they learn ethics may also be problematic
given recent findings in machine learning that machines pick up the prejudices
and biases embedded in their learning algorithms or data. This paper describes
a fuzzy reasoning approach to machine ethics. The paper shows how it is
possible for an ethics architecture to reason when taking over from a human
driver is morally justified. The design behind such an ethical reasoner is also
applied to an ethical dilemma resolution case. One major advantage of the
approach is that the ethical reasoner can generate its own data for learning
moral rules (hence, autometric) and thereby reduce the possibility of picking
up human biases and prejudices. The results show that a new type of
metric-based ethics appropriate for autonomous intelligent machines is feasible
and that our current concept of ethical reasoning being largely qualitative in
nature may need revising if want to construct future autonomous machines that
have an ethical dimension to their reasoning so that they become moral
machines.
","When is it right and good for an intelligent autonomous vehicle to take
  over control (and hand it back)?"
"  The current state-of-the-art Scrabble agents are not learning-based but
depend on truncated Monte Carlo simulations and the quality of such agents is
contingent upon the time available for running the simulations. This thesis
takes steps towards building a learning-based Scrabble agent using self-play.
Specifically, we try to find a better function approximation for the static
evaluation function used in Scrabble which determines the move goodness at a
given board configuration. In this work, we experimented with evolutionary
algorithms and Bayesian Optimization to learn the weights for an approximate
feature-based evaluation function. However, these optimization methods were not
quite effective, which lead us to explore the given problem from an Imitation
Learning point of view. We also tried to imitate the ranking of moves produced
by the Quackle simulation agent using supervised learning with a neural network
function approximator which takes the raw representation of the Scrabble board
as the input instead of using only a fixed number of handcrafted features.
",Evaluation Function Approximation for Scrabble
"  This is the Proceedings of AAAI 2019 Workshop on Network Interpretability for
Deep Learning
","Proceedings of AAAI 2019 Workshop on Network Interpretability for Deep
  Learning"
"  We apply the Ordered Weighted Averaging (OWA) operator in multi-criteria
decision-making. To satisfy different kinds of uncertainty, measure based
dominance has been presented to gain the order of different criterion. However,
this idea has not been applied in fuzzy system until now. In this paper, we
focus on the situation where the linguistic satisfactions are fuzzy measures
instead of the exact values. We review the concept of OWA operator and discuss
the order mechanism of fuzzy number. Then we combine with measure-based
dominance to give an overall score of each alternatives. An example is
illustrated to show the whole procedure.
","OWA aggregation of multi-criteria with mixed uncertain fuzzy
  satisfactions"
"  A person dependent network, called an AlterEgo net, is proposed for
development. The networks are created per person. It receives at input an
object descriptions and outputs a simulation of the internal person's
representation of the objects. The network generates a textual stream
resembling the narrative stream of consciousness depicting multitudinous
thoughts and feelings related to a perceived object. In this way, the object is
described not by a 'static' set of its properties, like a dictionary, but by
the stream of words and word combinations referring to the object. The network
simulates a person's dialogue with a representation of the object. It is based
on an introduced algorithmic scheme, where perception is modeled by two
interacting iterative cycles, reminding one respectively the forward and
backward propagation executed at training convolution neural networks. The
'forward' iterations generate a stream representing the 'internal world' of a
human. The 'backward' iterations generate a stream representing an internal
representation of the object. People perceive the world differently. Tuning
AlterEgo nets to a specific person or group of persons, will allow simulation
of their thoughts and feelings. Thereby these nets is potentially a new human
augmentation technology for various applications.
",AlteregoNets: a way to human augmentation
"  Many constraints restricting the result of some computations over an integer
sequence can be compactly represented by register automata. We improve the
propagation of the conjunction of such constraints on the same sequence by
synthesising a database of linear and non-linear invariants using their
register-automaton representation. The obtained invariants are formulae
parameterised by a function of the sequence length and proven to be true for
any long enough sequence. To assess the quality of such linear invariants, we
developed a method to verify whether a generated linear invariant is a facet of
the convex hull of the feasible points. This method, as well as the proof of
non-linear invariants, are based on the systematic generation of constant-size
deterministic finite automata that accept all integer sequences whose result
verifies some simple condition. We apply such methodology to a set of 44
time-series constraints and obtain 1400 linear invariants from which 70% are
facet defining, and 600 non-linear invariants, which were tested on short-term
electricity production problems.
","Synthesising a Database of Parameterised Linear and Non-Linear
  Invariants for Time-Series Constraints"
"  Meteorological forecasting provides reliable prediction about the future
weather within a given interval of time. Meteorological forecasting can be
viewed as a form of hybrid diagnostic reasoning and can be mapped onto an
integrated conceptual framework. The automation of the forecasting process
would be helpful in a number of contexts, in particular: when the amount of
data is too wide to be dealt with manually; to support forecasters education;
when forecasting about underpopulated geographic areas is not interesting for
everyday life (and then is out from human forecasters' tasks) but is central
for tourism sponsorship. We present logic MeteoLOG, a framework that models the
main steps of the reasoner the forecaster adopts to provide a bulletin.
MeteoLOG rests on several traditions, mainly on fuzzy, temporal and
probabilistic logics. On this basis, we also introduce the algorithm
Tournament, that transforms a set of MeteoLOG rules into a defeasible theory,
that can be implemented into an automatic reasoner. We finally propose an
example that models a real world forecasting scenario.
","It could be worse, it could be raining: reliable automatic
  meteorological forecasting"
"  Web Service Composition (WSC) is a particularly promising application of Web
services, where multiple individual services with specific functionalities are
composed to accomplish a more complex task, which must fulfil functional
requirements and optimise Quality of Service (QoS) attributes, simultaneously.
Additionally, large quantities of data, produced by technological advances,
need to be exchanged between services. Data-intensive Web services, which
manipulate and deal with those data, are of great interest to implement
data-intensive processes, such as distributed Data-intensive Web Service
Composition (DWSC). Researchers have proposed Evolutionary Computing (EC)
fully-automated WSC techniques that meet all the above factors. Some of these
works employed Memetic Algorithms (MAs) to enhance the performance of EC
through increasing its exploitation ability of in searching neighbourhood area
of a solution. However, those works are not efficient or effective. This paper
proposes an MA-based approach to solving the problem of distributed DWSC in an
effective and efficient manner. In particular, we develop an MA that hybridises
EC with a flexible local search technique incorporating distance of services.
An evaluation using benchmark datasets is carried out, comparing existing
state-of-the-art methods. Results show that our proposed method has the highest
quality and an acceptable execution time overall.
","Composing Distributed Data-intensive Web Services Using a Flexible
  Memetic Algorithm"
"  In several different applications, including data transformation and entity
resolution, rules are used to capture aspects of knowledge about the
application at hand. Often, a large set of such rules is generated
automatically or semi-automatically, and the challenge is to refine the
encapsulated knowledge by selecting a subset of rules based on the expected
operational behavior of the rules on available data. In this paper, we carry
out a systematic complexity-theoretic investigation of the following rule
selection problem: given a set of rules specified by Horn formulas, and a pair
of an input database and an output database, find a subset of the rules that
minimizes the total error, that is, the number of false positive and false
negative errors arising from the selected rules. We first establish
computational hardness results for the decision problems underlying this
minimization problem, as well as upper and lower bounds for its
approximability. We then investigate a bi-objective optimization version of the
rule selection problem in which both the total error and the size of the
selected rules are taken into account. We show that testing for membership in
the Pareto front of this bi-objective optimization problem is DP-complete.
Finally, we show that a similar DP-completeness result holds for a bi-level
optimization version of the rule selection problem, where one minimizes first
the total error and then the size.
",Knowledge Refinement via Rule Selection
"  Probability theory and Dempster-Shafer theory are two germane theories to
represent and handle uncertain information. Recent study suggested a
transformation to obtain the negation of a probability distribution based on
the maximum entropy. Correspondingly, determining the negation of a belief
structure, however, is still an open issue in Dempster-Shafer theory, which is
very important in theoretical research and practical applications. In this
paper, a negation transformation for belief structures is proposed based on
maximum uncertainty allocation, and several important properties satisfied by
the transformation have been studied. The proposed negation transformation is
more general and could totally compatible with existing transformation for
probability distributions.
","On the negation of a Dempster-Shafer belief structure based on maximum
  uncertainty allocation"
"  Problems arise when using reward functions to capture dependencies between
sequential time-constrained goal states because the state-space must be
prohibitively expanded to accommodate a history of successfully achieved
sub-goals. Also, policies and value functions derived with stationarity
assumptions are not readily decomposable, leading to a tension between reward
maximization and task generalization. We demonstrate a logic-compatible
approach using model-based knowledge of environment dynamics and deadline
information to directly infer non-stationary policies composed of reusable
stationary policies. The policies are constructed to maximize the probability
of satisfying time-sensitive goals while respecting time-varying obstacles. Our
approach explicitly maintains two different spaces, a high-level logical task
specification where the task-variables are grounded onto the low-level
state-space of a Markov decision process. Computing satisfiability at the
task-level is made possible by a Bellman-like equation which operates on a
tensor that links the temporal relationship between the two spaces; the
equation solves for a value function that can be explicitly interpreted as the
probability of sub-goal satisfaction under the synthesized non-stationary
policy, an approach we term Constraint Satisfaction Propagation (CSP).
","Constraint Satisfaction Propagation: Non-stationary Policy Synthesis for
  Temporal Logic Planning"
"  Humans are increasingly coming into contact with artificial intelligence and
machine learning systems. Human-centered artificial intelligence is a
perspective on AI and ML that algorithms must be designed with awareness that
they are part of a larger system consisting of humans. We lay forth an argument
that human-centered artificial intelligence can be broken down into two
aspects: (1) AI systems that understand humans from a sociocultural
perspective, and (2) AI systems that help humans understand them. We further
argue that issues of social responsibility such as fairness, accountability,
interpretability, and transparency.
",Human-Centered Artificial Intelligence and Machine Learning
"  Reinforcement Learning (RL) algorithms typically require millions of
environment interactions to learn successful policies in sparse reward
settings. Hindsight Experience Replay (HER) was introduced as a technique to
increase sample efficiency by reimagining unsuccessful trajectories as
successful ones by altering the originally intended goals. However, it cannot
be directly applied to visual environments where goal states are often
characterized by the presence of distinct visual features. In this work, we
show how visual trajectories can be hallucinated to appear successful by
altering agent observations using a generative model trained on relatively few
snapshots of the goal. We then use this model in combination with HER to train
RL agents in visual settings. We validate our approach on 3D navigation tasks
and a simulated robotics application and show marked improvement over baselines
derived from previous work.
","Addressing Sample Complexity in Visual Tasks Using HER and Hallucinatory
  GANs"
"  We investigate the problem whether two ALC ontologies are indistinguishable
(or inseparable) by means of queries in a given signature, which is fundamental
for ontology engineering tasks such as ontology versioning, modularisation,
update, and forgetting. We consider both knowledge base (KB) and TBox
inseparability. For KBs, we give model-theoretic criteria in terms of (finite
partial) homomorphisms and products and prove that this problem is undecidable
for conjunctive queries (CQs), but 2ExpTime-complete for unions of CQs (UCQs).
The same results hold if (U)CQs are replaced by rooted (U)CQs, where every
variable is connected to an answer variable. We also show that inseparability
by CQs is still undecidable if one KB is given in the lightweight DL EL and if
no restrictions are imposed on the signature of the CQs. We also consider the
problem whether two ALC TBoxes give the same answers to any query over any ABox
in a given signature and show that, for CQs, this problem is undecidable, too.
We then develop model-theoretic criteria for Horn-ALC TBoxes and show using
tree automata that, in contrast, inseparability becomes decidable and
2ExpTime-complete, even ExpTime-complete when restricted to (unions of) rooted
CQs.
",Query Inseparability for ALC Ontologies
"  Analogical reasoning has been a principal focus of various waves of AI
research. Analogy is particularly challenging for machines because it requires
relational structures to be represented such that they can be flexibly applied
across diverse domains of experience. Here, we study how analogical reasoning
can be induced in neural networks that learn to perceive and reason about raw
visual data. We find that the critical factor for inducing such a capacity is
not an elaborate architecture, but rather, careful attention to the choice of
data and the manner in which it is presented to the model. The most robust
capacity for analogical reasoning is induced when networks learn analogies by
contrasting abstract relational structures in their input domains, a training
method that uses only the input data to force models to learn about important
abstract features. Using this technique we demonstrate capacities for complex,
visual and symbolic analogy making and generalisation in even the simplest
neural network architectures.
",Learning to Make Analogies by Contrasting Abstract Relational Structure
"  Uplift modeling requires experimental data, preferably collected in random
fashion. This places a logistical and financial burden upon any organisation
aspiring such models. Once deployed, uplift models are subject to effects from
concept drift. Hence, methods are being developed that are able to learn from
newly gained experience, as well as handle drifting environments. As these new
methods attempt to eliminate the need for experimental data, another approach
to test such methods must be formulated. Therefore, we propose a method to
simulate environments that offer causal relationships in their parameters.
",Causal Simulations for Uplift Modeling
"  Generating explanation to explain its behavior is an essential capability for
a robotic teammate. Explanations help human partners better understand the
situation and maintain trust of their teammates. Prior work on robot generating
explanations focuses on providing the reasoning behind its decision making.
These approaches, however, fail to heed the cognitive requirement of
understanding an explanation. In other words, while they provide the right
explanations from the explainer's perspective, the explainee part of the
equation is ignored. In this work, we address an important aspect along this
direction that contributes to a better understanding of a given explanation,
which we refer to as the progressiveness of explanations. A progressive
explanation improves understanding by limiting the cognitive effort required at
each step of making the explanation. As a result, such explanations are
expected to be smoother and hence easier to understand. A general formulation
of progressive explanation is presented. Algorithms are provided based on
several alternative quantifications of cognitive effort as an explanation is
being made, which are evaluated in a standard planning competition domain.
",Progressive Explanation Generation for Human-robot Teaming
"  Projects consist of interconnected dimensions such as objective, time,
resource and environment. Use of these dimensions in a controlled way and their
effective scheduling brings the project success. Project scheduling process
includes defining project activities, and estimation of time and resources to
be used for the activities. At this point, the project resource-scheduling
problems have begun to attract more attention after Program Evaluation and
Review Technique (PERT) and Critical Path Method (CPM) are developed one after
the other. However, complexity and difficulty of CPM and PERT processes led to
the use of these techniques through artificial intelligence methods such as
Genetic Algorithm (GA). In this study, an algorithm was proposed and developed,
which determines critical path, critical activities and project completion
duration by using GA, instead of CPM and PERT techniques used for network
analysis within the scope of project management. The purpose of using GA was
that these algorithms are an effective method for solution of complex
optimization problems. Therefore, correct decisions can be made for implemented
project activities by using obtained results. Thus, optimum results were
obtained in a shorter time than the CPM and PERT techniques by using the model
based on the dynamic algorithm. It is expected that this study will contribute
to the performance field (time, speed, low error etc.) of other studies.
","Optimization of Project Scheduling Activities in Dynamic CPM and PERT
  Networks Using Genetic Algorithms"
"  Animal behavior is not driven simply by its current observations, but is
strongly influenced by internal states. Estimating the structure of these
internal states is crucial for understanding the neural basis of behavior. In
principle, internal states can be estimated by inverting behavior models, as in
inverse model-based Reinforcement Learning. However, this requires careful
parameterization and risks model-mismatch to the animal. Here we take a
data-driven approach to infer latent states directly from observations of
behavior, using a partially observable switching semi-Markov process. This
process has two elements critical for capturing animal behavior: it captures
non-exponential distribution of times between observations, and transitions
between latent states depend on the animal's actions, features that require
more complex non-markovian models to represent. To demonstrate the utility of
our approach, we apply it to the observations of a simulated optimal agent
performing a foraging task, and find that latent dynamics extracted by the
model has correspondences with the belief dynamics of the agent. Finally, we
apply our model to identify latent states in the behaviors of monkey performing
a foraging task, and find clusters of latent states that identify periods of
time consistent with expectant waiting. This data-driven behavioral model will
be valuable for inferring latent cognitive states, and thereby for measuring
neural representations of those states.
",Belief dynamics extraction
"  A new conceptual foundation for the notion of ""information"" is proposed,
based on the concept of a ""distinction graph"": a graph in which two nodes are
connected iff they cannot be distinguished by a particular observer. The
""graphtropy"" of a distinction graph is defined as the average connection
probability of two nodes; in the case where the distinction graph is a composed
of disconnected components that are fully connected subgraphs, this is
equivalent to Ellerman's logical entropy, which has straightforward
relationships to Shannon entropy. Probabilistic distinction graphs and
probabilistic graphtropy are also considered, as well as connections between
graphtropy and thermodynamic and quantum entropy. The semantics of the Second
Law of Thermodynamics and the Maximum Entropy Production Principle are unfolded
in a novel way, via analysis of the cognitive processes underlying the making
of distinction graphs This evokes an interpretation in which complex
intelligence is seen to correspond to states of consciousness with intermediate
graphtropy, which are associated with memory imperfections that violate the
assumptions leading to derivation of the Second Law. In the case where nodes of
a distinction graph are labeled by computable entities, graphtropy is shown to
be monotonically related to the average algorithmic information of the nodes
(relative to to the algorithmic information of the observer). A
quantum-mechanical version of distinction graphs is considered, in which
distinctions can exist in a superposed state; this yields to graphtropy as a
measure of the impurity of a mixed state, and to a concept of ""quangraphtropy.""
Finally, a novel computational model called Dynamic Distinction Graphs (DDGs)
is formulated, via enhancing distinction graphs with additional links
expressing causal implications, enabling a distinction-based model of
""observers.""
","Distinction Graphs and Graphtropy: A Formalized Phenomenological Layer
  Underlying Classical and Quantum Entropy, Observational Semantics and
  Cognitive Computation"
"  Dialogue systems have many applications such as customer support or question
answering. Typically they have been limited to shallow single turn
interactions. However more advanced applications such as career coaching or
planning a trip require a much more complex multi-turn dialogue. Current
limitations of conversational systems have made it difficult to support
applications that require personalization, customization and context dependent
interactions. We tackle this challenging problem by using domain-independent AI
planning to automatically create dialogue plans, customized to guide a dialogue
towards achieving a given goal. The input includes a library of atomic dialogue
actions, an initial state of the dialogue, and a goal. Dialogue plans are
plugged into a dialogue system capable to orchestrate their execution. Use
cases demonstrate the viability of the approach. Our work on dialogue planning
has been integrated into a product, and it is in the process of being deployed
into another.
",Generating Dialogue Agents via Automated Planning
"  Knowledge graphs have recently become the state-of-the-art tool for
representing the diverse and complex knowledge of the world. Examples include
the proprietary knowledge graphs of companies such as Google, Facebook, IBM, or
Microsoft, but also freely available ones such as YAGO, DBpedia, and Wikidata.
A distinguishing feature of Wikidata is that the knowledge is collaboratively
edited and curated. While this greatly enhances the scope of Wikidata, it also
makes it impossible for a single individual to grasp complex connections
between properties or understand the global impact of edits in the graph. We
apply Formal Concept Analysis to efficiently identify comprehensible
implications that are implicitly present in the data. Although the complex
structure of data modelling in Wikidata is not amenable to a direct approach,
we overcome this limitation by extracting contextual representations of parts
of Wikidata in a systematic fashion. We demonstrate the practical feasibility
of our approach through several experiments and show that the results may lead
to the discovery of interesting implicational knowledge. Besides providing a
method for obtaining large real-world data sets for FCA, we sketch potential
applications in offering semantic assistance for editing and curating Wikidata.
",Discovering Implicational Knowledge in Wikidata
"  Staff scheduling is a universal problem that can be encountered in many
organizations, such as call centers, educational institution, industry,
hospital, and any other public services. It is one of the most important
aspects of workforce management strategy and the one that is most prone to
errors or issues as there are many entities should be considered, such as the
staff turnover, employee availability, time between rotations, unusual periods
of activity, and even the last-minute shift changes. The nurse scheduling
problem is a variant of staff scheduling problems which appoints nurses to
shifts as well as rooms per day taking both hard constraints, i.e., hospital
requirements, and soft constraints, i.e., nurse preferences, into account. Most
algorithms used for scheduling problems fall short when it comes to the number
of inputs they can handle. In this paper, constraint programming was developed
to solve the nurse scheduling problem. The developed constraint programming
model was then implemented using python programming language.
",Solving Nurse Scheduling Problem Using Constraint Programming Technique
"  It is the efficient use of resources expected from an exam scheduling
application. There are various criteria for efficient use of resources and for
all tests to be carried out at minimum cost in the shortest possible time. It
is aimed that educational institutions with such criteria successfully carry
out central examination organizations. In the study, a two-stage genetic
algorithm was developed. In the first stage, the assignment of courses to
sessions was carried out. In the second stage, the students who participated in
the test session were assigned to examination rooms. Purposes of the study are
increasing the number of joint students participating in sessions, using the
minimum number of buildings in the same session, and reducing the number of
supervisors using the minimum number of classrooms possible. In this study, a
general purpose exam scheduling solution for educational institutions was
presented. The developed system can be used in different central examinations
to create originality. Given the results of the sample application, it is seen
that the proposed genetic algorithm gives successful results.1
","Solving The Exam Scheduling Problems in Central Exams With Genetic
  Algorithms"
"  Artificial Intelligence has an important place in the scientific community as
a result of its successful outputs in terms of different fields. In time, the
field of Artificial Intelligence has been divided into many sub-fields because
of increasing number of different solution approaches, methods, and techniques.
Machine Learning has the most remarkable role with its functions to learn from
samples from the environment. On the other hand, intelligent optimization done
by inspiring from nature and swarms had its own unique scientific literature,
with effective solutions provided for optimization problems from different
fields. Because intelligent optimization can be applied in different fields
effectively, this study aims to provide a general discussion on
multidisciplinary effects of Artificial Intelligence by considering its
optimization oriented solutions. The study briefly focuses on background of the
intelligent optimization briefly and then gives application examples of
intelligent optimization from a multidisciplinary perspective.
","Evaluation of Multidisciplinary Effects of Artificial Intelligence with
  Optimization Perspective"
"  Dungeon Crawl Stone Soup is a popular, single-player, free and open-source
rogue-like video game with a sufficiently complex decision space that makes it
an ideal testbed for research in cognitive systems and, more generally,
artificial intelligence. This paper describes the properties of Dungeon Crawl
Stone Soup that are conducive to evaluating new approaches of AI systems. We
also highlight an ongoing effort to build an API for AI researchers in the
spirit of recent game APIs such as MALMO, ELF, and the Starcraft II API.
Dungeon Crawl Stone Soup's complexity offers significant opportunities for
evaluating AI and cognitive systems, including human user studies. In this
paper we provide (1) a description of the state space of Dungeon Crawl Stone
Soup, (2) a description of the components for our API, and (3) the potential
benefits of evaluating AI agents in the Dungeon Crawl Stone Soup video game.
","Dungeon Crawl Stone Soup as an Evaluation Domain for Artificial
  Intelligence"
"  This is an integrative review that address the question, ""What makes for a
good explanation?"" with reference to AI systems. Pertinent literatures are
vast. Thus, this review is necessarily selective. That said, most of the key
concepts and issues are expressed in this Report. The Report encapsulates the
history of computer science efforts to create systems that explain and instruct
(intelligent tutoring systems and expert systems). The Report expresses the
explainability issues and challenges in modern AI, and presents capsule views
of the leading psychological theories of explanation. Certain articles stand
out by virtue of their particular relevance to XAI, and their methods, results,
and key points are highlighted. It is recommended that AI/XAI researchers be
encouraged to include in their research reports fuller details on their
empirical or experimental methods, in the fashion of experimental psychology
research reports: details on Participants, Instructions, Procedures, Tasks,
Dependent Variables (operational definitions of the measures and metrics),
Independent Variables (conditions), and Control Conditions.
","Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of
  Key Ideas and Publications, and Bibliography for Explainable AI"
"  In this paper, we argue that simulation platforms enable a novel type of
embodied spatial reasoning, one facilitated by a formal model of object and
event semantics that renders the continuous quantitative search space of an
open-world, real-time environment tractable. We provide examples for how a
semantically-informed AI system can exploit the precise, numerical information
provided by a game engine to perform qualitative reasoning about objects and
events, facilitate learning novel concepts from data, and communicate with a
human to improve its models and demonstrate its understanding. We argue that
simulation environments, and game engines in particular, bring together many
different notions of ""simulation"" and many different technologies to provide a
highly-effective platform for developing both AI systems and tools to
experiment in both machine and human intelligence.
",Situational Grounding within Multimodal Simulations
"  The fuzzy quantification model FA has been identified as one of the best
behaved quantification models in several revisions of the field of fuzzy
quantification. This model is, to our knowledge, the unique one fulfilling the
strict Determiner Fuzzification Scheme axiomatic framework that does not induce
the standard min and max operators. The main contribution of this paper is the
proof of a convergence result that links this quantification model with the
Zadeh's model when the size of the input sets tends to infinite. The
convergence proof is, in any case, more general than the convergence to the
Zadeh's model, being applicable to any quantitative quantifier. In addition,
recent revisions papers have presented some doubts about the existence of
suitable computational implementations to evaluate the FA model in practical
applications. In order to prove that this model is not only a theoretical
approach, we show exact algorithmic solutions for the most common linguistic
quantifiers as well as an approximate implementation by means of Monte Carlo.
Additionally, we will also give a general overview of the main properties
fulfilled by the FA model, as a single compendium integrating the whole set of
properties fulfilled by it has not been previously published.
","The FA Quantifier Fuzzification Mechanism: analysis of convergence and
  efficient implementations"
"  Optimizing compilers, as well as other translator systems, often work by
rewriting expressions according to equivalence preserving rules. Given an input
expression and its optimized form, finding the sequence of rules that were
applied is a non-trivial task. Most of the time, the tools provide no proof, of
any kind, of the equivalence between the original expression and its optimized
form. In this work, we propose to reconstruct proofs of equivalence of simple
mathematical expressions, after the fact, by finding paths of equivalence
preserving transformations between expressions. We propose to find those
sequences of transformations using a search algorithm, guided by a neural
network heuristic. Using a Tree-LSTM recursive neural network, we learn a
distributed representation of expressions where the Manhattan distance between
vectors approximately corresponds to the rewrite distance between expressions.
We then show how the neural network can be efficiently used to search for
transformation paths, leading to substantial gain in speed compared to an
uninformed exhaustive search. In one of our experiments, our neural-network
guided search algorithm is able to solve more instances with a 2 seconds
timeout per instance than breadth-first search does with a 5 minutes timeout
per instance.
",Neural-Network Guided Expression Transformation
"  We define a Causal Decision Problem as a Decision Problem where the available
actions, the family of uncertain events and the set of outcomes are related
through the variables of a Causal Graphical Model $\mathcal{G}$. A solution
criteria based on Pearl's Do-Calculus and the Expected Utility criteria for
rational preferences is proposed. The implementation of this criteria leads to
an on-line decision making procedure that has been shown to have similar
performance to classic Reinforcement Learning algorithms while allowing for a
causal model of an environment to be learned. Thus, we aim to provide the
theoretical guarantees of the usefulness and optimality of a decision making
procedure based on causal information.
",A Guiding Principle for Causal Decision Problems
"  This paper presents an adaptive level generation algorithm for the
physics-based puzzle game Angry Birds. The proposed algorithm is based on a
pre-existing level generator for this game, but where the difficulty of the
generated levels can be adjusted based on the player's performance. This allows
for the creation of personalised levels tailored specifically to the player's
own abilities. The effectiveness of our proposed method is evaluated using
several agents with differing strategies and AI techniques. By using these
agents as models / representations of real human player's characteristics, we
can optimise level properties efficiently over a large number of generations.
As a secondary investigation, we also demonstrate that by combining the
performance of several agents together it is possible to generate levels that
are especially challenging for certain players but not others.
","Agent-Based Adaptive Level Generation for Dynamic Difficulty Adjustment
  in Angry Birds"
"  Actor-critic algorithms learn an explicit policy (actor), and an accompanying
value function (critic). The actor performs actions in the environment, while
the critic evaluates the actor's current policy. However, despite their
stability and promising convergence properties, current actor-critic algorithms
do not outperform critic-only ones in practice. We believe that the fact that
the critic learns Q^pi, instead of the optimal Q-function Q*, prevents
state-of-the-art robust and sample-efficient off-policy learning algorithms
from being used. In this paper, we propose an elegant solution, the
Actor-Advisor architecture, in which a Policy Gradient actor learns from
unbiased Monte-Carlo returns, while being shaped (or advised) by the Softmax
policy arising from an off-policy critic. The critic can be learned
independently from the actor, using any state-of-the-art algorithm. Being
advised by a high-quality critic, the actor quickly and robustly learns the
task, while its use of the Monte-Carlo return helps overcome any bias the
critic may have. In addition to a new Actor-Critic formulation, the
Actor-Advisor, a method that allows an external advisory policy to shape a
Policy Gradient actor, can be applied to many other domains. By varying the
source of advice, we demonstrate the wide applicability of the Actor-Advisor to
three other important subfields of RL: safe RL with backup policies, efficient
leverage of domain knowledge, and transfer learning in RL. Our experimental
results demonstrate the benefits of the Actor-Advisor compared to
state-of-the-art actor-critic methods, illustrate its applicability to the
three other application scenarios listed above, and show that many important
challenges of RL can now be solved using a single elegant solution.
",The Actor-Advisor: Policy Gradient With Off-Policy Advice
"  Robotic mobile fulfillment systems (RMFSs) are a new type of warehousing
system, which has received more attention recently, due to increasing growth in
the e-commerce sector. Instead of sending pickers to the inventory area to
search for and pick the ordered items, robots carry shelves (called ""pods"")
including ordered items from the inventory area to picking stations. In the
picking stations, human pickers put ordered items into totes; then these items
are transported by a conveyor to the packing stations. This type of warehousing
system relieves the human pickers and improves the picking process. In this
paper, we concentrate on decisions about the assignment of pods to stations and
orders to stations to fulfill picking for each incoming customer's order. In
previous research for an RMFS with multiple picking stations, these decisions
are made sequentially. Instead, we present a new integrated model. To improve
the system performance even more, we extend our model by splitting orders. This
means parts of an order are allowed to be picked at different stations. To the
best of the authors' knowledge, this is the first publication on split orders
in an RMFS. We analyze different performance metrics, such as pile-on,
pod-station visits, robot moving distance and order turn-over time. We compare
the results of our models in different instances with the sequential method in
our open-source simulation framework RAWSim-O.
",Efficient order picking methods in robotic mobile fulfillment systems
"  Reinforcement learning (RL) problems often feature deceptive local optima,
and learning methods that optimize purely for reward signal often fail to learn
strategies for overcoming them. Deep neuroevolution and novelty search have
been proposed as effective alternatives to gradient-based methods for learning
RL policies directly from pixels. In this paper, we introduce and evaluate the
use of novelty search over agent action sequences by string edit metric
distance as a means for promoting innovation. We also introduce a method for
stagnation detection and population resampling inspired by recent developments
in the RL community that uses the same mechanisms as novelty search to promote
and develop innovative policies. Our methods extend a state-of-the-art method
for deep neuroevolution using a simple-yet-effective genetic algorithm (GA)
designed to efficiently learn deep RL policy network weights. Experiments using
four games from the Atari 2600 benchmark were conducted. Results provide
further evidence that GAs are competitive with gradient-based algorithms for
deep RL. Results also demonstrate that novelty search over action sequences is
an effective source of selection pressure that can be integrated into existing
evolutionary algorithms for deep RL.
","Novelty Search for Deep Reinforcement Learning Policy Network Weights by
  Action Sequence Edit Metric Distance"
"  In this paper, we introduce BINet, a neural network architecture for
real-time multi-perspective anomaly detection in business process event logs.
BINet is designed to handle both the control flow and the data perspective of a
business process. Additionally, we propose a set of heuristics for setting the
threshold of an anomaly detection algorithm automatically. We demonstrate that
BINet can be used to detect anomalies in event logs not only on a case level
but also on event attribute level. Finally, we demonstrate that a simple set of
rules can be used to utilize the output of BINet for anomaly classification. We
compare BINet to eight other state-of-the-art anomaly detection algorithms and
evaluate their performance on an elaborate data corpus of 29 synthetic and 15
real-life event logs. BINet outperforms all other methods both on the synthetic
as well as on the real-life datasets.
",BINet: Multi-perspective Business Process Anomaly Classification
"  Static stochastic VRPs aim at modeling real-life VRPs by considering
uncertainty on data. In particular, the SS-VRPTW-CR considers stochastic
customers with time windows and does not make any assumption on their reveal
times, which are stochastic as well. Based on customer request probabilities,
we look for an a priori solution composed preventive vehicle routes, minimizing
the expected number of unsatisfied customer requests at the end of the day. A
route describes a sequence of strategic vehicle relocations, from which nearby
requests can be rapidly reached. Instead of reoptimizing online, a so-called
recourse strategy defines the way the requests are handled, whenever they
appear. In this paper, we describe a new recourse strategy for the SS-VRPTW-CR,
improving vehicle routes by skipping useless parts. We show how to compute the
expected cost of a priori solutions, in pseudo-polynomial time, for this
recourse strategy. We introduce a new meta-heuristic, called Progressive Focus
Search (PFS), which may be combined with any local-search based algorithm for
solving static stochastic optimization problems. PFS accelerates the search by
using approximation factors: from an initial rough simplified problem, the
search progressively focuses to the actual problem description. We evaluate our
contributions on a new, real-world based, public benchmark.
","Progressive Focus Search for the Static and Stochastic VRPTW with both
  Random Customers and Reveal Times"
"  Of all novel computing methods, quantum computation (QC) is currently the
most likely to move from the realm of the unconventional into the conventional.
As a result some initial work has been done on applications of QC outside of
science: for example music. The small amount of arts research done in hardware
or with actual physical systems has not utilized any of the advantages of
quantum computation (QC): the main advantage being the potential speed increase
of quantum algorithms. This paper introduces a way of utilizing Grover's
algorithm - which has been shown to provide a quadratic speed-up over its
classical equivalent - in algorithmic rule-based music composition. The system
introduced - qgMuse - is simple but scalable. Example melodies are composed
using qgMuse using the ibmqx4 quantum hardware. The paper concludes with
discussion on how such an approach can grow with the improvement of quantum
computer hardware and software.
","Applying Quantum Hardware to non-Scientific Problems: Grover's Algorithm
  and Rule-based Algorithmic Music Composition"
"  We present VERIFAI, a software toolkit for the formal design and analysis of
systems that include artificial intelligence (AI) and machine learning (ML)
components. VERIFAI particularly seeks to address challenges with applying
formal methods to perception and ML components, including those based on neural
networks, and to model and analyze system behavior in the presence of
environment uncertainty. We describe the initial version of VERIFAI which
centers on simulation guided by formal models and specifications. Several use
cases are illustrated with examples, including temporal-logic falsification,
model-based systematic fuzz testing, parameter synthesis, counterexample
analysis, and data set augmentation.
","VERIFAI: A Toolkit for the Design and Analysis of Artificial
  Intelligence-Based Systems"
"  Interactive Fiction (IF) games are complex textual decision making problems.
This paper introduces NAIL, an autonomous agent for general parser-based IF
games. NAIL won the 2018 Text Adventure AI Competition, where it was evaluated
on twenty unseen games. This paper describes the architecture, development, and
insights underpinning NAIL's performance.
",NAIL: A General Interactive Fiction Agent
"  Rational decision making in its linguistic description means making logical
decisions. In essence, a rational agent optimally processes all relevant
information to achieve its goal. Rationality has two elements and these are the
use of relevant information and the efficient processing of such information.
In reality, relevant information is incomplete, imperfect and the processing
engine, which is a brain for humans, is suboptimal. Humans are risk averse
rather than utility maximizers. In the real world, problems are predominantly
non-convex and this makes the idea of rational decision-making fundamentally
unachievable and Herbert Simon called this bounded rationality. There is a
trade-off between the amount of information used for decision-making and the
complexity of the decision model used. This explores whether machine
rationality is subjective and concludes that indeed it is.
",Relative rationality: Is machine rationality subjective?
"  Rolling Horizon Evolutionary Algorithms (RHEA) are a class of online planning
methods for real-time game playing; their performance is closely related to the
planning horizon and the search time allowed. In this paper, we propose to
learn a prior for RHEA in an offline manner by training a value network and a
policy network. The value network is used to reduce the planning horizon by
providing an estimation of future rewards, and the policy network is used to
initialize the population, which helps to narrow down the search scope. The
proposed algorithm, named prior-based RHEA (p-RHEA), trains policy and value
networks by performing planning and learning iteratively. In the planning
stage, the horizon-limited search assisted with the policy network and value
network is performed to improve the policies and collect training samples. In
the learning stage, the policy network and value network are trained with the
collected samples to learn better prior knowledge. Experimental results on
OpenAI Gym MuJoCo tasks show that the performance of the proposed p-RHEA is
significantly improved compared to that of RHEA.
",Learn a Prior for RHEA for Better Online Planning
"  The desire to use reinforcement learning in safety-critical settings has
inspired a recent interest in formal methods for learning algorithms. Existing
formal methods for learning and optimization primarily consider the problem of
constrained learning or constrained optimization. Given a single correct model
and associated safety constraint, these approaches guarantee efficient learning
while provably avoiding behaviors outside the safety constraint. Acting well
given an accurate environmental model is an important pre-requisite for safe
learning, but is ultimately insufficient for systems that operate in complex
heterogeneous environments. This paper introduces verification-preserving model
updates, the first approach toward obtaining formal safety guarantees for
reinforcement learning in settings where multiple environmental models must be
taken into account. Through a combination of design-time model updates and
runtime model falsification, we provide a first approach toward obtaining
formal safety proofs for autonomous systems acting in heterogeneous
environments.
",Verifiably Safe Off-Model Reinforcement Learning
"  We pose an active perception problem where an autonomous agent actively
interacts with a second agent with potentially adversarial behaviors. Given the
uncertainty in the intent of the other agent, the objective is to collect
further evidence to help discriminate potential threats. The main technical
challenges are the partial observability of the agent intent, the adversary
modeling, and the corresponding uncertainty modeling. Note that an adversary
agent may act to mislead the autonomous agent by using a deceptive strategy
that is learned from past experiences. We propose an approach that combines
belief space planning, generative adversary modeling, and maximum entropy
reinforcement learning to obtain a stochastic belief space policy. By
accounting for various adversarial behaviors in the simulation framework and
minimizing the predictability of the autonomous agent's action, the resulting
policy is more robust to unmodeled adversarial strategies. This improved
robustness is empirically shown against an adversary that adapts to and
exploits the autonomous agent's policy when compared with a standard
Chance-Constraint Partially Observable Markov Decision Process robust approach.
","Active Perception in Adversarial Scenarios using Maximum Entropy Deep
  Reinforcement Learning"
"  PRAM puts agent-based models on a sound probabilistic footing as a basis for
integrating agent-based and probabilistic models. It extends the themes of
probabilistic relational models and lifted inference to incorporate dynamical
models and simulation. It can also be much more efficient than agent-based
simulation.
",Probabilistic Relational Agent-based Models
"  Web services are basic functions of a software system to support the concept
of service-oriented architecture. They are often composed together to provide
added values, known as web service composition. Researchers often employ
Evolutionary Computation techniques to efficiently construct composite services
with near-optimized functional quality (i.e., Quality of Semantic Matchmaking)
or non-functional quality (i.e., Quality of Service) or both due to the
complexity of this problem. With a significant increase in service composition
requests, many composition requests have similar input and output requirements
but may vary due to different preferences from different user segments. This
problem is often treated as a multi-objective service composition so as to cope
with different preferences from different user segments simultaneously. Without
taking a multi-objective approach that gives rise to a solution selection
challenge, we perceive multiple similar service composition requests as jointly
forming an evolutionary multi-tasking problem in this work. We propose an
effective permutation-based evolutionary multi-tasking approach that can
simultaneously generate a set of solutions, with one for each service request.
We also introduce a neighborhood structure over multiple tasks to allow newly
evolved solutions to be evaluated on related tasks. Our proposed method can
perform better at the cost of only a fraction of time, compared to one
state-of-art single-tasking EC-based method. We also found that the use of the
proper neighborhood structure can enhance the effectiveness of our approach.
",Evolutionary Multitasking for Semantic Web Service Composition
"  Revenue management can enable airline corporations to maximize the revenue
generated from each scheduled flight departing in their transportation network
by means of finding the optimal policies for differential pricing, seat
inventory control and overbooking. As different demand segments in the market
have different Willingness-To-Pay (WTP), airlines use differential pricing,
booking restrictions, and service amenities to determine different fare classes
or products targeted at each of these demand segments. Because seats are
limited for each flight, airlines also need to allocate seats for each of these
fare classes to prevent lower fare class passengers from displacing higher fare
class ones and set overbooking limits in anticipation of cancellations and
no-shows such that revenue is maximized. Previous work addresses these problems
using optimization techniques or classical Reinforcement Learning methods. This
paper focuses on the latter problem - the seat inventory control problem -
casting it as a Markov Decision Process to be able to find the optimal policy.
Multiple fare classes, concurrent continuous arrival of passengers of different
fare classes, overbooking and random cancellations that are independent of
class have been considered in the model. We have addressed this problem using
Deep Q-Learning with the goal of maximizing the reward for each flight
departure. The implementation of this technique allows us to employ large
continuous state space but also presents the potential opportunity to test on
real time airline data. To generate data and train the agent, a basic
air-travel market simulator was developed. The performance of the agent in
different simulated market scenarios was compared against theoretically optimal
solutions and was found to be nearly close to the expected optimal revenue.
","Autonomous Airline Revenue Management: A Deep Reinforcement Learning
  Approach to Seat Inventory Control and Overbooking"
"  We study the emergence of cooperative behaviors in reinforcement learning
agents by introducing a challenging competitive multi-agent soccer environment
with continuous simulated physics. We demonstrate that decentralized,
population-based training with co-play can lead to a progression in agents'
behaviors: from random, to simple ball chasing, and finally showing evidence of
cooperation. Our study highlights several of the challenges encountered in
large scale multi-agent training in continuous control. In particular, we
demonstrate that the automatic optimization of simple shaping rewards, not
themselves conducive to co-operative behavior, can lead to long-horizon team
behavior. We further apply an evaluation scheme, grounded by game theoretic
principals, that can assess agent performance in the absence of pre-defined
evaluation tasks or human baselines.
",Emergent Coordination Through Competition
"  Automatically reasoning with conflicting generic clinical guidelines is a
burning issue in patient-centric medical reasoning where patient-specific
conditions and goals need to be taken into account. It is even more challenging
in the presence of preferences such as patient's wishes and clinician's
priorities over goals. We advance a structured argumentation formalism for
reasoning with conflicting clinical guidelines, patient-specific information
and preferences. Our formalism integrates assumption-based reasoning and
goal-driven selection among reasoning outcomes. Specifically, we assume
applicability of guideline recommendations concerning the generic goal of
patient well-being, resolve conflicts among recommendations using patient's
conditions and preferences, and then consider prioritised patient-centered
goals to yield non-conflicting, goal-maximising and preference-respecting
recommendations. We rely on the state-of-the-art Transition-based Medical
Recommendation model for representing guideline recommendations and augment it
with context given by the patient's conditions, goals, as well as preferences
over recommendations and goals. We establish desirable properties of our
approach in terms of sensitivity to recommendation conflicts and patient
context.
",Resolving Conflicts in Clinical Guidelines using Argumentation
"  Project scheduling in manufacturing environments often requires flexibility
in terms of the selection and the exact length of alternative production
activities. Moreover, the simultaneous scheduling of multiple lots is mandatory
in many production planning applications. To meet these requirements, a new
resource-constrained project scheduling problem (RCPSP) is introduced where
both decisions (activity flexibility and time flexibility) are integrated.
Besides the minimization of makespan, two new alternative objectives are
presented: maximization of balanced length of selected activities (time
balance) and maximization of balanced resource utilization (resource balance).
New mixed integer and constraint programming (CP) models are proposed for the
developed integrated flexible project scheduling problem. Benchmark instances
on an already existing flexible RCPSP and the newly developed problem are
solved to optimality. The real-world applicability of the suggested CP models
is shown by additionally solving a large industry case.
","Resource-constrained multi-project scheduling with activity and time
  flexibility"
"  The human mind is still an unknown process of neuroscience in many aspects.
Nevertheless, for decades the scientific community has proposed computational
models that try to simulate their parts, specific applications, or their
behavior in different situations. The most complete model in this line is
undoubtedly the LIDA model, proposed by Stan Franklin with the aim of serving
as a generic computational architecture for several applications. The present
project is inspired by the LIDA model to apply it to the process of movie
recommendation, the model called MIRA (Movie Intelligent Recommender Agent)
presented percentages of precision similar to a traditional model when
submitted to the same assay conditions. Moreover, the proposed model reinforced
the precision indexes when submitted to tests with volunteers, proving once
again its performance as a cognitive model, when executed with small data
volumes. Considering that the proposed model achieved a similar behavior to the
traditional models under conditions expected to be similar for natural systems,
it can be said that MIRA reinforces the applicability of LIDA as a path to be
followed for the study and generation of computational agents inspired by
neural behaviors.
","MIRA: A Computational Neuro-Based Cognitive Architecture Applied to
  Movie Recommender Systems"
"  We study strategic similarity of game positions in two-player extensive games
of perfect information, by looking at the structure of their local game trees,
with the aim of improving the performance of game playing agents in detecting
forcing continuations. We present a range of measures over the induced game
trees and compare them against benchmark problems in chess, observing a
promising level of accuracy in matching up trap states.
",Similarity Measures based on Local Game Trees
"  The behavior of self-driving cars must be compatible with an enormous set of
conflicting and ambiguous objectives, from law, from ethics, from the local
culture, and so on. This paper describes a new way to conveniently define the
desired behavior for autonomous agents, which we use on the self-driving cars
developed at nuTonomy. We define a ""rulebook"" as a pre-ordered set of ""rules"",
each akin to a violation metric on the possible outcomes (""realizations""). The
rules are partially ordered by priority. The semantics of a rulebook imposes a
pre-order on the set of realizations. We study the compositional properties of
the rulebooks, and we derive which operations we can allow on the rulebooks to
preserve previously-introduced constraints. While we demonstrate the
application of these techniques in the self-driving domain, the methods are
domain-independent.
","Liability, Ethics, and Culture-Aware Behavior Specification using
  Rulebooks"
"  Traditional models of rational action treat the agent as though it is cleanly
separated from its environment, and can act on that environment from the
outside. Such agents have a known functional relationship with their
environment, can model their environment in every detail, and do not need to
reason about themselves or their internal parts.
  We provide an informal survey of obstacles to formalizing good reasoning for
agents embedded in their environment. Such agents must optimize an environment
that is not of type ""function""; they must rely on models that fit within the
modeled environment; and they must reason about themselves as just another
physical system, made of parts that can be modified and that can work at cross
purposes.
",Embedded Agency
"  It is crucial to generate crafted SAT formulas with predefined solutions for
the testing and development of SAT solvers since many SAT formulas from
real-world applications have solutions. Although some generating algorithms
have been proposed to generate SAT formulas with predefined solutions,
community structures of SAT formulas are not considered. We propose a 3-SAT
formula generating algorithm that not only guarantees the existence of a
predefined solution, but also simultaneously considers community structures and
clause distributions. The proposed 3-SAT formula generating algorithm controls
the quality of community structures through controlling (1) the number of
clauses whose variables have a common community, which we call intra-community
clauses, and (2) the number of variables that only belong to one community,
which we call intra-community variables. To study the combined effect of
community structures and clause distributions on the hardness of SAT formulas,
we measure solving runtimes of two solvers, gluHack (a leading CDCL solver) and
CPSparrow (a leading SLS solver), on the generated SAT formulas under different
groups of parameter settings. Through extensive experiments, we obtain some
noteworthy observations on the SAT formulas generated by the proposed
algorithm: (1) The community structure has little or no effects on the hardness
of SAT formulas with regard to CPSparrow but a strong effect with regard to
gluHack. (2) Only when the proportion of true literals in a SAT formula in
terms of the predefined solution is 0.5, SAT formulas are hard-to-solve with
regard to gluHack; when this proportion is below 0.5, SAT formulas are
hard-to-solve with regard to CPSparrow. (3) When the ratio of the number of
clauses to that of variables is around 4.25, the SAT formulas are hard-to-solve
with regard to both gluHack and CPSparrow.
",Community-based 3-SAT Formulas with a Predefined Solution
"  Reward functions are easy to misspecify; although designers can make
corrections after observing mistakes, an agent pursuing a misspecified reward
function can irreversibly change the state of its environment. If that change
precludes optimization of the correctly specified reward function, then
correction is futile. For example, a robotic factory assistant could break
expensive equipment due to a reward misspecification; even if the designers
immediately correct the reward function, the damage is done. To mitigate this
risk, we introduce an approach that balances optimization of the primary reward
function with preservation of the ability to optimize auxiliary reward
functions. Surprisingly, even when the auxiliary reward functions are randomly
generated and therefore uninformative about the correctly specified reward
function, this approach induces conservative, effective behavior.
",Conservative Agency via Attainable Utility Preservation
"  An embedding is a function that maps entities from one algebraic structure
into another while preserving certain characteristics. Embeddings are being
used successfully for mapping relational data or text into vector spaces where
they can be used for machine learning, similarity search, or similar tasks. We
address the problem of finding vector space embeddings for theories in the
Description Logic $\mathcal{EL}^{++}$ that are also models of the TBox. To find
such embeddings, we define an optimization problem that characterizes the
model-theoretic semantics of the operators in $\mathcal{EL}^{++}$ within
$\Re^n$, thereby solving the problem of finding an interpretation function for
an $\mathcal{EL}^{++}$ theory given a particular domain $\Delta$. Our approach
is mainly relevant to large $\mathcal{EL}^{++}$ theories and knowledge bases
such as the ontologies and knowledge graphs used in the life sciences. We
demonstrate that our method can be used for improved prediction of
protein--protein interactions when compared to semantic similarity measures or
knowledge graph embedding
","EL Embeddings: Geometric construction of models for the Description
  Logic EL ++"
"  In abstract argumentation, multiple argumentation semantics have been
proposed that allow to select sets of jointly acceptable arguments from a given
argumentation framework, i.e. based only on the attack relation between
arguments. The existence of multiple argumentation semantics raises the
question which of these semantics predicts best how humans evaluate arguments.
Previous empirical cognitive studies that have tested how humans evaluate sets
of arguments depending on the attack relation between them have been limited to
a small set of very simple argumentation frameworks, so that some semantics
studied in the literature could not be meaningfully distinguished by these
studies. In this paper we report on an empirical cognitive study that overcomes
these limitations by taking into consideration twelve argumentation frameworks
of three to eight arguments each. These argumentation frameworks were mostly
more complex than the argumentation frameworks considered in previous studies.
All twelve argumentation framework were systematically instantiated with
natural language arguments based on a certain fictional scenario, and
participants were shown both the natural language arguments and a graphical
depiction of the attack relation between them. Our data shows that grounded and
CF2 semantics were the best predictors of human argument evaluation. A detailed
analysis revealed that part of the participants chose a cognitively simpler
strategy that is predicted very well by grounded semantics, while another part
of the participants chose a cognitively more demanding strategy that is mostly
predicted well by CF2 semantics.
","Technical report of ""Empirical Study on Human Evaluation of Complex
  Argumentation Frameworks"""
"  Methods for learning and planning in sequential decision problems often
assume the learner is aware of all possible states and actions in advance. This
assumption is sometimes untenable. In this paper, we give a method to learn
factored markov decision problems from both domain exploration and expert
assistance, which guarantees convergence to near-optimal behaviour, even when
the agent begins unaware of factors critical to success. Our experiments show
our agent learns optimal behaviour on small and large problems, and that
conserving information on discovering new possibilities results in faster
convergence.
",Learning Factored Markov Decision Processes with Unawareness
"  Ensemble methods have been widely applied in Reinforcement Learning (RL) in
order to enhance stability, increase convergence speed, and improve
exploration. These methods typically work by employing an aggregation mechanism
over actions of different RL algorithms. We show that a variety of these
methods can be unified by drawing parallels from committee voting rules in
Social Choice Theory. We map the problem of designing an action aggregation
mechanism in an ensemble method to a voting problem which, under different
voting rules, yield popular ensemble-based RL algorithms like Majority Voting
Q-learning or Bootstrapped Q-learning. Our unification framework, in turn,
allows us to design new ensemble-RL algorithms with better performance. For
instance, we map two diversity-centered committee voting rules, namely Single
Non-Transferable Voting Rule and Chamberlin-Courant Rule, into new RL
algorithms that demonstrate excellent exploratory behavior in our experiments.
",Unifying Ensemble Methods for Q-learning via Social Choice Theory
"  Experience-based planning domains (EBPDs) have been recently proposed to
improve problem solving by learning from experience. EBPDs provide important
concepts for long-term learning and planning in robotics. They rely on
acquiring and using task knowledge, i.e., activity schemata, for generating
concrete solutions to problem instances in a class of tasks. Using Three-Valued
Logic Analysis (TVLA), we extend previous work to generate a set of conditions
as the scope of applicability for an activity schema. The inferred scope is a
bounded representation of a set of problems of potentially unbounded size, in
the form of a 3-valued logical structure, which allows an EBPD system to
automatically find an applicable activity schema for solving task problems. We
demonstrate the utility of our approach in a set of classes of problems in a
simulated domain and a class of real world tasks in a fully physically
simulated PR2 robot in Gazebo.
","Learning Task Knowledge and its Scope of Applicability in
  Experience-Based Planning Domains"
"  Autonomous agents need to make decisions in a sequential manner, under
partially observable environment, and in consideration of how other agents
behave. In critical situations, such decisions need to be made in real time for
example to avoid collisions and recover to safe conditions. We propose a
technique of tree search where a deterministic and pessimistic scenario is used
after a specified depth. Because there is no branching with the deterministic
scenario, the proposed technique allows us to take into account the events that
can occur far ahead in the future. The effectiveness of the proposed technique
is demonstrated in Pommerman, a multi-agent environment used in a NeurIPS 2018
competition, where the agents that implement the proposed technique have won
the first and third places.
",Real-time tree search with pessimistic scenarios
"  Choice functions constitute a simple, direct and very general mathematical
framework for modelling choice under uncertainty. In particular, they are able
to represent the set-valued choices that appear in imprecise-probabilistic
decision making. We provide these choice functions with a clear interpretation
in terms of desirability, use this interpretation to derive a set of basic
coherence axioms, and show that this notion of coherence leads to a
representation in terms of sets of strict preference orders. By imposing
additional properties such as totality, the mixing property and Archimedeanity,
we obtain representation in terms of sets of strict total orders, lexicographic
probability systems, coherent lower previsions or linear previsions.
","Interpreting, axiomatising and representing coherent choice functions in
  terms of desirability"
"  One of the main challenges in reinforcement learning is solving tasks with
sparse reward. We show that the difficulty of discovering a distant rewarding
state in an MDP is bounded by the expected cover time of a random walk over the
graph induced by the MDP's transition dynamics. We therefore propose to
accelerate exploration by constructing options that minimize cover time. The
proposed algorithm finds an option which provably diminishes the expected
number of steps to visit every state in the state space by a uniform random
walk. We show empirically that the proposed algorithm improves the learning
time in several domains with sparse rewards.
",Discovering Options for Exploration by Minimizing Cover Time
"  The game of bridge consists of two stages: bidding and playing. While playing
is proved to be relatively easy for computer programs, bidding is very
challenging. During the bidding stage, each player knowing only his/her own
cards needs to exchange information with his/her partner and interfere with
opponents at the same time. Existing methods for solving perfect-information
games cannot be directly applied to bidding. Most bridge programs are based on
human-designed rules, which, however, cannot cover all situations and are
usually ambiguous and even conflicting with each other. In this paper, we, for
the first time, propose a competitive bidding system based on deep learning
techniques, which exhibits two novelties. First, we design a compact
representation to encode the private and public information available to a
player for bidding. Second, based on the analysis of the impact of other
players' unknown cards on one's final rewards, we design two neural networks to
deal with imperfect information, the first one inferring the cards of the
partner and the second one taking the outputs of the first one as part of its
input to select a bid. Experimental results show that our bidding system
outperforms the top rule-based program.
",Competitive Bridge Bidding with Deep Neural Networks
"  This paper presents a novel approach for learning STRIPS action models from
examples that compiles this inductive learning task into a classical planning
task. Interestingly, the compilation approach is flexible to different amounts
of available input knowledge; the learning examples can range from a set of
plans (with their corresponding initial and final states) to just a pair of
initial and final states (no intermediate action or state is given). Moreover,
the compilation accepts partially specified action models and it can be used to
validate whether the observation of a plan execution follows a given STRIPS
action model, even if this model is not fully specified.
",Learning STRIPS Action Models with Classical Planning
"  The instrumentation of real systems is often designed for control purposes
and control inputs are designed to achieve nominal control objectives. Hence,
the available measurements may not be sufficient to isolate faults with
certainty and diagnoses are ambiguous. Active diagnosis formulates a planning
problem to generate a sequence of actions that, applied to the system, enforce
diagnosability and allow to iteratively refine ambiguous diagnoses. This paper
analyses the requirements for applying active diagnosis to space systems and
proposes ActHyDiag as an effective framework to solve this problem. It presents
the results of applying ActHyDiag to a real space case study and of
implementing the generated plans in the form of On-Board Control Procedures.
The case study is a redundant Spacewire Network where up to 6 instruments,
monitored and controlled by the on-board software hosted in the Satellite
Management Unit, are transferring science data to a mass memory unit through
Spacewire routers. Experiments have been conducted on a real physical benchmark
developed by Thales Alenia Space and demonstrate the effectiveness of the plans
proposed by ActHyDiag.
","Applying Active Diagnosis to Space Systems by On-Board Control
  Procedures"
"  Argumentation theory is a powerful paradigm that formalizes a type of
commonsense reasoning that aims to simulate the human ability to resolve a
specific problem in an intelligent manner. A classical argumentation process
takes into account only the properties related to the intrinsic logical
soundness of an argument in order to determine its acceptability status.
However, these properties are not always the only ones that matter to establish
the argument's acceptability---there exist other qualities, such as strength,
weight, social votes, trust degree, relevance level, and certainty degree,
among others.
","An Approach to Characterize Graded Entailment of Arguments through a
  Label-based Framework"
"  A Timed Argumentation Framework (TAF) is a formalism where arguments are only
valid for consideration in a given period of time, called availability
intervals, which are defined for every individual argument. The original
proposal is based on a single, abstract notion of attack between arguments that
remains static and permanent in time. Thus, in general, when identifying the
set of acceptable arguments, the outcome associated with a TAF will vary over
time. In this work we introduce an extension of TAF adding the capability of
modeling a support relation between arguments. In this sense, the resulting
framework provides a suitable model for different time-dependent issues. Thus,
the main contribution here is to provide an enhanced framework for modeling a
positive (support) and negative (attack) interaction varying over time, which
are relevant in many real-world situations. This leads to a Timed Bipolar
Argumentation Framework (T-BAF), where classical argument extensions can be
defined. The proposal aims at advancing in the integration of temporal
argumentation in different application domain.
",Bipolar in Temporal Argumentation Framework
"  In this paper, we introduce a formalism for single-agent decision making that
is based on Dynamic Argumentation Frameworks. The formalism can be used to
justify a choice, which is based on the current situation the agent is
involved. Taking advantage of the inference mechanism of the argumentation
formalism, it is possible to consider preference relations and conflicts among
the available alternatives for that reasoning. With this formalization, given a
particular set of evidence, the justified conclusions supported by warranted
arguments will be used by the agent's decision rules to determine which
alternatives will be selected. We also present an algorithm that implements a
choice function based on our formalization. Finally, we complete our
presentation by introducing formal results that relate the proposed framework
with approaches of classical decision theory.
",An approach to Decision Making based on Dynamic Argumentation Systems
"  In this work, we enrich a formalism for argumentation by including a formal
characterization of features related to the knowledge, in order to capture
proper reasoning in legal domains. We add meta-data information to the
arguments in the form of labels representing quantitative and qualitative data
about them. These labels are propagated through an argumentative graph
according to the relations of support, conflict, and aggregation between
arguments.
",Dealing with Qualitative and Quantitative Features in Legal Domains
"  State-of-the-art meta reinforcement learning algorithms typically assume the
setting of a single agent interacting with its environment in a sequential
manner. A negative side-effect of this sequential execution paradigm is that,
as the environment becomes more and more challenging, and thus requiring more
interaction episodes for the meta-learner, it needs the agent to reason over
longer and longer time-scales. To combat the difficulty of long time-scale
credit assignment, we propose an alternative parallel framework, which we name
""Concurrent Meta-Reinforcement Learning"" (CMRL), that transforms the temporal
credit assignment problem into a multi-agent reinforcement learning one. In
this multi-agent setting, a set of parallel agents are executed in the same
environment and each of these ""rollout"" agents are given the means to
communicate with each other. The goal of the communication is to coordinate, in
a collaborative manner, the most efficient exploration of the shared task the
agents are currently assigned. This coordination therefore represents the
meta-learning aspect of the framework, as each agent can be assigned or assign
itself a particular section of the current task's state space. This framework
is in contrast to standard RL methods that assume that each parallel rollout
occurs independently, which can potentially waste computation if many of the
rollouts end up sampling the same part of the state space. Furthermore, the
parallel setting enables us to define several reward sharing functions and
auxiliary losses that are non-trivial to apply in the sequential setting. We
demonstrate the effectiveness of our proposed CMRL at improving over sequential
methods in a variety of challenging tasks.
",Concurrent Meta Reinforcement Learning
"  In this paper, we study a courier dispatching problem (CDP) raised from an
online pickup-service platform of Alibaba. The CDP aims to assign a set of
couriers to serve pickup requests with stochastic spatial and temporal arrival
rate among urban regions. The objective is to maximize the revenue of served
requests given a limited number of couriers over a period of time. Many online
algorithms such as dynamic matching and vehicle routing strategy from existing
literature could be applied to tackle this problem. However, these methods rely
on appropriately predefined optimization objectives at each decision point,
which is hard in dynamic situations. This paper formulates the CDP as a Markov
decision process (MDP) and proposes a data-driven approach to derive the
optimal dispatching rule-set under different scenarios. Our method stacks
multi-layer images of the spatial-and-temporal map and apply multi-agent
reinforcement learning (MARL) techniques to evolve dispatching models. This
method solves the learning inefficiency caused by traditional centralized MDP
modeling. Through comprehensive experiments on both artificial dataset and
real-world dataset, we show: 1) By utilizing historical data and considering
long-term revenue gains, MARL achieves better performance than myopic online
algorithms; 2) MARL is able to construct the mapping between complex scenarios
to sophisticated decisions such as the dispatching rule. 3) MARL has the
scalability to adopt in large-scale real-world scenarios.
","Can Sophisticated Dispatching Strategy Acquired by Reinforcement
  Learning? - A Case Study in Dynamic Courier Dispatching System"
"  Maritime monitoring systems support safe shipping as they allow for the
real-time detection of dangerous, suspicious and illegal vessel activities. We
present such a system using the Run-Time Event Calculus, a composite event
recognition system with formal, declarative semantics. For effective
recognition, we developed a library of maritime patterns in close collaboration
with domain experts. We present a thorough evaluation of the system and the
patterns both in terms of predictive accuracy and computational efficiency,
using real-world datasets of vessel position streams and contextual
geographical information.
",Composite Event Recognition for Maritime Monitoring
"  We study lifted weight learning of Markov logic networks. We show that there
is an algorithm for maximum-likelihood learning of 2-variable Markov logic
networks which runs in time polynomial in the domain size. Our results are
based on existing lifted-inference algorithms and recent algorithmic results on
computing maximum entropy distributions.
",Lifted Weight Learning of Markov Logic Networks Revisited
"  Three-way decision theory, which trisects the universe with less risks or
costs, is considered as a powerful mathematical tool for handling uncertainty
in incomplete and imprecise information tables, and provides an effective tool
for conflict analysis decision making in real-time situations. In this paper,
we propose the concepts of the agreement, disagreement and neutral subsets of a
strategy with two evaluation functions, which establish the three-way
decisions-based conflict analysis models(TWDCAMs) for trisecting the universe
of agents, and employ a pair of two-way decisions models to interpret the
mechanism of the three-way decision rules for an agent. Subsequently, we
develop the concepts of the agreement, disagreement and neutral strategies of
an agent group with two evaluation functions, which build the TWDCAMs for
trisecting the universe of issues, and take a couple of two-way decisions
models to explain the mechanism of the three-way decision rules for an issue.
Finally, we reconstruct Fan, Qi and Wei's conflict analysis models(FQWCAMs) and
Sun, Ma and Zhao's conflict analysis models(SMZCAMs) with two evaluation
functions, and interpret FQWCAMs and SMZCAMs with a pair of two-day decisions
models, which illustrates that FQWCAMs and SMZCAMs are special cases of
TWDCAMs.
",Three-Way Decisions-Based Conflict Analysis Models
"  Mahjong is a very popular tile-based game commonly played by four players.
Each player begins with a hand of 13 tiles and, in turn, players draw and
discard (i.e., change) tiles until they complete a legal hand using a 14th
tile. In this paper, we initiate a mathematical and AI study of the Mahjong
game and try to answer two fundamental questions: how bad is a hand of 14
tiles? and which tile should I discard? We define and characterise the notion
of deficiency and present an optimal policy to discard a tile in order to
increase the chance of completing a legal hand within $k$ tile changes for each
$k\geq 1$.
",Let's Play Mahjong!
"  I use simulation of two multilayer neural networks to gain intuition into the
determinants of human learning. The first network, the teacher, is trained to
achieve a high accuracy in handwritten digit recognition. The second network,
the student, learns to reproduce the output of the first network. I show that
learning from the teacher is more effective than learning from the data under
the appropriate degree of regularization. Regularization allows the teacher to
distinguish the trends and to deliver ""big ideas"" to the student. I also model
other learning situations such as expert and novice teachers, high- and
low-ability students and biased learning experience due to, e.g., poverty and
trauma. The results from computer simulation accord remarkably well with
finding of the modern psychological literature. The code is written in MATLAB
and will be publicly available from the author's web page.
","How Machine (Deep) Learning Helps Us Understand Human Learning: the
  Value of Big Ideas"
"  The interaction between syntax (formal language) and its semantics (meanings
of language) is one which has been well studied in categorical logic. The
results of this particular study are employed to understand how the brain is
able to create meanings. To emphasize the toy character of the proposed model,
we prefer to speak of the homunculus brain rather than the brain per se. The
homunculus brain consists of neurons, each of which is modeled by a category,
and axons between neurons, which are modeled by functors between the
corresponding neuron-categories. Each neuron (category) has its own program
enabling its working, i.e. a theory of this neuron. In analogy to what is known
from categorical logic, we postulate the existence of a pair of adjoint
functors, called Lang and Syn, from a category, now called BRAIN, of
categories, to a category, now called MIND, of theories. Our homunculus is a
kind of ``mathematical robot'', the neuronal architecture of which is not
important. Its only aim is to provide us with the opportunity to study how such
a simple brain-like structure could ``create meanings'' and perform abstraction
operations out of its purely syntactic program. The pair of adjoint functors
Lang and Syn model the mutual dependencies between the syntactical structure of
a given theory of MIND and the internal logic of its semantics given by a
category of BRAIN. In this way, a formal language (syntax) and its meanings
(semantics) are interwoven with each other in a manner corresponding to the
adjointness of the functors Lang and Syn. Higher cognitive functions of
abstraction and realization of concepts are also modelled by a corresponding
pair of adjoint functors. The categories BRAIN and MIND interact with each
other with their entire structures and, at the same time, these very structures
are shaped by this interaction.
",The Homunculus Brain and Categorical Logic
"  We present a visual symptom checker that combines a pre-trained Convolutional
Neural Network (CNN) with a Reinforcement Learning (RL) agent as a Question
Answering (QA) model. This method increases the classification confidence and
accuracy of the visual symptom checker, and decreases the average number of
questions asked to narrow down the differential diagnosis. A Deep Q-Network
(DQN)-based RL agent learns how to ask the patient about the presence of
symptoms in order to maximize the probability of correctly identifying the
underlying condition. The RL agent uses the visual information provided by CNN
in addition to the answers to the asked questions to guide the QA system. We
demonstrate that the RL-based approach increases the accuracy more than 20%
compared to the CNN-only approach, which only uses the visual information to
predict the condition. Moreover, the increased accuracy is up to 10% compared
to the approach that uses the visual information provided by CNN along with a
conventional decision tree-based QA system. We finally show that the RL-based
approach not only outperforms the decision tree-based approach, but also
narrows down the diagnosis faster in terms of the average number of asked
questions.
","Improving Skin Condition Classification with a Visual Symptom Checker
  Trained using Reinforcement Learning"
"  This paper introduces, philosophically and to a degree formally, the novel
concept of learning $\textit{ex nihilo}$, intended (obviously) to be analogous
to the concept of creation $\textit{ex nihilo}$. Learning $\textit{ex nihilo}$
is an agent's learning ""from nothing,"" by the suitable employment of schemata
for deductive and inductive reasoning. This reasoning must be in
machine-verifiable accord with a formal proof/argument theory in a
$\textit{cognitive calculus}$ (i.e., roughly, an intensional higher-order
multi-operator quantified logic), and this reasoning is applied to percepts
received by the agent, in the context of both some prior knowledge, and some
prior and current interests. Learning $\textit{ex nihilo}$ is a challenge to
contemporary forms of ML, indeed a severe one, but the challenge is offered in
the spirt of seeking to stimulate attempts, on the part of non-logicist ML
researchers and engineers, to collaborate with those in possession of
learning-$\textit{ex nihilo}$ frameworks, and eventually attempts to integrate
directly with such frameworks at the implementation level. Such integration
will require, among other things, the symbiotic interoperation of
state-of-the-art automated reasoners and high-expressivity planners, with
statistical/connectionist ML technology.
",Learning $\textit{Ex Nihilo}$
"  When creating benchmarks for SAT solvers, we need SAT instances that are easy
to build but hard to solve. A recent development in the search for such methods
has led to the Balanced SAT algorithm, which can create k-SAT instances with m
clauses of high difficulty, for arbitrary k and m. In this paper we introduce
the No-Triangle SAT algorithm, a SAT instance generator based on the cluster
coefficient graph statistic. We empirically compare the two algorithms by
fixing the arity and the number of variables, but varying the number of
clauses. The hardest instances that we find are produced by No-Triangle SAT.
Furthermore, difficult instances from No-Triangle SAT have a different number
of clauses than difficult instances from Balanced SAT, potentially allowing a
combination of the two methods to find hard SAT instances for a larger array of
parameters.
",Generating Difficult SAT Instances by Preventing Triangles
"  The online programing services, such as Github,TopCoder, and EduCoder, have
promoted a lot of social interactions among the service users. However, the
existing social interactions is rather limited and inefficient due to the rapid
increasing of source-code repositories, which is difficult to explore manually.
The emergence of source-code mining provides a promising way to analyze those
source codes, so that those source codes can be relatively easy to understand
and share among those service users. Among all the source-code mining
attempts,program classification lays a foundation for various tasks related to
source-code understanding, because it is impossible for a machine to understand
a computer program if it cannot classify the program correctly. Although
numerous machine learning models, such as the Natural Language Processing (NLP)
based models and the Abstract Syntax Tree (AST) based models, have been
proposed to classify computer programs based on their corresponding source
codes, the existing works cannot fully characterize the source codes from the
perspective of both the syntax and semantic information. To address this
problem, we proposed a Graph Neural Network (GNN) based model, which integrates
data flow and function call information to the AST,and applies an improved GNN
model to the integrated graph, so as to achieve the state-of-art program
classification accuracy. The experiment results have shown that the proposed
work can classify programs with accuracy over 97%.
","Program Classification Using Gated Graph Attention Neural Network for
  Online Programming Service"
"  It is incredibly easy for a system designer to misspecify the objective for
an autonomous system (""robot''), thus motivating the desire to have the robot
learn the objective from human behavior instead. Recent work has suggested that
people have an interest in the robot performing well, and will thus behave
pedagogically, choosing actions that are informative to the robot. In turn,
robots benefit from interpreting the behavior by accounting for this pedagogy.
In this work, we focus on misspecification: we argue that robots might not know
whether people are being pedagogic or literal and that it is important to ask
which assumption is safer to make. We cast objective learning into the more
general form of a common-payoff game between the robot and human, and prove
that in any such game literal interpretation is more robust to
misspecification. Experiments with human data support our theoretical results
and point to the sensitivity of the pedagogic assumption.
","Literal or Pedagogic Human? Analyzing Human Model Misspecification in
  Objective Learning"
"  Process-Mining techniques aim to use event data about past executions to gain
insight into how processes are executed. While these techniques are proven to
be very valuable, they are less successful to reach their goal if the process
is flexible and, hence, events can potentially occur in any order. Furthermore,
information systems can record events at very low level, which do not match the
high-level concepts known at business level. Without abstracting sequences of
events to high-level concepts, the results of applying process mining (e.g.,
discovered models) easily become very complex and difficult to interpret, which
ultimately means that they are of little use. A large body of research exists
on event abstraction but typically a large amount of domain knowledge is
required to be fed in, which is often not readily available. Other abstraction
techniques are unsupervised, which give lower accuracy. This paper puts forward
a technique that requires limited domain knowledge that can be easily provided.
Traces are divided in sessions, and each session is abstracted as one single
high-level activity execution. The abstraction is based on a combination of
automatic clustering and visualization methods. The technique was assessed on
two case studies that evidently exhibits a large amount of behavior. The
results clearly illustrate the benefits of the abstraction to convey knowledge
to stakeholders.
","From Low-Level Events to Activities -- A Session-Based Approach
  (Extended Version)"
"  Electric Vehicle (EV) sharing systems have recently experienced unprecedented
growth across the globe. Many car sharing service providers as well as
automobile manufacturers are entering this competition by expanding both their
EV fleets and renting/returning station networks, aiming to seize a share of
the market and bring car sharing to the zero emissions level. During their fast
expansion, one fundamental determinant for success is the capability of
dynamically predicting the demand of stations. In this paper we propose a novel
demand prediction approach, which is able to model the dynamics of the system
and predict demand accordingly. We use a local temporal encoding process to
handle the available historical data at individual stations, and a spatial
encoding process to take correlations between stations into account with graph
convolutional neural networks. The encoded features are fed to a prediction
network, which forecasts both the long-term expected demand of the stations. We
evaluate the proposed approach on real-world data collected from a major EV
sharing platform. Experimental results demonstrate that our approach
significantly outperforms the state of the art.
",Demand Prediction for Electric Vehicle Sharing
"  A key goal in the design of probabilistic inference algorithms is identifying
and exploiting properties of the distribution that make inference tractable.
Lifted inference algorithms identify symmetry as a property that enables
efficient inference and seek to scale with the degree of symmetry of a
probability model. A limitation of existing exact lifted inference techniques
is that they do not apply to non-relational representations like factor graphs.
In this work we provide the first example of an exact lifted inference
algorithm for arbitrary discrete factor graphs. In addition we describe a
lifted Markov-Chain Monte-Carlo algorithm that provably mixes rapidly in the
degree of symmetry of the distribution.
",Generating and Sampling Orbits for Lifted Probabilistic Inference
"  The Set-union Knapsack Problem (SUKP) is a generalization of the popular 0-1
knapsack problem. Given a set of weighted elements and a set of items with
profits where each item is composed of a subset of elements, the SUKP involves
packing a subset of items in a capacity-constrained knapsack such that the
total profit of the selected items is maximized while their weights do not
exceed the knapsack capacity. In this work, we present an effective iterated
two-phase local search algorithm for this NP-hard combinatorial optimization
problem. The proposed algorithm iterates through two search phases: a local
optima exploration phase that alternates between a variable neighborhood
descent search and a tabu search to explore local optimal solutions, and a
local optima escaping phase to drive the search to unexplored regions. We show
the competitiveness of the algorithm compared to the state-of-the-art methods
in the literature. Specifically, the algorithm discovers 18 improved best
results (new lower bounds) for the 30 benchmark instances and matches the
best-known results for the 12 remaining instances. We also report the first
computational results with the general CPLEX solver, including 6 proven optimal
solutions. Finally, we investigate the effectiveness of the key ingredients of
the algorithm on its performance.
",Iterated two-phase local search for the Set-Union Knapsack Problem
"  This paper presents an explainable AI (XAI) system that provides explanations
for its predictions. The system consists of two key components -- namely, the
prediction And-Or graph (AOG) model for recognizing and localizing concepts of
interest in input data, and the XAI model for providing explanations to the
user about the AOG's predictions. In this work, we focus on the XAI model
specified to interact with the user in natural language, whereas the AOG's
predictions are considered given and represented by the corresponding parse
graphs (pg's) of the AOG. Our XAI model takes pg's as input and provides
answers to the user's questions using the following types of reasoning: direct
evidence (e.g., detection scores), part-based inference (e.g., detected parts
provide evidence for the concept asked), and other evidences from
spatio-temporal context (e.g., constraints from the spatio-temporal surround).
We identify several correlations between user's questions and the XAI answers
using Youtube Action dataset.
",Natural Language Interaction with Explainable AI Models
"  We propose a framework for learning discrete deterministic planning domains.
In this framework, an agent learns the domain by observing the action effects
through continuous features that describe the state of the environment after
the execution of each action. Besides, the agent learns its perception
function, i.e., a probabilistic mapping between state variables and sensor data
represented as a vector of continuous random variables called perception
variables. We define an algorithm that updates the planning domain and the
perception function by (i) introducing new states, either by extending the
possible values of state variables, or by weakening their constraints; (ii)
adapts the perception function to fit the observed data (iii) adapts the
transition function on the basis of the executed actions and the effects
observed via the perception function. The framework is able to deal with
exogenous events that happen in the environment.
","Incremental Learning of Discrete Planning Domains from Continuous
  Perceptions"
"  Experience-based planning domains have been proposed to improve problem
solving by learning from experience. They rely on acquiring and using task
knowledge, i.e., activity schemata, for generating solutions to problem
instances in a class of tasks. Using Three-Valued Logic Analysis (TVLA), we
extend previous work to generate a set of conditions that determine the scope
of applicability of an activity schema. The inferred scope is a bounded
representation of a set of problems of potentially unbounded size, in the form
of a 3-valued logical structure, which is used to automatically find an
applicable activity schema for solving task problems. We validate this work in
two classical planning domains.
","Computing the Scope of Applicability for Acquired Task Knowledge in
  Experience-Based Planning Domains"
"  As AI becomes an integral part of our lives, the development of explainable
AI, embodied in the decision-making process of an AI or robotic agent, becomes
imperative. For a robotic teammate, the ability to generate explanations to
justify its behavior is one of the key requirements of explainable agency.
Prior work on explanation generation has been focused on supporting the
rationale behind the robot's decision or behavior. These approaches, however,
fail to consider the mental demand for understanding the received explanation.
In other words, the human teammate is expected to understand an explanation no
matter how much information is presented. In this work, we argue that
explanations, especially those of a complex nature, should be made in an online
fashion during the execution, which helps spread out the information to be
explained and thus reduce the mental workload of humans in highly cognitive
demanding tasks. However, a challenge here is that the different parts of an
explanation may be dependent on each other, which must be taken into account
when generating online explanations. To this end, a general formulation of
online explanation generation is presented with three variations satisfying
different ""online"" properties. The new explanation generation methods are based
on a model reconciliation setting introduced in our prior work. We evaluated
our methods both with human subjects in a simulated rover domain, using NASA
Task Load Index (TLX), and synthetically with ten different problems across two
standard IPC domains. Results strongly suggest that our methods generate
explanations that are perceived as less cognitively demanding and much
preferred over the baselines and are computationally efficient.
",Online Explanation Generation for Human-Robot Teaming
"  Affective Computing is a rapidly growing field spurred by advancements in
artificial intelligence, but often, held back by the inability to translate
psychological theories of emotion into tractable computational models. To
address this, we propose a probabilistic programming approach to affective
computing, which models psychological-grounded theories as generative models of
emotion, and implements them as stochastic, executable computer programs. We
first review probabilistic approaches that integrate reasoning about emotions
with reasoning about other latent mental states (e.g., beliefs, desires) in
context. Recently-developed probabilistic programming languages offer several
key desidarata over previous approaches, such as: (i) flexibility in
representing emotions and emotional processes; (ii) modularity and
compositionality; (iii) integration with deep learning libraries that
facilitate efficient inference and learning from large, naturalistic data; and
(iv) ease of adoption. Furthermore, using a probabilistic programming framework
allows a standardized platform for theory-building and experimentation:
Competing theories (e.g., of appraisal or other emotional processes) can be
easily compared via modular substitution of code followed by model comparison.
To jumpstart adoption, we illustrate our points with executable code that
researchers can easily modify for their own models. We end with a discussion of
applications and future directions of the probabilistic programming approach.
",Applying Probabilistic Programming to Affective Computing
"  From the beginning if the history of AI, there has been interest in games as
a platform of research. As the field developed, human-level competence in
complex games became a target researchers worked to reach. Only relatively
recently has this target been finally met for traditional tabletop games such
as Backgammon, Chess and Go. Current research focus has shifted to electronic
games, which provide unique challenges. As is often the case with AI research,
these results are liable to be exaggerated or misrepresented by either authors
or third parties. The extent to which these games benchmark consist of fair
competition between human and AI is also a matter of debate. In this work, we
review the statements made by authors and third parties in the general media
and academic circle about these game benchmark results and discuss factors that
can impact the perception of fairness in the contest between humans and
machines
","Leveling the Playing Field -- Fairness in AI Versus Human Game
  Benchmarks"
"  Designing agents capable of explaining complex sequential decisions remain a
significant open problem in automated decision-making. Recently, there has been
a lot of interest in developing approaches for generating such explanations for
various decision-making paradigms. One such approach has been the idea of {\em
explanation as model-reconciliation}. The framework hypothesizes that one of
the common reasons for the user's confusion could be the mismatch between the
user's model of the task and the one used by the system to generate the
decisions. While this is a general framework, most works that have been
explicitly built on this explanatory philosophy have focused on settings where
the model of user's knowledge is available in a declarative form. Our goal in
this paper is to adapt the model reconciliation approach to the cases where
such user models are no longer explicitly provided. We present a simple and
easy to learn labeling model that can help an explainer decide what information
could help achieve model reconciliation between the user and the agent.
",Model-Free Model Reconciliation
"  Due to the complication of the presented problem, intelligent algorithms show
great power to solve the parts logistics optimization problem related to the
vehicle routing problem (VRP). However, most of the existing research to VRP
are incomprehensive and failed to solve a real-work parts logistics problem.
  In this work, towards SAIC logistics problem, we propose a systematic
solution to this 2-Dimensional Loading Capacitated Multi-Depot Heterogeneous
VRP with Time Windows by integrating diverse types of intelligent algorithms,
including, a heuristic algorithm to initialize feasible logistics planning
schemes by imitating manual planning, the core Tabu Search algorithm for global
optimization, accelerated by a novel bundle technique, heuristically algorithms
for routing, packing and queuing associated, and a heuristic post-optimization
process to promote the optimal solution.
  Based on these algorithms, the SAIC Motor has successfully established an
intelligent management system to give a systematic solution for the parts
logistics planning, superior than manual planning in its performance,
customizability and expandability.
",Intelligent Solution System towards Parts Logistics Optimization
"  In this work, we present a new planning formalism called Expectation-Aware
planning for decision making with humans in the loop where the human's
expectations about an agent may differ from the agent's own model. We show how
this formulation allows agents to not only leverage existing strategies for
handling model differences but can also exhibit novel behaviors that are
generated through the combination of these different strategies. Our
formulation also reveals a deep connection to existing approaches in epistemic
planning. Specifically, we show how we can leverage classical planning
compilations for epistemic planning to solve Expectation-Aware planning
problems. To the best of our knowledge, the proposed formulation is the first
complete solution to decision-making in the presence of diverging user
expectations that is amenable to a classical planning compilation while
successfully combining previous works on explanation and explicability. We
empirically show how our approach provides a computational advantage over
existing approximate approaches that unnecessarily try to search in the space
of models while also failing to facilitate the full gamut of behaviors enabled
by our framework.
","Expectation-Aware Planning: A Unifying Framework for Synthesizing and
  Executing Self-Explaining Plans for Human-Aware Planning"
"  Explainable planning is widely accepted as a prerequisite for autonomous
agents to successfully work with humans. While there has been a lot of research
on generating explanations of solutions to planning problems, explaining the
absence of solutions remains an open and under-studied problem, even though
such situations can be the hardest to understand or debug. In this paper, we
show that hierarchical abstractions can be used to efficiently generate reasons
for unsolvability of planning problems. In contrast to related work on
computing certificates of unsolvability, we show that these methods can
generate compact, human-understandable reasons for unsolvability. Empirical
analysis and user studies show the validity of our methods as well as their
computational efficacy on a number of benchmark planning domains.
","Why Couldn't You do that? Explaining Unsolvability of Classical Planning
  Problems in the Presence of Plan Advice"
"  In this paper, we propose a constraint-based modeling approach for the
problem of discovering frequent gradual patterns in a numerical dataset. This
SAT-based declarative approach offers an additional possibility to benefit from
the recent progress in satisfiability testing and to exploit the efficiency of
modern SAT solvers for enumerating all frequent gradual patterns in a numerical
dataset. Our approach can easily be extended with extra constraints, such as
temporal constraints in order to extract more specific patterns in a broad
range of gradual patterns mining applications. We show the practical
feasibility of our SAT model by running experiments on two real world datasets.
",Extracting Frequent Gradual Patterns Using Constraints Modeling
"  The TriRhenaTech alliance universities and their partners presented their
competences in the field of artificial intelligence and their cross-border
cooperations with the industry at the tri-national conference 'Artificial
Intelligence : from Research to Application' on March 13th, 2019 in Offenburg.
The TriRhenaTech alliance is a network of universities in the Upper Rhine
Trinational Metropolitan Region comprising of the German universities of
applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, and Offenburg, the
Baden-Wuerttemberg Cooperative State University Loerrach, the French university
network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of
engineering, architecture and management) and the University of Applied
Sciences and Arts Northwestern Switzerland. The alliance's common goal is to
reinforce the transfer of knowledge, research, and technology, as well as the
cross-border mobility of students.
","Artificial Intelligence : from Research to Application ; the Upper-Rhine
  Artificial Intelligence Symposium (UR-AI 2019)"
"  We present a machine-readable movement writing for sleight-of-hand moves with
cards -- a ""Labanotation of card magic."" This scheme of movement writing
contains 440 categories of motion, and appears to taxonomize all card sleights
that have appeared in over 1500 publications. The movement writing is
axiomatized in $\mathcal{SROIQ}$(D) Description Logic, and collected formally
as an Ontology of Card Sleights, a computational ontology that extends the
Basic Formal Ontology and the Information Artifact Ontology. The Ontology of
Card Sleights is implemented in OWL DL, a Description Logic fragment of the Web
Ontology Language. While ontologies have historically been used to classify at
a less granular level, the algorithmic nature of card tricks allows us to
transcribe a performer's actions step by step. We conclude by discussing design
criteria we have used to ensure the ontology can be accessed and modified with
a simple click-and-drag interface. This may allow database searches and
performance transcriptions by users with card magic knowledge, but no ontology
background.
",Ontology of Card Sleights
"  In this paper, we use reinforcement learning for safety driving in adversary
settings. In our work, the knowledge in state-of-art planning methods is reused
by single-step options whose action suggestions are compared in parallel with
primitive actions. We show two advantages by doing so. First, training this
reinforcement learning agent is easier and faster than training the
primitive-action agent. Second, our new agent outperforms the primitive-action
reinforcement learning agent, human testers as well as the state-of-art
planning methods that our agent queries as skill options.
",Single-step Options for Adversary Driving
"  Research in Artificial Intelligence (AI) has focused mostly on two extremes:
either on small improvements in narrow AI domains, or on universal theoretical
frameworks which are usually uncomputable, incompatible with theories of
biological intelligence, or lack practical implementations. The goal of this
work is to combine the main advantages of the two: to follow a big picture
view, while providing a particular theory and its implementation. In contrast
with purely theoretical approaches, the resulting architecture should be usable
in realistic settings, but also form the core of a framework containing all the
basic mechanisms, into which it should be easier to integrate additional
required functionality.
  In this paper, we present a novel, purposely simple, and interpretable
hierarchical architecture which combines multiple different mechanisms into one
system: unsupervised learning of a model of the world, learning the influence
of one's own actions on the world, model-based reinforcement learning,
hierarchical planning and plan execution, and symbolic/sub-symbolic integration
in general. The learned model is stored in the form of hierarchical
representations with the following properties: 1) they are increasingly more
abstract, but can retain details when needed, and 2) they are easy to
manipulate in their local and symbolic-like form, thus also allowing one to
observe the learning process at each level of abstraction. On all levels of the
system, the representation of the data can be interpreted in both a symbolic
and a sub-symbolic manner. This enables the architecture to learn efficiently
using sub-symbolic methods and to employ symbolic inference.
","ToyArchitecture: Unsupervised Learning of Interpretable Models of the
  World"
"  The no-wait flowshop scheduling problem is a variant of the classical
permutation flowshop problem, with the additional constraint that jobs have to
be processed by the successive machines without waiting time. To efficiently
address this NP-hard combinatorial optimization problem we conduct an analysis
of the structure of good quality solutions. This analysis shows that the
No-Wait specificity gives them a common structure: they share identical
sub-sequences of jobs, we call super-jobs. After a discussion on the way to
identify these super-jobs, we propose IG-SJ, an algorithm that exploits
super-jobs within the state-of-the-art algorithm for the classical permutation
flowshop, the well-known Iterated Greedy (IG) algorithm. An iterative approach
of IG-SJ is also proposed. Experiments are conducted on Taillard's instances.
The experimental results show that exploiting super-jobs is successful since
IG-SJ is able to find 64 new best solutions.
","Exploiting Promising Sub-Sequences of Jobs to solve the No-Wait Flowshop
  Scheduling Problem"
"  Recent progress in AI and Reinforcement learning has shown great success in
solving complex problems with high dimensional state spaces. However, most of
these successes have been primarily in simulated environments where failure is
of little or no consequence. Most real-world applications, however, require
training solutions that are safe to operate as catastrophic failures are
inadmissible especially when there is human interaction involved. Currently,
Safe RL systems use human oversight during training and exploration in order to
make sure the RL agent does not go into a catastrophic state. These methods
require a large amount of human labor and it is very difficult to scale up. We
present a hybrid method for reducing the human intervention time by combining
model-based approaches and training a supervised learner to improve sample
efficiency while also ensuring safety. We evaluate these methods on various
grid-world environments using both standard and visual representations and show
that our approach achieves better performance in terms of sample efficiency,
number of catastrophic states reached as well as overall task performance
compared to traditional model-free approaches
","Improving Safety in Reinforcement Learning Using Model-Based
  Architectures and Human Intervention"
"  Researchers on artificial intelligence have achieved human-level intelligence
in large-scale perfect-information games, but it is still a challenge to
achieve (nearly) optimal results (in other words, an approximate Nash
Equilibrium) in large-scale imperfect-information games (i.e. war games,
football coach or business strategies). Neural Fictitious Self Play (NFSP) is
an effective algorithm for learning approximate Nash equilibrium of
imperfect-information games from self-play without prior domain knowledge.
However, it relies on Deep Q-Network, which is off-line and is hard to converge
in online games with changing opponent strategy, so it can't approach
approximate Nash equilibrium in games with large search scale and deep search
depth. In this paper, we propose Monte Carlo Neural Fictitious Self Play
(MC-NFSP), an algorithm combines Monte Carlo tree search with NFSP, which
greatly improves the performance on large-scale zero-sum imperfect-information
games. Experimentally, we demonstrate that the proposed Monte Carlo Neural
Fictitious Self Play can converge to approximate Nash equilibrium in games with
large-scale search depth while the Neural Fictitious Self Play can't.
Furthermore, we develop Asynchronous Neural Fictitious Self Play (ANFSP). It
use asynchronous and parallel architecture to collect game experience. In
experiments, we show that parallel actor-learners have a further accelerated
and stabilizing effect on training.
","Monte Carlo Neural Fictitious Self-Play: Approach to Approximate Nash
  equilibrium of Imperfect-Information Games"
"  In trick-taking card games, a two-step process of state sampling and
evaluation is widely used to approximate move values. While the evaluation
component is vital, the accuracy of move value estimates is also fundamentally
linked to how well the sampling distribution corresponds the true distribution.
Despite this, recent work in trick-taking card game AI has mainly focused on
improving evaluation algorithms with limited work on improving sampling. In
this paper, we focus on the effect of sampling on the strength of a player and
propose a novel method of sampling more realistic states given move history. In
particular, we use predictions about locations of individual cards made by a
deep neural network --- trained on data from human gameplay - in order to
sample likely worlds for evaluation. This technique, used in conjunction with
Perfect Information Monte Carlo (PIMC) search, provides a substantial increase
in cardplay strength in the popular trick-taking card game of Skat.
",Improving Search with Supervised Learning in Trick-Based Card Games
"  This paper addresses a variant of multi-agent path finding (MAPF) in
continuous space and time. We present a new solving approach based on
satisfiability modulo theories (SMT) to obtain makespan optimal solutions. The
standard MAPF is a task of navigating agents in an undirected graph from given
starting vertices to given goal vertices so that agents do not collide with
each other in vertices of the graph. In the continuous version
(MAPF$^\mathcal{R}$) agents move in an $n$-dimensional Euclidean space along
straight lines that interconnect predefined positions. For simplicity, we work
with circular omni-directional agents having constant velocities in the 2D
plane. As agents can have different sizes and move smoothly along lines, a
non-colliding movement along certain lines with small agents can result in a
collision if the same movement is performed with larger agents. Our SMT-based
approach for MAPF$^\mathcal{R}$ called SMT-CBS$^\mathcal{R}$ reformulates the
Conflict-based Search (CBS) algorithm in terms of SMT concepts. We suggest lazy
generation of decision variables and constraints. Each time a new conflict is
discovered, the underlying encoding is extended with new variables and
constraints to eliminate the conflict. We compared SMT-CBS$^\mathcal{R}$ and
adaptations of CBS for the continuous variant of MAPF experimentally.
","Multi-agent Path Finding with Continuous Time Viewed Through
  Satisfiability Modulo Theories (SMT)"
"  Information Retrieval (IR) aims at retrieving documents that are most
relevant to a query provided by a user. Traditional techniques rely mostly on
syntactic methods. In some cases, however, links at a deeper semantic level
must be considered. In this paper, we explore a type of IR task in which
documents describe sequences of events, and queries are about the state of the
world after such events. In this context, successfully matching documents and
query requires considering the events' possibly implicit, uncertain effects and
side-effects. We begin by analyzing the problem, then propose an action
language based formalization, and finally automate the corresponding IR task
using Answer Set Programming.
",Action-Centered Information Retrieval
"  A framework and methodology---termed LogiKEy---for the design and engineering
of ethical reasoners, normative theories and deontic logics is presented. The
overall motivation is the development of suitable means for the control and
governance of intelligent autonomous systems. LogiKEy's unifying formal
framework is based on semantical embeddings of deontic logics, logic
combinations and ethico-legal domain theories in expressive classic
higher-order logic (HOL). This meta-logical approach enables the provision of
powerful tool support in LogiKEy: off-the-shelf theorem provers and model
finders for HOL are assisting the LogiKEy designer of ethical intelligent
agents to flexibly experiment with underlying logics and their combinations,
with ethico-legal domain theories, and with concrete examples---all at the same
time. Continuous improvements of these off-the-shelf provers, without further
ado, leverage the reasoning performance in LogiKEy. Case studies, in which the
LogiKEy framework and methodology has been applied and tested, give evidence
that HOL's undecidability often does not hinder efficient experimentation.
","Designing Normative Theories for Ethical and Legal Reasoning: LogiKEy
  Framework, Methodology, and Tool Support"
"  Philosophers frequently struggle with the relation of metaphysics to the
everyday world, with its practical value, and with its relation to empirical
science. This paper distinguishes several different models of the relation
between philosophical ontology and applied (scientific) ontology that have been
advanced in the history of philosophy. Adoption of a strong participation model
for the philosophical ontologist in science is urged, and requirements and
consequences of the participation model are explored. This approach provides
both a principled view and justification of the role of the philosophical
ontologist in contemporary empirical science as well as guidelines for
integrating philosophers and philosophical contributions into the practice of
science.
","Ontology, Ontologies, and Science"
"  The Turing Machine is the paradigmatic case of computing machines, but there
are others such as Artificial Neural Networks, quantum computing, holography,
and diverse forms of analogical computing, each based on a particular intuition
of the phenomenon of computing. This variety can be captured in terms of system
levels, re-interpreting and generalizing Newell's hierarchy, which includes the
knowledge level at the top and the symbol level immediately below it. In this
re-interpretation the knowledge level consists of human knowledge and the
symbol level is generalized into a new level that here is called The Mode of
Computing. Natural computing performed by brains of humans and non-human
animals with a developed enough neural system should be understood in terms of
a hierarchy of system levels too. By analogy from standard computing machinery
there must be a system level above the neural circuitry and directly below the
knowledge level that is named here The mode of Natural Computing. A central
question for Cognition is the characterization of this mode. The Mode of
Computing provides a novel perspective on the phenomena of computing,
interpreting, the representational and non-representational views of cognition,
and consciousness.
",The Mode of Computing
"  Off-Policy reinforcement learning (RL) is an important class of methods for
many problem domains, such as robotics, where the cost of collecting data is
high and on-policy methods are consequently intractable. Standard methods for
applying Q-learning to continuous-valued action domains involve iteratively
sampling the Q-function to find a good action (e.g. via hill-climbing), or by
learning a policy network at the same time as the Q-function (e.g. DDPG). Both
approaches make tradeoffs between stability, speed, and accuracy. We propose a
novel approach, called Cross-Entropy Guided Policies, or CGP, that draws
inspiration from both classes of techniques. CGP aims to combine the stability
and performance of iterative sampling policies with the low computational cost
of a policy network. Our approach trains the Q-function using iterative
sampling with the Cross-Entropy Method (CEM), while training a policy network
to imitate CEM's sampling behavior. We demonstrate that our method is more
stable to train than state of the art policy network methods, while preserving
equivalent inference time compute costs, and achieving competitive total reward
on standard benchmarks.
",Q-Learning for Continuous Actions with Cross-Entropy Guided Policies
"  Search-based procedural content generation uses stochastic global
optimization algorithms to search for game content. However, standard tree
search algorithms can be competitive with evolution on some optimization
problems. We investigate the applicability of several tree search methods to
level generation and compare them systematically with several optimization
algorithms, including evolutionary algorithms. We compare them on three
different game level generation problems: Binary, Zelda, and Sokoban. We
introduce two new representations that can help tree search algorithms deal
with the large branching factor of the generation problem. We find that in
general, optimization algorithms clearly outperform tree search algorithms, but
given the right problem representation certain tree search algorithms perform
similarly to optimization algorithms, and in one particular problem, we see
surprisingly strong results from MCTS.
",Tree Search vs Optimization Approaches for Map Generation
"  The Semantic Web Rule Language (SWRL) is a direct extension of OWL 2 DL with
a subset of RuleML, and it is designed to be the rule language of the Semantic
Web. This paper explores the state-of-the-art of SWRL's expressiveness
extensions proposed over time. As a motivation, the effectiveness of the
SWRL/OWL combination in modeling domain facts is discussed while some of the
common expressive limitations of the combination are also highlighted. The
paper then classifies and presents the relevant language extensions of the SWRL
and their added expressive powers to the original SWRL definition. Furthermore,
it provides a comparative analysis of the syntax and semantics of the proposed
extensions. In conclusion, the decidability requirement and usability of each
expressiveness extension are evaluated towards an efficient inclusion into the
OWL ontologies.
",The Semantic Web Rule Language Expressiveness Extensions-A Survey
"  Epistemic planning --- planning with knowledge and belief --- is essential in
many multi-agent and human-agent interaction domains. Most state-of-the-art
epistemic planners solve this problem by compiling to propositional classical
planning, for example, generating all possible knowledge atoms, or compiling
epistemic formula to normal forms. However, these methods become
computationally infeasible as problems grow. In this paper, we decompose
epistemic planning by delegating reasoning about epistemic formula to an
external solver. We do this by modelling the problem using \emph{functional
STRIPS}, which is more expressive than standard STRIPS and supports the use of
external, black-box functions within action models. Exploiting recent work that
demonstrates the relationship between what an agent `sees' and what it knows,
we allow modellers to provide new implementations of externals functions. These
define what agents see in their environment, allowing new epistemic logics to
be defined without changing the planner. As a result, it increases the
capability and flexibility of the epistemic model itself, and avoids the
exponential pre-compilation step. We ran evaluations on well-known epistemic
planning benchmarks to compare with an existing state-of-the-art planner, and
on new scenarios based on different external functions. The results show that
our planner scales significantly better than the state-of-the-art planner
against which we compared, and can express problems more succinctly.
","What you get is what you see: Decomposing Epistemic Planning using
  Functional STRIPS"
"  In multiple attribute decision analysis (MADA) problems, one often needs to
deal with assessment information with uncertainty. The evidential reasoning
approach is one of the most effective methods to deal with such MADA problems.
As kernel of the evidential reasoning approach, an original evidential
reasoning (ER) algorithm was firstly proposed by Yang et al, and later they
modified the ER algorithm in order to satisfy the proposed four synthesis
axioms with which a rational aggregation process needs to satisfy. However, up
to present, the essential difference of the two ER algorithms as well as the
rationality of the synthesis axioms are still unclear. In this paper, we
analyze the ER algorithms in Dempster-Shafer theory (DST) framework and prove
that the original ER algorithm follows the reliability discounting and
combination scheme, while the modified one follows the importance discounting
and combination scheme. Further we reveal that the four synthesis axioms are
not valid criteria to check the rationality of one attribute aggregation
algorithm. Based on these new findings, an extended ER algorithm is proposed to
take into account both the reliability and importance of different attributes,
which provides a more general attribute aggregation scheme for MADA with
uncertainty. A motorcycle performance assessment problem is examined to
illustrate the proposed algorithm.
","Analysis and Extension of the Evidential Reasoning Algorithm for
  Multiple Attribute Decision Analysis with Uncertainty"
"  This paper investigates the effect of learning a forward model on the
performance of a statistical forward planning agent. We transform Conway's Game
of Life simulation into a single-player game where the objective can be either
to preserve as much life as possible or to extinguish all life as quickly as
possible.
  In order to learn the forward model of the game, we formulate the problem in
a novel way that learns the local cell transition function by creating a set of
supervised training data and predicting the next state of each cell in the grid
based on its current state and immediate neighbours. Using this method we are
able to harvest sufficient data to learn perfect forward models by observing
only a few complete state transitions, using either a look-up table, a decision
tree or a neural network.
  In contrast, learning the complete state transition function is a much harder
task and our initial efforts to do this using deep convolutional auto-encoders
were less successful.
  We also investigate the effects of imperfect learned models on prediction
errors and game-playing performance, and show that even models with significant
errors can provide good performance.
","A Local Approach to Forward Model Learning: Results on the Game of Life
  Game"
"  An effective way to achieve intelligence is to simulate various intelligent
behaviors in the human brain. In recent years, bio-inspired learning methods
have emerged, and they are different from the classical mathematical
programming principle. In the perspective of brain inspiration, reinforcement
learning has gained additional interest in solving decision-making tasks as
increasing neuroscientific research demonstrates that significant links exist
between reinforcement learning and specific neural substrates. Because of the
tremendous research that focuses on human brains and reinforcement learning,
scientists have investigated how robots can autonomously tackle complex tasks
in the form of a self-driving agent control in a human-like way. In this study,
we propose an end-to-end architecture using novel deep-Q-network architecture
in conjunction with a recurrence to resolve the problem in the field of
simulated self-driving. The main contribution of this study is that we trained
the driving agent using a brain-inspired trial-and-error technique, which was
in line with the real world situation. Besides, there are three innovations in
the proposed learning network: raw screen outputs are the only information
which the driving agent can rely on, a weighted layer that enhances the
differences of the lengthy episode, and a modified replay mechanism that
overcomes the problem of sparsity and accelerates learning. The proposed
network was trained and tested under a third-partied OpenAI Gym environment.
After training for several episodes, the resulting driving agent performed
advanced behaviors in the given scene. We hope that in the future, the proposed
brain-inspired learning system would inspire practicable self-driving control
solutions.
","Towards Brain-inspired System: Deep Recurrent Reinforcement Learning for
  Simulated Self-driving Agent"
"  Metaheuristics are general methods that guide application of concrete
heuristic(s) to problems that are too hard to solve using exact algorithms.
However, even though a growing body of literature has been devoted to their
statistical evaluation, the approaches proposed so far are able to assess only
coupled effects of metaheuristics and heuristics. They do not reveal us
anything about how efficient the examined metaheuristic is at guiding its
subordinate heuristic(s), nor do they provide us information about how much the
heuristic component of the combined algorithm contributes to the overall
performance. In this paper, we propose a simple yet effective methodology of
doing so by deriving a naive, placebo metaheuristic from the one being studied
and comparing the distributions of chosen performance metrics for the two
methods. We propose three measures of difference between the two distributions.
Those measures, which we call BER values (benefit, equivalence, risk) are based
on a preselected threshold of practical significance which represents the
minimal difference between two performance scores required for them to be
considered practically different. We illustrate usefulness of our methodology
on the example of Simulated Annealing, Boolean Satisfiability Problem, and the
Flip heuristic.
","How to Estimate the Ability of a Metaheuristic Algorithm to Guide
  Heuristics During Optimization"
"  When ontologies reach a certain size and complexity, faults such as
inconsistencies, unsatisfiable classes or wrong entailments are hardly
avoidable. Locating the incorrect axioms that cause these faults is a hard and
time-consuming task. Addressing this issue, several techniques for
semi-automatic fault localization in ontologies have been proposed. Often,
these approaches involve a human expert who provides answers to
system-generated questions about the intended (correct) ontology in order to
reduce the possible fault locations. To suggest as informative questions as
possible, existing methods draw on various algorithmic optimizations as well as
heuristics. However, these computations are often based on certain assumptions
about the interacting user.
  In this work, we characterize and discuss different user types and show that
existing approaches do not achieve optimal efficiency for all of them. As a
remedy, we suggest a new type of expert question which aims at fitting the
answering behavior of all analyzed experts. Moreover, we present an algorithm
to optimize this new query type which is fully compatible with the (tried and
tested) heuristics used in the field. Experiments on faulty real-world
ontologies show the potential of the new querying method for minimizing the
expert consultation time, independent of the expert type. Besides, the gained
insights can inform the design of interactive debugging tools towards better
meeting their users' needs.
","A New Expert Questioning Approach to More Efficient Fault Localization
  in Ontologies"
"  We explore deep Reinforcement Learning(RL) algorithms for scalping trading
and knew that there is no appropriate trading gym and agent examples. Thus we
propose gym and agent like Open AI gym in finance. Not only that, we introduce
new RL framework based on our hybrid algorithm which leverages between
supervised learning and RL algorithm and uses meaningful observations such
order book and settlement data from experience watching scalpers trading. That
is very crucial information for traders behavior to be decided. To feed these
data into our model, we use spatio-temporal convolution layer, called Conv3D
for order book data and temporal CNN, called Conv1D for settlement data. Those
are preprocessed by episode filter we developed. Agent consists of four sub
agents divided to clarify their own goal to make best decision. Also, we
adopted value and policy based algorithm to our framework. With these features,
we could make agent mimic scalpers as much as possible. In many fields, RL
algorithm has already begun to transcend human capabilities in many domains.
This approach could be a starting point to beat human in the financial stock
market, too and be a good reference for anyone who wants to design RL algorithm
in real world domain. Finally, weexperiment our framework and gave you
experiment progress.
","Cooperative Multi-Agent Reinforcement Learning Framework for Scalping
  Trading"
"  We extend probabilistic action language pBC+ with the notion of utility as in
decision theory. The semantics of the extended pBC+ can be defined as a
shorthand notation for a decision-theoretic extension of the probabilistic
answer set programming language LPMLN. Alternatively, the semantics of pBC+ can
also be defined in terms of Markov Decision Process (MDP), which in turn allows
for representing MDP in a succinct and elaboration tolerant way as well as to
leverage an MDP solver to compute pBC+. The idea led to the design of the
system pbcplus2mdp, which can find an optimal policy of a pBC+ action
description using an MDP solver. This paper is under consideration in Theory
and Practice of Logic Programming (TPLP).
","Elaboration Tolerant Representation of Markov Decision Process via
  Decision-Theoretic Extension of Probabilistic Action Language pBC+"
"  Real-world semantic or knowledge-based systems, e.g., in the biomedical
domain, can become large and complex. Tool support for the localization and
repair of faults within knowledge bases of such systems can therefore be
essential for their practical success. Correspondingly, a number of knowledge
base debugging approaches, in particular for ontology-based systems, were
proposed throughout recent years. Query-based debugging is a comparably recent
interactive approach that localizes the true cause of an observed problem by
asking knowledge engineers a series of questions. Concrete implementations of
this approach exist, such as the OntoDebug plug-in for the ontology editor
Prot\'eg\'e.
  To validate that a newly proposed method is favorable over an existing one,
researchers often rely on simulation-based comparisons. Such an evaluation
approach however has certain limitations and often cannot fully inform us about
a method's true usefulness. We therefore conducted different user studies to
assess the practical value of query-based ontology debugging. One main insight
from the studies is that the considered interactive approach is indeed more
efficient than an alternative algorithmic debugging based on test cases. We
also observed that users frequently made errors in the process, which
highlights the importance of a careful design of the queries that users need to
answer.
",Are Query-Based Ontology Debuggers Really Helping Knowledge Engineers?
"  In the light of ongoing progresses of research on artificial intelligent
systems exhibiting a steadily increasing problem-solving ability, the
identification of practicable solutions to the value alignment problem in AGI
Safety is becoming a matter of urgency. In this context, one preeminent
challenge that has been addressed by multiple researchers is the adequate
formulation of utility functions or equivalents reliably capturing human
ethical conceptions. However, the specification of suitable utility functions
harbors the risk of ""perverse instantiation"" for which no final consensus on
responsible proactive countermeasures has been achieved so far. Amidst this
background, we propose a novel socio-technological ethical framework denoted
Augmented Utilitarianism which directly alleviates the perverse instantiation
problem. We elaborate on how augmented by AI and more generally science and
technology, it might allow a society to craft and update ethical utility
functions while jointly undergoing a dynamical ethical enhancement. Further, we
elucidate the need to consider embodied simulations in the design of utility
functions for AGIs aligned with human values. Finally, we discuss future
prospects regarding the usage of the presented scientifically grounded ethical
framework and mention possible challenges.
",Augmented Utilitarianism for AGI Safety
"  Game-based benchmarks have been playing an essential role in the development
of Artificial Intelligence (AI) techniques. Providing diverse challenges is
crucial to push research toward innovation and understanding in modern
techniques. Rinascimento provides a parameterised partially-observable
multiplayer card-based board game, these parameters can easily modify the
rules, objectives and items in the game. We describe the framework in all its
features and the game-playing challenge providing baseline game-playing AIs and
analysis of their skills. We reserve to agents' hyper-parameter tuning a
central role in the experiments highlighting how it can heavily influence the
performance. The base-line agents contain several additional contribution to
Statistical Forward Planning algorithms.
","Rinascimento: Optimising Statistical Forward Planning Agents for Playing
  Splendor"
"  Planning in stochastic and partially observable environments is a central
issue in artificial intelligence. One commonly used technique for solving such
a problem is by constructing an accurate model firstly. Although some recent
approaches have been proposed for learning optimal behaviour under model
uncertainty, prior knowledge about the environment is still needed to guarantee
the performance of the proposed algorithms. With the benefits of the Predictive
State Representations~(PSRs) approach for state representation and model
prediction, in this paper, we introduce an approach for planning from scratch,
where an offline PSR model is firstly learned and then combined with online
Monte-Carlo tree search for planning with model uncertainty. By comparing with
the state-of-the-art approach of planning with model uncertainty, we
demonstrated the effectiveness of the proposed approaches along with the proof
of their convergence. The effectiveness and scalability of our proposed
approach are also tested on the RockSample problem, which are infeasible for
the state-of-the-art BA-POMDP based approaches.
","Combining Offline Models and Online Monte-Carlo Tree Search for Planning
  from Scratch"
"  Approaches to goal-directed behaviour including online planning and
opportunistic planning tackle a change in the environment by generating
alternative goals to avoid failures or seize opportunities. However, current
approaches only address unanticipated changes related to objects or object
types already defined in the planning task that is being solved. This article
describes a domain-independent approach that advances the state of the art by
extending the knowledge of a planning task with relevant objects of new types.
The approach draws upon the use of ontologies, semantic measures, and ontology
alignment to accommodate newly acquired data that trigger the formulation of
goal opportunities inducing a better-valued plan.
",Extending planning knowledge using ontologies for goal opportunities
"  Pattern-based, modular ontologies have several beneficial properties that
lend themselves to FAIR data practices, especially as it pertains to
Interoperability and Reusability. However, developing such ontologies has a
high upfront cost, e.g. reusing a pattern is predicated upon being aware of its
existence in the first place. Thus, to help overcome these barriers, we have
developed MODL: a modular ontology design library. MODL is a curated collection
of well-documented ontology design patterns, drawn from a wide variety of
interdisciplinary use-cases. In this paper we present MODL as a resource,
discuss its use, and provide some examples of its contents.
",MODL: A Modular Ontology Design Library
"  Humans can learn many novel tasks from a very small number (1--5) of
demonstrations, in stark contrast to the data requirements of nearly tabula
rasa deep learning methods. We propose an expressive class of policies, a
strong but general prior, and a learning algorithm that, together, can learn
interesting policies from very few examples. We represent policies as logical
combinations of programs drawn from a domain-specific language (DSL), define a
prior over policies with a probabilistic grammar, and derive an approximate
Bayesian inference algorithm to learn policies from demonstrations. In
experiments, we study five strategy games played on a 2D grid with one shared
DSL. After a few demonstrations of each game, the inferred policies generalize
to new game instances that differ substantially from the demonstrations. Our
policy learning is 20--1,000x more data efficient than convolutional and fully
convolutional policy learning and many orders of magnitude more computationally
efficient than vanilla program induction. We argue that the proposed method is
an apt choice for tasks that have scarce training data and feature significant,
structured variation between task instances.
",Few-Shot Bayesian Imitation Learning with Logical Program Policies
"  Reinforcement learning (RL) is a branch of machine learning which is employed
to solve various sequential decision making problems without proper
supervision. Due to the recent advancement of deep learning, the newly proposed
Deep-RL algorithms have been able to perform extremely well in sophisticated
high-dimensional environments. However, even after successes in many domains,
one of the major challenge in these approaches is the high magnitude of
interactions with the environment required for efficient decision making.
Seeking inspiration from the brain, this problem can be solved by incorporating
instance based learning by biasing the decision making on the memories of high
rewarding experiences. This paper reviews various recent reinforcement learning
methods which incorporate external memory to solve decision making and a survey
of them is presented. We provide an overview of the different methods - along
with their advantages and disadvantages, applications and the standard
experimentation settings used for memory based models. This review hopes to be
a helpful resource to provide key insight of the recent advances in the field
and provide help in further future development of it.
",A Short Survey On Memory Based Reinforcement Learning
"  Width-based planning has demonstrated great success in recent years due to
its ability to scale independently of the size of the state space. For example,
Bandres et al. (2018) introduced a rollout version of the Iterated Width
algorithm whose performance compares well with humans and learning methods in
the pixel setting of the Atari games suite. In this setting, planning is done
on-line using the ""screen"" states and selecting actions by looking ahead into
the future. However, this algorithm is purely exploratory and does not leverage
past reward information. Furthermore, it requires the state to be factored into
features that need to be pre-defined for the particular task, e.g., the B-PROST
pixel features. In this work, we extend width-based planning by incorporating
an explicit policy in the action selection mechanism. Our method, called
$\pi$-IW, interleaves width-based planning and policy learning using the
state-actions visited by the planner. The policy estimate takes the form of a
neural network and is in turn used to guide the planning step, thus reinforcing
promising paths. Surprisingly, we observe that the representation learned by
the neural network can be used as a feature space for the width-based planner
without degrading its performance, thus removing the requirement of pre-defined
features for the planner. We compare $\pi$-IW with previous width-based methods
and with AlphaZero, a method that also interleaves planning and learning, in
simple environments, and show that $\pi$-IW has superior performance. We also
show that $\pi$-IW algorithm outperforms previous width-based methods in the
pixel setting of Atari games suite.
",Deep Policies for Width-Based Planning in Pixel Domains
"  We propose a method for constructing artificial intelligence (AI) of mahjong,
which is a multiplayer imperfect information game. Since the size of the game
tree is huge, constructing an expert-level AI player of mahjong is challenging.
We define multiple Markov decision processes (MDPs) as abstractions of mahjong
to construct effective search trees. We also introduce two methods of inferring
state values of the original mahjong using these MDPs. We evaluated the
effectiveness of our method using gameplays vis-\`{a}-vis the current strongest
AI player.
","Method for Constructing Artificial Intelligence Player with Abstraction
  to Markov Decision Processes in Multiplayer Game of Mahjong"
"  This paper describes a design that can be used for Explainable AI. The lower
level is a nested ensemble of patterns created by self-organisation. The upper
level is a hierarchical tree, where nodes are linked through individual
concepts, so there is a transition from mixed ensemble masses to specific
categories. Lower-level pattern ensembles are learned in an unsupervsised
manner and then split into branches when it is clear that the category has
changed. Links between the two levels define that the concepts are learned and
missing links define that they are guessed only. This paper proposes some new
clustering algorithms for producing the pattern ensembles, that are themselves
an ensemble which converges through aggregations. Multiple solutions are also
combined, to make the final result more robust. One measure of success is how
coherent these ensembles are, which means that every data row in the cluster
belongs to the same category. The total number of clusters is also important
and the teaching phase can correct the ensemble estimates with respect to both
of these. A teaching phase would then help the classifier to learn the true
category for each input row. During this phase, any classifier can learn or
infer correct classifications from some other classifier's knowledge, thereby
reducing the required number of presentations. As the information is added,
cross-referencing between the two structures allows it to be used more widely,
where a unique structure can build up that would not be possible by either
method separately.
",A Pattern-Hierarchy Classifier for Reduced Teaching
"  This paper presents a taxonomy of explainability in Human-Agent Systems. We
consider fundamental questions about the Why, Who, What, When and How of
explainability. First, we define explainability, and its relationship to the
related terms of interpretability, transparency, explicitness, and
faithfulness. These definitions allow us to answer why explainability is needed
in the system, whom it is geared to and what explanations can be generated to
meet this need. We then consider when the user should be presented with this
information. Last, we consider how objective and subjective measures can be
used to evaluate the entire system. This last question is the most encompassing
as it will need to evaluate all other issues regarding explainability.
",Explainability in Human-Agent Systems
"  Application of decision support systems for conflict modeling in information
operations recognition is presented. An information operation is considered as
a complex weakly structured system. The model of conflict between two subjects
is proposed based on the second-order rank reflexive model. The method is
described for construction of the design pattern for knowledge bases of
decision support systems. In the talk, the methodology is proposed for using of
decision support systems for modeling of conflicts in information operations
recognition based on the use of expert knowledge and content monitoring.
","Usage of Decision Support Systems for Conflicts Modelling during
  Information Operations Recognition"
"  Big data solutions are designed to cope with data of huge Volume and wide
Variety, that need to be ingested at high Velocity and have potential Veracity
issues, challenging characteristics that are usually referred to as the ""4Vs of
Big Data"". In order to evaluate possibly complex big data solutions, stress
tests require to assess a large number of combinations of sub-components
jointly with the possible big data variations. A formalization of the Design of
Experiments (DoE) on big data solutions is aimed at ensuring the
reproducibility of the experiments, facilitating their partitioning in
sub-experiments and guaranteeing the consistency of their outcomes in a global
assessment. In this paper, an ontology-based approach is proposed to support
the evaluation of a big data system in two ways. Firstly, the approach
formalizes a decomposition and recombination of the big data solution, allowing
for the aggregation of component evaluation results at inter-component level.
Secondly, existing work on DoE is translated into an ontology for supporting
the selection of experiments. The proposed ontology-based approach offers the
possibility to combine knowledge from the evaluation domain and the application
domain. It exploits domain and inter-domain specific restrictions on the factor
combinations in order to reduce the number of experiments. Contrary to existing
approaches, the proposed use of ontologies is not limited to the assertional
description and exploitation of past experiments but offers richer
terminological descriptions for the development of a DoE from scratch. As an
application example, a maritime big data solution to the problem of detecting
and predicting vessel suspicious behaviour through mobility analysis is
selected. The article is concluded with a sketch of future works.
",Ontology-based Design of Experiments on Big Data Solutions
"  Answer Set Programming (ASP) is a prominent knowledge representation language
with roots in logic programming and non-monotonic reasoning. Biennial ASP
competitions are organized in order to furnish challenging benchmark
collections and assess the advancement of the state of the art in ASP solving.
  In this paper, we report on the design and results of the Seventh ASP
Competition, jointly organized by the University of Calabria (Italy), the
University of Genova (Italy), and the University of Potsdam (Germany), in
affiliation with the 14th International Conference on Logic Programming and
Non-Monotonic Reasoning (LPNMR 2017). (Under consideration for acceptance in
TPLP).
",The Seventh Answer Set Programming Competition: Design and Results
"  Optimal planning with respect to learned neural network (NN) models in
continuous action and state spaces using mixed-integer linear programming
(MILP) is a challenging task for branch-and-bound solvers due to the poor
linear relaxation of the underlying MILP model. For a given set of features,
potential heuristics provide an efficient framework for computing bounds on
cost (reward) functions. In this paper, we model the problem of finding optimal
potential bounds for learned NN models as a bilevel program, and solve it using
a novel finite-time constraint generation algorithm. We then strengthen the
linear relaxation of the underlying MILP model by introducing constraints to
bound the reward function based on the precomputed reward potentials.
Experimentally, we show that our algorithm efficiently computes reward
potentials for learned NN models, and that the overhead of computing reward
potentials is justified by the overall strengthening of the underlying MILP
model for the task of planning over long horizons.
","Reward Potentials for Planning with Learned Neural Network Transition
  Models"
"  Predictive analysis in business process monitoring aims at forecasting the
future information of a running business process. The prediction is typically
made based on the model extracted from historical process execution logs (event
logs). In practice, different business domains might require different kinds of
predictions. Hence, it is important to have a means for properly specifying the
desired prediction tasks, and a mechanism to deal with these various prediction
tasks. Although there have been many studies in this area, they mostly focus on
a specific prediction task. This work introduces a language for specifying the
desired prediction tasks, and this language allows us to express various kinds
of prediction tasks. This work also presents a mechanism for automatically
creating the corresponding prediction model based on the given specification.
Differently from previous studies, instead of focusing on a particular
prediction task, we present an approach to deal with various prediction tasks
based on the given specification of the desired prediction tasks. We also
provide an implementation of the approach which is used to conduct experiments
using real-life event logs.
",Specification-Driven Predictive Business Process Monitoring
"  Web Ontology Language (OWL) reasoners are used to infer new logical relations
from ontologies. While inferring new facts, these reasoners can be further
optimized, e.g., by properly ordering disjuncts in disjunction expressions of
ontologies for satisfiability testing of concepts. Different expansion-ordering
heuristics have been developed for this purpose. The built-in heuristics in
these reasoners determine the order for branches in search trees while each
heuristic choice causes different effects for various ontologies depending on
the ontologies' syntactic structure and probably other features as well. A
learning-based approach that takes into account the features aims to select an
appropriate expansion-ordering heuristic for each ontology. The proper choice
is expected to accelerate the reasoning process for the reasoners. In this
paper, the effect of our methodology is investigated on a well-known reasoner
that is JFact. Our experiments show the average speedup by a factor of one to
two orders of magnitude for satisfiability testing after applying learning
methodology for selecting the right expansion-ordering heuristics.
","Learning the Right Expansion-ordering Heuristics for Satisfiability
  Testing in OWL Reasoners"
"  Supplier selection problem has gained extensive attention in the prior
studies. However, research based on Fuzzy Multi-Attribute Decision Making
(F-MADM) approach in ranking resilient suppliers in logistic 4 is still in its
infancy. Traditional MADM approach fails to address the resilient supplier
selection problem in logistic 4 primarily because of the large amount of data
concerning some attributes that are quantitative, yet difficult to process
while making decisions. Besides, some qualitative attributes prevalent in
logistic 4 entail imprecise perceptual or judgmental decision relevant
information, and are substantially different than those considered in
traditional suppler selection problems. This study develops a Decision Support
System (DSS) that will help the decision maker to incorporate and process such
imprecise heterogeneous data in a unified framework to rank a set of resilient
suppliers in the logistic 4 environment. The proposed framework induces a
triangular fuzzy number from large-scale temporal data using
probability-possibility consistency principle. Large number of non-temporal
data presented graphically are computed by extracting granular information that
are imprecise in nature. Fuzzy linguistic variables are used to map the
qualitative attributes. Finally, fuzzy based TOPSIS method is adopted to
generate the ranking score of alternative suppliers. These ranking scores are
used as input in a Multi-Choice Goal Programming (MCGP) model to determine
optimal order allocation for respective suppliers. Finally, a sensitivity
analysis assesses how the Suppliers Cost versus Resilience Index (SCRI) changes
when differential priorities are set for respective cost and resilience
attributes.
","Resilient Supplier Selection in Logistics 4.0 with Heterogeneous
  Information"
"  Automated planning technology has developed significantly. Designing a
planning model that allows an automated agent to be capable of reacting
intelligently to unexpected events in a real execution environment yet remains
a challenge. This article describes a domain-independent approach to allow the
agent to be context-aware of its execution environment and the task it
performs, acquire new information that is guaranteed to be related and more
importantly manageable, and integrate such information into its model through
the use of ontologies and semantic operations to autonomously formulate new
objectives, resulting in a more human-like behaviour for handling unexpected
events in the context of opportunities.
","A context-aware knowledge acquisition for planning applications using
  ontologies"
"  A state-of-the-art criterion to evaluate the importance of a given learned
clause is called Literal Block Distance (LBD) score. It measures the number of
distinct decision levels in a given learned clause. The lower the LBD score of
a learned clause, the better is its quality. The learned clauses with LBD score
of 2, called glue clauses, are known to possess high pruning power which are
never deleted from the clause databases of the modern CDCL SAT solvers. In this
work, we relate glue clauses to decision variables. We call the variables that
appeared in at least one glue clause up to the current search state Glue
Variables. We first show experimentally, by running the state-of-the-art CDCL
SAT solver MapleLCMDist on benchmarks from SAT Competition-2017 and 2018, that
branching decisions with glue variables are categorically more inference and
conflict efficient than nonglue variables. Based on this observation, we
develop a structure aware CDCL variable bumping scheme, which bumps the
activity score of a glue variable based on its appearance count in the glue
clauses that are learned so far by the search. Empirical evaluation shows
effectiveness of the new method over the main track instances from SAT
Competition 2017 and 2018.
",Characterization of Glue Variables in CDCL SAT Solving
"  The task of recognizing goals and plans from missing and full observations
can be done efficiently by using automated planning techniques. In many
applications, it is important to recognize goals and plans not only accurately,
but also quickly. To address this challenge, we develop novel goal recognition
approaches based on planning techniques that rely on planning landmarks. In
automated planning, landmarks are properties (or actions) that cannot be
avoided to achieve a goal. We show the applicability of a number of planning
techniques with an emphasis on landmarks for goal and plan recognition tasks in
two settings: (1) we use the concept of landmarks to develop goal recognition
heuristics; and (2) we develop a landmark-based filtering method to refine
existing planning-based goal and plan recognition approaches. These recognition
approaches are empirically evaluated in experiments over several classical
planning domains. We show that our goal recognition approaches yield not only
accuracy comparable to (and often higher than) other state-of-the-art
techniques, but also substantially faster recognition time over such
techniques.
",Landmark-Based Approaches for Goal Recognition as Planning
"  FRI methods are less popular in the practical application domain. One
possible reason is the missing common framework. There are many FRI methods
developed independently, having different interpolation concepts and features.
One trial for setting up a common FRI framework was the MATLAB FRI Toolbox,
developed by Johany\'ak et. al. in 2006. The goals of this paper are divided as
follows: firstly, to present a brief introduction of the FRI methods. Secondly,
to introduce a brief description of the refreshed and extended version of the
original FRI Toolbox. And thirdly, to use different unified numerical benchmark
examples to evaluate and analyze the Fuzzy Rule Interpolation Techniques (FRI)
(KH, KH Stabilized, MACI, IMUL, CRF, VKK, GM, FRIPOC, LESFRI, and SCALEMOVE),
that will be classified and compared based on different features by following
the abnormality and linearity conditions [15].
",Fuzzy Rule Interpolation Methods and Fri Toolbox
"  In this paper, we consider the problem of choosing the optimal scenario of
the impact between nodes based on of the introduced criteria for the optimality
of the impact. Two criteria for the optimality of the impact, which are called
the force of impact and the speed of implementation of the scenario, are
considered. To obtain a unique solution of the problem, a multi-criterial
assessment of the received scenarios using the Pareto principle was applied.
Based on the criteria of a force of impact and the speed of implementation of
the scenario, the choice of the optimal scenario of impact was justified. The
results and advantages of the proposed approach in comparison with the Kosko
model are presented.
","Method for Searching of an Optimal Scenario of Impact in Cognitive Maps
  during Information Operations Recognition"
"  Despite significant advancements in developing autonomous agents,
communication between them often relies on a set of pre-specified symbols for a
given domain. In this paper, we investigate the automatic construction of these
symbols from abstractions to form ""natural languages"" for such agents. The
focus of this initial investigation is on a task planning setting where one
agent (the speaker) directly communicates a ""plan sketch"" to another agent (the
listener) to achieve coordination. In contrast to prior work, we view language
formation as a fundamental requirement for resolving miscoordination. This view
enables us to ""compute"" a language from the ground up by mapping physical
states to symbols, thus reverse engineering the function of languages.
Languages that arise from this process are only approximately expressive of the
actual plans, meaning that they specify abstractions over the plan space, which
is not only theoretically appealing as it provides the desired flexibility to
the listener to choose its plan during execution, but also practically useful
since it both reduces the communication cost of the speaker and computational
cost of the listener. We formulate this language construction problem and show
that it is NEXP-complete. An approximate solution is then developed to relate
this problem to task planning problems that have efficient off-the-shelf
solutions. Finally, we discuss a multi-agent path-finding domain in our
evaluation to provide a comprehensive set of results to illustrate the benefits
of the constructed languages and their applications.
","From Abstractions to ""Natural Languages"" for Coordinating Planning
  Agents"
"  In this paper, we propose an intelligence approach based on fuzzy logic to
modeling human intelligence in washing clothes. At first, an intelligent
feedback loop is designed for perception-based sensing of dirt inspired by
human color understanding. Then, when color stains leak out of some colored
clothes the human probabilistic decision making is computationally modeled to
detect this stain leakage and thus the problem of recognizing dirt from stain
can be considered in the washing process. Finally, we discuss the fuzzy control
of washing clothes and design and simulate a smart controller based on the
fuzzy intelligence feedback loop.
","A knowledge-based intelligent system for control of dirt recognition
  process in the smart washing machines"
"  In recent years, descriptive evaluation has been introduced as a new model
for educational evaluation of Iranian students. The current descriptive
evaluation method is based on four-valued logic. Assessing all students with
only four values is led to a lack of relative justice and the creation of
unrealistic equality. Also, the complexity of the evaluation process in the
current method increases teacher errors likelihood. As a suitable solution, in
this paper, a fuzzy descriptive evaluation system has been proposed. The
proposed method is based on fuzzy logic, which is an infinite-valued logic and
it can perform approximate reasoning on natural language propositions. By the
proposed fuzzy system, student assessment is performed over the school year
with infinite values instead of four values. But to eliminate the diversity of
assigned values to students, at the end of the school year, the calculated
values for each student will be rounded to the nearest value of the four
standard values of the current descriptive evaluation system. It can be
implemented easily in an appropriate smartphone app, which makes it much easier
for the teachers to evaluate the evaluation process. In this paper, the
evaluation process of the elementary third-grade mathematics course in Iran
during the period from the beginning of the MEHR (The Seventh month of Iran) to
the end of BAHMAN (The Eleventh Month of Iran) is examined by the proposed
system. To evaluate the validity of this system, the proposed method has been
simulated in MATLAB software.
",Descriptive evaluation of students using fuzzy approximate reasoning
"  Artificial intelligence (AI) researchers claim that they have made great
`achievements' in clinical realms. However, clinicians point out the so-called
`achievements' have no ability to implement into natural clinical settings. The
root cause for this huge gap is that many essential features of natural
clinical tasks are overlooked by AI system developers without medical
background. In this paper, we propose that the clinical benchmark suite is a
novel and promising direction to capture the essential features of the
real-world clinical tasks, hence qualifies itself for guiding the development
of AI systems, promoting the implementation of AI in real-world clinical
practice.
","A new direction to promote the implementation of artificial intelligence
  in natural clinical settings"
"  Image retrieval utilizes image descriptors to retrieve the most similar
images to a given query image. Convolutional neural network (CNN) is becoming
the dominant approach to extract image descriptors for image retrieval. For
low-power hardware implementation of image retrieval, the drawback of CNN-based
feature descriptor is that it requires hundreds of megabytes of storage. To
address this problem, this paper applies deep model quantization and
compression to CNN in ASIC chip for image retrieval. It is demonstrated that
the CNN-based features descriptor can be extracted using as few as 2-bit
weights quantization to deliver a similar performance as floating-point model
for image retrieval. In addition, to implement CNN in ASIC, especially for
large scale images, the limited buffer size of chips should be considered. To
retrieve large scale images, we propose an improved pooling strategy, region
nested invariance pooling (RNIP), which uses cropped sub-images for CNN.
Testing results on chip show that integrating RNIP with the proposed 2-bit CNN
model compression approach is capable of retrieving large scale images.
","2-bit Model Compression of Deep Convolutional Neural Network on ASIC
  Engine for Image Retrieval"
"  A general and fast method is conceived for computing the cyclic convolution
of n points, where n is a prime number. This method fully exploits the internal
structure of the cyclic matrix, and hence leads to significant reduction of the
multiplication complexity in terms of CPU time by 50%, as compared with
Winograd's algorithm. In this paper, we only consider the real and complex
fields due to their most important applications, but in general, the idea
behind this method can be extended to any finite field of interest. Clearly, it
is well-known that the discrete Fourier transform (DFT) can be expressed in
terms of cyclic convolution, so it can be utilized to compute the DFT when the
block length is a prime.
",General Method for Prime-point Cyclic Convolution over the Real Field
"  Artificial Intelligence (AI) has the opportunity to revolutionize the way the
United States Department of Defense (DoD) and Intelligence Community (IC)
address the challenges of evolving threats, data deluge, and rapid courses of
action. Developing an end-to-end artificial intelligence system involves
parallel development of different pieces that must work together in order to
provide capabilities that can be used by decision makers, warfighters and
analysts. These pieces include data collection, data conditioning, algorithms,
computing, robust artificial intelligence, and human-machine teaming. While
much of the popular press today surrounds advances in algorithms and computing,
most modern AI systems leverage advances across numerous different fields.
Further, while certain components may not be as visible to end-users as others,
our experience has shown that each of these interrelated components play a
major role in the success or failure of an AI system. This article is meant to
highlight many of these technologies that are involved in an end-to-end AI
system. The goal of this article is to provide readers with an overview of
terminology, technical details and recent highlights from academia, industry
and government. Where possible, we indicate relevant resources that can be used
for further reading and understanding.
",AI Enabling Technologies: A Survey
"  State-of-the-art approaches to partially observable planning like POMCP are
based on stochastic tree search. While these approaches are computationally
efficient, they may still construct search trees of considerable size, which
could limit the performance due to restricted memory resources. In this paper,
we propose Partially Observable Stacked Thompson Sampling (POSTS), a memory
bounded approach to open-loop planning in large POMDPs, which optimizes a fixed
size stack of Thompson Sampling bandits. We empirically evaluate POSTS in four
large benchmark problems and compare its performance with different tree-based
approaches. We show that POSTS achieves competitive performance compared to
tree-based open-loop planning and offers a performance-memory tradeoff, making
it suitable for partially observable planning with highly restricted
computational and memory resources.
","Memory Bounded Open-Loop Planning in Large POMDPs using Thompson
  Sampling"
"  Goal recognition aims to recognize the set of candidate goals that are
compatible with the observed behavior of an agent. In this paper, we develop a
method based on the operator-counting framework that efficiently computes
solutions that satisfy the observations and uses the information generated to
solve goal recognition tasks. Our method reasons explicitly about both partial
and noisy observations: estimating uncertainty for the former, and satisfying
observations given the unreliability of the sensor for the latter. We evaluate
our approach empirically over a large data set, analyzing its components on how
each can impact the quality of the solutions. In general, our approach is
superior to previous methods in terms of agreement ratio, accuracy, and spread.
Finally, our approach paves the way for new research on combinatorial
optimization to solve goal recognition tasks.
",An LP-Based Approach for Goal Recognition as Planning
"  Intrinsic rewards were introduced to simulate how human intelligence works;
they are usually evaluated by intrinsically-motivated play, i.e., playing games
without extrinsic rewards but evaluated with extrinsic rewards. However, none
of the existing intrinsic reward approaches can achieve human-level performance
under this very challenging setting of intrinsically-motivated play. In this
work, we propose a novel megalomania-driven intrinsic reward (called
mega-reward), which, to our knowledge, is the first approach that achieves
human-level performance in intrinsically-motivated play. Intuitively,
mega-reward comes from the observation that infants' intelligence develops when
they try to gain more control on entities in an environment; therefore,
mega-reward aims to maximize the control capabilities of agents on given
entities in a given environment. To formalize mega-reward, a relational
transition model is proposed to bridge the gaps between direct and latent
control. Experimental studies show that mega-reward (i) can greatly outperform
all state-of-the-art intrinsic reward approaches, (ii) generally achieves the
same level of performance as Ex-PPO and professional human-level scores, and
(iii) has also a superior performance when it is incorporated with extrinsic
rewards.
",Mega-Reward: Achieving Human-Level Play without Extrinsic Rewards
"  While current General Game Playing (GGP) systems facilitate useful research
in Artificial Intelligence (AI) for game-playing, they are often somewhat
specialised and computationally inefficient. In this paper, we describe the
""ludemic"" general game system Ludii, which has the potential to provide an
efficient tool for AI researchers as well as game designers, historians,
educators and practitioners in related fields. Ludii defines games as
structures of ludemes -- high-level, easily understandable game concepts --
which allows for concise and human-understandable game descriptions. We
formally describe Ludii and outline its main benefits: generality,
extensibility, understandability and efficiency. Experimentally, Ludii
outperforms one of the most efficient Game Description Language (GDL)
reasoners, based on a propositional network, in all games available in the
Tiltyard GGP repository. Moreover, Ludii is also competitive in terms of
performance with the more recently proposed Regular Boardgames (RBG) system,
and has various advantages in qualitative aspects such as generality.
",Ludii -- The Ludemic General Game System
"  We propose an alternative and unifying framework for decision-making that, by
using quantum mechanics, provides more generalised cognitive and decision
models with the ability to represent more information than classical models.
This framework can accommodate and predict several cognitive biases reported in
Lieder & Griffiths without heavy reliance on heuristics nor on assumptions of
the computational resources of the mind.
",Towards a Quantum-Like Cognitive Architecture for Decision-Making
"  Automated Planning is one of the main research field of Artificial
Intelligence since its beginnings. Research in Automated Planning aims at
developing general reasoners (i.e., planners) capable of automatically solve
complex problems. Broadly speaking, planners rely on a general model
characterizing the possible states of the world and the actions that can be
performed in order to change the status of the world. Given a model and an
initial known state, the objective of a planner is to synthesize a set of
actions needed to achieve a particular goal state. The classical approach to
planning roughly corresponds to the description given above. The timeline-based
approach is a particular planning paradigm capable of integrating causal and
temporal reasoning within a unified solving process. This approach has been
successfully applied in many real-world scenarios although a common
interpretation of the related planning concepts is missing. Indeed, there are
significant differences among the existing frameworks that apply this
technique. Each framework relies on its own interpretation of timeline-based
planning and therefore it is not easy to compare these systems. Thus, the
objective of this work is to investigate the timeline-based approach to
planning by addressing several aspects ranging from the semantics of the
related planning concepts to the modeling and solving techniques. Specifically,
the main contributions of this PhD work consist of: (i) the proposal of a
formal characterization of the timeline-based approach capable of dealing with
temporal uncertainty; (ii) the proposal of a hierarchical modeling and solving
approach; (iii) the development of a general purpose framework for planning and
execution with timelines; (iv) the validation{\dag}of this approach in
real-world manufacturing scenarios.
","Timeline-based Planning and Execution with Uncertainty: Theory, Modeling
  Methodologies and Practice"
"  Current advances in Artificial Intelligence and machine learning in general,
and deep learning in particular have reached unprecedented impact not only
across research communities, but also over popular media channels. However,
concerns about interpretability and accountability of AI have been raised by
influential thinkers. In spite of the recent impact of AI, several works have
identified the need for principled knowledge representation and reasoning
mechanisms integrated with deep learning-based systems to provide sound and
explainable models for such systems. Neural-symbolic computing aims at
integrating, as foreseen by Valiant, two most fundamental cognitive abilities:
the ability to learn from the environment, and the ability to reason from what
has been learned. Neural-symbolic computing has been an active topic of
research for many years, reconciling the advantages of robust learning in
neural networks and reasoning and interpretability of symbolic representation.
In this paper, we survey recent accomplishments of neural-symbolic computing as
a principled methodology for integrated machine learning and reasoning. We
illustrate the effectiveness of the approach by outlining the main
characteristics of the methodology: principled integration of neural learning
with symbolic knowledge representation and reasoning allowing for the
construction of explainable AI systems. The insights provided by
neural-symbolic computing shed new light on the increasingly prominent need for
interpretable and accountable AI systems.
","Neural-Symbolic Computing: An Effective Methodology for Principled
  Integration of Machine Learning and Reasoning"
"  A fundamental concern in real-time planning is the presence of dead-ends in
the state space, from which no goal is reachable. Recently, the SafeRTS
algorithm was proposed for searching in such spaces. SafeRTS exploits a
user-provided predicate to identify safe states, from which a goal is likely
reachable, and attempts to maintain a backup plan for reaching a safe state at
all times. In this paper, we study the SafeRTS approach, identify certain
properties of its behavior, and design an improved framework for safe real-time
search. We prove that the new approach performs at least as well as SafeRTS and
present experimental results showing that its promise is fulfilled in practice.
",Improved Safe Real-time Heuristic Search
"  In the context of Industry 4.0, data management is a key point for decision
aid approaches. Large amounts of manufacturing digital data are collected on
the shop floor. Their analysis can then require a large amount of computing
power. The Big Data issue can be solved by aggregation, generating smart and
meaningful data. This paper presents a new knowledge-based multi-level
aggregation strategy to support decision making. Manufacturing knowledge is
used at each level to design the monitoring criteria or aggregation operators.
The proposed approach has been implemented as a demonstrator and successfully
applied to a real machining database from the aeronautic industry. Decision
Making; Machining; Knowledge based system
","Knowledge-based multi-level aggregation for decision aid in the
  machining industry"
"  This paper surveys an approach to the XAI problem, using post-hoc explanation
by example, that hinges on twinning Artificial Neural Networks (ANNs) with
Case-Based Reasoning (CBR) systems, so-called ANN-CBR twins. A systematic
survey of 1100+ papers was carried out to identify the fragmented literature on
this topic and to trace it influence through to more recent work involving Deep
Neural Networks (DNNs). The paper argues that this twin-system approach,
especially using ANN-CBR twins, presents one possible coherent, generic
solution to the XAI problem (and, indeed, XCBR problem). The paper concludes by
road-mapping some future directions for this XAI solution involving (i) further
tests of feature-weighting techniques, (iii) explorations of how explanatory
cases might best be deployed (e.g., in counterfactuals, near-miss cases, a
fortori cases), and (iii) the raising of the unwelcome and, much ignored, issue
of human user evaluation.
","How Case Based Reasoning Explained Neural Networks: An XAI Survey of
  Post-Hoc Explanation-by-Example in ANN-CBR Twins"
"  The notion of twin systems is proposed to address the eXplainable AI (XAI)
problem, where an uninterpretable black-box system is mapped to a white-box
'twin' that is more interpretable. In this short paper, we overview very recent
work that advances a generic solution to the XAI problem, the so called twin
system approach. The most popular twinning in the literature is that between an
Artificial Neural Networks (ANN ) as a black box and Case Based Reasoning (CBR)
system as a white-box, where the latter acts as an interpretable proxy for the
former. We outline how recent work reviving this idea has applied it to deep
learning methods. Furthermore, we detail the many fruitful directions in which
this work may be taken; such as, determining the most (i) accurate
feature-weighting methods to be used, (ii) appropriate deployments for
explanatory cases, (iii) useful cases of explanatory value to users.
","The Twin-System Approach as One Generic Solution for XAI: An Overview of
  ANN-CBR Twins for Explaining Deep Learning"
"  Concrete is the most widely used engineered material in the world with more
than 10 billion tons produced annually. Unfortunately, with that scale comes a
significant burden in terms of energy, water, and release of greenhouse gases
and other pollutants. As such, there is interest in creating concrete formulas
that minimize this environmental burden, while satisfying engineering
performance requirements. Recent advances in artificial intelligence have
enabled machines to generate highly plausible artifacts, such as images of
realistic looking faces. Semi-supervised generative models allow generation of
artifacts with specific, desired characteristics. In this work, we use
Conditional Variational Autoencoders (CVAE), a type of semi-supervised
generative model, to discover concrete formulas with desired properties. Our
model is trained using open data from the UCI Machine Learning Repository
joined with environmental impact data computed using a web-based tool. We
demonstrate CVAEs can design concrete formulas with lower emissions and natural
resource usage while meeting design requirements. To ensure fair comparison
between extant and generated formulas, we also train regression models to
predict the environmental impacts and strength of discovered formulas. With
these results, a construction engineer may create a formula that meets
structural needs and best addresses local environmental concerns.
",Accelerated Discovery of Sustainable Building Materials
"  While research on iterated revision is predominant in the field of iterated
belief change, the class of iterated contraction operators received more
attention in recent years. In this article, we examine a non-prioritized
generalisation of iterated contraction. In particular, the class of weak
decrement operators is introduced, which are operators that by multiple steps
achieve the same as a contraction. Inspired by Darwiche and Pearl's work on
iterated revision the subclass of decrement operators is defined. For both,
decrement and weak decrement operators, postulates are presented and for each
of them a representation theorem in the framework of total preorders is given.
Furthermore, we present two sub-types of decrement operators.
",Decrement Operators in Belief Change
"  In this paper, we demonstrate a data-driven methodology for modelling the
local similarity measures of various attributes in a dataset. We analyse the
spread in the numerical attributes and estimate their distribution using
polynomial function to showcase an approach for deriving strong initial value
ranges of numerical attributes and use a non-overlapping distribution for
categorical attributes such that the entire similarity range [0,1] is utilized.
We use an open source dataset for demonstrating modelling and development of
the similarity measures and will present a case-based reasoning (CBR) system
that can be used to search for the most relevant similar cases.
","Similarity Measure Development for Case-Based Reasoning- A Data-driven
  Approach"
"  Deep learning is bringing remarkable contributions to the field of
argumentation mining, but the existing approaches still need to fill the gap
toward performing advanced reasoning tasks. In this position paper, we posit
that neural-symbolic and statistical relational learning could play a crucial
role in the integration of symbolic and sub-symbolic methods to achieve this
goal.
","Neural-Symbolic Argumentation Mining: an Argument in Favor of Deep
  Learning and Reasoning"
"  Reduced models of large Markov decision processes accelerate planning by
considering a subset of outcomes for each state-action pair. This reduction in
reachable states leads to replanning when the agent encounters states without a
precomputed action during plan execution. However, not all states are suitable
for replanning. In the worst case, the agent may not be able to reach the goal
from the newly encountered state. Agents should be better prepared to handle
such risky situations and avoid replanning in risky states. Hence, we consider
replanning in states that are unsafe for deliberation as a negative side effect
of planning with reduced models. While the negative side effects can be
minimized by always using the full model, this defeats the purpose of using
reduced models. The challenge is to plan with reduced models, but somehow
account for the possibility of encountering risky situations. An agent should
thus only replan in states that the user has approved as safe for replanning.
To that end, we propose planning using a portfolio of reduced models, a
planning paradigm that minimizes the negative side effects of planning using
reduced models by alternating between different outcome selection approaches.
We empirically demonstrate the effectiveness of our approach on three domains:
an electric vehicle charging domain using real-world data from a university
campus and two benchmark planning problems.
",Minimizing the Negative Side Effects of Planning with Reduced Models
"  Background. Most tutorial ontologies focus on illustrating one aspect of
ontology development, notably language features and automated reasoners, but
ignore ontology development factors, such as emergent modelling guidelines and
ontological principles. Yet, novices replicate examples from the exercises they
carry out. Not providing good examples holistically causes the propagation of
sub-optimal ontology development, which may negatively affect the quality of a
real domain ontology. Results. We identified 22 requirements that a good
tutorial ontology should satisfy regarding subject domain, logics and
reasoning, and engineering aspects. We developed a set of ontologies about
African Wildlife to serve as tutorial ontologies. A majority of the
requirements have been met with the set of African Wildlife Ontology tutorial
ontologies, which are introduced in this paper. The African Wildlife Ontology
is mature and has been used yearly in an ontology engineering course or
tutorial since 2010 and is included in a recent ontology engineering textbook
with relevant examples and exercises. Conclusion. The African Wildlife Ontology
provides a wide range of options concerning examples and exercises for ontology
engineering well beyond illustrating only language features and automated
reasoning. It assists in demonstrating tasks about ontology quality, such as
alignment to a foundational ontology and satisfying competency questions,
versioning, and multilingual ontologies.
","The African Wildlife Ontology tutorial ontologies: requirements, design,
  and content"
"  In this work we describe a new learning-based proof guidance -- ENIGMAWatch
-- for saturation-style first-order theorem provers. ENIGMAWatch combines two
guiding approaches for the given-clause selection implemented for the E ATP
system: ProofWatch and ENIGMA. ProofWatch is motivated by the watchlist (hints)
method and based on symbolic matching of multiple related proofs, while ENIGMA
is based on statistical machine learning. The two methods are combined by using
the evolving information about symbolic proof matching as an additional
information that characterizes the saturation-style proof search for the
statistical learning methods. The new system is experimentally evaluated on a
large set of problems from the Mizar Library. We show that the added
proof-matching information is considered important by the statistical machine
learners, and that it leads to improvements in E's Performance over ProofWatch
and ENIGMA.
",ENIGMAWatch: ProofWatch Meets ENIGMA
"  Dynamic Epistemic Logic (DEL) is a family of multimodal logics that has
proved to be very successful for epistemic reasoning in planning tasks. In this
logic, the agent's knowledge is captured by modal epistemic operators whereas
the system evolution is described in terms of (some subset of) dynamic logic
modalities in which actions are usually represented as semantic objects called
event models. In this paper, we study a variant of DEL, that wecall DEL[ASP],
where actions are syntactically described by using an Answer Set Programming
(ASP) representation instead of event models. This representation directly
inherits high level expressive features like indirect effects, qualifications,
state constraints, defaults, or recursive fluents that are common in ASP
descriptions of action domains. Besides, we illustrate how this approach can be
applied for obtaining conditional plans in single-agent, partially observable
domains where knowledge acquisition may be represented as indirect effects of
actions.
","Dynamic Epistemic Logic with ASP Updates: Application to Conditional
  Planning"
"  In order to be useful in the real world, AI agents need to plan and act in
the presence of others, who may include adversarial and cooperative entities.
In this paper, we consider the problem where an autonomous agent needs to act
in a manner that clarifies its objectives to cooperative entities while
preventing adversarial entities from inferring those objectives. We show that
this problem is solvable when cooperative entities and adversarial entities use
different types of sensors and/or prior knowledge. We develop two new solution
approaches for computing such plans. One approach provides an optimal solution
to the problem by using an IP solver to provide maximum obfuscation for
adversarial entities while providing maximum legibility for cooperative
entities in the environment, whereas the other approach provides a satisficing
solution using heuristic-guided forward search to achieve preset levels of
obfuscation and legibility for adversarial and cooperative entities
respectively. We show the feasibility and utility of our algorithms through
extensive empirical evaluation on problems derived from planning benchmarks.
","Signaling Friends and Head-Faking Enemies Simultaneously: Balancing Goal
  Obfuscation and Goal Legibility"
"  Ensemble Decision Systems offer a unique form of decision making that allows
a collection of algorithms to reason together about a problem. Each individual
algorithm has its own inherent strengths and weaknesses, and often it is
difficult to overcome the weaknesses while retaining the strengths. Instead of
altering the properties of the algorithm, the Ensemble Decision System augments
the performance with other algorithms that have complementing strengths. This
work outlines different options for building an Ensemble Decision System as
well as providing analysis on its performance compared to the individual
components of the system with interesting results, showing an increase in the
generality of the algorithms without significantly impeding performance.
",Ensemble Decision Systems for General Video Game Playing
"  We develop a new model that can be applied to any perfect information
two-player zero-sum game to target a high score, and thus a perfect play. We
integrate this model into the Monte Carlo tree search-policy iteration learning
pipeline introduced by Google DeepMind with AlphaGo. Training this model on 9x9
Go produces a superhuman Go player, thus proving that it is stable and robust.
We show that this model can be used to effectively play with both positional
and score handicap, and to minimize suboptimal moves. We develop a family of
agents that can target high scores against any opponent, and recover from very
severe disadvantage against weak opponents. To the best of our knowledge, these
are the first effective achievements in this direction.
","SAI: a Sensible Artificial Intelligence that plays with handicap and
  targets high scores in 9x9 Go (extended version)"
"  Decision-making in large imperfect information games is difficult. Thanks to
recent success in Poker, Counterfactual Regret Minimization (CFR) methods have
been at the forefront of research in these games. However, most of the success
in large games comes with the use of a forward model and powerful state
abstractions. In trick-taking card games like Bridge or Skat, large information
sets and an inability to advance the simulation without fully determinizing the
state make forward search problematic. Furthermore, state abstractions can be
especially difficult to construct because the precise holdings of each player
directly impact move values.
  In this paper we explore learning model-free policies for Skat from human
game data using deep neural networks (DNN). We produce a new state-of-the-art
system for bidding and game declaration by introducing methods to a) directly
vary the aggressiveness of the bidder and b) declare games based on expected
value while mitigating issues with rarely observed state-action pairs. Although
cardplay policies learned through imitation are slightly weaker than the
current best search-based method, they run orders of magnitude faster. We also
explore how these policies could be learned directly from experience in a
reinforcement learning setting and discuss the value of incorporating human
data for this task.
",Learning Policies from Human Data for Skat
"  Trick-taking card games feature a large amount of private information that
slowly gets revealed through a long sequence of actions. This makes the number
of histories exponentially large in the action sequence length, as well as
creating extremely large information sets. As a result, these games become too
large to solve. To deal with these issues many algorithms employ inference, the
estimation of the probability of states within an information set. In this
paper, we demonstrate a Policy Based Inference (PI) algorithm that uses player
modelling to infer the probability we are in a given state. We perform
experiments in the German trick-taking card game Skat, in which we show that
this method vastly improves the inference as compared to previous work, and
increases the performance of the state-of-the-art Skat AI system Kermit when it
is employed into its determinized search algorithm.
",Policy Based Inference in Trick-Taking Card Games
"  Perhaps the most ambitious scientific quest in human history is the creation
of general artificial intelligence, which roughly means AI that is as smart or
smarter than humans. The dominant approach in the machine learning community is
to attempt to discover each of the pieces required for intelligence, with the
implicit assumption that some future group will complete the Herculean task of
figuring out how to combine all of those pieces into a complex thinking
machine. I call this the ""manual AI approach"". This paper describes another
exciting path that ultimately may be more successful at producing general AI.
It is based on the clear trend in machine learning that hand-designed solutions
eventually are replaced by more effective, learned solutions. The idea is to
create an AI-generating algorithm (AI-GA), which automatically learns how to
produce general AI. Three Pillars are essential for the approach: (1)
meta-learning architectures, (2) meta-learning the learning algorithms
themselves, and (3) generating effective learning environments. I argue that
either approach could produce general AI first, and both are scientifically
worthwhile irrespective of which is the fastest path. Because both are
promising, yet the ML community is currently committed to the manual approach,
I argue that our community should increase its research investment in the AI-GA
approach. To encourage such research, I describe promising work in each of the
Three Pillars. I also discuss AI-GA-specific safety and ethical considerations.
Because it it may be the fastest path to general AI and because it is
inherently scientifically interesting to understand the conditions in which a
simple algorithm can produce general AI (as happened on Earth where Darwinian
evolution produced human intelligence), I argue that the pursuit of AI-GAs
should be considered a new grand challenge of computer science research.
","AI-GAs: AI-generating algorithms, an alternate paradigm for producing
  general artificial intelligence"
"  Weighted A* (wA*) is a widely used algorithm for rapidly, but suboptimally,
solving planning and search problems. The cost of the solution it produces is
guaranteed to be at most W times the optimal solution cost, where W is the
weight wA* uses in prioritizing open nodes. W is therefore a suboptimality
bound for the solution produced by wA*. There is broad consensus that this
bound is not very accurate, that the actual suboptimality of wA*'s solution is
often much less than W times optimal. However, there is very little published
evidence supporting that view, and no existing explanation of why W is a poor
bound. This paper fills in these gaps in the literature. We begin with a
large-scale experiment demonstrating that, across a wide variety of domains and
heuristics for those domains, W is indeed very often far from the true
suboptimality of wA*'s solution. We then analytically identify the potential
sources of error. Finally, we present a practical method for correcting for two
of these sources of error and experimentally show that the correction
frequently eliminates much of the error.
","Error Analysis and Correction for Weighted A*'s Suboptimality (Extended
  Version)"
"  This paper describes a possible way to improve computer security by
implementing a program which implements the following three features related to
a weak notion of artificial consciousness: (partial) self-monitoring, ability
to compute the truth of quantifier-free propositions and the ability to
communicate with the user. The integrity of the program could be enhanced by
using a trusted computing approach, that is to say a hardware module that is at
the root of a chain of trust. This paper outlines a possible approach but does
not refer to an implementation (which would need further work), but the author
believes that an implementation using current processors, a debugger, a
monitoring program and a trusted processing module is currently possible.
",Artificial Consciousness and Security
"  General intelligence, the ability to solve arbitrary solvable problems, is
supposed by many to be artificially constructible. Narrow intelligence, the
ability to solve a given particularly difficult problem, has seen impressive
recent development. Notable examples include self-driving cars, Go engines,
image classifiers, and translators. Artificial General Intelligence (AGI)
presents dangers that narrow intelligence does not: if something smarter than
us across every domain were indifferent to our concerns, it would be an
existential threat to humanity, just as we threaten many species despite no ill
will. Even the theory of how to maintain the alignment of an AGI's goals with
our own has proven highly elusive. We present the first algorithm we are aware
of for asymptotically unambitious AGI, where ""unambitiousness"" includes not
seeking arbitrary power. Thus, we identify an exception to the Instrumental
Convergence Thesis, which is roughly that by default, an AGI would seek power,
including over us.
",Asymptotically Unambitious Artificial General Intelligence
"  We propose a set of compositional design patterns to describe a large variety
of systems that combine statistical techniques from machine learning with
symbolic techniques from knowledge representation. As in other areas of
computer science (knowledge engineering, software engineering, ontology
engineering, process mining and others), such design patterns help to
systematize the literature, clarify which combinations of techniques serve
which purposes, and encourage re-use of software components. We have validated
our set of compositional design patterns against a large body of recent
literature.
",A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems
"  In Case-Based Reasoning, when the similarity assumption does not hold, the
retrieval of a set of cases structurally similar to the query does not
guarantee to get a reusable or revisable solution. Knowledge about the
adaptability of solutions has to be exploited, in order to define a method for
adaptation-guided retrieval. We propose a novel approach to address this
problem, where knowledge about the adaptability of the solutions is captured
inside a metric Markov Random Field (MRF). Nodes of the MRF represent cases and
edges connect nodes whose solutions are close in the solution space. States of
the nodes represent different adaptation levels with respect to the potential
query. Metric-based potentials enforce connected nodes to share the same state,
since cases having similar solutions should have the same adaptability level
with respect to the query. The main goal is to enlarge the set of potentially
adaptable cases that are retrieved without significantly sacrificing the
precision and accuracy of retrieval. We will report on some experiments
concerning a retrieval architecture where a simple kNN retrieval (on the
problem description) is followed by a further retrieval step based on MRF
inference.
","Approaching Adaptation Guided Retrieval in Case-Based Reasoning through
  Inference in Undirected Graphical Models"
"  Over the past few years the Angry Birds AI competition has been held in an
attempt to develop intelligent agents that can successfully and efficiently
solve levels for the video game Angry Birds. Many different agents and
strategies have been developed to solve the complex and challenging physical
reasoning problems associated with such a game. However none of these agents
attempt one of the key strategies which humans employ to solve Angry Birds
levels, which is restarting levels. Restarting is important in Angry Birds
because sometimes the level is no longer solvable or some given shot made has
little to no benefit towards the ultimate goal of the game. This paper proposes
a framework and experimental evaluation for when to restart levels in Angry
Birds. We demonstrate that restarting is a viable strategy to improve agent
performance in many cases.
",Using Restart Heuristics to Improve Agent Performance in Angry Birds
"  We propose a novel reinforcement learning algorithm, AlphaNPI, that
incorporates the strengths of Neural Programmer-Interpreters (NPI) and
AlphaZero. NPI contributes structural biases in the form of modularity,
hierarchy and recursion, which are helpful to reduce sample complexity, improve
generalization and increase interpretability. AlphaZero contributes powerful
neural network guided search algorithms, which we augment with recursion.
AlphaNPI only assumes a hierarchical program specification with sparse rewards:
1 when the program execution satisfies the specification, and 0 otherwise.
Using this specification, AlphaNPI is able to train NPI models effectively with
RL for the first time, completely eliminating the need for strong supervision
in the form of execution traces. The experiments show that AlphaNPI can sort as
well as previous strongly supervised NPI variants. The AlphaNPI agent is also
trained on a Tower of Hanoi puzzle with two disks and is shown to generalize to
puzzles with an arbitrary number of disk
","Learning Compositional Neural Programs with Recursive Tree Search and
  Planning"
"  Rankings, representing preferences over a set of candidates, are widely used
in many information systems, e.g., group decision making and information
retrieval. It is of great importance to evaluate the consensus of the obtained
rankings from multiple agents. An overall measure of the consensus degree
provides an insight into the ranking data. Moreover, it could provide a
quantitative indicator for consensus comparison between groups and further
improvement of a ranking system. Existing studies are insufficient in assessing
the overall consensus of a ranking set. They did not provide an evaluation of
the consensus degree of preference patterns in most rankings. In this paper, a
novel consensus quantifying approach, without the need for any correlation or
distance functions as in existing studies of consensus, is proposed based on a
concept of q-support patterns of rankings. The q-support patterns represent the
commonality embedded in a set of rankings. A method for detecting outliers in a
set of rankings is naturally derived from the proposed consensus quantifying
approach. Experimental studies are conducted to demonstrate the effectiveness
of the proposed approach.
",Quantifying consensus of rankings based on q-support patterns
"  Digital Archaeoludology (DAL) is a new field of study involving the analysis
and reconstruction of ancient games from incomplete descriptions and
archaeological evidence using modern computational techniques. The aim is to
provide digital tools and methods to help game historians and other researchers
better understand traditional games, their development throughout recorded
human history, and their relationship to the development of human culture and
mathematical knowledge. This work is being explored in the ERC-funded Digital
Ludeme Project.
  The aim of this inaugural international research meeting on DAL is to gather
together leading experts in relevant disciplines - computer science, artificial
intelligence, machine learning, computational phylogenetics, mathematics,
history, archaeology, anthropology, etc. - to discuss the key themes and
establish the foundations for this new field of research, so that it may
continue beyond the lifetime of its initiating project.
",Foundations of Digital Arch{\ae}oludology
"  Many of the strongest game playing programs use a combination of Monte Carlo
tree search (MCTS) and deep neural networks (DNN), where the DNNs are used as
policy or value evaluators. Given a limited budget, such as online playing or
during the self-play phase of AlphaZero (AZ) training, a balance needs to be
reached between accurate state estimation and more MCTS simulations, both of
which are critical for a strong game playing agent. Typically, larger DNNs are
better at generalization and accurate evaluation, while smaller DNNs are less
costly, and therefore can lead to more MCTS simulations and bigger search trees
with the same budget. This paper introduces a new method called the multiple
policy value MCTS (MPV-MCTS), which combines multiple policy value neural
networks (PV-NNs) of various sizes to retain advantages of each network, where
two PV-NNs f_S and f_L are used in this paper. We show through experiments on
the game NoGo that a combined f_S and f_L MPV-MCTS outperforms single PV-NN
with policy value MCTS, called PV-MCTS. Additionally, MPV-MCTS also outperforms
PV-MCTS for AZ training.
",Multiple Policy Value Monte Carlo Tree Search
"  In this research work, probabilistic decision-making approaches are studied,
e.g. Bayesian and Boltzmann strategies, along with various deterministic
exploration strategies, e.g. greedy, epsilon-Greedy and random approaches. In
this research work, a comparative study has been done between probabilistic and
deterministic decision-making approaches, the experiments are performed in
OpenAI gym environment, solving Cart Pole problem. This research work discusses
about the Bayesian approach to decision-making in deep reinforcement learning,
and about dropout, how it can reduce the computational cost. All the
exploration approaches are compared. It also discusses about the importance of
exploration in deep reinforcement learning, and how improving exploration
strategies may help in science and technology. This research work shows how
probabilistic decision-making approaches are better in the long run as compared
to the deterministic approaches. When there is uncertainty, Bayesian dropout
approach proved to be better than all other approaches in this research work.
",Decision-Making in Reinforcement Learning
"  The problem of learning logical rules from examples arises in diverse fields,
including program synthesis, logic programming, and machine learning. Existing
approaches either involve solving computationally difficult combinatorial
problems, or performing parameter estimation in complex statistical models.
  In this paper, we present Difflog, a technique to extend the logic
programming language Datalog to the continuous setting. By attaching
real-valued weights to individual rules of a Datalog program, we naturally
associate numerical values with individual conclusions of the program.
Analogous to the strategy of numerical relaxation in optimization problems, we
can now first determine the rule weights which cause the best agreement between
the training labels and the induced values of output tuples, and subsequently
recover the classical discrete-valued target program from the continuous
optimum.
  We evaluate Difflog on a suite of 34 benchmark problems from recent
literature in knowledge discovery, formal verification, and database
query-by-example, and demonstrate significant improvements in learning complex
programs with recursive rules, invented predicates, and relations of arbitrary
arity.
",Synthesizing Datalog Programs Using Numerical Relaxation
"  In this paper, we present a new methodology that employs tester agents to
automate video game testing. We introduce two types of agents -synthetic and
human-like- and two distinct approaches to create them. Our agents are derived
from Reinforcement Learning (RL) and Monte Carlo Tree Search (MCTS) agents, but
focus on finding defects. The synthetic agent uses test goals generated from
game scenarios, and these goals are further modified to examine the effects of
unintended game transitions. The human-like agent uses test goals extracted by
our proposed multiple greedy-policy inverse reinforcement learning (MGP-IRL)
algorithm from tester trajectories. MGPIRL captures multiple policies executed
by human testers. These testers' aims are finding defects while interacting
with the game to break it, which is considerably different from game playing.
We present interaction states to model such interactions. We use our agents to
produce test sequences, run the game with these sequences, and check the game
for each run with an automated test oracle. We analyze the proposed method in
two parts: we compare the success of human-like and synthetic agents in bug
finding, and we evaluate the similarity between humanlike agents and human
testers. We collected 427 trajectories from human testers using the General
Video Game Artificial Intelligence (GVG-AI) framework and created three games
with 12 levels that contain 45 bugs. Our experiments reveal that human-like and
synthetic agents compete with human testers' bug finding performances.
Moreover, we show that MGP-IRL increases the human-likeness of agents while
improving the bug finding performance.
",Automated Video Game Testing Using Synthetic and Human-Like Agents
"  Kandinsky Figures and Kandinsky Patterns are mathematically describable,
simple self-contained hence controllable test data sets for the development,
validation and training of explainability in artificial intelligence. Whilst
Kandinsky Patterns have these computationally manageable properties, they are
at the same time easily distinguishable from human observers. Consequently,
controlled patterns can be described by both humans and computers. We define a
Kandinsky Pattern as a set of Kandinsky Figures, where for each figure an
""infallible authority"" defines that the figure belongs to the Kandinsky
Pattern. With this simple principle we build training and validation data sets
for automatic interpretability and context learning. In this paper we describe
the basic idea and some underlying principles of Kandinsky Patterns and provide
a Github repository to invite the international machine learning research
community to a challenge to experiment with our Kandinsky Patterns to expand
and thus make progress in the field of explainable AI and to contribute to the
upcoming field of explainability and causability.
",Kandinsky Patterns
"  We analyze the type of learned optimization that occurs when a learned model
(such as a neural network) is itself an optimizer - a situation we refer to as
mesa-optimization, a neologism we introduce in this paper. We believe that the
possibility of mesa-optimization raises two important questions for the safety
and transparency of advanced machine learning systems. First, under what
circumstances will learned models be optimizers, including when they should not
be? Second, when a learned model is an optimizer, what will its objective be -
how will it differ from the loss function it was trained under - and how can it
be aligned? In this paper, we provide an in-depth analysis of these two primary
questions and provide an overview of topics for future research.
",Risks from Learned Optimization in Advanced Machine Learning Systems
"  This paper investigates the use of intrinsic reward to guide exploration in
multi-agent reinforcement learning. We discuss the challenges in applying
intrinsic reward to multiple collaborative agents and demonstrate how
unreliable reward can prevent decentralized agents from learning the optimal
policy. We address this problem with a novel framework, Independent
Centrally-assisted Q-learning (ICQL), in which decentralized agents share
control and an experience replay buffer with a centralized agent. Only the
centralized agent is intrinsically rewarded, but the decentralized agents still
benefit from improved exploration, without the distraction of unreliable
incentives.
","Exploration with Unreliable Intrinsic Reward in Multi-Agent
  Reinforcement Learning"
"  In order to collaborate and co-create with humans, an AI system must be
capable of both reactive and anticipatory behavior. We present a case study of
such a system in the domain of musical improvisation. We consider a duo
consisting of a human pianist accompained by an off-the-shelf virtual drummer,
and we design an AI system to control the perfomance parameters of the drummer
(e.g., patterns, intensity, or complexity) as a function of what the human
pianist is playing. The AI system utilizes a model elicited from the musicians
and encoded through fuzzy logic. This paper outlines the methodology, design,
and development process of this system. An evaluation in public concerts is
upcoming. This case study is seen as a step in the broader investigation of
anticipation and creative processes in mixed human-robot, or ""anthrobotic""
systems.
","Anticipation in collaborative music performance using fuzzy systems: a
  case study"
"  The Maximum k-plex Problem is an important combinatorial optimization problem
with increasingly wide applications. Due to its exponential time complexity,
many heuristic methods have been proposed which can return a good-quality
solution in a reasonable time. However, most of the heuristic algorithms are
memoryless and unable to utilize the experience during the search. Inspired by
the multi-armed bandit (MAB) problem in reinforcement learning (RL), we propose
a novel perturbation mechanism named BLP, which can learn online to select a
good vertex for perturbation when getting stuck in local optima. To our best of
knowledge, this is the first attempt to combine local search with RL for the
maximum $ k $-plex problem.
  Besides, we also propose a novel strategy, named Dynamic-threshold
Configuration Checking (DTCC), which extends the original Configuration
Checking (CC) strategy from two aspects.
  Based on the BLP and DTCC, we develop a local search algorithm named BDCC and
improve it by a hyperheuristic strategy. The experimental result shows that our
algorithms dominate on the standard DIMACS and BHOSLIB benchmarks and achieve
state-of-the-art performance on massive graphs.
","Combining Reinforcement Learning and Configuration Checking for Maximum
  k-plex Problem"
"  Iterative deepening search is used in applications where the best cost bound
for state-space search is unknown. The iterative deepening process is used to
avoid overshooting the appropriate cost bound and doing too much work as a
result. However, iterative deepening search also does too much work if the cost
bound grows too slowly. This paper proposes a new framework for iterative
deepening search called exponential-binary state-space search. The approach
interleaves exponential and binary searches to find the desired cost bound,
reducing the worst-case overhead from polynomial to logarithmic.
Exponential-binary search can be used with bounded depth-first search to
improve the worst-case performance of IDA* and with breadth-first heuristic
search to improve the worst-case performance of search with inconsistent
heuristics.
",Exponential-Binary State-Space Search
"  This paper introduces the Contextual Evaluation Model (CEM), a novel method
for knowledge representation and manipulation. The CEM differs from existing
models in that it integrates facts, patterns and sequences into a single
contextual framework. V5, an implementation of the model is presented and
demonstrated with multiple annotated examples. The paper includes simulations
demonstrating how the model reacts to pleasure/pain stimuli. The 'thought' is
defined within the model and examples are given converting thoughts to
language, converting language to thoughts and how 'meaning' arises from
thoughts. A pattern learning algorithm is described. The algorithm is applied
to multiple problems ranging from recognizing a voice to the autonomous
learning of a simplified natural language.
",Representing and Using Knowledge with the Contextual Evaluation Model
"  The application of rough set theory in incomplete information systems is a
key problem in practice since missing values almost always occur in knowledge
acquisition due to the error of data measuring, the limitation of data
collection, or the limitation of data comprehension, etc. An incomplete
information system is mainly processed by compressing the indiscernibility
relation. The existing rough set extension models based on tolerance or
symmetric similarity relations typically discard one relation among the
reflexive, symmetric and transitive relations, especially the transitive
relation. In order to overcome the limitations of the current rough set
extension models, we define a new relation called the positive transitive
relation and then propose a novel rough set extension model built upon which.
The new model holds the merit of the existing rough set extension models while
avoids their limitations of discarding transitivity or symmetry. In comparison
to the existing extension models, the proposed model has a better performance
in processing the incomplete information systems while substantially reducing
the computational complexity, taking into account the relation of tolerance and
similarity of positive transitivity, and supplementing the related theories in
accordance to the intuitive classification of incomplete information. In
summary, the positive transitive relation can improve current theoretical
analysis of incomplete information systems and the newly proposed extension
model is more suitable for processing incomplete information systems and has a
broad application prospect.
",Extension of Rough Set Based on Positive Transitive Relation
"  In multi-agent planning, preserving the agents' privacy has become an
increasingly popular research topic. For preserving the agents' privacy, agents
jointly compute a plan that achieves mutual goals by keeping certain
information private to the individual agents. Unfortunately, this can severely
restrict the accuracy of the heuristic functions used while searching for
solutions. It has been recently shown that, for centralized planning, the
performance of goal oriented search can be improved by combining goal oriented
search and width-based search. The combination of these techniques has been
called best-first width search. In this paper, we investigate the usage of
best-first width search in the context of (decentralised) multi-agent
privacy-preserving planning, addressing the challenges related to the agents'
privacy and performance. In particular, we show that best-first width search is
a very effective approach over several benchmark domains, even when the search
is driven by heuristics that roughly estimate the distance from goal states,
computed without using the private information of other agents. An experimental
study analyses the effectiveness of our techniques and compares them with the
state-of-the-art.
",Best-First Width Search for Multi Agent Privacy-preserving Planning
"  In a multi-agent pathfinding (MAPF) problem, agents need to navigate from
their start to their goal locations without colliding into each other. There
are various MAPF algorithms, including Windowed Hierarchical Cooperative A*,
Flow Annotated Replanning, and Bounded Multi-Agent A*. It is often the case
that there is no a single algorithm that dominates all MAPF instances.
Therefore, in this paper, we investigate the use of deep learning to
automatically select the best MAPF algorithm from a portfolio of algorithms for
a given MAPF problem instance. Empirical results show that our automatic
algorithm selection approach, which uses an off-the-shelf convolutional neural
network, is able to outperform any individual MAPF algorithm in our portfolio.
",Automatic Algorithm Selection In Multi-agent Pathfinding
"  The Hearthstone AI framework and competition motivates the development of
artificial intelligence agents that can play collectible card games. A special
feature of those games is the high variety of cards, which can be chosen by the
players to create their own decks. In contrast to simpler card games, the value
of many cards is determined by their possible synergies. The vast amount of
possible decks, the randomness of the game, as well as the restricted
information during the player's turn offer quite a hard challenge for the
development of game-playing agents. This short paper introduces the competition
framework and goes into more detail on the problems and challenges that need to
be faced during the development process.
",Introducing the Hearthstone-AI Competition
"  In the last decades we have witnessed the success of applications of
Artificial Intelligence to playing games. In this work we address the
challenging field of games with hidden information and card games in
particular. Jass is a very popular card game in Switzerland and is closely
connected with Swiss culture. To the best of our knowledge, performances of
Artificial Intelligence agents in the game of Jass do not outperform top
players yet. Our contribution to the community is two-fold. First, we provide
an overview of the current state-of-the-art of Artificial Intelligence methods
for card games in general. Second, we discuss their application to the use-case
of the Swiss card game Jass. This paper aims to be an entry point for both
seasoned researchers and new practitioners who want to join in the Jass
challenge.
","Survey of Artificial Intelligence for Card Games and Its Application to
  the Swiss Game Jass"
"  This paper presents a two-step generative approach for creating dungeons in
the rogue-like puzzle game MiniDungeons 2. Generation is split into two steps,
initially producing the architectural layout of the level as its walls and
floor tiles, and then furnishing it with game objects representing the player's
start and goal position, challenges and rewards. Three layout creators and
three furnishers are introduced in this paper, which can be combined in
different ways in the two-step generative process for producing diverse
dungeons levels. Layout creators generate the floors and walls of a level,
while furnishers populate it with monsters, traps, and treasures. We test the
generated levels on several expressivity measures, and in simulations with
procedural persona agents.
",Two-step Constructive Approaches for Dungeon Generation
"  Probabilistic epistemic argumentation allows for reasoning about
argumentation problems in a way that is well founded by probability theory.
Epistemic states are represented by probability functions over possible worlds
and can be adjusted to new beliefs using update operators. While the use of
probability functions puts this approach on a solid foundational basis, it also
causes computational challenges as the amount of data to process depends
exponentially on the number of arguments. This leads to bottlenecks in
applications such as modelling opponent's beliefs for persuasion dialogues. We
show how update operators over probability functions can be related to update
operators over much more compact representations that allow polynomial-time
updates. We discuss the cognitive and probabilistic-logical plausibility of
this approach and demonstrate its applicability in computational persuasion.
","Polynomial-time Updates of Epistemic States in a Fragment of
  Probabilistic Epistemic Argumentation (Technical Report)"
"  How an agent can act optimally in stochastic, partially observable domains is
a challenge problem, the standard approach to address this issue is to learn
the domain model firstly and then based on the learned model to find the (near)
optimal policy. However, offline learning the model often needs to store the
entire training data and cannot utilize the data generated in the planning
phase. Furthermore, current research usually assumes the learned model is
accurate or presupposes knowledge of the nature of the unobservable part of the
world. In this paper, for systems with discrete settings, with the benefits of
Predictive State Representations~(PSRs), a model-based planning approach is
proposed where the learning and planning phases can both be executed online and
no prior knowledge of the underlying system is required. Experimental results
show compared to the state-of-the-art approaches, our algorithm achieved a high
level of performance with no prior knowledge provided, along with theoretical
advantages of PSRs. Source code is available at
https://github.com/DMU-XMU/PSR-MCTS-Online.
","Online Learning and Planning in Partially Observable Domains without
  Prior Knowledge"
"  We introduce the General Video Game Rule Generation problem, and the
eponymous software framework which will be used in a new track of the General
Video Game AI (GVGAI) competition. The problem is, given a game level as input,
to generate the rules of a game that fits that level. This can be seen as the
inverse of the General Video Game Level Generation problem. Conceptualizing
these two problems as separate helps breaking the very hard problem of
generating complete games into smaller, more manageable subproblems. The
proposed framework builds on the GVGAI software and thus asks the rule
generator for rules defined in the Video Game Description Language. We describe
the API, and three different rule generators: a random, a constructive and a
search-based generator. Early results indicate that the constructive generator
generates playable and somewhat interesting game rules but has a limited
expressive range, whereas the search-based generator generates remarkably
diverse rulesets, but with an uneven quality.
",General Video Game Rule Generation
"  Critical to successful human interaction is a capacity for empathy - the
ability to understand and share the thoughts and feelings of another. As
Artificial Intelligence (AI) systems are increasingly required to interact with
humans in a myriad of settings, it is important to enable AI to wield empathy
as a tool to benefit those it interacts with. In this paper, we work towards
this goal by bringing together a number of important concepts: empathy, AI
planning, and reasoning in the presence of knowledge and belief. We formalize
the notion of Empathetic Planning which is informed by the beliefs and
affective state of the empathizee. We appeal to an epistemic logic framework to
represent the beliefs of the empathizee and propose AI planning-based
computational approaches to compute empathetic solutions. We illustrate the
potential benefits of our approach by conducting a study where we evaluate
participants' perceptions of the agent's empathetic abilities and assistive
capabilities.
",Towards Empathetic Planning
"  Amao is a cognitive agent framework that tackles the invention of predicates
with a different strategy as compared to recent advances in Inductive Logic
Programming (ILP) approaches like Meta-Intepretive Learning (MIL) technique. It
uses a Neural Multi-Space (NeMuS) graph structure to anti-unify atoms from the
Herbrand base, which passes in the inductive momentum check. Inductive Clause
Learning (ICL), as it is called, is extended here by using the weights of
logical components, already present in NeMuS, to support inductive learning by
expanding clause candidates with anti-unified atoms. An efficient invention
mechanism is achieved, including the learning of recursive hypotheses, while
restricting the shape of the hypothesis by adding bias definitions or
idiosyncrasies of the language.
","Efficient predicate invention using shared ""NeMuS"""
"  Neural Multi-Space (NeMuS) is a weighted multi-space representation for a
portion of first-order logic designed for use with machine learning and neural
network methods. It was demonstrated that it can be used to perform reasoning
based on regions forming patterns of refutation and also in the process of
inductive learning in ILP-like style. Initial experiments were carried out to
investigate whether a self-organizing the approach is suitable to generate
similar concept regions according to the attributes that form such concepts. We
present the results and make an analysis of the suitability of the method in
the process of inductive learning with NeMuS.
",Self-organized inductive reasoning with NeMuS
"  We propose multi-type probabilistic serial (MPS) and multi-type random
priority (MRP) as extensions of the well known PS and RP mechanisms to the
multi-type resource allocation problem (MTRA) with partial preferences. In our
setting, there are multiple types of divisible items, and a group of agents who
have partial order preferences over bundles consisting of one item of each
type. We show that for the unrestricted domain of partial order preferences, no
mechanism satisfies both sd-efficiency and sd-envy-freeness. Notwithstanding
this impossibility result, our main message is positive: When agents'
preferences are represented by acyclic CP-nets, MPS satisfies sd-efficiency,
sd-envy-freeness, ordinal fairness, and upper invariance, while MRP satisfies
ex-post-efficiency, sd-strategy-proofness, and upper invariance, recovering the
properties of PS and RP.
",Multi-type Resource Allocation with Partial Preferences
"  Conventional reinforcement learning (RL) allows an agent to learn policies
via environmental rewards only, with a long and slow learning curve, especially
at the beginning stage. On the contrary, human learning is usually much faster
because prior and general knowledge and multiple information resources are
utilized. In this paper, we propose a
\textbf{P}lanner-\textbf{A}ctor-\textbf{C}ritic architecture for
hu\textbf{MAN}-centered planning and learning (\textbf{PACMAN}), where an agent
uses prior, high-level, deterministic symbolic knowledge to plan for
goal-directed actions. PACMAN integrates Actor-Critic algorithm of RL to
fine-tune its behavior towards both environmental rewards and human feedback.
To the best our knowledge, This is the first unified framework where
knowledge-based planning, RL, and human teaching jointly contribute to the
policy learning of an agent. Our experiments demonstrate that PACMAN leads to a
significant jump-start at the early stage of learning, converges rapidly and
with small variance, and is robust to inconsistent, infrequent, and misleading
feedback.
",A Joint Planning and Learning Framework for Human-Aided Decision-Making
"  Data-driven approaches are becoming more common as problem-solving techniques
in many areas of research and industry. In most cases, machine learning models
are the key component of these solutions, but a solution involves multiple such
models, along with significant levels of reasoning with the models' output and
input. Current technologies do not make such techniques easy to use for
application experts who are not fluent in machine learning nor for machine
learning experts who aim at testing ideas and models on real-world data in the
context of the overall AI system. We review key efforts made by various AI
communities to provide languages for high-level abstractions over learning and
reasoning techniques needed for designing complex AI systems. We classify the
existing frameworks based on the type of techniques and the data and knowledge
representations they use, provide a comparative study of the way they address
the challenges of programming real-world applications, and highlight some
shortcomings and future directions.
",Declarative Learning-Based Programming as an Interface to AI Systems
"  In multi-agent planning, agents jointly compute a plan that achieves mutual
goals, keeping certain information private to the individual agents. Agents'
coordination is achieved through the transmission of messages. These messages
can be a source of privacy leakage as they can permit a malicious agent to
collect information about other agents' actions and search states. In this
paper, we investigate the usage of novelty techniques in the context of
(decentralised) multi-agent privacy-preserving planning, addressing the
challenges related to the agents' privacy and performance. In particular, we
show that the use of novelty based techniques can significantly reduce the
number of messages transmitted among agents, better preserving their privacy
and improving their performance. An experimental study analyses the
effectiveness of our techniques and compares them with the state-of-the-art.
Finally, we evaluate the robustness of our approach, considering different
delays in the transmission of messages as they would occur in overloaded
networks, due for example to massive attacks or critical situations.
",Novelty Messages Filtering for Multi Agent Privacy-preserving Planning
"  In this work we present a novel approach to solving concurrent multiagent
planning problems in which several agents act in parallel. Our approach relies
on a compilation from concurrent multiagent planning to classical planning,
allowing us to use an off-the-shelf classical planner to solve the original
multiagent problem. The solution can be directly interpreted as a concurrent
plan that satisfies a given set of concurrency constraints, while avoiding the
exponential blowup associated with concurrent actions. Our planner is the first
to handle action effects that are conditional on what other agents are doing.
Theoretically, we show that the compilation is sound and complete. Empirically,
we show that our compilation can solve challenging multiagent planning problems
that require concurrent actions.
",Solving Multiagent Planning Problems with Concurrent Conditional Effects
"  Explainability in Artificial Intelligence has been revived as a topic of
active research by the need of conveying safety and trust to users in the `how'
and `why' of automated decision-making. Whilst a plethora of approaches have
been developed for post-hoc explainability, only a few focus on how to use
domain knowledge, and how this influences the understandability of global
explanations from the users' perspective. In this paper, we show how ontologies
help the understandability of global post-hoc explanations, presented in the
form of symbolic models. In particular, we build on Trepan, an algorithm that
explains artificial neural networks by means of decision trees, and we extend
it to include ontologies modeling domain knowledge in the process of generating
explanations. We present the results of a user study that measures the
understandability of decision trees using a syntactic complexity measure, and
through time and accuracy of responses as well as reported user confidence and
understandability. The user study considers domains where explanations are
critical, namely, in finance and medicine. The results show that decision trees
generated with our algorithm, taking into account domain knowledge, are more
understandable than those generated by standard Trepan without the use of
ontologies.
","Trepan Reloaded: A Knowledge-driven Approach to Explaining Artificial
  Neural Networks"
"  ""Theorem proving is similar to the game of Go. So, we can probably improve
our provers using deep learning, like DeepMind built the super-human computer
Go program, AlphaGo."" Such optimism has been observed among participants of
AITP2017. But is theorem proving really similar to Go? In this paper, we first
identify the similarities and differences between them and then propose a
system in which various provers keep competing against each other and changing
themselves until they prove conjectures provided by users.
",Designing Game of Theorems
"  Proposals for safe AGI systems are typically made at the level of frameworks,
specifying how the components of the proposed system should be trained and
interact with each other. In this paper, we model and compare the most
promising AGI safety frameworks using causal influence diagrams. The diagrams
show the optimization objective and causal assumptions of the framework. The
unified representation permits easy comparison of frameworks and their
assumptions. We hope that the diagrams will serve as an accessible and visual
introduction to the main AGI safety frameworks.
",Modeling AGI Safety Frameworks with Causal Influence Diagrams
"  Sequential decision problems in applications such as manipulation in
warehouses, multi-step meal preparation, and routing in autonomous vehicle
networks often involve reasoning about uncertainty, planning over discrete
modes as well as continuous states, and reacting to dynamic updates. To
formalize such problems generally, we introduce a class of Markov Decision
Processes (MDPs) called Dynamic Multimodal Stochastic Shortest Paths (DMSSPs).
Much of the work in these domains solves deterministic variants, which can
yield poor results when the uncertainty has downstream effects. We develop a
Hybrid Stochastic Planning (HSP) algorithm, which uses domain-agnostic
abstractions to efficiently unify heuristic search for planning over discrete
modes, approximate dynamic programming for stochastic planning over continuous
states, and hierarchical interleaved planning and execution. In the domain of
autonomous multimodal routing, HSP obtains significantly higher quality
solutions than a state-of-the-art Upper Confidence Trees algorithm and a
two-level Receding Horizon Control algorithm.
",Hybrid Planning for Dynamic Multimodal Stochastic Shortest Paths
"  $\textit{Embedded agents}$ are not explicitly separated from their
environment, lacking clear I/O channels. Such agents can reason about and
modify their internal parts, which they are incentivized to shortcut or
$\textit{wirehead}$ in order to achieve the maximal reward. In this paper, we
provide a taxonomy of ways by which wireheading can occur, followed by a
definition of wirehead-vulnerable agents. Starting from the fully dualistic
universal agent AIXI, we introduce a spectrum of partially embedded agents and
identify wireheading opportunities that such agents can exploit, experimentally
demonstrating the results with the GRL simulation platform AIXIjs. We
contextualize wireheading in the broader class of all misalignment problems -
where the goals of the agent conflict with the goals of the human designer -
and conjecture that the only other possible type of misalignment is
specification gaming. Motivated by this taxonomy, we define wirehead-vulnerable
agents as embedded agents that choose to behave differently from fully
dualistic agents lacking access to their internal parts.
",Categorizing Wireheading in Partially Embedded Agents
"  Mixed Integer Programming (MIP) is one of the most widely used modeling
techniques for combinatorial optimization problems. In many applications, a
similar MIP model is solved on a regular basis, maintaining remarkable
similarities in model structures and solution appearances but differing in
formulation coefficients. This offers the opportunity for machine learning
methods to explore the correlations between model structures and the resulting
solution values. To address this issue, we propose to represent an MIP instance
using a tripartite graph, based on which a Graph Convolutional Network (GCN) is
constructed to predict solution values for binary variables. The predicted
solutions are used to generate a local branching type cut which can be either
treated as a global (invalid) inequality in the formulation resulting in a
heuristic approach to solve the MIP, or as a root branching rule resulting in
an exact approach. Computational evaluations on 8 distinct types of MIP
problems show that the proposed framework improves the primal solution finding
performance significantly on a state-of-the-art open-source MIP solver.
","Accelerating Primal Solution Findings for Mixed Integer Programs Based
  on Solution Prediction"
"  We consider the problem of answering queries about formulas of first-order
logic based on background knowledge partially represented explicitly as other
formulas, and partially represented as examples independently drawn from a
fixed probability distribution. PAC semantics, introduced by Valiant, is one
rigorous, general proposal for learning to reason in formal languages: although
weaker than classical entailment, it allows for a powerful model theoretic
framework for answering queries while requiring minimal assumptions about the
form of the distribution in question. To date, however, the most significant
limitation of that approach, and more generally most machine learning
approaches with robustness guarantees, is that the logical language is
ultimately essentially propositional, with finitely many atoms. Indeed, the
theoretical findings on the learning of relational theories in such generality
have been resoundingly negative. This is despite the fact that first-order
logic is widely argued to be most appropriate for representing human knowledge.
In this work, we present a new theoretical approach to robustly learning to
reason in first-order logic, and consider universally quantified clauses over a
countably infinite domain. Our results exploit symmetries exhibited by
constants in the language, and generalize the notion of implicit learnability
to show how queries can be computed against (implicitly) learned first-order
background knowledge.
",Implicitly Learning to Reason in First-Order Logic
"  We consider the problem of learning rules from a data set that support a
proof of a given query, under Valiant's PAC-Semantics. We show how any backward
proof search algorithm that is sufficiently oblivious to the contents of its
knowledge base can be modified to learn such rules while it searches for a
proof using those rules. We note that this gives such algorithms for standard
logics such as chaining and resolution.
",Query-driven PAC-Learning for Reasoning
"  In a commonly-used version of the Simple Assembly Line Balancing Problem
(SALBP-1) tasks are assigned to stations along an assembly line with a fixed
cycle time in order to minimize the required number of stations. It has
traditionally been assumed that the total work needed for each product unit has
been partitioned into economically indivisible tasks. However, in practice, it
is sometimes possible to divide particular tasks in limited ways at additional
time penalty cost. Despite the penalties, task division where possible, now and
then leads to a reduction in the minimum number of stations. Deciding which
allowable tasks to divide creates a new assembly line balancing problem, TDALBP
(Task Division Assembly Line Balancing Problem). We propose a mathematical
model of the TDALBP, an exact solution procedure for it and present promising
computational results for the adaptation of some classical SALBP instances from
the research literature. The results demonstrate that the TDALBP sometimes has
the potential to significantly improve assembly line performance.
",Assembly line balancing with task division
"  An ontology is a formal representation of domain knowledge, which can be
interpreted by machines. In recent years, ontologies have become a major tool
for domain knowledge representation and a core component of many knowledge
management systems, decision support systems and other intelligent systems,
inter alia, in the context of agriculture. A review of the existing literature
on agricultural ontologies, however, reveals that most of the studies, which
propose agricultural ontologies, are lacking an explicit evaluation procedure.
This is undesired because without well-structured evaluation processes, it is
difficult to consider the value of ontologies to research and practice.
Moreover, it is difficult to rely on such ontologies and share them on the
Semantic Web or between semantic aware applications. With the growing number of
ontology-based agricultural systems and the increasing popularity of the
Semantic Web, it becomes essential that such development and evaluation methods
are put forward to guide future efforts of ontology development. Our work
contributes to the literature on agricultural ontologies, by presenting a
method for evaluating agricultural ontologies, which seems to be missing from
most existing studies on agricultural ontologies. The framework supports the
matching of appropriate evaluation methods for a given ontology based on the
ontology's purpose.
",A Framework for Evaluating Agricultural Ontologies
"  This paper reveals a trap for artificial general intelligence (AGI) theorists
who use economists' standard method of discounting. This trap is implicitly and
falsely assuming that a rational AGI would have time-consistent preferences. An
agent with time-inconsistent preferences knows that its future self will
disagree with its current self concerning intertemporal decision making. Such
an agent cannot automatically trust its future self to carry out plans that its
current self considers optimal.
",An AGI with Time-Inconsistent Preferences
"  We develop new voting mechanisms for the case when voters and candidates are
located in an arbitrary unknown metric space, and the goal is to choose a
candidate minimizing social cost: the total distance from the voters to this
candidate. Previous work has often assumed that only ordinal preferences of the
voters are known (instead of their true costs), and focused on minimizing
distortion: the quality of the chosen candidate as compared with the best
possible candidate. In this paper, we instead assume that a (very small) amount
of information is known about the voter preference strengths, not just about
their ordinal preferences. We provide mechanisms with much better distortion
when this extra information is known as compared to mechanisms which use only
ordinal information. We quantify tradeoffs between the amount of information
known about preference strengths and the achievable distortion. We further
provide advice about which type of information about preference strengths seems
to be the most useful. Finally, we conclude by quantifying the ideal candidate
distortion, which compares the quality of the chosen outcome with the best
possible candidate that could ever exist, instead of only the best candidate
that is actually in the running.
","Awareness of Voter Passion Greatly Improves the Distortion of Metric
  Social Choice"
"  This article describes the application of soft computing methods for solving
the problem of locating garbage accumulation points in urban scenarios. This is
a relevant problem in modern smart cities, in order to reduce negative
environmental and social impacts in the waste management process, and also to
optimize the available budget from the city administration to install waste
bins. A specific problem model is presented, which accounts for reducing the
investment costs, enhance the number of citizens served by the installed bins,
and the accessibility to the system. A family of single- and multi-objective
heuristics based on the PageRank method and two mutiobjective evolutionary
algorithms are proposed. Experimental evaluation performed on real scenarios on
the cities of Montevideo (Uruguay) and Bahia Blanca (Argentina) demonstrates
the effectiveness of the proposed approaches. The methods allow computing
plannings with different trade-off between the problem objectives. The computed
results improve over the current planning in Montevideo and provide a
reasonable budget cost and quality of service for Bahia Blanca.
","Soft computing methods for multiobjective location of garbage
  accumulation points in smart cities"
"  This paper aims to question the suitability of the Turing Test, for testing
machine intelligence, in the light of advances made in the last 60 years in
science, medicine, and philosophy of mind. While the main concept of the test
may seem sound and valid, a detailed analysis of what is required to pass the
test highlights a significant flow. Once the analysis of the test is presented,
a systematic approach is followed in analysing what is needed to devise a test
or tests for intelligent machines. The paper presents a plausible generic
framework based on categories of factors implied by subjective perception of
intelligence. An evaluative discussion concludes the paper highlighting some of
the unaddressed issues within this generic framework.
",Turing Test Revisited: A Framework for an Alternative
"  Dempster-Shafer evidence theory has been widely used in various fields of
applications, because of the flexibility and effectiveness in modeling
uncertainties without prior information. However, the existing evidence theory
is insufficient to consider the situations where it has no capability to
express the fluctuations of data at a given phase of time during their
execution, and the uncertainty and imprecision which are inevitably involved in
the data occur concurrently with changes to the phase or periodicity of the
data. In this paper, therefore, a generalized Dempster-Shafer evidence theory
is proposed. To be specific, a mass function in the generalized Dempster-Shafer
evidence theory is modeled by a complex number, called as a complex basic
belief assignment, which has more powerful ability to express uncertain
information. Based on that, a generalized Dempster's combination rule is
exploited. In contrast to the classical Dempster's combination rule, the
condition in terms of the conflict coefficient between the evidences K<1 is
released in the generalized Dempster's combination rule. Hence, it is more
general and applicable than the classical Dempster's combination rule. When the
complex mass function is degenerated from complex numbers to real numbers, the
generalized Dempster's combination rule degenerates to the classical evidence
theory under the condition that the conflict coefficient between the evidences
K is less than 1. In a word, this generalized Dempster-Shafer evidence theory
provides a promising way to model and handle more uncertain information.
",Generalization of Dempster-Shafer theory: A complex belief function
"  Scientific models describe natural phenomena at different levels of
abstraction. Abstract descriptions can provide the basis for interventions on
the system and explanation of observed phenomena at a level of granularity that
is coarser than the most fundamental account of the system. Beckers and Halpern
(2019), building on work of Rubenstein et al. (2017), developed an account of
abstraction for causal models that is exact. Here we extend this account to the
more realistic case where an abstract causal model offers only an approximation
of the underlying system. We show how the resulting account handles the
discrepancy that can arise between low- and high-level causal models of the
same system, and in the process provide an account of how one causal model
approximates another, a topic of independent interest. Finally, we extend the
account of approximate abstractions to probabilistic causal models, indicating
how and where uncertainty can enter into an approximate abstraction.
",Approximate Causal Abstraction
"  Anticipatory thinking is a complex cognitive process for assessing and
managing risk in many contexts. Humans use anticipatory thinking to identify
potential future issues and proactively take actions to manage their risks. In
this paper we define a cognitive systems approach to anticipatory thinking as a
metacognitive goal reasoning mechanism. The contributions of this paper include
(1) defining anticipatory thinking in the MIDCA cognitive architecture, (2)
operationalizing anticipatory thinking as a three step process for managing
risk in plans, and (3) a numeric risk assessment calculating an expected
cost-benefit ratio for modifying a plan with anticipatory actions.
",Anticipatory Thinking: A Metacognitive Capability
"  Our ignorance of the winnability percentage of the game in the Windows
Solitaire program, more properly called 'Klondike', has been described as ""one
of the embarrassments of applied mathematics"". Klondike is just one of many
single-player card games, generically called 'patience' or 'solitaire' games,
for which players have long wanted to know how likely a particular game is to
be winnable. A number of different games have been studied empirically in the
academic literature and by non-academic enthusiasts. Here we show that a single
general purpose Artificial Intelligence program, called ""Solvitaire"", can be
used to determine the winnability percentage of 45 different single-player card
games with a 95% confidence interval of +/- 0.1% or better. For example, we
report the winnability of Klondike as 81.956% +/- 0.096% (in the 'thoughtful'
variant where the player knows the location of all cards), a 30-fold reduction
in confidence interval over the best previous result. Almost all our results
are either entirely new or represent significant improvements on previous
knowledge.
",The Winnability of Klondike Solitaire and Many Other Patience Games
"  The Digital Ludeme Project (DLP) aims to reconstruct and analyse over 1000
traditional strategy games using modern techniques. One of the key aspects of
this project is the development of Ludii, a general game system that will be
able to model and play the complete range of games required by this project.
Such an undertaking will create a wide range of possibilities for new AI
challenges. In this paper we describe many of the features of Ludii that can be
used. This includes designing and modifying games using the Ludii game
description language, creating agents capable of playing these games, and
several advantages the system has over prior general game software.
",An Overview of the Ludii General Game System
"  Although General Game Playing (GGP) systems can facilitate useful research in
Artificial Intelligence (AI) for game-playing, they are often computationally
inefficient and somewhat specialised to a specific class of games. However,
since the start of this year, two General Game Systems have emerged that
provide efficient alternatives to the academic state of the art -- the Game
Description Language (GDL). In order of publication, these are the Regular
Boardgames language (RBG), and the Ludii system. This paper offers an
experimental evaluation of Ludii. Here, we focus mainly on a comparison between
the two new systems in terms of two key properties for any GGP system:
simplicity/clarity (e.g. human-readability), and efficiency.
",An Empirical Evaluation of Two General Game Systems: Ludii and RBG
"  Many of the famous single-player games, commonly called puzzles, can be shown
to be NP-Complete. Indeed, this class of complexity contains hundreds of
puzzles, since people particularly appreciate completing an intractable puzzle,
such as Sudoku, but also enjoy the ability to check their solution easily once
it's done. For this reason, using constraint programming is naturally suited to
solve them. In this paper, we focus on logic puzzles described in the Ludii
general game system and we propose using the XCSP formalism in order to solve
them with any CSP solver.
",Ludii and XCSP: Playing and Solving Logic Puzzles
"  Ludii is a general game system being developed as part of the ERC-funded
Digital Ludeme Project (DLP). While its primary aim is to model, play, and
analyse the full range of traditional strategy games, Ludii also has the
potential to support a wide range of AI research topics and competitions. This
paper describes some of the future competitions and challenges that we intend
to run using the Ludii system, highlighting some of its most important aspects
that can potentially lead to many algorithm improvements and new avenues of
research. We compare and contrast our proposed competition motivations, goals
and frameworks against those of existing general game playing competitions,
addressing the strengths and weaknesses of each platform.
",Ludii as a Competition Platform
"  How should a robot that collaborates with multiple people decide upon the
distribution of resources (e.g. social attention, or parts needed for an
assembly)? People are uniquely attuned to how resources are distributed. A
decision to distribute more resources to one team member than another might be
perceived as unfair with potentially detrimental effects for trust. We
introduce a multi-armed bandit algorithm with fairness constraints, where a
robot distributes resources to human teammates of different skill levels. In
this problem, the robot does not know the skill level of each human teammate,
but learns it by observing their performance over time. We define fairness as a
constraint on the minimum rate that each human teammate is selected throughout
the task. We provide theoretical guarantees on performance and perform a
large-scale user study, where we adjust the level of fairness in our algorithm.
Results show that fairness in resource distribution has a significant effect on
users' trust in the system.
","Multi-Armed Bandits with Fairness Constraints for Distributing Resources
  to Human Teammates"
"  Being a complex subject of major importance in AI Safety research, value
alignment has been studied from various perspectives in the last years.
However, no final consensus on the design of ethical utility functions
facilitating AI value alignment has been achieved yet. Given the urgency to
identify systematic solutions, we postulate that it might be useful to start
with the simple fact that for the utility function of an AI not to violate
human ethical intuitions, it trivially has to be a model of these intuitions
and reflect their variety $ - $ whereby the most accurate models pertaining to
human entities being biological organisms equipped with a brain constructing
concepts like moral judgements, are scientific models. Thus, in order to better
assess the variety of human morality, we perform a transdisciplinary analysis
applying a security mindset to the issue and summarizing variety-relevant
background knowledge from neuroscience and psychology. We complement this
information by linking it to augmented utilitarianism as a suitable ethical
framework. Based on that, we propose first practical guidelines for the design
of approximate ethical goal functions that might better capture the variety of
human moral judgements. Finally, we conclude and address future possible
challenges.
",Requisite Variety in Ethical Utility Functions for AI Value Alignment
"  In this paper, an evidential distance measure is proposed which can measure
the difference or dissimilarity between complex basic belief assignments
(CBBAs), in which the CBBAs are composed of complex numbers. When the CBBAs are
degenerated from complex numbers to real numbers, i.e., BBAs, the proposed
distance will degrade into the Jousselme et al.'s distance. Therefore, the
proposed distance provides a promising way to measure the differences between
evidences in a more general framework of complex plane space.
",Evidential distance measure in complex belief function theory
"  Fuzzy Rule Interpolation (FRI) reasoning methods have been introduced to
address sparse fuzzy rule bases and reduce complexity. The first FRI method was
the Koczy and Hirota (KH) proposed ""Linear Interpolation"". Besides, several
conditions and criteria have been suggested for unifying the common
requirements FRI methods have to satisfy. One of the most conditions is
restricted the fuzzy set of the conclusion must preserve a Piece-Wise Linearity
(PWL) if all antecedents and consequents of the fuzzy rules are preserving on
PWL sets at {\alpha}-cut levels. The KH FRI is one of FRI methods which cannot
satisfy this condition. Therefore, the goal of this paper is to investigate
equations and notations related to PWL property, which is aimed to highlight
the problematic properties of the KH FRI method to prove its efficiency with
PWL condition. In addition, this paper is focusing on constructing benchmark
examples to be a baseline for testing other FRI methods against situations that
are not satisfied with the linearity condition for KH FRI.
","Investigating The Piece-Wise Linearity And Benchmark Related To
  Koczy-Hirota Fuzzy Linear Interpolation"
"  Recent work has considered the problem of extending to the case of iterated
belief change the so-called `Harper Identity' (HI), which defines single-shot
contraction in terms of single-shot revision. The present paper considers the
prospects of providing a similar extension of the Levi Identity (LI), in which
the direction of definition runs the other way. We restrict our attention here
to the three classic iterated revision operators--natural, restrained and
lexicographic, for which we provide here the first collective characterisation
in the literature, under the appellation of `elementary' operators. We consider
two prima facie plausible ways of extending (LI). The first proposal involves
the use of the rational closure operator to offer a `reductive' account of
iterated revision in terms of iterated contraction. The second, which doesn't
commit to reductionism, was put forward some years ago by Nayak et al. We
establish that, for elementary revision operators and under mild assumptions
regarding contraction, Nayak's proposal is equivalent to a new set of
postulates formalising the claim that contraction by $\neg A$ should be
considered to be a kind of `mild' revision by $A$. We then show that these, in
turn, under slightly weaker assumptions, jointly amount to the conjunction of a
pair of constraints on the extension of (HI) that were recently proposed in the
literature. Finally, we consider the consequences of endorsing both suggestions
and show that this would yield an identification of rational revision with
natural revision. We close the paper by discussing the general prospects for
defining iterated revision in terms of iterated contraction.
",Elementary Iterated Revision and the Levi Identity
"  Values are things that are important to us. Actions activate values - they
either go against our values or they promote our values. Values themselves can
either be conforming or conflicting depending on the action that is taken. In
this short paper, we argue that values may be classified as one of two types -
conflicting and inherently conflicting values. They are distinguished by the
fact that the latter in some sense can be thought of as being independent of
actions. This allows us to do two things: i) check whether a set of values is
consistent and ii) check whether it is in conflict with other sets of values.
",On Conforming and Conflicting Values
"  Procedural generation of initial states of state-space search problems have
applications in human and machine learning as well as in the evaluation of
planning systems. In this paper we deal with the task of generating hard and
solvable initial states of Sokoban puzzles. We propose hardness metrics based
on pattern database heuristics and the use of novelty to improve the
exploration of search methods in the task of generating initial states. We then
present a system called Beta that uses our hardness metrics and novelty to
generate initial states. Experiments show that Beta is able to generate initial
states that are harder to solve by a specialized solver than those designed by
human experts.
",Procedural Generation of Initial States of Sokoban
"  We present a scheme for sequential decision making with a risk-sensitive
objective and constraints in a dynamic environment. A neural network is trained
as an approximator of the mapping from parameter space to space of risk and
policy with risk-sensitive constraints. For a given risk-sensitive problem, in
which the objective and constraints are, or can be estimated by, functions of
the mean and variance of return, we generate a synthetic dataset as training
data. Parameters defining a targeted process might be dynamic, i.e., they might
vary over time, so we sample them within specified intervals to deal with these
dynamics. We show that: i). Most risk measures can be estimated using return
variance; ii). By virtue of the state-augmentation transformation, practical
problems modeled by Markov decision processes with stochastic rewards can be
solved in a risk-sensitive scenario; and iii). The proposed scheme is validated
by a numerical experiment.
",A Scheme for Dynamic Risk-Sensitive Sequential Decision Making
"  We discuss the objectives of any endeavor in creating artificial
intelligence, AI, and provide a possible alternative. Intelligence might be an
unintended consequence of curiosity left to roam free, best exemplified by a
frolicking infant. This suggests that our attempts at AI could have been
misguided. What we actually need to strive for can be termed artificial
curiosity, AC, and intelligence happens as a consequence of those efforts. For
this unintentional yet welcome aftereffect to set in a foundational list of
guiding principles needs to be present. We start with the intuition for this
line of reasoning and formalize it with a series of definitions, assumptions,
ingredients, models and iterative improvements that will be necessary to make
the incubation of intelligence a reality. Our discussion provides conceptual
modifications to the Turing Test and to Searle's Chinese room argument. We
discuss the future implications for society as AI becomes an integral part of
life.
  We provide a road-map for creating intelligence with the technical parts
relegated to the appendix so that the article is accessible to a wide audience.
The central techniques in our formal approach to creating intelligence draw
upon tools and concepts widely used in physics, cognitive science, psychology,
evolutionary biology, statistics, linguistics, communication systems, pattern
recognition, marketing, economics, finance, information science and
computational theory highlighting that solutions for creating artificial
intelligence have to transcend the artificial barriers between various fields
and be highly multi-disciplinary.
",Artificial Intelligence: A Child's Play
"  Recently, there has been much research into modelling of uncertainty in human
perception through Fuzzy Sets (FSs). Most of this research has focused on
allowing respondents to express their (intra) uncertainty using intervals.
Here, depending on the technique used and types of uncertainties being modelled
different types of FSs can be obtained (e.g., Type-1, Interval Type-2, General
Type-2). Arguably, one of the most flexible techniques is the Interval
Agreement Approach (IAA) as it allows to model the perception of all
respondents without making assumptions such as outlier removal or predefined
membership function types (e.g. Gaussian). A key aspect in the analysis of
interval-valued data and indeed, IAA based agreement models of said data, is to
determine the position and strengths of agreement across all the
sources/participants. While previously, the Agreement Ratio was proposed to
measure the strength of agreement in fuzzy set based models of interval data,
said measure has only been applicable to type-1 fuzzy sets. In this paper, we
extend the Agreement Ratio to capture the degree of inter-group agreement
modelled by a General Type-2 Fuzzy Set when using the IAA. This measure relies
on using a similarity measure to quantitatively express the relation between
the different levels of agreement in a given FS. Synthetic examples are
provided in order to demonstrate both behaviour and calculation of the measure.
Finally, an application to real-world data is provided in order to show the
potential of this measure to assess the divergence of opinions for ambiguous
concepts when heterogeneous groups of participants are involved.
","Measuring Inter-group Agreement on zSlice Based General Type-2 Fuzzy
  Sets"
"  In this paper, we generalize the belief function on complex plane from
another point of view. We first propose a new concept of complex mass function
based on the complex number, called complex basic belief assignment, which is a
generalization of the traditional mass function in Dempster-Shafer evidence
theory. On the basis of the de nition of complex mass function, the belief
function and plausibility function are generalized. In particular, when the
complex mass function is degenerated from complex numbers to real numbers, the
generalized belief and plausibility functions degenerate into the traditional
belief and plausibility functions in DSE theory, respectively.
","Generalized Belief Function: A new concept for uncertainty modelling and
  processing"
"  Many real-world human behaviors can be characterized as a sequential decision
making processes, such as urban travelers choices of transport modes and routes
(Wu et al. 2017). Differing from choices controlled by machines, which in
general follows perfect rationality to adopt the policy with the highest
reward, studies have revealed that human agents make sub-optimal decisions
under bounded rationality (Tao, Rohde, and Corcoran 2014). Such behaviors can
be modeled using maximum causal entropy (MCE) principle (Ziebart 2010). In this
paper, we define and investigate a general reward trans-formation problem
(namely, reward advancement): Recovering the range of additional reward
functions that transform the agent's policy from original policy to a
predefined target policy under MCE principle. We show that given an MDP and a
target policy, there are infinite many additional reward functions that can
achieve the desired policy transformation. Moreover, we propose an algorithm to
further extract the additional rewards with minimum ""cost"" to implement the
policy transformation.
","Reward Advancement: Transforming Policy under Maximum Causal Entropy
  Principle"
"  Airspace models have played an important role in the development and
evaluation of aircraft collision avoidance systems for both manned and unmanned
aircraft. As Urban Air Mobility (UAM) systems are being developed, we need new
encounter models that are representative of their operational environment.
Developing such models is challenging due to the lack of data on UAM behavior
in the airspace. While previous encounter models for other aircraft types rely
on large datasets to produce realistic trajectories, this paper presents an
approach to encounter modeling that instead relies on expert knowledge. In
particular, recent advances in preference-based learning are extended to tune
an encounter model from expert preferences. The model takes the form of a
stochastic policy for a Markov decision process (MDP) in which the reward
function is learned from pairwise queries of a domain expert. We evaluate the
performance of two querying methods that seek to maximize the information
obtained from each query. Ultimately, we demonstrate a method for generating
realistic encounter trajectories with only a few minutes of an expert's time.
",Learning an Urban Air Mobility Encounter Model from Expert Preferences
"  One of the main, long-term objectives of artificial intelligence is the
creation of thinking machines. To that end, substantial effort has been placed
into designing cognitive systems; i.e. systems that can manipulate
semantic-level information. A substantial part of that effort is oriented
towards designing the mathematical machinery underlying cognition in a way that
is very efficiently implementable in hardware. In this work we propose a
'semi-holographic' representation system that can be implemented in hardware
using only multiplexing and addition operations, thus avoiding the need for
expensive multiplication. The resulting architecture can be readily constructed
by recycling standard microprocessor elements and is capable of performing two
key mathematical operations frequently used in cognition, superposition and
binding, within a budget of below 6 pJ for 64- bit operands. Our proposed
'cognitive processing unit' (CoPU) is intended as just one (albeit crucial)
part of much larger cognitive systems where artificial neural networks of all
kinds and associative memories work in concord to give rise to intelligence.
","A semi-holographic hyperdimensional representation system for
  hardware-friendly cognitive computing"
"  We propose Stable Yet Memory Bounded Open-Loop (SYMBOL) planning, a general
memory bounded approach to partially observable open-loop planning. SYMBOL
maintains an adaptive stack of Thompson Sampling bandits, whose size is bounded
by the planning horizon and can be automatically adapted according to the
underlying domain without any prior domain knowledge beyond a generative model.
We empirically test SYMBOL in four large POMDP benchmark problems to
demonstrate its effectiveness and robustness w.r.t. the choice of
hyperparameters and evaluate its adaptive memory consumption. We also compare
its performance with other open-loop planning algorithms and POMCP.
",Adaptive Thompson Sampling Stacks for Memory Bounded Open-Loop Planning
"  Our game Pommerman is based on the console game Bommerman. The game starts on
an 11 by 11 platform. Pommerman is a multi-agent environment and is made up of
a set of different situations and contains four agents.
",Automated Gaming Pommerman: FFA
"  Recent research has introduced ideas from concept drift into process mining
to enable the analysis of changes in business processes over time. This stream
of research, however, has not yet addressed the challenges of drift
categorization, drilling-down, and quantification. In this paper, we propose a
novel technique for managing process drifts, called Visual Drift Detection
(VDD), which fulfills these requirements. The technique starts by clustering
declarative process constraints discovered from recorded logs of executed
business processes based on their similarity and then applies change point
detection on the identified clusters to detect drifts. VDD complements these
features with detailed visualizations and explanations of drifts. Our
evaluation, both on synthetic and real-world logs, demonstrates all the
aforementioned capabilities of the technique.
",Comprehensive Process Drift Detection with Visual Analytics
"  Games have benchmarked AI methods since the inception of the field, with
classic board games such as Chess and Go recently leaving room for video games
with related yet different sets of challenges. The set of AI problems
associated with video games has in recent decades expanded from simply playing
games to win, to playing games in particular styles, generating game content,
modeling players etc. Different games pose very different challenges for AI
systems, and several different AI challenges can typically be posed by the same
game. In this article we analyze the popular collectible card game Hearthstone
(Blizzard 2014) and describe a varied set of interesting AI challenges posed by
this game. Collectible card games are relatively understudied in the AI
community, despite their popularity and the interesting challenges they pose.
Analyzing a single game in-depth in the manner we do here allows us to see the
entire field of AI and Games through the lens of a single game, discovering a
few new variations on existing research topics.
",The Many AI Challenges of Hearthstone
"  Matching tile games are an extremely popular game genre. Arguably the most
popular iteration, Match-3 games, are simple to understand puzzle games, making
them great benchmarks for research. In this paper, we propose developing
different procedural personas for Match-3 games in order to approximate
different human playstyles to create an automated playtesting system. The
procedural personas are realized through evolving the utility function for the
Monte Carlo Tree Search agent. We compare the performance and results of the
evolution agents with the standard Vanilla Monte Carlo Tree Search
implementation as well as to a random move-selection agent. We then observe the
impacts on both the game's design and the game design process. Lastly, a user
study is performed to compare the agents to human play traces.
",Automated Playtesting of Matching Tile Games
"  We introduce DeepProbLog, a neural probabilistic logic programming language
that incorporates deep learning by means of neural predicates. We show how
existing inference and learning techniques of the underlying probabilistic
logic programming language ProbLog can be adapted for the new language. We
theoretically and experimentally demonstrate that DeepProbLog supports (i) both
symbolic and subsymbolic representations and inference, (ii) program induction,
(iii) probabilistic (logic) programming, and (iv) (deep) learning from
examples. To the best of our knowledge, this work is the first to propose a
framework where general-purpose neural networks and expressive
probabilistic-logical modeling and reasoning are integrated in a way that
exploits the full expressiveness and strengths of both worlds and can be
trained end-to-end based on examples.
",Neural Probabilistic Logic Programming in DeepProbLog
"  Specifying a complete domain model is time-consuming, which has been a
bottleneck of AI planning technique application in many real-world scenarios.
Most classical domain-model learning approaches output a domain model in the
form of the declarative planning language, such as STRIPS or PDDL, and solve
new planning instances by invoking an existing planner. However, planning in
such a representation is sensitive to the accuracy of the learned domain model
which probably cannot be used to solve real planning problems. In this paper,
to represent domain models in a vectorization representation way, we propose a
novel framework based on graph neural network (GNN) integrating model-free
learning and model-based planning, called LP-GNN. By embedding propositions and
actions in a graph, the latent relationship between them is explored to form a
domain-specific heuristics. We evaluate our approach on five classical planning
domains, comparing with the classical domain-model learner ARMS. The
experimental results show that the domain models learned by our approach are
much more effective on solving real planning problems.
","Representation Learning for Classical Planning from Partially Observed
  Traces"
"  Magic sets are a Datalog to Datalog rewriting technique to optimize query
answering. The rewritten program focuses on a portion of the stable model(s) of
the input program which is sufficient to answer the given query. However, the
rewriting may introduce new recursive definitions, which can involve even
negation and aggregations, and may slow down program evaluation. This paper
enhances the magic set technique by preventing the creation of (new) recursive
definitions in the rewritten program. It turns out that the new version of
magic sets is closed for Datalog programs with stratified negation and
aggregations, which is very convenient to obtain efficient computation of the
stable model of the rewritten program. Moreover, the rewritten program is
further optimized by the elimination of subsumed rules and by the efficient
handling of the cases where binding propagation is lost. The research was
stimulated by a challenge on the exploitation of Datalog/\textsc{dlv} for
efficient reasoning on large ontologies. All proposed techniques have been
hence implemented in the \textsc{dlv} system, and tested for ontological
reasoning, confirming their effectiveness.
  Under consideration for publication in Theory and Practice of Logic
Programming.
",Enhancing magic sets with an application to ontological reasoning
"  This paper describes an implementation of a bot assistant in Minecraft, and
the tools and platform allowing players to interact with the bot and to record
those interactions. The purpose of building such an assistant is to facilitate
the study of agents that can complete tasks specified by dialogue, and
eventually, to learn from dialogue interactions.
",CraftAssist: A Framework for Dialogue-enabled Interactive Agents
"  The Generalised Travelling Salesman Problem (GTSP) is a well-known problem
that, among other applications, arises in warehouse order picking, where each
stock is distributed between several locations -- a typical approach in large
modern warehouses. However, the instances commonly used in the literature have
a completely different structure, and the methods are designed with those
instances in mind. In this paper, we give a new pseudo-random instance
generator that reflects the warehouse order picking and publish new benchmark
testbeds. We also use the Conditional Markov Chain Search framework to
automatically generate new GTSP metaheuristics trained specifically for
warehouse order picking. Finally, we report the computational results of our
metaheuristics to enable further competition between solvers.
","Conditional Markov Chain Search for the Generalised Travelling Salesman
  Problem for Warehouse Order Picking"
"  Transportation service providers that dispatch drivers and vehicles to riders
start to support both on-demand ride requests posted in real time and rides
scheduled in advance, leading to new challenges which, to the best of our
knowledge, have not been addressed by existing works. To fill the gap, we
design novel trip-vehicle dispatch algorithms to handle both types of requests
while taking into account an estimated request distribution of on-demand
requests. At the core of the algorithms is the newly proposed Constrained
Spatio-Temporal value function (CST-function), which is polynomial-time
computable and represents the expected value a vehicle could gain with the
constraint that it needs to arrive at a specific location at a given time.
Built upon CST-function, we design a randomized best-fit algorithm for
scheduled requests and an online planning algorithm for on-demand requests
given the scheduled requests as constraints. We evaluate the algorithms through
extensive experiments on a real-world dataset of an online ride-hailing
platform.
",Dynamic Trip-Vehicle Dispatch with Scheduled and On-Demand Requests
"  This paper presents a new algorithm: TSRuleGrowth, looking for
partially-ordered rules over a time series. This algorithm takes principles
from the state of the art of rule mining and applies them to time series via a
new notion of support. We apply this algorithm to real data from a connected
environment, which extract user habits through different connected objects.
","TSRuleGrowth : Extraction de r\`egles de pr\'ediction semi-ordonn\'ees
  \`a partir d'une s\'erie temporelle d'\'el\'ements discrets, application dans
  un contexte d'intelligence ambiante"
"  Deep reinforcement learning has over the past few years shown great potential
in learning near-optimal control in complex simulated environments with little
visible information. Rainbow (Q-Learning) and PPO (Policy Optimisation) have
shown outstanding performance in a variety of tasks, including Atari 2600,
MuJoCo, and Roboschool test suite. While these algorithms are fundamentally
different, both suffer from high variance, low sample efficiency, and
hyperparameter sensitivity that in practice, make these algorithms a no-go for
critical operations in the industry.
  On the other hand, model-based reinforcement learning focuses on learning the
transition dynamics between states in an environment. If these environment
dynamics are adequately learned, a model-based approach is perhaps the most
sample efficient method for learning agents to act in an environment optimally.
The traits of model-based reinforcement are ideal for real-world environments
where sampling is slow and for mission-critical operations. In the warehouse
industry, there is an increasing motivation to minimise time and to maximise
production. Currently, autonomous agents act suboptimally using handcrafted
policies for significant portions of the state-space.
  In this paper, we present The Dreaming Variational Autoencoder v2 (DVAE-2), a
model-based reinforcement learning algorithm that increases sample efficiency,
hence enable algorithms with low sample efficiency function better in
real-world environments. We introduce Deep Warehouse, a simulated environment
for industry-near testing of autonomous agents in grid-based warehouses.
Finally, we illustrate that DVAE-2 improves the sample efficiency for the Deep
Warehouse compared to model-free methods.
","Towards Model-based Reinforcement Learning for Industry-near
  Environments"
"  The prevalence of e-learning systems and on-line courses has made educational
material widely accessible to students of varying abilities and backgrounds.
There is thus a growing need to accommodate for individual differences in
e-learning systems. This paper presents an algorithm called EduRank for
personalizing educational content to students that combines a collaborative
filtering algorithm with voting methods. EduRank constructs a difficulty
ranking for each student by aggregating the rankings of similar students using
different aspects of their performance on common questions. These aspects
include grades, number of retries, and time spent solving questions. It infers
a difficulty ranking directly over the questions for each student, rather than
ordering them according to the student's predicted score. The EduRank algorithm
was tested on two data sets containing thousands of students and a million
records. It was able to outperform the state-of-the-art ranking approaches as
well as a domain expert. EduRank was used by students in a classroom activity,
where a prior model was incorporated to predict the difficulty rankings of
students with no prior history in the system. It was shown to lead students to
solve more difficult questions than an ordering by a domain expert, without
reducing their performance.
",A difficulty ranking approach to personalization in E-learning
"  Stream reasoning systems are designed for complex decision-making from
possibly infinite, dynamic streams of data. Modern approaches to stream
reasoning are usually performing their computations using stand-alone solvers,
which incrementally update their internal state and return results as the new
portions of data streams are pushed. However, the performance of such
approaches degrades quickly as the rates of the input data and the complexity
of decision problems are growing. This problem was already recognized in the
area of stream processing, where systems became distributed in order to
allocate vast computing resources provided by clouds. In this paper we propose
a distributed approach to stream reasoning that can efficiently split
computations among different solvers communicating their results over data
streams. Moreover, in order to increase the throughput of the distributed
system, we suggest an interval-based semantics for the LARS language, which
enables significant reductions of network traffic. Performed evaluations
indicate that the distributed stream reasoning significantly outperforms
existing stand-alone LARS solvers when the complexity of decision problems and
the rate of incoming data are increasing. Under consideration for acceptance in
Theory and Practice of Logic Programming.
",A Distributed Approach to LARS Stream Reasoning (System paper)
"  Whereas the operation of forgetting has recently seen a considerable amount
of attention in the context of Answer Set Programming (ASP), most of it has
focused on theoretical aspects, leaving the practical issues largely untouched.
Recent studies include results about what sets of properties operators should
satisfy, as well as the abstract characterization of several operators and
their theoretical limits. However, no concrete operators have been
investigated.
  In this paper, we address this issue by presenting the first concrete
operator that satisfies strong persistence - a property that seems to best
capture the essence of forgetting in the context of ASP - whenever this is
possible, and many other important properties. The operator is syntactic,
limiting the computation of the forgetting result to manipulating the rules in
which the atoms to be forgotten occur, naturally yielding a forgetting result
that is close to the original program.
  This paper is under consideration for acceptance in TPLP.
",A Syntactic Operator for Forgetting that Satisfies Strong Persistence
"  The architecture described in this paper encodes a theory of intentions based
on the the key principles of non-procrastination, persistence, and
automatically limiting reasoning to relevant knowledge and observations. The
architecture reasons with transition diagrams of any given domain at two
different resolutions, with the fine-resolution description defined as a
refinement of, and hence tightly-coupled to, a coarse-resolution description.
Non-monotonic logical reasoning with the coarse-resolution description computes
an activity (i.e., plan) comprising abstract actions for any given goal. Each
abstract action is implemented as a sequence of concrete actions by
automatically zooming to and reasoning with the part of the fine-resolution
transition diagram relevant to the current coarse-resolution transition and the
goal. Each concrete action in this sequence is executed using probabilistic
models of the uncertainty in sensing and actuation, and the corresponding
fine-resolution outcomes are used to infer coarse-resolution observations that
are added to the coarse-resolution history. The architecture's capabilities are
evaluated in the context of a simulated robot assisting humans in an office
domain, on a physical robot (Baxter) manipulating tabletop objects, and on a
wheeled robot (Turtlebot) moving objects to particular places or people. The
experimental results indicate improvements in reliability and computational
efficiency compared with an architecture that does not include the theory of
intentions, and an architecture that does not include zooming for
fine-resolution reasoning.
",Towards a Theory of Intentions for Human-Robot Collaboration
"  In this article we present an implementation of non-monotonic reasoning in an
embedded system. As a part of an autonomous motor-glider, it simulates piloting
decisions of an airplane. A real pilot must take care not only about the
information arising from the cockpit (airspeed, altitude, variometer,
compass...) but also from outside the cabin. Throughout a flight, a pilot is
constantly in communication with the control tower to follow orders, because
there is an airspace regulation to respect. In addition, if the control tower
sends orders while the pilot has an emergency, he may have to violate these
orders and airspace regulations to solve his problem (e.g. emergency landing).
On the other hand, climate changes constantly (wind, snow, hail..) and can
affect the sensors. All these cases easily lead to contradictions. Switching to
reasoning under uncertainty, a pilot must make decisions to carry out a flight.
The objective of this implementation is to validate a non-monotonic model which
allows to solve the question of incomplete and contradictory information. We
formalize the problem using default logic, a non-monotonic logic which allows
to find fixed-points in the face of contradictions. For the implementation, the
Prolog language is used in an embedded computer running at 1 GHz single core
with 512 Mb of RAM and 0.8 watts of energy consumption.
","An Implementation of a Non-monotonic Logic in an Embedded Computer for a
  Motor-glider"
"  To be responsive to dynamically changing real-world environments, an
intelligent agent needs to perform complex sequential decision-making tasks
that are often guided by commonsense knowledge. The previous work on this line
of research led to the framework called ""interleaved commonsense reasoning and
probabilistic planning"" (icorpp), which used P-log for representing
commmonsense knowledge and Markov Decision Processes (MDPs) or Partially
Observable MDPs (POMDPs) for planning under uncertainty. A main limitation of
icorpp is that its implementation requires non-trivial engineering efforts to
bridge the commonsense reasoning and probabilistic planning formalisms. In this
paper, we present a unified framework to integrate icorpp's reasoning and
planning components. In particular, we extend probabilistic action language
pBC+ to express utility, belief states, and observation as in POMDP models.
Inheriting the advantages of action languages, the new action language provides
an elaboration tolerant representation of POMDP that reflects commonsense
knowledge. The idea led to the design of the system pbcplus2pomdp, which
compiles a pBC+ action description into a POMDP model that can be directly
processed by off-the-shelf POMDP solvers to compute an optimal policy of the
pBC+ action description. Our experiments show that it retains the advantages of
icorpp while avoiding the manual efforts in bridging the commonsense reasoner
and the probabilistic planner.
","Bridging Commonsense Reasoning and Probabilistic Planning via a
  Probabilistic Action Language"
"  We investigate the problem of cost-optimal planning in ASP. Current ASP
planners can be trivially extended to a cost-optimal one by adding weak
constraints, but only for a given makespan (number of steps). It is desirable
to have a planner that guarantees global optimality. In this paper, we present
two approaches to addressing this problem. First, we show how to engineer a
cost-optimal planner composed of two ASP programs running in parallel. Using
lessons learned from this, we then develop an entirely new approach to
cost-optimal planning, stepless planning, which is completely free of makespan.
Experiments to compare the two approaches with the only known cost-optimal
planner in SAT reveal good potentials for stepless planning in ASP. The paper
is under consideration for acceptance in TPLP.
",Domain-Independent Cost-Optimal Planning in ASP
"  We consider the Deduction Theorem used in the literature of game theory to
run a purported proof by contradiction. In the context of game theory, it is
stated that if we have a proof of $\phi \vdash \varphi$, then we also have a
proof of $\phi \Rightarrow \varphi$. Hence, the proof of $\phi \Rightarrow
\varphi$ is deduced from a previously known statement. However, we argue that
one has to manage to establish that a proof exists for the clauses $\phi$ and
$\varphi$, i.e., they are known true statements in order to show that $\phi
\vdash \varphi$ is provable, and that therefore $\phi \Rightarrow \varphi$ is
provable as well. Thus, we are not allowed to assume that the clause $\phi$ or
$\varphi$ is a true statement. This leads immediately to a wrong conclusion.
Apart from this, we stress to other facts why the Deduction Theorem is not
applicable to run a proof by contradiction. Finally, we present an example from
industrial cooperation where the Deduction Theorem is not correctly applied
with the consequence that the obtained result contradicts the well-known
aggregation issue.
","Deduction Theorem: The Problematic Nature of Common Practice in Game
  Theory"
"  In this paper, we discuss the learning of generalised policies for
probabilistic and classical planning problems using Action Schema Networks
(ASNets). The ASNet is a neural network architecture that exploits the
relational structure of (P)PDDL planning problems to learn a common set of
weights that can be applied to any problem in a domain. By mimicking the
actions chosen by a traditional, non-learning planner on a handful of small
problems in a domain, ASNets are able to learn a generalised reactive policy
that can quickly solve much larger instances from the domain. This work extends
the ASNet architecture to make it more expressive, while still remaining
invariant to a range of symmetries that exist in PPDDL problems. We also
present a thorough experimental evaluation of ASNets, including a comparison
with heuristic search planners on seven probabilistic and deterministic
domains, an extended evaluation on over 18,000 Blocksworld instances, and an
ablation study. Finally, we show that sparsity-inducing regularisation can
produce ASNets that are compact enough for humans to understand, yielding
insights into how the structure of ASNets allows them to generalise across a
domain.
",ASNets: Deep Learning for Generalised Planning
"  Game designers use human playtesting to gather feedback about game design
elements when iteratively improving a game. Playtesting, however, is expensive:
human testers must be recruited, playtest results must be aggregated and
interpreted, and changes to game designs must be extrapolated from these
results. Can automated methods reduce this expense? We show how active learning
techniques can formalize and automate a subset of playtesting goals.
Specifically, we focus on the low-level parameter tuning required to balance a
game once the mechanics have been chosen. Through a case study on a
shoot-`em-up game we demonstrate the efficacy of active learning to reduce the
amount of playtesting needed to choose the optimal set of game parameters for
two classes of (formal) design objectives. This work opens the potential for
additional methods to reduce the human burden of performing playtesting for a
variety of relevant design concerns.
",Automatic Playtesting for Game Parameter Tuning via Active Learning
"  Game designs often center on the game mechanics---rules governing the logical
evolution of the game. We seek to develop an intelligent system that generates
computer games. As first steps towards this goal we present a composable and
cross-domain representation for game mechanics that draws from AI planning
action representations. We use a constraint solver to generate mechanics
subject to design requirements on the form of those mechanics---what they do in
the game. A planner takes a set of generated mechanics and tests whether those
mechanics meet playability requirements---controlling how mechanics function in
a game to affect player behavior. We demonstrate our system by modeling and
generating mechanics in a role-playing game, platformer game, and combined
role-playing-platformer game.
",Automatic Game Design via Mechanic Generation
"  Games are often designed to shape player behavior in a desired way; however,
it can be unclear how design decisions affect the space of behaviors in a game.
Designers usually explore this space through human playtesting, which can be
time-consuming and of limited effectiveness in exhausting the space of possible
behaviors. In this paper, we propose the use of automated planning agents to
simulate humans of varying skill levels to generate game playthroughs. Metrics
can then be gathered from these playthroughs to evaluate the current game
design and identify its potential flaws. We demonstrate this technique in two
games: the popular word game Scrabble and a collectible card game of our own
design named Cardonomicon. Using these case studies, we show how using
simulated agents to model humans of varying skill levels allows us to extract
metrics to describe game balance (in the case of Scrabble) and highlight
potential design flaws (in the case of Cardonomicon).
",Monte-Carlo Tree Search for Simulation-based Strategy Analysis
"  The singularity refers to an idea that once a machine having an artificial
intelligence surpassing the human intelligence capacity is created, it will
trigger explosive technological and intelligence growth. I propose to test the
hypothesis that machine intelligence capacity can grow autonomously starting
with an intelligence comparable to that of bacteria - microbial intelligence.
The goal will be to demonstrate that rapid growth in intelligence capacity can
be realized at all in artificial computing systems. I propose the following
three properties that may allow an artificial intelligence to exhibit a steady
growth in its intelligence capacity: (i) learning with the ability to modify
itself when exposed to more data, (ii) acquiring new functionalities (skills),
and (iii) expanding or replicating itself. The algorithms must demonstrate a
rapid growth in skills of dataprocessing and analysis and gain qualitatively
different functionalities, at least until the current computing technology
supports their scalable development. The existing algorithms that already
encompass some of these or similar properties, as well as missing abilities
that must yet be implemented, will be reviewed in this work. Future
computational tests could support or oppose the hypothesis that artificial
intelligence can potentially grow to the level of superintelligence which
overcomes the limitations in hardware by producing necessary processing
resources or by changing the physical realization of computation from using
chip circuits to using quantum computing principles.
",Seeding the Singularity for A.I
"  Inference and decision making under uncertainty are key processes in every
autonomous system and numerous robotic problems. In recent years, the
similarities between inference and decision making triggered much work, from
developing unified computational frameworks to pondering about the duality
between the two. In spite of these efforts, inference and control, as well as
inference and belief space planning (BSP) are still treated as two separate
processes. In this paper we propose a paradigm shift, a novel approach which
deviates from conventional Bayesian inference and utilizes the similarities
between inference and BSP. We make the key observation that inference can be
efficiently updated using predictions made during the decision making stage,
even in light of inconsistent data association between the two. We developed a
two staged process that implements our novel approach and updates inference
using calculations from the precursory planning phase. Using autonomous
navigation in an unknown environment along with iSAM2 efficient methodologies
as a test case, we benchmarked our novel approach against standard Bayesian
inference, both with synthetic and real-world data (KITTI dataset). Results
indicate that not only our approach improves running time by at least a factor
of two while providing the same estimation accuracy, but it also alleviates the
computational burden of state dimensionality and loop closures.
","Bayesian Incremental Inference Update by Re-using Calculations from
  Belief Space Planning: A New Paradigm"
"  Consistent and reproducible evaluation of Deep Reinforcement Learning (DRL)
is not straightforward. In the Arcade Learning Environment (ALE), small changes
in environment parameters such as stochasticity or the maximum allowed play
time can lead to very different performance. In this work, we discuss the
difficulties of comparing different agents trained on ALE. In order to take a
step further towards reproducible and comparable DRL, we introduce SABER, a
Standardized Atari BEnchmark for general Reinforcement learning algorithms. Our
methodology extends previous recommendations and contains a complete set of
environment parameters as well as train and test procedures. We then use SABER
to evaluate the current state of the art, Rainbow. Furthermore, we introduce a
human world records baseline, and argue that previous claims of expert or
superhuman performance of DRL might not be accurate. Finally, we propose
Rainbow-IQN by extending Rainbow with Implicit Quantile Networks (IQN) leading
to new state-of-the-art performance. Source code is available for
reproducibility.
","Is Deep Reinforcement Learning Really Superhuman on Atari? Leveling the
  playing field"
"  With the increasing complexity of CPSs, their behavior and decisions become
increasingly difficult to understand and comprehend for users and other
stakeholders. Our vision is to build self-explainable systems that can, at
run-time, answer questions about the system's past, current, and future
behavior. As hitherto no design methodology or reference framework exists for
building such systems, we propose the MAB-EX framework for building
self-explainable systems that leverage requirements- and explainability models
at run-time. The basic idea of MAB-EX is to first Monitor and Analyze a certain
behavior of a system, then Build an explanation from explanation models and
convey this EXplanation in a suitable way to a stakeholder. We also take into
account that new explanations can be learned, by updating the explanation
models, should new and yet un-explainable behavior be detected by the system.
",Towards Self-Explainable Cyber-Physical Systems
"  Explainable AI is an important area of research within which Explainable
Planning is an emerging topic. In this paper, we argue that Explainable
Planning can be designed as a service -- that is, as a wrapper around an
existing planning system that utilises the existing planner to assist in
answering contrastive questions. We introduce a prototype framework to
facilitate this, along with some examples of how a planner can be used to
address certain types of contrastive questions. We discuss the main advantages
and limitations of such an approach and we identify open questions for
Explainable Planning as a service that identify several possible research
directions.
",Towards Explainable AI Planning as a Service
"  This paper presents Knowledge-Based Reinforcement Learning (KB-RL) as a
method that combines a knowledge-based approach and a reinforcement learning
(RL) technique into one method for intelligent problem solving. The proposed
approach focuses on multi-expert knowledge acquisition, with the reinforcement
learning being applied as a conflict resolution strategy aimed at integrating
the knowledge of multiple exerts into one knowledge base.
  The article describes the KB-RL approach in detail and applies the reported
method to one of the most challenging problems of current Artificial
Intelligence (AI) research, namely playing a strategy game. The results show
that the KB-RL system is able to play and complete the full FreeCiv game, and
to win against the computer players in various game settings. Moreover, with
more games played, the system improves the gameplay by shortening the number of
rounds that it takes to win the game.
  Overall, the reported experiment supports the idea that, based on human
knowledge and empowered by reinforcement learning, the KB-RL system can deliver
a strong solution to the complex, multi-strategic problems, and, mainly, to
improve the solution with increased experience.
",Playing a Strategy Game with Knowledge-Based Reinforcement Learning
"  This paper focuses on ""tracing player knowledge"" in educational games.
Specifically, given a set of concepts or skills required to master a game, the
goal is to estimate the likelihood with which the current player has mastery of
each of those concepts or skills. The main contribution of the paper is an
approach that integrates machine learning and domain knowledge rules to find
when the player applied a certain skill and either succeeded or failed. This is
then given as input to a standard knowledge tracing module (such as those from
Intelligent Tutoring Systems) to perform knowledge tracing. We evaluate our
approach in the context of an educational game called ""Parallel"" to teach
parallel and concurrent programming with data collected from real users,
showing our approach can predict students skills with a low mean-squared error.
",Tracing Player Knowledge in a Parallel Programming Educational Game
"  This paper describes a new approach on optimization of constraint
satisfaction problems (CSPs) by means of substituting sub-CSPs with locally
consistent regular membership constraints. The purpose of this approach is to
reduce the number of fails in the resolution process, to improve the inferences
made during search by the constraint solver by strengthening constraint
propagation, and to maintain the level of propagation while reducing the cost
of propagating the constraints. Our experimental results show improvements in
terms of the resolution speed compared to the original CSPs and a
competitiveness to the recent tabulation approach. Besides, our approach can be
realized in a preprocessing step, and therefore wouldn't collide with
redundancy constraints or parallel computing if implemented.
",The Regularization of Small Sub-Constraint Satisfaction Problems
"  Icosoku is a challenging and interesting puzzle that exhibits highly
symmetrical and combinatorial nature. In this paper, we pose the questions
derived from the puzzle, but with more difficulty and generality. In addition,
we also present a constraint programming model for the proposed questions,
which can provide the answers to our first two questions. The purpose of this
paper is to share our preliminary result and problems to encourage researchers
in both group theory and constraint communities to consider this topic further.
",Exploring Properties of Icosoku by Constraint Satisfaction Approach
"  his paper presents two novel approaches to solving the classic board game
mastermind, including a variant of simulated annealing (SA) and a technique we
term maximum expected reduction in consistency (MERC). In addition, we compare
search results for these algorithms to two baseline search methods: a random,
uninformed search and the method of minimizing maximum query partition sets as
originally developed by both Donald Knuth and Peter Norvig.
",Search Algorithms for Mastermind
"  ConArg is a suite of tools that offers a wide series of applications for
dealing with argumentation problems. In this work, we present the advances we
made in implementing a ranking-based semantics, based on computational choice
power indexes, within ConArg. Such kind of semantics represents a method for
sorting the arguments of an abstract argumentation framework, according to some
preference relation. The ranking-based semantics we implement relies on
Shapley, Banzhaf, Deegan-Packel and Johnston power index, transferring well
know properties from computational social choice to argumentation framework
ranking-based semantics.
",Implementing Ranking-Based Semantics in ConArg: a Preliminary Report
"  Recently, a higher competition in logistics business introduces new
challenges to the vehicle routing problem (VRP). Re-route planning, also known
as dynamic VRP, is one of the important challenges. The re-route planning has
to be performed when new customers request for deliveries while the delivery
vehicles, i.e., trucks, are serving other customers. While the re-route
planning has been studied in the literature, most of the existing works do not
consider different uncertainties. Therefore, in this paper, we propose two
systems, i.e., (i) an offline package pickup and delivery planning with
stochastic demands (PDPSD) and (ii) a re-route package pickup and delivery
planning with stochastic demands (Re-route PDPSD). Accordingly, we formulate
the PDPSD system as a two-stage stochastic optimization. We then extend the
PDPSD system to the Re-route PDPSD system with a re-route algorithm.
Furthermore, we evaluate performance of the proposed systems by using the
dataset from Solomon Benchmark suite and a real data from a Singapore logistics
1company. The results show that the PDPSD system can achieve the lower cost
than that of the baseline model. In addition, the Re-route PDPSD system can
help the supplier efficiently and successfully to serve more customers while
the trucks are already on the road.
",Re-route Package Pickup and Delivery Planning with Random Demands
"  In this paper, we introduce the Double Traveling Salesman Problem with
Partial Last-In-First-Out Loading Constraints (DTSPPL). It is a
pickup-and-delivery single-vehicle routing problem, where all pickup operations
must be performed before any delivery one because the pickup and delivery areas
are geographically separated. The vehicle collects items in the pickup area and
loads them into its container, a horizontal stack. After performing all pickup
operations, the vehicle begins delivering the items in the delivery area.
Loading and unloading operations must obey a partial Last-In-First-Out (LIFO)
policy, i.e., a version of the LIFO policy that may be violated within a given
reloading depth. The objective of the DTSPPL is to minimize the total cost,
which involves the total distance traveled by the vehicle and the number of
items that are unloaded and then reloaded due to violations of the standard
LIFO policy. We formally describe the DTSPPL through two Integer Linear
Programming (ILP) formulations and propose a heuristic algorithm based on the
Biased Random-Key Genetic Algorithm (BRKGA) to find high-quality solutions. The
performance of the proposed solution approaches is assessed over a broad set of
instances. Computational results have shown that both ILP formulations have
been able to solve only the smaller instances, whereas the BRKGA obtained good
quality solutions for almost all instances, requiring short computational
times.
","The double traveling salesman problem with partial last-in-first-out
  loading constraints"
"  There is increasing awareness in the planning community that the burden of
specifying complete domain models is too high, which impedes the applicability
of planning technology in many real-world domains. Although there have many
learning systems that help automatically learning domain models, most existing
work assumes that the input traces are completely correct. A more realistic
situation is that the plan traces are disordered and noisy, such as plan traces
described by natural language. In this paper we propose and evaluate an
approach for doing this. Our approach takes as input a set of plan traces with
disordered actions and noise and outputs action models that can best explain
the plan traces. We use a MAX-SAT framework for learning, where the constraints
are derived from the given plan traces. Unlike traditional action models
learners, the states in plan traces can be partially observable and noisy as
well as the actions in plan traces can be disordered and parallel. We
demonstrate the effectiveness of our approach through a systematic empirical
evaluation with both IPC domains and the real-world dataset extracted from
natural language documents.
",Learning Action Models from Disordered and Noisy Plan Traces
"  This paper presents a novel model-free Reinforcement Learning algorithm for
learning behavior in continuous action, state, and goal spaces. The algorithm
approximates optimal value functions using non-parametric estimators. It is
able to efficiently learn to reach multiple arbitrary goals in deterministic
and nondeterministic environments. To improve generalization in the goal space,
we propose a novel sample augmentation technique. Using these methods, robots
learn faster and overall better controllers. We benchmark the proposed
algorithms using simulation and a real-world voltage controlled robot that
learns to maneuver in a non-observable Cartesian task space.
","Continuous Value Iteration (CVI) Reinforcement Learning and Imaginary
  Experience Replay (IER) for learning multi-goal, continuous action and state
  space controllers"
"  Artificial Intelligence (AI) has received tremendous attention from academia,
industry, and the general public in recent years. The integration of geography
and AI, or GeoAI, provides novel approaches for addressing a variety of
problems in the natural environment and our human society. This entry briefly
reviews the recent development of AI with a focus on machine learning and deep
learning approaches. We discuss the integration of AI with geography and
particularly geographic information science, and present a number of GeoAI
applications and possible future directions.
",Artificial Intelligence Approaches
"  Nowadays, model-free reinforcement learning algorithms have achieved
remarkable performance on many decision making and control tasks, but high
sample complexity and low sample efficiency still hinder the wide use of
model-free reinforcement learning algorithms. In this paper, we argue that if
we intend to design an intelligent agent that learns fast and transfers well,
the agent must be able to reflect key elements of intelligence, like intuition,
Memory, PredictionandCuriosity. We propose an agent framework that integrates
off-policy reinforcement learning with world model learning, so as to embody
the important features of intelligence in our algorithm design. We adopt the
state-of-art model-free reinforcement learning algorithm, Soft Actor-Critic, as
the agent intuition, and world model learning through RNN to endow the agent
with memory, curiosity, and the ability to predict. We show that these ideas
can work collaboratively with each other and our agent (RMC) can give new
state-of-art results while maintaining sample efficiency and training
stability. Moreover, our agent framework can be easily extended from MDP to
POMDP problems without performance loss.
",Reinforcement learning with world model
"  This paper examines learning approaches for forward models based on local
cell transition functions. We provide a formal definition of local forward
models for which we propose two basic learning approaches. Our analysis is
based on the game Sokoban, where a wrong action can lead to an unsolvable game
state. Therefore, an accurate prediction of an action's resulting state is
necessary to avoid this scenario.
  In contrast to learning the complete state transition function, local forward
models allow extracting multiple training examples from a single state
transition. In this way, the Hash Set model, as well as the Decision Tree
model, quickly learn to predict upcoming state transitions of both the training
and the test set. Applying the model using a statistical forward planner showed
that the best models can be used to satisfying degree even in cases in which
the test levels have not yet been seen.
  Our evaluation includes an analysis of various local neighbourhood patterns
and sizes to test the learners' capabilities in case too few or too many
attributes are extracted, of which the latter has shown do degrade the
performance of the model learner.
",Learning Local Forward Models on Unforgiving Games
"  Argumentation is a major topic in the study of Artificial Intelligence. Since
the first edition in 2015, advancements in solving (abstract) argumentation
frameworks are assessed in competition events, similar to other closely related
problem solving technologies. In this paper, we report about the design and
results of the Second International Competition on Computational Models of
Argumentation, which has been jointly organized by TU Dresden (Germany), TU
Wien (Austria), and the University of Genova (Italy), in affiliation with the
2017 International Workshop on Theory and Applications of Formal Argumentation.
This second edition maintains some of the design choices made in the first
event, e.g. the I/O formats, the basic reasoning problems, and the organization
into tasks and tracks. At the same time, it introduces significant novelties,
e.g. three additional prominent semantics, and an instance selection stage for
classifying instances according to their empirical hardness.
","Design and Results of the Second International Competition on
  Computational Models of Argumentation"
"  The disruptive potential of the upcoming digital transformations for the
industrial manufacturing domain have led to several reference frameworks and
numerous standardization approaches. On the other hand, the Semantic Web
community has made significant contributions in the field, for instance on data
and service description, integration of heterogeneous sources and devices, and
AI techniques in distributed systems. These two streams of work are, however,
mostly unrelated and only briefly regard each others requirements, practices
and terminology. We contribute to closing this gap by providing the Semantic
Asset Administration Shell, an RDF-based representation of the Industrie 4.0
Component. We provide an ontology for the latest data model specification,
created a RML mapping, supply resources to validate the RDF entities and
introduce basic reasoning on the Asset Administration Shell data model.
Furthermore, we discuss the different assumptions and presentation patterns,
and analyze the implications of a semantic representation on the original data.
We evaluate the thereby created overheads, and conclude that the semantic
lifting is manageable, also for restricted or embedded devices, and therefore
meets the needs of Industrie 4.0 scenarios.
",The Semantic Asset Administration Shell
"  Allen's Interval Algebra constitutes a framework for reasoning about temporal
information in a qualitative manner. In particular, it uses intervals, i.e.,
pairs of endpoints, on the timeline to represent entities corresponding to
actions, events, or tasks, and binary relations such as precedes and overlaps
to encode the possible configurations between those entities. Allen's calculus
has found its way in many academic and industrial applications that involve,
most commonly, planning and scheduling, temporal databases, and healthcare. In
this paper, we present a novel encoding of Interval Algebra using answer-set
programming (ASP) extended by difference constraints, i.e., the fragment
abbreviated as ASP(DL), and demonstrate its performance via a preliminary
experimental evaluation. Although our ASP encoding is presented in the case of
Allen's calculus for the sake of clarity, we suggest that analogous encodings
can be devised for other point-based calculi, too.
",Allen's Interval Algebra Makes the Difference
"  The paper introduces an extension of the proposal according to which
conceptual representations in cognitive agents should be intended as
heterogeneous proxytypes. The main contribution of this paper is in that it
details how to reconcile, under a heterogeneous representational perspective,
different theories of typicality about conceptual representation and reasoning.
In particular, it provides a novel theoretical hypothesis - as well as a novel
categorization algorithm called DELTA - showing how to integrate the
representational and reasoning assumptions of the theory-theory of concepts
with the those ascribed to the prototype and exemplars-based theories.
","Heterogeneous Proxytypes Extended: Integrating Theory-like
  Representations and Mechanisms with Prototypes and Exemplars"
"  In picker-to-parts warehouses, order picking is a cost- and labor-intensive
operation that must be designed efficiently. It comprises the construction of
order batches and the associated order picker routes, and the assignment and
sequencing of those batches to multiple order pickers. The ever-increasing
competitiveness among e-commerce companies has made the joint optimization of
this order picking process inevitable. Inspired by the large number of product
returns and the many but small-sized customer orders, we address a new
integrated order picking process problem. We integrate the restocking of
returned products into regular order picking routes and we allow for the
decomposition of customer orders so that multiple batches may contain products
from the same customer order. We thereby generalize the existing models on
order picking processing. We provide Mixed Integer Programming (MIP)
formulations and a tailored adaptive large neighborhood search heuristic that,
amongst others, exploits these MIPs. We propose a new set of practically-sized
benchmark instances, consisting of up to 5547 to be picked products and 2491 to
be restocked products. On those large-scale instances, we show that integrating
the restocking of returned products into regular order picker routes results in
cost-savings of 10 to 15%. Allowing for the decomposition of the customer
orders' products results in cost savings of up to 44% compared to not allowing
this. Finally, we show that on average cost-savings of 17.4% can be obtained by
using our ALNS instead of heuristics typically used in practice.
","Integration of returns and decomposition of customer orders in
  e-commerce warehouses"
"  We consider the problem of eliciting expert assessments of an uncertain
parameter. The context is risk control, where there are, in fact, three
uncertain parameters to be estimates. Two of these are probabilities, requiring
the that the experts be guided in the concept of ""uncertainty about
uncertainty."" We propose a novel formulation for expert estimates, which relies
on the range and the median, rather than the variance and the mean. We discuss
the process of elicitation, and provide precise formulas for these new
distributions.
",Soft Triangles for Expert Aggregation
"  This paper formally compares some central notions from two well-known
formalisms for rule-based argumentation, DeLP and ASPIC+. The comparisons
especially focus on intuitive adequacy and inter-translatability, consistency,
and closure properties. As for differences in the definitions of arguments and
attack, it turns out that DeLP's definitions are intuitively appealing but that
they may not fully comply with Caminada and Amgoud's rationality postulates of
strict closure and indirect consistency. For some special cases, the DeLP
definitions are shown to fare better than ASPIC+. Next, it is argued that there
are reasons to consider a variant of DeLP with grounded semantics, since in
some examples its current notion of warrant arguably has counterintuitive
consequences and may lead to sets of warranted arguments that are not
admissible. Finally, under some minimality and consistency assumptions on
ASPIC+ arguments, a one-to-many correspondence between ASPIC+ arguments and
DeLP arguments is identified in such a way that if the DeLP warranting
procedure is changed to grounded semantics, then DeLP notion of warrant and
ASPIC+'s notion of justification are equivalent. This result is proven for
three alternative definitions of attack.
",A Comparative Study of Some Central Notions of ASPIC+ and DeLP
"  We present a new method of automatic critical mechanic discovery for video
games using a combination of game description parsing and playtrace
information. This method is applied to several games within the General Video
Game Artificial Intelligence (GVG-AI) framework. In a user study,
human-identified mechanics are compared against system-identified critical
mechanics to verify alignment between humans and the system. The results of the
study demonstrate that the new method is able to match humans with higher
consistency than baseline. Our system is further validated by comparing MCTS
agents augmented with critical mechanics and vanilla MCTS agents on $4$ games
from GVG-AI. Our new playtrace method shows a significant performance
improvement over the baseline for all 4 tested games. The proposed method also
shows either matched or improved performance over the old method, demonstrating
that playtrace information is responsible for more complete critical mechanic
discovery.
",Automatic Critical Mechanic Discovery Using Playtraces in Video Games
"  AGVs are driverless robotic vehicles that picks up and delivers materials.
How to improve the efficiency while preventing deadlocks is the core issue in
designing AGV systems. In this paper, we propose an approach to tackle this
problem.The proposed approach includes a traditional AGV scheduling algorithm,
which aims at solving deadlock problems, and an artificial neural network based
component, which predict future tasks of the AGV system, and make decisions on
whether to send an AGV to the predicted starting location of the upcoming
task,so as to save the time of waiting for an AGV to go to there first when the
upcoming task is created. Simulation results show that the proposed method
significantly improves the efficiency as against traditional method, up to 20%
to 30%.
",Artificial intelligence empowered multi-AGVs in manufacturing systems
"  From marketing to politics, exploitation of incomplete information through
selective communication of arguments is ubiquitous. In this work, we focus on
development of an argumentation-theoretic model for manipulable multi-agent
argumentation, where each agent may transmit deceptive information to others
for tactical motives. In particular, we study characterisation of epistemic
states, and their roles in deception/honesty detection and (mis)trust-building.
To this end, we propose the use of intra-agent preferences to handle
deception/honesty detection and inter-agent preferences to determine which
agent(s) to believe in more. We show how deception/honesty in an argumentation
of an agent, if detected, would alter the agent's perceived trustworthiness,
and how that may affect their judgement as to which arguments should be
acceptable.
","Formulating Manipulable Argumentation with Intra-/Inter-Agent
  Preferences"
"  Low Back Pain (LBP) is a common medical condition that deprives many
individuals worldwide of their normal routine activities. In the absence of
external biomarkers, diagnosis of LBP is quite challenging. It requires dealing
with several clinical variables, which have no precisely quantified values.
Aiming at the development of a fuzzy medical expert system for LBP management,
this research proposes an attractive lattice-based knowledge representation
scheme for handling imprecision in knowledge, offering a suitable design
methodology for a fuzzy knowledge base and a fuzzy inference system. The fuzzy
knowledge base is constructed in modular fashion, with each module capturing
interrelated medical knowledge about the relevant clinical history, clinical
examinations and laboratory investigation results. This approach in design
ensures optimality, consistency and preciseness in the knowledge base and
scalability. The fuzzy inference system, which uses the Mamdani method, adopts
the triangular membership function for fuzzification and the Centroid of Area
technique for defuzzification. A prototype of this system has been built using
the knowledge extracted from the domain expert physicians. The inference of the
system against a few available patient records at the ESI Hospital, Sealdah has
been checked. It was found to be acceptable by the verifying medical experts.
",Lattice-Based Fuzzy Medical Expert System for Low Back Pain Management
"  Aiming at developing a medical expert system for low back pain management,
the paper proposes an efficient knowledge representation scheme using frame
data structures, and also derives a reliable resolution logic through Bayesian
Network. When a patient comes to the intended expert system for diagnosis, the
proposed inference engine outputs a number of probable diseases in sorted
order, with each disease being associated with a numeric measure to indicate
its possibility of occurrence. When two or more diseases in the list have the
same or closer possibility of occurrence, Bayesian Network is used for conflict
resolution. The proposed scheme has been validated with cases of empirically
selected thirty patients. Considering the expected value 0.75 as level of
acceptance, the proposed system offers the diagnostic inference with the
standard deviation of 0.029. The computational value of Chi-Squared test has
been obtained as 11.08 with 12 degree of freedom, implying that the derived
results from the designed system conform the homogeneity with the expected
outcomes. Prior to any clinical investigations on the selected low back pain
patients, the accuracy level (average) of 73.89% has been achieved by the
proposed system, which is quite close to the expected clinical accuracy level
of 75%.
","Addressing Design Issues in Medical Expert System for Low Back Pain
  Management: Knowledge Representation, Inference Mechanism, and Conflict
  Resolution Using Bayesian Network"
"  We present a method for pursuit/evasion that is highly efficient and and
scales to large teams of aircraft. The underlying algorithm is an efficient
algorithm for solving Markov Decision Processes (MDPs) that supports fully
continuous state spaces. We demonstrate the algorithm in a team pursuit/evasion
setting in a 3D environment using a pseudo-6DOF model and study performance by
varying sizes of team members. We show that as the number of aircraft in the
simulation grows, computational performance remains efficient and is suitable
for real-time systems. We also define probability-to-win and survivability
metrics that describe the teams' performance over multiple trials, and show
that the algorithm performs consistently. We provide numerical results showing
control inputs for a typical 1v1 encounter and provide videos for 1v1, 2v2,
3v3, 4v4, and 10v10 contests to demonstrate the ability of the algorithm to
adapt seamlessly to complex environments.
","An Efficient Algorithm for Multiple-Pursuer-Multiple-Evader
  Pursuit/Evasion Game"
"  Transferring high-level knowledge from a source task to a target task is an
effective way to expedite reinforcement learning (RL). For example,
propositional logic and first-order logic have been used as representations of
such knowledge. We study the transfer of knowledge between tasks in which the
timing of the events matters. We call such tasks temporal tasks. We concretize
similarity between temporal tasks through a notion of logical transferability,
and develop a transfer learning approach between different yet similar temporal
tasks. We first propose an inference technique to extract metric interval
temporal logic (MITL) formulas in sequential disjunctive normal form from
labeled trajectories collected in RL of the two tasks. If logical
transferability is identified through this inference, we construct a timed
automaton for each sequential conjunctive subformula of the inferred MITL
formulas from both tasks. We perform RL on the extended state which includes
the locations and clock valuations of the timed automata for the source task.
We then establish mappings between the corresponding components (clocks,
locations, etc.) of the timed automata from the two tasks, and transfer the
extended Q-functions based on the established mappings. Finally, we perform RL
on the extended state for the target task, starting with the transferred
extended Q-functions. Our results in two case studies show, depending on how
similar the source task and the target task are, that the sampling efficiency
for the target task can be improved by up to one order of magnitude by
performing RL in the extended state space, and further improved by up to
another order of magnitude using the transferred extended Q-functions.
",Transfer of Temporal Logic Formulas in Reinforcement Learning
"  Prior access to domain knowledge could significantly improve the performance
of a reinforcement learning agent. In particular, it could help agents avoid
potentially catastrophic exploratory actions, which would otherwise have to be
experienced during learning. In this work, we identify consistently undesirable
actions in a set of previously learned tasks, and use pseudo-rewards associated
with them to learn a prior policy. In addition to enabling safer exploratory
behaviors in subsequent tasks in the domain, we show that these priors are
transferable to similar environments, and can be learned off-policy and in
parallel with the learning of other tasks in the domain. We compare our
approach to established, state-of-the-art algorithms in both discrete as well
as continuous environments, and demonstrate that it exhibits a safer
exploratory behavior while learning to perform arbitrary tasks in the domain.
We also present a theoretical analysis to support these results, and briefly
discuss the implications and some alternative formulations of this approach,
which could also be useful in certain scenarios.
","Learning Transferable Domain Priors for Safe Exploration in
  Reinforcement Learning"
"  Over the last year, the amount of research in hierarchical planning has
increased, leading to significant improvements in the performance of planners.
However, the research is diverging and planners are somewhat hard to compare
against each other. This is mostly caused by the fact that there is no standard
set of benchmark domains, nor even a common description language for
hierarchical planning problems. As a consequence, the available planners
support a widely varying set of features and (almost) none of them can solve
(or even parse) any problem developed for another planner. With this paper, we
propose to create a new track for the IPC in which hierarchical planners will
compete. This competition will result in a standardised description language,
broader support for core features of that language among planners, a set of
benchmark problems, a means to fairly and objectively compare HTN planners, and
for new challenges for planners.
",Hierarchical Planning in the IPC
"  One of the main obstacles for developing flexible AI systems is the split
between data-based learners and model-based solvers. Solvers such as classical
planners are very flexible and can deal with a variety of problem instances and
goals but require first-order symbolic models. Data-based learners, on the
other hand, are robust but do not produce such representations. In this work we
address this split by showing how the first-order symbolic representations that
are used by planners can be learned from non-symbolic inputs that encode the
structure of the state space. The representation learning problem is formulated
as the problem of inferring planning instances over a common but unknown
first-order domain that account for the structure of the observed state space.
This means to infer a complete first-order representation (i.e. general action
schemas, relational symbols, and objects) that explains the observed state
space structures. The inference problem is cast as a two-level combinatorial
search where the outer level searches for values of a small set of
hyperparameters and the inner level, solved via SAT, searches for a first-order
symbolic model. The framework is shown to produce general and correct
first-order representations for standard problems like Gripper, Blocksworld,
and Hanoi from input graphs that encode the flat state-space structure of a
single instance.
","Learning First-Order Symbolic Representations for Planning from the
  Structure of the State Space"
"  Some preliminary results are reported on the equivalence of any n-queens
problem with the roots of a Boolean valued quadratic form via a generic
dimensional reduction scheme. It is then proven that the solutions set is
encoded in the entries of a special matrix. Further examination reveals a
direct association with pointwise Boolean fractal operators applied on certain
integer sequences associated with this matrix suggesting the presence of an
underlying special geometry of the solutions set.
",Hidden Structure in the Solutions Set of the N Queens Problem
"  Incorporating high-level knowledge is an effective way to expedite
reinforcement learning (RL), especially for complex tasks with sparse rewards.
We investigate an RL problem where the high-level knowledge is in the form of
reward machines, i.e., a type of Mealy machine that encodes the reward
functions. We focus on a setting in which this knowledge is a priori not
available to the learning agent. We develop an iterative algorithm that
performs joint inference of reward machines and policies for RL (more
specifically, q-learning). In each iteration, the algorithm maintains a
hypothesis reward machine and a sample of RL episodes. It derives q-functions
from the current hypothesis reward machine, and performs RL to update the
q-functions. While performing RL, the algorithm updates the sample by adding RL
episodes along which the obtained rewards are inconsistent with the rewards
based on the current hypothesis reward machine. In the next iteration, the
algorithm infers a new hypothesis reward machine from the updated sample. Based
on an equivalence relationship we defined between states of reward machines, we
transfer the q-functions between the hypothesis reward machines in consecutive
iterations. We prove that the proposed algorithm converges almost surely to an
optimal policy in the limit if a minimal reward machine can be inferred and the
maximal length of each RL episode is sufficiently long. The experiments show
that learning high-level knowledge in the form of reward machines can lead to
fast convergence to optimal policies in RL, while standard RL methods such as
q-learning and hierarchical RL methods fail to converge to optimal policies
after a substantial number of training steps in many tasks.
","Joint Inference of Reward Machines and Policies for Reinforcement
  Learning"
"  Machine education is an emerging research field that focuses on the problem
which is inverse to machine learning. To date, the literature on educating
machines is still in its infancy. A fairly low number of methodology and method
papers are scattered throughout various formal and informal publication
avenues, mainly because the field is not yet well coalesced (with no well
established discussion forums or investigation pathways), but also due to the
breadth of its potential ramifications and research directions. In this study
we bring together the existing literature and organise the discussion into a
small number of research directions (out of many) which are to date
sufficiently explored to form a minimal critical mass that can push the machine
education concept further towards a standalone research field status.
",On educating machines
"  Many intelligent systems currently interact with others using at least one of
fixed communication inputs or preset responses, resulting in rigid interaction
experiences and extensive efforts developing a variety of scenarios for the
system. Fixed inputs limit the natural behavior of the user in order to
effectively communicate, and preset responses prevent the system from adapting
to the current situation unless it was specifically implemented. Closed-loop
interaction instead focuses on dynamic responses that account for what the user
is currently doing based on interpretations of their perceived activity. Agents
employing closed-loop interaction can also monitor their interactions to ensure
that the user responds as expected. We introduce a closed-loop interactive
agent framework that integrates planning and recognition to predict what the
user is trying to accomplish and autonomously decide on actions to take in
response to these predictions. Based on a recent demonstration of such an
assistive interactive agent in a turn-based simulated game, we also discuss new
research challenges that are not present in the areas of artificial
intelligence planning or recognition alone.
",Responsive Planning and Recognition for Closed-Loop Interaction
"  The goal of this paper is to investigate a decision support system for
vehicle routing, where the routing engine learns from the subjective decisions
that human planners have made in the past, rather than optimizing a
distance-based objective criterion. This is an alternative to the practice of
formulating a custom VRP for every company with its own routing requirements.
Instead, we assume the presence of past vehicle routing solutions over similar
sets of customers, and learn to make similar choices. The approach is based on
the concept of learning a first-order Markov model, which corresponds to a
probabilistic transition matrix, rather than a deterministic distance matrix.
This nevertheless allows us to use existing arc routing VRP software in
creating the actual route plans. For the learning, we explore different schemes
to construct the probabilistic transition matrix. Our results on a use-case
with a small transportation company show that our method is able to generate
results that are close to the manually created solutions, without needing to
characterize all constraints and sub-objectives explicitly. Even in the case of
changes in the client sets, our method is able to find solutions that are
closer to the actual route plans than when using distances, and hence,
solutions that would require fewer manual changes to transform into the actual
route plan.
",Vehicle routing by learning from historical solutions
"  In medical decision making, we have to choose among several expensive
diagnostic tests such that the certainty about a patient's health is maximized
while remaining within the bounds of resources like time and money. The
expected increase in certainty in the patient's condition due to performing a
test is called the value of information (VoI) for that test. In general, VoI
relates to acquiring additional information to improve decision-making based on
probabilistic reasoning in an uncertain system. This paper presents a framework
for acquiring information based on VoI in uncertain systems modeled as
Probabilistic Logic Programs (PLPs). Optimal decision-making in uncertain
systems modeled as PLPs have already been studied before. But, acquiring
additional information to further improve the results of making the optimal
decision has remained open in this context.
  We model decision-making in an uncertain system with a PLP and a set of
top-level queries, with a set of utility measures over the distributions of
these queries. The PLP is annotated with a set of atoms labeled as
""observable""; in the medical diagnosis example, the observable atoms will be
results of diagnostic tests. Each observable atom has an associated cost. This
setting of optimally selecting observations based on VoI is more general than
that considered by any prior work. Given a limited budget, optimally choosing
observable atoms based on VoI is intractable in general. We give a greedy
algorithm for constructing a ""conditional plan"" of observations: a schedule
where the selection of what atom to observe next depends on earlier
observations. We show that, preempting the algorithm anytime before completion
provides a usable result, the result improves over time, and, in the absence of
a well-defined budget, converges to the optimal solution.
",Value of Information in Probabilistic Logic Programs
"  In this work we design a narrative understanding tool Text2ALM. This tool
uses an action language ALM to perform inferences on complex interactions of
events described in narratives. The methodology used to implement the Text2ALM
system was originally outlined by Lierler, Inclezan, and Gelfond (2017) via a
manual process of converting a narrative to an ALM model. It relies on a
conglomeration of resources and techniques from two distinct fields of
artificial intelligence, namely, natural language processing and knowledge
representation and reasoning. The effectiveness of system Text2ALM is measured
by its ability to correctly answer questions from the bAbI tasks published by
Facebook Research in 2015. This tool matched or exceeded the performance of
state-of-the-art machine learning methods in six of the seven tested tasks. We
also illustrate that the Text2ALM approach generalizes to a broader spectrum of
narratives.
","Information Extraction Tool Text2ALM: From Narratives to Action Language
  System Descriptions"
"  We present an approach to representing large sets of mutual exclusions, also
known as mutexes or mutex constraints. These are the types of constraints that
specify the exclusion of some properties, events, processes, and so on. They
are ubiquitous in many areas of applications. The size of these constraints for
a given problem can be overwhelming enough to present a bottleneck for the
solving efficiency of the underlying solver. In this paper, we propose a novel
graph-theoretic technique based on multicliques for a compact representation of
mutex constraints and apply it to domain-independent planning in ASP. As
computing a minimum multiclique covering from a mutex graph is NP-hard, we
propose an efficient approximation algorithm for multiclique covering and show
experimentally that it generates substantially smaller grounding size for mutex
constraints in ASP than the previously known work in SAT.
",Mutex Graphs and Multicliques: Reducing Grounding Size for Planning
"  It is common for search and optimization problems to have alternative
equivalent encodings in ASP. Typically none of them is uniformly better than
others when evaluated on broad classes of problem instances. We claim that one
can improve the solving ability of ASP by using machine learning techniques to
select encodings likely to perform well on a given instance. We substantiate
this claim by studying the hamiltonian cycle problem. We propose several
equivalent encodings of the problem and several classes of hard instances. We
build models to predict the behavior of each encoding, and then show that
selecting encodings for a given instance using the learned performance
predictors leads to significant performance gains.
",Encoding Selection for Solving Hamiltonian Cycle Problems with ASP
"  For the diagnostic inference under uncertainty Bayesian networks are
investigated. The method is based on an adequate uniform representation of the
necessary knowledge. This includes both generic and experience-based specific
knowledge, which is stored in a knowledge base. For knowledge processing, a
combination of the problem-solving methods of concept-based and case-based
reasoning is used. Concept-based reasoning is used for the diagnosis, therapy
and medication recommendation and evaluation of generic knowledge. Exceptions
in the form of specific patient cases are processed by case-based reasoning. In
addition, the use of Bayesian networks allows to deal with uncertainty,
fuzziness and incompleteness. Thus, the valid general concepts can be issued
according to their probability. To this end, various inference mechanisms are
introduced and subsequently evaluated within the context of a developed
prototype. Tests are employed to assess the classification of diagnoses by the
network.
","Knowledge representation and diagnostic inference using Bayesian
  networks in the medical discourse"
"  From a set of technical drawings and expert knowledge, we automatically learn
a parser to interpret such a drawing. This enables automatic reasoning and
learning on top of a large database of technical drawings. In this work, we
develop a similarity based search algorithm to help engineers and designers
find or complete designs more easily and flexibly. This is part of an ongoing
effort to build an automated engineering assistant. The proposed methods make
use of both neural methods to learn to interpret images, and symbolic methods
to learn to interpret the structure in the technical drawing and incorporate
expert knowledge.
","An Automated Engineering Assistant: Learning Parsers for Technical
  Drawings"
"  The main purposes of this study are to distinguish the trends of research in
publication exits for the utilisations of the fuzzy expert and knowledge-based
systems that is done based on the classification of studies in the last decade.
The present investigation covers 60 articles from related scholastic journals,
International conference proceedings and some major literature review papers.
Our outcomes reveal an upward trend in the up-to-date publications number, that
is evidence of growing notoriety on the various applications of fuzzy expert
systems. This raise in the reports is mainly in the medical neuro-fuzzy and
fuzzy expert systems. Moreover, another most critical observation is that many
modern industrial applications are extended, employing knowledge-based systems
by extracting the experts' knowledge.
","A literature review on current approaches and applications of fuzzy
  expert systems"
"  Police officer presence at an intersection discourages a potential traffic
violator from violating the law. It also alerts the motorists' consciousness to
take precaution and follow the rules. However, due to the abundant
intersections and shortage of human resources, it is not possible to assign a
police officer to every intersection. In this paper, we propose an intelligent
and optimal policing strategy for traffic violation prevention. Our model
consists of a specific number of targeted intersections and two police officers
with no prior knowledge on the number of the traffic violations in the
designated intersections. At each time interval, the proposed strategy, assigns
the two police officers to different intersections such that at the end of the
time horizon, maximum traffic violation prevention is achieved. Our proposed
methodology adapts the PROLA (Play and Random Observe Learning Algorithm)
algorithm [1] to achieve an optimal traffic violation prevention strategy.
Finally, we conduct a case study to evaluate and demonstrate the performance of
the proposed method.
",Intelligent Policing Strategy for Traffic Violation Prevention
"  Weighted model integration (WMI) is a very appealing framework for
probabilistic inference: it allows to express the complex dependencies of
real-world hybrid scenarios where variables are heterogeneous in nature (both
continuous and discrete) via the language of Satisfiability Modulo Theories
(SMT); as well as computing probabilistic queries with arbitrarily complex
logical constraints. Recent work has shown WMI inference to be reducible to a
model integration (MI) problem, under some assumptions, thus effectively
allowing hybrid probabilistic reasoning by volume computations. In this paper,
we introduce a novel formulation of MI via a message passing scheme that allows
to efficiently compute the marginal densities and statistical moments of all
the variables in linear time. As such, we are able to amortize inference for
arbitrarily rich MI queries when they conform to the problem structure, here
represented as the primal graph associated to the SMT formula. Furthermore, we
theoretically trace the tractability boundaries of exact MI. Indeed, we prove
that in terms of the structural requirements on the primal graph that make our
MI algorithm tractable - bounding its diameter and treewidth - the bounds are
not only sufficient, but necessary for tractable inference via MI.
","Hybrid Probabilistic Inference with Logical Constraints: Tractability
  and Message Passing"
"  Bike Sharing Systems (BSSs) have been adopted in many major cities of the
world due to traffic congestion and carbon emissions. Although there have been
approaches to exploiting either bike trailers via crowdsourcing or carrier
vehicles to reposition bikes in the ``right'' stations in the ``right'' time,
they do not jointly consider the usage of both bike trailers and carrier
vehicles. In this paper, we aim to take advantage of both bike trailers and
carrier vehicles to reduce the loss of demand with regard to the crowdsourcing
of bike trailers and the fuel cost of carrier vehicles. In the experiment, we
exhibit that our approach outperforms baselines in several datasets from bike
sharing companies.
","Repositioning Bikes with Carrier Vehicles and Bike Trailers in Bike
  Sharing Systems"
"  We build a bridge between neural network-based machine learning and
graph-based natural language processing and introduce a unified approach to
keyphrase, summary and relation extraction by aggregating dependency graphs
from links provided by a deep-learning based dependency parser.
  We reorganize dependency graphs to focus on the most relevant content
elements of a sentence, integrate sentence identifiers as graph nodes and after
ranking the graph, we extract our keyphrases and summaries from its largest
strongly-connected component. We take advantage of the implicit structural
information that dependency links bring to extract subject-verb-object, is-a
and part-of relations.
  We put it all together into a proof-of-concept dialog engine that specializes
the text graph with respect to a query and reveals interactively the document's
most relevant content elements. The open-source code of the integrated system
is available at https://github.com/ptarau/DeepRank .
  Keywords: graph-based natural language processing, dependency graphs,
keyphrase, summary and relation extraction, query-driven salient sentence
extraction, logic-based dialog engine, synergies between neural and symbolic
processing.
","Dependency-based Text Graphs for Keyphrase and Summary Extraction with
  Applications to Interactive Content Retrieval"
"  With the recent rise of toxicity in online conversations on social media
platforms, using modern machine learning algorithms for toxic comment detection
has become a central focus of many online applications. Researchers and
companies have developed a variety of models to identify toxicity in online
conversations, reviews, or comments with mixed successes. However, many
existing approaches have learned to incorrectly associate non-toxic comments
that have certain trigger-words (e.g. gay, lesbian, black, muslim) as a
potential source of toxicity. In this paper, we evaluate several
state-of-the-art models with the specific focus of reducing model bias towards
these commonly-attacked identity groups. We propose a multi-task learning model
with an attention layer that jointly learns to predict the toxicity of a
comment as well as the identities present in the comments in order to reduce
this bias. We then compare our model to an array of shallow and deep-learning
models using metrics designed especially to test for unintended model bias
within these identity groups.
","Empirical Analysis of Multi-Task Learning for Reducing Model Bias in
  Toxic Comment Detection"
"  Latest addition to the toolbox of human species is Artificial
Intelligence(AI). Thus far, AI has made significant progress in low stake low
risk scenarios such as playing Go and we are currently in a transition toward
medium stake scenarios such as Visual Dialog. In my thesis, I argue that we
need to incorporate controlled de-entanglement as first class object to succeed
in this transition. I present mathematical analysis from information theory to
show that employing stochasticity leads to controlled de-entanglement of
relevant factors of variation at various levels. Based on this, I highlight
results from initial experiments that depict efficacy of the proposed
framework. I conclude this writeup by a roadmap of experiments that show the
applicability of this framework to scalability, flexibility and
interpretibility.
",On Controlled DeEntanglement for Natural Language Processing
"  Network attack is a significant security issue for modern society. From small
mobile devices to large cloud platforms, almost all computing products, used in
our daily life, are networked and potentially under the threat of network
intrusion. With the fast-growing network users, network intrusions become more
and more frequent, volatile and advanced. Being able to capture intrusions in
time for such a large scale network is critical and very challenging. To this
end, the machine learning (or AI) based network intrusion detection (NID), due
to its intelligent capability, has drawn increasing attention in recent years.
Compared to the traditional signature-based approaches, the AI-based solutions
are more capable of detecting variants of advanced network attacks. However,
the high detection rate achieved by the existing designs is usually accompanied
by a high rate of false alarms, which may significantly discount the overall
effectiveness of the intrusion detection system. In this paper, we consider the
existence of spatial and temporal features in the network traffic data and
propose a hierarchical CNN+RNN neural network, LuNet. In LuNet, the
convolutional neural network (CNN) and the recurrent neural network (RNN) learn
input traffic data in sync with a gradually increasing granularity such that
both spatial and temporal features of the data can be effectively extracted.
Our experiments on two network traffic datasets show that compared to the
state-of-the-art network intrusion detection techniques, LuNet not only offers
a high level of detection capability but also has a much low rate of false
positive-alarm.
",LuNet: A Deep Neural Network for Network Intrusion Detection
"  This paper presents an initial design concept and specification of a civilian
Unmanned Aerial Vehicle (UAV) management simulation system that focuses on
explainability for the human-in-the-loop control of semi-autonomous UAVs. The
goal of the system is to facilitate the operator intervention in critical
scenarios (e.g. avoid safety issues or financial risks). Explainability is
supported via user-friendly abstractions on Belief-Desire-Intention agents. To
evaluate the effectiveness of the system, a human-computer interaction study is
proposed.
","Towards Explainability for a Civilian UAV Fleet Management using an
  Agent-based Approach"
"  This paper proposes a unique active relative localization mechanism for
multi-agent Simultaneous Localization and Mapping(SLAM),in which a agent to be
observed are considered as a task, which is performed by others assisting that
agent by relative observation. A task allocation algorithm based on deep
reinforcement learning are proposed for this mechanism. Each agent can choose
whether to localize other agents or to continue independent SLAM on it own
initiative. By this way, the process of each agent SLAM will be interacted by
the collaboration. Firstly, based on the characteristics of ORBSLAM, a unique
observation function which models the whole MAS is obtained. Secondly, a novel
type of Deep Q network(DQN) called MAS-DQN is deployed to learn correspondence
between Q Value and state-action pair,abstract representation of agents in MAS
are learned in the process of collaboration among agents. Finally, each agent
must act with a certain degree of freedom according to MAS-DQN. The simulation
results of comparative experiments prove that this mechanism improves the
efficiency of cooperation in the process of multi-agent SLAM.
","Active collaboration in relative observation for Multi-agent visual SLAM
  based on Deep Q Network"
"  A number of researchers have recently questioned the necessity of
increasingly complex neural network (NN) architectures. In particular, several
recent papers have shown that simpler, properly tuned models are at least
competitive across several NLP tasks. In this work, we show that this is also
the case for text generation from structured and unstructured data. We consider
neural table-to-text generation and neural question generation (NQG) tasks for
text generation from structured and unstructured data, respectively.
Table-to-text generation aims to generate a description based on a given table,
and NQG is the task of generating a question from a given passage where the
generated question can be answered by a certain sub-span of the passage using
NN models. Experimental results demonstrate that a basic attention-based
seq2seq model trained with the exponential moving average technique achieves
the state of the art in both tasks. Code is available at
https://github.com/h-shahidi/2birds-gen.
","Two Birds, One Stone: A Simple, Unified Model for Text Generation from
  Structured and Unstructured Data"
"  Factored stochastic constraint programming (FSCP) is a formalism to represent
multi-stage decision making problems under uncertainty. FSCP models support
factorized probabilistic models and involve constraints over decision and
random variables. These models have many applications in real-world problems.
However, solving these problems requires evaluating the best course of action
for each possible outcome of the random variables and hence is computationally
challenging. FSCP problems often involve repeated subproblems which ideally
should be solved once. In this paper we show how identifying and exploiting
these identical subproblems can simplify solving them and leads to a compact
representation of the solution. We compile an And-Or search tree to a compact
decision diagram. Preliminary experiments show that our proposed method
significantly improves the search efficiency by reducing the size of the
problem and outperforms the existing methods.
",Compiling Stochastic Constraint Programs to And-Or Decision Diagrams
"  This paper describes methods for comparative evaluation of the
interpretability of models of high dimensional time series data inferred by
unsupervised machine learning algorithms. The time series data used in this
investigation were logs from an immersive simulation like those commonly used
in education and healthcare training. The structures learnt by the models
provide representations of participants' activities in the simulation which are
intended to be meaningful to people's interpretation. To choose the model that
induces the best representation, we designed two interpretability tests, each
of which evaluates the extent to which a model's output aligns with people's
expectations or intuitions of what has occurred in the simulation. We compared
the performance of the models on these interpretability tests to their
performance on statistical information criteria. We show that the models that
optimize interpretability quality differ from those that optimize (statistical)
information theoretic criteria. Furthermore, we found that a model using a
fully Bayesian approach performed well on both the statistical and
human-interpretability measures. The Bayesian approach is a good candidate for
fully automated model selection, i.e., when direct empirical investigations of
interpretability are costly or infeasible.
",Interpretable Models for Understanding Immersive Simulations
"  To coordinate with other systems, agents must be able to determine what the
systems are currently doing and predict what they will be doing in the
future---plan and goal recognition. There are many methods for plan and goal
recognition, but they assume a passive observer that continually monitors the
target system. Real-world domains, where information gathering has a cost
(e.g., moving a camera or a robot, or time taken away from another task), will
often require a more active observer. We propose to combine goal recognition
with other observer tasks in order to obtain \emph{active goal recognition}
(AGR). We discuss this problem and provide a model and preliminary experimental
results for one form of this composite problem. As expected, the results show
that optimal behavior in AGR problems balance information gathering with other
actions (e.g., task completion) such as to achieve all tasks jointly and
efficiently. We hope that our formulation opens the door for extensive further
research on this interesting and realistic problem.
",Active Goal Recognition
"  Automated temporal planning is the technology of choice when controlling
systems that can execute more actions in parallel and when temporal
constraints, such as deadlines, are needed in the model. One limitation of
several action-based planning systems is that actions are modeled as intervals
having conditions and effects only at the extremes and as invariants, but no
conditions nor effects can be specified at arbitrary points or sub-intervals.
In this paper, we address this limitation by providing an effective
heuristic-search technique for temporal planning, allowing the definition of
actions with conditions and effects at any arbitrary time within the action
duration. We experimentally demonstrate that our approach is far better than
standard encodings in PDDL 2.1 and is competitive with other approaches that
can (directly or indirectly) represent intermediate action conditions or
effects.
",Temporal Planning with Intermediate Conditions and Effects
"  Despite a tremendous amount of work in the literature and in the commercial
sectors, current approaches to multi-modal trip planning still fail to
consistently generate plans that users deem optimal in practice. We believe
that this is due to the fact that current planners fail to capture the true
preferences of users, e.g., their preferences depend on aspects that are not
modeled. An example of this could be a preference not to walk through an unsafe
area at night. We present a novel multi-modal trip planner that allows users to
upload auxiliary geographic data (e.g., crime rates) and to specify temporal
constraints and preferences over these data in combination with typical metrics
such as time and cost. Concretely, our planner supports the modes walking,
biking, driving, public transit, and taxi, uses linear temporal logic to
capture temporal constraints, and preferential cost functions to represent
preferences. We show by examples that this allows the expression of very
interesting preferences and constraints that, naturally, lead to quite diverse
optimal plans.
",An Extensible and Personalizable Multi-Modal Trip Planner
"  Co-operative learning in heterogeneous teams refers to learning methods in
which teams are organised both to accomplish academic tasks and for individuals
to gain knowledge. Competencies, personality and the gender of team members are
key factors that influence team performance. Here, we introduce a team
composition problem, the so-called synergistic team composition problem (STCP),
which incorporates such key factors when arranging teams. Thus, the goal of the
STCP is to partition a set of individuals into a set of synergistic teams:
teams that are diverse in personality and gender and whose members cover all
required competencies to complete a task. Furthermore, the STCP requires that
all teams are balanced in that they are expected to exhibit similar
performances when completing the task. We propose two efficient algorithms to
solve the STCP. Our first algorithm is based on a linear programming
formulation and is appropriate to solve small instances of the problem. Our
second algorithm is an anytime heuristic that is effective for large instances
of the STCP. Finally, we thoroughly study the computational properties of both
algorithms in an educational context when grouping students in a classroom into
teams using actual-world data.
","Synergistic Team Composition: A Computational Approach to Foster
  Diversity in Teams"
"  Valuation-Based~System can represent knowledge in different domains including
probability theory, Dempster-Shafer theory and possibility theory. More recent
studies show that the framework of VBS is also appropriate for representing and
solving Bayesian decision problems and optimization problems.
  In this paper after introducing the valuation based system (VBS) framework,
we present Markov-like properties of VBS and a method for resolving queries to
VBS.
",Query Optimization Properties of Modified VBS
"  In the presence of non-admissible heuristics, A* and other best-first
algorithms can be converted into anytime optimal algorithms over OR graphs, by
simply continuing the search after the first solution is found. The same trick,
however, does not work for best-first algorithms over AND/OR graphs, that must
be able to expand leaf nodes of the explicit graph that are not necessarily
part of the best partial solution. Anytime optimal variants of AO* must thus
address an exploration-exploitation tradeoff: they cannot just ""exploit"", they
must keep exploring as well. In this work, we develop one such variant of AO*
and apply it to finite-horizon MDPs. This Anytime AO* algorithm eventually
delivers an optimal policy while using non-admissible random heuristics that
can be sampled, as when the heuristic is the cost of a base policy that can be
sampled with rollouts. We then test Anytime AO* for action selection over large
infinite-horizon MDPs that cannot be solved with existing off-line heuristic
search and dynamic programming algorithms, and compare it with UCT.
",Action Selection for MDPs: Anytime AO* vs. UCT
"  Potential heuristics for state-space search are defined as weighted sums over
simple state features. Atomic features consider the value of a single state
variable in a factored state representation, while binary features consider
joint assignments to two state variables. Previous work showed that the set of
all admissible and consistent potential heuristics using atomic features can be
characterized by a compact set of linear constraints. We generalize this result
to binary features and prove a hardness result for features of higher
dimension. Furthermore, we prove a tractability result based on the treewidth
of a new graphical structure we call the context-dependency graph. Finally, we
study the relationship of potential heuristics to transition cost partitioning.
Experimental results show that binary potential heuristics are significantly
more informative than the previously considered atomic ones.
",Higher-Dimensional Potential Heuristics for Optimal Classical Planning
"  Human beings are particularly good at reasoning and inference from just a few
examples. When facing new tasks, humans will leverage knowledge and skills
learned before, and quickly integrate them with the new task. In addition to
learning by experimentation, human also learn socio-culturally through
instructions and learning by example. In this way humans can learn much faster
compared with most current artificial intelligence algorithms in many tasks. In
this paper, we test the idea of speeding up machine learning through social
learning. We argue that in solving real-world problems, especially when the
task is designed by humans, and/or for humans, there are typically instructions
from user manuals and/or human experts which give guidelines on how to better
accomplish the tasks. We argue that these instructions have tremendous value in
designing a reinforcement learning system which can learn in human fashion, and
we test the idea by playing the Atari games Tennis and Pong. We experimentally
demonstrate that the instructions provide key information about the task, which
can be used to decompose the learning task into sub-systems and construct
options for the temporally extended planning, and dramatically accelerate the
learning process.
",Playing Atari Ball Games with Hierarchical Reinforcement Learning
"  In 1999, we introduced CSPLib, a benchmark library for the constraints
community. Our CP-1999 poster paper about CSPLib discussed the advantages and
disadvantages of building such a library. Unlike some other domains such as
theorem proving, or machine learning, representation was then and remains today
a major issue in the success or failure to solve problems. Benchmarks in CSPLib
are therefore specified in natural language as this allows users to find good
representations for themselves. The community responded positively and CSPLib
has become a valuable resource but, as we discuss here, we cannot rest.
",CSPLib: Twenty Years On
"  This is a review of ""The Book of Why"", by Judea Pearl.
",The Book of Why: Review
"  Belief tracking is a basic problem in planning with sensing. While the
problem is intractable, it has been recently shown that for both deterministic
and non-deterministic systems expressed in compact form, it can be done in time
and space that are exponential in the problem width. The width measures the
maximum number of state variables that are all relevant to a given precondition
or goal. In this work, we extend this result both theoretically and
practically. First, we introduce an alternative decomposition scheme and
algorithm with the same time complexity but different completeness guarantees,
whose space complexity is much smaller: exponential in the causal width of the
problem that measures the number of state variables that are causally relevant
to a given precondition, goal, or observable. Second, we introduce a fast,
meaningful, and powerful approximation that trades completeness by speed, and
is both time and space exponential in the problem causal width. It is then
shown empirically that the algorithm combined with simple heuristics yields
state-of-the-art real-time performance in domains with high widths but low
causal widths such as Minesweeper, Battleship, and Wumpus.
","Causal Belief Decomposition for Planning with Sensing: Completeness
  Results and Practical Approximation"
"  The problem of belief tracking in the presence of stochastic actions and
observations is pervasive and yet computationally intractable. In this work we
show however that probabilistic beliefs can be maintained in factored form
exactly and efficiently across a number of causally closed beams, when the
state variables that appear in more than one beam obey a form of backward
determinism. Since computing marginals from the factors is still
computationally intractable in general, and variables appearing in several
beams are not always backward-deterministic, the basic formulation is extended
with two approximations: forms of belief propagation for computing marginals
from factors, and sampling of non-backward-deterministic variables for making
such variables backward-deterministic given their sampled history. Unlike,
Rao-Blackwellized particle-filtering, the sampling is not used for making
inference tractable but for making the factorization sound. The resulting
algorithm involves sampling and belief propagation or just one of them as
determined by the structure of the model.
",Factored Probabilistic Belief Tracking
"  When a model makes a consequential decision, e.g., denying someone a loan, it
needs to additionally generate actionable, realistic feedback on what the
person can do to favorably change the decision. We cast this problem through
the lens of program synthesis, in which our goal is to synthesize an optimal
(realistically cheapest or simplest) sequence of actions that if a person
executes successfully can change their classification. We present a novel and
general approach that combines search-based program synthesis and test-time
adversarial attacks to construct action sequences over a domain-specific set of
actions. We demonstrate the effectiveness of our approach on a number of deep
neural networks.
",Synthesizing Action Sequences for Modifying Model Decisions
"  Nowadays, more and more process data are automatically recorded by
information systems, and made available in the form of event logs. Process
mining techniques enable process-centric analysis of data, including
automatically discovering process models and checking if event data conform to
a certain model. In this paper we analyze the previously unexplored setting of
uncertain event logs: logs where quantified uncertainty is recorded together
with the corresponding data. We define a taxonomy of uncertain event logs and
models, and we examine the challenges that uncertainty poses on process
discovery and conformance checking. Finally, we show how upper and lower bounds
for conformance can be obtained aligning an uncertain trace onto a regular
process model.
",Mining Uncertain Event Data in Process Mining
"  In 2000, I published a relatively comprehensive study of mappings between
propositional satisfiability (SAT) and constraint satisfaction problems (CSPs)
[Wal00]. I analysed four different mappings of SAT problems into CSPs, and two
of CSPs into SAT problems. For each mapping, I compared the impact of achieving
arc-consistency on the CSP with unit propagation on the corresponding SAT
problems, and lifted these results to CSP algorithms that maintain (some level
of ) arc-consistency during search like FC and MAC, and to the Davis- Putnam
procedure (which performs unit propagation at each search node). These results
helped provide some insight into the relationship between propositional
satisfiability and constraint satisfaction that set the scene for an important
and valuable body of work that followed. I discuss here what prompted the
paper, and what followed.
",SAT vs CSP: a commentary
"  We present an experimental comparison of the efficiency of three General Game
Playing systems in their current versions: Regular Boardgames (RBG 1.0),
Ludii~0.3.0, and a Game Description Language (GDL) propnet. We show that in
general, RBG is currently the fastest GGP system. For example, for chess, we
demonstrate that RBG is about 37 times faster than Ludii, and Ludii is about 3
times slower than a GDL propnet. Referring to the recent comparison [An
Empirical Evaluation of Two General Game Systems: Ludii and RBG, CoG 2019], we
show evidences that the benchmark presented there contains a number of
significant flaws that lead to wrong conclusions.
",A note on the empirical comparison of RBG and Ludii
"  The question of whether deep neural networks are good at generalising beyond
their immediate training experience is of critical importance for
learning-based approaches to AI. Here, we consider tests of out-of-sample
generalisation that require an agent to respond to never-seen-before
instructions by manipulating and positioning objects in a 3D Unity simulated
room. We first describe a comparatively generic agent architecture that
exhibits strong performance on these tests. We then identify three aspects of
the training regime and environment that make a significant difference to its
performance: (a) the number of object/word experiences in the training set; (b)
the visual invariances afforded by the agent's perspective, or frame of
reference; and (c) the variety of visual input inherent in the perceptual
aspect of the agent's perception. Our findings indicate that the degree of
generalisation that networks exhibit can depend critically on particulars of
the environment in which a given task is instantiated. They further suggest
that the propensity for neural networks to generalise in systematic ways may
increase if, like human children, those networks have access to many frames of
richly varying, multi-modal observations as they learn.
","Environmental drivers of systematicity and generalization in a situated
  agent"
"  There are notable examples of online search improving over hand-coded or
learned policies (e.g. AlphaZero) for sequential decision making. It is not
clear, however, whether or not policy improvement is guaranteed for many of
these approaches, even when given a perfect evaluation function and transition
model. Indeed, simple counter examples show that seemingly reasonable online
search procedures can hurt performance compared to the original policy. To
address this issue, we introduce the choice function framework for analyzing
online search procedures for policy improvement. A choice function specifies
the actions to be considered at every node of a search tree, with all other
actions being pruned. Our main contribution is to give sufficient conditions
for stationary and non-stationary choice functions to guarantee that the value
achieved by online search is no worse than the original policy. In addition, we
describe a general parametric class of choice functions that satisfy those
conditions and present an illustrative use case of the framework's empirical
utility.
",The Choice Function Framework for Online Policy Improvement
"  While AI techniques have found many successful applications in autonomous
systems, many of them permit behaviours that are difficult to interpret and may
lead to uncertain results. We follow the ""verification as planning"" paradigm
and propose to use model checking techniques to solve planning and goal
reasoning problems for autonomous systems. We give a new formulation of Goal
Task Network (GTN) that is tailored for our model checking based framework. We
then provide a systematic method that models GTNs in the model checker Process
Analysis Toolkit (PAT). We present our planning and goal reasoning system as a
framework called Goal Reasoning And Verification for Independent Trusted
Autonomous Systems (GRAVITAS) and discuss how it helps provide trustworthy
plans in an uncertain environment. Finally, we demonstrate the proposed ideas
in an experiment that simulates a survey mission performed by the REMUS-100
autonomous underwater vehicle.
","GRAVITAS: A Model Checking Based Planning and Goal Reasoning Framework
  for Autonomous Systems"
"  The CP 2002 paper entitled ""Breaking Row and Column Symmetries in Matrix
Models"" by Flener et al.
(https://link.springer.com/chapter/10.1007%2F3-540-46135-3_31) describes some
of the first work for identifying and analyzing row and column symmetry in
matrix models and for efficiently and effectively dealing with such symmetry
using static symmetry-breaking ordering constraints. This commentary provides a
retrospective on that work and highlights some of the subsequent work on the
topic.
","A Commentary on ""Breaking Row and Column Symmetries in Matrix Models"""
"  This paper presents a method for semantic indexing and describes its
application in the field of knowledge representation. Starting point of the
semantic indexing is the knowledge represented by concept hierarchies. The goal
is to assign keys to nodes (concepts) that are hierarchically ordered and
syntactically and semantically correct. With the indexing algorithm, keys are
computed such that concepts are partially unifiable with all more specific
concepts and only semantically correct concepts are allowed to be added. The
keys represent terminological relationships. Correctness and completeness of
the underlying indexing algorithm are proven. The use of classical relational
databases for the storage of instances is described. Because of the uniform
representation, inference can be done using case-based reasoning and generic
problem solving methods.
","Method for the semantic indexing of concept hierarchies, uniform
  representation, use of relational database systems and generic and case-based
  reasoning"
"  Angry Birds is a popular video game in which the player is provided with a
sequence of birds to shoot from a slingshot. The task of the game is to destroy
all green pigs with maximum possible score. Angry Birds appears to be a
difficult task to solve for artificially intelligent agents due to the
sequential decision-making, non-deterministic game environment, enormous state
and action spaces and requirement to differentiate between multiple birds,
their abilities and optimum tapping times. We describe the application of Deep
Reinforcement learning by implementing Double Dueling Deep Q-network to play
Angry Birds game. One of our main goals was to build an agent that is able to
compete with previous participants and humans on the first 21 levels. In order
to do so, we have collected a dataset of game frames that we used to train our
agent on. We present different approaches and settings for DQN agent. We
evaluate our agent using results of the previous participants of AIBirds
competition, results of volunteer human players and present the results of
AIBirds 2018 competition.
",Deep Q-Network for Angry Birds
"  Discounted reinforcement learning is fundamentally incompatible with function
approximation for control in continuing tasks. It is not an optimization
problem in its usual formulation, so when using function approximation there is
no optimal policy. We substantiate these claims, then go on to address some
misconceptions about discounting and its connection to the average reward
formulation. We encourage researchers to adopt rigorous optimization
approaches, such as maximizing average reward, for reinforcement learning in
continuing tasks.
",Discounted Reinforcement Learning Is Not an Optimization Problem
"  This paper attempts to answer a central question in unsupervised learning:
what does it mean to ""make sense"" of a sensory sequence? In our formalization,
making sense involves constructing a symbolic causal theory that both explains
the sensory sequence and also satisfies a set of unity conditions. The unity
conditions insist that the constituents of the causal theory -- objects,
properties, and laws -- must be integrated into a coherent whole. On our
account, making sense of sensory input is a type of program synthesis, but it
is unsupervised program synthesis.
  Our second contribution is a computer implementation, the Apperception
Engine, that was designed to satisfy the above requirements. Our system is able
to produce interpretable human-readable causal theories from very small amounts
of data, because of the strong inductive bias provided by the unity conditions.
A causal theory produced by our system is able to predict future sensor
readings, as well as retrodict earlier readings, and impute (fill in the blanks
of) missing sensory readings, in any combination.
  We tested the engine in a diverse variety of domains, including cellular
automata, rhythms and simple nursery tunes, multi-modal binding problems,
occlusion tasks, and sequence induction intelligence tests. In each domain, we
test our engine's ability to predict future sensor values, retrodict earlier
sensor values, and impute missing sensory data. The engine performs well in all
these domains, significantly out-performing neural net baselines. We note in
particular that in the sequence induction intelligence tests, our system
achieved human-level performance. This is notable because our system is not a
bespoke system designed specifically to solve intelligence tests, but a
general-purpose system that was designed to make sense of any sensory sequence.
",Making sense of sensory input
"  The aim of multi-agent reinforcement learning systems is to provide
interacting agents with the ability to collaboratively learn and adapt to the
behavior of other agents. In many real-world applications, the agents can only
acquire a partial view of the world. However, in realistic settings, one or
more agents that show arbitrarily faulty or malicious behavior may suffice to
let the current coordination mechanisms fail. In this paper, we study a
practical scenario considering the security issues in the presence of agents
with arbitrarily faulty or malicious behavior. Under these circumstances,
learning an optimal policy becomes particularly challenging, even in the
unrealistic case that an agent's policy can be made conditional upon all other
agents' observations. To overcome these difficulties, we present an
Attention-based Fault-Tolerant (FT-Attn) algorithm which selects correct and
relevant information for each agent at every time-step. The multi-head
attention mechanism enables the agents to learn effective communication
policies through experience concurrently to the action policies. Empirical
results have shown that FT-Attn beats previous state-of-the-art methods in some
complex environments and can adapt to various kinds of noisy environments
without tuning the complexity of the algorithm. Furthermore, FT-Attn can
effectively deal with the complex situation where an agent needs to reach
multiple agents' correct observation at the same time.
","Attention-based Fault-tolerant Approach for Multi-agent Reinforcement
  Learning Systems"
"  The capability of making interpretable and self-explanatory decisions is
essential for developing responsible machine learning systems. In this work, we
study the learning to explain problem in the scope of inductive logic
programming (ILP). We propose Neural Logic Inductive Learning (NLIL), an
efficient differentiable ILP framework that learns first-order logic rules that
can explain the patterns in the data. In experiments, compared with the
state-of-the-art methods, we find NLIL can search for rules that are x10 times
longer while remaining x3 times faster. We also show that NLIL can scale to
large image datasets, i.e. Visual Genome, with 1M entities.
",Learn to Explain Efficiently via Neural Logic Inductive Learning
"  Combining neural networks with continuous logic and multicriteria decision
making tools can reduce the black box nature of neural models. In this study,
we show that nilpotent logical systems offer an appropriate mathematical
framework for a hybridization of continuous nilpotent logic and neural models,
helping to improve the interpretability and safety of machine learning. In our
concept, perceptrons model soft inequalities; namely membership functions and
continuous logical operators. We design the network architecture before
training, using continuous logical operators and multicriteria decision tools
with given weights working in the hidden layers. Designing the structure
appropriately leads to a drastic reduction in the number of parameters to be
learned. The theoretical basis offers a straightforward choice of activation
functions (the cutting function or its differentiable approximation, the
squashing function), and also suggests an explanation to the great success of
the rectified linear unit (ReLU). In this study, we focus on the architecture
of a hybrid model and introduce the building blocks for future application in
deep neural networks. The concept is illustrated with some toy examples taken
from an extended version of the tensorflow playground.
","Interpretable neural networks based on continuous-valued logic and
  multicriteria decision operators"
"  We build a profitable electronic trading agent with Reinforcement Learning
that places buy and sell orders in the stock market. An environment model is
built only with historical observational data, and the RL agent learns the
trading policy by interacting with the environment model instead of with the
real-market to minimize the risk and potential monetary loss. Trained in
unsupervised and self-supervised fashion, our environment model learned a
temporal and causal representation of the market in latent space through deep
neural networks. We demonstrate that the trading policy trained entirely within
the environment model can be transferred back into the real market and maintain
its profitability. We believe that this environment model can serve as a robust
simulator that predicts market movement as well as trade impact for further
studies.
","Model-based Reinforcement Learning for Predictions and Control for Limit
  Order Books"
"  Evidence-based reasoning is at the core of many problem-solving and
decision-making tasks in a wide variety of domains. Generalizing from the
research and development of cognitive agents in several such domains, this
paper presents progress toward a computational theory for the development of
instructable cognitive agents for evidence-based reasoning tasks. The paper
also illustrates the application of this theory to the development of four
prototype cognitive agents in domains that are critical to the government and
the public sector. Two agents function as cognitive assistants, one in
intelligence analysis, and the other in science education. The other two agents
operate autonomously, one in cybersecurity and the other in intelligence,
surveillance, and reconnaissance. The paper concludes with the directions of
future research on the proposed computational theory.
","Toward a Computational Theory of Evidence-Based Reasoning for
  Instructable Cognitive Agents"
"  Over its lifetime, a reinforcement learning agent is often tasked with
different tasks. How to efficiently adapt a previously learned control policy
from one task to another, remains an open research question. In this paper, we
investigate how instructions formulated in natural language can enable faster
and more effective task adaptation. This can serve as the basis for developing
language instructed skills, which can be used in a lifelong learning setting.
Our method is capable of assessing, given a set of developed base control
policies, which policy will adapt best to a new unseen task.
","Fast Task-Adaptation for Tasks Labeled Using Natural Language in
  Reinforcement Learning"
"  RLCard is an open-source toolkit for reinforcement learning research in card
games. It supports various card environments with easy-to-use interfaces,
including Blackjack, Leduc Hold'em, Texas Hold'em, UNO, Dou Dizhu and Mahjong.
The goal of RLCard is to bridge reinforcement learning and imperfect
information games, and push forward the research of reinforcement learning in
domains with multiple agents, large state and action space, and sparse reward.
In this paper, we provide an overview of the key components in RLCard, a
discussion of the design principles, a brief introduction of the interfaces,
and comprehensive evaluations of the environments. The codes and documents are
available at https://github.com/datamllab/rlcard
",RLCard: A Toolkit for Reinforcement Learning in Card Games
"  Explanation is necessary for humans to understand and accept decisions made
by an AI system when the system's goal is known. It is even more important when
the AI system makes decisions in multi-agent environments where the human does
not know the systems' goals since they may depend on other agents' preferences.
In such situations, explanations should aim to increase user satisfaction,
taking into account the system's decision, the user's and the other agents'
preferences, the environment settings and properties such as fairness, envy and
privacy. Generating explanations that will increase user satisfaction is very
challenging; to this end, we propose a new research direction: xMASE. We then
review the state of the art and discuss research directions towards efficient
methodologies and algorithms for generating explanations that will increase
users' satisfaction from AI system's decisions in multi-agent environments.
",AI for Explaining Decisions in Multi-Agent Environments
"  Artificial Intelligence (AI) provides many opportunities to improve private
and public life. Discovering patterns and structures in large troves of data in
an automated manner is a core component of data science, and currently drives
applications in computational biology, finance, law and robotics. However, such
a highly positive impact is coupled with significant challenges: How do we
understand the decisions suggested by these systems in order that we can trust
them? How can they be held accountable for those decisions?
  In this short survey, we cover some of the motivations and trends in the area
that attempt to address such questions.
",The Quest for Interpretable and Responsible Artificial Intelligence
"  An agent who interacts with a wide population of other agents needs to be
aware that there may be variations in their understanding of the world.
Furthermore, the machinery which they use to perceive may be inherently
different, as is the case between humans and machines. In this work, we present
both an image reference game between a speaker and a population of listeners
where reasoning about the concepts other agents can comprehend is necessary and
a model formulation with this capability. We focus on reasoning about the
conceptual understanding of others, as well as adapting to novel gameplay
partners and dealing with differences in perceptual machinery. Our experiments
on three benchmark image/attribute datasets suggest that our learner indeed
encodes information directly pertaining to the understanding of other agents,
and that leveraging this information is crucial for maximizing gameplay
performance.
",Modeling Conceptual Understanding in Image Reference Games
"  Generalized planning is the task of generating a single solution that is
valid for a set of planning problems. In this paper we show how to represent
and compute generalized plans using procedural Domain Control Knowledge (DCK).
We define a {\it divide and conquer} approach that first generates the
procedural DCK solving a set of planning problems representative of certain
subtasks and then compile it as callable procedures of the overall generalized
planning problem. Our procedure calling mechanism allows nested and recursive
procedure calls and is implemented in PDDL so that classical planners can
compute and exploit procedural DCK. Experiments show that an off-the-shelf
classical planner, using procedural DCK as callable procedures, can compute
generalized plans in a wide range of domains including non-trivial ones, such
as sorting variable-size lists or DFS traversal of binary trees with variable
size.
",Generalized Planning With Procedural Domain Control Knowledge
"  Predictive business process monitoring aims at providing predictions about
running instances by analyzing logs of completed cases in a business process.
Recently, a lot of research focuses on increasing productivity and efficiency
in a business process by forecasting potential problems during its executions.
However, most of the studies lack suggesting concrete actions to improve the
process. They leave it up to the subjective judgment of a user. In this paper,
we propose a novel method to connect the results from predictive business
process monitoring to actual business process improvements. More in detail, we
optimize the resource allocation in a non-clairvoyant online environment, where
we have limited information required for scheduling, by exploiting the
predictions. The proposed method integrates the offline prediction model
construction that predicts the processing time and the next activity of an
ongoing instance using Bayesian Neural Networks (BNNs) with the online resource
allocation that is extended from the minimum cost and maximum flow algorithm.
To validate the proposed method, we performed experiments using an artificial
event log and a real-life event log from a global financial organization.
","Prediction-based Resource Allocation using Bayesian Neural Networks and
  Minimum Cost and Maximum Flow Algorithm"
"  We present in this paper our solver for logic grid puzzles. The approach used
by our algorithm mimics the way a human would try to solve the same problem.
Every progress made during the solving process is accompanied by a detailed
explanation of our program's reasoning. Since this reasoning is based on the
same heuristics that a human would employ, the user can easily follow the given
explanation.
","Solving Logic Grid Puzzles with an Algorithm that Imitates Human
  Behavior"
"  How we store information in our mind has been a major intriguing open
question. We approach this question not from a physiological standpoint as to
how information is physically stored in the brain, but from a conceptual and
algorithm standpoint as to the right data structures to be used to organize and
index information. Here we propose a memory architecture directly based on the
recursive sketching ideas from the paper ""Recursive Sketches for Modular Deep
Networks"", ICML 2019 (arXiv:1905.12730), to store information in memory as
concise sketches. We also give a high level, informal exposition of the
recursive sketching idea from the paper that makes use of subspace embeddings
to capture deep network computations into a concise sketch. These sketches form
an implicit knowledge graph that can be used to find related information via
sketches from the past while processing an event.
",How does the Mind store Information?
"  An interval-valued fuzzy answer set programming paradigm is proposed for
nonmonotonic reasoning with vague and uncertain information. The set of
sub-intervals of $[0,1]$ is considered as truth-space. The intervals are
ordered using preorder-based truth and knowledge ordering. The preorder based
ordering is an enhanced version of bilattice-based ordering. The system can
represent and reason with prioritized rules, rules with exceptions. An
iterative method for answer set computation is proposed. The sufficient
conditions for termination of iterations are identified for a class of logic
programs using the notion of difference equations.
","A Unified Framework for Nonmonotonic Reasoning with Vagueness and
  Uncertainty"
"  A prototype for automated reasoning over legal texts, called NAI, is
presented. As an input, NAI accepts formalized logical representations of such
legal texts that can be created and curated using an integrated annotation
interface. The prototype supports automated reasoning over the given text
representation and multiple quality assurance procedures. The pragmatics of the
NAI suite as well its feasibility in practical applications is studied on a
fragment of the Smoking Prohibition (Children in Motor Vehicles) (Scotland) Act
2016 of the Scottish Parliament.
",The NAI Suite -- Drafting and Reasoning over Legal Texts
"  Generating complex multi-turn goal-oriented dialogue agents is a difficult
problem that has seen a considerable focus from many leaders in the tech
industry, including IBM, Google, Amazon, and Microsoft. This is in large part
due to the rapidly growing market demand for dialogue agents capable of
goal-oriented behaviour. Due to the business process nature of these
conversations, end-to-end machine learning systems are generally not a viable
option, as the generated dialogue agents must be deployable and verifiable on
behalf of the businesses authoring them.
  In this work, we propose a paradigm shift in the creation of goal-oriented
complex dialogue systems that dramatically eliminates the need for a designer
to manually specify a dialogue tree, which nearly all current systems have to
resort to when the interaction pattern falls outside standard patterns such as
slot filling. We propose a declarative representation of the dialogue agent to
be processed by state-of-the-art planning technology. Our proposed approach
covers all aspects of the process; from model solicitation to the execution of
the generated plans/dialogue agents. Along the way, we introduce novel planning
encodings for declarative dialogue synthesis, a variety of interfaces for
working with the specification as a dialogue architect, and a robust executor
for generalized contingent plans. We have created prototype implementations of
all components, and in this paper, we further demonstrate the resulting system
empirically.
",Planning for Goal-Oriented Dialogue Systems
"  Today, image classification is a common way for systems to process visual
content. Although neural network approaches to classification have seen great
progress in reducing error rates, it is not clear what this means for a
cognitive system that needs to make sense of the multiple and competing
predictions from its own classifiers. As a step to address this, we present a
novel framework that uses meta-reasoning and meta-operations to unify
predictions into abstractions, properties, or relationships. Using the
framework on images from ImageNet, we demonstrate systems that unify 41% to 46%
of predictions in general and unify 67% to 75% of predictions when the systems
can explain their conceptual differences. We also demonstrate a system in ""the
wild"" by feeding live video images through it and show it unifying 51% of
predictions in general and 69% of predictions when their differences can be
explained conceptually by the system. In a survey given to 24 participants, we
found that 87% of the unified predictions describe their corresponding images.
",Reflecting After Learning for Understanding
"  Decisions in public health are almost always made in the context of
uncertainty. Policy makers are responsible for making important decisions,
faced with the daunting task of choosing from amongst many possible options.
This task is called planning under uncertainty, and is particularly acute when
addressing complex systems, such as issues of global health and development.
Uncertainty leads to cautious or incorrect decisions that cost time, money, and
human life. It is with this understanding that we pursue greater clarity on,
and methods to address optimal policy making in health. Decision making under
uncertainty is a challenging task, and all too often this uncertainty is
averaged away to simplify results for policy makers. Our goal in this work is
to implement dynamic programming which provides basis for compiling planning
results into reactive strategies. We present here a description of an AI-based
method and illustrate how this method can improve our ability to find an
optimal vaccination strategy. We model the problem as a partially observable
Markov decision process, POMDP and show how a re-active policy can be computed
using dynamic programming. In this paper, we developed a framework for optimal
health policy design in an uncertain dynamic setting. We apply a stochastic
dynamic programming approach to identify the optimal time to change the health
intervention policy and the value of decision relevant information for
improving the impact of the policy.
",Optimal Immunization Policy Using Dynamic Programming
"  The Newcomb's paradox is one of the most known paradox in Game Theory about
the Oracles. We will define the graph associated to the time lines of the Game.
After this Studying its topology and using only the Expected Utility Principle
we will formulate a solution of the paradox able to explain all the classical
cases.
","Studying Topology of Time Lines Graph leads to an alternative approach
  to the Newcomb's Paradox"
"  While cyclic scheduling is involved in numerous real-world applications,
solving the derived problem is still of exponential complexity. This paper
focuses specifically on modelling the manufacturing application as a cyclic job
shop problem and we have developed an efficient neural network approach to
minimise the cycle time of a schedule. Our approach introduces an interesting
model for a manufacturing production, and it is also very efficient, adaptive
and flexible enough to work with other techniques. Experimental results
validated the approach and confirmed our hypotheses about the system model and
the efficiency of neural networks for such a class of problems.
",Recurrent neural network approach for cyclic job shop scheduling problem
"  The runtime performance of modern SAT solvers is deeply connected to the
phase transition behavior of CNF formulas. While CNF solving has witnessed
significant runtime improvement over the past two decades, the same does not
hold for several other classes such as the conjunction of cardinality and XOR
constraints, denoted as CARD-XOR formulas. The problem of determining the
satisfiability of CARD-XOR formulas is a fundamental problem with a wide
variety of applications ranging from discrete integration in the field of
artificial intelligence to maximum likelihood decoding in coding theory. The
runtime behavior of random CARD-XOR formulas is unexplored in prior work. In
this paper, we present the first rigorous empirical study to characterize the
runtime behavior of 1-CARD-XOR formulas. We show empirical evidence of a
surprising phase-transition that follows a non-linear tradeoff between CARD and
XOR constraints.
",Phase Transition Behavior of Cardinality and XOR Constraints
"  Deep reinforcement learning has been successfully used in many dynamic
decision making domains, especially those with very large state spaces.
However, it is also well-known that deep reinforcement learning can be very
slow and resource intensive. The resulting system is often brittle and
difficult to explain. In this paper, we attempt to address some of these
problems by proposing a framework of Rule-interposing Learning (RIL) that
embeds high level rules into the deep reinforcement learning. With some good
rules, this framework not only can accelerate the learning process, but also
keep it away from catastrophic explorations, thus making the system relatively
stable even during the very early stage of training. Moreover, given the rules
are high level and easy to interpret, they can be easily maintained, updated
and shared with other similar tasks.
","Faster and Safer Training by Embedding High-Level Knowledge into Deep
  Reinforcement Learning"
"  A novel general intelligence model is proposed with three types of learning.
A unified sequence of the foreground percept trace and the command trace
translates into direct and time-hop observation paths to form the basis of Raw
learning. Raw learning includes the formation of image-image associations,
which lead to the perception of temporal and spatial relationships among
objects and object parts; and the formation of image-audio associations, which
serve as the building blocks of language. Offline identification of similar
segments in the observation paths and their subsequent reduction into a common
segment through merging of memory nodes leads to Generalized learning.
Generalization includes the formation of interpolated sensory nodes for robust
and generic matching, the formation of sensory properties nodes for specific
matching and superimposition, and the formation of group nodes for simpler
logic pathways. Online superimposition of memory nodes across multiple
predictions, primarily the superimposition of images on the internal projection
canvas, gives rise to Innovative learning and thought. The learning of actions
happens the same way as raw learning while the action determination happens
through the utility model built into the raw learnings, the utility function
being the pleasure and pain of the physical senses.
",RTOP: A Conceptual and Computational Framework for General Intelligence
"  Distributed data mining (DDM) deals with the problem of finding patterns or
models, called knowledge, in an environment with distributed data and
computations. Today, a massive amounts of data which are often geographically
distributed and owned by different organisation are being mined. As
consequence, a large mount of knowledge are being produced. This causes
problems of not only knowledge management but also visualization in data
mining. Besides, the main aim of DDM is to exploit fully the benefit of
distributed data analysis while minimising the communication. Existing DDM
techniques perform partial analysis of local data at individual sites and then
generate a global model by aggregating these local results. These two steps are
not independent since naive approaches to local analysis may produce an
incorrect and ambiguous global data model. The integrating and cooperating of
these two steps need an effective knowledge management, concretely an efficient
map of knowledge in order to take the advantage of mined knowledge to guide
mining the data. In this paper, we present ""knowledge map"", a representation of
knowledge about mined knowledge. This new approach aims to manage efficiently
mined knowledge in large scale distributed platform such as Grid. This
knowledge map is used to facilitate not only the visualization, evaluation of
mining results but also the coordinating of local mining process and existing
knowledge to increase the accuracy of final model.
","Knowledge Map: Toward a New Approach Supporting the Knowledge Management
  in Distributed Data Mining"
"  Finding the optimum path for a robot for moving from start to the goal
position through obstacles is still a challenging issue. This paper presents a
novel path planning method, named D-point trigonometric, based on Q-learning
algorithm for dynamic and uncertain environments, in which all the obstacles
and the target are moving. We define a new state, action and reward functions
for the Q-learning by which the agent can find the best action in every state
to reach the goal in the most appropriate path. The D-point approach minimizes
the possible number of states. Moreover, the experiments in Unity3D confirmed
the high convergence speed, the high hit rate, as well as the low dependency on
environmental parameters of the proposed method compared with an opponent
approach.
","D-Point Trigonometric Path Planning based on Q-Learning in Uncertain
  Environments"
"  User knowledge modeling systems are used as the most effective technology for
grabbing new user's attention. Moreover, the quality of service (QOS) is
increased by these intelligent services. This paper proposes two user knowledge
classifiers based on artificial neural networks used as one of the influential
parts of knowledge modeling systems. We employed multi-layer perceptron (MLP)
and adaptive neural fuzzy inference system (ANFIS) as the classifiers.
Moreover, we used real data contains the user's degree of study time,
repetition number, their performance in exam, as well as the learning
percentage, as our classifier's inputs. Compared with well-known methods like
KNN and Bayesian classifiers used in other research with the same data sets,
our experiments present better performance. Although, the number of samples in
the train set is not large enough, the performance of the neuro-fuzzy
classifier in the test set is 98.6% which is the best result in comparison with
others. However, the comparison of MLP toward the ANFIS results presents
performance reduction, although the MLP performance is more efficient than
other methods like Bayesian and KNN. As our goal is evaluating and reporting
the efficiency of a neuro-fuzzy classifier for user knowledge modeling systems,
we utilized many different evaluation metrics such as Receiver Operating
Characteristic and the Area Under its Curve, Total Accuracy, and Kappa
statistics.
","On the Efficiency of the Neuro-Fuzzy Classifier for User Knowledge
  Modeling Systems"
"  Recently, the traffic congestion in modern cities has become a growing worry
for the residents. As presented in Baidu traffic report, the commuting stress
index has reached surprising 1.973 in Beijing during rush hours, which results
in longer trip time and increased vehicular queueing. Previous works have
demonstrated that by reasonable scheduling, e.g, rebalancing bike-sharing
systems and optimized bus transportation, the traffic efficiency could be
significantly improved with little resource consumption. However, there are
still two disadvantages that restrict their performance: (1) they only consider
single scheduling in a short time, but ignoring the layout after first
reposition, and (2) they only focus on the single transport. However, the
multi-modal characteristics of urban public transportation are largely
under-exploited. In this paper, we propose an efficient and economical
multi-modal traffic scheduling scheme named JLRLS based on spatio -temporal
prediction, which adopts reinforcement learning to obtain optimal long-term and
joint schedule. In JLRLS, we combines multiple transportation to conduct
scheduling by their own characteristics, which potentially helps the system to
reach the optimal performance. Our implementation of an example by PaddlePaddle
is available at https://github.com/bigdata-ustc/Long-term-Joint-Scheduling,
with an explaining video at https://youtu.be/t5M2wVPhTyk.
",Long-term Joint Scheduling for Urban Traffic
"  The AlphaZero algorithm has achieved superhuman performance in two-player,
deterministic, zero-sum games where perfect information of the game state is
available. This success has been demonstrated in Chess, Shogi, and Go where
learning occurs solely through self-play. Many real-world applications (e.g.,
equity trading) require the consideration of a multiplayer environment. In this
work, we suggest novel modifications of the AlphaZero algorithm to support
multiplayer environments, and evaluate the approach in two simple 3-player
games. Our experiments show that multiplayer AlphaZero learns successfully and
consistently outperforms a competing approach: Monte Carlo tree search. These
results suggest that our modified AlphaZero can learn effective strategies in
multiplayer game scenarios. Our work supports the use of AlphaZero in
multiplayer games and suggests future research for more complex environments.
",Multiplayer AlphaZero
"  We consider a vehicle routing problem which seeks to minimize cost subject to
time window and synchronization constraints. In this problem, the fleet of
vehicles is categorized into regular and special vehicles. Some customers
require both vehicles' services, whose starting service times at the customer
are synchronized. Despite its important real-world application, this problem
has rarely been studied in the literature. To solve the problem, we propose a
Constraint Programming (CP) model and an Adaptive Large Neighborhood Search
(ALNS) in which the design of insertion operators is based on solving linear
programming (LP) models to check the insertion feasibility. A number of
acceleration techniques is also proposed to significantly reduce the
computational time. The computational experiments show that our new CP model
finds better solutions than an existing CP-based ANLS, when used on small
instances with 25 customers and with a much shorter running time. Our LP-based
ALNS dominates the cp-ALNS, in terms of solution quality, when it provides
solutions with better objective values, on average, for all instance classes.
This demonstrates the advantage of using linear programming instead of
constraint programming when dealing with a variant of vehicle routing problems
with relatively tight constraints, which is often considered to be more
favorable for CP-based methods.
","A new constraint programming model and a linear programming-based
  adaptive large neighborhood search for the vehicle routing problem with
  synchronization constraints"
"  $\varepsilon$-greedy is a policy used to balance exploration and exploitation
in many reinforcement learning setting. In cases where the agent uses some
on-policy algorithm to learn optimal behaviour, it makes sense for the agent to
explore more initially and eventually exploit more as it approaches the target
behaviour. This shift from heavy exploration to heavy exploitation can be
represented as decay in the $\varepsilon$ value, where $\varepsilon$ depicts
the how much an agent is allowed to explore. This paper proposes a new approach
to this $\varepsilon$ decay where the decay is based on feedback from the
environment. This paper also compares and contrasts one such approach based on
rewards and compares it against standard exponential decay. The new approach,
in the environments tested, produces more consistent results that on average
perform better.
",RBED: Reward Based Epsilon Decay
"  Reasoning about observed effects and their causes is important in multi-agent
contexts. While there has been much work on causality from an objective
standpoint, causality from the point of view of some particular agent has
received much less attention. In this paper, we address this issue by
incorporating an epistemic dimension to an existing formal model of causality.
We define what it means for an agent to know the causes of an effect. Then
using a counterexample, we prove that epistemic causality is a different notion
from its objective counterpart.
",Towards A Logical Account of Epistemic Causality
"  We consider Monte-Carlo Tree Search (MCTS) applied to Markov Decision
Processes (MDPs) and Partially Observable MDPs (POMDPs), and the well-known
Upper Confidence bound for Trees (UCT) algorithm. In UCT, a tree with nodes
(states) and edges (actions) is incrementally built by the expansion of nodes,
and the values of nodes are updated through a backup strategy based on the
average value of child nodes. However, it has been shown that with enough
samples the maximum operator yields more accurate node value estimates than
averaging. Instead of settling for one of these value estimates, we go a step
further proposing a novel backup strategy which uses the power mean operator,
which computes a value between the average and maximum value. We call our new
approach Power-UCT, and argue how the use of the power mean operator helps to
speed up the learning in MCTS. We theoretically analyze our method providing
guarantees of convergence to the optimum. Finally, we empirically demonstrate
the effectiveness of our method in well-known MDP and POMDP benchmarks, showing
significant improvement in performance and convergence speed w.r.t. state of
the art algorithms.
",Generalized Mean Estimation in Monte-Carlo Tree Search
"  Based on the online transaction data of COSCO group's centralized procurement
platform, this paper studies the clustering method of time series type data.
The different methods of similarity calculation, different clustering methods
with different K values are analysed, and the best clustering method suitable
for centralized purchasing data is determined. The company list under the
corresponding cluster is obtained. The time series motif discovery algorithm is
used to model the centroid of each cluster. Through ARIMA method, we also made
12 periods of prediction for the centroid of each category. This paper
constructs a matrix of ""Customer Lifecycle Theory - Five Elements of Marketing
"", and puts forward corresponding marketing suggestions for customers at
different life cycle stages.
","Research and application of time series algorithms in centralized
  purchasing data"
"  Proceedings of the AAAI Fall Symposium on Artificial Intelligence in
Government and Public Sector, Arlington, Virginia, USA, November 7-8, 2019
","AAAI FSS-19: Artificial Intelligence in Government and Public Sector
  Proceedings"
"  A referring expression (RE) is a description that identifies a set of
instances unambiguously. Mining REs from data finds applications in natural
language generation, algorithmic journalism, and data maintenance. Since there
may exist multiple REs for a given set of entities, it is common to focus on
the most intuitive ones, i.e., the most concise and informative. In this paper
we present REMI, a system that can mine intuitive REs on large RDF knowledge
bases. Our experimental evaluation shows that REMI finds REs deemed intuitive
by users. Moreover we show that REMI is several orders of magnitude faster than
an approach based on inductive logic programming.
",REMI: Mining Intuitive Referring Expressions on Knowledge Bases
"  While using shaped rewards can be beneficial when solving sparse reward
tasks, their successful application often requires careful engineering and is
problem specific. For instance, in tasks where the agent must achieve some goal
state, simple distance-to-goal reward shaping often fails, as it renders
learning vulnerable to local optima. We introduce a simple and effective
model-free method to learn from shaped distance-to-goal rewards on tasks where
success depends on reaching a goal state. Our method introduces an auxiliary
distance-based reward based on pairs of rollouts to encourage diverse
exploration. This approach effectively prevents learning dynamics from
stabilizing around local optima induced by the naive distance-to-goal reward
shaping and enables policies to efficiently solve sparse reward tasks. Our
augmented objective does not require any additional reward engineering or
domain expertise to implement and converges to the original sparse objective as
the agent learns to solve the task. We demonstrate that our method successfully
solves a variety of hard-exploration tasks (including maze navigation and 3D
construction in a Minecraft environment), where naive distance-based reward
shaping otherwise fails, and intrinsic curiosity and reward relabeling
strategies exhibit poor performance.
","Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing
  Shaped Rewards"
"  To make deliberate progress towards more intelligent and more human-like
artificial systems, we need to be following an appropriate feedback signal: we
need to be able to define and evaluate intelligence in a way that enables
comparisons between two systems, as well as comparisons with humans. Over the
past hundred years, there has been an abundance of attempts to define and
measure intelligence, across both the fields of psychology and AI. We summarize
and critically assess these definitions and evaluation approaches, while making
apparent the two historical conceptions of intelligence that have implicitly
guided them. We note that in practice, the contemporary AI community still
gravitates towards benchmarking intelligence by comparing the skill exhibited
by AIs and humans at specific tasks such as board games and video games. We
argue that solely measuring skill at any given task falls short of measuring
intelligence, because skill is heavily modulated by prior knowledge and
experience: unlimited priors or unlimited training data allow experimenters to
""buy"" arbitrary levels of skills for a system, in a way that masks the system's
own generalization power. We then articulate a new formal definition of
intelligence based on Algorithmic Information Theory, describing intelligence
as skill-acquisition efficiency and highlighting the concepts of scope,
generalization difficulty, priors, and experience. Using this definition, we
propose a set of guidelines for what a general AI benchmark should look like.
Finally, we present a benchmark closely following these guidelines, the
Abstraction and Reasoning Corpus (ARC), built upon an explicit set of priors
designed to be as close as possible to innate human priors. We argue that ARC
can be used to measure a human-like form of general fluid intelligence and that
it enables fair general intelligence comparisons between AI systems and humans.
",On the Measure of Intelligence
"  In this paper we present the first steps towards hardening the science of
measuring AI systems, by adopting metrology, the science of measurement and its
application, and applying it to human (crowd) powered evaluations. We begin
with the intuitive observation that evaluating the performance of an AI system
is a form of measurement. In all other science and engineering disciplines, the
devices used to measure are called instruments, and all measurements are
recorded with respect to the characteristics of the instruments used. One does
not report mass, speed, or length, for example, of a studied object without
disclosing the precision (measurement variance) and resolution (smallest
detectable change) of the instrument used. It is extremely common in the AI
literature to compare the performance of two systems by using a crowd-sourced
dataset as an instrument, but failing to report if the performance difference
lies within the capability of that instrument to measure. To illustrate the
adoption of metrology to benchmark datasets we use the word similarity
benchmark WS353 and several previously published experiments that use it for
evaluation.
",Metrology for AI: From Benchmarks to Instruments
"  Predicting user churn and taking personalized measures to retain users is a
set of common and effective practices for online game operators. However,
different from the traditional user churn relevant researches that can involve
demographic, economic, and behavioral data, most online games can only obtain
logs of user behavior and have no access to users' latent feelings. There are
mainly two challenges in this work: 1. The latent feelings, which cannot be
directly observed in this work, need to be estimated and verified; 2. User
churn needs to be predicted with only behavioral data. In this work, a
Recurrent Neural Network(RNN) called LaFee (Latent Feeling) is proposed, which
can get the users' latent feelings while predicting user churn. Besides, we
proposed a method named BMM-UCP (Behavior-based Modeling Method for User Churn
Prediction) to help models predict user churn with only behavioral data. The
latent feelings are names as satisfaction and aspiration in this work. We
designed experiments on a real dataset and the results show that our methods
outperform baselines and are more suitable for long-term sequential learning.
The latent feelings learned are fully discussed and proven meaningful.
","A Latent Feelings-aware RNN Model for User Churn Prediction with
  Behavioral Data"
"  Finite State Controllers (FSCs) are an effective way to represent sequential
plans compactly. By imposing appropriate conditions on transitions, FSCs can
also represent generalized plans that solve a range of planning problems from a
given domain. In this paper we introduce the concept of {\it hierarchical FSCs}
for planning by allowing controllers to call other controllers. We show that
hierarchical FSCs can represent generalized plans more compactly than
individual FSCs. Moreover, our call mechanism makes it possible to generate
hierarchical FSCs in a modular fashion, or even to apply recursion. We also
introduce a compilation that enables a classical planner to generate
hierarchical FSCs that solve challenging generalized planning problems. The
compilation takes as input a set of planning problems from a given domain and
outputs a single classical planning problem, whose solution corresponds to a
hierarchical FSC.
",Hierarchical Finite State Controllers for Generalized Planning
"  The alpha-beta pruning algorithms have been popular in game tree searching
ever since they were discovered. Numerous enhancements are proposed in
literature and it is often overwhelming as to which would be the best for
implementation. A certain enhancement can take far too long to fine tune its
hyper parameters or to decide whether it is going to not make much of a
difference due to the memory limitations. On the other hand are the best first
pruning techniques, mostly the counterparts of the infamous SSS* algorithm, the
algorithm which proved out to be disruptive at the time of its discovery but
gradually became outcast as being too memory intensive and having a higher time
complexity. Later research doesn't see the best first approaches to be
completely different from the depth first based enhancements but both seem to
be transitionary in the sense that a best first approach could be looked as a
depth first approach with a certain set of enhancements and with the growing
power of the computers, SSS* didn't seem to be as taxing on the memory either.
Even so, there seems to be quite difficulty in understanding the nature of the
SSS* algorithm, why it does what it does and it being termed as being too
complex to fathom, visualize and understand on an intellectual level. This
article tries to bridge this gap and provide some experimental results
comparing the two with the most promising advances.
",A different take on the best-first game tree pruning algorithms
"  In this paper we deal with a complex real world scheduling problem closely
related to the well-known Resource-Constrained Project Scheduling Problem
(RCPSP). The problem concerns industrial test laboratories in which a large
number of tests has to be performed by qualified personnel using specialised
equipment, while respecting deadlines and other constraints. We present
different constraint programming models and search strategies for this problem.
Furthermore, we propose a Very Large Neighborhood Search approach based on our
CP methods. Our models are evaluated using CP solvers and a MIP solver both on
real-world test laboratory data and on a set of generated instances of
different sizes based on the real-world data. Further, we compare the exact
approaches with VLNS and a Simulated Annealing heuristic. We could find
feasible solutions for all instances and several optimal solutions and we show
that using VLNS we can improve upon the results of the other approaches.
","Investigating Constraint Programming and Hybrid Methods for Real World
  Industrial Test Laboratory Scheduling"
"  This project challenges the car racing problem from OpenAI gym environment.
The problem is very challenging since it requires computer to finish the
continuous control task by learning from pixels. To tackle this challenging
problem, we explored two approaches including evolutionary algorithm based
genetic multi-layer perceptron and double deep Q-learning network. The result
shows that the genetic multi-layer perceptron can converge fast but when
training many episodes, double deep Q-learning can get better score. We analyze
the result and draw a conclusion that for limited hardware resources, using
genetic multi-layer perceptron sometimes can be more efficient.
",Challenging On Car Racing Problem from OpenAI gym
"  Causal Models are increasingly suggested as a means to reason about the
behavior of cyber-physical systems in socio-technical contexts. They allow us
to analyze courses of events and reason about possible alternatives. Until now,
however, such reasoning is confined to the technical domain and limited to
single systems or at most groups of systems. The humans that are an integral
part of any such socio-technical system are usually ignored or dealt with by
""expert judgment"". We show how a technical causal model can be extended with
models of human behavior to cover the complexity and interplay between humans
and technical systems. This integrated socio-technical causal model can then be
used to reason not only about actions and decisions taken by the machine, but
also about those taken by humans interacting with the system. In this paper we
demonstrate the feasibility of merging causal models about machines with causal
models about humans and illustrate the usefulness of this approach with a
highly automated vehicle example.
",Extending Causal Models from Machines into Humans
"  Expert estimation of objects takes place when there are no benchmark values
of object weights, but these weights still have to be defined. That is why it
is problematic to define the efficiency of expert estimation methods. We
propose to define efficiency of such methods based on stability of their
results under perturbations of input data. We compare two modifications of
combinatorial method of expert data aggregation (spanning tree enumeration).
Using the example of these two methods, we illustrate two approaches to
efficiency evaluation. The first approach is based on usage of real data,
obtained through estimation of a set of model objects by a group of experts.
The second approach is based on simulation of the whole expert examination
cycle (including expert estimates). During evaluation of efficiency of the two
listed modifications of combinatorial expert data aggregation method the
simulation-based approach proved more robust and credible. Our experimental
study confirms that if weights of spanning trees are taken into consideration,
the results of combinatorial data aggregation method become more stable. So,
weighted spanning tree enumeration method has an advantage over non-weighted
method (and, consequently, over logarithmic least squares and row geometric
mean methods).
",Comparing Efficiency of Expert Data Aggregation Methods
"  The goal of this paper is twofold. Once to highlight some basic problematic
properties of the KH Fuzzy Rule Interpolation through examples, secondly to set
up a brief Benchmark set of Examples, which is suitable for testing other Fuzzy
Rule Interpolation (FRI) methods against these ill conditions. Fuzzy Rule
Interpolation methods were originally proposed to handle the situation of
missing fuzzy rules (sparse rule-bases) and to reduce the decision complexity.
Fuzzy Rule Interpolation is an important technique for implementing inference
with sparse fuzzy rule-bases. Even if a given observation has no overlap with
the antecedent of any rule from the rule-base, FRI may still conclude a
conclusion. The first FRI method was the Koczy and Hirota proposed ""Linear
Interpolation"", which was later renamed to ""KH Fuzzy Interpolation"" by the
followers. There are several conditions and criteria have been suggested for
unifying the common requirements an FRI methods have to satisfy. One of the
most common one is the demand for a convex and normal fuzzy (CNF) conclusion,
if all the rule antecedents and consequents are CNF sets. The KH FRI is the
one, which cannot fulfill this condition. This paper is focusing on the
conditions, where the KH FRI fails the demand for the CNF conclusion. By
setting up some CNF rule examples, the paper also defines a Benchmark, in which
other FRI methods can be tested if they can produce CNF conclusion where the KH
FRI fails.
","Some Considerations and a Benchmark Related to the CNF Property of the
  Koczy-Hirota Fuzzy Rule Interpolation"
"  The research in hierarchical planning has made considerable progress in the
last few years. Many recent systems do not rely on hand-tailored advice anymore
to find solutions, but are supposed to be domain-independent systems that come
with sophisticated solving techniques. In principle, this development would
make the comparison between systems easier (because the domains are not
tailored to a single system anymore) and -- much more important -- also the
integration into other systems, because the modeling process is less tedious
(due to the lack of advice) and there is no (or less) commitment to a certain
planning system the model is created for. However, these advantages are
destroyed by the lack of a common input language and feature set supported by
the different systems. In this paper, we propose an extension to PDDL, the
description language used in non-hierarchical planning, to the needs of
hierarchical planning systems. We restrict our language to a basic feature set
shared by many recent systems, give an extension of PDDL's EBNF syntax
definition, and discuss our extensions with respect to several planner-specific
input languages from related work.
",HDDL -- A Language to Describe Hierarchical Planning Problems
"  This work aims to make plan recognition as planning more ready for real-world
scenarios by adapting previous compilations to work with partial-order,
half-seen observations of both fluents and actions. We first redefine what
observations can be and what it means to satisfy each kind. We then provide a
compilation from plan recognition problem to classical planning problem,
similar to original work by Ramirez and Geffner, but accommodating these more
complex observation types. This compilation can be adapted towards other
planning-based plan recognition techniques. Lastly we evaluate this method
against an ""ignore complexity"" strategy that uses the original method by
Ramirez and Geffner. Our experimental results suggest that, while slower, our
method is equally or more accurate than baseline methods; our technique
sometimes significantly reduces the size of the solution to the plan
recognition problem, i.e, the size of the optimal goal set. We discuss these
findings in the context of plan recognition problem difficulty and present an
avenue for future work.
","Partial-Order, Partially-Seen Observations of Fluents or Actions for
  Plan Recognition as Planning"
"  In this paper, we advocate the use of setwise contests for aggregating a set
of input rankings into an output ranking. We propose a generalization of the
Kemeny rule where one minimizes the number of k-wise disagreements instead of
pairwise disagreements (one counts 1 disagreement each time the top choice in a
subset of alternatives of cardinality at most k differs between an input
ranking and the output ranking). After an algorithmic study of this k-wise
Kemeny aggregation problem, we introduce a k-wise counterpart of the majority
graph. It reveals useful to divide the aggregation problem into several
sub-problems. We conclude with numerical tests.
","Beyond Pairwise Comparisons in Social Choice: A Setwise Kemeny
  Aggregation Problem"
"  As machine learning black boxes are increasingly being deployed in critical
domains such as healthcare and criminal justice, there has been a growing
emphasis on developing techniques for explaining these black boxes in a human
interpretable manner. It has recently become apparent that a high-fidelity
explanation of a black box ML model may not accurately reflect the biases in
the black box. As a consequence, explanations have the potential to mislead
human users into trusting a problematic black box. In this work, we rigorously
explore the notion of misleading explanations and how they influence user trust
in black-box models. More specifically, we propose a novel theoretical
framework for understanding and generating misleading explanations, and carry
out a user study with domain experts to demonstrate how these explanations can
be used to mislead users. Our work is the first to empirically establish how
user trust in black box models can be manipulated via misleading explanations.
","""How do I fool you?"": Manipulating User Trust via Misleading Black Box
  Explanations"
"  An important use of sensors and actuator networks is to comply with health
and safety policies in hazardous environments. In order to deal with
increasingly large and dynamic environments, and to quickly react to
emergencies, tools are needed to simplify the process of translating high-level
policies into executable queries and rules. We present a framework to produce
such tools, which uses rules to aggregate low-level sensor data, described
using the Semantic Sensor Network Ontology, into more useful and actionable
abstractions. Using the schema of the underlying data sources as an input, we
automatically generate abstractions which are relevant to the use case at hand.
In this demonstration we present a policy editor tool and a simulation on which
policies can be tested.
",A Policy Editor for Semantic Sensor Networks
"  Evidence often grounds temporal probabilistic relational models over time,
which makes reasoning infeasible. To counteract groundings over time and to
keep reasoning polynomial by restoring a lifted representation, we present
temporal approximate merging (TAMe), which incorporates (i) clustering for
grouping submodels as well as (ii) statistical significance checks to test the
fitness of the clustering outcome. In exchange for faster runtimes, TAMe
introduces a bounded error that becomes negligible over time. Empirical results
show that TAMe significantly improves the runtime performance of inference,
while keeping errors small.
",Taming Reasoning in Temporal Probabilistic Relational Models
"  We investigate the complexity of learning query inseparable ELH ontologies in
a variant of Angluin's exact learning model. Given a fixed data instance A* and
a query language Q, we are interested in computing an ontology H that entails
the same queries as a target ontology T on A*, that is, H and T are inseparable
w.r.t. A* and Q. The learner is allowed to pose two kinds of questions. The
first is `Does (T,A)\models q?', with A an arbitrary data instance and q and
query in Q. An oracle replies this question with `yes' or `no'. In the second,
the learner asks `Are H and T inseparable w.r.t. A* and Q?'. If so, the
learning process finishes, otherwise, the learner receives (A*,q) with q in Q,
(T,A*)\models q and (H,A*)\not\models q (or vice-versa). Then, we analyse
conditions in which query inseparability is preserved if A* changes. Finally,
we consider the PAC learning model and a setting where the algorithms learn
from a batch of classified data, limiting interactions with the oracles.
",Learning Query Inseparable ELH Ontologies
"  One of the major limitations for the employment of model-based planning and
scheduling in practical applications is the need of costly re-planning when an
incongruence between the observed reality and the formal model is encountered
during execution. Robustness Envelopes characterize the set of possible
contingencies that a plan is able to address without re-planning, but their
exact computation is extremely expensive; furthermore, general robustness
envelopes are not amenable for efficient execution. In this paper, we present a
novel, anytime algorithm to approximate Robustness Envelopes, making them
scalable and executable. This is proven by an experimental analysis showing the
efficiency of the algorithm, and by a concrete case study where the execution
of robustness envelopes significantly reduces the number of re-plannings.
","Towards Efficient Anytime Computation and Execution of Decoupled
  Robustness Envelopes for Temporal Plans"
"  Existing value-factorized based Multi-Agent deep Reinforce-ment Learning
(MARL) approaches are well-performing invarious multi-agent cooperative
environment under thecen-tralized training and decentralized execution(CTDE)
scheme,where all agents are trained together by the centralized valuenetwork
and each agent execute its policy independently. How-ever, an issue remains
open: in the centralized training process,when the environment for the team is
partially observable ornon-stationary, i.e., the observation and action
informationof all the agents cannot represent the global states,
existingmethods perform poorly and sample inefficiently. Regret Min-imization
(RM) can be a promising approach as it performswell in partially observable and
fully competitive settings.However, it tends to model others as opponents and
thus can-not work well under the CTDE scheme. In this work, wepropose a novel
team RM based Bayesian MARL with threekey contributions: (a) we design a novel
RM method to traincooperative agents as a team and obtain a team
regret-basedpolicy for that team; (b) we introduce a novel method to de-compose
the team regret to generate the policy for each agentfor decentralized
execution; (c) to further improve the perfor-mance, we leverage a differential
particle filter (a SequentialMonte Carlo method) network to get an accurate
estimation ofthe state for each agent. Experimental results on two-step ma-trix
games (cooperative game) and battle games (large-scalemixed
cooperative-competitive games) demonstrate that ouralgorithm significantly
outperforms state-of-the-art methods.
","Inducing Cooperation via Team Regret Minimization based Multi-Agent Deep
  Reinforcement Learning"
"  State-of-the-art inference approaches in probabilistic logic programming
typically start by computing the relevant ground program with respect to the
queries of interest, and then use this program for probabilistic inference
using knowledge compilation and weighted model counting. We propose an
alternative approach that uses efficient Datalog techniques to integrate
knowledge compilation with forward reasoning with a non-ground program. This
effectively eliminates the grounding bottleneck that so far has prohibited the
application of probabilistic logic programming in query answering scenarios
over knowledge graphs, while also providing fast approximations on classical
benchmarks in the field.
","Beyond the Grounding Bottleneck: Datalog Techniques for Inference in
  Probabilistic Logic Programs (Technical Report)"
"  {\alpha}{\mu} is an anytime heuristic search algorithm for incomplete
information games that assumes perfect information for the opponents.
{\alpha}{\mu} addresses the strategy fusion and non-locality problems
encountered by Perfect Information Monte Carlo sampling. In this paper
{\alpha}{\mu} is applied to the game of Bridge.
",The {\alpha}{\mu} Search Algorithm for the Game of Bridge
"  With the needs of science and business, data sharing and re-use has become an
intensive activity for various areas. In many cases, governance imposes rules
concerning data use, but there is no existing computational technique to help
data-users comply with such rules. We argue that intelligent systems can be
used to improve the situation, by recording provenance records during
processing, encoding the rules and performing reasoning. We present our initial
work, designing formal models for data rules and flow rules and the reasoning
system, as the first step towards helping data providers and data users sustain
productive relationships.
","Towards a computer-interpretable actionable formal model to encode data
  governance rules"
"  According to Boutillier, Darwiche, Pearl and others, principles for iterated
revision can be characterised in terms of changing beliefs about conditionals.
For iterated contraction a similar formulation is not known. This is especially
because for iterated belief change the connection between revision and
contraction via the Levi and Harper identity is not straightforward, and
therefore, characterisation results do not transfer easily between iterated
revision and contraction. In this article, we develop an axiomatisation of
iterated contraction in terms of changing conditional beliefs. We prove that
the new set of postulates conforms semantically to the class of operators like
the ones given by Konieczny and Pino P\'erez for iterated contraction.
",A Conditional Perspective for Iterated Belief Contraction
"  We investigate the application of inconsistency measures to the problem of
analysing business rule bases. Due to some intricacies of the domain of
business rule bases, a straightforward application is not feasible. We
therefore develop some new rationality postulates for this setting as well as
adapt and modify existing inconsistency measures. We further adapt the notion
of inconsistency values (or culpability measures) for this setting and give a
comprehensive feasibility study.
",Towards Inconsistency Measurement in Business Rule Bases
"  Generalized planning aims at computing an algorithm-like structure
(generalized plan) that solves a set of multiple planning instances. In this
paper we define negative examples for generalized planning as planning
instances that must not be solved by a generalized plan. With this regard the
paper extends the notion of validation of a generalized plan as the problem of
verifying that a given generalized plan solves the set of input positives
instances while it fails to solve a given input set of negative examples. This
notion of plan validation allows us to define quantitative metrics to asses the
generalization capacity of generalized plans. The paper also shows how to
incorporate this new notion of plan validation into a compilation for plan
synthesis that takes both positive and negative instances as input. Experiments
show that incorporating negative examples can accelerate plan synthesis in
several domains and leverage quantitative metrics to evaluate the
generalization capacity of the synthesized plans.
",Generalized Planning with Positive and Negative Examples
"  Heuristic forward search is currently the dominant paradigm in classical
planning. Forward search algorithms typically rely on a single, relatively
simple variation of best-first search and remain fixed throughout the process
of solving a planning problem. Existing work combining multiple search
techniques usually aims at supporting best-first search with an additional
exploratory mechanism, triggered using a handcrafted criterion. A notable
exception is very recent work which combines various search techniques using a
trainable policy. It is, however, confined to a discrete action space
comprising several fixed subroutines.
  In this paper, we introduce a parametrized search algorithm template which
combines various search techniques within a single routine. The template's
parameter space defines an infinite space of search algorithms, including,
among others, BFS, local and random search. We further introduce a neural
architecture for designating the values of the search parameters given the
state of the search. This enables expressing neural search policies that change
the values of the parameters as the search progresses. The policies can be
learned automatically, with the objective of maximizing the planner's
performance on a given distribution of planning problems. We consider a
training setting based on a stochastic optimization algorithm known as the
cross-entropy method (CEM). Experimental evaluation of our approach shows that
it is capable of finding effective distribution-specific search policies,
outperforming the relevant baselines.
",Learning Neural Search Policies for Classical Planning
"  We present, FT-SWRL, a fuzzy temporal extension to the Semantic Web Rule
Language (SWRL), which combines fuzzy theories based on the valid-time temporal
model to provide a standard approach for modeling imprecise temporal domain
knowledge in OWL ontologies. The proposal introduces a fuzzy temporal model for
the semantic web, which is syntactically defined as a fuzzy temporal SWRL
ontology (SWRL-FTO) with a new set of fuzzy temporal SWRL built-ins for
defining their semantics. The SWRL-FTO hierarchically defines the necessary
linguistic terminologies and variables for the fuzzy temporal model. An example
model demonstrating the usefulness of the fuzzy temporal SWRL built-ins to
model imprecise temporal information is also represented. Fuzzification process
of interval-based temporal logic is further discussed as a reasoning paradigm
for our FT-SWRL rules, with the aim of achieving a complete OWL-based fuzzy
temporal reasoning. Literature review on fuzzy temporal representation
approaches, both within and without the use of ontologies, led to the
conclusion that the FT-SWRL model can authoritatively serve as a formal
specification for handling imprecise temporal expressions on the semantic web.
",FT-SWRL: A Fuzzy-Temporal Extension of Semantic Web Rule Language
"  Hierarchical Task Network (HTN) planning is showing its power in real-world
planning. Although domain experts have partial hierarchical domain knowledge,
it is time-consuming to specify all HTN methods, leaving them incomplete. On
the other hand, traditional HTN learning approaches focus only on declarative
goals, omitting the hierarchical domain knowledge. In this paper, we propose a
novel learning framework to refine HTN methods via task insertion with
completely preserving the original methods. As it is difficult to identify
incomplete methods without designating declarative goals for compound tasks, we
introduce the notion of prioritized preference to capture the incompleteness
possibility of methods. Specifically, the framework first computes the
preferred completion profile w.r.t. the prioritized preference to refine the
incomplete methods. Then it finds the minimal set of refined methods via a
method substitution operation. Experimental analysis demonstrates that our
approach is effective, especially in solving new HTN planning instances.
",Refining HTN Methods via Task Insertion with Preferences
"  Procedural Content Generation (PCG) refers to the practice, in videogames and
other games, of generating content such as levels, quests, or characters
algorithmically. Motivated by the need to make games replayable, as well as to
reduce authoring burden, limit storage space requirements, and enable
particular aesthetics, a large number of PCG methods have been devised by game
developers. Additionally, researchers have explored adapting methods from
machine learning, optimization, and constraint solving to PCG problems. Games
have been widely used in AI research since the inception of the field, and in
recent years have been used to develop and benchmark new machine learning
algorithms. Through this practice, it has become more apparent that these
algorithms are susceptible to overfitting. Often, an algorithm will not learn a
general policy, but instead a policy that will only work for a particular
version of a particular task with particular initial parameters. In response,
researchers have begun exploring randomization of problem parameters to
counteract such overfitting and to allow trained policies to more easily
transfer from one environment to another, such as from a simulated robot to a
robot in the real world. Here we review the large amount of existing work on
PCG, which we believe has an important role to play in increasing the
generality of machine learning methods. The main goal here is to present RL/AI
with new tools from the PCG toolbox, and its secondary goal is to explain to
game developers and researchers a way in which their work is relevant to AI
research.
","Increasing Generality in Machine Learning through Procedural Content
  Generation"
"  As a generalization of Dempster-Shafer theory, D number theory provides a
framework to deal with uncertain information with non-exclusiveness and
incompleteness. However, some basic concepts in D number theory are not well
defined. In this note, the belief and plausibility measures for D numbers have
been proposed, and basic properties of these measures have been revealed as
well.
",Belief and plausibility measures for D numbers
"  Inhibition is one of the core concepts in Cognitive Psychology. The idea of
inhibitory mechanisms actively weakening representations in the human mind has
inspired a great number of studies in various research domains. In contrast,
Computer Science only recently has begun to consider inhibition as a second
basic processing quality beside activation. Here, we review psychological
research on inhibition in memory and link the gained insights with the current
efforts in Computer Science of incorporating inhibitory principles for
optimizing information retrieval in Personal Information Management. Four
common aspects guide this review in both domains: 1. The purpose of inhibition
to increase processing efficiency. 2. Its relation to activation. 3. Its links
to contexts. 4. Its temporariness. In summary, the concept of inhibition has
been used by Computer Science for enhancing software in various ways already.
Yet, we also identify areas for promising future developments of inhibitory
mechanisms, particularly context inhibition.
","Temporarily Unavailable: Memory Inhibition in Cognitive and Computer
  Science"
"  In the vision and language navigation task, the agent may encounter ambiguous
situations that are hard to interpret by just relying on visual information and
natural language instructions. We propose an interactive learning framework to
endow the agent with the ability to ask for users' help in such situations. As
part of this framework, we investigate multiple learning approaches for the
agent with different levels of complexity. The simplest model-confusion-based
method lets the agent ask questions based on its confusion, relying on the
predefined confidence threshold of a next action prediction model. To build on
this confusion-based method, the agent is expected to demonstrate more
sophisticated reasoning such that it discovers the timing and locations to
interact with a human. We achieve this goal using reinforcement learning (RL)
with a proposed reward shaping term, which enables the agent to ask questions
only when necessary. The success rate can be boosted by at least 15% with only
one question asked on average during the navigation. Furthermore, we show that
the RL agent is capable of adjusting dynamically to noisy human responses.
Finally, we design a continual learning strategy, which can be viewed as a data
augmentation method, for the agent to improve further utilizing its interaction
history with a human. We demonstrate the proposed strategy is substantially
more realistic and data-efficient compared to previously proposed
pre-exploration techniques.
","Just Ask:An Interactive Learning Framework for Vision and Language
  Navigation"
"  Social psychology and real experiences show that cognitive consistency plays
an important role to keep human society in order: if people have a more
consistent cognition about their environments, they are more likely to achieve
better cooperation. Meanwhile, only cognitive consistency within a neighborhood
matters because humans only interact directly with their neighbors. Inspired by
these observations, we take the first step to introduce \emph{neighborhood
cognitive consistency} (NCC) into multi-agent reinforcement learning (MARL).
Our NCC design is quite general and can be easily combined with existing MARL
methods. As examples, we propose neighborhood cognition consistent deep
Q-learning and Actor-Critic to facilitate large-scale multi-agent cooperations.
Extensive experiments on several challenging tasks (i.e., packet routing, wifi
configuration, and Google football player control) justify the superior
performance of our methods compared with state-of-the-art MARL approaches.
",Neighborhood Cognition Consistent Multi-Agent Reinforcement Learning
"  We present SafeLife, a publicly available reinforcement learning environment
that tests the safety of reinforcement learning agents. It contains complex,
dynamic, tunable, procedurally generated levels with many opportunities for
unsafe behavior. Agents are graded both on their ability to maximize their
explicit reward and on their ability to operate safely without unnecessary side
effects. We train agents to maximize rewards using proximal policy optimization
and score them on a suite of benchmark levels. The resulting agents are
performant but not safe -- they tend to cause large side effects in their
environments -- but they form a baseline against which future safety research
can be measured.
",SafeLife 1.0: Exploring Side Effects in Complex Environments
"  Some researchers speculate that intelligent reinforcement learning (RL)
agents would be incentivized to seek resources and power in pursuit of their
objectives. Other researchers are skeptical, because RL agents need not have
human-like power-seeking instincts. To clarify this debate, we develop the
first formal theory of the statistical tendencies of optimal policies. In the
context of Markov decision processes, we prove that certain environmental
symmetries are sufficient for optimal policies to tend to seek power over the
environment. These symmetries exist in many environments in which the agent can
be shut down or destroyed. We prove that in these environments, most reward
functions make it optimal to seek power by keeping a range of options available
and, when maximizing average reward, by navigating towards larger sets of
potential terminal states.
",Optimal Policies Tend to Seek Power
"  We study the interpretability of predictive systems that use high-dimensonal
behavioral and textual data. Examples include predicting product interest based
on online browsing data and detecting spam emails or objectionable web content.
Recently, counterfactual explanations have been proposed for generating insight
into model predictions, which focus on what is relevant to a particular
instance. Conducting a complete search to compute counterfactuals is very
time-consuming because of the huge dimensionality. To our knowledge, for
behavioral and text data, only one model-agnostic heuristic algorithm (SEDC)
for finding counterfactual explanations has been proposed in the literature.
However, there may be better algorithms for finding counterfactuals quickly.
This study aligns the recently proposed Linear Interpretable Model-agnostic
Explainer (LIME) and Shapley Additive Explanations (SHAP) with the notion of
counterfactual explanations, and empirically benchmarks their effectiveness and
efficiency against SEDC using a collection of 13 data sets. Results show that
LIME-Counterfactual (LIME-C) and SHAP-Counterfactual (SHAP-C) have low and
stable computation times, but mostly, they are less efficient than SEDC.
However, for certain instances on certain data sets, SEDC's run time is
comparably large. With regard to effectiveness, LIME-C and SHAP-C find
reasonable, if not always optimal, counterfactual explanations. SHAP-C,
however, seems to have difficulties with highly unbalanced data. Because of its
good overall performance, LIME-C seems to be a favorable alternative to SEDC,
which failed for some nonlinear models to find counterfactuals because of the
particular heuristic search algorithm it uses. A main upshot of this paper is
that there is a good deal of room for further research. For example, we propose
algorithmic adjustments that are direct upshots of the paper's findings.
",Counterfactual Explanation Algorithms for Behavioral and Textual Data
"  In recent years we have seen fast progress on a number of benchmark problems
in AI, with modern methods achieving near or super human performance in Go,
Poker and Dota. One common aspect of all of these challenges is that they are
by design adversarial or, technically speaking, zero-sum. In contrast to these
settings, success in the real world commonly requires humans to collaborate and
communicate with others, in settings that are, at least partially, cooperative.
In the last year, the card game Hanabi has been established as a new benchmark
environment for AI to fill this gap. In particular, Hanabi is interesting to
humans since it is entirely focused on theory of mind, i.e., the ability to
effectively reason over the intentions, beliefs and point of view of other
agents when observing their actions. Learning to be informative when observed
by others is an interesting challenge for Reinforcement Learning (RL):
Fundamentally, RL requires agents to explore in order to discover good
policies. However, when done naively, this randomness will inherently make
their actions less informative to others during training. We present a new deep
multi-agent RL method, the Simplified Action Decoder (SAD), which resolves this
contradiction exploiting the centralized training phase. During training SAD
allows other agents to not only observe the (exploratory) action chosen, but
agents instead also observe the greedy action of their team mates. By combining
this simple intuition with best practices for multi-agent learning, SAD
establishes a new SOTA for learning methods for 2-5 players on the self-play
part of the Hanabi challenge. Our ablations show the contributions of SAD
compared with the best practice components. All of our code and trained agents
are available at https://github.com/facebookresearch/Hanabi_SAD.
",Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning
"  The standard RL world model is that of a Markov Decision Process (MDP). A
basic premise of MDPs is that the rewards depend on the last state and action
only. Yet, many real-world rewards are non-Markovian. For example, a reward for
bringing coffee only if requested earlier and not yet served, is non-Markovian
if the state only records current requests and deliveries. Past work considered
the problem of modeling and solving MDPs with non-Markovian rewards (NMR), but
we know of no principled approaches for RL with NMR. Here, we address the
problem of policy learning from experience with such rewards. We describe and
evaluate empirically four combinations of the classical RL algorithm Q-learning
and R-max with automata learning algorithms to obtain new RL algorithms for
domains with NMR. We also prove that some of these variants converge to an
optimal policy in the limit.
",Reinforcement Learning with Non-Markovian Rewards
"  Powerful formalisms for abstract argumentation have been proposed, among them
abstract dialectical frameworks (ADFs) that allow for a succinct and flexible
specification of the relationship between arguments, and the GRAPPA framework
which allows argumentation scenarios to be represented as arbitrary
edge-labelled graphs. The complexity of ADFs and GRAPPA is located beyond NP
and ranges up to the third level of the polynomial hierarchy. The combined
complexity of Answer Set Programming (ASP) exactly matches this complexity when
programs are restricted to predicates of bounded arity. In this paper, we
exploit this coincidence and present novel efficient translations from ADFs and
GRAPPA to ASP. More specifically, we provide reductions for the five main ADF
semantics of admissible, complete, preferred, grounded, and stable
interpretations, and exemplify how these reductions need to be adapted for
GRAPPA for the admissible, complete and preferred semantics. Under
consideration in Theory and Practice of Logic Programming (TPLP).
",Solving Advanced Argumentation Problems with Answer Set Programming
"  Rapid advancements in the Internet of Things (IoT) have facilitated more
efficient deployment of smart environment solutions for specific user
requirement. With the increase in the number of IoT devices, it has become
difficult for the user to control or operate every individual smart device into
achieving some desired goal like optimized power consumption, scheduled
appliance running time, etc. Furthermore, existing solutions to automatically
adapt the IoT devices are not capable enough to incorporate the user behavior.
This paper presents a novel approach to accurately configure IoT devices while
achieving the twin objectives of energy optimization along with conforming to
user preferences. Our work comprises of unsupervised clustering of devices'
data to find the states of operation for each device, followed by
probabilistically analyzing user behavior to determine their preferred states.
Eventually, we deploy an online reinforcement learning (RL) agent to find the
best device settings automatically. Results for three different smart homes'
data-sets show the effectiveness of our methodology. To the best of our
knowledge, this is the first time that a practical approach has been adopted to
achieve the above mentioned objectives without any human interaction within the
system.
","Making Smart Homes Smarter: Optimizing Energy Consumption with Human in
  the Loop"
"  Qualitative numerical planning is classical planning extended with
non-negative real variables that can be increased or decreased ""qualitatively"",
i.e., by positive indeterminate amounts. While deterministic planning with
numerical variables is undecidable in general, qualitative numerical planning
is decidable and provides a convenient abstract model for generalized planning.
The solutions to qualitative numerical problems (QNPs) were shown to correspond
to the strong cyclic solutions of an associated fully observable
non-deterministic (FOND) problem that terminate. This leads to a
generate-and-test algorithm for solving QNPs where solutions to a FOND problem
are generated one by one and tested for termination. The computational
shortcomings of this approach for solving QNPs, however, are that it is not
simple to amend FOND planners to generate all solutions, and that the number of
solutions to check can be doubly exponential in the number of variables. In
this work we address these limitations while providing additional insights on
QNPs. More precisely, we introduce two polynomial-time reductions, one from
QNPs to FOND problems and the other from FOND problems to QNPs both of which do
not involve termination tests. A result of these reductions is that QNPs are
shown to have the same expressive power and the same complexity as FOND
problems.
",Qualitative Numeric Planning: Reductions and Complexity
"  In most fuzzy control applications (applying classical fuzzy reasoning), the
reasoning method requires a complete fuzzy rule-base, i.e all the possible
observations must be covered by the antecedents of the fuzzy rules, which is
not always available. Fuzzy control systems based on the Fuzzy Rule
Interpolation (FRI) concept play a major role in different platforms, in case
if only a sparse fuzzy rule-base is available. This cases the fuzzy model
contains only the most relevant rules, without covering all the antecedent
universes. The first FRI toolbox being able to handle different FRI methods was
developed by Johanyak et. al. in 2006 for the MATLAB environment. The goal of
this paper is to introduce some details of the adaptation of the FRI toolbox to
support the GNU/OCTAVE programming language. The OCTAVE Fuzzy Rule
Interpolation (OCTFRI) Toolbox is an open-source toolbox for OCTAVE programming
language, providing a large functionally compatible subset of the MATLAB FRI
toolbox as well as many extensions. The OCTFRI Toolbox includes functions that
enable the user to evaluate Fuzzy Inference Systems (FISs) from the command
line and from OCTAVE scripts, read/write FISs and OBS to/from files, and
produce a graphical visualisation of both the membership functions and the FIS
outputs. Future work will focus on implementing advanced fuzzy inference
techniques and GUI tools.
",Fuzzy Rule Interpolation Toolbox for the GNU Open-Source OCTAVE
"  We combine Adaptive Dynamic Programming (ADP), a reinforcement learning
method and UCB applied to trees (UCT) algorithm with a more powerful heuristic
function based on Progressive Bias method and two pruning strategies for a
traditional board game Gomoku. For the Adaptive Dynamic Programming part, we
train a shallow forward neural network to give a quick evaluation of Gomoku
board situations. UCT is a general approach in MCTS as a tree policy. Our
framework use UCT to balance the exploration and exploitation of Gomoku game
trees while we also apply powerful pruning strategies and heuristic function to
re-select the available 2-adjacent grids of the state and use ADP instead of
simulation to give estimated values of expanded nodes. Experiment result shows
that this method can eliminate the search depth defect of the simulation
process and converge to the correct value faster than single UCT. This approach
can be applied to design new Gomoku AI and solve other Gomoku-like board game.
",UCT-ADP Progressive Bias Algorithm for Solving Gomoku
"  A linear algorithm is described for solving the n-Queens Completion problem
for an arbitrary composition of k queens, consistently distributed on a
chessboard of size n x n. Two important rules are used in the algorithm: a) the
rule of sequential risk elimination for the entire system as a whole; b) the
rule of formation of minimal damage in the given selection conditions. For any
composition of k queens (1<= k<n), a solution is provided, or a decision is
made that this composition can't be completed. The probability of an error in
making such a decision does not exceed 0.0001, and its value decreases, with
increasing n. It is established that the average time, required for the queen
to be placed on one row, decreases with increasing value of n. A description is
given of two random selection models and the results of their comparative
analysis. A model for organizing the Back Tracking procedure is proposed based
on the separation of the solution matrix into two basic levels. Regression
formulas are given for the dependence of basic levels on the value of n. It was
found that for n=(7-100000) the number of solutions in which the Back Tracking
procedure has never been used exceeds 35%. Moreover, for n=(320-22500), the
number of such cases exceeds 50 %. A quick algorithm for verifying the
correctness of n-Queens problem solution or arbitrary composition of k queens
is given.
",Linear algorithm for solution n-Queens Completion problem
"  The main goal of this paper is to describe an axiomatic utility theory for
Dempster-Shafer belief function lotteries. The axiomatic framework used is
analogous to von Neumann-Morgenstern's utility theory for probabilistic
lotteries as described by Luce and Raiffa. Unlike the probabilistic case, our
axiomatic framework leads to interval-valued utilities, and therefore, to a
partial (incomplete) preference order on the set of all belief function
lotteries. If the belief function reference lotteries we use are Bayesian
belief functions, then our representation theorem coincides with Jaffray's
representation theorem for his linear utility theory for belief functions. We
illustrate our representation theorem using some examples discussed in the
literature, and we propose a simple model for assessing utilities based on an
interval-valued pessimism index representing a decision-maker's attitude to
ambiguity and indeterminacy. Finally, we compare our decision theory with those
proposed by Jaffray, Smets, Dubois et al., Giang and Shenoy, and Shafer.
","An Interval-Valued Utility Theory for Decision Making with
  Dempster-Shafer Belief Functions"
"  This paper proposes a tentative and original survey of meeting points between
Knowledge Representation and Reasoning (KRR) and Machine Learning (ML), two
areas which have been developing quite separately in the last three decades.
Some common concerns are identified and discussed such as the types of used
representation, the roles of knowledge and data, the lack or the excess of
information, or the need for explanations and causal understanding. Then some
methodologies combining reasoning and learning are reviewed (such as inductive
logic programming, neuro-symbolic reasoning, formal concept analysis,
rule-based representations and ML, uncertainty in ML, or case-based reasoning
and analogical reasoning), before discussing examples of synergies between KRR
and ML (including topics such as belief functions on regression, EM algorithm
versus revision, the semantic description of vector representations, the
combination of deep learning with high level inference, knowledge graph
completion, declarative frameworks for data mining, or preferences and
recommendation). This paper is the first step of a work in progress aiming at a
better mutual understanding of research in KRR and ML, and how they could
cooperate.
","From Shallow to Deep Interactions Between Knowledge Representation,
  Reasoning and Machine Learning (Kay R. Amel group)"
"  We present a method for learning intrinsic reward functions to drive the
learning of an agent during periods of practice in which extrinsic task rewards
are not available. During practice, the environment may differ from the one
available for training and evaluation with extrinsic rewards. We refer to this
setup of alternating periods of practice and objective evaluation as
practice-match, drawing an analogy to regimes of skill acquisition common for
humans in sports and games. The agent must effectively use periods in the
practice environment so that performance improves during matches. In the
proposed method the intrinsic practice reward is learned through a
meta-gradient approach that adapts the practice reward parameters to reduce the
extrinsic match reward loss computed from matches. We illustrate the method on
a simple grid world, and evaluate it in two games in which the practice
environment differs from match: Pong with practice against a wall without an
opponent, and PacMan with practice in a maze without ghosts. The results show
gains from learning in practice in addition to match periods over learning in
matches only.
",How Should an Agent Practice?
"  We consider the problem of learning generalized first-order representations
of concepts from a single example. To address this challenging problem, we
augment an inductive logic programming learner with two novel algorithmic
contributions. First, we define a distance measure between candidate concept
representations that improves the efficiency of search for target concept and
generalization. Second, we leverage richer human inputs in the form of advice
to improve the sample-efficiency of learning. We prove that the proposed
distance measure is semantically valid and use that to derive a PAC bound. Our
experimental analysis on diverse concept learning tasks demonstrates both the
effectiveness and efficiency of the proposed approach over a first-order
concept learner using only examples.
",One-Shot Induction of Generalized Logical Concepts via Human Guidance
"  We present Hierarchical Deep Q-Network (HDQfD) that took first place in the
MineRL competition. HDQfD works on imperfect demonstrations and utilizes the
hierarchical structure of expert trajectories. We introduce the procedure of
extracting an effective sequence of meta-actions and subgoals from
demonstration data. We present a structured task-dependent replay buffer and
adaptive prioritizing technique that allow the HDQfD agent to gradually erase
poor-quality expert data from the buffer. In this paper, we present the details
of the HDQfD algorithm and give the experimental results in the Minecraft
domain.
",Hierarchical Deep Q-Network from Imperfect Demonstrations in Minecraft
"  Recent success in Artificial Intelligence (AI) and Machine Learning (ML)
allow problem solving automatically without any human intervention. Autonomous
approaches can be very convenient. However, in certain domains, e.g., in the
medical domain, it is necessary to enable a domain expert to understand, why an
algorithm came up with a certain result. Consequently, the field of Explainable
AI (xAI) rapidly gained interest worldwide in various domains, particularly in
medicine. Explainable AI studies transparency and traceability of opaque AI/ML
and there are already a huge variety of methods. For example with layer-wise
relevance propagation relevant parts of inputs to, and representations in, a
neural network which caused a result, can be highlighted. This is a first
important step to ensure that end users, e.g., medical professionals, assume
responsibility for decision making with AI/ML and of interest to professionals
and regulators. Interactive ML adds the component of human expertise to AI/ML
processes by enabling them to re-enact and retrace AI/ML results, e.g. let them
check it for plausibility. This requires new human-AI interfaces for
explainable AI. In order to build effective and efficient interactive human-AI
interfaces we have to deal with the question of how to evaluate the quality of
explanations given by an explainable AI system. In this paper we introduce our
System Causability Scale (SCS) to measure the quality of explanations. It is
based on our notion of Causability (Holzinger et al., 2019) combined with
concepts adapted from a widely accepted usability scale.
","Measuring the Quality of Explanations: The System Causability Scale
  (SCS). Comparing Human and Machine Explanations"
"  This is the Proceedings of the twelfth Workshop on Answer Set Programming and
Other Computing Paradigms (ASPOCP) 2019, which was held in Philadelphia, USA,
June 3rd , 2019.
","Proceedings of the twelfth Workshop on Answer Set Programming and Other
  Computing Paradigms 2019"
"  Was investigated in this paper the use of a search strategy in the problem of
finding the best path among multiple origins and multiple destinations. In this
kind of problem, it must be decided within a lot of combinations which is the
best origin and the best destination, and also the best path between these two
regions. One remarkable difficulty to answer this sort of problem is to perform
the search in a reduced time. This monography is a extension of previous
research in which the problem described here was studied only in a bus network
in the city of Fortaleza. This extension consisted of an exploration of the
search strategy in graphs that represent public ways in cities like Fortaleza,
Mumbai and Tokyo. Using this strategy with a heuristic algorithm, Haversine
distance, was noticed that is possible to reduce substantially the time of the
search, but introducing an error because of the loss of the admissible
characteristic of the heuristic function applied.
","Busca de melhor caminho entre m\'ultiplas origens e m\'ultiplos destinos
  em redes complexas que representam cidades"
"  The overarching goal of this paper is to develop a general model of the state
space of AI. Given the breathtaking progress in AI research and technologies in
recent years, such conceptual work is of substantial theoretical interest. The
present AI hype is mainly driven by the triumph of deep learning neural
networks. As the distinguishing feature of such networks is the ability to
self-learn, self-learning is identified as one important dimension of the AI
state space. Another main dimension lies in the possibility to go over from
specific to more general types of problems. The third main dimension is
provided by semantic grounding. Since this is a philosophically complex and
controversial dimension, a larger part of the paper is devoted to it. We take a
fresh look at known foundational arguments in the philosophy of mind and
cognition that are gaining new relevance in view of the recent AI developments
including the blockhead objection, the Turing test, the symbol grounding
problem, the Chinese room argument, and general use-theoretic considerations of
meaning. Finally, the AI state space, spanned by the main dimensions
generalization, grounding and ""selfx-ness"", possessing self-x properties such
as self-learning, is outlined.
","Does AlphaGo actually play Go? Concerning the State Space of Artificial
  Intelligence"
"  This paper describes a competition proposal for evolving Intelligent Agents
for the game-playing framework called EvoMan. The framework is based on the
boss fights of the game called Mega Man II developed by Capcom. For this
particular competition, the main goal is to beat all of the eight bosses using
a generalist strategy. In other words, the competitors should train the agent
to beat a set of the bosses and then the agent will be evaluated by its
performance against all eight bosses. At the end of this paper, the competitors
are provided with baseline results so that they can have an intuition on how
good their results are.
",EvoMan: Game-playing Competition
"  In this article, we present a new data type agnostic algorithm calculating a
concept lattice from heterogeneous and complex data. Our NextPriorityConcept
algorithm is first introduced and proved in the binary case as an extension of
Bordat's algorithm with the notion of strategies to select only some
predecessors of each concept, avoiding the generation of unreasonably large
lattices. The algorithm is then extended to any type of data in a generic way.
It is inspired from pattern structure theory, where data are locally described
by predicates independent of their types, allowing the management of
heterogeneous data.
","Next Priority Concept: A new and generic algorithm computing concepts
  from complex and heterogeneous data"
"  We present a Spades bidding algorithm that is superior to recreational human
players and to publicly available bots. Like in Bridge, the game of Spades is
composed of two independent phases, \textit{bidding} and \textit{playing}. This
paper focuses on the bidding algorithm, since this phase holds a precise
challenge: based on the input, choose the bid that maximizes the agent's
winning probability. Our \emph{Bidding-in-Spades} (BIS) algorithm heuristically
determines the bidding strategy by comparing the expected utility of each
possible bid. A major challenge is how to estimate these expected utilities. To
this end, we propose a set of domain-specific heuristics, and then correct them
via machine learning using data from real-world players. The \BIS algorithm we
present can be attached to any playing algorithm. It beats rule-based bidding
bots when all use the same playing component. When combined with a rule-based
playing algorithm, it is superior to the average recreational human.
",Bidding in Spades
"  We introduce pattern injection local search (PILS), an optimization strategy
that uses pattern mining to explore high-order local-search neighborhoods, and
illustrate its application on the vehicle routing problem. PILS operates by
storing a limited number of frequent patterns from elite solutions. During the
local search, each pattern is used to define one move in which 1) incompatible
edges are disconnected, 2) the edges defined by the pattern are reconnected,
and 3) the remaining solution fragments are optimally reconnected. Each such
move is accepted only in case of solution improvement. As visible in our
experiments, this strategy results in a new paradigm of local search, which
complements and enhances classical search approaches in a controllable amount
of computational time. We demonstrate that PILS identifies useful high-order
moves (e.g., 9-opt and 10-opt) which would otherwise not be found by
enumeration, and that it significantly improves the performance of
state-of-the-art population-based and neighborhood-centered metaheuristics.
",PILS: Exploring high-order neighborhoods by pattern mining and injection
"  To make machine exhibit human-like abilities in the domains like robotics and
conversation, social commonsense knowledge (SCK), i.e., common sense about
social contexts and social roles, is absolutely necessarily. Therefor, our
ultimate goal is to acquire large-scale SCK to support much more intelligent
applications. Before that, we need to know clearly what is SCK and how to
represent it, since automatic information processing requires data and
knowledge are organized in structured and semantically related ways. For this
reason, in this paper, we identify and formalize three basic types of SCK based
on first-order theory. Firstly, we identify and formalize the
interrelationships, such as having-role and having-social_relation, among
social contexts, roles and players from the perspective of considering both
contexts and roles as first-order citizens and not generating role instances.
Secondly, we provide a four level structure to identify and formalize the
intrinsic information, such as events and desires, of social contexts, roles
and players, and illustrate the way of harvesting the intrinsic information of
social contexts and roles from the exhibition of players in concrete contexts.
And thirdly, enlightened by some observations of actual contexts, we further
introduce and formalize the embedding of social contexts, and depict the way of
excavating the intrinsic information of social contexts and roles from the
embedded smaller and simpler contexts. The results of this paper lay the
foundation not only for formalizing much more complex SCK but also for
acquiring these three basic types of SCK.
",A Logical Model for Supporting Social Commonsense Knowledge Acquisition
"  This paper develops a new approach for estimating an interpretable,
relational model of a black-box autonomous agent that can plan and act. Our
main contributions are a new paradigm for estimating such models using a
minimal query interface with the agent, and a hierarchical querying algorithm
that generates an interrogation policy for estimating the agent's internal
model in a vocabulary provided by the user. Empirical evaluation of our
approach shows that despite the intractable search space of possible agent
models, our approach allows correct and scalable estimation of interpretable
agent models for a wide class of black-box autonomous agents. Our results also
show that this approach can use predicate classifiers to learn interpretable
models of planning agents that represent states as images.
","Asking the Right Questions: Learning Interpretable Action Models Through
  Query Answering"
"  We study what are the factors that can accelerate the emergence of
collaborative behaviours among independent selfish learning agents. We depart
from the ""Battle of the Exes"" (BoE), a spatial repeated game from which human
behavioral data has been obtained (by Hawkings and Goldstone, 2016) that we
find interesting because it considers two cases: a classic game theory version,
called ballistic, in which agents can only make one action/decision (equivalent
to the Battle of the Sexes) and a spatial version, called dynamic, in which
agents can change decision (a spatial continuous version). We model both
versions of the game with independent reinforcement learning agents and we
manipulate the reward function transforming it into an utility introducing
""loss aversion"": the reward that an agent obtains can be perceived as less
valuable when compared to what the other got. We prove experimentally the
introduction of loss aversion fosters cooperation by accelerating its
appearance, and by making it possible in some cases like in the dynamic
condition. We suggest that this may be an important factor explaining the rapid
converge of human behaviour towards collaboration reported in the experiment of
Hawkings and Goldstone.
","Loss aversion fosters coordination among independent reinforcement
  learners"
"  The CoRg system is a system to solve commonsense reasoning problems. The core
of the CoRg system is the automated theorem prover Hyper that is fed with large
amounts of background knowledge. This background knowledge plays a crucial role
in solving commonsense reasoning problems. In this paper we present different
ways to use knowledge graphs as background knowledge and discuss challenges
that arise.
",Using ConceptNet to Teach Common Sense to an Automated Theorem Prover
"  We have proposed going beyond traditional ontologies to use rich semantics
implemented in programming languages for modeling. In this paper, we discuss
the application of executable semantic models to two examples, first a
structured definition of a waterfall and second the cardiopulmonary system. We
examine the components of these models and the way those components interact.
Ultimately, such models should provide the basis for direct representation.
","Definitions and Semantic Simulations Based on Object-Oriented Analysis
  and Modeling"
"  Robotic Process Automation (RPA) is a technology for automating repetitive
routines consisting of sequences of user interactions with one or more
applications. In order to fully exploit the opportunities opened by RPA,
companies need to discover which specific routines may be automated, and how.
In this setting, this paper addresses the problem of analyzing User Interaction
(UI) logs in order to discover routines where a user transfers data from one
spreadsheet or (Web) form to another. The paper maps this problem to that of
discovering data transformations by example - a problem for which several
techniques are available. The paper shows that a naive application of a
state-of-the-art technique for data transformation discovery is computationally
inefficient. Accordingly, the paper proposes two optimizations that take
advantage of the information in the UI log and the fact that data transfers
across applications typically involve copying alphabetic and numeric tokens
separately. The proposed approach and its optimizations are evaluated using UI
logs that replicate a real-life repetitive data transfer routine.
","Automated Discovery of Data Transformations for Robotic Process
  Automation"
"  In this paper, we evolve a card-choice strategy for the arena mode of Legends
of Code and Magic, a programming game inspired by popular collectible card
games like Hearthstone or TES: Legends. In the arena game mode, before each
match, a player has to construct his deck choosing cards one by one from the
previously unknown options. Such a scenario is difficult from the optimization
point of view, as not only the fitness function is non-deterministic, but its
value, even for a given problem instance, is impossible to be calculated
directly and can only be estimated with simulation-based approaches. We propose
a variant of the evolutionary algorithm that uses a concept of an active gene
to reduce the range of the operators only to generation-specific subsequences
of the genotype. Thus, we batched learning process and constrained evolutionary
updates only to the cards relevant for the particular draft, without forgetting
the knowledge from the previous tests. We developed and tested various
implementations of this idea, investigating their performance by taking into
account the computational cost of each variant. Performed experiments show that
some of the introduced active-genes algorithms tend to learn faster and produce
statistically better draft policies than the compared methods.
","Evolutionary Approach to Collectible Card Game Arena Deckbuilding using
  Active Genes"
"  Reinforcement learning (RL) has become an increasingly active area of
research in recent years. Although there are many algorithms that allow an
agent to solve tasks efficiently, they often ignore the possibility that prior
experience related to the task at hand might be available. For many practical
applications, it might be unfeasible for an agent to learn how to solve a task
from scratch, given that it is generally a computationally expensive process;
however, prior experience could be leveraged to make these problems tractable
in practice. In this paper, we propose a framework for exploiting existing
experience by learning reusable options. We show that after an agent learns
policies for solving a small number of problems, we are able to use the
trajectories generated from those policies to learn reusable options that allow
an agent to quickly learn how to solve novel and related problems.
",Learning Reusable Options for Multi-Task Reinforcement Learning
"  We converted the recently developed BabyAI grid world platform to a
sender/receiver setup in order to test the hypothesis that established deep
reinforcement learning techniques are sufficient to incentivize the emergence
of a grounded discrete communication protocol between generalized agents. This
is in contrast to previous experiments that employed straight-through
estimation or specialized inductive biases. Our results show that these can
indeed be avoided, by instead providing proper environmental incentives.
Moreover, they show that a longer interval between communications incentivized
more abstract semantics. In some cases, the communicating agents adapted to new
environments more quickly than a monolithic agent, showcasing the potential of
emergent communication for transfer learning and generalization in general.
",Generalizing Emergent Communication
"  To deal with uncertainty in reasoning, interval-valued logic has been
developed. But uniform intervals cannot capture the difference in degrees of
belief for different values in the interval. To salvage the problem triangular
and trapezoidal fuzzy numbers are used as the set of truth values along with
traditional intervals. Preorder-based truth and knowledge ordering are defined
over the set of fuzzy numbers defined over $[0,1]$. Based on this enhanced set
of epistemic states, an answer set framework is developed, with properly
defined logical connectives. This type of framework is efficient in knowledge
representation and reasoning with vague and uncertain information under
nonmonotonic environment where rules may posses exceptions.
","Modeling Uncertainty and Imprecision in Nonmonotonic Reasoning using
  Fuzzy Numbers"
"  Large probabilistic models are often shaped by a pool of known individuals (a
universe) and relations between them. Lifted inference algorithms handle sets
of known individuals for tractable inference. Universes may not always be
known, though, or may only described by assumptions such as ""small universes
are more likely"". Without a universe, inference is no longer possible for
lifted algorithms, losing their advantage of tractable inference. The aim of
this paper is to define a semantics for models with unknown universes decoupled
from a specific constraint language to enable lifted and thereby, tractable
inference.
",Exploring Unknown Universes in Probabilistic Relational Models
"  Transportation occupies one-third of the amount in the logistics costs, and
accordingly transportation systems largely influence the performance of the
logistics system. This work presents an adaptive data-driven innovative modular
approach for solving the real-world Vehicle Routing Problems (VRP) in the field
of logistics. The work consists of two basic units: (i) an innovative
multi-step algorithm for successful and entirely feasible solving of the VRP
problems in logistics, (ii) an adaptive approach for adjusting and setting up
parameters and constants of the proposed algorithm. The proposed algorithm
combines several data transformation approaches, heuristics and Tabu search.
Moreover, as the performance of the algorithm depends on the set of control
parameters and constants, a predictive model that adaptively adjusts these
parameters and constants according to historical data is proposed. A comparison
of the acquired results has been made using the Decision Support System with
predictive models: Generalized Linear Models (GLM) and Support Vector Machine
(SVM). The algorithm, along with the control parameters, which using the
prediction method were acquired, was incorporated into a web-based enterprise
system, which is in use in several big distribution companies in Bosnia and
Herzegovina. The results of the proposed algorithm were compared with a set of
benchmark instances and validated over real benchmark instances as well. The
successful feasibility of the given routes, in a real environment, is also
presented.
","An adaptive data-driven approach to solve real-world vehicle routing
  problems in logistics"
"  For the past decades we have experienced an enormous expansion of the
accumulated data that humanity produces. Daily a numerous number of smart
devices, usually interconnected over internet, produce vast, real-values
datasets. Time series representing datasets from completely irrelevant domains
such as finance, weather, medical applications, traffic control etc. become
more and more crucial in human day life. Analyzing and clustering these time
series, or in general any kind of curves, could be critical for several human
activities. In the current paper, the new Curves Clustering Using Common
Patterns (3CP) methodology is introduced, which applies a repeated pattern
detection algorithm in order to cluster sequences according to their shape and
the similarities of common patterns between time series, data curves and
eventually any kind of discrete sequences. For this purpose, the Longest
Expected Repeated Pattern Reduced Suffix Array (LERP-RSA) data structure has
been used in combination with the All Repeated Patterns Detection (ARPaD)
algorithm in order to perform highly accurate and efficient detection of
similarities among data curves that can be used for clustering purposes and
which also provides additional flexibility and features.
",Data Curves Clustering Using Common Patterns Detection
"  How do people decide how long to continue in a task, when to switch, and to
which other task? Understanding the mechanisms that underpin task interleaving
is a long-standing goal in the cognitive sciences. Prior work suggests greedy
heuristics and a policy maximizing the marginal rate of return. However, it is
unclear how such a strategy would allow for adaptation to everyday environments
that offer multiple tasks with complex switch costs and delayed rewards. Here
we develop a hierarchical model of supervisory control driven by reinforcement
learning (RL). The supervisory level learns to switch using task-specific
approximate utility estimates, which are computed on the lower level. A
hierarchically optimal value function decomposition can be learned from
experience, even in conditions with multiple tasks and arbitrary and uncertain
reward and cost structures. The model reproduces known empirical effects of
task interleaving. It yields better predictions of individual-level data than a
myopic baseline in a six-task problem (N=211). The results support hierarchical
RL as a plausible model of task interleaving.
","Hierarchical Reinforcement Learning as a Model of Human Task
  Interleaving"
"  This paper builds upon recent work in the declarative design of dialogue
agents and proposes an exciting new tool -- D3BA -- Declarative Design for
Digital Business Automation, built to optimize business processes using the
power of AI planning. The tool provides a powerful framework to build,
optimize, and maintain complex business processes and optimize them by
composing with services that automate one or more subtasks. We illustrate
salient features of this composition technique, compare with other philosophies
of composition, and highlight exciting opportunities for research in this
emerging field of business process automation.
","D3BA: A Tool for Optimizing Business Processes Using Non-Deterministic
  Planning"
"  Connecting consumers with relevant products is a very important problem in
both online and offline commerce. In physical retail, product placement is an
effective way to connect consumers with products. However, selecting product
locations within a store can be a tedious process. Moreover, learning important
spatial patterns in offline retail is challenging due to the scarcity of data
and the high cost of exploration and experimentation in the physical world. To
address these challenges, we propose a stochastic model of spatial demand in
physical retail. We show that the proposed model is more predictive of demand
than existing baselines. We also perform a preliminary study into different
automation techniques and show that an optimal product allocation policy can be
learned through Deep Q-Learning.
",A Probabilistic Simulator of Spatial Demand for Product Allocation
"  Business process automation is a booming multi-billion-dollar industry that
promises to remove menial tasks from workers' plates -- through the
introduction of autonomous agents -- and free up their time and brain power for
more creative and engaging tasks. However, an essential component to the
successful deployment of such autonomous agents is the ability of business
users to monitor their performance and customize their execution. A simple and
user-friendly interface with a low learning curve is necessary to increase the
adoption of such agents in banking, insurance, retail and other domains. As a
result, proactive chatbots will play a crucial role in the business automation
space. Not only can they respond to users' queries and perform actions on their
behalf but also initiate communication with the users to inform them of the
system's behavior. This will provide business users a natural language
interface to interact with, monitor and control autonomous agents. In this
work, we present a multi-agent orchestration framework to develop such
proactive chatbots by discussing the types of skills that can be composed into
agents and how to orchestrate these agents. Two use cases on a travel
preapproval business process and a loan application business process are
adopted to qualitatively analyze the proposed framework based on four criteria:
performance, coding overhead, scalability, and agent overlap.
","A Unified Conversational Assistant Framework for Business Process
  Automation"
"  Autonomous systems are often required to operate in partially observable
environments. They must reliably execute a specified objective even with
incomplete information about the state of the environment. We propose a
methodology to synthesize policies that satisfy a linear temporal logic formula
in a partially observable Markov decision process (POMDP). By formulating a
planning problem, we show how to use point-based value iteration methods to
efficiently approximate the maximum probability of satisfying a desired logical
formula and compute the associated belief state policy. We demonstrate that our
method scales to large POMDP domains and provides strong bounds on the
performance of the resulting policy.
","Point-Based Methods for Model Checking in Partially Observable Markov
  Decision Processes"
"  Recent research in behaviour understanding through language grounding has
shown it is possible to automatically generate behaviour models from textual
instructions. These models usually have goal-oriented structure and are
modelled with different formalisms from the planning domain such as the
Planning Domain Definition Language. One major problem that still remains is
that there are no benchmark datasets for comparing the different model
generation approaches, as each approach is usually evaluated on domain-specific
application. To allow the objective comparison of different methods for model
generation from textual instructions, in this report we introduce a dataset
consisting of 83 textual instructions in English language, their refinement in
a more structured form as well as manually developed plans for each of the
instructions. The dataset is publicly available to the community.
",Towards Evaluating Plan Generation Approaches with Instructional Texts
"  Modern board games are a rich source of entertainment for many people, but
also contain interesting and challenging structures for game playing research
and implementing game playing agents. This paper studies the game Patchwork, a
two player strategy game using polyomino tile drafting and placement. The core
polyomino placement mechanic is implemented in a constraint model using regular
constraints, extending and improving the model in (Lagerkvist, Pesant, 2008)
with: explicit rotation handling; optional placements; and new constraints for
resource usage. Crucial for implementing good game playing agents is to have
great heuristics for guiding the search when faced with large branching
factors. This paper divides placing tiles into two parts: a policy used for
placing parts and an evaluation used to select among different placements.
Policies are designed based on classical packing literature as well as common
standard constraint programming heuristics. For evaluation, global propagation
guided regret is introduced, choosing placements based on not ruling out later
placements. Extensive evaluations are performed, showing the importance of
using a good evaluation and that the proposed global propagation guided regret
is a very effective guide.
",State Representation and Polyomino Placement for the Game Patchwork
"  Modern board games are a rich source of interesting and new challenges for
combinatorial problems. The game Nmbr9 is a solitaire style puzzle game using
polyominoes. The rules of the game are simple to explain, but modelling the
game effectively using constraint programming is hard. This abstract presents
the game, contributes new generalized variants of the game suitable for
benchmarking and testing, and describes a model for the presented variants. The
question of the top possible score in the standard game is an open challenge.
",Nmbr9 as a Constraint Programming Challenge
"  The feasibility of autonomous artificial thinking systems needs to compare
the way the human beings acquire their information and develops the thought
with the current capacities of the autonomous information systems. Our model
uses four hierarchies: the hierarchy of information systems, the cognitive
hierarchy, the linguistic hierarchy and the digital informative hierarchy that
combines artificial intelligence, the power of computers models, methods and
tools to develop autonomous information systems. The question of the capability
of autonomous system to provide a form of artificial thought arises with the
ethical consequences on the social life and the perspective of transhumanism.
",Perspectives and Ethics of the Autonomous Artificial Thinking Systems
"  In this work, we present an alternative approach to making an agent
compositional through the use of a diagnostic classifier. Because of the need
for explainable agents in automated decision processes, we attempt to interpret
the latent space from an RL agent to identify its current objective in a
complex language instruction. Results show that the classification process
causes changes in the hidden states which makes them more easily interpretable,
but also causes a shift in zero-shot performance to novel instructions. Lastly,
we limit the supervisory signal on the classification, and observe a similar
but less notable effect.
","Exploiting Language Instructions for Interpretable and Compositional
  Reinforcement Learning"
"  The increased use of electronic health records has made possible the
automated extraction of medical policies from patient records to aid in the
development of clinical decision support systems. We adapted a boosted
Statistical Relational Learning (SRL) framework to learn probabilistic rules
from clinical hospital records for the management of physiologic parameters of
children with severe cardiac or respiratory failure who were managed with
extracorporeal membrane oxygenation. In this preliminary study, the results
were promising. In particular, the algorithm returned logic rules for medical
actions that are consistent with medical reasoning.
","A Preliminary Approach for Learning Relational Policies for the
  Management of Critically Ill Children"
"  Probabilistic inference in the hybrid domain, i.e. inference over
discrete-continuous domains, requires tackling two well known #P-hard problems
1)~weighted model counting (WMC) over discrete variables and 2)~integration
over continuous variables. For both of these problems inference techniques have
been developed separately in order to manage their #P-hardness, such as
knowledge compilation for WMC and Monte Carlo (MC) methods for (approximate)
integration in the continuous domain. Weighted model integration (WMI), the
extension of WMC to the hybrid domain, has been proposed as a formalism to
study probabilistic inference over discrete and continuous variables alike.
Recently developed WMI solvers have focused on exploiting structure in WMI
problems, for which they rely on symbolic integration to find the primitive of
an integrand, i.e. to perform anti-differentiation. To combine these advances
with state-of-the-art Monte Carlo integration techniques, we introduce
\textit{Monte Carlo anti-differentiation} (MCAD), which computes MC
approximations of anti-derivatives. In our empirical evaluation we substitute
the exact symbolic integration backend in an existing WMI solver with an MCAD
backend. Our experiments show that that equipping existing WMI solvers with
MCAD yields a fast yet reliable approximate inference scheme.
","Monte Carlo Anti-Differentiation for Approximate Weighted Model
  Integration"
"  Algorithmic fairness in decision-making has been studied extensively in
static settings where one-shot decisions are made on tasks such as
classification. However, in practice most decision-making processes are of a
sequential nature, where decisions made in the past may have an impact on
future data. This is particularly the case when decisions affect the
individuals or users generating the data used for future decisions. In this
survey, we review existing literature on the fairness of data-driven sequential
decision-making. We will focus on two types of sequential decisions: (1) past
decisions have no impact on the underlying user population and thus no impact
on future data; (2) past decisions have an impact on the underlying user
population and therefore the future data, which can then impact future
decisions. In each case the impact of various fairness interventions on the
underlying population is examined.
",Fairness in Learning-Based Sequential Decision Algorithms: A Survey
"  We present a general algorithm to order moves so as to speedup exact game
solvers. It uses online learning of playout policies and Monte Carlo Tree
Search. The learned policy and the information in the Monte Carlo tree are used
to order moves in game solvers. They improve greatly the solving time for
multiple games.
",Monte Carlo Game Solver
"  This is the Proceedings of the AAAI-20 Workshop on Intelligent Process
Automation (IPA-20) which took place in New York, NY, USA on February 7th 2020.
","Proceedings of the AAAI-20 Workshop on Intelligent Process Automation
  (IPA-20)"
"  In this research, an extensive literature review was performed on the recent
developments of the ambulance routing problem (ARP) and ambulance location
problem (ALP). Both are respective modifications of the vehicle routing problem
(VRP) and maximum covering problem (MCP), with modifications to objective
functions and constraints. Although alike, a key distinction is emergency
service systems (EMS) are considered critical and the optimization of these has
become all the more important as a result. Similar to their parent problems,
these are NP-hard and must resort to approximations if the space size is too
large. Much of the current work has simply been on modifying existing systems
through simulation to achieve a more acceptable result. There has been attempts
towards using meta-heuristics, though practical experimentation is lacking when
compared to VRP or MCP. The contributions of this work are a comprehensive
survey of current methodologies, summarized models, and suggested future
improvements.
",A Comprehensive Survey on the Ambulance Routing and Location Problems
"  In a disaster situation, air ambulance rapid response will often be the
determining factor in patient survival. Obstacles intensify this circumstance,
with geographical remoteness and limitations in vehicle placement making it an
arduous task. Considering these elements, the arrangement of responders is a
critical decision of the utmost importance. Utilizing real mission data, this
research structured an optimal coverage problem with integer linear
programming. For accurate comparison, the Gurobi optimizer was programmed with
the developed model and timed for performance. A solution implementing base
ranking followed by both local and Tabu search-based algorithms was created.
The local search algorithm proved insufficient for maximizing coverage, while
the Tabu search achieved near-optimal results. In the latter case, the total
vehicle travel distance was minimized and the runtime significantly
outperformed the one generated by Gurobi. Furthermore, variations utilizing
parallel CUDA processing further decreased the algorithmic runtime. These
proved superior as the number of test missions increased, while also
maintaining the same minimized distance.
",Algorithms for Optimizing Fleet Staging of Air Ambulances
"  This paper shows how knowledge representation and reasoning techniques can be
used to support organizations in complying with the GDPR, that is, the new
European data protection regulation. This work is carried out in a European
H2020 project called SPECIAL. Data usage policies, the consent of data
subjects, and selected fragments of the GDPR are encoded in a fragment of OWL2
called PL (policy language); compliance checking and policy validation are
reduced to subsumption checking and concept consistency checking. This work
proposes a satisfactory tradeoff between the expressiveness requirements on PL
posed by the GDPR, and the scalability requirements that arise from the use
cases provided by SPECIAL's industrial partners. Real-time compliance checking
is achieved by means of a specialized reasoner, called PLR, that leverages
knowledge compilation and structural subsumption techniques. The performance of
a prototype implementation of PLR is analyzed through systematic experiments,
and compared with the performance of other important reasoners. Moreover, we
show how PL and PLR can be extended to support richer ontologies, by means of
import-by-query techniques. PL and its integration with OWL2's profiles
constitute new tractable fragments of OWL2. We prove also some negative
results, concerning the intractability of unrestricted reasoning in PL, and the
limitations posed on ontology import.
",Real Time Reasoning in OWL2 for GDPR Compliance
"  We introduce the multimodal car- and ride-sharing problem (MMCRP), in which a
pool of cars is used to cover a set of ride requests, while uncovered requests
are assigned to other modes of transport (MOT). A car's route consists of one
or more trips. Each trip must have a specific but non-predetermined driver,
start in a depot and finish in a (possibly different) depot. Ride-sharing
between users is allowed, even when two rides do not have the same origin
and/or destination. A user has always the option of using other modes of
transport according to an individual list of preferences.
  The problem can be formulated as a vehicle scheduling problem. In order to
solve the problem, an auxiliary graph is constructed in which each trip
starting and ending in a depot, and covering possible ride-shares, is modeled
as an edge in a time-space graph. We propose a two-layer decomposition
algorithm based on column generation, where the master problem ensures that
each request can only be covered at most once, and the pricing problem
generates new promising routes by solving a kind of shortest path problem in a
time-space network. Computational experiments based on realistic instances are
reported. The benchmark instances are based on demographic, spatial, and
economic data of Vienna, Austria. We solve large instances with the column
generation based approach to near optimality in reasonable time, and we further
investigate various exact and heuristic pricing schemes.
",Modeling and solving the multimodal car- and ride-sharing problem
"  The semantics as to which set of arguments in a given argumentation graph may
be acceptable (acceptability semantics) can be characterised in a few different
ways. Among them, labelling-based approach allows for concise and flexible
determination of acceptability statuses of arguments through assignment of a
label indicating acceptance, rejection, or undecided to each argument. In this
work, we contemplate a way of broadening it by accommodating may- and must-
conditions for an argument to be accepted or rejected, as determined by the
number(s) of rejected and accepted attacking arguments. We show that the
broadened label-based semantics can be used to express more mild indeterminacy
than inconsistency for acceptability judgement when, for example, it may be the
case that an argument is accepted and when it may also be the case that it is
rejected. We identify that finding which conditions a labelling satisfies for
every argument can be an undecidable problem, which has an unfavourable
implication to existence of a semantics. We propose to address this problem by
enforcing a labelling to maximally respect the conditions, while keeping the
rest that would necessarily cause non-termination labelled undecided. Several
semantics will be presented and the relation among them will be noted. Towards
the end, we will touch upon possible research directions that can be pursued
further.
","Broadening Label-based Argumentation Semantics with May-Must Scales
  (May-Must Argumentation)"
"  In this paper, we present a visual emulator of the emotions seen in
characters in stories. This system is based on a simplified view of the
cognitive structure of emotions proposed by Ortony, Clore and Collins (OCC
Model). The goal of this paper is to provide a visual platform that allows us
to observe changes in the characters' different emotions, and the intricate
interrelationships between: 1) each character's emotions, 2) their affective
relationships and actions, 3) The events that take place in the development of
a plot, and 4) the objects of desire that make up the emotional map of any
story. This tool was tested on stories with a contrasting variety of emotional
and affective environments: Othello, Twilight, and Harry Potter, behaving
sensibly and in keeping with the atmosphere in which the characters were
immersed.
",Visual Simplified Characters' Emotion Emulator Implementing OCC Model
"  We address the problem of complying with the GDPR while processing and
transferring personal data on the web. For this purpose we introduce an
extensible profile of OWL2 for representing data protection policies. With this
language, a company's data usage policy can be checked for compliance with data
subjects' consent and with a formalized fragment of the GDPR by means of
subsumption queries. The outer structure of the policies is restricted in order
to make compliance checking highly scalable, as required when processing
high-frequency data streams or large data volumes. However, the vocabularies
for specifying policy properties can be chosen rather freely from expressive
Horn fragments of OWL2. We exploit IBQ reasoning to integrate specialized
reasoners for the policy language and the vocabulary's language. Our
experiments show that this approach significantly improves performance.
",Fast Compliance Checking with General Vocabularies
"  Reinforcement learning has been successful in training autonomous agents to
accomplish goals in complex environments. Although this has been adapted to
multiple settings, including robotics and computer games, human players often
find it easier to obtain higher rewards in some environments than reinforcement
learning algorithms. This is especially true of high-dimensional state spaces
where the reward obtained by the agent is sparse or extremely delayed. In this
paper, we seek to effectively integrate feedback signals supplied by a human
operator with deep reinforcement learning algorithms in high-dimensional state
spaces. We call this FRESH (Feedback-based REward SHaping). During training, a
human operator is presented with trajectories from a replay buffer and then
provides feedback on states and actions in the trajectory. In order to
generalize feedback signals provided by the human operator to previously unseen
states and actions at test-time, we use a feedback neural network. We use an
ensemble of neural networks with a shared network architecture to represent
model uncertainty and the confidence of the neural network in its output. The
output of the feedback neural network is converted to a shaping reward that is
augmented to the reward provided by the environment. We evaluate our approach
on the Bowling and Skiing Atari games in the arcade learning environment.
Although human experts have been able to achieve high scores in these
environments, state-of-the-art deep learning algorithms perform poorly. We
observe that FRESH is able to achieve much higher scores than state-of-the-art
deep learning algorithms in both environments. FRESH also achieves a 21.4%
higher score than a human expert in Bowling and does as well as a human expert
in Skiing.
","FRESH: Interactive Reward Shaping in High-Dimensional State Spaces using
  Human Feedback"
"  The usefulness and usability of knowledge bases (KBs) is often limited by
quality issues. One common issue is the presence of erroneous assertions, often
caused by lexical or semantic confusion. We study the problem of correcting
such assertions, and present a general correction framework which combines
lexical matching, semantic embedding, soft constraint mining and semantic
consistency checking. The framework is evaluated using DBpedia and an
enterprise medical KB.
",Correcting Knowledge Base Assertions
"  Reinforcement learning is one of the core components in designing an
artificial intelligent system emphasizing real-time response. Reinforcement
learning influences the system to take actions within an arbitrary environment
either having previous knowledge about the environment model or not. In this
paper, we present a comprehensive study on Reinforcement Learning focusing on
various dimensions including challenges, the recent development of different
state-of-the-art techniques, and future directions. The fundamental objective
of this paper is to provide a framework for the presentation of available
methods of reinforcement learning that is informative enough and simple to
follow for the new researchers and academics in this domain considering the
latest concerns. First, we illustrated the core techniques of reinforcement
learning in an easily understandable and comparable way. Finally, we analyzed
and depicted the recent developments in reinforcement learning approaches. My
analysis pointed out that most of the models focused on tuning policy values
rather than tuning other things in a particular state of reasoning.
","A Survey of Reinforcement Learning Techniques: Strategies, Recent
  Development, and Future Directions"
"  Emergency Response Management (ERM) is a critical problem faced by
communities across the globe. Despite this, it is common for ERM systems to
follow myopic decision policies in the real world. Principled approaches to aid
ERM decision-making under uncertainty have been explored but have failed to be
accepted into real systems. We identify a key issue impeding their adoption ---
algorithmic approaches to emergency response focus on reactive, post-incident
dispatching actions, i.e. optimally dispatching a responder \textit{after}
incidents occur. However, the critical nature of emergency response dictates
that when an incident occurs, first responders always dispatch the closest
available responder to the incident. We argue that the crucial period of
planning for ERM systems is not post-incident, but between incidents. This is
not a trivial planning problem --- a major challenge with dynamically balancing
the spatial distribution of responders is the complexity of the problem. An
orthogonal problem in ERM systems is planning under limited communication,
which is particularly important in disaster scenarios that affect communication
networks. We address both problems by proposing two partially decentralized
multi-agent planning algorithms that utilize heuristics and exploit the
structure of the dispatch problem. We evaluate our proposed approach using
real-world data, and find that in several contexts, dynamic re-balancing the
spatial distribution of emergency responders reduces both the average response
time as well as its variance.
","On Algorithmic Decision Procedures in Emergency Response Systems in
  Smart and Connected Communities"
"  Clinical decision support systems combine knowledge and data from a variety
of sources, represented by quantitative models based on stochastic methods, or
qualitative based rather on expert heuristics and deductive reasoning. At the
same time, case-based reasoning (CBR) memorizes and returns the experience of
solving similar problems. The cooperation of heterogeneous clinical knowledge
bases (knowledge objects, semantic distances, evaluation functions, logical
rules, databases...) is based on medical ontologies. A multi-agent decision
support system (MADSS) enables the integration and cooperation of agents
specialized in different fields of knowledge (semiology, pharmacology, clinical
cases, etc.). Each specialist agent operates a knowledge base defining the
conduct to be maintained in conformity with the state of the art associated
with an ontological basis that expresses the semantic relationships between the
terms of the domain in question. Our approach is based on the specialization of
agents adapted to the knowledge models used during the clinical steps and
ontologies. This modular approach is suitable for the realization of MADSS in
many areas.
",A multi-agent ontologies-based clinical decision support system
"  Business processes underpin a large number of enterprise operations including
processing loan applications, managing invoices, and insurance claims. There is
a large opportunity for infusing AI to reduce cost or provide better customer
experience, and the business process management (BPM) literature is rich in
machine learning solutions including unsupervised learning to gain insights on
clusters of process traces, classification models to predict the outcomes,
duration, or paths of partial process traces, extracting business process from
documents, and models to recommend how to optimize a business process or
navigate decision points. More recently, deep learning models including those
from the NLP domain have been applied to process predictions.
  Unfortunately, very little of these innovations have been applied and adopted
by enterprise companies. We assert that a large reason for the lack of adoption
of AI models in BPM is that business users are risk-averse and do not
implicitly trust AI models. There has, unfortunately, been little attention
paid to explaining model predictions to business users with process context. We
challenge the BPM community to build on the AI interpretability literature, and
the AI Trust community to understand
",AI Trust in business processes: The need for process-aware explanations
"  Increasingly complex and autonomous systems require machine ethics to
maximize the benefits and minimize the risks to society arising from the new
technology. It is challenging to decide which type of ethical theory to employ
and how to implement it effectively. This survey provides a threefold
contribution. First, it introduces a trimorphic taxonomy to analyze machine
ethics implementations with respect to their object (ethical theories), as well
as their nontechnical and technical aspects. Second, an exhaustive selection
and description of relevant works is presented. Third, applying the new
taxonomy to the selected works, dominant research patterns, and lessons for the
field are identified, and future directions for research are suggested.
",Implementations in Machine Ethics: A Survey
"  Explaining sophisticated machine-learning based systems is an important issue
at the foundations of AI. Recent efforts have shown various methods for
providing explanations. These approaches can be broadly divided into two
schools: those that provide a local and human interpreatable approximation of a
machine learning algorithm, and logical approaches that exactly characterise
one aspect of the decision. In this paper we focus upon the second school of
exact explanations with a rigorous logical foundation. There is an
epistemological problem with these exact methods. While they can furnish
complete explanations, such explanations may be too complex for humans to
understand or even to write down in human readable form. Interpretability
requires epistemically accessible explanations, explanations humans can grasp.
Yet what is a sufficiently complete epistemically accessible explanation still
needs clarification. We do this here in terms of counterfactuals, following
[Wachter et al., 2017]. With counterfactual explanations, many of the
assumptions needed to provide a complete explanation are left implicit. To do
so, counterfactual explanations exploit the properties of a particular data
point or sample, and as such are also local as well as partial explanations. We
explore how to move from local partial explanations to what we call complete
local explanations and then to global ones. But to preserve accessibility we
argue for the need for partiality. This partiality makes it possible to hide
explicit biases present in the algorithm that may be injurious or unfair.We
investigate how easy it is to uncover these biases in providing complete and
fair explanations by exploiting the structure of the set of counterfactuals
providing a complete local explanation.
",Adequate and fair explanations
"  Bridge is a trick-taking card game requiring the ability to evaluate
probabilities since it is a game of incomplete information where each player
only sees its cards. In order to choose a strategy, a player needs to gather
information about the hidden cards in the other players' hand. We present a
methodology allowing us to model a part of card playing in Bridge using
Probabilistic Logic Programming.
",StarAI: Reducing incompleteness in the game of Bridge using PLP
"  In problem-solving, a path towards solutions can be viewed as a sequence of
decisions. The decisions, made by humans or computers, describe a trajectory
through a high-dimensional representation space of the problem. By means of
dimensionality reduction, these trajectories can be visualized in
lower-dimensional space. Such embedded trajectories have previously been
applied to a wide variety of data, but analysis has focused almost exclusively
on the self-similarity of single trajectories. In contrast, we describe
patterns emerging from drawing many trajectories---for different initial
conditions, end states, and solution strategies---in the same embedding space.
We argue that general statements about the problem-solving tasks and solving
strategies can be made by interpreting these patterns. We explore and
characterize such patterns in trajectories resulting from human and
machine-made decisions in a variety of application domains: logic puzzles
(Rubik's cube), strategy games (chess), and optimization problems (neural
network training). We also discuss the importance of suitably chosen
representation spaces and similarity metrics for the embedding.
","ProjectionPathExplorer: Exploring Visual Patterns in Projected
  Decision-Making Paths"
"  There are situations in which an agent should receive rewards only after
having accomplished a series of previous tasks. In other words, the reward that
the agent receives is non-Markovian. One natural and quite general way to
represent history-dependent rewards is via a Mealy machine; a finite state
automaton that produces output sequences (rewards in our case) from input
sequences (state/action observations in our case). In our formal setting, we
consider a Markov decision process (MDP) that models the dynamic of the
environment in which the agent evolves and a Mealy machine synchronised with
this MDP to formalise the non-Markovian reward function. While the MDP is known
by the agent, the reward function is unknown from the agent and must be learnt.
  Learning non-Markov reward functions is a challenge. Our approach to overcome
this challenging problem is a careful combination of the Angluin's L* active
learning algorithm to learn finite automata, testing techniques for
establishing conformance of finite model hypothesis and optimisation techniques
for computing optimal strategies in Markovian (immediate) reward MDPs. We also
show how our framework can be combined with classical heuristics such as Monte
Carlo Tree Search. We illustrate our algorithms and a preliminary
implementation on two typical examples for AI.
",Learning Non-Markovian Reward Models in MDPs
"  The Boolean satisfiability problem (SAT) is a famous NP-complete problem in
computer science. An effective way for solving a satisfiable SAT problem is the
stochastic local search (SLS). However, in this method, the initialization is
assigned in a random manner, which impacts the effectiveness of SLS solvers. To
address this problem, we propose NLocalSAT. NLocalSAT combines SLS with a
solution prediction model, which boosts SLS by changing initialization
assignments with a neural network. We evaluated NLocalSAT on five SLS solvers
(CCAnr, Sparrow, CPSparrow, YalSAT, and probSAT) with instances in the random
track of SAT Competition 2018. The experimental results show that solvers with
NLocalSAT achieve 27% ~ 62% improvement over the original SLS solvers.
",NLocalSAT: Boosting Local Search with Solution Prediction
"  Algorithmic trading, due to its inherent nature, is a difficult problem to
tackle; there are too many variables involved in the real world which make it
almost impossible to have reliable algorithms for automated stock trading. The
lack of reliable labelled data that considers physical and physiological
factors that dictate the ups and downs of the market, has hindered the
supervised learning attempts for dependable predictions. To learn a good policy
for trading, we formulate an approach using reinforcement learning which uses
traditional time series stock price data and combines it with news headline
sentiments, while leveraging knowledge graphs for exploiting news about
implicit relationships.
","Sentiment and Knowledge Based Algorithmic Trading with Deep
  Reinforcement Learning"
"  This paper aims at demonstrating how a first-order logic reasoning system in
combination with a large knowledge base can be understood as an artificial
consciousness system. For this we review some aspects from the area of
philosophy of mind and in particular Tononi's Information Integration Theory
(IIT) and Baars' Global Workspace Theory. These will be applied to the
reasoning system Hyper with ConceptNet as a knowledge base within a scenario of
commonsense and cognitive reasoning. Finally we demonstrate that such a system
is very well able to do conscious mind wandering.
",Consciousness and Automated Reasoning
"  Machine teaching is an algorithmic framework for teaching a target hypothesis
via a sequence of examples or demonstrations. We investigate machine teaching
for temporal logic formulas -- a novel and expressive hypothesis class amenable
to time-related task specifications. In the context of teaching temporal logic
formulas, an exhaustive search even for a myopic solution takes exponential
time (with respect to the time span of the task). We propose an efficient
approach for teaching parametric linear temporal logic formulas. Concretely, we
derive a necessary condition for the minimal time length of a demonstration to
eliminate a set of hypotheses. Utilizing this condition, we propose a myopic
teaching algorithm by solving a sequence of integer programming problems. We
further show that, under two notions of teaching complexity, the proposed
algorithm has near-optimal performance. The results strictly generalize the
previous results on teaching preference-based version space learners. We
evaluate our algorithm extensively under a variety of learner types (i.e.,
learners with different preference models) and interactive protocols (e.g.,
batched and adaptive). The results show that the proposed algorithms can
efficiently teach a given target temporal logic formula under various settings,
and that there are significant gains of teaching efficacy when the teacher
adapts to the learner's current hypotheses or uses oracles.
","Adaptive Teaching of Temporal Logic Formulas to Learners with
  Preferences"
"  Conformance checking techniques are widely adopted to pinpoint possible
discrepancies between process models and the execution of the process in
reality. However, state of the art approaches adopt a crisp evaluation of
deviations, with the result that small violations are considered at the same
level of significant ones. This affects the quality of the provided
diagnostics, especially when there exists some tolerance with respect to
reasonably small violations, and hampers the flexibility of the process. In
this work, we propose a novel approach which allows to represent actors'
tolerance with respect to violations and to account for severity of deviations
when assessing executions compliance. We argue that besides improving the
quality of the provided diagnostics, allowing some tolerance in deviations
assessment also enhances the flexibility of conformance checking techniques
and, indirectly, paves the way for improving the resilience of the overall
process management system.
",Towards Multi-perspective conformance checking with fuzzy sets
"  This paper studies large scale instances of a fairly general arc-routing
problem as well as incorporate practical constraints in particular coming from
the scheduling problem of the winter road maintenance (e.g. different
priorities for and methods of road maintenance). We develop a new algorithm
based on a bin-packing heuristic which is well-scalable and able to solve road
networks on thousands of crossroads and road segments in few minutes. Since it
is impossible to find an optimal solution for such a large instances to compare
it with a result of our algorithm, we also develop techniques to compute lower
bounds which are based on Integer Linear Programming and Lazy Constraints.
",A New Arc-Routing Algorithm Applied to Winter Road Maintenance
"  In recent years, there has been an increasing interest in studying
causality-related properties in machine learning models generally, and in
generative models in particular. While that is well motivated, it inherits the
fundamental computational hardness of probabilistic inference, making exact
reasoning intractable. Probabilistic tractable models have also recently
emerged, which guarantee that conditional marginals can be computed in time
linear in the size of the model, where the model is usually learned from data.
Although initially limited to low tree-width models, recent tractable models
such as sum product networks (SPNs) and probabilistic sentential decision
diagrams (PSDDs) exploit efficient function representations and also capture
high tree-width models.
  In this paper, we ask the following technical question: can we use the
distributions represented or learned by these models to perform causal queries,
such as reasoning about interventions and counterfactuals? By appealing to some
existing ideas on transforming such models to Bayesian networks, we answer
mostly in the negative. We show that when transforming SPNs to a causal graph
interventional reasoning reduces to computing marginal distributions; in other
words, only trivial causal reasoning is possible. For PSDDs the situation is
only slightly better. We first provide an algorithm for constructing a causal
graph from a PSDD, which introduces augmented variables. Intervening on the
original variables, once again, reduces to marginal distributions, but when
intervening on the augmented variables, a deterministic but nonetheless
causal-semantics can be provided for PSDDs.
","Interventions and Counterfactuals in Tractable Probabilistic Models:
  Limitations of Contemporary Transformations"
"  Lindenmayer systems (L-systems) are a grammar system that consist of string
rewriting rules. The rules replace every symbol in a string in parallel with a
successor to produce the next string, and this procedure iterates. In a
stochastic context-free L-system (S0L-system), every symbol may have one or
more rewriting rule, each with an associated probability of selection. Properly
constructed rewriting rules have been found to be useful for modeling and
simulating some natural and human engineered processes where each derived
string describes a step in the simulation. Typically, processes are modeled by
experts who meticulously construct the rules based on measurements or domain
knowledge of the process. This paper presents an automated approach to finding
stochastic L-systems, given a set of string sequences as input. The implemented
tool is called the Plant Model Inference Tool for S0L-systems (PMIT-S0L).
PMIT-S0L is evaluated using 960 procedurally generated S0L-systems in a test
suite, which are each used to generate input strings, and PMIT-S0L is then used
to infer the system from only the sequences. The evaluation shows that PMIT-S0L
infers S0L-systems with up to 9 rewriting rules each in under 12 hours.
Additionally, it is found that 3 sequences of strings is sufficient to find the
correct original rewriting rules in 100% of the cases in the test suite, and 6
sequences of strings reduces the difference in the associated probabilities to
approximately 1% or less.
",Stochastic L-system Inference from Multiple String Sequence Inputs
"  A great number of computer vision publications have focused on distinguishing
between human action recognition and classification rather than the intensity
of actions performed. Indexing the intensity which determines the performance
of human actions is a challenging task due to the uncertainty and information
deficiency that exists in the video inputs. To remedy this uncertainty, in this
paper we coupled fuzzy logic rules with the neural-based action recognition
model to rate the intensity of a human action as intense or mild. In our
approach, we used a Spatio-Temporal LSTM to generate the weights of the
fuzzy-logic model, and then demonstrate through experiments that indexing of
the action intensity is possible. We analyzed the integrated model by applying
it to videos of human actions with different action intensities and were able
to achieve an accuracy of 89.16% on our intensity indexing generated dataset.
The integrated model demonstrates the ability of a neuro-fuzzy inference module
to effectively estimate the intensity index of human actions.
","Human Action Performance using Deep Neuro-Fuzzy Recurrent Attention
  Model"
"  In this paper, we present an extension to an air traffic conflicts resolution
method consisting in generating a large number of trajectories for a set of
aircraft, and efficiently selecting the best compatible ones. We propose a
multimanoeuvre version which encapsulates different conflict-solving
algorithms, in particular an original ""smart brute-force"" method and the
well-known ToulBar2 CSP toolset. Experiments on several benchmarks show that
the first one is very efficient on cases involving few aircraft (representative
of what actually happens in operations), allowing us to search through a large
pool of manoeuvres and trajectories; however, this method is overtaken by its
complexity when the number of aircraft increases to 7 and more. Conversely,
within acceptable times, the ToulBar2 toolset can handle conflicts involving
more aircraft, but with fewer possible trajectories for each.
","Tackling Air Traffic Conflicts as a Weighted CSP : Experiments with the
  Lumberjack Method"
"  The problem of specifying high-level knowledge bases for planning becomes a
hard task in realistic environments. This knowledge is usually handcrafted and
is hard to keep updated, even for system experts. Recent approaches have shown
the success of classical planning at synthesizing action models even when all
intermediate states are missing. These approaches can synthesize action schemas
in Planning Domain Definition Language (PDDL) from a set of execution traces
each consisting, at least, of an initial and final state. In this paper, we
propose a new algorithm to unsupervisedly synthesize STRIPS action models with
a classical planner when action signatures are unknown. In addition, we
contribute with a compilation to classical planning that mitigates the problem
of learning static predicates in the action model preconditions, exploits the
capabilities of SAT planners with parallel encodings to compute action schemas
and validate all instances. Our system is flexible in that it supports the
inclusion of partial input information that may speed up the search. We show
through several experiments how learned action models generalize over unseen
planning instances.
",STRIPS Action Discovery
"  Vector Symbolic Architectures combine a high-dimensional vector space with a
set of carefully designed operators in order to perform symbolic computations
with large numerical vectors. Major goals are the exploitation of their
representational power and ability to deal with fuzziness and ambiguity. Over
the past years, several VSA implementations have been proposed. The available
implementations differ in the underlying vector space and the particular
implementations of the required VSA operators - with important ramifications
for the properties of these architectures. For example, not every VSA is
equally well suited to address each task, including complete incompatibility.
This paper provides an overview of eleven available VSA implementations and
discusses their commonalities and differences in the underlying vector space,
bundling, and binding/unbinding operations. We create a taxonomy of available
binding/unbinding operations and show an important ramification for non
self-inverse binding operations using an example from analogical reasoning. A
main contribution is the experimental comparison of the available
implementations in order to evaluate (1) the capacity of bundles, (2) the
approximation quality of non-exact unbinding operations, (3) the influence of
combining binding and bundling operations on the query answering performance,
and (4) the performance on two example applications: visual place and language
recognition. An overall good performance is shown by the HRR VSA in the
frequency domain. However, its non-self-inverse binding mechanism can
negatively influence its applicability, e.g. to analogical reasoning. We expect
this systematization and comparison to be relevant for development and
evaluation of new VSAs, but most importantly, to support the selection of an
appropriate VSA for a particular task. The implementations are available in
form of a MATLAB toolbox.
",A comparison of Vector Symbolic Architectures
"  Causality has traditionally been a scientific way to generate knowledge by
relating causes to effects. From an imaginery point of view, causal graphs are
a helpful tool for representing and infering new causal information. In
previous works, we have generated automatically causal graphs associated to a
given concept by analyzing sets of documents and extracting and representing
the found causal information in that visual way. The retrieved information
shows that causality is frequently imperfect rather than exact, feature
gathered by the graph. In this work we will attempt to go a step further
modelling the uncertainty in the graph through probabilistic improving the
management of the imprecision in the quoted graph.
",Uncertainty Weighted Causal Graphs
"  Automated driving in urban settings is challenging. Human participant
behavior is difficult to model, and conventional, rule-based Automated Driving
Systems (ADSs) tend to fail when they face unmodeled dynamics. On the other
hand, the more recent, end-to-end Deep Reinforcement Learning (DRL) based
model-free ADSs have shown promising results. However, pure learning-based
approaches lack the hard-coded safety measures of model-based controllers. Here
we propose a hybrid approach for integrating a path planning pipe into a vision
based DRL framework to alleviate the shortcomings of both worlds. In summary,
the DRL agent is trained to follow the path planner's waypoints as close as
possible. The agent learns this policy by interacting with the environment. The
reward function contains two major terms: the penalty of straying away from the
path planner and the penalty of having a collision. The latter has precedence
in the form of having a significantly greater numerical value. Experimental
results show that the proposed method can plan its path and navigate between
randomly chosen origin-destination points in CARLA, a dynamic urban simulation
environment. Our code is open-source and available online.
","Integrating Deep Reinforcement Learning with Model-based Path Planners
  for Automated Driving"
"  Recent developments in machine learning have pushed the tasks that machines
can do outside the boundaries of what was thought to be possible years ago.
Methodologies such as deep learning or generative models have achieved complex
tasks such as generating art pictures or literature automatically. On the other
hand, symbolic resources have also been developed further and behave well in
problems such as the ones proposed by common sense reasoning. Machine
Consciousness is a field that has been deeply studied and several theories
based in the functionalism philosophical theory like the global workspace
theory or information integration have been proposed that try to explain the
ariseness of consciousness in machines. In this work, we propose an
architecture that may arise consciousness in a machine based in the global
workspace theory and in the assumption that consciousness appear in machines
that has cognitive processes and exhibit conscious behaviour. This architecture
is based in processes that use the recent developments in artificial
intelligence models which output are these correlated activities. For every one
of the modules of this architecture, we provide detailed explanations of the
models involved and how they communicate with each other to create the
cognitive architecture.
","A Machine Consciousness architecture based on Deep Learning and Gaussian
  Processes"
"  As increasingly complex AI systems are introduced into our daily lives, it
becomes important for such systems to be capable of explaining the rationale
for their decisions and allowing users to contest these decisions. A
significant hurdle to allowing for such explanatory dialogue could be the
vocabulary mismatch between the user and the AI system. This paper introduces
methods for providing contrastive explanations in terms of user-specified
concepts for sequential decision-making settings where the system's model of
the task may be best represented as an inscrutable model. We do this by
building partial symbolic models of a local approximation of the task that can
be leveraged to answer the user queries. We test these methods on a popular
Atari game (Montezuma's Revenge) and variants of Sokoban (a well-known planning
benchmark) and report the results of user studies to evaluate whether people
find explanations generated in this form useful.
","Bridging the Gap: Providing Post-Hoc Symbolic Explanations for
  Sequential Decision-Making Problems with Inscrutable Representations"
"  The recent successes of deep learning and deep reinforcement learning have
firmly established their statuses as state-of-the-art artificial learning
techniques. However, longstanding drawbacks of these approaches, such as their
poor sample efficiencies and limited generalization capabilities point to a
need for re-thinking the way such systems are designed and deployed. In this
paper, we emphasize how the use of these learning systems, in conjunction with
a specific variation of evolutionary algorithms could lead to the emergence of
unique characteristics such as the automated acquisition of a variety of
desirable behaviors and useful sets of behavior priors. This could pave the way
for learning to occur in a generalized and continual manner, with minimal
interactions with the environment. We discuss the anticipated improvements from
such neuro-evolutionary frameworks, along with the associated challenges, as
well as its potential for application to a number of research areas.
",Neuro-evolutionary Frameworks for Generalized Learning Agents
"  Task-allocation is an important problem in multi-agent systems. It becomes
more challenging when the team-members are humans with imperfect knowledge
about their teammates' costs and the overall performance metric. While
distributed task-allocation methods let the team-members engage in iterative
dialog to reach a consensus, the process can take a considerable amount of time
and communication. On the other hand, a centralized method that simply outputs
an allocation may result in discontented human team-members who, due to their
imperfect knowledge and limited computation capabilities, perceive the
allocation to be unfair. To address these challenges, we propose a centralized
Artificial Intelligence Task Allocation (AITA) that simulates a negotiation and
produces a negotiation-aware task allocation that is fair. If a team-member is
unhappy with the proposed allocation, we allow them to question the proposed
allocation using a counterfactual. By using parts of the simulated negotiation,
we are able to provide contrastive explanations that providing minimum
information about other's costs to refute their foil. With human studies, we
show that (1) the allocation proposed using our method does indeed appear fair
to the majority, and (2) when a counterfactual is raised, explanations
generated are easy to comprehend and convincing. Finally, we empirically study
the effect of different kinds of incompleteness on the explanation-length and
find that underestimation of a teammate's costs often increases it.
","`Why didn't you allocate this task to them?' Negotiation-Aware Task
  Allocation and Contrastive Explanation Generation"
"  Hierarchical reinforcement learning (HRL) helps address large-scale and
sparse reward issues in reinforcement learning. In HRL, the policy model has an
inner representation structured in levels. With this structure, the
reinforcement learning task is expected to be decomposed into corresponding
levels with sub-tasks, and thus the learning can be more efficient. In HRL,
although it is intuitive that a high-level policy only needs to make macro
decisions in a low frequency, the exact frequency is hard to be simply
determined. Previous HRL approaches often employed a fixed-time skip strategy
or learn a terminal condition without taking account of the context, which,
however, not only requires manual adjustments but also sacrifices some decision
granularity. In this paper, we propose the \emph{temporal-adaptive hierarchical
policy learning} (TEMPLE) structure, which uses a temporal gate to adaptively
control the high-level policy decision frequency. We train the TEMPLE structure
with PPO and test its performance in a range of environments including 2-D
rooms, Mujoco tasks, and Atari games. The results show that the TEMPLE
structure can lead to improved performance in these environments with a
sequential adaptive high-level control.
",Temporal-adaptive Hierarchical Reinforcement Learning
"  In a standard Turing test, a machine has to prove its humanness to the
judges. By successfully imitating a thinking entity such as a human, this
machine then proves that it can also think. Some objections claim that Turing
test is not a tool to demonstrate the existence of general intelligence or
thinking activity. A compelling alternative is the Lovelace test, in which the
agent must originate a product that the agent's creator cannot explain.
Therefore, the agent must be the owner of an original product. However, for
this to happen the agent must exhibit the idea of self and distinguish oneself
from others. Sustaining the idea of self within the Turing test is still
possible if the judge decides to act as a textual mirror. Self-recognition
tests applied on animals through mirrors appear to be viable tools to
demonstrate the existence of a type of general intelligence. Methodology here
constructs a textual version of the mirror test by placing the agent as the one
and only judge to figure out whether the contacted one is an other, a mimicker,
or oneself in an unsupervised manner. This textual version of the mirror test
is objective, self-contained, and devoid of humanness. Any agent passing this
textual mirror test should have or can acquire a thought mechanism that can be
referred to as the inner-voice, answering the original and long lasting
question of Turing ""Can machines think?"" in a constructive manner still within
the bounds of the Turing test. Moreover, it is possible that a successful
self-recognition might pave way to stronger notions of self-awareness in
artificial beings.
",Self-recognition in conversational agents
"  Transfer learning is an important new subfield of multiagent reinforcement
learning that aims to help an agent learn about a problem by using knowledge
that it has gained solving another problem, or by using knowledge that is
communicated to it by an agent who already knows the problem. This is useful
when one wishes to change the architecture or learning algorithm of an agent
(so that the new knowledge need not be built ""from scratch""), when new agents
are frequently introduced to the environment with no knowledge, or when an
agent must adapt to similar but different problems. Great progress has been
made in the agent-to-agent case using the Teacher/Student framework proposed by
(Torrey and Taylor 2013). However, that approach requires that learning from a
teacher be treated differently from learning in every other reinforcement
learning context. In this paper, I propose a method which allows the
teacher/student framework to be applied in a way that fits directly and
naturally into the more general reinforcement learning framework by integrating
the teacher feedback into the reward signal received by the learning agent. I
show that this approach can significantly improve the rate of learning for an
agent playing a one-player stochastic game; I give examples of potential
pitfalls of the approach; and I propose further areas of research building on
this framework.
",Student/Teacher Advising through Reward Augmentation
"  The ethical concept of fairness has recently been applied in machine learning
(ML) settings to describe a wide range of constraints and objectives. When
considering the relevance of ethical concepts to subset selection problems, the
concepts of diversity and inclusion are additionally applicable in order to
create outputs that account for social power and access differentials. We
introduce metrics based on these concepts, which can be applied together,
separately, and in tandem with additional fairness constraints. Results from
human subject experiments lend support to the proposed criteria. Social choice
methods can additionally be leveraged to aggregate and choose preferable sets,
and we detail how these may be applied.
",Diversity and Inclusion Metrics in Subset Selection
"  Recent research efforts aiming to bridge the Neural-Symbolic gap for RDFS
reasoning proved empirically that deep learning techniques can be used to learn
RDFS inference rules. However, one of their main deficiencies compared to
rule-based reasoners is the lack of derivations for the inferred triples (i.e.
explainability in AI terms). In this paper, we build on these approaches to
provide not only the inferred graph but also explain how these triples were
inferred. In the graph words approach, RDF graphs are represented as a sequence
of graph words where inference can be achieved through neural machine
translation. To achieve explainability in RDFS reasoning, we revisit this
approach and introduce a new neural network model that gets the input graph--as
a sequence of graph words-- as well as the encoding of the inferred triple and
outputs the derivation for the inferred triple. We evaluated our justification
model on two datasets: a synthetic dataset-- LUBM benchmark-- and a real-world
dataset --ScholarlyData about conferences-- where the lowest validation
accuracy approached 96%.
",Explainable Deep RDFS Reasoner
"  We study the design of stochastic local search methods to prove
unsatisfiability of a constraint satisfaction problem (CSP). For a binary CSP,
such methods have been designed using the microstructure of the CSP. Here, we
develop a method to decompose the microstructure into graph tensors. We show
how to use the tensor decomposition to compute a proof of unsatisfiability
efficiently and in parallel. We also offer substantial empirical evidence that
our approach improves the praxis. For instance, one decomposition yields proofs
of unsatisfiability in half the time without sacrificing the quality. Another
decomposition is twenty times faster and effective three-tenths of the times
compared to the prior method. Our method is applicable to arbitrary CSPs using
the well known dual and hidden variable transformations from an arbitrary CSP
to a binary CSP.
","Testing Unsatisfiability of Constraint Satisfaction Problems via Tensor
  Products"
"  Ontologies order and interconnect knowledge of a certain field in a formal
and semantic way so that they are machine-parsable. They try to define allwhere
acceptable definition of concepts and objects, classify them, provide
properties as well as interconnect them with relations (e.g. ""A is a special
case of B""). More precisely, Tom Gruber defines Ontologies as a ""specification
of a conceptualization; [...] a description (like a formal specification of a
program) of the concepts and relationships that can exist for an agent or a
community of agents."" [1] An Ontology is made of Individuals which are
organized in Classes. Both can have Attributes and Relations among themselves.
Some complex Ontologies define Restrictions, Rules and Events which change
attributes or relations. To be computer accessible they are written in certain
ontology languages, like the OBO language or the more used Common Algebraic
Specification Language. With the rising of a digitalized, interconnected and
globalized world, where common standards have to be found, ontologies are of
great interest. [2] Yet, the development of chemical ontologies is in the
beginning. Indeed, some interesting basic approaches towards chemical
ontologies can be found, but nevertheless they suffer from two main flaws.
Firstly, we found that they are mostly only fragmentary completed or are still
in an architecture state. Secondly, apparently no chemical ontology is
widespread accepted. Therefore, we herein try to describe the major
ontology-developments in the chemical related fields Ontologies about chemical
analytical methods, Ontologies about name reactions and Ontologies about
scientific units.
",Overview of chemical ontologies
"  This paper introduces a fully automatic method of mechanic illumination for
general video game level generation. Using the Constrained MAP-Elites algorithm
and the GVG-AI framework, this system generates the simplest tile based levels
that contain specific sets of game mechanics and also satisfy playability
constraints. We apply this method to illuminate mechanic space for $4$
different games in GVG-AI: Zelda, Solarfox, Plants, and RealPortals.
",Mech-Elites: Illuminating the Mechanic Space of GVGAI
"  We present a heuristic strategy for marginal MAP (MMAP) queries in graphical
models. The algorithm is based on a reduction of the task to a polynomial
number of marginal inference computations. Given an input evidence, the
marginals mass functions of the variables to be explained are computed.
Marginal information gain is used to decide the variables to be explained
first, and their most probable marginal states are consequently moved to the
evidence. The sequential iteration of this procedure leads to a MMAP
explanation and the minimum information gain obtained during the process can be
regarded as a confidence measure for the explanation. Preliminary experiments
show that the proposed confidence measure is properly detecting instances for
which the algorithm is accurate and, for sufficiently high confidence levels,
the algorithm gives the exact solution or an approximation whose Hamming
distance from the exact one is small.
",Approximate MMAP by Marginal Search
"  This article proposes a method for automated service selection to improve
treatment efficacy and reduce re-hospitalization costs. A predictive model is
developed using the National Home and Hospice Care Survey (NHHCS) dataset to
quantify the effect of care services on the risk of re-hospitalization. By
taking the patient's characteristics and other selected services into account,
the model is able to indicate the overall effectiveness of a combination of
services for a specific NHHCS patient. The developed model is incorporated in
Monte-Carlo Tree Search (MCTS) to determine optimal combinations of services
that minimize the risk of emergency re-hospitalization. MCTS serves as a risk
minimization algorithm in this case, using the predictive model for guidance
during the search. Using this method on the NHHCS dataset, a significant
reduction in risk of re-hospitalization is observed compared to the original
selections made by clinicians. An 11.89 percentage points risk reduction is
achieved on average. Higher reductions of roughly 40 percentage points on
average are observed for NHHCS patients in the highest risk categories. These
results seem to indicate that there is enormous potential for improving service
selection in the near future.
",Service Selection using Predictive Models and Monte-Carlo Tree Search
"  If uncertainty is modelled by a probability measure, decisions are typically
made by choosing the option with the highest expected utility. If an imprecise
probability model is used instead, this decision rule can be generalised in
several ways. We here focus on two such generalisations that apply to sets of
probability measures: E-admissibility and maximality. Both of them can be
regarded as special instances of so-called choice functions, a very general
mathematical framework for decision making. For each of these two decision
rules, we provide a set of necessary and sufficient conditions on choice
functions that uniquely characterises this rule, thereby providing an axiomatic
foundation for imprecise decision making with sets of probabilities. A
representation theorem for Archimedean choice functions in terms of coherent
lower previsions lies at the basis of both results.
","Archimedean Choice Functions: an Axiomatic Foundation for Imprecise
  Decision Making"
"  I introduce and study a new notion of Archimedeanity for binary and
non-binary choice between options that live in an abstract Banach space,
through a very general class of choice models, called sets of desirable option
sets. In order to be able to bring an important diversity of contexts into the
fold, amongst which choice between horse lottery options, I pay special
attention to the case where these linear spaces don't include all `constant'
options.I consider the frameworks of conservative inference associated with
Archimedean (and coherent) choice models, and also pay quite a lot of attention
to representation of general (non-binary) choice models in terms of the
simpler, binary ones.The representation theorems proved here provide an
axiomatic characterisation for, amongst many other choice methods, Levi's
E-admissibility and Walley-Sen maximality.
",Coherent and Archimedean choice in general Banach spaces
"  Multi-vehicle routing problem with soft time windows (MVRPSTW) is an
indispensable constituent in urban logistics distribution systems. Over the
past decade, numerous methods for MVRPSTW have been proposed, but most are
based on heuristic rules that require a large amount of computation time. With
the current rapid increase of logistics demands, traditional methods incur the
dilemma between computational efficiency and solution quality. To efficiently
solve the problem, we propose a novel reinforcement learning algorithm called
the Multi-Agent Attention Model that can solve routing problem instantly
benefit from lengthy offline training. Specifically, the vehicle routing
problem is regarded as a vehicle tour generation process, and an
encoder-decoder framework with attention layers is proposed to generate tours
of multiple vehicles iteratively. Furthermore, a multi-agent reinforcement
learning method with an unsupervised auxiliary network is developed for the
model training. By evaluated on four synthetic networks with different scales,
the results demonstrate that the proposed method consistently outperforms
Google OR-Tools and traditional methods with little computation time. In
addition, we validate the robustness of the well-trained model by varying the
number of customers and the capacities of vehicles.
","Multi-Vehicle Routing Problems with Soft Time Windows: A Multi-Agent
  Reinforcement Learning Approach"
"  Recurrent neural networks (RNNs) have emerged as an effective representation
of control policies in sequential decision-making problems. However, a major
drawback in the application of RNN-based policies is the difficulty in
providing formal guarantees on the satisfaction of behavioral specifications,
e.g. safety and/or reachability. By integrating techniques from formal methods
and machine learning, we propose an approach to automatically extract a
finite-state controller (FSC) from an RNN, which, when composed with a
finite-state system model, is amenable to existing formal verification tools.
Specifically, we introduce an iterative modification to the so-called quantized
bottleneck insertion technique to create an FSC as a randomized policy with
memory. For the cases in which the resulting FSC fails to satisfy the
specification, verification generates diagnostic information. We utilize this
information to either adjust the amount of memory in the extracted FSC or
perform focused retraining of the RNN. While generally applicable, we detail
the resulting iterative procedure in the context of policy synthesis for
partially observable Markov decision processes (POMDPs), which is known to be
notoriously hard. The numerical experiments show that the proposed approach
outperforms traditional POMDP synthesis methods by 3 orders of magnitude within
2% of optimal benchmark values.
","Verifiable RNN-Based Policies for POMDPs Under Temporal Logic
  Constraints"
"  Planning is useful. It lets people take actions that have desirable long-term
consequences. But, planning is hard. It requires thinking about consequences,
which consumes limited computational and cognitive resources. Thus, people
should plan their actions, but they should also be smart about how they deploy
resources used for planning their actions. Put another way, people should also
""plan their plans"". Here, we formulate this aspect of planning as a
meta-reasoning problem and formalize it in terms of a recursive Bellman
objective that incorporates both task rewards and information-theoretic
planning costs. Our account makes quantitative predictions about how people
should plan and meta-plan as a function of the overall structure of a task,
which we test in two experiments with human participants. We find that people's
reaction times reflect a planned use of information processing, consistent with
our account. This formulation of planning to plan provides new insight into the
function of hierarchical planning, state abstraction, and cognitive control in
both humans and machines.
","The Efficiency of Human Cognition Reflects Planned Information
  Processing"
"  There has been significant progress in recent years in the field of Natural
Language Processing thanks to the introduction of the Transformer architecture.
Current state-of-the-art models, via a large number of parameters and
pre-training on massive text corpus, have shown impressive results on several
downstream tasks. Many researchers have studied previous (non-Transformer)
models to understand their actual behavior under different scenarios, showing
that these models are taking advantage of clues or failures of datasets and
that slight perturbations on the input data can severely reduce their
performance. In contrast, recent models have not been systematically tested
with adversarial-examples in order to show their robustness under severe stress
conditions. For that reason, this work evaluates three Transformer-based models
(RoBERTa, XLNet, and BERT) in Natural Language Inference (NLI) and Question
Answering (QA) tasks to know if they are more robust or if they have the same
flaws as their predecessors. As a result, our experiments reveal that RoBERTa,
XLNet and BERT are more robust than recurrent neural network models to stress
tests for both NLI and QA tasks. Nevertheless, they are still very fragile and
demonstrate various unexpected behaviors, thus revealing that there is still
room for future improvement in this field.
","Stress Test Evaluation of Transformer-based Models in Natural Language
  Understanding Tasks"
"  The promise of AI is huge. AI systems have already achieved good enough
performance to be in our streets and in our homes. However, they can be brittle
and unfair. For society to reap the benefits of AI systems, society needs to be
able to trust them. Inspired by decades of progress in trustworthy computing,
we suggest what trustworthy properties would be desired of AI systems. By
enumerating a set of new research questions, we explore one approach--formal
verification--for ensuring trust in AI. Trustworthy AI ups the ante on both
trustworthy computing and formal methods.
",Trustworthy AI
"  We introduce a reinforcement learning environment based on Heroic - Magic
Duel, a 1 v 1 action strategy game. This domain is non-trivial for several
reasons: it is a real-time game, the state space is large, the information
given to the player before and at each step of a match is imperfect, and
distribution of actions is dynamic. Our main contribution is a deep
reinforcement learning agent playing the game at a competitive level that we
trained using PPO and self-play with multiple competing agents, employing only
a simple reward of $\pm 1$ depending on the outcome of a single match. Our best
self-play agent, obtains around $65\%$ win rate against the existing AI and
over $50\%$ win rate against a top human player.
",Deep RL Agent for a Real-Time Action Strategy Game
"  We present PDDLGym, a framework that automatically constructs OpenAI Gym
environments from PDDL domains and problems. Observations and actions in
PDDLGym are relational, making the framework particularly well-suited for
research in relational reinforcement learning and relational sequential
decision-making. PDDLGym is also useful as a generic framework for rapidly
building numerous, diverse benchmarks from a concise and familiar specification
language. We discuss design decisions and implementation details, and also
illustrate empirical variations between the 20 built-in environments in terms
of planning and model-learning difficulty. We hope that PDDLGym will facilitate
bridge-building between the reinforcement learning community (from which Gym
emerged) and the AI planning community (which produced PDDL). We look forward
to gathering feedback from all those interested and expanding the set of
available environments and features accordingly. Code:
https://github.com/tomsilver/pddlgym
",PDDLGym: Gym Environments from PDDL Problems
"  Weighted model counting (WMC) consists of computing the weighted sum of all
satisfying assignments of a propositional formula. WMC is well-known to be
#P-hard for exact solving, but admits a fully polynomial randomized
approximation scheme (FPRAS) when restricted to DNF structures. In this work,
we study weighted model integration, a generalization of weighted model
counting which involves real variables in addition to propositional variables,
and pose the following question: Does weighted model integration on DNF
structures admit an FPRAS? Building on classical results from approximate
volume computation and approximate weighted model counting, we show that
weighted model integration on DNF structures can indeed be approximated for a
class of weight functions. Our approximation algorithm is based on three
subroutines, each of which can be a weak (i.e., approximate), or a strong
(i.e., exact) oracle, and in all cases, comes along with accuracy guarantees.
We experimentally verify our approach over randomly generated DNF instances of
varying sizes, and show that our algorithm scales to large problem instances,
involving up to 1K variables, which are currently out of reach for existing,
general-purpose weighted model integration solvers.
",On the Approximability of Weighted Model Integration on DNF Structures
"  Reinforcement learning agents usually learn from scratch, which requires a
large number of interactions with the environment. This is quite different from
the learning process of human. When faced with a new task, human naturally have
the common sense and use the prior knowledge to derive an initial policy and
guide the learning process afterwards. Although the prior knowledge may be not
fully applicable to the new task, the learning process is significantly sped up
since the initial policy ensures a quick-start of learning and intermediate
guidance allows to avoid unnecessary exploration. Taking this inspiration, we
propose knowledge guided policy network (KoGuN), a novel framework that
combines human prior suboptimal knowledge with reinforcement learning. Our
framework consists of a fuzzy rule controller to represent human knowledge and
a refine module to fine-tune suboptimal prior knowledge. The proposed framework
is end-to-end and can be combined with existing policy-based reinforcement
learning algorithm. We conduct experiments on both discrete and continuous
control tasks. The empirical results show that our approach, which combines
human suboptimal knowledge and RL, achieves significant improvement on learning
efficiency of flat RL algorithms, even with very low-performance human prior
knowledge.
","KoGuN: Accelerating Deep Reinforcement Learning via Integrating Human
  Suboptimal Knowledge"
"  Motivated by distinct, though related, criteria, a growing number of
attribution methods have been developed tointerprete deep learning. While each
relies on the interpretability of the concept of ""importance"" and our ability
to visualize patterns, explanations produced by the methods often differ. As a
result, input attribution for vision models fail to provide any level of human
understanding of model behaviour. In this work we expand the foundationsof
human-understandable concepts with which attributionscan be interpreted beyond
""importance"" and its visualization; we incorporate the logical concepts of
necessity andsufficiency, and the concept of proportionality. We definemetrics
to represent these concepts as quantitative aspectsof an attribution. This
allows us to compare attributionsproduced by different methods and interpret
them in novelways: to what extent does this attribution (or this
method)represent the necessity or sufficiency of the highlighted inputs, and to
what extent is it proportional? We evaluate our measures on a collection of
methods explaining convolutional neural networks (CNN) for image
classification. We conclude that some attribution methods are more appropriate
for interpretation in terms of necessity while others are in terms of
sufficiency, while no method is always the most appropriate in terms of both.
",Interpreting Interpretations: Organizing Attribution Methods by Criteria
"  An increasing number of data and knowledge sources are accessible by human
and software agents in the expanding Semantic Web. Sources may differ in
granularity or completeness, and thus be complementary. Consequently, they
should be reconciled in order to unlock the full potential of their conjoint
knowledge. In particular, units should be matched within and across sources,
and their level of relatedness should be classified into equivalent, more
specific, or similar. This task is challenging since knowledge units can be
heterogeneously represented in sources (e.g., in terms of vocabularies). In
this paper, we focus on matching n-ary tuples in a knowledge base with a
rule-based methodology. To alleviate heterogeneity issues, we rely on domain
knowledge expressed by ontologies. We tested our method on the biomedical
domain of pharmacogenomics by searching alignments among 50,435 n-ary tuples
from four different real-world sources. Results highlight noteworthy agreements
and particularities within and across sources.
",Knowledge-Based Matching of $n$-ary Tuples
"  In recent years, a great variety of nature- and bio-inspired algorithms has
been reported in the literature. This algorithmic family simulates different
biological processes observed in Nature in order to efficiently address complex
optimization problems. In the last years the number of bio-inspired
optimization approaches in literature has grown considerably, reaching
unprecedented levels that dark the future prospects of this field of research.
This paper addresses this problem by proposing two comprehensive,
principle-based taxonomies that allow researchers to organize existing and
future algorithmic developments into well-defined categories, considering two
different criteria: the source of inspiration and the behavior of each
algorithm. Using these taxonomies we review more than three hundred
publications dealing with nature-inspired and bio-inspired algorithms, and
proposals falling within each of these categories are examined, leading to a
critical summary of design trends and similarities between them, and the
identification of the most similar classical algorithm for each reviewed paper.
From our analysis we conclude that a poor relationship is often found between
the natural inspiration of an algorithm and its behavior. Furthermore,
similarities in terms of behavior between different algorithms are greater than
what is claimed in their public disclosure: specifically, we show that more
than one-third of the reviewed bio-inspired solvers are versions of classical
algorithms. Grounded on the conclusions of our critical analysis, we give
several recommendations and points of improvement for better methodological
practices in this active and growing research field.
","Comprehensive Taxonomies of Nature- and Bio-inspired Optimization:
  Inspiration versus Algorithmic Behavior, Critical Analysis and
  Recommendations"
"  No comprehensive review of Bayesian networks (BNs) in healthcare has been
published in the past, making it difficult to organize the research
contributions in the present and identify challenges and neglected areas that
need to be addressed in the future. This unique and novel scoping review of BNs
in healthcare provides an analytical framework for comprehensively
characterizing the domain and its current state. The review shows that: (1) BNs
in healthcare are not used to their full potential; (2) a generic BN
development process is lacking; (3) limitations exists in the way BNs in
healthcare are presented in the literature, which impacts understanding,
consensus towards systematic methodologies, practice and adoption of BNs; and
(4) a gap exists between having an accurate BN and a useful BN that impacts
clinical practice. This review empowers researchers and clinicians with an
analytical framework and findings that will enable understanding of the need to
address the problems of restricted aims of BNs, ad hoc BN development methods,
and the lack of BN adoption in practice. To map the way forward, the paper
proposes future research directions and makes recommendations regarding BN
development methods and adoption in practice.
","A Comprehensive Scoping Review of Bayesian Networks in Healthcare: Past,
  Present and Future"
"  Cyber-attacks can occur at machine speeds that are far too fast for
human-in-the-loop (or sometimes on-the-loop) decision making to be a viable
option. Although human inputs are still important, a defensive Artificial
Intelligence (AI) system must have considerable autonomy in these
circumstances. When the AI system is model-based, its behavior responses can be
aligned with risk-aware cost/benefit tradeoffs that are defined by
user-supplied preferences that capture the key aspects of how human operators
understand the system, the adversary and the mission. This paper describes an
approach to automated cyber response that is designed along these lines. We
combine a simulation of the system to be defended with an anytime online
planner to solve cyber defense problems characterized as partially observable
Markov decision problems (POMDPs).
","A Model-Based, Decision-Theoretic Perspective on Automated Cyber
  Response"
"  Automated game design is the problem of automatically producing games through
computational processes. Traditionally, these methods have relied on the
authoring of search spaces by a designer, defining the space of all possible
games for the system to author. In this paper, we instead learn representations
of existing games from gameplay video and use these to approximate a search
space of novel games. In a human subject study we demonstrate that these novel
games are indistinguishable from human games in terms of challenge, and that
one of the novel games was equivalent to one of the human games in terms of
fun, frustration, and likeability.
",Conceptual Game Expansion
"  In Constraint Programming, constraints are usually represented as predicates
allowing or forbidding combinations of values. However, some algorithms exploit
a finer representation: error functions. Their usage comes with a price though:
it makes problem modeling significantly harder. Here, we propose a method to
automatically learn an error function corresponding to a constraint, given a
function deciding if assignments are valid or not. This is, to the best of our
knowledge, the first attempt to automatically learn error functions for hard
constraints. Our method uses a variant of neural networks we named
Interpretable Compositional Networks, allowing us to get interpretable results,
unlike regular artificial neural networks. Experiments on 5 different
constraints show that our system can learn functions that scale to high
dimensions, and can learn fairly good functions over incomplete spaces.
","Learning Interpretable Error Functions for Combinatorial Optimization
  Problem Modeling"
"  This paper addresses the challenge of modeling human reasoning, within a new
framework called Cognitive Argumentation. This framework rests on the
assumption that human logical reasoning is inherently a process of dialectic
argumentation and aims to develop a cognitive model for human reasoning that is
computational and implementable. To give logical reasoning a human cognitive
form the framework relies on cognitive principles, based on empirical and
theoretical work in Cognitive Science, to suitably adapt a general and abstract
framework of computational argumentation from AI. The approach of Cognitive
Argumentation is evaluated with respect to Byrne's suppression task, where the
aim is not only to capture the suppression effect between different groups of
people but also to account for the variation of reasoning within each group.
Two main cognitive principles are particularly important to capture human
conditional reasoning that explain the participants' responses: (i) the
interpretation of a condition within a conditional as sufficient and/or
necessary and (ii) the mode of reasoning either as predictive or explanatory.
We argue that Cognitive Argumentation provides a coherent and cognitively
adequate model for human conditional reasoning that allows a natural
distinction between definite and plausible conclusions, exhibiting the
important characteristics of context-sensitive and defeasible reasoning.
",Cognitive Argumentation and the Suppression Task
"  Robotic agents should be able to learn from sub-symbolic sensor data, and at
the same time, be able to reason about objects and communicate with humans on a
symbolic level. This raises the question of how to overcome the gap between
symbolic and sub-symbolic artificial intelligence. We propose a semantic world
modeling approach based on bottom-up object anchoring using an object-centered
representation of the world. Perceptual anchoring processes continuous
perceptual sensor data and maintains a correspondence to a symbolic
representation. We extend the definitions of anchoring to handle multi-modal
probability distributions and we couple the resulting symbol anchoring system
to a probabilistic logic reasoner for performing inference. Furthermore, we use
statistical relational learning to enable the anchoring framework to learn
symbolic knowledge in the form of a set of probabilistic logic rules of the
world from noisy and sub-symbolic sensor input. The resulting framework, which
combines perceptual anchoring and statistical relational learning, is able to
maintain a semantic world model of all the objects that have been perceived
over time, while still exploiting the expressiveness of logical rules to reason
about the state of objects which are not directly observed through sensory
input data. To validate our approach we demonstrate, on the one hand, the
ability of our system to perform probabilistic reasoning over multi-modal
probability distributions, and on the other hand, the learning of probabilistic
logical rules from anchored objects produced by perceptual observations. The
learned logical rules are, subsequently, used to assess our proposed
probabilistic anchoring procedure. We demonstrate our system in a setting
involving object interactions where object occlusions arise and where
probabilistic inference is needed to correctly anchor objects.
","Symbolic Learning and Reasoning with Noisy Data for Probabilistic
  Anchoring"
"  Deep learning technology is making great progress in solving the challenging
problems of artificial intelligence, hence machine learning based on artificial
neural networks is in the spotlight again. In some areas, artificial
intelligence based on deep learning is beyond human capabilities. It seemed
extremely difficult for a machine to beat a human in a Go game, but AlphaGo has
shown to beat a professional player in the game. By looking at the statistical
distribution of the distance in which the Go stones are laid in succession, we
find a clear trace that Alphago has surpassed human abilities. The AlphaGo than
professional players and professional players than ordinary players shows the
laying of stones in the distance becomes more frequent. In addition, AlphaGo
shows a much more pronounced difference than that of ordinary players and
professional players.
","Very simple statistical evidence that AlphaGo has exceeded human limits
  in playing GO game"
"  This paper will discuss the role of an artificially-intelligent computer
system as critique-based, implicit-organizational, and an inherently necessary
device, deployed in synchrony with parallel governmental policy, as a genuine
means of capturing nation-population complexity in quantitative form, public
contentment in societal-cooperative economic groups, regulatory proposition,
and governance-effectiveness domains. It will discuss a solution involving a
well-known algorithm and proffer an improved mechanism for
knowledge-representation, thereby increasing range of utility, scope of
influence (in terms of differentiating class sectors) and operational
efficiency. It will finish with a discussion of these and other historical
implications.
","A machine-learning software-systems approach to capture social,
  regulatory, governance, and climate problems"
"  TCSPs (Temporal Constraint Satisfaction Problems), as defined in [Dechter et
al., 1991], get rid of unary constraints by binarizing them after having added
an ""origin of the world"" variable. In this work, we look at the constraints
between the ""origin of the world"" variable and the other variables, as the
(binarized) domains of these other variables. With this in mind, we define a
notion of arc-consistency for TCSPs, which we will refer to as
binarized-domains Arc-Consistency, or bdArc-Consistency for short. We provide
an algorithm achieving bdArc-Consistency for a TCSP, which we will refer to as
bdAC-3, for it is an adaptation of Mackworth's [1977] well-known
arc-consistency algorithm AC-3. We show that if a convex TCSP, referred to in
[Dechter et al., 1991] as an STP (Simple Temporal Problem), is
bdArc-Consistent, and its ""origin of the world"" variable disconnected from none
of the other variables, its binarized domains are minimal. We provide two
polynomial backtrack-free procedures: one for the task of getting, from a
bdArc-Consistent STP, either that it is inconsistent or, in case of
consistency, a bdArc-Consistent STP refinement whose ""origin of the world""
variable is disconnected from none of the other variables; the other for the
task of getting a solution from a bdArc-Consistent STP whose ""origin of the
world"" variable is disconnected from none of the other variables. We then show
how to use our results both in a general TCSP solver and in a TCSP-based job
shop scheduler. From our work can be extracted a one-to-all all-to-one shortest
paths algorithm of an IR-labelled directed graph. Finally, we show that an
existing adaptation to TCSPs of Mackworth's [1977] path-consistency algorithm
PC-2 is not guaranteed to always terminate, and correct it.
","A binarized-domains arc-consistency algorithm for TCSPs: its
  computational analysis and its use as a filtering procedure in solution
  search algorithms"
"  Proper scheduling of air assets can be the difference between life and death
for a patient. While poor scheduling can be incredibly problematic during
hospital transfers, it can be potentially catastrophic in the case of a
disaster. These issues are amplified in the case of an air emergency medical
service (EMS) system where populations are dispersed, and resources are
limited. There are exact methodologies existing for scheduling missions,
although actual calculation times can be quite significant given a large enough
problem space. For this research, known coordinates of air and health
facilities were used in conjunction with a formulated integer linear
programming model. This was the programmed through Gurobi so that performance
could be compared against custom algorithmic solutions. Two methods were
developed, one based on neighbourhood search and the other on Tabu search.
While both were able to achieve results quite close to the Gurobi solution, the
Tabu search outperformed the former algorithm. Additionally, it was able to do
so in a greatly decreased time, with Gurobi actually being unable to resolve to
optimal in larger examples. Parallel variations were also developed with the
compute unified device architecture (CUDA), though did not improve the timing
given the smaller sample size.
",Algorithms for Optimizing Fleet Scheduling of Air Ambulances
"  Approaches based on computing with words find good applicability in decision
making systems. Predominantly finding their basis in type-1 fuzzy sets,
computing with words approaches employ type-1 fuzzy sets as semantics of the
linguistic terms. However, type-2 fuzzy sets have been proven to be
scientifically more appropriate to represent linguistic information in
practical systems. They take into account both the intra-uncertainty as well as
the inter-uncertainty in cases where the linguistic information comes from a
group of experts. Hence in this paper, we propose to introduce linguistic terms
whose semantics are denoted by interval type-2 fuzzy sets within the hesitant
fuzzy linguistic term set framework, resulting in type-2 fuzzy sets based
hesitant fuzzy linguistic term sets. We also introduce a novel method of
computing type-2 fuzzy envelopes out of multiple interval type-2 fuzzy sets
with trapezoidal membership functions. Furthermore, the proposed framework with
interval type-2 fuzzy sets is applied on a supplier performance evaluation
scenario. Since humans are predominantly involved in the entire process of
supply chain, their feedback is crucial while deciding many factors. Towards
the end of the paper, we compare our presented model with various existing
models and demonstrate the advantages of the former.
","Type-2 Fuzzy Set based Hesitant Fuzzy Linguistic Term Sets for
  Linguistic Decision Making"
"  Crowdsourcing is defined as the outsourcing of tasks to a crowd of
contributors. The crowd is very diverse on these platforms and includes
malicious contributors attracted by the remuneration of tasks and not
conscientiously performing them. It is essential to identify these contributors
in order to avoid considering their responses. As not all contributors have the
same aptitude for a task, it seems appropriate to give weight to their answers
according to their qualifications. This paper, published at the ICTAI 2019
conference, proposes a method, MONITOR, for estimating the profile of the
contributor and aggregating the responses using belief function theory.
","Modelisation de l'incertitude et de l'imprecision de donnees de
  crowdsourcing : MONITOR"
"  The maximum vertex weight clique problem (MVWCP) is an important
generalization of the maximum clique problem (MCP) that has a wide range of
real-world applications. In situations where rigorous guarantees regarding the
optimality of solutions are not required, MVWCP is usually solved using
stochastic local search (SLS) algorithms, which also define the state of the
art for solving this problem. However, there is no single SLS algorithm which
gives the best performance across all classes of MVWCP instances, and it is
challenging to effectively identify the most suitable algorithm for each class
of MVWCP instances. In this work, we follow the paradigm of Programming by
Optimization (PbO) to develop a new, flexible and highly parametric SLS
framework for solving MVWCP, combining, for the first time, a broad range of
effective heuristic mechanisms. By automatically configuring this PbO-MWC
framework, we achieve substantial advances in the state-of-the-art in solving
MVWCP over a broad range of prominent benchmarks, including two derived from
real-world applications in transplantation medicine (kidney exchange) and
assessment of research excellence.
","Improving the Performance of Stochastic Local Search for Maximum Vertex
  Weight Clique Problem Using Programming by Optimization"
"  This paper introduces the 2019 version of \us{}, a novel Constraint
Programming framework for floating point verification problems expressed with
the SMT language of SMTLIB. SMT solvers decompose their task by delegating to
specific theories (e.g., floating point, bit vectors, arrays, ...) the task to
reason about combinatorial or otherwise complex constraints for which the SAT
encoding would be cumbersome or ineffective. This decomposition and encoding
processes lead to the obfuscation of the high-level constraints and a loss of
information on the structure of the combinatorial model. In \us{}, constraints
over the floats are first class objects, and the purpose is to expose and
exploit structures of floating point domains to enhance the search process. A
symbolic phase rewrites each SMTLIB instance to elementary constraints, and
eliminates auxiliary variables whose presence is counterproductive. A
diversification technique within the search steers it away from costly
enumerations in unproductive areas of the search space. The empirical
evaluation demonstrates that the 2019 version of \us{} is competitive on
computationally challenging floating point benchmarks that induce significant
search efforts even for other CP solvers. It highlights that the ability to
harness both inference and search is critical. Indeed, it yields a factor 3
improvement over Colibri and is up to 10 times faster than SMT solvers. The
evaluation was conducted over 214 benchmarks (The Griggio suite) which is a
standard within SMTLIB.
","An efficient constraint based framework forhandling floating point SMT
  problems"
"  A planning domain, as any model, is never complete and inevitably makes
assumptions on the environment's dynamic. By allowing the specification of just
one domain model, the knowledge engineer is only able to make one set of
assumptions, and to specify a single objective-goal. Borrowing from work in
Software Engineering, we propose a multi-tier framework for planning that
allows the specification of different sets of assumptions, and of different
corresponding objectives. The framework aims to support the synthesis of
adaptive behavior so as to mitigate the intrinsic risk in any planning modeling
task. After defining the multi-tier planning task and its solution concept, we
show how to solve problem instances by a succinct compilation to a form of
non-deterministic planning. In doing so, our technique justifies the
applicability of planning with both fair and unfair actions, and the need for
more efforts in developing planning systems supporting dual fairness
assumptions.
",Multi-tier Automated Planning for Adaptive Behavior (Extended Version)
"  Program verification techniques typically focus on finding counter-examples
that violate properties of a program. Constraint programming offers a
convenient way to verify programs by modeling their state transformations and
specifying searches that seek counter-examples. Floating-point computations
present additional challenges for verification given the semantic subtleties of
floating point arithmetic. % This paper focuses on search strategies for CSPs
using floating point numbers constraint systems and dedicated to program
verification. It introduces a new search heuristic based on the global number
of occurrences that outperforms state-of-the-art strategies. More importantly,
it demonstrates that a new technique that only branches on input variables of
the verified program improve performance. It composes with a diversification
technique that prevents the selection of the same variable within a fixed
horizon further improving performances and reduces disparities between various
variable choice heuristics. The result is a robust methodology that can tailor
the search strategy according to the sought properties of the counter example.
","Bringing freedom in variable choice when searching counter-examples in
  floating point programs"
"  This paper considers the problem of learning a model in model-based
reinforcement learning (MBRL). We examine how the planning module of an MBRL
algorithm uses the model, and propose that the model learning module should
incorporate the way the planner is going to use the model. This is in contrast
to conventional model learning approaches, such as those based on maximum
likelihood estimate, that learn a predictive model of the environment without
explicitly considering the interaction of the model and the planner. We focus
on policy gradient type of planning algorithms and derive new loss functions
for model learning that incorporate how the planner uses the model. We call
this approach Policy-Aware Model Learning (PAML). We theoretically analyze a
generic model-based policy gradient algorithm and provide a convergence
guarantee for the optimized policy. We also empirically evaluate PAML on some
benchmark problems, showing promising results.
",Policy-Aware Model Learning for Policy Gradient Methods
"  Weighted model integration (WMI) is a very appealing framework for
probabilistic inference: it allows to express the complex dependencies of
real-world problems where variables are both continuous and discrete, via the
language of Satisfiability Modulo Theories (SMT), as well as to compute
probabilistic queries with complex logical and arithmetic constraints. Yet,
existing WMI solvers are not ready to scale to these problems. They either
ignore the intrinsic dependency structure of the problem at all, or they are
limited to too restrictive structures. To narrow this gap, we derive a
factorized formalism of WMI enabling us to devise a scalable WMI solver based
on message passing, MP-WMI. Namely, MP-WMI is the first WMI solver which allows
to: 1) perform exact inference on the full class of tree-structured WMI
problems; 2) compute all marginal densities in linear time; 3) amortize
inference inter query. Experimental results show that our solver dramatically
outperforms the existing WMI solvers on a large set of benchmarks.
","Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic
  Constraints via Message Passing"
"  Knowledge Graphs (KGs) are graph-structured knowledge bases storing factual
information about real-world entities. Understanding the uniqueness of each
entity is crucial to the analyzing, sharing, and reusing of KGs. Traditional
profiling technologies encompass a vast array of methods to find distinctive
features in various applications, which can help to differentiate entities in
the process of human understanding of KGs. In this work, we present a novel
profiling approach to identify distinctive entity features. The distinctiveness
of features is carefully measured by a HAS model, which is a scalable
representation learning model to produce a multi-pattern entity embedding. We
fully evaluate the quality of entity profiles generated from real KGs. The
results show that our approach facilitates human understanding of entities in
KGs.
",Entity Profiling in Knowledge Graphs
"  Morphological analyzers are the essential milestones for many linguistic
applications like; machine translation, word sense disambiguation, spells
checkers, and search engines etc. Therefore, development of an effective
morphological analyzer has a greater impact on the computational recognition of
a language. In this paper, we present a finite state transducer based
inflectional morphological analyzer for a resource poor language of India,
known as Maithili. Maithili is an eastern Indo-Aryan language spoken in the
eastern and northern regions of Bihar in India and the southeastern plains,
known as tarai of Nepal. This work can be recognized as the first work towards
the computational development of Maithili which may attract researchers around
the country to up-rise the language to establish in computational world.
","A Finite State Transducer Based Morphological Analyzer of Maithili
  Language"
"  Recent drought and population growth are planting unprecedented demand for
the use of available limited water resources. Irrigated agriculture is one of
the major consumers of freshwater. A large amount of water in irrigated
agriculture is wasted due to poor water management practices. To improve water
management in irrigated areas, models for estimation of future water
requirements are needed. Developing a model for forecasting irrigation water
demand can improve water management practices and maximise water productivity.
Data mining can be used effectively to build such models.
  In this study, we prepare a dataset containing information on suitable
attributes for forecasting irrigation water demand. The data is obtained from
three different sources namely meteorological data, remote sensing images and
water delivery statements. In order to make the prepared dataset useful for
demand forecasting and pattern extraction, we pre-process the dataset using a
novel approach based on a combination of irrigation and data mining knowledge.
We then apply and compare the effectiveness of different data mining methods
namely decision tree (DT), artificial neural networks (ANNs), systematically
developed forest (SysFor) for multiple trees, support vector machine (SVM),
logistic regression, and the traditional Evapotranspiration (ETc) methods and
evaluate the performance of these models to predict irrigation water demand.
Our experimental results indicate the usefulness of data pre-processing and the
effectiveness of different classifiers. Among the six methods we used, SysFor
produces the best prediction with 97.5% accuracy followed by a decision tree
with 96% and ANN with 95% respectively by closely matching the predictions with
actual water usage. Therefore, we recommend using SysFor and DT models for
irrigation water demand forecasting.
","Data Pre-Processing and Evaluating the Performance of Several Data
  Mining Methods for Predicting Irrigation Water Requirement"
"  Explainability and interpretability of AI models is an essential factor
affecting the safety of AI. While various explainable AI (XAI) approaches aim
at mitigating the lack of transparency in deep networks, the evidence of the
effectiveness of these approaches in improving usability, trust, and
understanding of AI systems are still missing. We evaluate multimodal
explanations in the setting of a Visual Question Answering (VQA) task, by
asking users to predict the response accuracy of a VQA agent with and without
explanations. We use between-subjects and within-subjects experiments to probe
explanation effectiveness in terms of improving user prediction accuracy,
confidence, and reliance, among other factors. The results indicate that the
explanations help improve human prediction accuracy, especially in trials when
the VQA system's answer is inaccurate. Furthermore, we introduce active
attention, a novel method for evaluating causal attentional effects through
intervention by editing attention maps. User explanation ratings are strongly
correlated with human prediction accuracy and suggest the efficacy of these
explanations in human-machine AI collaboration tasks.
","A Study on Multimodal and Interactive Explanations for Visual Question
  Answering"
"  Differential Evolution (DE) is quite powerful for real parameter single
objective optimization. However, the ability of extending or changing search
area when falling into a local optimum is still required to be developed in DE
for accommodating extremely complicated fitness landscapes with a huge number
of local optima. We propose a new flow of DE, termed DE with individuals
redistribution, in which a process of individuals redistribution will be called
when progress on fitness is low for generations. In such a process, mutation
and crossover are standardized, while trial vectors are all kept in selection.
Once diversity exceeds a predetermined threshold, our opposition replacement is
executed, then algorithm behavior returns to original mode. In our experiments
based on two benchmark test suites, we apply individuals redistribution in ten
DE algorithms. Versions of the ten DE algorithms based on individuals
redistribution are compared with not only original version but also version
based on complete restart, where individuals redistribution and complete
restart are based on the same entry criterion. Experimental results indicate
that, for most of the DE algorithms, version based on individuals
redistribution performs better than both original version and version based on
complete restart.
","Differential Evolution with Individuals Redistribution for Real
  Parameter Single Objective Optimization"
"  In the big data era, data labeling can be obtained through crowdsourcing.
Nevertheless, the obtained labels are generally noisy, unreliable or even
adversarial. In this paper, we propose a probabilistic graphical annotation
model to infer the underlying ground truth and annotator's behavior. To
accommodate both discrete and continuous application scenarios (e.g.,
classifying scenes vs. rating videos on a Likert scale), the underlying ground
truth is considered following a distribution rather than a single value. In
this way, the reliable but potentially divergent opinions from ""good""
annotators can be recovered. The proposed model is able to identify whether an
annotator has worked diligently towards the task during the labeling procedure,
which could be used for further selection of qualified annotators. Our model
has been tested on both simulated data and real-world data, where it always
shows superior performance than the other state-of-the-art models in terms of
accuracy and robustness.
","GPM: A Generic Probabilistic Model to Recover Annotator's Behavior and
  Ground Truth Labeling"
"  Graph convolutional networks (GCNs) update a node's feature vector by
aggregating features from its neighbors in the graph. This ignores potentially
useful contributions from distant nodes. Identifying such useful distant
contributions is challenging due to scalability issues (too many nodes can
potentially contribute) and oversmoothing (aggregating features from too many
nodes risks swamping out relevant information and may result in nodes having
different labels but indistinguishable features). We introduce a global
attention mechanism where a node can selectively attend to, and aggregate
features from, any other node in the graph. The attention coefficients depend
on the Euclidean distance between learnable node embeddings, and we show that
the resulting attention-based global aggregation scheme is analogous to
high-dimensional Gaussian filtering. This makes it possible to use efficient
approximate Gaussian filtering techniques to implement our attention-based
global aggregation scheme. By employing an approximate filtering method based
on the permutohedral lattice, the time complexity of our proposed global
aggregation scheme only grows linearly with the number of nodes. The resulting
GCNs, which we term permutohedral-GCNs, are differentiable and trained
end-to-end, and they achieve state of the art performance on several node
classification benchmarks.
",Permutohedral-GCN: Graph Convolutional Networks with Global Attention
"  Unbiased data collection is essential to guaranteeing fairness in artificial
intelligence models. Implicit bias, a form of behavioral conditioning that
leads us to attribute predetermined characteristics to members of certain
groups and informs the data collection process. This paper quantifies implicit
bias in viewer ratings of TEDTalks, a diverse social platform assessing social
and professional performance, in order to present the correlations of different
kinds of bias across sensitive attributes. Although the viewer ratings of these
videos should purely reflect the speaker's competence and skill, our analysis
of the ratings demonstrates the presence of overwhelming and predominant
implicit bias with respect to race and gender. In our paper, we present
strategies to detect and mitigate bias that are critical to removing unfairness
in AI.
",Detection and Mitigation of Bias in Ted Talk Ratings
"  Explainability in AI is gaining attention in the computer science community
in response to the increasing success of deep learning and the important need
of justifying how such systems make predictions in life-critical applications.
The focus of explainability in AI has predominantly been on trying to gain
insights into how machine learning systems function by exploring relationships
between input data and predicted outcomes or by extracting simpler
interpretable models. Through literature surveys of philosophy and social
science, authors have highlighted the sharp difference between these generated
explanations and human-made explanations and claimed that current explanations
in AI do not take into account the complexity of human interaction to allow for
effective information passing to not-expert users. In this paper we instantiate
the concept of structure of scientific explanation as the theoretical
underpinning for a general framework in which explanations for AI systems can
be implemented. This framework aims to provide the tools to build a
""mental-model"" of any AI system so that the interaction with the user can
provide information on demand and be closer to the nature of human-made
explanations. We illustrate how we can utilize this framework through two very
different examples: an artificial neural network and a Prolog solver and we
provide a possible implementation for both examples.
",A general framework for scientifically inspired explanations in AI
"  Learning from demonstrations (LfD) is an efficient paradigm to train AI
agents. But major issues arise when there are differences between (a) the
demonstrator's own sensory input, (b) our sensors that observe the demonstrator
and (c) the sensory input of the agent we train. In this paper, we propose a
causal model-based framework for transfer learning under such ""sensor-shifts"",
for two common LfD tasks: (1) inferring the effect of the demonstrator's
actions and (2) imitation learning. First we rigorously analyze, on the
population-level, to what extent the relevant underlying mechanisms (the action
effects and the demonstrator policy) can be identified and transferred from the
available observations together with prior knowledge of sensor characteristics.
And we device an algorithm to infer these mechanisms. Then we introduce several
proxy methods which are easier to calculate, estimate from finite data and
interpret than the exact solutions, alongside theoretical bounds on their
closeness to the exact ones. We validate our two main methods on simulated and
semi-real world data.
","Causal Transfer for Imitation Learning and Decision Making under
  Sensor-shift"
"  Text generation system has made massive promising progress contributed by
deep learning techniques and has been widely applied in our life. However,
existing end-to-end neural models suffer from the problem of tending to
generate uninformative and generic text because they cannot ground input
context with background knowledge. In order to solve this problem, many
researchers begin to consider combining external knowledge in text generation
systems, namely knowledge-enhanced text generation. The challenges of knowledge
enhanced text generation including how to select the appropriate knowledge from
large-scale knowledge bases, how to read and understand extracted knowledge,
and how to integrate knowledge into generation process. This survey gives a
comprehensive review of knowledge-enhanced text generation systems, summarizes
research progress to solving these challenges and proposes some open issues and
research directions.
","Towards information-rich, logical text generation with
  knowledge-enhanced neural models"
"  Regular Decision Processes (RDPs) are a recently introduced model that
extends MDPs with non-Markovian dynamics and rewards. The non-Markovian
behavior is restricted to depend on regular properties of the history. These
can be specified using regular expressions or formulas in linear dynamic logic
over finite traces. Fully specified RDPs can be solved by compiling them into
an appropriate MDP. Learning RDPs from data is a challenging problem that has
yet to be addressed, on which we focus in this paper. Our approach rests on a
new representation for RDPs using Mealy Machines that emit a distribution and
an expected reward for each state-action pair. Building on this representation,
we combine automata learning techniques with history clustering to learn such a
Mealy machine and solve it by adapting MCTS to it. We empirically evaluate this
approach, demonstrating its feasibility.
",Learning and Solving Regular Decision Processes
"  In many complex, real-world situations, problem solving and decision making
require effective reasoning about causation and uncertainty. However, human
reasoning in these cases is prone to confusion and error. Bayesian networks
(BNs) are an artificial intelligence technology that models uncertain
situations, supporting probabilistic and causal reasoning and decision making.
However, to date, BN methodologies and software require significant upfront
training, do not provide much guidance on the model building process, and do
not support collaboratively building BNs. BARD (Bayesian ARgumentation via
Delphi) is both a methodology and an expert system that utilises (1) BNs as the
underlying structured representations for better argument analysis, (2) a
multi-user web-based software platform and Delphi-style social processes to
assist with collaboration, and (3) short, high-quality e-courses on demand, a
highly structured process to guide BN construction, and a variety of helpful
tools to assist in building and reasoning with BNs, including an automated
explanation tool to assist effective report writing. The result is an
end-to-end online platform, with associated online training, for groups without
prior BN expertise to understand and analyse a problem, build a model of its
underlying probabilistic causal structure, validate and reason with the causal
model, and use it to produce a written analytic report. Initial experimental
results demonstrate that BARD aids in problem solving, reasoning and
collaboration.
","BARD: A structured technique for group elicitation of Bayesian networks
  to support analytic reasoning"
"  We consider the problem of zero-shot coordination - constructing AI agents
that can coordinate with novel partners they have not seen before (e.g.
humans). Standard Multi-Agent Reinforcement Learning (MARL) methods typically
focus on the self-play (SP) setting where agents construct strategies by
playing the game with themselves repeatedly. Unfortunately, applying SP naively
to the zero-shot coordination problem can produce agents that establish highly
specialized conventions that do not carry over to novel partners they have not
been trained with. We introduce a novel learning algorithm called other-play
(OP), that enhances self-play by looking for more robust strategies, exploiting
the presence of known symmetries in the underlying problem. We characterize OP
theoretically as well as experimentally. We study the cooperative card game
Hanabi and show that OP agents achieve higher scores when paired with
independently trained agents. In preliminary results we also show that our OP
agents obtains higher average scores when paired with human players, compared
to state-of-the-art SP agents.
","""Other-Play"" for Zero-Shot Coordination"
"  We describe nearly fifteen years of General Game Playing experimental
research history in the context of reproducibility and fairness of comparisons
between various GGP agents and systems designed to play games described by
different formalisms. We think our survey may provide an interesting
perspective of how chaotic methods were allowed when nothing better was
possible. Finally, from our experience-based view, we would like to propose a
few recommendations of how such specific heterogeneous branch of research
should be handled appropriately in the future. The goal of this note is to
point out common difficulties and problems in the experimental research in the
area. We hope that our recommendations will help in avoiding them in future
works and allow more fair and reproducible comparisons.
",Experimental Studies in General Game Playing: An Experience Report
"  A belief base revision is developed. The belief base is represented using
Unified Answer Set Programs which is capable of representing imprecise and
uncertain information and perform nonomonotonic reasoning with them. The base
revision operator is developed using Removed Set Revision strategy. The
operator is characterized with respect to the postulates for base revisions
operator satisfies.
","Belief Base Revision for Further Improvement of Unified Answer Set
  Programming"
"  This work investigates Monte-Carlo planning for agents in stochastic
environments, with multiple objectives. We propose the Convex Hull Monte-Carlo
Tree-Search (CHMCTS) framework, which builds upon Trial Based Heuristic Tree
Search and Convex Hull Value Iteration (CHVI), as a solution to multi-objective
planning in large environments. Moreover, we consider how to pose the problem
of approximating multiobjective planning solutions as a contextual multi-armed
bandits problem, giving a principled motivation for how to select actions from
the view of contextual regret. This leads us to the use of Contextual Zooming
for action selection, yielding Zooming CHMCTS. We evaluate our algorithm using
the Generalised Deep Sea Treasure environment, demonstrating that Zooming
CHMCTS can achieve a sublinear contextual regret and scales better than CHVI on
a given computational budget.
",Convex Hull Monte-Carlo Tree Search
"  This work focuses on a comparison between the performances of two well-known
Swarm algorithms: Cuckoo Search (CS) and Firefly Algorithm (FA), in estimating
the parameters of Software Reliability Growth Models. This study is further
reinforced using Particle Swarm Optimization (PSO) and Ant Colony Optimization
(ACO). All algorithms are evaluated according to real software failure data,
the tests are performed and the obtained results are compared to show the
performance of each of the used algorithms. Furthermore, CS and FA are also
compared with each other on bases of execution time and iteration number.
Experimental results show that CS is more efficient in estimating the
parameters of SRGMs, and it has outperformed FA in addition to PSO and ACO for
the selected Data sets and employed models.
","A Comparative Study on Parameter Estimation in Software Reliability
  Modeling using Swarm Intelligence"
"  A successful intelligent control of patient food for treatment purpose must
combines patient interesting food list and doctors efficient treatment food
list. Actually, many rural communities in Sudan have extremely limited access
to diabetic diet centers. People travel long distances to clinics or medical
facilities, and there is a shortage of medical experts in most of these
facilities. This results in slow service, and patients end up waiting long
hours without receiving any attention. Hence diabetic diet expert systems can
play a significant role in such cases where medical experts are not readily
available. This paper presents the design and implementation of an intelligent
medical expert system for diabetes diet that intended to be used in Sudan. The
development of the proposed expert system went through a number of stages such
problem and need identification, requirements analysis, knowledge acquisition,
formalization, design and implementation. Visual prolog was used for designing
the graphical user interface and the implementation of the system. The proposed
expert system is a promising helpful tool that reduces the workload for
physicians and provides diabetics with simple and valuable assistance.
",Development of an Expert System for Diabetic Type-2 Diet
"  Understanding the fundamentals of human reasoning is central to the
development of any system built to closely interact with humans. Cognitive
science pursues the goal of modeling human-like intelligence from a
theory-driven perspective with a strong focus on explainability. Syllogistic
reasoning as one of the core domains of human reasoning research has seen a
surge of computational models being developed over the last years. However,
recent analyses of models' predictive performances revealed a stagnation in
improvement. We believe that most of the problems encountered in cognitive
science are not due to the specific models that have been developed but can be
traced back to the peculiarities of behavioral data instead.
  Therefore, we investigate potential data-related reasons for the problems in
human reasoning research by comparing model performances on human and
artificially generated datasets. In particular, we apply collaborative
filtering recommenders to investigate the adversarial effects of
inconsistencies and noise in data and illustrate the potential for data-driven
methods in a field of research predominantly concerned with gaining high-level
theoretical insight into a domain.
  Our work (i) provides insight into the levels of noise to be expected from
human responses in reasoning data, (ii) uncovers evidence for an upper-bound of
performance that is close to being reached urging for an extension of the
modeling task, and (iii) introduces the tools and presents initial results to
pioneer a new paradigm for investigating and modeling reasoning focusing on
predicting responses for individual human reasoners.
","Uncovering the Data-Related Limits of Human Reasoning Research: An
  Analysis based on Recommender Systems"
"  One of the most fundamental questions in Biology or Artificial Intelligence
is how the human brain performs mathematical functions. How does a neural
architecture that may organise itself mostly through statistics, know what to
do? One possibility is to extract the problem to something more abstract. This
becomes clear when thinking about how the brain handles large numbers, for
example to the power of something, when simply summing to an answer is not
feasible. In this paper, the author suggests that the maths question can be
answered more easily if the problem is changed into one of symbol manipulation
and not just number counting. If symbols can be compared and manipulated, maybe
without understanding completely what they are, then the mathematical
operations become relative and some of them might even be rote learned. The
proposed system may also be suggested as an alternative to the traditional
computer binary system. Any of the actual maths still breaks down into binary
operations, while a more symbolic level above that can manipulate the numbers
and reduce the problem size, thus making the binary operations simpler. An
interesting result of looking at this is the possibility of a new fractal
equation resulting from division, that can be used as a measure of good fit and
would help the brain decide how to solve something through self-replacement and
a comparison with this good fit.
",How the Brain might use Division
"  Large ontologies still pose serious challenges to state-of-the-art ontology
alignment systems. In this paper we present an approach that combines a neural
embedding model and logic-based modules to accurately divide an input ontology
matching task into smaller and more tractable matching (sub)tasks. We have
conducted a comprehensive evaluation using the datasets of the Ontology
Alignment Evaluation Initiative. The results are encouraging and suggest that
the proposed method is adequate in practice and can be integrated within the
workflow of systems unable to cope with very large ontologies.
","Dividing the Ontology Alignment Task with Semantic Embeddings and
  Logic-based Modules"
"  To achieve social interactions within Human-Robot Interaction (HRI)
environments is a very challenging task. Most of the current research focuses
on Wizard-of-Oz approaches, which neglect the recent development of intelligent
robots. On the other hand, real-world scenarios usually do not provide the
necessary control and reproducibility which are needed for learning algorithms.
In this paper, we propose a virtual simulation environment that implements the
Chef's Hat card game, designed to be used in HRI scenarios, to provide a
controllable and reproducible scenario for reinforcement-learning algorithms.
","The Chef's Hat Simulation Environment for Reinforcement-Learning-Based
  Agents"
"  Smart home environments equipped with distributed sensor networks are capable
of helping people by providing services related to health, emergency detection
or daily routine management. A backbone to these systems relies often on the
system's ability to track and detect activities performed by the users in their
home. Despite the continuous progress in the area of activity recognition in
smart homes, many systems make a strong underlying assumption that the number
of occupants in the home at any given moment of time is always known.
Estimating the number of persons in a Smart Home at each time step remains a
challenge nowadays. Indeed, unlike most (crowd) counting solution which are
based on computer vision techniques, the sensors considered in a Smart Home are
often very simple and do not offer individually a good overview of the
situation. The data gathered needs therefore to be fused in order to infer
useful information. This paper aims at addressing this challenge and presents a
probabilistic approach able to estimate the number of persons in the
environment at each time step. This approach works in two steps: first, an
estimate of the number of persons present in the environment is done using a
Constraint Satisfaction Problem solver, based on the topology of the sensor
network and the sensor activation pattern at this time point. Then, a Hidden
Markov Model refines this estimate by considering the uncertainty related to
the sensors. Using both simulated and real data, our method has been tested and
validated on two smart homes of different sizes and configuration and
demonstrates the ability to accurately estimate the number of inhabitants.
","Online Guest Detection in a Smart Home using Pervasive Sensors and
  Probabilistic Reasoning"
"  Increasingly growing Cryptocurrency markets have become a hive for scammers
to run pump and dump schemes which is considered as an anomalous activity in
exchange markets. Anomaly detection in time series is challenging since
existing methods are not sufficient to detect the anomalies in all contexts. In
this paper, we propose a novel hybrid pump and dump detection method based on
distance and density metrics. First, we propose a novel automatic thresh-old
setting method for distance-based anomaly detection. Second, we propose a novel
metric called density score for density-based anomaly detection. Finally, we
exploit the combination of density and distance metrics successfully as a
hybrid approach. Our experiments show that, the proposed hybrid approach is
reliable to detect the majority of alleged P & D activities in top ranked
exchange pairs by outperforming both density-based and distance-based methods.
",Hybrid Cryptocurrency Pump and Dump Detection
"  Learning constraint networks is known to require a number of membership
queries exponential in the number of variables. In this paper, we learn
constraint networks by asking the user partial queries. That is, we ask the
user to classify assignments to subsets of the variables as positive or
negative. We provide an algorithm, called QUACQ, that, given a negative
example, focuses onto a constraint of the target network in a number of queries
logarithmic in the size of the example. The whole constraint network can then
be learned with a polynomial number of partial queries. We give information
theoretic lower bounds for learning some simple classes of constraint networks
and show that our generic algorithm is optimal in some cases.
",Partial Queries for Constraint Acquisition
"  In this paper, we study the effects of several Monte Carlo Tree Search (MCTS)
modifications for video game testing. Although MCTS modifications are highly
studied in game playing, their impacts on finding bugs are blank. We focused on
bug finding in our previous study where we introduced synthetic and human-like
test goals and we used these test goals in Sarsa and MCTS agents to find bugs.
In this study, we extend the MCTS agent with several modifications for game
testing purposes. Furthermore, we present a novel tree reuse strategy. We
experiment with these modifications by testing them on three testbed games,
four levels each, that contain 45 bugs in total. We use the General Video Game
Artificial Intelligence (GVG-AI) framework to create the testbed games and
collect 427 human tester trajectories using the GVG-AI framework. We analyze
the proposed modifications in three parts: we evaluate their effects on bug
finding performances of agents, we measure their success under two different
computational budgets, and we assess their effects on human-likeness of the
human-like agent. Our results show that MCTS modifications improve the bug
finding performance of the agents.
",Enhancing the Monte Carlo Tree Search Algorithm for Video Game Testing
"  Neuro-symbolic and statistical relational artificial intelligence both
integrate frameworks for learning with logical reasoning. This survey
identifies several parallels across seven different dimensions between these
two fields. These cannot only be used to characterize and position
neuro-symbolic artificial intelligence approaches but also to identify a number
of directions for further research.
",From Statistical Relational to Neuro-Symbolic Artificial Intelligence
"  Placement Optimization is an important problem in systems and chip design,
which consists of mapping the nodes of a graph onto a limited set of resources
to optimize for an objective, subject to constraints. In this paper, we start
by motivating reinforcement learning as a solution to the placement problem. We
then give an overview of what deep reinforcement learning is. We next formulate
the placement problem as a reinforcement learning problem and show how this
problem can be solved with policy gradient optimization. Finally, we describe
lessons we have learned from training deep reinforcement learning policies
across a variety of placement optimization problems.
",Placement Optimization with Deep Reinforcement Learning
"  We present a solution to real-world train scheduling problems, involving
routing, scheduling, and optimization, based on Answer Set Programming (ASP).
To this end, we pursue a hybrid approach that extends ASP with difference
constraints to account for a fine-grained timing. More precisely, we
exemplarily show how the hybrid ASP system clingo[DL] can be used to tackle
demanding planning-and-scheduling problems. In particular, we investigate how
to boost performance by combining distinct ASP solving techniques, such as
approximations and heuristics, with preprocessing and encoding techniques for
tackling large-scale, real-world train scheduling instances. Under
consideration in Theory and Practice of Logic Programming (TPLP)
",Train Scheduling with Hybrid Answer Set Programming
"  Decentralized online planning can be an attractive paradigm for cooperative
multi-agent systems, due to improved scalability and robustness. A key
difficulty of such approach lies in making accurate predictions about the
decisions of other agents. In this paper, we present a trainable online
decentralized planning algorithm based on decentralized Monte Carlo Tree
Search, combined with models of teammates learned from previous episodic runs.
By only allowing one agent to adapt its models at a time, under the assumption
of ideal policy approximation, successive iterations of our method are
guaranteed to improve joint policies, and eventually lead to convergence to a
Nash equilibrium. We test the efficiency of the algorithm by performing
experiments in several scenarios of the spatial task allocation environment
introduced in [Claes et al., 2015]. We show that deep learning and
convolutional neural networks can be employed to produce accurate policy
approximators which exploit the spatial features of the problem, and that the
proposed algorithm improves over the baseline planning performance for
particularly challenging domain configurations.
",Decentralized MCTS via Learned Teammate Models
"  Being able to infer the goal of people we observe, interact with, or read
stories about is one of the hallmarks of human intelligence. A prominent idea
in current goal-recognition research is to infer the likelihood of an agent's
goal from the estimations of the costs of plans to the different goals the
agent might have. Different approaches implement this idea by relying only on
handcrafted symbolic representations. Their application to real-world settings
is, however, quite limited, mainly because extracting rules for the factors
that influence goal-oriented behaviors remains a complicated task. In this
paper, we introduce a novel idea of using a symbolic planner to compute
plan-cost insights, which augment a deep neural network with an imagination
capability, leading to improved goal recognition accuracy in real and synthetic
domains compared to a symbolic recognizer or a deep-learning goal recognizer
alone.
",Imagination-Augmented Deep Learning for Goal Recognition
"  Reinforcement learning is one of the most popular approaches for automated
game playing. This method allows an agent to estimate the expected utility of
its state in order to make optimal actions in an unknown environment. We seek
to apply reinforcement learning algorithms to the game Flappy Bird. We
implement SARSA and Q-Learning with some modifications such as
$\epsilon$-greedy policy, discretization and backward updates. We find that
SARSA and Q-Learning outperform the baseline, regularly achieving scores of
1400+, with the highest in-game score of 2069.
","FlapAI Bird: Training an Agent to Play Flappy Bird Using Reinforcement
  Learning Techniques"
"  As a generalization of Dempster-Shafer theory, D number theory (DNT) aims to
provide a framework to deal with uncertain information with non-exclusiveness
and incompleteness. Although there are some advances on DNT in previous
studies, however, they lack of systematicness, and many important issues have
not yet been solved. In this paper, several crucial aspects in constructing a
perfect and systematic framework of DNT are considered. At first the
non-exclusiveness in DNT is formally defined and discussed. Secondly, a method
to combine multiple D numbers is proposed by extending previous exclusive
conflict redistribution (ECR) rule. Thirdly, a new pair of belief and
plausibility measures for D numbers are defined and many desirable properties
are satisfied by the proposed measures. Fourthly, the combination of
information-incomplete D numbers is studied specially to show how to deal with
the incompleteness of information in DNT. In this paper, we mainly give
relative math definitions, properties, and theorems, concrete examples and
applications will be considered in the future study.
","Basic concepts, definitions, and methods in D number theory"
"  Reasoning over OWL 2 is a very expensive task in general, and therefore the
W3C identified tractable profiles exhibiting good computational properties.
Ontological reasoning for many fragments of OWL 2 can be reduced to the
evaluation of Datalog queries. This paper surveys some of these compilations,
and in particular the one addressing queries over Horn-$\mathcal{SHIQ}$
knowledge bases and its implementation in DLV2 enanched by a new version of the
Magic Sets algorithm.
",Large-scale Ontological Reasoning via Datalog
"  Adaptive Informative Path Planning (AIPP) problems model an agent tasked with
obtaining information subject to resource constraints in unknown, partially
observable environments. Existing work on AIPP has focused on representing
observations about the world as a result of agent movement. We formulate the
more general setting where the agent may choose between different sensors at
the cost of some energy, in addition to traversing the environment to gather
information. We call this problem AIPPMS (MS for Multimodal Sensing). AIPPMS
requires reasoning jointly about the effects of sensing and movement in terms
of both energy expended and information gained. We frame AIPPMS as a Partially
Observable Markov Decision Process (POMDP) and solve it with online planning.
Our approach is based on the Partially Observable Monte Carlo Planning
framework with modifications to ensure constraint feasibility and a heuristic
rollout policy tailored for AIPPMS. We evaluate our method on two domains: a
simulated search-and-rescue scenario and a challenging extension to the classic
RockSample problem. We find that our approach outperforms a classic AIPP
algorithm that is modified for AIPPMS, as well as online planning using a
random rollout policy.
",Adaptive Informative Path Planning with Multimodal Sensing
"  Nested Rollout Policy Adaptation (NRPA) is a Monte Carlo search algorithm for
single player games. In this paper we propose to generalize NRPA with a
temperature and a bias and to analyze theoretically the algorithms. The
generalized algorithm is named GNRPA. Experiments show it improves on NRPA for
different application domains: SameGame and the Traveling Salesman Problem with
Time Windows.
",Generalized Nested Rollout Policy Adaptation
"  The N-Tuple Bandit Evolutionary Algorithm (NTBEA) has proven very effective
in optimising algorithm parameters in Game AI. A potential weakness is the use
of a simple average of all component Tuples in the model. This study
investigates a refinement to the N-Tuple model used in NTBEA by weighting these
component Tuples by their level of information and specificity of match. We
introduce weighting functions to the model to obtain Weighted- NTBEA and test
this on four benchmark functions and two game environments. These tests show
that vanilla NTBEA is the most reliable and performant of the algorithms
tested. Furthermore we show that given an iteration budget it is better to
execute several independent NTBEA runs, and use part of the budget to find the
best recommendation from these runs.
",Weighting NTBEA for Game AI Optimisation
"  Access to a fast and easily copied forward model of a game is essential for
model-based reinforcement learning and for algorithms such as Monte Carlo tree
search, and is also beneficial as a source of unlimited experience data for
model-free algorithms. Learning forward models is an interesting and important
challenge in order to address problems where a model is not available. Building
upon previous work on the Neural GPU, this paper introduces the Neural Game
Engine, as a way to learn models directly from pixels. The learned models are
able to generalise to different size game levels to the ones they were trained
on without loss of accuracy. Results on 10 deterministic General Video Game AI
games demonstrate competitive performance, with many of the games models being
learned perfectly both in terms of pixel predictions and reward predictions.
The pre-trained models are available through the OpenAI Gym interface and are
available publicly for future research here:
\url{https://github.com/Bam4d/Neural-Game-Engine}
","Neural Game Engine: Accurate learning of generalizable forward models
  from pixels"
"  The European Materials and Modelling Ontology (EMMO) has recently been
advanced in the computational molecular engineering and multiscale modelling
communities as a top-level ontology, aiming to support semantic
interoperability and data integration solutions, e.g., for research data
infrastructures. The present work explores how top-level ontologies that are
based on the same paradigm - the same set of fundamental postulates - as the
EMMO can be applied to models of physical systems and their use in
computational engineering practice. This paradigm, which combines mereology (in
its extension as mereotopology) and semiotics (following Peirce's approach), is
here referred to as mereosemiotics. Multiple conceivable ways of implementing
mereosemiotics are compared, and the design space consisting of the possible
types of top-level ontologies following this paradigm is characterized.
","Semantic interoperability based on the European Materials and Modelling
  Ontology and its ontological paradigm: Mereosemiotics"
"  Interventions are of fundamental importance in Pearl's probabilistic
causality regime. In this paper, we will inspect how interventions influence
the interpretation of causation in causal models in specific situation. To this
end, we will introduce a priori relationships as non-causal relationships in a
causal system. Then, we will proceed to discuss the cases that interventions
can lead to spurious causation interpretations. This includes the
interventional detection of a priori relationships, and cases where the
interventional detection of causality forms structural causal models that are
not valid in natural situations. We will also discuss other properties of a
priori relations and SCMs that have a priori information in their structural
equations.
",A Critique on the Interventional Detection of Causal Relationships
"  Medical Triage is of paramount importance to healthcare systems, allowing for
the correct orientation of patients and allocation of the necessary resources
to treat them adequately. While reliable decision-tree methods exist to triage
patients based on their presentation, those trees implicitly require human
inference and are not immediately applicable in a fully automated setting. On
the other hand, learning triage policies directly from experts may correct for
some of the limitations of hard-coded decision-trees. In this work, we present
a Deep Reinforcement Learning approach (a variant of DeepQ-Learning) to triage
patients using curated clinical vignettes. The dataset, consisting of 1374
clinical vignettes, was created by medical doctors to represent real-life
cases. Each vignette is associated with an average of 3.8 expert triage
decisions given by medical doctors relying solely on medical history. We show
that this approach is on a par with human performance, yielding safe triage
decisions in 94% of cases, and matching expert decisions in 85% of cases. The
trained agent learns when to stop asking questions, acquires optimized decision
policies requiring less evidence than supervised approaches, and adapts to the
novelty of a situation by asking for more information. Overall, we demonstrate
that a Deep Reinforcement Learning approach can learn effective medical triage
policies directly from expert decisions, without requiring expert knowledge
engineering. This approach is scalable and can be deployed in healthcare
settings or geographical regions with distinct triage specifications, or where
trained experts are scarce, to improve decision making in the early stage of
care.
",Learning medical triage from clinicians using Deep Q-Learning
"  Commonsense reasoning has long been considered as one of the holy grails of
artificial intelligence. Most of the recent progress in the field has been
achieved by novel machine learning algorithms for natural language processing.
However, without incorporating logical reasoning, these algorithms remain
arguably shallow. With some notable exceptions, developers of practical
automated logic-based reasoners have mostly avoided focusing on the problem.
The paper argues that the methods and algorithms used by existing automated
reasoners for classical first-order logic can be extended towards commonsense
reasoning. Instead of devising new specialized logics we propose a framework of
extensions to the mainstream resolution-based search methods to make these
capable of performing search tasks for practical commonsense reasoning with
reasonable efficiency. The proposed extensions mostly rely on operating on
ordinary proof trees and are devised to handle commonsense knowledge bases
containing inconsistencies, default rules, taxonomies, topics, relevance,
confidence and similarity measures. We claim that machine learning is best
suited for the construction of commonsense knowledge bases while the extended
logic-based methods would be well-suited for actually answering queries from
these knowledge bases.
",Extending Automated Deduction for Commonsense Reasoning
"  This paper presents an original method of fuzzy approximate reasoning that
can open a new direction of research in the uncertainty inference of Artificial
Intelligence(AI) and Computational Intelligence(CI). Fuzzy modus ponens (FMP)
and fuzzy modus tollens(FMT) are two fundamental and basic models of general
fuzzy approximate reasoning in various fuzzy systems. And the reductive
property is one of the essential and important properties in the approximate
reasoning theory and it is a lot of applications. This paper suggests a kind of
extended distance measure (EDM) based approximate reasoning method in the
single input single output(SISO) fuzzy system with discrete fuzzy set vectors
of different dimensions. The EDM based fuzzy approximate reasoning method is
consists of two part, i.e., FMP-EDM and FMT-EDM. The distance measure based
fuzzy reasoning method that the dimension of the antecedent discrete fuzzy set
is equal to one of the consequent discrete fuzzy set has already solved in
other paper. In this paper discrete fuzzy set vectors of different dimensions
mean that the dimension of the antecedent discrete fuzzy set differs from one
of the consequent discrete fuzzy set in the SISO fuzzy system. That is, this
paper is based on EDM. The experimental results highlight that the proposed
approximate reasoning method is comparatively clear and effective with respect
to the reductive property, and in accordance with human thinking than existing
fuzzy reasoning methods.
","A Novel Fuzzy Approximate Reasoning Method Based on Extended Distance
  Measure in SISO Fuzzy System"
"  Artificial Intelligence (AI) has achieved great success in many domains, and
game AI is widely regarded as its beachhead since the dawn of AI. In recent
years, studies on game AI have gradually evolved from relatively simple
environments (e.g., perfect-information games such as Go, chess, shogi or
two-player imperfect-information games such as heads-up Texas hold'em) to more
complex ones (e.g., multi-player imperfect-information games such as
multi-player Texas hold'em and StartCraft II). Mahjong is a popular
multi-player imperfect-information game worldwide but very challenging for AI
research due to its complex playing/scoring rules and rich hidden information.
We design an AI for Mahjong, named Suphx, based on deep reinforcement learning
with some newly introduced techniques including global reward prediction,
oracle guiding, and run-time policy adaptation. Suphx has demonstrated stronger
performance than most top human players in terms of stable rank and is rated
above 99.99% of all the officially ranked human players in the Tenhou platform.
This is the first time that a computer program outperforms most top human
players in Mahjong.
",Suphx: Mastering Mahjong with Deep Reinforcement Learning
"  A novel bioinspired metaheuristic is proposed in this work, simulating how
the coronavirus spreads and infects healthy people. From an initial individual
(the patient zero), the coronavirus infects new patients at known rates,
creating new populations of infected people. Every individual can either die or
infect and, afterwards, be sent to the recovered population. Relevant terms
such as re-infection probability, super-spreading rate or traveling rate are
introduced in the model in order to simulate as accurately as possible the
coronavirus activity. The Coronavirus Optimization Algorithm has two major
advantages compared to other similar strategies. First, the input parameters
are already set according to the disease statistics, preventing researchers
from initializing them with arbitrary values. Second, the approach has the
ability of ending after several iterations, without setting this value either.
Infected population initially grows at an exponential rate but after some
iterations, when considering social isolation measures and the high number
recovered and dead people, the number of infected people starts decreasing in
subsequent iterations. Furthermore, a parallel multi-virus version is proposed
in which several coronavirus strains evolve over time and explore wider search
space areas in less iterations. Finally, the metaheuristic has been combined
with deep learning models, in order to find optimal hyperparameters during the
training phase. As application case, the problem of electricity load time
series forecasting has been addressed, showing quite remarkable performance.
","Coronavirus Optimization Algorithm: A bioinspired metaheuristic based on
  the COVID-19 propagation model"
"  Automated negotiation has been used in a variety of distributed settings,
such as privacy in the Internet of Things (IoT) devices and power distribution
in Smart Grids. The most common protocol under which these agents negotiate is
the Alternating Offers Protocol (AOP). Under this protocol, agents cannot
express any additional information to each other besides a counter offer. This
can lead to unnecessarily long negotiations when, for example, negotiations are
impossible, risking to waste bandwidth that is a precious resource at the edge
of the network. While alternative protocols exist which alleviate this problem,
these solutions are too complex for low power devices, such as IoT sensors
operating at the edge of the network. To improve this bottleneck, we introduce
an extension to AOP called Alternating Constrained Offers Protocol (ACOP), in
which agents can also express constraints to each other. This allows agents to
both search the possibility space more efficiently and recognise impossible
situations sooner. We empirically show that agents using ACOP can significantly
reduce the number of messages a negotiation takes, independently of the
strategy agents choose. In particular, we show our method significantly reduces
the number of messages when an agreement is not possible. Furthermore, when an
agreement is possible it reaches this agreement sooner with no negative effect
on the utility.
",Increasing negotiation performance at the edge of the network
"  Peer review forms the backbone of modern scientific manuscript evaluation.
But after two hundred and eighty-nine years of egalitarian service to the
scientific community, does this protocol remain fit for purpose in 2020? In
this work, we answer this question in the negative (strong reject, high
confidence) and propose instead State-Of-the-Art Review (SOAR), a neoteric
reviewing pipeline that serves as a 'plug-and-play' replacement for peer
review. At the heart of our approach is an interpretation of the review process
as a multi-objective, massively distributed and extremely-high-latency
optimisation, which we scalarise and solve efficiently for PAC and CMT-optimal
solutions. We make the following contributions: (1) We propose a highly
scalable, fully automatic methodology for review, drawing inspiration from
best-practices from premier computer vision and machine learning conferences;
(2) We explore several instantiations of our approach and demonstrate that SOAR
can be used to both review prints and pre-review pre-prints; (3) We wander
listlessly in vain search of catharsis from our latest rounds of savage CVPR
rejections.
","State-of-Art-Reviewing: A Radical Proposal to Improve Scientific
  Publication"
"  Evolution gave rise to human and animal intelligence here on Earth. We argue
that the path to developing artificial human-like-intelligence will pass
through mimicking the evolutionary process in a nature-like simulation. In
Nature, there are two processes driving the development of the brain: evolution
and learning. Evolution acts slowly, across generations, and amongst other
things, it defines what agents learn by changing their internal reward
function. Learning acts fast, across one's lifetime, and it quickly updates
agents' policy to maximise pleasure and minimise pain. The reward function is
slowly aligned with the fitness function by evolution, however, as agents
evolve the environment and its fitness function also change, increasing the
misalignment between reward and fitness. It is extremely computationally
expensive to replicate these two processes in simulation. This work proposes
Evolution via Evolutionary Reward (EvER) that allows learning to
single-handedly drive the search for policies with increasingly evolutionary
fitness by ensuring the alignment of the reward function with the fitness
function. In this search, EvER makes use of the whole state-action trajectories
that agents go through their lifetime. In contrast, current evolutionary
algorithms discard this information and consequently limit their potential
efficiency at tackling sequential decision problems. We test our algorithm in
two simple bio-inspired environments and show its superiority at generating
more capable agents at surviving and reproducing their genes when compared with
a state-of-the-art evolutionary algorithm.
",Mimicking Evolution with Reinforcement Learning
"  Decades of research have been invested in making computer programs for
playing games such as Chess and Go. This paper focuses on a new game, Tetris
Link, a board game that is still lacking any scientific analysis. Tetris Link
has a large branching factor, hampering a traditional heuristic planning
approach. We explore heuristic planning and two other approaches: Reinforcement
Learning, Monte Carlo tree search. We document our approach and report on their
relative performance in a tournament. Curiously, the heuristic approach is
stronger than the planning/learning approaches. However, experienced human
players easily win the majority of the matches against the heuristic planning
AIs. We, therefore, surmise that Tetris Link is more difficult than expected.
We offer our findings to the community as a challenge to improve upon.
",A New Challenge: Approaching Tetris Link with AI
"  This paper addresses a lot-sizing and scheduling problem variant arising from
the study of the curing process of a tire factory. The aim is to find the
minimum makespan needed for producing enough tires to meet the demand
requirements on time, considering the availability and compatibility of
different resources involved. To solve this problem, we suggest a hybrid
approach that consists in first applying a heuristic to obtain an estimated
value of the makespan and then solving a mathematical model to determine the
minimum value. We note that the size of the model (number of variables and
constraints) depends significantly on the estimated makespan. Extensive
numerical experiments over different instances based on real data are presented
to evaluate the effectiveness of the hybrid procedure proposed. From the
results obtained we can note that the hybrid approach is able to achieve the
optimal makespan for many of the instances, even large ones, since the results
provided by the heuristic allow to reduce significantly the size of the
mathematical model.
","A hybrid optimization procedure for solving a tire curing scheduling
  problem"
"  Transportation is quickly evolving in the emerging smart city ecosystem with
personalized ride sharing services quickly advancing. Yet, the public bus
infrastructure has been slow to respond to these trends. With our research, we
propose a semi-dynamic bus routing framework that is data-driven and responsive
to relevant parameters in bus transport. We use newly published bus event data
from a bus line in Boston and several algorithmic heuristics to create this
framework and demonstrate the capabilities and results. We find that this
approach yields a very promising routing infrastructure that is smarter and
more dynamic than the existing system.
",A Semi-Dynamic Bus Routing Infrastructure based on MBTA Bus Data
"  In this article, we present the anytime tree search algorithm we designed for
the 2018 ROADEF/EURO challenge glass cutting problem proposed by the French
company Saint-Gobain. The resulting program was ranked first among 64
participants. Its key components are: a new search algorithm called Memory
Bounded A* (MBA*) with guide functions, a symmetry breaking strategy, and a
pseudo-dominance rule. We perform a comprehensive study of these components
showing that each of them contributes to the algorithm global performances. In
addition, we designed a second tree search algorithm fully based on the
pseudo-dominance rule and dedicated to some of the challenge instances with
strong precedence constraints. On these instances, it finds the best-known
solutions very quickly.
","An anytime tree search algorithm for the 2018 ROADEF/EURO challenge
  glass cutting problem"
"  Reinforcement learning (RL) has been successful in training agents in various
learning environments, including video-games. However, such work modifies and
shrinks the action space from the game's original. This is to avoid trying
""pointless"" actions and to ease the implementation. Currently, this is mostly
done based on intuition, with little systematic research supporting the design
decisions. In this work, we aim to gain insight on these action space
modifications by conducting extensive experiments in video-game environments.
Our results show how domain-specific removal of actions and discretization of
continuous actions can be crucial for successful learning. With these insights,
we hope to ease the use of RL in new environments, by clarifying what
action-spaces are easy to learn.
",Action Space Shaping in Deep Reinforcement Learning
"  Behavioural cloning, where a computer is taught to perform a task based on
demonstrations, has been successfully applied to various video games and
robotics tasks, with and without reinforcement learning. This also includes
end-to-end approaches, where a computer plays a video game like humans do: by
looking at the image displayed on the screen, and sending keystrokes to the
game. As a general approach to playing video games, this has many inviting
properties: no need for specialized modifications to the game, no lengthy
training sessions and the ability to re-use the same tools across different
games. However, related work includes game-specific engineering to achieve the
results. We take a step towards a general approach and study the general
applicability of behavioural cloning on twelve video games, including six
modern video games (published after 2010), by using human demonstrations as
training data. Our results show that these agents cannot match humans in raw
performance but do learn basic dynamics and rules. We also demonstrate how the
quality of the data matters, and how recording data from humans is subject to a
state-action mismatch, due to human reflexes.
",Benchmarking End-to-End Behavioural Cloning on Video Games
"  Interactive adaptive systems powered by Reinforcement Learning (RL) have many
potential applications, such as intelligent tutoring systems. In such systems
there is typically an external human system designer that is creating,
monitoring and modifying the interactive adaptive system, trying to improve its
performance on the target outcomes. In this paper we focus on algorithmic
foundation of how to help the system designer choose the set of sensors or
features to define the observation space used by reinforcement learning agent.
We present an algorithm, value driven representation (VDR), that can
iteratively and adaptively augment the observation space of a reinforcement
learning agent so that is sufficient to capture a (near) optimal policy. To do
so we introduce a new method to optimistically estimate the value of a policy
using offline simulated Monte Carlo rollouts. We evaluate the performance of
our approach on standard RL benchmarks with simulated humans and demonstrate
significant improvement over prior baselines.
",Value Driven Representation for Human-in-the-Loop Reinforcement Learning
"  Many areas of research are characterised by the deluge of large-scale
highly-dimensional time-series data. However, using the data available for
prediction and decision making is hampered by the current lag in our ability to
uncover and quantify true interactions that explain the outcomes.We are
interested in areas such as intensive care medicine, which are characterised by
i) continuous monitoring of multivariate variables and non-uniform sampling of
data streams, ii) the outcomes are generally governed by interactions between a
small set of rare events, iii) these interactions are not necessarily definable
by specific values (or value ranges) of a given group of variables, but rather,
by the deviations of these values from the normal state recorded over time, iv)
the need to explain the predictions made by the model. Here, while numerous
data mining models have been formulated for outcome prediction, they are unable
to explain their predictions.
  We present a model for uncovering interactions with the highest likelihood of
generating the outcomes seen from highly-dimensional time series data.
Interactions among variables are represented by a relational graph structure,
which relies on qualitative abstractions to overcome non-uniform sampling and
to capture the semantics of the interactions corresponding to the changes and
deviations from normality of variables of interest over time. Using the
assumption that similar templates of small interactions are responsible for the
outcomes (as prevalent in the medical domains), we reformulate the discovery
task to retrieve the most-likely templates from the data.
","Modeling Rare Interactions in Time Series Data Through Qualitative
  Change: Application to Outcome Prediction in Intensive Care Units"
"  Procedural generation is used across game design to achieve a wide variety of
ends, and has led to the creation of several game subgenres by injecting
variance, surprise or unpredictability into otherwise static designs.
Information games are a type of mystery game in which the player is tasked with
gathering knowledge and developing an understanding of an event or system.
Their reliance on player knowledge leaves them vulnerable to spoilers and hard
to replay. In this paper we introduce the notion of generative forensics games,
a subgenre of information games that challenge the player to understand the
output of a generative system. We introduce information games, show how
generative forensics develops the idea, report on two prototype games we
created, and evaluate our work on generative forensics so far from a player and
a designer perspective.
",Generative Forensics: Procedural Generation and Information Games
"  In this paper, we study novel variants of the well-known two-echelon vehicle
routing problem in which a truck works on the first echelon to transport
parcels and a fleet of drones to intermediate depots while in the second
echelon, the drones are used to deliver parcels from intermediate depots to
customers. The objective is to minimize the completion time instead of the
transportation cost as in classical 2-echelon vehicle routing problems.
Depending on the context, a drone can be launched from the truck at an
intermediate depot once (single trip drone) or several times (multiple trip
drone). Mixed Integer Linear Programming (MILP) models are first proposed to
formulate mathematically the problems and solve to optimality small-size
instances. To handle larger instances, a metaheuristic based on the idea of
Greedy Randomized Adaptive Search Procedure (GRASP) is introduced. Experimental
results obtained on instances of different contexts are reported and analyzed.
",The two-echelon routing problem with truck and drones
"  At present, artificial intelligence in the form of machine learning is making
impressive progress, especially the field of deep learning (DL) [1]. Deep
learning algorithms have been inspired from the beginning by nature,
specifically by the human brain, in spite of our incomplete knowledge about its
brain function. Learning from nature is a two-way process as discussed in
[2][3][4], computing is learning from neuroscience, while neuroscience is
quickly adopting information processing models. The question is, what can the
inspiration from computational nature at this stage of the development
contribute to deep learning and how much models and experiments in machine
learning can motivate, justify and lead research in neuroscience and cognitive
science and to practical applications of artificial intelligence.
","Morphological Computation and Learning to Learn In Natural Intelligent
  Systems And AI"
"  Cardiovascular Diseases (CVD) and strokes produce immense health and economic
burdens globally. Coronary Artery Disease (CAD) is the most common type of
cardiovascular disease. Coronary Angiography, which is an invasive treatment,
is also the standard procedure for diagnosing CAD. In this work, we illustrate
a Medical Decision Support System for the prediction of Coronary Artery Disease
(CAD) utilizing Fuzzy Cognitive Maps (FCMs). FCMs are a promising modeling
methodology, based on human knowledge, capable of dealing with ambiguity and
uncertainty, and learning how to adapt to the unknown or changing environment.
The newly proposed MDSS is developed using the basic notions of Fuzzy Logic and
Fuzzy Cognitive Maps, with some adjustments to improve the results. The
proposed model, tested on a labelled CAD dataset of 303 patients, obtains an
accuracy of 78.2% outmatching several state-of-the-art classification
algorithms.
","Non-invasive modelling methodology for the diagnosis of Coronary Artery
  Disease using Fuzzy Cognitive Maps"
"  [libralesso_anytime_2020] proposed an anytime tree search algorithm for the
2018 ROADEF/EURO challenge glass cutting problem
(https://www.roadef.org/challenge/2018/en/index.php). The resulting program was
ranked first among 64 participants. In this article, we generalize it and show
that it is not only effective for the specific problem it was originally
designed for, but is also very competitive and even returns state-of-the-art
solutions on a large variety of Cutting and Packing problems from the
literature. We adapted the algorithm for two-dimensional Bin Packing, Multiple
Knapsack, and Strip Packing Problems, with two- or three-staged exact or
non-exact guillotine cuts, the orientation of the first cut being imposed or
not, and with or without item rotation. The combination of efficiency, ability
to provide good solutions fast, simplicity and versatility makes it
particularly suited for industrial applications, which require quickly
developing algorithms implementing several business-specific constraints. The
algorithm is implemented in a new software package called PackingSolver.
","An anytime tree search algorithm for two-dimensional two- and
  three-staged guillotine packing problems"
"  Automated negotiation can be an efficient method for resolving conflict and
redistributing resources in a coalition setting. Automated negotiation has
already seen increased usage in fields such as e-commerce and power
distribution in smart girds, and recent advancements in opponent modelling have
proven to deliver better outcomes. However, significant barriers to more
widespread adoption remain, such as lack of predictable outcome over time and
user trust. Additionally, there have been many recent advancements in the field
of reasoning about uncertainty, which could help alleviate both those problems.
As there is no recent survey on these two fields, and specifically not on their
possible intersection we aim to provide such a survey here.
",The current state of automated negotiation theory: a literature review
"  The process of information fusion needs to deal with a large number of
uncertain information with multi-source, heterogeneity, inaccuracy,
unreliability, and incompleteness. In practical engineering applications,
Dempster-Shafer evidence theory is widely used in multi-source information
fusion owing to its effectiveness in data fusion. Information sources have an
important impact on multi-source information fusion in an environment of
complex, unstable, uncertain, and incomplete characteristics. To address
multi-source information fusion problem, this paper considers the situation of
uncertain information modeling from the closed world to the open world
assumption and studies the generation of basic probability assignment (BPA)
with incomplete information. In this paper, a new method is proposed to
generate generalized basic probability assignment (GBPA) based on the
triangular fuzzy number model under the open world assumption. The proposed
method can not only be used in different complex environments simply and
flexibly, but also have less information loss in information processing.
Finally, a series of comprehensive experiments basing on the UCI data sets are
used to verify the rationality and superiority of the proposed method.
","A new approach for generation of generalized basic probability
  assignment in the evidence theory"
"  Learning how to adapt to complex and dynamic environments is one of the most
important factors that contribute to our intelligence. Endowing artificial
agents with this ability is not a simple task, particularly in competitive
scenarios. In this paper, we present a broad study on how popular reinforcement
learning algorithms can be adapted and implemented to learn and to play a
real-world implementation of a competitive multiplayer card game. We propose
specific training and validation routines for the learning agents, in order to
evaluate how the agents learn to be competitive and explain how they adapt to
each others' playing style. Finally, we pinpoint how the behavior of each agent
derives from their learning style and create a baseline for future research on
this scenario.
","Learning from Learners: Adapting Reinforcement Learning Agents to be
  Competitive in a Card Game"
"  In this demo, we present ConsciousControlFlow(CCF), a prototype system to
demonstrate conscious Artificial Intelligence (AI). The system is based on the
computational model for consciousness and the hierarchy of needs. CCF supports
typical scenarios to show the behaviors and the mental activities of conscious
AI. We demonstrate that CCF provides a useful tool for effective machine
consciousness demonstration and human behavior study assistance.
","ConsciousControlFlow(CCF): A Demonstration for conscious Artificial
  Intelligence"
"  A novel computational model (CoDD) utilizing combinatory logic to create
higher-order decision trees is presented. A theoretical analysis of general
intelligence in terms of the formal theory of pattern recognition and pattern
formation is outlined, and shown to take especially natural form in the case
where patterns are expressed in CoDD language. Relationships between logical
entropy and algorithmic information, and Shannon entropy and runtime
complexity, are shown to be elucidated by this approach. Extension to the
quantum computing case is also briefly discussed.
","Combinatorial Decision Dags: A Natural Computational Model for General
  Intelligence"
"  A formal theory of simplicity is introduced, in the context of a
""combinational"" computation model that views computation as comprising the
iterated transformational and compositional activity of a population of agents
upon each other. Conventional measures of simplicity in terms of algorithmic
information etc. are shown to be special cases of a broader understanding of
the core ""symmetry"" properties constituting what is defined here as a
Compositional Simplicity Measure (CoSM).
  This theory of CoSMs is extended to a theory of CoSMOS (Combinational
Simplicity Measure Operating Sets) which involve multiple simplicity measures
utilized together. Given a vector of simplicity measures, an entity is
associated not with an individual simplicity value but with a ""simplicity
bundles"" of Pareto-optimal simplicity-value vectors.
  CoSMs and CoSMOS are then used as a foundation for a theory of pattern and
multipattern, and a theory of hierarchy and heterarchy in systems of patterns.
A formalization of the cognitive-systems notion of a ""coherent dual network""
interweaving hierarchy and heterarchy in a consistent way is presented.
  The high level end result of this investigation is to re-envision Occam's
Razor as something like: When in doubt, prefer hypotheses whose simplicity
bundles are Pareto optimal, partly because doing so both permits and benefits
from the construction of coherent dual networks comprising coordinated and
consistent multipattern hierarchies and heterarchies.
",Grounding Occam's Razor in a Formal Theory of Simplicity
"  We address the problem of accelerating column generation for set cover
problems in which we relax the state space of the columns to do efficient
pricing. We achieve this by adapting the recently introduced smooth and
flexible dual optimal inequalities (DOI) for use with relaxed columns. Smooth
DOI exploit the observation that similar items are nearly fungible, and hence
should be associated with similarly valued dual variables. Flexible DOI exploit
the observation that the change in cost of a column induced by removing an item
can be bounded. We adapt these DOI to the problem of capacitated vehicle
routing in the context of ng-route relaxations. We demonstrate significant
speed ups on a benchmark data set, while provably not weakening the relaxation.
","Relaxed Dual Optimal Inequalities for Relaxed Columns: with Application
  to Vehicle Routing"
"  The report suggests the concept of risk, outlining two mathematical
structures necessary for risk genesis: the set of outcomes and, in a general
case, partial order of preference on it. It is shown that this minimum partial
order should constitute the structure of a semilattice. In some cases, there
should be a system of semilattices nested in a certain way. On this basis, the
classification of risk theory tasks is given in the context of specialization
of mathematical knowledge. In other words, we are talking about the development
of a new rela-tional risk theory. The problem of political decision making in
game-theoretic formulation in terms of having partial order of preference on
the set of outcomes for each par-ticipant of the game forming a certain system
of nested semilattices is consid-ered as an example of a relational risk
concept implementation. Solutions to the problem obtained through the use of
various optimality principles are investi-gated.
",Game-theoretic applications of a relational risk model
"  An integral function of fully autonomous robots and humans is the ability to
focus attention on a few relevant percepts to reach a certain goal while
disregarding irrelevant percepts. Humans and animals rely on the interactions
between the Pre-Frontal Cortex (PFC) and the Basal Ganglia (BG) to achieve this
focus called Working Memory (WM). The Working Memory Toolkit (WMtk) was
developed based on a computational neuroscience model of this phenomenon with
Temporal Difference (TD) Learning for autonomous systems. Recent adaptations of
the toolkit either utilize Abstract Task Representations (ATRs) to solve
Non-Observable (NO) tasks or storage of past input features to solve
Partially-Observable (PO) tasks, but not both. We propose a new model,
PONOWMtk, which combines both approaches, ATRs and input storage, with a static
or dynamic number of ATRs. The results of our experiments show that PONOWMtk
performs effectively for tasks that exhibit PO, NO, or both properties.
","Combined Model for Partially-Observable and Non-Observable Task
  Switching: Solving Hierarchical Reinforcement Learning Problems Statically
  and Dynamically with Transfer Learning"
"  The Thief Orienteering Problem (ThOP) is a multi-component problem that
combines features of two classic combinatorial optimization problems:
Orienteering Problem and Knapsack Problem. The ThOP is challenging due to the
given time constraint and the interaction between its components. We propose an
Ant Colony Optimization algorithm together with a new packing heuristic to deal
individually and interactively with problem components. Our approach
outperforms existing work on more than 90% of the benchmarking instances, with
an average improvement of over 300%.
",Ants can orienteer a thief in their robbery
"  In this report, we will define a new approach to the problem of non
deterministic planning for extended temporal goals. In particular, we will give
a solution to this problem reducing it to a fully observable non deterministic
(FOND) planning problem and taking advantage of the LTLfToDFA tool. First of
all, we will introduce the main idea and motivations supporting our approach.
Then, we will give some preliminaries explaining the Planning Domain Definition
Language (PDDL) language and the FOND planning problem formally. After that, we
will illustrate our FOND4LTLfPLTLf (also available online) approach with the
encoding of temporal goals into a PDDL domain and problem. Finally, we will
present some of the results obtained through the application of the proposed
solution.
",FOND Planning for LTLf and PLTLf Goals
"  Prior work on generating explanations in a planning and decision-making
context has focused on providing the rationale behind an AI agent's decision
making. While these methods provide the right explanations from the explainer's
perspective, they fail to heed the cognitive requirement of understanding an
explanation from the explainee's (the human's) perspective. In this work, we
set out to address this issue by first considering the influence of information
order in an explanation, or the progressiveness of explanations. Intuitively,
progression builds later concepts on previous ones and is known to contribute
to better learning. In this work, we aim to investigate similar effects during
explanation generation when an explanation is broken into multiple parts that
are communicated sequentially. The challenge here lies in modeling the humans'
preferences for information order in receiving such explanations to assist
understanding. Given this sequential process, a formulation based on goal-based
MDP for generating progressive explanations is presented. The reward function
of this MDP is learned via inverse reinforcement learning based on explanations
that are retrieved via human subject studies. We first evaluated our approach
on a scavenger-hunt domain to demonstrate its effectively in capturing the
humans' preferences. Upon analyzing the results, it revealed something more
fundamental: the preferences arise strongly from both domain dependent and
independence features. The correlation with domain independent features pushed
us to verify this result further in an escape room domain. Results confirmed
our hypothesis that the process of understanding an explanation was a dynamic
process. The human preference that reflected this aspect corresponded exactly
to the progression for knowledge assimilation hidden deeper in our cognitive
process.
","Order Matters: Generating Progressive Explanations for Planning Tasks in
  Human-Robot Teaming"
"  The Expected Free Energy (EFE) is a central quantity in the theory of active
inference. It is the quantity that all active inference agents are mandated to
minimize through action, and its decomposition into extrinsic and intrinsic
value terms is key to the balance of exploration and exploitation that active
inference agents evince. Despite its importance, the mathematical origins of
this quantity and its relation to the Variational Free Energy (VFE) remain
unclear. In this paper, we investigate the origins of the EFE in detail and
show that it is not simply ""the free energy in the future"". We present a
functional that we argue is the natural extension of the VFE, but which
actively discourages exploratory behaviour, thus demonstrating that exploration
does not directly follow from free energy minimization into the future. We then
develop a novel objective, the Free-Energy of the Expected Future (FEEF), which
possesses both the epistemic component of the EFE as well as an intuitive
mathematical grounding as the divergence between predicted and desired futures.
",Whence the Expected Free Energy?
"  Robot sequential decision-making in the real world is a challenge because it
requires the robots to simultaneously reason about the current world state and
dynamics, while planning actions to accomplish complex tasks. On the one hand,
declarative languages and reasoning algorithms well support representing and
reasoning with commonsense knowledge. But these algorithms are not good at
planning actions toward maximizing cumulative reward over a long, unspecified
horizon. On the other hand, probabilistic planning frameworks, such as Markov
decision processes (MDPs) and partially observable MDPs (POMDPs), well support
planning to achieve long-term goals under uncertainty. But they are
ill-equipped to represent or reason about knowledge that is not directly
related to actions.
  In this article, we present a novel algorithm, called iCORPP, to
simultaneously estimate the current world state, reason about world dynamics,
and construct task-oriented controllers. In this process, robot decision-making
problems are decomposed into two interdependent (smaller) subproblems that
focus on reasoning to ""understand the world"" and planning to ""achieve the goal""
respectively. Contextual knowledge is represented in the reasoning component,
which makes the planning component epistemic and enables active information
gathering. The developed algorithm has been implemented and evaluated both in
simulation and on real robots using everyday service tasks, such as indoor
navigation, dialog management, and object delivery. Results show significant
improvements in scalability, efficiency, and adaptiveness, compared to
competitive baselines including handcrafted action policies.
","iCORPP: Interleaved Commonsense Reasoning and Probabilistic Planning on
  Robots"
"  In this work we describe preferential Description Logics of typicality, a
nonmonotonic extension of standard Description Logics by means of a typicality
operator T allowing to extend a knowledge base with inclusions of the form T(C)
v D, whose intuitive meaning is that normally/typically Cs are also Ds. This
extension is based on a minimal model semantics corresponding to a notion of
rational closure, built upon preferential models. We recall the basic concepts
underlying preferential Description Logics. We also present two extensions of
the preferential semantics: on the one hand, we consider probabilistic
extensions, based on a distributed semantics that is suitable for tackling the
problem of commonsense concept combination, on the other hand, we consider
other strengthening of the rational closure semantics and construction to avoid
the so-called blocking of property inheritance problem.
","Reasoning about Typicality and Probabilities in Preferential Description
  Logics"
"  Seemingly since the inception of virtual humans, there has been an effort to
make their behaviors more natural and human-like. In additions to improving
movement's visual quality, there has been considerable research focused on
creating more intelligent virtual characters. This paper presents a framework
inspired by natural language constructs that aims to author more reasonable
virtual human behaviors using structured English input. We focus mainly on
object types and properties, quantifiers, determiners, and spatial relations.
The framework provides a natural, flexible authoring system for simulating
human behaviors.
",Imperatives for Virtual Humans
"  This paper presents a conceptual refinement of agent cognitive architecture
inspired from the beliefs-desires-intentions (BDI) and the theory of planned
behaviour (TPB) models, with an emphasis on different belief facets. This
enables us to investigate the impact of personality and the way that an agent
weights its internal beliefs and social sanctions on an agent's actions. The
study also uses the concept of cognitive dissonance associated with the
fairness of institutions to investigate the agents' behaviour. To showcase our
model, we simulate two historical long-distance trading societies, namely
Armenian merchants of New-Julfa and the English East India Company. The results
demonstrate the importance of internal beliefs of agents as a pivotal aspect
for following institutional rules.
","Impact of different belief facets on agents' decision -- a refined
  cognitive architecture to model the interaction between organisations'
  institutional characteristics and agents' behaviour"
"  As a comprehensive indicator of mathematical thinking and intelligence, the
number sense (Dehaene 2011) bridges the induction of symbolic concepts and the
competence of problem-solving. To endow such a crucial cognitive ability to
machine intelligence, we propose a dataset, Machine Number Sense (MNS),
consisting of visual arithmetic problems automatically generated using a
grammar model--And-Or Graph (AOG). These visual arithmetic problems are in the
form of geometric figures: each problem has a set of geometric shapes as its
context and embedded number symbols. Solving such problems is not trivial; the
machine not only has to recognize the number, but also to interpret the number
with its contexts, shapes, and relations (e.g., symmetry) together with proper
operations. We benchmark the MNS dataset using four predominant neural network
models as baselines in this visual reasoning task. Comprehensive experiments
show that current neural-network-based models still struggle to understand
number concepts and relational operations. We show that a simple brute-force
search algorithm could work out some of the problems without context
information. Crucially, taking geometric context into account by an additional
perception module would provide a sharp performance gain with fewer search
steps. Altogether, we call for attention in fusing the classic search-based
algorithms with modern neural networks to discover the essential number
concepts in future research.
","Machine Number Sense: A Dataset of Visual Arithmetic Problems for
  Abstract and Relational Reasoning"
"  The promise of reinforcement learning is to solve complex sequential decision
problems autonomously by specifying a high-level reward function only. However,
reinforcement learning algorithms struggle when, as is often the case, simple
and intuitive rewards provide sparse and deceptive feedback. Avoiding these
pitfalls requires thoroughly exploring the environment, but creating algorithms
that can do so remains one of the central challenges of the field. We
hypothesise that the main impediment to effective exploration originates from
algorithms forgetting how to reach previously visited states (""detachment"") and
from failing to first return to a state before exploring from it
(""derailment""). We introduce Go-Explore, a family of algorithms that addresses
these two challenges directly through the simple principles of explicitly
remembering promising states and first returning to such states before
intentionally exploring. Go-Explore solves all heretofore unsolved Atari games
and surpasses the state of the art on all hard-exploration games, with orders
of magnitude improvements on the grand challenges Montezuma's Revenge and
Pitfall. We also demonstrate the practical potential of Go-Explore on a
sparse-reward pick-and-place robotics task. Additionally, we show that adding a
goal-conditioned policy can further improve Go-Explore's exploration efficiency
and enable it to handle stochasticity throughout training. The substantial
performance gains from Go-Explore suggest that the simple principles of
remembering states, returning to them, and exploring from them are a powerful
and general approach to exploration, an insight that may prove critical to the
creation of truly intelligent learning agents.
","First return, then explore"
"  Multi-agent path finding in continuous space and time with geometric agents
MAPF$^\mathcal{R}$ is addressed in this paper. The task is to navigate agents
that move smoothly between predefined positions to their individual goals so
that they do not collide. We introduce a novel solving approach for obtaining
makespan optimal solutions called SMT-CBS$^\mathcal{R}$ based on {\em
satisfiability modulo theories} (SMT). The new algorithm combines collision
resolution known from conflict-based search (CBS) with previous generation of
incomplete SAT encodings on top of a novel scheme for selecting decision
variables in a potentially uncountable search space. We experimentally compare
SMT-CBS$^\mathcal{R}$ and previous CCBS algorithm for MAPF$^\mathcal{R}$.
","Pushing the Envelope: From Discrete to Continuous Movements in
  Multi-Agent Path Finding via Lazy Encodings"
"  Computer-based assistants have recently attracted much interest due to its
applicability to ambient assisted living. Such assistants have to detect and
recognize the high-level activities and goals performed by the assisted human
beings. In this work, we demonstrate activity recognition in an indoor
environment in order to identify the goal towards which the subject of the
video is pursuing. Our hybrid approach combines an action recognition module
and a goal recognition algorithm to identify the ultimate goal of the subject
in the video.
",HAPRec: Hybrid Activity and Plan Recognizer
"  Imitation from observation is a computational technique that teaches an agent
on how to mimic the behavior of an expert by observing only the sequence of
states from the expert demonstrations. Recent approaches learn the inverse
dynamics of the environment and an imitation policy by interleaving epochs of
both models while changing the demonstration data. However, such approaches
often get stuck into sub-optimal solutions that are distant from the expert,
limiting their imitation effectiveness. We address this problem with a novel
approach that overcomes the problem of reaching bad local minima by exploring:
(I) a self-attention mechanism that better captures global features of the
states; and (ii) a sampling strategy that regulates the observations that are
used for learning. We show empirically that our approach outperforms the
state-of-the-art approaches in four different environments by a large margin.
",Augmented Behavioral Cloning from Observation
"  In some agent designs like inverse reinforcement learning an agent needs to
learn its own reward function. Learning the reward function and optimising for
it are typically two different processes, usually performed at different
stages. We consider a continual (``one life'') learning approach where the
agent both learns the reward function and optimises for it at the same time. We
show that this comes with a number of pitfalls, such as deliberately
manipulating the learning process in one direction, refusing to learn,
``learning'' facts already known to the agent, and making decisions that are
strictly dominated (for all relevant reward functions). We formally introduce
two desirable properties: the first is `unriggability', which prevents the
agent from steering the learning process in the direction of a reward function
that is easier to optimise. The second is `uninfluenceability', whereby the
reward-function learning process operates by learning facts about the
environment. We show that an uninfluenceable process is automatically
unriggable, and if the set of possible environments is sufficiently rich, the
converse is true too.
",Pitfalls of learning a reward function online
"  One of the arduous tasks in supply chain modelling is to build robust models
against irregular variations. During the proliferation of time-series analyses
and machine learning models, several modifications were proposed such as
acceleration of the classical levenberg-marquardt algorithm, weight decaying
and normalization, which introduced an algorithmic optimization approach to
this problem. In this paper, we have introduced a novel methodology namely,
Pareto Optimization to handle uncertainties and bound the entropy of such
uncertainties by explicitly modelling them under some apriori assumptions. We
have implemented Pareto Optimization using a genetic approach and compared the
results with classical genetic algorithms and Mixed-Integer Linear Programming
(MILP) models. Our results yields empirical evidence suggesting that Pareto
Optimization can elude such non-deterministic errors and is a formal approach
towards producing robust and reactive supply chain models.
","Uncertainty Modelling in Risk-averse Supply Chain Systems Using
  Multi-objective Pareto Optimization"
"  Computing with words (CWW) has emerged as a powerful tool for processing the
linguistic information, especially the one generated by human beings. Various
CWW approaches have emerged since the inception of CWW, such as perceptual
computing, extension principle based CWW approach, symbolic method based CWW
approach, and 2-tuple based CWW approach. Furthermore, perceptual computing can
use interval approach (IA), enhanced interval approach (EIA), or Hao-Mendel
approach (HMA), for data processing. There have been numerous works in which
HMA was shown to be better at word modelling than EIA, and EIA better than IA.
But, a deeper study of these works reveals that HMA captures lesser fuzziness
than the EIA or IA. Thus, we feel that EIA is more suited for word modelling in
multi-person systems and HMA for single-person systems (as EIA is an
improvement over IA). Furthermore, another set of works, compared the
performances perceptual computing to the other above said CWW approaches. In
all these works, perceptual computing was shown to be better than other CWW
approaches. However, none of the works tried to investigate the reason behind
this observed better performance of perceptual computing. Also, no comparison
has been performed for scenarios where the inputs are differentially weighted.
Thus, the aim of this work is to empirically establish that EIA is suitable for
multi-person systems and HMA for single-person systems. Another dimension of
this work is also to empirically prove that perceptual computing gives better
performance than other CWW approaches based on extension principle, symbolic
method and 2-tuple especially in scenarios where inputs are differentially
weighted.
","An empirical study of computing with words approaches for multi-person
  and single-person systems"
"  Decision making in real-life scenarios may often be modeled as an
optimization problem. It requires the consideration of various attributes like
human preferences and thinking, which constrain achieving the optimal value of
the problem objectives. The value of the objectives may be maximized or
minimized, depending on the situation. Numerous times, the values of these
problem parameters are in linguistic form, as human beings naturally understand
and express themselves using words. These problems are therefore termed as
linguistic optimization problems (LOPs), and are of two types, namely single
objective linguistic optimization problems (SOLOPs) and multi-objective
linguistic optimization problems (MOLOPs). In these LOPs, the value of the
objective function(s) may not be known at all points of the decision space, and
therefore, the objective function(s) as well as problem constraints are linked
by the if-then rules. Tsukamoto inference method has been used to solve these
LOPs; however, it suffers from drawbacks. As, the use of linguistic information
inevitably calls for the utilization of computing with words (CWW), and
therefore, 2-tuple linguistic model based solution methodologies were proposed
for LOPs. However, we found that 2-tuple linguistic model based solution
methodologies represent the semantics of the linguistic information using a
combination of type-1 fuzzy sets and ordinal term sets. As, the semantics of
linguistic information are best modeled using the interval type-2 fuzzy sets,
hence we propose solution methodologies for LOPs based on CWW approach of
perceptual computing, in this paper. The perceptual computing based solution
methodologies use a novel design of CWW engine, called the perceptual reasoning
(PR). PR in the current form is suitable for solving SOLOPs and, hence, we have
also extended it to the MOLOPs.
","Perceptual reasoning based solution methodology for linguistic
  optimization problems"
"  In the era of Industry 4.0, the focus is on the minimization of human element
and maximizing the automation in almost all the industrial and manufacturing
establishments. These establishments contain numerous processing systems, which
can execute a number of tasks, in parallel with minimum number of human beings.
This parallel execution of tasks is done in accordance to a scheduling policy.
However, the minimization of human element beyond a certain point is difficult.
In fact, the expertise and experience of a group of humans, called the experts,
becomes imminent to design a fruitful scheduling policy. The aim of the
scheduling policy is to achieve the optimal value of an objective, like
production time, cost, etc. In real-life situations, there are more often than
not, multiple objectives in any parallel processing scenario. Furthermore, the
experts generally provide their opinions, about various scheduling criteria
(pertaining to the scheduling policies) in linguistic terms or words. Word
semantics are best modeled using fuzzy sets (FSs). Thus, all these factors have
motivated us to model the parallel processing scenario as a multi-objective
linguistic optimization problem (MOLOP) and use the novel perceptual reasoning
(PR) based methodology for solving it. We have also compared the results of the
PR based solution methodology with those obtained from the 2-tuple based
solution methodology. PR based solution methodology offers three main
advantages viz., it generates unique recommendations, here the linguistic
recommendations match a codebook word, and also the word model comes before the
word. 2-tuple based solution methodology fails to give all these advantages.
Thus, we feel that our work is novel and will provide directions for the future
research.
","Parallel processor scheduling: formulation as multi-objective linguistic
  optimization and solution using Perceptual Reasoning based methodology"
"  In the framework of Granular Computing (GC), Interval type 2 Fuzzy Sets (IT2
FSs) play a prominent role by facilitating a better representation of uncertain
linguistic information. Perceptual Computing (Per C), a well known computing
with words (CWW) approach, and its various applications have nicely exploited
this advantage. This paper reports a novel Per C based approach for student
strategy evaluation. Examinations are generally oriented to test the subject
knowledge of students. The number of questions that they are able to solve
accurately judges success rates of students in the examinations. However, we
feel that not only the solutions of questions, but also the strategy adopted
for finding those solutions are equally important. More marks should be awarded
to a student, who solves a question with a better strategy compared to a
student, whose strategy is relatively not that good. Furthermore, the students
strategy can be taken as a measure of his or her learning outcome as perceived
by a faculty member. This can help to identify students, whose learning
outcomes are not good, and, thus, can be provided with any relevant help, for
improvement. The main contribution of this paper is to illustrate the use of
CWW for student strategy evaluation and present a comparison of the
recommendations generated by different CWW approaches. CWW provides us with two
major advantages. First, it generates a numeric score for the overall
evaluation of strategy adopted by a student in the examination. This enables
comparison and ranking of the students based on their performances. Second, a
linguistic evaluation describing the student strategy is also obtained from the
system. Both these numeric score and linguistic recommendation are together
used to assess the quality of a students strategy. We found that Per-C
generates unique recommendations in all cases and outperforms other CWW
approaches.
",Computing With Words for Student Strategy Evaluation in an Examination
"  We address the problem of building a decision model for a specific bidding
situation in the game of Bridge. We propose the following multi-step
methodology i) Build a set of examples for the decision problem and use
simulations to associate a decision to each example ii) Use supervised
relational learning to build an accurate and readable model iii) Perform a
joint analysis between domain experts and data scientists to improve the
learning language, including the production by experts of a handmade model iv)
Build a better, more readable and accurate model.
",Construction and Elicitation of a Black Box Model in the Game of Bridge
"  Ontologies are the prime way of organizing data in the Semantic Web. Often,
it is necessary to combine several, independently developed ontologies to
obtain a knowledge graph fully representing a domain of interest. The
complementarity of existing ontologies can be leveraged by merging them.
Existing approaches for ontology merging mostly implement a binary merge.
However, with the growing number and size of relevant ontologies across
domains, scalability becomes a central challenge. A multi-ontology merging
technique offers a potential solution to this problem. We present CoMerger, a
scalable multiple ontologies merging method. For efficient processing, rather
than successively merging complete ontologies pairwise, we group related
concepts across ontologies into partitions and merge first within and then
across those partitions. The experimental results on well-known datasets
confirm the feasibility of our approach and demonstrate its superiority over
binary strategies. A prototypical implementation is freely accessible through a
live web portal.
","Towards Building Knowledge by Merging Multiple Ontologies with CoMerger:
  A Partitioning-based Approach"
"  This paper analyses the application of artificial intelligence techniques to
various areas of archaeology and more specifically: a) The use of software
tools as a creative stimulus for the organization of exhibitions; the use of
humanoid robots and holographic displays as guides that interact and involve
museum visitors; b) The analysis of methods for the classification of fragments
found in archaeological excavations and for the reconstruction of ceramics,
with the recomposition of the parts of text missing from historical documents
and epigraphs; c) The cataloguing and study of human remains to understand the
social and historical context of belonging with the demonstration of the
effectiveness of the AI techniques used; d) The detection of particularly
difficult terrestrial archaeological sites with the analysis of the
architectures of the Artificial Neural Networks most suitable for solving the
problems presented by the site; the design of a study for the exploration of
marine archaeological sites, located at depths that cannot be reached by man,
through the construction of a freely explorable 3D version.
",The computerization of archaeology: survey on AI techniques
"  Research in developmental psychology consistently shows that children explore
the world thoroughly and efficiently and that this exploration allows them to
learn. In turn, this early learning supports more robust generalization and
intelligent behavior later in life. While much work has gone into developing
methods for exploration in machine learning, artificial agents have not yet
reached the high standard set by their human counterparts. In this work we
propose using DeepMind Lab (Beattie et al., 2016) as a platform to directly
compare child and agent behaviors and to develop new exploration techniques. We
outline two ongoing experiments to demonstrate the effectiveness of a direct
comparison, and outline a number of open research questions that we believe can
be tested using this methodology.
","Exploring Exploration: Comparing Children with RL Agents in Unified
  Environments"
"  Theory of Mind is commonly defined as the ability to attribute mental states
(e.g., beliefs, goals) to oneself, and to others. A large body of previous work
- from the social sciences to artificial intelligence - has observed that
Theory of Mind capabilities are central to providing an explanation to another
agent or when explaining that agent's behaviour. In this paper, we build and
expand upon previous work by providing an account of explanation in terms of
the beliefs of agents and the mechanism by which agents revise their beliefs
given possible explanations. We further identify a set of desiderata for
explanations that utilize Theory of Mind. These desiderata inform our
belief-based account of explanation.
",Towards the Role of Theory of Mind in Explanation
"  Recent approaches to goal and plan recognition using classical planning
domains have achieved state of the art results in terms of both recognition
time and accuracy by using heuristics based on planning landmarks. To achieve
such fast recognition time these approaches use efficient, but incomplete,
algorithms to extract only a subset of landmarks for planning domains and
problems, at the cost of some accuracy. In this paper, we investigate the
impact and effect of using various landmark extraction algorithms capable of
extracting a larger proportion of the landmarks for each given planning
problem, up to exhaustive landmark extraction. We perform an extensive
empirical evaluation of various landmark-based heuristics when using different
percentages of the full set of landmarks. Results show that having more
landmarks does not necessarily mean achieving higher accuracy and lower spread,
as the additional extracted landmarks may not necessarily increase be helpful
towards the goal recognition task.
","The More the Merrier?! Evaluating the Effect of Landmark Extraction
  Algorithms on Landmark-Based Goal Recognition"
"  We study how to infer new choices from previous choices in a conservative
manner. To make such inferences, we use the theory of choice functions: a
unifying mathematical framework for conservative decision making that allows
one to impose axioms directly on the represented decisions. We here adopt the
coherence axioms of De Bock and De Cooman (2019). We show how to naturally
extend any given choice assessment to such a coherent choice function, whenever
possible, and use this natural extension to make new choices. We present a
practical algorithm to compute this natural extension and provide several
methods that can be used to improve its scalability.
",Inference with Choice Functions Made Practical
"  In the future we can expect that artificial intelligent agents, once
deployed, will be required to learn continually from their experience during
their operational lifetime. Such agents will also need to communicate with
humans and other agents regarding the content of their experience, in the
context of passing along their learnings, for the purpose of explaining their
actions in specific circumstances or simply to relate more naturally to humans
concerning experiences the agent acquires that are not necessarily related to
their assigned tasks. We argue that to support these goals, an agent would
benefit from an episodic memory; that is, a memory that encodes the agent's
experience in such a way that the agent can relive the experience, communicate
about it and use its past experience, inclusive of the agents own past actions,
to learn more effective models and policies. In this short paper, we propose
one potential approach to provide an AI agent with such capabilities. We draw
upon the ever-growing body of work examining the function and operation of the
Medial Temporal Lobe (MTL) in mammals to guide us in adding an episodic memory
capability to an AI agent composed of artificial neural networks (ANNs). Based
on that, we highlight important aspects to be considered in the memory
organization and we propose an architecture combining ANNs and standard
Computer Science techniques for supporting storage and retrieval of episodic
memories. Despite being initial work, we hope this short paper can spark
discussions around the creation of intelligent agents with memory or, at least,
provide a different point of view on the subject.
",A Proposal for Intelligent Agents with Episodic Memory
"  Business processes are prone to unexpected changes, as process workers may
suddenly or gradually start executing a process differently in order to adjust
to changes in workload, season, or other external factors. Early detection of
business process changes enables managers to identify and act upon changes that
may otherwise affect process performance. Business process drift detection
refers to a family of methods to detect changes in a business process by
analyzing event logs extracted from the systems that support the execution of
the process. Existing methods for business process drift detection are based on
an explorative analysis of a potentially large feature space and in some cases
they require users to manually identify specific features that characterize the
drift. Depending on the explored feature space, these methods miss various
types of changes. Moreover, they are either designed to detect sudden drifts or
gradual drifts but not both. This paper proposes an automated and statistically
grounded method for detecting sudden and gradual business process drifts under
a unified framework. An empirical evaluation shows that the method detects
typical change patterns with significantly higher accuracy and lower detection
delay than existing methods, while accurately distinguishing between sudden and
gradual drifts.
","Detecting sudden and gradual drifts in business processes from execution
  traces"
"  This paper describes a new technique, called ""knowledge patterns"", for
helping construct axiom-rich, formal ontologies, based on identifying and
explicitly representing recurring patterns of knowledge (theory schemata) in
the ontology, and then stating how those patterns map onto domain-specific
concepts in the ontology. From a modeling perspective, knowledge patterns
provide an important insight into the structure of a formal ontology: rather
than viewing a formal ontology simply as a list of terms and axioms, knowledge
patterns views it as a collection of abstract, modular theories (the ""knowledge
patterns"") plus a collection of modeling decisions stating how different
aspects of the world can be modeled using those theories. Knowledge patterns
make both those abstract theories and their mappings to the domain of interest
explicit, thus making modeling decisions clear, and avoiding some of the
ontological confusion that can otherwise arise. In addition, from a
computational perspective, knowledge patterns provide a simple and
computationally efficient mechanism for facilitating knowledge reuse. We
describe the technique and an application built using them, and then critique
its strengths and weaknesses. We conclude that this technique enables us to
better explicate both the structure and modeling decisions made when
constructing a formal axiom-rich ontology.
",Knowledge Patterns
"  Current pseudo-Boolean solvers implement different variants of the cutting
planes proof system to infer new constraints during conflict analysis. One of
these variants is generalized resolution, which allows to infer strong
constraints, but suffers from the growth of coefficients it generates while
combining pseudo-Boolean constraints. Another variant consists in using
weakening and division, which is more efficient in practice but may infer
weaker constraints. In both cases, weakening is mandatory to derive conflicting
constraints. However, its impact on the performance of pseudo-Boolean solvers
has not been assessed so far. In this paper, new application strategies for
this rule are studied, aiming to infer strong constraints with small
coefficients. We implemented them in Sat4j and observed that each of them
improves the runtime of the solver. While none of them performs better than the
others on all benchmarks, applying weakening on the conflict side has
surprising good performance, whereas applying partial weakening and division on
both the conflict and the reason sides provides the best results overall.
",On Weakening Strategies for PB Solvers
"  Ideas and formalisms from far-from-equilibrium thermodynamics are ported to
the context of stochastic computational processes, via following and extending
Tadaki's algorithmic thermodynamics. A Principle of Maximum Algorithmic Caliber
is proposed, providing guidance as to what computational processes one should
hypothesize if one is provided constraints to work within. It is conjectured
that, under suitable assumptions, computational processes obeying algorithmic
Markov conditions will maximize algorithmic caliber. It is proposed that in
accordance with this, real-world cognitive systems may operate in substantial
part by modeling their environments and choosing their actions to be
(approximate and compactly represented) algorithmic Markov networks. These
ideas are suggested as potential early steps toward a general theory of the
operation of pragmatic generally intelligent systems.
","Maximal Algorithmic Caliber and Algorithmic Causal Network Inference:
  General Principles of Real-World General Intelligence?"
"  Despite significant effort, building models that are both interpretable and
accurate is an unresolved challenge for many pattern recognition problems. In
general, rule-based and linear models lack accuracy, while deep learning
interpretability is based on rough approximations of the underlying inference.
Using a linear combination of conjunctive clauses in propositional logic,
Tsetlin Machines (TMs) have shown competitive performance on diverse
benchmarks. However, to do so, many clauses are needed, which impacts
interpretability. Here, we address the accuracy-interpretability challenge in
machine learning by equipping the TM clauses with integer weights. The
resulting Integer Weighted TM (IWTM) deals with the problem of learning which
clauses are inaccurate and thus must team up to obtain high accuracy as a team
(low weight clauses), and which clauses are sufficiently accurate to operate
more independently (high weight clauses). Since each TM clause is formed
adaptively by a team of Tsetlin Automata, identifying effective weights becomes
a challenging online learning problem. We address this problem by extending
each team of Tsetlin Automata with a stochastic searching on the line (SSL)
automaton. In our novel scheme, the SSL automaton learns the weight of its
clause in interaction with the corresponding Tsetlin Automata team, which, in
turn, adapts the composition of the clause by the adjusting weight. We evaluate
IWTM empirically using five datasets, including a study of interpetability. On
average, IWTM uses 6.5 times fewer literals than the vanilla TM and 120 times
fewer literals than a TM with real-valued weights. Furthermore, in terms of
average F1-Score, IWTM outperforms simple Multi-Layered Artificial Neural
Networks, Decision Trees, Support Vector Machines, K-Nearest Neighbor, Random
Forest, XGBoost, Explainable Boosting Machines, and standard and real-value
weighted TMs.
","Extending the Tsetlin Machine With Integer-Weighted Clauses for
  Increased Interpretability"
"  This paper describes implementation details for a 3-level cognitive model,
described in the paper series. The whole architecture is now modular, with
different levels using different types of information. The ensemble-hierarchy
relationship is maintained and placed in the bottom optimising and middle
aggregating levels, to store memory objects and their relations. The top-level
cognitive layer has been re-designed to model the Cognitive Process Language
(CPL) of an earlier paper, by refactoring it into a network structure with a
light scheduler. The cortex brain region is thought to be hierarchical -
clustering from simple to more complex features. The refactored network might
therefore challenge conventional thinking on that brain region. It is also
argued that the function and structure in particular, of the new top level, is
similar to the psychology theory of chunking. The model is still only a
framework and does not have enough information for real intelligence. But a
framework is now implemented over the whole design and so can give a more
complete picture about the potential for results.
",New Ideas for Brain Modelling 6
"  Experts in Artificial Intelligence (AI) development predict that advances in
the development of intelligent systems and agents will reshape vital areas in
our society. Nevertheless, if such an advance isn't done with prudence, it can
result in negative outcomes for humanity. For this reason, several researchers
in the area are trying to develop a robust, beneficial, and safe concept of
artificial intelligence. Currently, several of the open problems in the field
of AI research arise from the difficulty of avoiding unwanted behaviors of
intelligent agents, and at the same time specifying what we want such systems
to do. It is of utmost importance that artificial intelligent agents have their
values aligned with human values, given the fact that we cannot expect an AI to
develop our moral preferences simply because of its intelligence, as discussed
in the Orthogonality Thesis. Perhaps this difficulty comes from the way we are
addressing the problem of expressing objectives, values, and ends, using
representational cognitive methods. A solution to this problem would be the
dynamic cognitive approach proposed by Dreyfus, whose phenomenological
philosophy defends that the human experience of being-in-the-world cannot be
represented by the symbolic or connectionist cognitive methods. A possible
approach to this problem would be to use theoretical models such as SED
(situated embodied dynamics) to address the values learning problem in AI.
",Dynamic Cognition Applied to Value Learning in Artificial Intelligence
"  Goal recognition is the problem of recognizing the intended goal of
autonomous agents or humans by observing their behavior in an environment. Over
the past years, most existing approaches to goal and plan recognition have been
ignoring the need to deal with imperfections regarding the domain model that
formalizes the environment where autonomous agents behave. In this thesis, we
introduce the problem of goal recognition over imperfect domain models, and
develop solution approaches that explicitly deal with two distinct types of
imperfect domains models: (1) incomplete discrete domain models that have
possible, rather than known, preconditions and effects in action descriptions;
and (2) approximate continuous domain models, where the transition function is
approximated from past observations and not well-defined. We develop novel goal
recognition approaches over imperfect domains models by leveraging and adapting
existing recognition approaches from the literature. Experiments and evaluation
over these two types of imperfect domains models show that our novel goal
recognition approaches are accurate in comparison to baseline approaches from
the literature, at several levels of observability and imperfections.
",Goal Recognition over Imperfect Domain Models
"  Various structured argumentation frameworks utilize preferences as part of
their standard inference procedure to enable reasoning with preferences. In
this paper, we consider an inverse of the standard reasoning problem, seeking
to identify what preferences over assumptions could lead to a given set of
conclusions being drawn. We ground our work in the Assumption-Based
Argumentation (ABA) framework, and present an algorithm which computes and
enumerates all possible sets of preferences over the assumptions in the system
from which a desired conflict free set of conclusions can be obtained under a
given semantic. After describing our algorithm, we establish its soundness,
completeness and complexity.
",Preference Elicitation in Assumption-Based Argumentation
"  Artificial Intelligence (AI) is being increasingly used to develop systems
that produce intelligent solutions. However, there is a major concern that
whether the systems built will be trusted by humans. In order to establish
trust in AI systems, there is a need for the user to understand the reasoning
behind their solutions and therefore, the system should be able to explain and
justify its output. In this paper, we use argumentation to provide explanations
in the domain of AI planning. We present argument schemes to create arguments
that explain a plan and its components; and a set of critical questions that
allow interaction between the arguments and enable the user to obtain further
information regarding the key elements of the plan. Finally, we present some
properties of the plan arguments.
",Argument Schemes for Explainable Planning
"  This article identifies a critical incompatibility between European notions
of discrimination and existing statistical measures of fairness. First, we
review the evidential requirements to bring a claim under EU non-discrimination
law. Due to the disparate nature of algorithmic and human discrimination, the
EU's current requirements are too contextual, reliant on intuition, and open to
judicial interpretation to be automated. Second, we show how the legal
protection offered by non-discrimination law is challenged when AI, not humans,
discriminate. Humans discriminate due to negative attitudes (e.g. stereotypes,
prejudice) and unintentional biases (e.g. organisational practices or
internalised stereotypes) which can act as a signal to victims that
discrimination has occurred. Finally, we examine how existing work on fairness
in machine learning lines up with procedures for assessing cases under EU
non-discrimination law. We propose ""conditional demographic disparity"" (CDD) as
a standard baseline statistical measurement that aligns with the European Court
of Justice's ""gold standard."" Establishing a standard set of statistical
evidence for automated discrimination cases can help ensure consistent
procedures for assessment, but not judicial interpretation, of cases involving
AI and automated systems. Through this proposal for procedural regularity in
the identification and assessment of automated discrimination, we clarify how
to build considerations of fairness into automated systems as far as possible
while still respecting and enabling the contextual approach to judicial
interpretation practiced under EU non-discrimination law.
  N.B. Abridged abstract
","Why Fairness Cannot Be Automated: Bridging the Gap Between EU
  Non-Discrimination Law and AI"
"  We propose a novel activity learning framework based on Edge Cloud
architecture for the purpose of recognizing and predicting human activities.
Although activity recognition has been vastly studied by many researchers, the
temporal features that constitute an activity, which can provide useful
insights for activity models, have not been exploited to their full potentials
by mining algorithms. In this paper, we utilize temporal features for activity
recognition and prediction in a single smart home setting. We discover activity
patterns and temporal relations such as the order of activities from real data
to develop a prompting system. Analysis of real data collected from smart homes
was used to validate the proposed method.
",Enabling Edge Cloud Intelligence for Activity Learning in Smart Home
"  We present a Cognitive Amplifier framework to augment things part of an IoT,
with cognitive capabilities for the purpose of improving life convenience.
Specifically, the Cognitive Amplifier consists of knowledge discovery and
prediction components. The knowledge discovery component focuses on finding
natural activity patterns considering their regularity, variations, and
transitions in real life setting. The prediction component takes the discovered
knowledge as the base for inferring what, when, and where the next activity
will happen. Experimental results on real-life data validate the feasibility
and applicability of the proposed approach.
",Cognitive Amplifier for Internet of Things
"  Deep reinforcement learning has been successfully applied to many control
tasks, but the application of such agents in safety-critical scenarios has been
limited due to safety concerns. Rigorous testing of these controllers is
challenging, particularly when they operate in probabilistic environments due
to, for example, hardware faults or noisy sensors. We propose MOSAIC, an
algorithm for measuring the safety of deep reinforcement learning agents in
stochastic settings. Our approach is based on the iterative construction of a
formal abstraction of a controller's execution in an environment, and leverages
probabilistic model checking of Markov decision processes to produce
probabilistic guarantees on safe behaviour over a finite time horizon. It
produces bounds on the probability of safe operation of the controller for
different initial configurations and identifies regions where correct behaviour
can be guaranteed. We implement and evaluate our approach on agents trained for
several benchmark control problems.
",Probabilistic Guarantees for Safe Deep Reinforcement Learning
"  While humans and animals learn incrementally during their lifetimes and
exploit their experience to solve new tasks, standard deep reinforcement
learning methods specialize to solve only one task at a time and, as a result,
the information they acquire is hardly reusable in new situations. Here, we
introduce a new perspective on the problem of leveraging prior knowledge to
solve future unknown tasks. We show that learning discrete concept-like
representations of sensory inputs can provide a high-level abstraction that is
common across multiple tasks, thus facilitating the transference of
information. In particular, we show that it is possible to learn such
representations by self-supervision, following an information theoretic
approach, and that they improve the sample efficiency by providing prior
policies that guide the policy learning process. Our method is able to learn
concepts in locomotive tasks that reduce the number of optimization steps in
both known and unknown tasks, opening a new path to endow artificial agents
with generalization abilities.
",Learning Transferable Concepts in Deep Reinforcement Learning
"  Here we understand 'intelligence' as referring to items of knowledge
collected for the sake of assessing and maintaining national security. The
intelligence community (IC) of the United States (US) is a community of
organizations that collaborate in collecting and processing intelligence for
the US. The IC relies on human-machine-based analytic strategies that 1) access
and integrate vast amounts of information from disparate sources, 2)
continuously process this information, so that, 3) a maximally comprehensive
understanding of world actors and their behaviors can be developed and updated.
Herein we describe an approach to utilizing outcomes-based learning (OBL) to
support these efforts that is based on an ontology of the cognitive processes
performed by intelligence analysts. Of particular importance to the Cognitive
Process Ontology is the class Representation that is Warranted. Such a
representation is descriptive in nature and deserving of trust in its
veridicality. The latter is because a Representation that is Warranted is
always produced by a process that was vetted (or successfully designed) to
reliably produce veridical representations. As such, Representations that are
Warranted are what in other contexts we might refer to as 'items of knowledge'.
",Ontology and Cognitive Outcomes
"  We describe an Object Oriented Model for building Expert Systems. This model
and the detection of similarities allow to implement reasoning modes as
induction, deduction and simulation. We specially focus on similarity and its
use in induction. We propose original algorithms which deal with total and
partial structural similitude of objects to facilitate knowledge acquisition.
",Automatic Knowledge Acquisition for Object-Oriented Expert Systems
"  We can break symmetry by eliminating solutions within a symmetry class that
are not least in the lexicographical ordering. This is often referred to as the
lex-leader method. Unfortunately, as symmetry groups can be large, the
lexleader method is not tractable in general. We prove that using other total
orderings besides the usual lexicographical ordering will not reduce the
computational complexity of breaking symmetry in general. It follows that
breaking symmetry with other orderings like the Gray code ordering or the
Snake-Lex ordering is intractable in general.
",On the Complexity of Breaking Symmetry
"  General conversational intelligence appears to be an important part of
artificial general intelligence. Respectively, it requires accessible measures
of the intelligence quality and controllable ways of its achievement, ideally -
having the linguistic and semantic models represented in a reasonable way. Our
work is suggesting to use Baby Turing Test approach to extend the classic
Turing Test for conversational intelligence and controlled language based on
semantic graph representation extensible for arbitrary subject domain. We
describe how the two can be used together to build a general-purpose
conversational system such as an intelligent assistant for online media and
social network data processing.
","Controlled Language and Baby Turing Test for General Conversational
  Intelligence"
"  In scenarios that require teamwork, we usually have at hand a variety of
specific tasks, for which we need to form a team in order to carry out each
one. Here we target the problem of matching teams with tasks within the context
of education, and specifically in the context of forming teams of students and
allocating them to internship programs. First we provide a formalization of the
Team Allocation for Internship Programs Problem, and show the computational
hardness of solving it optimally. Thereafter, we propose TAIP, a heuristic
algorithm that generates an initial team allocation which later on attempts to
improve in an iterative process. Moreover, we conduct a systematic evaluation
to show that TAIP reaches optimality, and outperforms CPLEX in terms of time.
","TAIP: an anytime algorithm for allocating student teams to internship
  programs"
"  Monte Carlo Tree Search (MCTS) efficiently balances exploration and
exploitation in tree search based on count-derived uncertainty. However, these
local visit counts ignore a second type of uncertainty induced by the size of
the subtree below an action. We first show how, due to the lack of this second
uncertainty type, MCTS may completely fail in well-known sparse exploration
problems, known from the reinforcement learning community. We then introduce a
new algorithm, which estimates the size of the subtree below an action, and
leverages this information in the UCB formula to better direct exploration.
Subsequently, we generalize these ideas by showing that loops, i.e., the
repeated occurrence of (approximately) the same state in the same trace, are
actually a special case of subtree depth variation. Testing on a variety of
tasks shows that our algorithms increase sample efficiency, especially when the
planning budget per timestep is small.
",The Second Type of Uncertainty in Monte Carlo Tree Search
"  The efficient and fair allocation of limited resources is a classical problem
in economics and computer science. In kidney exchanges, a central market maker
allocates living kidney donors to patients in need of an organ. Patients and
donors in kidney exchanges are prioritized using ad-hoc weights decided on by
committee and then fed into an allocation algorithm that determines who gets
what--and who does not. In this paper, we provide an end-to-end methodology for
estimating weights of individual participant profiles in a kidney exchange. We
first elicit from human subjects a list of patient attributes they consider
acceptable for the purpose of prioritizing patients (e.g., medical
characteristics, lifestyle choices, and so on). Then, we ask subjects
comparison queries between patient profiles and estimate weights in a
principled way from their responses. We show how to use these weights in kidney
exchange market clearing algorithms. We then evaluate the impact of the weights
in simulations and find that the precise numerical values of the weights we
computed matter little, other than the ordering of profiles that they imply.
However, compared to not prioritizing patients at all, there is a significant
effect, with certain classes of patients being (de)prioritized based on the
human-elicited value judgments.
",Adapting a Kidney Exchange Algorithm to Align with Human Values
"  Reinforcement learning and probabilistic reasoning algorithms aim at learning
from interaction experiences and reasoning with probabilistic contextual
knowledge respectively. In this research, we develop algorithms for robot task
completions, while looking into the complementary strengths of reinforcement
learning and probabilistic reasoning techniques. The robots learn from
trial-and-error experiences to augment their declarative knowledge base, and
the augmented knowledge can be used for speeding up the learning process in
potentially different tasks. We have implemented and evaluated the developed
algorithms using mobile robots conducting dialog and navigation tasks. From the
results, we see that our robot's performance can be improved by both reasoning
with human knowledge and learning from task-completion experience. More
interestingly, the robot was able to learn from navigation tasks to improve its
dialog strategies.
",Learning and Reasoning for Robot Dialog and Navigation Tasks
"  The RNA Inverse Folding problem comes from computational biology. The goal is
to find a molecule that has a given folding. It is important for scientific
fields such as bioengineering, pharmaceutical research, biochemistry, synthetic
biology and RNA nanostructures. Nested Monte Carlo Search has given excellent
results for this problem. We propose to adapt and evaluate different Monte
Carlo Search algorithms for the RNA Inverse Folding problem.
",Monte Carlo Inverse Folding
"  In many real-world settings, a decision-maker must combine information
provided by different experts in order to decide on an effective policy.
Alrajeh, Chockler, and Halpern [2018] showed how to combine causal models that
are compatible in the sense that, for variables that appear in both models, the
experts agree on the causal structure. In this work we show how causal models
can be combined in cases where the experts might disagree on the causal
structure for variables that appear in both models due to having different
focus areas. We provide a new formal definition of compatibility of models in
this setting and show how compatible models can be combined. We also consider
the complexity of determining whether models are compatible. We believe that
the notions defined in this work are of direct relevance to many practical
decision making scenarios that come up in natural, social, and medical science
settings.
","Combining the Causal Judgments of Experts with Possibly Different Focus
  Areas"
"  We introduce a theoretical model of information acquisition under resource
limitations in a noisy environment. An agent must guess the truth value of a
given Boolean formula $\varphi$ after performing a bounded number of noisy
tests of the truth values of variables in the formula. We observe that, in
general, the problem of finding an optimal testing strategy for $\phi$ is hard,
but we suggest a useful heuristic. The techniques we use also give insight into
two apparently unrelated, but well-studied problems: (1) \emph{rational
inattention}, that is, when it is rational to ignore pertinent information (the
optimal strategy may involve hardly ever testing variables that are clearly
relevant to $\phi$), and (2) what makes a formula hard to learn/remember.
","Information Acquisition Under Resource Limitations in a Noisy
  Environment"
"  In this paper, we provide an overview of the existing methods for integrating
human advice into a Reinforcement Learning process. We first propose a taxonomy
of the different forms of advice that can be provided to a learning agent. We
then describe the methods that can be used for interpreting advice when its
meaning is not determined beforehand. Finally, we review different approaches
for integrating advice into the learning process.
",Reinforcement learning with human advice: a survey
"  This competition paper presents microPhantom, a bot playing microRTS and
participating in the 2020 microRTS AI competition. microPhantom is based on our
previous bot POAdaptive which won the partially observable track of the 2018
and 2019 microRTS AI competitions. In this paper, we focus on decision-making
under uncertainty, by tackling the Unit Production Problem with a method based
on a combination of Constraint Programming and decision theory. We show that
using our method to decide which units to train improves significantly the win
rate against the second-best microRTS bot from the partially observable track.
We also show that our method is resilient in chaotic environments, with a very
small loss of efficiency only. To allow replicability and to facilitate further
research, the source code of microPhantom is available, as well as the
Constraint Programming toolkit it uses.
",microPhantom: Playing microRTS under uncertainty and chaos
"  The General Video Game Artificial Intelligence (GVGAI) competition has been
running for several years with various tracks. This paper focuses on the
challenge of the GVGAI learning track in which 3 games are selected and 2
levels are given for training, while 3 hidden levels are left for evaluation.
This setup poses a difficult challenge for current Reinforcement Learning (RL)
algorithms, as they typically require much more data. This work investigates 3
versions of the Advantage Actor-Critic (A2C) algorithm trained on a maximum of
2 levels from the available 5 from the GVGAI framework and compares their
performance on all levels. The selected sub-set of games have different
characteristics, like stochasticity, reward distribution and objectives. We
found that stochasticity improves the generalisation, but too much can cause
the algorithms to fail to learn the training levels. The quality of the
training levels also matters, different sets of training levels can boost
generalisation over all levels. In the GVGAI competition agents are scored
based on their win rates and then their scores achieved in the games. We found
that solely using the rewards provided by the game might not encourage winning.
",Evaluating Generalisation in General Video Game Playing
"  This paper presents a new approach to generate samples from conditional
belief functions for a restricted but non trivial subset of conditional belief
functions. It assumes the factorization (decomposition) of a belief function
along a bayesian network structure. It applies general conditional belief
functions.
",Non-Destructive Sample Generation From Conditional Belief Functions
"  This paper analyses properties of conceptual hierarchy obtained via
incremental concept formation method called ""flexible prediction"" in order to
determine what kind of ""relevance"" of participating attributes may be requested
for meaningful conceptual hierarchy. The impact of selection of simple and
combined attributes, of scaling and of distribution of individual attributes
and of correlation strengths among them is investigated. Paradoxically, both:
attributes weakly and strongly related with other attributes have deteriorating
impact onto the overall classification. Proper construction of derived
attributes as well as selection of scaling of individual attributes strongly
influences the obtained concept hierarchy. Attribute density of distribution
seems to influence the classification weakly
  It seems also, that concept hierarchies (taxonomies) reflect a compromise
between the data and our interests in some objective truth about the data. To
obtain classifications more suitable for one's purposes, breaking the symmetry
among attributes (by dividing them into dependent and independent and applying
differing evaluation formulas for their contribution) is suggested. Both
continuous and discrete variables are considered. Some methodologies for the
former are considered.
",On Irrelevance of Attributes in Flexible Prediction
"  Multi-agent learning is a challenging problem in machine learning that has
applications in different domains such as distributed control, robotics, and
economics. We develop a prescriptive model of multi-agent behavior using Markov
games. Since in many multi-agent systems, agents do not necessary select their
optimum strategies against other agents (e.g., multi-pedestrian interaction),
we focus on models in which the agents play ""exploration but near optimum
strategies"". We model such policies using the Boltzmann-Gibbs distribution.
This leads to a set of coupled Bellman equations that describes the behavior of
the agents. We introduce a set of conditions under which the set of equations
admit a unique solution and propose two algorithms that provably provide the
solution in finite and infinite time horizon scenarios. We also study a
practical setting in which the interactions can be described using the
occupancy measures and propose a simplified Markov game with less complexity.
Furthermore, we establish the connection between the Markov games with
exploration strategies and the principle of maximum causal entropy for
multi-agent systems. Finally, we evaluate the performance of our algorithms via
several well-known games from the literature and some games that are designed
based on real world applications.
",Non-cooperative Multi-agent Systems with Exploring Agents
"  Standard reinforcement learning (RL) algorithms assume that the observation
of the next state comes instantaneously and at no cost. In a wide variety of
sequential decision making tasks ranging from medical treatment to scientific
discovery, however, multiple classes of state observations are possible, each
of which has an associated cost. We propose the active measure RL framework
(Amrl) as an initial solution to this problem where the agent learns to
maximize the costed return, which we define as the discounted sum of rewards
minus the sum of observation costs. Our empirical evaluation demonstrates that
Amrl-Q agents are able to learn a policy and state estimator in parallel during
online training. During training the agent naturally shifts from its reliance
on costly measurements of the environment to its state estimator in order to
increase its reward. It does this without harm to the learned policy. Our
results show that the Amrl-Q agent learns at a rate similar to standard
Q-learning and Dyna-Q. Critically, by utilizing an active strategy, Amrl-Q
achieves a higher costed return.
",Active Measure Reinforcement Learning for Observation Cost Minimization
"  A conditional knowledge base R is a set of conditionals of the form ""If A,
the usually B"". Using structural information derived from the conditionals in
R, we introduce the preferred structure relation on worlds. The preferred
structure relation is the core ingredient of a new inference relation called
system W inference that inductively completes the knowledge given explicitly in
R. We show that system W exhibits desirable inference properties like
satisfying system P and avoiding, in contrast to e.g. system Z, the drowning
problem. It fully captures and strictly extends both system Z and skeptical
c-inference. In contrast to skeptical c-inference, it does not require to solve
a complex constraint satisfaction problem, but is as tractable as system Z.
","Nonmonotonic Inferences with Qualitative Conditionals based on Preferred
  Structures on Worlds"
"  Tracking dialogue states to better interpret user goals and feed downstream
policy learning is a bottleneck in dialogue management. Common practice has
been to treat it as a problem of classifying dialogue content into a set of
pre-defined slot-value pairs, or generating values for different slots given
the dialogue history. Both have limitations on considering dependencies that
occur on dialogues, and are lacking of reasoning capabilities. This paper
proposes to track dialogue states gradually with reasoning over dialogue turns
with the help of the back-end data. Empirical results demonstrate that our
method significantly outperforms the state-of-the-art methods by 38.6% in terms
of joint belief accuracy for MultiWOZ 2.1, a large-scale human-human dialogue
dataset across multiple domains.
",Rethinking Dialogue State Tracking with Reasoning
"  The Traveling-Salesperson-Problem (TSP) is arguably one of the best-known
NP-hard combinatorial optimization problems. The two sophisticated heuristic
solvers LKH and EAX and respective (restart) variants manage to calculate
close-to optimal or even optimal solutions, also for large instances with
several thousand nodes in reasonable time. In this work we extend existing
benchmarking studies by addressing anytime behaviour of inexact TSP solvers
based on empirical runtime distributions leading to an increased understanding
of solver behaviour and the respective relation to problem hardness. It turns
out that performance ranking of solvers is highly dependent on the focused
approximation quality. Insights on intersection points of performances offer
huge potential for the construction of hybridized solvers depending on instance
features. Moreover, instance features tailored to anytime performance and
corresponding performance indicators will highly improve automated algorithm
selection models by including comprehensive information on solver quality.
","Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated
  Algorithm Selection"
"  We use neural graph networks with a message-passing architecture and an
attention mechanism to enhance the branching heuristic in two SAT-solving
algorithms. We report improvements of learned neural heuristics compared with
two standard human-designed heuristics.
",Neural heuristics for SAT solving
"  Modern algorithms in the domain of Deep Reinforcement Learning (DRL)
demonstrated remarkable successes; most widely known are those in game-based
scenarios, from ATARI video games to Go and the StarCraft~\textsc{II} real-time
strategy game. However, applications in the domain of modern Cyber-Physical
Systems (CPS) that take advantage a vast variety of DRL algorithms are few. We
assume that the benefits would be considerable: Modern CPS have become
increasingly complex and evolved beyond traditional methods of modelling and
analysis. At the same time, these CPS are confronted with an increasing amount
of stochastic inputs, from volatile energy sources in power grids to broad user
participation stemming from markets. Approaches of system modelling that use
techniques from the domain of Artificial Intelligence (AI) do not focus on
analysis and operation. In this paper, we describe the concept of Adversarial
Resilience Learning (ARL) that formulates a new approach to complex environment
checking and resilient operation: It defines two agent classes, attacker and
defender agents. The quintessence of ARL lies in both agents exploring the
system and training each other without any domain knowledge. Here, we introduce
the ARL software architecture that allows to use a wide range of model-free as
well as model-based DRL-based algorithms, and document results of concrete
experiment runs on a complex power grid.
","The Adversarial Resilience Learning Architecture for AI-based Modelling,
  Exploration, and Operation of Complex Cyber-Physical Systems"
"  Recently, a groundswell of research has identified the use of counterfactual
explanations as a potentially significant solution to the Explainable AI (XAI)
problem. It is argued that (a) technically, these counterfactual cases can be
generated by permuting problem-features until a class change is found, (b)
psychologically, they are much more causally informative than factual
explanations, (c) legally, they are GDPR-compliant. However, there are issues
around the finding of good counterfactuals using current techniques (e.g.
sparsity and plausibility). We show that many commonly-used datasets appear to
have few good counterfactuals for explanation purposes. So, we propose a new
case based approach for generating counterfactuals using novel ideas about the
counterfactual potential and explanatory coverage of a case-base. The new
technique reuses patterns of good counterfactuals, present in a case-base, to
generate analogous counterfactuals that can explain new problems and their
solutions. Several experiments show how this technique can improve the
counterfactual potential and explanatory coverage of case-bases that were
previously found wanting.
","Good Counterfactuals and Where to Find Them: A Case-Based Technique for
  Generating Counterfactuals for Explainable AI (XAI)"
"  Recommendation system or also known as a recommender system is a tool to help
the user in providing a suggestion of a specific dilemma. Thus, recently, the
interest in developing a recommendation system in many fields has increased.
Fuzzy Logic system (FLSs) is one of the approaches that can be used to model
the recommendation systems as it can deal with uncertainty and imprecise
information. However, one of the fundamental issues in FLS is the problem of
the curse of dimensionality. That is, the number of rules in FLSs is increasing
exponentially with the number of input variables. One effective way to overcome
this problem is by using Hierarchical Fuzzy System (HFSs). This paper aims to
explore the use of HFSs for Recommendation system. Specifically, we are
interested in exploring and comparing the HFS and FLS for the Career path
recommendation system (CPRS) based on four key criteria, namely topology, the
number of rules, the rules structures and interpretability. The findings
suggested that the HFS has advantages over FLS towards improving the
interpretability models, in the context of a recommendation system example.
This study contributes to providing an insight into the development of
interpretable HFSs in the Recommendation systems.
","An Exploratory Study of Hierarchical Fuzzy Systems Approach in
  Recommendation System"
"  LWF chain graphs combine directed acyclic graphs and undirected graphs. We
present a PC-like algorithm that finds the structure of chain graphs under the
faithfulness assumption to resolve the problem of scalability of the proposed
algorithm by Studeny (1997). We prove that our PC-like algorithm is order
dependent, in the sense that the output can depend on the order in which the
variables are given. This order dependence can be very pronounced in
high-dimensional settings. We propose two modifications of the PC-like
algorithm that remove part or all of this order dependence. Simulation results
under a variety of settings demonstrate the competitive performance of the
PC-like algorithms in comparison with the decomposition-based method, called
LCD algorithm, proposed by Ma et al. (2008) in low-dimensional settings and
improved performance in high-dimensional settings.
",Learning LWF Chain Graphs: an Order Independent Algorithm
"  The Travelling Salesman Problem (TSP) is a classical NP-hard problem and has
broad applications in many disciplines and industries. In a large scale
location-based services system, users issue TSP queries concurrently, where a
TSP query is a TSP instance with $n$ points. In the literature, many advanced
TSP solvers are developed to find high-quality solutions. Such solvers can
solve some TSP instances efficiently but may take an extremely long time for
some other instances. Due to the diversity of TSP instances, it is well-known
that there exists no universal best solver dominating all other solvers on all
possible TSP instances. To solve TSP efficiently, in addition to developing new
TSP solvers, it needs to find a per-instance solver for each TSP instance,
which is known as the TSP solver selection problem. In this paper, for the
first time, we propose a deep learning framework, \CTAS, for TSP solver
selection in an end-to-end manner. Specifically, \CTAS exploits deep
convolutional neural networks to extract informative features from TSP
instances and involves data argumentation strategies to handle the scarcity of
labeled TSP instances. Moreover, to support large scale TSP solver selection,
we construct a challenging TSP benchmark dataset with 6,000 instances, which is
known as the largest TSP benchmark. Our \CTAS achieves over 2$\times$ speedup
of the average running time, comparing the single best solver, and outperforms
the state-of-the-art statistical models.
",Towards Feature-free TSP Solver Selection: A Deep Learning Approach
"  We devise a method to exactly compute the length of the longest simple path
in factored state spaces, like state spaces encountered in classical planning.
Although the complexity of this problem is NEXP-Hard, we show that our method
can be used to compute practically useful upper-bounds on lengths of plans. We
show that the computed upper-bounds are significantly (in many cases, orders of
magnitude) better than bounds produced by previous bounding techniques and that
they can be used to improve the SAT-based planning.
",Computing Plan-Length Bounds Using Lengths of Longest Paths
"  Safe-interval path planning (SIPP) is a powerful algorithm for finding a path
in the presence of dynamic obstacles. SIPP returns provably optimal solutions.
However, in many practical applications of SIPP such as path planning for
robots, one would like to trade-off optimality for shorter planning time. In
this paper we explore different ways to build a bounded-suboptimal SIPP and
discuss their pros and cons. We compare the different bounded-suboptimal
versions of SIPP experimentally. While there is no universal winner, the
results provide insights into when each method should be used.
",Revisiting Bounded-Suboptimal Safe Interval Path Planning
"  Descriptor revision by Hansson is a framework for addressing the problem of
belief change. In descriptor revision, different kinds of change processes are
dealt with in a joint framework. Individual change requirements are qualified
by specific success conditions expressed by a belief descriptor, and belief
descriptors can be combined by logical connectives. This is in contrast to the
currently dominating AGM paradigm shaped by Alchourr\'on, G\""ardenfors, and
Makinson, where different kinds of changes, like a revision or a contraction,
are dealt with separately. In this article, we investigate the realisation of
descriptor revision for a conditional logic while restricting descriptors to
the conjunction of literal descriptors. We apply the principle of conditional
preservation developed by Kern-Isberner to descriptor revision for
conditionals, show how descriptor revision for conditionals under these
restrictions can be characterised by a constraint satisfaction problem, and
implement it using constraint logic programming. Since our conditional logic
subsumes propositional logic, our approach also realises descriptor revision
for propositional logic.
","Descriptor Revision for Conditionals: Literal Descriptors and
  Conditional Preservation"
"  In this paper we schedule the travel path of a set of drones across a graph
where the nodes need to be visited multiple times at pre-defined points in
time. This is an extension of the well-known multiple traveling salesman
problem. The proposed formulation can be applied in several domains such as the
monitoring of traffic flows in a transportation network, or the monitoring of
remote locations to assist search and rescue missions. Aiming to find the
optimal schedule, the problem is formulated as an Integer Linear Program (ILP).
Given that the problem is highly combinatorial, the optimal solution scales
only for small sized problems. Thus, a greedy algorithm is also proposed that
uses a one-step look ahead heuristic search mechanism. In a detailed
evaluation, it is observed that the greedy algorithm has near-optimal
performance as it is on average at 92.06% of the optimal, while it can
potentially scale up to settings with hundreds of drones and locations.
","Extending the Multiple Traveling Salesman Problem for Scheduling a Fleet
  of Drones Performing Monitoring Missions"
"  SAT research has a long history of source code and binary releases, thanks to
competitions organized every year. However, since every cycle of competitions
has its own set of rules and an adhoc way of publishing source code and
binaries, compiling or even running any solver may be harder than what it
seems. Moreover, there has been more than a thousand solvers published so far,
some of them released in the early 90's. If the SAT community wants to archive
and be able to keep track of all the solvers that made its history, it urgently
needs to deploy an important effort. We propose to initiate a community-driven
effort to archive and to allow easy compilation and running of all SAT solvers
that have been released so far. We rely on the best tools for archiving and
building binaries (thanks to Docker, GitHub and Zenodo) and provide a
consistent and easy way for this. Thanks to our tool, building (or running) a
solver from its source (or from its binary) can be done in one line.
","SAT Heritage: a community-driven effort for archiving, building and
  running more than thousand SAT solvers"
"  This paper provides the foundations of a unified cognitive decision-making
framework (QulBIT) which is derived from quantum theory. The main advantage of
this framework is that it can cater for paradoxical and irrational human
decision making. Although quantum approaches for cognition have demonstrated
advantages over classical probabilistic approaches and bounded rationality
models, they still lack explanatory power. To address this, we introduce a
novel explanatory analysis of the decision-maker's belief space. This is
achieved by exploiting quantum interference effects as a way of both
quantifying and explaining the decision-maker's uncertainty. We detail the main
modules of the unified framework, the explanatory analysis method, and
illustrate their application in situations violating the Sure Thing Principle.
","QuLBIT: Quantum-Like Bayesian Inference Technologies for Cognition and
  Decision"
"  We introduce Logic Guided Machine Learning (LGML), a novel approach that
symbiotically combines machine learning (ML) and logic solvers with the goal of
learning mathematical functions from data. LGML consists of two phases, namely
a learning-phase and a logic-phase with a corrective feedback loop, such that,
the learning-phase learns symbolic expressions from input data, and the
logic-phase cross verifies the consistency of the learned expression with known
auxiliary truths. If inconsistent, the logic-phase feeds back ""counterexamples""
to the learning-phase. This process is repeated until the learned expression is
consistent with auxiliary truth. Using LGML, we were able to learn expressions
that correspond to the Pythagorean theorem and the sine function, with several
orders of magnitude improvements in data efficiency compared to an approach
based on an out-of-the-box multi-layered perceptron (MLP).
",LGML: Logic Guided Machine Learning
"  In studying robots and planning problems, a basic question is what is the
minimal information a robot must obtain to guarantee task completion. Erdmann's
theory of action-based sensors is a classical approach to characterizing
fundamental information requirements. That approach uses a plan to derive a
type of virtual sensor which prescribes actions that make progress toward a
goal. We show that the established theory is incomplete: the previous method
for obtaining such sensors, using backchained plans, overlooks some sensors.
Furthermore, there are plans, that are guaranteed to achieve goals, where the
existing methods are unable to provide any action-based sensor. We identify the
underlying feature common to all such plans. Then, we show how to produce
action-based sensors even for plans where the existing treatment is inadequate,
although for these cases they have no single canonical sensor. Consequently,
the approach is generalized to produce sets of sensors. Finally, we show also
that this is a complete characterization of action-based sensors for planning
problems and discuss how an action-based sensor translates into the traditional
conception of a sensor.
",Every Action Based Sensor
"  WordNet-like Lexical Databases (WLDs) group English words into sets of
synonyms called ""synsets."" Although the standard WLDs are being used in many
successful Text-Mining applications, they have the limitation that word-senses
are considered to represent the meaning associated to their corresponding
synsets, to the same degree, which is not generally true. In order to overcome
this limitation, several fuzzy versions of synsets have been proposed. A common
trait of these studies is that, to the best of our knowledge, they do not aim
to produce fuzzified versions of the existing WLD's, but build new WLDs from
scratch, which has limited the attention received from the Text-Mining
community, many of whose resources and applications are based on the existing
WLDs. In this study, we present an algorithm for constructing fuzzy versions of
WLDs of any language, given a corpus of documents and a word-sense
disambiguation (WSD) system for that language. Then, using the
Open-American-National-Corpus and UKB WSD as algorithm inputs, we construct and
publish online the fuzzified version of English WordNet (FWN). We also propose
a theoretical (mathematical) proof of the validity of its results.
","An Algorithm for Fuzzification of WordNets, Supported by a Mathematical
  Proof"
"  While the biomedical community has published several ""open data"" sources in
the last decade, most researchers still endure severe logistical and technical
challenges to discover, query, and integrate heterogeneous data and knowledge
from multiple sources. To tackle these challenges, the community has
experimented with Semantic Web and linked data technologies to create the Life
Sciences Linked Open Data (LSLOD) cloud. In this paper, we extract schemas from
more than 80 publicly available biomedical linked data graphs into an LSLOD
schema graph and conduct an empirical meta-analysis to evaluate the extent of
semantic heterogeneity across the LSLOD cloud. We observe that several LSLOD
sources exist as stand-alone data sources that are not inter-linked with other
sources, use unpublished schemas with minimal reuse or mappings, and have
elements that are not useful for data integration from a biomedical
perspective. We envision that the LSLOD schema graph and the findings from this
research will aid researchers who wish to query and integrate data and
knowledge from multiple biomedical sources simultaneously on the Web.
","An Empirical Meta-analysis of the Life Sciences (Linked?) Open Data on
  the Web"
"  Our concern is the problem of efficiently determining the data complexity of
answering queries mediated by description logic ontologies and constructing
their optimal rewritings to standard database queries. Originated in
ontology-based data access and datalog optimisation, this problem is known to
be computationally very complex in general, with no explicit syntactic
characterisations available. In this article, aiming to understand the
fundamental roots of this difficulty, we strip the problem to the bare bones
and focus on Boolean conjunctive queries mediated by a simple covering axiom
stating that one class is covered by the union of two other classes. We show
that, on the one hand, these rudimentary ontology-mediated queries, called
disjunctive sirups (or d-sirups), capture many features and difficulties of the
general case. For example, answering d-sirups is Pi^p_2-complete for combined
complexity and can be in AC0 or LogSpace-, NL-, P-, or coNP-complete for data
complexity (with the problem of recognising FO-rewritability of d-sirups being
2ExpTime-hard); some d-sirups only have exponential-size resolution proofs,
some only double-exponential-size positive existential FO-rewritings and
single-exponential-size nonrecursive datalog rewritings. On the other hand, we
prove a few partial sufficient and necessary conditions of FO- and
(symmetric/linear-) datalog rewritability of d-sirups. Our main technical
result is a complete and transparent syntactic AC0/NL/P/coNP tetrachotomy of
d-sirups with disjoint covering classes and a path-shaped Boolean conjunctive
query. To obtain this tetrachotomy, we develop new techniques for establishing
P- and coNP-hardness of answering non-Horn ontology-mediated queries as well as
showing that they can be answered in NL.
",A tetrachotomy of ontology-mediated queries with a covering axiom
"  In this paper we develop a concept aware multi-preferential semantics for
dealing with typicality in description logics, where preferences are associated
with concepts, starting from a collection of ranked TBoxes containing
defeasible concept inclusions. Preferences are combined to define a
preferential interpretation in which defeasible inclusions can be evaluated.
The construction of the concept-aware multipreference semantics is related to
Brewka's framework for qualitative preferences. We exploit Answer Set
Programming (in particular, asprin) to achieve defeasible reasoning under the
multipreference approach for the lightweight description logic EL+bot.
  The paper is under consideration for acceptance in TPLP.
","An ASP approach for reasoning in a concept-aware multipreferential
  lightweight DL"
"  Computational intractability has for decades motivated the development of a
plethora of methodologies that mainly aimed at a quality-time trade-off. The
use of Machine Learning techniques has finally emerged as one of the possible
tools to obtain approximate solutions to ${\cal NP}$-hard combinatorial
optimization problems. In a recent article, Dai et al. introduced a method for
computing such approximate solutions for instances of the Vertex Cover problem.
In this paper we consider the effectiveness of selecting a proper training
strategy by considering special problem instances called ""obstructions"" that we
believe carry some intrinsic properties of the problem itself. Capitalizing on
the recent work of Dai et al. on the Vertex Cover problem, and using the same
case study as well as 19 other problem instances, we show the utility of using
obstructions for training neural networks. Experiments show that training with
obstructions results in a huge reduction in number of iterations needed for
convergence, thus gaining a substantial reduction in the time needed for
training the model.
",Graph Minors Meet Machine Learning: the Power of Obstructions
"  An ambitious goal for machine learning is to create agents that behave
ethically: The capacity to abide by human moral norms would greatly expand the
context in which autonomous agents could be practically and safely deployed,
e.g. fully autonomous vehicles will encounter charged moral decisions that
complicate their deployment. While ethical agents could be trained by rewarding
correct behavior under a specific moral theory (e.g. utilitarianism), there
remains widespread disagreement about the nature of morality. Acknowledging
such disagreement, recent work in moral philosophy proposes that ethical
behavior requires acting under moral uncertainty, i.e. to take into account
when acting that one's credence is split across several plausible ethical
theories. This paper translates such insights to the field of reinforcement
learning, proposes two training methods that realize different points among
competing desiderata, and trains agents in simple environments to act under
moral uncertainty. The results illustrate (1) how such uncertainty can help
curb extreme behavior from commitment to single theories and (2) several
technical complications arising from attempting to ground moral philosophy in
RL (e.g. how can a principled trade-off between two competing but incomparable
reward functions be reached). The aim is to catalyze progress towards
morally-competent agents and highlight the potential of RL to contribute
towards the computational grounding of moral philosophy.
",Reinforcement Learning Under Moral Uncertainty
"  We consider the problem of coordinating a fleet of robots in a warehouse so
as to maximize the reward achieved within a time limit while respecting problem
and robot specific constraints. We formulate the problem as a weighted set
packing problem where elements are defined as being the space-time positions a
robot can occupy and the items that can be picked up and delivered. We enforce
that robots do not collide, that each item is delivered at most once, and that
the number of robots active at any time does not exceed the total number
available. Since the set of robot routes is not enumerable, we attack
optimization using column generation where pricing is a resource-constrained
shortest-path problem.
","Integer Programming for Multi-Robot Planning: A Column Generation
  Approach"
"  Simulated annealing-based ontology matching (SANOM) participates for the
second time at the ontology alignment evaluation initiative (OAEI) 2019. This
paper contains the configuration of SANOM and its results on the anatomy and
conference tracks. In comparison to the OAEI 2017, SANOM has improved
significantly, and its results are competitive with the state-of-the-art
systems. In particular, SANOM has the highest recall rate among the
participated systems in the conference track, and is competitive with AML, the
best performing system, in terms of F-measure. SANOM is also competitive with
LogMap on the anatomy track, which is the best performing system in this track
with no usage of particular biomedical background knowledge. SANOM has been
adapted to the HOBBIT platfrom and is now available for the registered users.
",SANOM Results for OAEI 2019
"  In the realm of games research, Artificial General Intelligence algorithms
often use score as main reward signal for learning or playing actions. However
this has shown its severe limitations when the point rewards are very rare or
absent until the end of the game. This paper proposes a new approach based on
event logging: the game state triggers an event every time one of its features
changes. These events are processed by an Event-value Function (EF) that
assigns a value to a single action or a sequence. The experiments have shown
that such approach can mitigate the problem of scarce point rewards and improve
the AI performance. Furthermore this represents a step forward in controlling
the strategy adopted by the artificial agent, by describing a much richer and
controllable behavioural space through the EF. Tuned EF are able to neatly
synthesise the relevance of the events in the game. Agents using an EF show
more robust when playing games with several opponents.
",Rinascimento: using event-value functions for playing Splendor
"  The At-Most-One (AMO) constraint is a special case of cardinality constraint
that requires at most one variable from a set of Boolean variables to be set to
TRUE. AMO is important for modeling problems as Boolean satisfiability (SAT)
from domains where decision variables represent spatial or temporal placements
of some objects that cannot share the same spatial or temporal slot. The AMO
constraint can be used for more efficient representation and problem solving in
mutex networks consisting of pair-wise mutual exclusions forbidding pairs of
Boolean variable to be simultaneously TRUE. An on-line method for automated
detection of cliques for efficient representation of incremental mutex networks
where new mutexes arrive using AMOs is presented. A comparison of SAT-based
problem solving in mutex networks represented by AMO constraints using various
encodings is shown.
",At-Most-One Constraints in Efficient Representations of Mutex Networks
"  Sample-based planning is a powerful family of algorithms for generating
intelligent behavior from a model of the environment. Generating good candidate
actions is critical to the success of sample-based planners, particularly in
continuous or large action spaces. Typically, candidate action generation
exhausts the action space, uses domain knowledge, or more recently, involves
learning a stochastic policy to provide such search guidance. In this paper we
explore explicitly learning a candidate action generator by optimizing a novel
objective, marginal utility. The marginal utility of an action generator
measures the increase in value of an action over previously generated actions.
We validate our approach in both curling, a challenging stochastic domain with
continuous state and action spaces, and a location game with a discrete but
large action space. We show that a generator trained with the marginal utility
objective outperforms hand-coded schemes built on substantial domain knowledge,
trained stochastic policies, and other natural objectives for generating
actions for sampled-based planners.
","Marginal Utility for Planning in Continuous or Large Discrete Action
  Spaces"
"  Imitation learning is an approach for generating intelligent behavior when
the cost function is unknown or difficult to specify. Building upon work in
inverse reinforcement learning (IRL), Generative Adversarial Imitation Learning
(GAIL) aims to provide effective imitation even for problems with large or
continuous state and action spaces. Driver modeling is one example of a problem
where the state and action spaces are continuous. Human driving behavior is
characterized by non-linearity and stochasticity, and the underlying cost
function is unknown. As a result, learning from human driving demonstrations is
a promising approach for generating human-like driving behavior. This article
describes the use of GAIL for learning-based driver modeling. Because driver
modeling is inherently a multi-agent problem, where the interaction between
agents needs to be modeled, this paper describes a parameter-sharing extension
of GAIL called PS-GAIL to tackle multi-agent driver modeling. In addition, GAIL
is domain agnostic, making it difficult to encode specific knowledge relevant
to driving in the learning process. This paper describes Reward Augmented
Imitation Learning (RAIL), which modifies the reward signal to provide
domain-specific knowledge to the agent. Finally, human demonstrations are
dependent upon latent factors that may not be captured by GAIL. This paper
describes Burn-InfoGAIL, which allows for disentanglement of latent variability
in demonstrations. Imitation learning experiments are performed using NGSIM, a
real-world highway driving dataset. Experiments show that these modifications
to GAIL can successfully model highway driving behavior, accurately replicating
human demonstrations and generating realistic, emergent behavior in the traffic
flow arising from the interaction between driving agents.
","Modeling Human Driving Behavior through Generative Adversarial Imitation
  Learning"
"  Reward function specification can be difficult. Rewarding the agent for
making a widget may be easy, but penalizing the multitude of possible negative
side effects is hard. In toy environments, Attainable Utility Preservation
(AUP) avoided side effects by penalizing shifts in the ability to achieve
randomly generated goals. We scale this approach to large, randomly generated
environments based on Conway's Game of Life. By preserving optimal value for a
single randomly generated reward function, AUP incurs modest overhead while
leading the agent to complete the specified task and avoid many side effects.
Videos and code are available at https://avoiding-side-effects.github.io/.
",Avoiding Side Effects in Complex Environments
"  During the last decade, various approaches have been put forward to integrate
business processes with different types of data. Each of such approaches
reflects specific demands in the whole process-data integration spectrum. One
particular important point is the capability of these approaches to flexibly
accommodate processes with multiple cases that need to co-evolve. In this work,
we introduce and study an extension of coloured Petri nets, called
catalog-nets, providing two key features to capture this type of processes. On
the one hand, net transitions are equipped with guards that simultaneously
inspect the content of tokens and query facts stored in a read-only, persistent
database. On the other hand, such transitions can inject data into tokens by
extracting relevant values from the database or by generating genuinely fresh
ones. We systematically encode catalog-nets into one of the reference
frameworks for the (parameterised) verification of data and processes. We show
that fresh-value injection is a particularly complex feature to handle, and
discuss strategies to tame it. Finally, we discuss how catalog nets relate to
well-known formalisms in this area.
","Petri Nets with Parameterised Data: Modelling and Verification (Extended
  Version)"
"  Local structure such as context-specific independence (CSI) has received much
attention in the probabilistic graphical model (PGM) literature, as it
facilitates the modeling of large complex systems, as well as for reasoning
with them. In this paper, we provide a new perspective on how to learn CSIs
from data. We propose to first learn a functional and parameterized
representation of a conditional probability table (CPT), such as a neural
network. Next, we quantize this continuous function, into an arithmetic circuit
representation that facilitates efficient inference. In the first step, we can
leverage the many powerful tools that have been developed in the machine
learning literature. In the second step, we exploit more recently-developed
analytic tools from explainable AI, for the purposes of learning CSIs. Finally,
we contrast our approach, empirically and conceptually, with more traditional
variable-splitting approaches, that search for CSIs more explicitly.
",A New Perspective on Learning Context-Specific Independence
"  People routinely infer the goals of others by observing their actions over
time. Remarkably, we can do so even when those actions lead to failure,
enabling us to assist others when we detect that they might not achieve their
goals. How might we endow machines with similar capabilities? Here we present
an architecture capable of inferring an agent's goals online from both optimal
and non-optimal sequences of actions. Our architecture models agents as
boundedly-rational planners that interleave search with execution by
replanning, thereby accounting for sub-optimal behavior. These models are
specified as probabilistic programs, allowing us to represent and perform
efficient Bayesian inference over an agent's goals and internal planning
processes. To perform such inference, we develop Sequential Inverse Plan Search
(SIPS), a sequential Monte Carlo algorithm that exploits the online replanning
assumption of these models, limiting computation by incrementally extending
inferred plans as new actions are observed. We present experiments showing that
this modeling and inference architecture outperforms Bayesian inverse
reinforcement learning baselines, accurately inferring goals from both optimal
and non-optimal trajectories involving failure and back-tracking, while
generalizing across domains with compositional structure and sparse rewards.
",Online Bayesian Goal Inference for Boundedly-Rational Planning Agents
"  Morpion Solitaire is a popular single player game, performed with paper and
pencil. Due to its large state space (on the order of the game of Go)
traditional search algorithms, such as MCTS, have not been able to find good
solutions. A later algorithm, Nested Rollout Policy Adaptation, was able to
find a new record of 82 steps, albeit with large computational resources. After
achieving this record, to the best of our knowledge, there has been no further
progress reported, for about a decade.
  In this paper we take the recent impressive performance of deep self-learning
reinforcement learning approaches from AlphaGo/AlphaZero as inspiration to
design a searcher for Morpion Solitaire. A challenge of Morpion Solitaire is
that the state space is sparse, there are few win/loss signals. Instead, we use
an approach known as ranked reward to create a reinforcement learning self-play
framework for Morpion Solitaire. This enables us to find medium-quality
solutions with reasonable computational effort. Our record is a 67 steps
solution, which is very close to the human best (68) without any other
adaptation to the problem than using ranked reward. We list many further
avenues for potential improvement.
","Tackling Morpion Solitaire with AlphaZero-likeRanked Reward
  Reinforcement Learning"
"  Decision and policy-makers in multi-criteria decision-making analysis take
into account some strategies in order to analyze outcomes and to finally make
an effective and more precise decision. Among those strategies, the
modification of the normalization process in the multiple-criteria
decision-making algorithm is still a question due to the confrontation of many
normalization tools. Normalization is the basic action in defining and solving
a MADM problem and a MADM model. Normalization is the first, also necessary,
step in solving, i.e. the application of a MADM method. It is a fact that the
selection of normalization methods has a direct effect on the results. One of
the latest normalization methods introduced is the Logarithmic Normalization
(LN) method. This new method has a distinguished advantage, reflecting in that
a sum of the normalized values of criteria always equals 1. This normalization
method had never been applied in any MADM methods before. This research study
is focused on the analysis of the classical MADM methods based on logarithmic
normalization. VIKOR and TOPSIS, as the two famous MADM methods, were selected
for this reanalysis research study. Two numerical examples were checked in both
methods, based on both the classical and the novel ways based on the LN. The
results indicate that there are differences between the two approaches.
Eventually, a sensitivity analysis is also designed to illustrate the
reliability of the final results.
","A VIKOR and TOPSIS focused reanalysis of the MADM methods based on
  logarithmic normalization"
"  We present the technical side of reasoning in Regular Boardgames (RBG)
language -- a universal General Game Playing (GGP) formalism for the class of
finite deterministic games with perfect information, encoding rules in the form
of regular expressions. RBG serves as a research tool that aims to aid in the
development of generalized algorithms for knowledge inference, analysis,
generation, learning, and playing games. In all these tasks, both generality
and efficiency are important.
  In the first part, this paper describes optimizations used by the RBG
compiler. The impact of these optimizations ranges from 1.7 to even 33-fold
efficiency improvement when measuring the number of possible game playouts per
second. Then, we perform an in-depth efficiency comparison with three other
modern GGP systems (GDL, Ludii, Ai Ai). We also include our own highly
optimized game-specific reasoners to provide a point of reference of the
maximum speed. Our experiments show that RBG is currently the fastest among the
abstract general game playing languages, and its efficiency can be competitive
to common interface-based systems that rely on handcrafted game-specific
implementations. Finally, we discuss some issues and methodology of computing
benchmarks like this.
",Efficient Reasoning in Regular Boardgames
"  Causal loop and stock and flow diagrams are broadly used in System Dynamics
because they help organize relationships and convey meaning. Using the
analytical work of Schoenberg (2019) to select what to include in a compressed
model, this paper demonstrates how that information can be clearly presented in
an automatically generated causal loop diagram. The diagrams are generated
using tools developed by people working in graph theory and the generated
diagrams are clear and aesthetically pleasing. This approach can also be built
upon to generate stock and flow diagrams. Automated stock and flow diagram
generation opens the door to representing models developed using only
equations, regardless or origin, in a clear and easy to understand way. Because
models can be large, the application of grouping techniques, again developed
for graph theory, can help structure the resulting diagrams in the most usable
form. This paper describes the algorithms developed for automated diagram
generation and shows a number of examples of their uses in large models. The
application of these techniques to existing, but inaccessible, equation-based
models can help broaden the knowledge base for System Dynamics modeling. The
techniques can also be used to improve layout in all, or part, of existing
models with diagrammatic informtion.
",Automated Diagram Generation to Build Understanding and Usability
"  Machine common sense remains a broad, potentially unbounded problem in
artificial intelligence (AI). There is a wide range of strategies that can be
employed to make progress on this challenge. This article deals with the
aspects of modeling commonsense reasoning focusing on such domain as
interpersonal interactions. The basic idea is that there are several types of
commonsense reasoning: one is manifested at the logical level of physical
actions, the other deals with the understanding of the essence of human-human
interactions. Existing approaches, based on formal logic and artificial neural
networks, allow for modeling only the first type of common sense. To model the
second type, it is vital to understand the motives and rules of human behavior.
This model is based on real-life heuristics, i.e., the rules of thumb,
developed through knowledge and experience of different generations. Such
knowledge base allows for development of an expert system with inference and
explanatory mechanisms (commonsense reasoning algorithms and personal models).
Algorithms provide tools for a situation analysis, while personal models make
it possible to identify personality traits. The system so designed should
perform the function of amplified intelligence for interactions, including
human-machine.
",Machine Common Sense
"  The Loops that Matter method (Schoenberg et. al, 2019) for understanding
model behavior provides metrics showing the contribution of the feedback loops
in a model to behavior at each point in time. To provide these metrics, it is
necessary find the set of loops on which to compute them. We show in this paper
the necessity of including loops that are important at different points in the
simulation. These important loops may not be independent of one another and
cannot be determined from static analysis of the model structure. We then
describe an algorithm that can be used to discover the most important loops in
models that are too feedback rich for exhaustive loop discovery. We demonstrate
the use of this algorithm in terms of its ability to find the most explanatory
loops, and its computational performance for large models. By using this
approach, the Loops that Matter method can be applied to models of any size or
complexity.
",Finding the Loops that Matter
"  Opponent Modelling tries to predict the future actions of opponents, and is
required to perform well in multi-player games. There is a deep literature on
learning an opponent model, but much less on how accurate such models must be
to be useful. We investigate the sensitivity of Monte Carlo Tree Search (MCTS)
and a Rolling Horizon Evolutionary Algorithm (RHEA) to the accuracy of their
modelling of the opponent in a simple Real-Time Strategy game. We find that in
this domain RHEA is much more sensitive to the accuracy of an opponent model
than MCTS. MCTS generally does better even with an inaccurate model, while this
will degrade RHEA's performance. We show that faced with an unknown opponent
and a low computational budget it is better not to use any explicit model with
RHEA, and to model the opponent's actions within the tree as part of the MCTS
algorithm.
","Does it matter how well I know what you're thinking? Opponent Modelling
  in an RTS game"
"  Spirtes, Glymour and Scheines formulated a Conjecture that a direct
dependence test and a head-to-head meeting test would suffice to construe
directed acyclic graph decompositions of a joint probability distribution
(Bayesian network) for which Pearl's d-separation applies. This Conjecture was
later shown to be a direct consequence of a result of Pearl and Verma. This
paper is intended to prove this Conjecture in a new way, by exploiting the
concept of p-d-separation (partial dependency separation). While Pearl's
d-separation works with Bayesian networks, p-d-separation is intended to apply
to causal networks: that is partially oriented networks in which orientations
are given to only to those edges, that express statistically confirmed causal
influence, whereas undirected edges express existence of direct influence
without possibility of determination of direction of causation. As a
consequence of the particular way of proving the validity of this Conjecture,
an algorithm for construction of all the directed acyclic graphs (dags)
carrying the available independence information is also presented. The notion
of a partially oriented graph (pog) is introduced and within this graph the
notion of p-d-separation is defined. It is demonstrated that the p-d-separation
within the pog is equivalent to d-separation in all derived dags.
","p-d-Separation -- A Concept for Expressing Dependence/Independence
  Relations in Causal Networks"
"  As we deploy autonomous agents in safety-critical domains, it becomes
important to develop an understanding of their internal mechanisms and
representations. We outline an approach to imitation learning for
reverse-engineering black box agent policies in MDP environments, yielding
simplified, interpretable models in the form of decision trees. As part of this
process, we explicitly model and learn agents' latent state representations by
selecting from a large space of candidate features constructed from the Markov
state. We present initial promising results from an implementation in a
multi-agent traffic environment.
",Modelling Agent Policies with Interpretable Imitation Learning
"  Constraint Optimization Problems (COP) are often considered without
sufficient knowledge on the boundaries of the objective variable to optimize.
When available, tight boundaries are helpful to prune the search space or
estimate problem characteristics. Finding close boundaries, that correctly
under- and overestimate the optimum, is almost impossible without actually
solving the COP. This paper introduces Bion, a novel approach for boundary
estimation by learning from previously solved instances of the COP. Based on
supervised machine learning, Bion is problem-specific and solver-independent
and can be applied to any COP which is repeatedly solved with different data
inputs. An experimental evaluation over seven realistic COPs shows that an
estimation model can be trained to prune the objective variables' domains by
over 80%. By evaluating the estimated boundaries with various COP solvers, we
find that Bion improves the solving process for some problems, although the
effect of closer bounds is generally problem-dependent.
",Learning Objective Boundaries for Constraint Optimization Problems
"  Hierarchical DQN (h-DQN) is a two-level architecture of feedforward neural
networks where the meta level selects goals and the lower level takes actions
to achieve the goals. We show tasks that cannot be solved by h-DQN,
exemplifying the limitation of this type of hierarchical framework (HF). We
describe the recurrent hierarchical framework (RHF), generalizing architectures
that use a recurrent neural network at the meta level. We analyze the
expressiveness of HF and RHF using context-sensitive grammars. We show that RHF
is more expressive than HF. We perform experiments comparing an implementation
of RHF with two HF baselines; the results corroborate our theoretical findings.
","Hierarchical Reinforcement Learning for Deep Goal Reasoning: An
  Expressiveness Analysis"
"  With this paper, we aim to put an issue on the agenda of AI ethics that in
our view is overlooked in the current discourse. The current discussions are
dominated by topics suchas trustworthiness and bias, whereas the issue we like
to focuson is counter to the debate on trustworthiness. We fear that the
overuse of currently dominant AI systems that are driven by short-term
objectives and optimized for avoiding error leads to a society that loses its
diversity and flexibility needed for true progress. We couch our concerns in
the discourse around the term anti-fragility and show with some examples what
threats current methods used for decision making pose for society.
",A blindspot of AI ethics: anti-fragility in statistical prediction
"  This volume contains revised versions of the papers selected for the first
volume of the Online Handbook of Argumentation for AI (OHAAI). Previously,
formal theories of argument and argument interaction have been proposed and
studied, and this has led to the more recent study of computational models of
argument. Argumentation, as a field within artificial intelligence (AI), is
highly relevant for researchers interested in symbolic representations of
knowledge and defeasible reasoning. The purpose of this handbook is to provide
an open access and curated anthology for the argumentation research community.
OHAAI is designed to serve as a research hub to keep track of the latest and
upcoming PhD-driven research on the theory and application of argumentation in
all areas related to AI.
",Online Handbook of Argumentation for AI: Volume 1
"  In this work, the online printing shop scheduling problem introduced in
(Lunardi et al., Mixed Integer Linear Programming and Constraint Programming
Models for the Online Printing Shop Scheduling Problem, Computers & Operations
Research, to appear) is considered. This challenging real scheduling problem,
that emerged in the nowadays printing industry, corresponds to a flexible job
shop scheduling problem with sequencing flexibility; and it presents several
complicating specificities such as resumable operations, periods of
unavailability of the machines, sequence-dependent setup times, partial
overlapping between operations with precedence constraints, and fixed
operations, among others. A local search strategy and metaheuristic approaches
for the problem are proposed and evaluated. Based on a common representation
scheme, trajectory and populational metaheuristics are considered. Extensive
numerical experiments with large-sized instances show that the proposed methods
are suitable for solving practical instances of the problem; and that they
outperform a half-heuristic-half-exact off-the-shelf solver by a large extent.
Numerical experiments with classical instances of the flexible job shop
scheduling problem show that the introduced methods are also competitive when
applied to this particular case.
",Metaheuristics for the Online Printing Shop Scheduling Problem
"  The development of robotics and AI agents has enabled their wider usage in
human surroundings. AI agents are more trusted to make increasingly important
decisions with potentially critical outcomes. It is essential to consider the
ethical consequences of the decisions made by these systems. In this paper, we
present how contrastive explanations can be used for comparing the ethics of
plans. We build upon an existing ethical framework to allow users to make
suggestions to plans and receive contrastive explanations.
",Towards Contrastive Explanations for Comparing the Ethics of Plans
"  Multi-step (also called n-step) methods in reinforcement learning (RL) have
been shown to be more efficient than the 1-step method due to faster
propagation of the reward signal, both theoretically and empirically, in tasks
exploiting tabular representation of the value-function. Recently, research in
Deep Reinforcement Learning (DRL) also shows that multi-step methods improve
learning speed and final performance in applications where the value-function
and policy are represented with deep neural networks. However, there is a lack
of understanding about what is actually contributing to the boost of
performance. In this work, we analyze the effect of multi-step methods on
alleviating the overestimation problem in DRL, where multi-step experiences are
sampled from a replay buffer. Specifically building on top of Deep
Deterministic Policy Gradient (DDPG), we propose Multi-step DDPG (MDDPG), where
different step sizes are manually set, and its variant called Mixed Multi-step
DDPG (MMDDPG) where an average over different multi-step backups is used as
update target of Q-value function. Empirically, we show that both MDDPG and
MMDDPG are significantly less affected by the overestimation problem than DDPG
with 1-step backup, which consequently results in better final performance and
learning speed. We also discuss the advantages and disadvantages of different
ways to do multi-step expansion in order to reduce approximation error, and
expose the tradeoff between overestimation and underestimation that underlies
offline multi-step methods. Finally, we compare the computational resource
needs of Twin Delayed Deep Deterministic Policy Gradient (TD3), a state-of-art
algorithm proposed to address overestimation in actor-critic methods, and our
proposed methods, since they show comparable final performance and learning
speed.
","The Effect of Multi-step Methods on Overestimation in Deep Reinforcement
  Learning"
"  Can one build a knowledge graph (KG) for all products in the world? Knowledge
graphs have firmly established themselves as valuable sources of information
for search and question answering, and it is natural to wonder if a KG can
contain information about products offered at online retail sites. There have
been several successful examples of generic KGs, but organizing information
about products poses many additional challenges, including sparsity and noise
of structured data for products, complexity of the domain with millions of
product types and thousands of attributes, heterogeneity across large number of
categories, as well as large and constantly growing number of products. We
describe AutoKnow, our automatic (self-driving) system that addresses these
challenges. The system includes a suite of novel techniques for taxonomy
construction, product property identification, knowledge extraction, anomaly
detection, and synonym discovery. AutoKnow is (a) automatic, requiring little
human intervention, (b) multi-scalable, scalable in multiple dimensions (many
domains, many products, and many attributes), and (c) integrative, exploiting
rich customer behavior logs. AutoKnow has been operational in collecting
product knowledge for over 11K product types.
","AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of
  Types"
"  Circuit routing is a fundamental problem in designing electronic systems such
as integrated circuits (ICs) and printed circuit boards (PCBs) which form the
hardware of electronics and computers. Like finding paths between pairs of
locations, circuit routing generates traces of wires to connect contacts or
leads of circuit components. It is challenging because finding paths between
dense and massive electronic components involves a very large search space.
Existing solutions are either manually designed with domain knowledge or
tailored to specific design rules, hence, difficult to adapt to new problems or
design needs. Therefore, a general routing approach is highly desired. In this
paper, we model the circuit routing as a sequential decision-making problem,
and solve it by Monte Carlo tree search (MCTS) with deep neural network (DNN)
guided rollout. It could be easily extended to routing cases with more routing
constraints and optimization goals. Experiments on randomly generated
single-layer circuits show the potential to route complex circuits. The
proposed approach can solve the problems that benchmark methods such as
sequential A* method and Lee's algorithm cannot solve, and can also outperform
the vanilla MCTS approach.
",Circuit Routing Using Monte Carlo Tree Search and Deep Neural Networks
"  Description logics (DLs) are standard knowledge representation languages for
modelling ontologies, i.e. knowledge about concepts and the relations between
them. Unfortunately, DL ontologies are difficult to learn from data and
time-consuming to encode manually. As a result, ontologies for broad domains
are almost inevitably incomplete. In recent years, several data-driven
approaches have been proposed for automatically extending such ontologies. One
family of methods rely on characterizations of concepts that are derived from
text descriptions. While such characterizations do not capture ontological
knowledge directly, they encode information about the similarity between
different concepts, which can be exploited for filling in the gaps in existing
ontologies. To this end, several inductive inference mechanisms have already
been proposed, but these have been defined and used in a heuristic fashion. In
this paper, we instead propose an inductive inference mechanism which is based
on a clear model-theoretic semantics, and can thus be tightly integrated with
standard deductive reasoning. We particularly focus on interpolation, a
powerful commonsense reasoning mechanism which is closely related to cognitive
models of category-based induction. Apart from the formalization of the
underlying semantics, as our main technical contribution we provide
computational complexity bounds for reasoning in EL with this interpolation
mechanism.
",Plausible Reasoning about EL-Ontologies using Concept Interpolation
"  Human explanation (e.g., in terms of feature importance) has been recently
used to extend the communication channel between human and agent in interactive
machine learning. Under this setting, human trainers provide not only the
ground truth but also some form of explanation. However, this kind of human
guidance was only investigated in supervised learning tasks, and it remains
unclear how to best incorporate this type of human knowledge into deep
reinforcement learning. In this paper, we present the first study of using
human visual explanations in human-in-the-loop reinforcement learning (HRL). We
focus on the task of learning from feedback, in which the human trainer not
only gives binary evaluative ""good"" or ""bad"" feedback for queried state-action
pairs, but also provides a visual explanation by annotating relevant features
in images. We propose EXPAND (EXPlanation AugmeNted feeDback) to encourage the
model to encode task-relevant features through a context-aware data
augmentation that only perturbs irrelevant features in human salient
information. We choose five tasks, namely Pixel-Taxi and four Atari games, to
evaluate the performance and sample efficiency of this approach. We show that
our method significantly outperforms methods leveraging human explanation that
are adapted from supervised learning, and Human-in-the-loop RL baselines that
only utilize evaluative feedback.
","Widening the Pipeline in Human-Guided Reinforcement Learning with
  Explanation and Context-Aware Data Augmentation"
"  Euclidean Markov decision processes are a powerful tool for modeling control
problems under uncertainty over continuous domains. Finite state imprecise,
Markov decision processes can be used to approximate the behavior of these
infinite models. In this paper we address two questions: first, we investigate
what kind of approximation guarantees are obtained when the Euclidean process
is approximated by finite state approximations induced by increasingly fine
partitions of the continuous state space. We show that for cost functions over
finite time horizons the approximations become arbitrarily precise. Second, we
use imprecise Markov decision process approximations as a tool to analyse and
validate cost functions and strategies obtained by reinforcement learning. We
find that, on the one hand, our new theoretical results validate basic design
choices of a previously proposed reinforcement learning approach. On the other
hand, the imprecise Markov decision process approximations reveal some
inaccuracies in the learned cost functions.
",Approximating Euclidean by Imprecise Markov Decision Processes
"  The belief revision literature has largely focussed on the issue of how to
revise one's beliefs in the light of information regarding matters of fact.
Here we turn to an important but comparatively neglected issue: How might one
extend a revision operator to handle conditionals as input? Our approach to
this question of 'conditional revision' is distinctive insofar as it abstracts
from the controversial details of how to revise by factual sentences. We
introduce a 'plug and play' method for uniquely extending any iterated belief
revision operator to the conditional case. The flexibility of our approach is
achieved by having the result of a conditional revision by a Ramsey Test
conditional ('arrow') determined by that of a plain revision by its
corresponding material conditional ('hook'). It is shown to satisfy a number of
new constraints that are of independent interest.
",Revision by Conditionals: From Hook to Arrow
"  Bayesian Networks (BNs) are graphical probabilistic models that have proven
popular in medical applications. While numerous medical BNs have been
published, most are presented fait accompli without explanation of how the
network structure was developed or justification of why it represents the
correct structure for the given medical application. This means that the
process of building medical BNs from experts is typically ad hoc and offers
little opportunity for methodological improvement. This paper proposes
generally applicable and reusable medical reasoning patterns to aid those
developing medical BNs. The proposed method complements and extends the
idiom-based approach introduced by Neil, Fenton, and Nielsen in 2000. We
propose instances of their generic idioms that are specific to medical BNs. We
refer to the proposed medical reasoning patterns as medical idioms. In
addition, we extend the use of idioms to represent interventional and
counterfactual reasoning. We believe that the proposed medical idioms are
logical reasoning patterns that can be combined, reused and applied generically
to help develop medical BNs. All proposed medical idioms have been illustrated
using medical examples on coronary artery disease. The method has also been
applied to other ongoing BNs being developed with medical experts. Finally, we
show that applying the proposed medical idioms to published BN models results
in models with a clearer structure.
",Medical idioms for clinical Bayesian network development
"  We propose a Deep Reinforcement Learning (Deep RL) algorithm for solving the
online 3D bin packing problem for an arbitrary number of bins and any bin size.
The focus is on producing decisions that can be physically implemented by a
robotic loading arm, a laboratory prototype used for testing the concept. The
problem considered in this paper is novel in two ways. First, unlike the
traditional 3D bin packing problem, we assume that the entire set of objects to
be packed is not known a priori. Instead, a fixed number of upcoming objects is
visible to the loading system, and they must be loaded in the order of arrival.
Second, the goal is not to move objects from one point to another via a
feasible path, but to find a location and orientation for each object that
maximises the overall packing efficiency of the bin(s). Finally, the learnt
model is designed to work with problem instances of arbitrary size without
retraining. Simulation results show that the RL-based method outperforms
state-of-the-art online bin packing heuristics in terms of empirical
competitive ratio and volume efficiency.
",A Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing
"  Designing robots capable of generating interpretable behavior is a
prerequisite for achieving effective human-robot collaboration. This means that
the robots need to be capable of generating behavior that aligns with human
expectations and, when required, provide explanations to the humans in the
loop. However, exhibiting such behavior in arbitrary environments could be
quite expensive for robots, and in some cases, the robot may not even be able
to exhibit the expected behavior. Given structured environments (like
warehouses and restaurants), it may be possible to design the environment so as
to boost the interpretability of the robot's behavior or to shape the human's
expectations of the robot's behavior. In this paper, we investigate the
opportunities and limitations of environment design as a tool to promote a type
of interpretable behavior -- known in the literature as explicable behavior. We
formulate a novel environment design framework that considers design over
multiple tasks and over a time horizon. In addition, we explore the
longitudinal aspect of explicable behavior and the trade-off that arises
between the cost of design and the cost of generating explicable behavior over
a time horizon.
",Designing Environments Conducive to Interpretable Robot Behavior
"  The rule extraction literature contains the notion of a fidelity-accuracy
dilemma: when building an interpretable model of a black box function,
optimising for fidelity is likely to reduce performance on the underlying task,
and vice versa. I reassert the relevance of this dilemma for the modern field
of explainable artificial intelligence, and highlight how it is compounded when
the black box is an agent interacting with a dynamic environment. I then
discuss two independent research directions - building white box agents and
interpreting black box agents - which are both coherent and worthy of
attention, but must not be conflated by researchers embarking on projects in
the domain of agent interpretability.
",Am I Building a White Box Agent or Interpreting a Black Box Agent?
"  While traditionally a labour intensive task, the testing of game content is
progressively becoming more automated. Among the many directions in which this
automation is taking shape, automatic play-testing is one of the most promising
thanks also to advancements of many supervised and reinforcement learning (RL)
algorithms. However these type of algorithms, while extremely powerful, often
suffer in production environments due to issues with reliability and
transparency in their training and usage.
  In this research work we are investigating and evaluating strategies to apply
the popular RL method Proximal Policy Optimization (PPO) in a casual mobile
puzzle game with a specific focus on improving its reliability in training and
generalization during game playing.
  We have implemented and tested a number of different strategies against a
real-world mobile puzzle game (Lily's Garden from Tactile Games). We isolated
the conditions that lead to a failure in either training or generalization
during testing and we identified a few strategies to ensure a more stable
behaviour of the algorithm in this game genre.
",Strategies for Using Proximal Policy Optimization in Mobile Puzzle Games
"  Human learning and intelligence work differently from the supervised pattern
recognition approach adopted in most deep learning architectures. Humans seem
to learn rich representations by exploration and imitation, build causal models
of the world, and use both to flexibly solve new tasks. We suggest a simple but
effective unsupervised model which develops such characteristics. The agent
learns to represent the dynamical physical properties of its environment by
intrinsically motivated exploration, and performs inference on this
representation to reach goals. For this, a set of self-organizing maps which
represent state-action pairs is combined with a causal model for sequence
prediction. The proposed system is evaluated in the cartpole environment. After
an initial phase of playful exploration, the agent can execute kinematic
simulations of the environment's future, and use those for action planning. We
demonstrate its performance on a set of several related, but different one-shot
imitation tasks, which the agent flexibly solves in an active inference style.
","Learning intuitive physics and one-shot imitation using
  state-action-prediction self-organizing maps"
"  Mission schedule of satellites is an important part of space operation
nowadays, since the number and types of satellites in orbit are increasing
tremendously and their corresponding tasks are also becoming more and more
complicated. In this paper, a mission schedule model combined with Proximal
Policy Optimization Algorithm(PPO) is proposed. Different from the traditional
heuristic planning method, this paper incorporate reinforcement learning
algorithms into it and find a new way to describe the problem. Several
constraints including data download are considered in this paper.
","Mission schedule of agile satellites based on Proximal Policy
  Optimization Algorithm"
"  While supporting the execution of business processes, information systems
record event logs. Conformance checking relies on these logs to analyze whether
the recorded behavior of a process conforms to the behavior of a normative
specification. A key assumption of existing conformance checking techniques,
however, is that all events are associated with timestamps that allow to infer
a total order of events per process instance. Unfortunately, this assumption is
often violated in practice. Due to synchronization issues, manual event
recordings, or data corruption, events are only partially ordered. In this
paper, we put forward the problem of partial order resolution of event logs to
close this gap. It refers to the construction of a probability distribution
over all possible total orders of events of an instance. To cope with the order
uncertainty in real-world data, we present several estimators for this task,
incorporating different notions of behavioral abstraction. Moreover, to reduce
the runtime of conformance checking based on partial order resolution, we
introduce an approximation method that comes with a bounded error in terms of
accuracy. Our experiments with real-world and synthetic data reveal that our
approach improves accuracy over the state-of-the-art considerably.
",Partial Order Resolution of Event Logs for Process Conformance Checking
"  Inferential relations govern our concept use. In order to understand a
concept it has to be located in a space of implications. There are different
kinds of conditions for statements, i.e. that the conditions represent
different kinds of explanations, e.g. causal or conceptual explanations. The
crucial questions is: How can the conditionality of language use be
represented. The conceptual background of representation in models is discussed
and in the end I propose how implications of propositional logic and conceptual
determinations can be represented in a model of a neural network.
",Space of Reasons and Mathematical Model
"  In Hierarchical Control, compositionality, abstraction, and task-transfer are
crucial for designing versatile algorithms which can solve a variety of
problems with maximal representational reuse. We propose a novel hierarchical
and compositional framework called Jump-Operator Dynamic Programming for
quickly computing solutions within a super-exponential space of sequential
sub-goal tasks with ordering constraints, while also providing a fast
linearly-solvable algorithm as an implementation. This approach involves
controlling over an ensemble of reusable goal-conditioned polices functioning
as temporally extended actions, and utilizes transition operators called
feasibility functions, which are used to summarize initial-to-final state
dynamics of the polices. Consequently, the added complexity of grounding a
high-level task space onto a larger ambient state-space can be mitigated by
optimizing in a lower-dimensional subspace defined by the grounding,
substantially improving the scalability of the algorithm while effecting
transferable solutions. We then identify classes of objective functions on this
subspace whose solutions are invariant to the grounding, resulting in optimal
zero-shot transfer.
","Jump Operator Planning: Goal-Conditioned Policy Ensembles and Zero-Shot
  Transfer"
"  While games have been used extensively as milestones to evaluate game-playing
AI, there exists no standardised framework for reporting the obtained
observations. As a result, it remains difficult to draw general conclusions
about the strengths and weaknesses of different game-playing AI algorithms. In
this paper, we propose reporting guidelines for AI game-playing performance
that, if followed, provide information suitable for unbiased comparisons
between different AI approaches. The vision we describe is to build benchmarks
and competitions based on such guidelines in order to be able to draw more
general conclusions about the behaviour of different AI algorithms, as well as
the types of challenges different games pose.
",Towards Game-Playing AI Benchmarks via Performance Reporting Standards
"  This research is about the development a fuzzy decision support system for
the diagnosis of coronary artery disease based on evidence. The coronary artery
disease data sets taken from University California Irvine (UCI) are used. The
knowledge base of fuzzy decision support system is taken by using rules
extraction method based on Rough Set Theory. The rules then are selected and
fuzzified based on information from discretization of numerical attributes.
Fuzzy rules weight is proposed using the information from support of extracted
rules. UCI heart disease data sets collected from U.S., Switzerland and
Hungary, data from Ipoh Specialist Hospital Malaysia are used to verify the
proposed system. The results show that the system is able to give the
percentage of coronary artery blocking better than cardiologists and
angiography. The results of the proposed system were verified and validated by
three expert cardiologists and are considered to be more efficient and useful.
","Diagnosis of Coronary Artery Disease Using Artificial Intelligence Based
  Decision Support System"
"  Multi agent strategies in mixed cooperative-competitive environments can be
hard to craft by hand because each agent needs to coordinate with its teammates
while competing with its opponents. Learning based algorithms are appealing but
many scenarios require heterogeneous agent behavior for the team's success and
this increases the complexity of the learning algorithm. In this work, we
develop a competitive multi agent environment called FortAttack in which two
teams compete against each other. We corroborate that modeling agents with
Graph Neural Networks and training them with Reinforcement Learning leads to
the evolution of increasingly complex strategies for each team. We observe a
natural emergence of heterogeneous behavior amongst homogeneous agents when
such behavior can lead to the team's success. Such heterogeneous behavior from
homogeneous agents is appealing because any agent can replace the role of
another agent at test time. Finally, we propose ensemble training, in which we
utilize the evolved opponent strategies to train a single policy for friendly
agents.
","Natural Emergence of Heterogeneous Strategies in Artificially
  Intelligent Competitive Teams"
"  Motivated by kidney exchange, we study a stochastic cycle and chain packing
problem, where we aim to identify structures in a directed graph to maximize
the expectation of matched edge weights. All edges are subject to failure, and
the failures can have nonidentical probabilities. To the best of our knowledge,
the state-of-the-art approaches are only tractable when failure probabilities
are identical. We formulate a relevant non-convex optimization problem and
propose a tractable mixed-integer linear programming reformulation to solve it.
In addition, we propose a model that integrates both risks and the expected
utilities of the matching by incorporating conditional value at risk (CVaR)
into the objective function, providing a robust formulation for this problem.
Subsequently, we propose a sample-average-approximation (SAA) based approach to
solve this problem. We test our approaches on data from the United Network for
Organ Sharing (UNOS) and compare against state-of-the-art approaches. Our model
provides better performance with the same running time as a leading
deterministic approach (PICEF). Our CVaR extensions with an SAA-based method
improves the $\alpha \times 100\%$ ($0<\alpha\leqslant 1$) worst-case
performance substantially compared to existing models.
",Kidney Exchange with Inhomogeneous Edge Existence Uncertainty
"  Solving sparse reward tasks through exploration is one of the major
challenges in deep reinforcement learning, especially in three-dimensional,
partially-observable environments. Critically, the algorithm proposed in this
article uses a single human demonstration to solve hard-exploration problems.
We train an agent on a combination of demonstrations and own experience to
solve problems with variable initial conditions. We adapt this idea and
integrate it with the proximal policy optimization (PPO). The agent is able to
increase its performance and to tackle harder problems by replaying its own
past trajectories prioritizing them based on the obtained reward and the
maximum value of the trajectory. We compare different variations of this
algorithm to behavioral cloning on a set of hard-exploration tasks in the
Animal-AI Olympics environment. To the best of our knowledge, learning a task
in a three-dimensional environment with comparable difficulty has never been
considered before using only one human demonstration.
","Guided Exploration with Proximal Policy Optimization using a Single
  Demonstration"
"  Generalizing the attack structure in argumentation frameworks (AFs) has been
studied in different ways. Most prominently, the binary attack relation of Dung
frameworks has been extended to the notion of collective attacks. The resulting
formalism is often termed SETAFs. Another approach is provided via abstract
dialectical frameworks (ADFs), where acceptance conditions specify the relation
between arguments; restricting these conditions naturally allows for so-called
support-free ADFs. The aim of the paper is to shed light on the relation
between these two different approaches. To this end, we investigate and compare
the expressiveness of SETAFs and support-free ADFs under the lens of 3-valued
semantics. Our results show that it is only the presence of unsatisfiable
acceptance conditions in support-free ADFs that discriminate the two
approaches.
",Expressiveness of SETAFs and Support-Free ADFs under 3-valued Semantics
"  Processing driving data and investigating driving behavior has been receiving
an increasing interest in the last decades, with applications ranging from car
insurance pricing to policy making. A common strategy to analyze driving
behavior is to study the maneuvers being performance by the driver. In this
paper, we propose TripMD, a system that extracts the most relevant driving
patterns from sensor recordings (such as acceleration) and provides a
visualization that allows for an easy investigation. Additionally, we test our
system using the UAH-DriveSet dataset, a publicly available naturalistic
driving dataset. We show that (1) our system can extract a rich number of
driving patterns from a single driver that are meaningful to understand driving
behaviors and (2) our system can be used to identify the driving behavior of an
unknown driver from a set of drivers whose behavior we know.
",TripMD: Driving patterns investigation via Motif Analysis
"  We show that preferred, stable, complete, and grounded semantics satisfy
attack removal monotonicity. This means that if an attack from b to a is
removed, the status of a cannot worsen, e.g. if a was skeptically accepted, it
cannot become rejected.
",Dung's semantics satisfy attack removal monotonicity
"  What do Cyberpunk and AI Ethics have to do with each other? Cyberpunk is a
sub-genre of science fiction that explores the post-human relationships between
human experience and technology. One similarity between AI Ethics and Cyberpunk
literature is that both seek to explore future social and ethical problems that
our technological advances may bring upon society. In recent years, an
increasing number of ethical matters involving AI have been pointed and
debated, and several ethical principles and guides have been suggested as
governance policies for the tech industry. However, would this be the role of
AI Ethics? To serve as a soft and ambiguous version of the law? We would like
to advocate in this article for a more Cyberpunk way of doing AI Ethics, with a
more democratic way of governance. In this study, we will seek to expose some
of the deficits of the underlying power structures of the AI industry, and
suggest that AI governance be subject to public opinion, so that good AI can
become good AI for all.
",Good AI for the Present of Humanity Democratizing AI Governance
"  In this work, we present a learning-based approach to analysis cyberspace
configuration. Unlike prior methods, our approach has the ability to learn from
past experience and improve over time. In particular, as we train over a
greater number of agents as attackers, our method becomes better at rapidly
finding attack paths for previously hidden paths, especially in multiple domain
cyberspace. To achieve these results, we pose finding attack paths as a
Reinforcement Learning (RL) problem and train an agent to find multiple domain
attack paths. To enable our RL policy to find more hidden attack paths, we
ground representation introduction an multiple domain action select module in
RL. By designing a simulated cyberspace experimental environment to verify our
method. Our objective is to find more hidden attack paths, to analysis the
weakness of cyberspace configuration. The experimental results show that our
method can find more hidden multiple domain attack paths than existing
baselines methods.
","Weakness Analysis of Cyberspace Configuration Based on Reinforcement
  Learning"
"  An unconstrained crossword puzzle is a generalization of the constrained
crossword problem. In this problem, only the word vocabulary, and optionally
the grid dimensions are known. Hence, it not only requires the algorithm to
determine the word locations, but it also needs to come up with the grid
geometry. This paper discusses algorithmic strategies for automatic crossword
puzzle generation in such an unconstrained setting. The strategies proposed
cover the tasks of selection of words from a given vocabulary, selection of
grid sizes, grid resizing and adjustments, metrics for word fitting,
back-tracking techniques, and also clue generation. The strategies have been
formulated based on a study of the effect of word sequence permutation order on
grid fitting. An end-to-end algorithm that combines these strategies is
presented, and its performance is analyzed. The techniques have been found to
be successful in quickly producing well-packed puzzles of even large sizes.
Finally, a few example puzzles generated by our algorithm are also provided.
",Automation Strategies for Unconstrained Crossword Puzzle Generation
"  In neuroscience, attention has been shown to bidirectionally interact with
reinforcement learning (RL) processes. This interaction is thought to support
dimensionality reduction of task representations, restricting computations to
relevant features. However, it remains unclear whether these properties can
translate into real algorithmic advantages for artificial agents, especially in
dynamic environments. We design a model incorporating a self-attention
mechanism that implements task-state representations in semantic feature-space,
and test it on a battery of Atari games. To evaluate the agent's selective
properties, we add a large volume of task-irrelevant features to observations.
In line with neuroscience predictions, self-attention leads to increased
robustness to noise compared to benchmark models. Strikingly, this
self-attention mechanism is general enough, such that it can be naturally
extended to implement a transient working-memory, able to solve a partially
observable maze task. Lastly, we highlight the predictive quality of attended
stimuli. Because we use semantic observations, we can uncover not only which
features the agent elects to base decisions on, but also how it chooses to
compile more complex, relational features from simpler ones. These results
formally illustrate the benefits of attention in deep RL and provide evidence
for the interpretability of self-attention mechanisms.
",Attention or memory? Neurointerpretable agents in space and time
"  Possibilistic fuzzy c-means (PFCM) algorithm is a reliable algorithm has been
proposed to deal the weakness of two popular algorithms for clustering, fuzzy
c-means (FCM) and possibilistic c-means (PCM). PFCM algorithm deals with the
weaknesses of FCM in handling noise sensitivity and the weaknesses of PCM in
the case of coincidence clusters. However, the PFCM algorithm can be only
applied to cluster complete data sets. Therefore, in this study, we propose a
modification of the PFCM algorithm that can be applied to incomplete data sets
clustering. We modified the PFCM algorithm to OCSPFCM and NPSPFCM algorithms
and measured performance on three things: 1) accuracy percentage, 2) a number
of iterations to termination, and 3) centroid errors. Based on the results that
both algorithms have the potential for clustering incomplete data sets.
However, the performance of the NPSPFCM algorithm is better than the OCSPFCM
algorithm for clustering incomplete data sets.
","Modified Possibilistic Fuzzy C-Means Algorithm for Clustering Incomplete
  Data Sets"
"  The Travelling Salesman Problem (TSP) is a challenging graph task in
combinatorial optimization that requires reasoning about both local node
neighborhoods and global graph structure. In this paper, we propose to use the
novel Graph Learning Network (GLN), a generative approach, to approximately
solve the TSP. GLN model learns directly the pattern of TSP instances as
training dataset, encodes the graph properties, and merge the different node
embeddings to output node-to-node an optimal tour directly or via graph search
technique that validates the final tour. The preliminary results of the
proposed novel approach proves its applicability to this challenging problem
providing a low optimally gap with significant computation saving compared to
the optimal solution.
",A Generative Graph Method to Solve the Travelling Salesman Problem
"  The Clustered Traveling Salesman Problem (CTSP) is a variant of the popular
Traveling Salesman Problem (TSP) arising from a number of real-life
applications. In this work, we explore an uncharted solution approach that
solves the CTSP by transforming it to the well-studied TSP. For this purpose,
we first investigate a technique to convert a CTSP instance to a TSP and then
apply popular TSP solvers (including exact and heuristic solvers) to solve the
resulting TSP instance. We want to answer the following questions: How do
state-of-the-art TSP solvers perform on clustered instances converted from the
CTSP? Do state-of-the-art TSP solvers compete well with the best performing
methods specifically designed for the CTSP? For this purpose, we present
intensive computational experiments on various benchmark instances to draw
conclusions.
",Solving the Clustered Traveling Salesman Problem via TSP methods
"  Recently, abstract argumentation-based models of case-based reasoning
($AA{\text -}CBR$ in short) have been proposed, originally inspired by the
legal domain, but also applicable as classifiers in different scenarios,
including image classification, sentiment analysis of text, and in predicting
the passage of bills in the UK Parliament. However, the formal properties of
$AA{\text -}CBR$ as a reasoning system remain largely unexplored. In this
paper, we focus on analysing the non-monotonicity properties of a regular
version of $AA{\text -}CBR$ (that we call $AA{\text -}CBR_{\succeq}$).
Specifically, we prove that $AA{\text -}CBR_{\succeq}$ is not cautiously
monotonic, a property frequently considered desirable in the literature of
non-monotonic reasoning. We then define a variation of $AA{\text
-}CBR_{\succeq}$ which is cautiously monotonic, and provide an algorithm for
obtaining it. Further, we prove that such variation is equivalent to using
$AA{\text -}CBR_{\succeq}$ with a restricted casebase consisting of all
""surprising"" cases in the original casebase.
","Cautious Monotonicity in Case-Based Reasoning with Abstract
  Argumentation"
"  The Apperception Engine is an unsupervised learning system. Given a sequence
of sensory inputs, it constructs a symbolic causal theory that both explains
the sensory sequence and also satisfies a set of unity conditions. The unity
conditions insist that the constituents of the theory - objects, properties,
and laws - must be integrated into a coherent whole. Once a theory has been
constructed, it can be applied to predict future sensor readings, retrodict
earlier readings, or impute missing readings.
  In this paper, we evaluate the Apperception Engine in a diverse variety of
domains, including cellular automata, rhythms and simple nursery tunes,
multi-modal binding problems, occlusion tasks, and sequence induction
intelligence tests. In each domain, we test our engine's ability to predict
future sensor values, retrodict earlier sensor values, and impute missing
sensory data. The engine performs well in all these domains, significantly
outperforming neural net baselines and state of the art inductive logic
programming systems. These results are significant because neural nets
typically struggle to solve the binding problem (where information from
different modalities must somehow be combined together into different aspects
of one unified object) and fail to solve occlusion tasks (in which objects are
sometimes visible and sometimes obscured from view). We note in particular that
in the sequence induction intelligence tests, our system achieved human-level
performance. This is notable because our system is not a bespoke system
designed specifically to solve intelligence tests, but a general-purpose system
that was designed to make sense of any sensory sequence.
",Evaluating the Apperception Engine
"  While it is still unclear if agents with Artificial General Intelligence
(AGI) could ever be built, we can already use mathematical models to
investigate potential safety systems for these agents. We present an AGI safety
layer that creates a special dedicated input terminal to support the iterative
improvement of an AGI agent's utility function. The humans who switched on the
agent can use this terminal to close any loopholes that are discovered in the
utility function's encoding of agent goals and constraints, to direct the agent
towards new goals, or to force the agent to switch itself off. An AGI agent may
develop the emergent incentive to manipulate the above utility function
improvement process, for example by deceiving, restraining, or even attacking
the humans involved. The safety layer will partially, and sometimes fully,
suppress this dangerous incentive. The first part of this paper generalizes
earlier work on AGI emergency stop buttons. We aim to make the mathematical
methods used to construct the layer more accessible, by applying them to an MDP
model. We discuss two provable properties of the safety layer, and show ongoing
work in mapping it to a Causal Influence Diagram (CID). In the second part, we
develop full mathematical proofs, and show that the safety layer creates a type
of bureaucratic blindness. We then present the design of a learning agent, a
design that wraps the safety layer around either a known machine learning
system, or a potential future AGI-level learning system. The resulting agent
will satisfy the provable safety properties from the moment it is first
switched on. Finally, we show how this agent can be mapped from its model to a
real-life implementation. We review the methodological issues involved in this
step, and discuss how these are typically resolved.
",AGI Agent Safety by Iteratively Improving the Utility Function
"  Propagators are central to the success of constraint programming, that is
contracting functions removing values proven not to be in any solution of a
given constraint. The literature contains numerous propagation algorithms, for
many different constraints, and common to all these propagation algorithms is
the notion of correctness: only values that appear in no solution to the
respective constraint may be removed. In this paper half-checking propagators
are introduced, for which the only requirements are that identified solutions
(by the propagators) are actual solutions (to the corresponding constraints),
and that the propagators are contracting. In particular, a half-checking
propagator may remove solutions resulting in an incomplete solving process, but
with the upside that (good) solutions may be found faster. Overall completeness
can be obtained by running half-checking propagators as one component in a
portfolio solving process. Half-checking propagators opens up a wider variety
of techniques to be used when designing propagation algorithms, compared to
what is currently available.
  A formal model for half-checking propagators is introduced, together with a
detailed description of how to support such propagators in a constraint
programming system. Three general directions for creating half-checking
propagation algorithms are introduced, and used for designing new half-checking
propagators for the cost-circuit constraint as examples. The new propagators
are implemented in the Gecode system.
",Half-checking propagators
"  Generative adversarial networks (GANs) are quickly becoming a ubiquitous
approach to procedurally generating video game levels. While GAN generated
levels are stylistically similar to human-authored examples, human designers
often want to explore the generative design space of GANs to extract
interesting levels. However, human designers find latent vectors opaque and
would rather explore along dimensions the designer specifies, such as number of
enemies or obstacles. We propose using state-of-the-art quality diversity
algorithms designed to optimize continuous spaces, i.e. MAP-Elites with a
directional variation operator and Covariance Matrix Adaptation MAP-Elites, to
efficiently explore the latent space of a GAN to extract levels that vary
across a set of specified gameplay measures. In the benchmark domain of Super
Mario Bros, we demonstrate how designers may specify gameplay measures to our
system and extract high-quality (playable) levels with a diverse range of level
mechanics, while still maintaining stylistic similarity to human authored
examples. An online user study shows how the different mechanics of the
automatically generated levels affect subjective ratings of their perceived
difficulty and appearance.
","Illuminating Mario Scenes in the Latent Space of a Generative
  Adversarial Network"
"  Reinforcement learning (RL) agents are often designed specifically for a
particular problem and they generally have uninterpretable working processes.
Statistical methods-based agent algorithms can be improved in terms of
generalizability and interpretability using symbolic Artificial Intelligence
(AI) tools such as logic programming. In this study, we present a model-free RL
architecture that is supported with explicit relational representations of the
environmental objects. For the first time, we use the PrediNet network
architecture in a dynamic decision-making problem rather than image-based
tasks, and Multi-Head Dot-Product Attention Network (MHDPA) as a baseline for
performance comparisons. We tested two networks in two environments ---i.e.,
the baseline Box-World environment and our novel environment,
Relational-Grid-World (RGW). With the procedurally generated RGW environment,
which is complex in terms of visual perceptions and combinatorial selections,
it is easy to measure the relational representation performance of the RL
agents. The experiments were carried out using different configurations of the
environment so that the presented module and the environment were compared with
the baselines. We reached similar policy optimization performance results with
the PrediNet architecture and MHDPA; additionally, we achieved to extract the
propositional representation explicitly ---which makes the agent's statistical
policy logic more interpretable and tractable. This flexibility in the agent's
policy provides convenience for designing non-task-specific agent
architectures. The main contributions of this study are two-fold ---an RL agent
that can explicitly perform relational reasoning, and a new environment that
measures the relational reasoning capabilities of RL agents.
","Relational-Grid-World: A Novel Relational Reasoning Environment and An
  Agent Model for Relational Information Extraction"
"  Knapsack problems are classic models that can formulate a wide range of
applications. In this work, we deal with the Budgeted Maximum Coverage Problem
(BMCP), which is a generalized 0-1 knapsack problem. Given a set of items with
nonnegative weights and a set of elements with nonnegative profits, where each
item is composed of a subset of elements, BMCP aims to pack a subset of items
in a capacity-constrained knapsack such that the total weight of the selected
items does not exceed the knapsack capacity, and the total profit of the
associated elements is maximized. Note that each element is counted once even
if it is covered multiple times. BMCP is closely related to the Set-Union
Knapsack Problem (SUKP) that is well studied in recent years. As the
counterpart problem of SUKP, however, BMCP was introduced early in 1999 but
since then it has been rarely studied, especially there is no practical
algorithm proposed. By combining the reinforcement learning technique to the
local search procedure, we propose a probability learning based tabu search
(PLTS) algorithm for addressing this NP-hard problem. The proposed algorithm
iterates through two distinct phases, namely a tabu search phase and a
probability learning based perturbation phase. As there is no benchmark
instances proposed in the literature, we generate 30 benchmark instances with
varied properties. Experimental results demonstrate that our PLTS algorithm
significantly outperforms the general CPLEX solver for solving the challenging
BMCP in terms of the solution quality.
","Probability Learning based Tabu Search for the Budgeted Maximum Coverage
  Problem"
"  Tabletop roleplaying games (TTRPGs) and procedural content generators can
both be understood as systems of rules for producing content. In this paper, we
argue that TTRPG design can usefully be viewed as procedural content generator
design. We present several case studies linking key concepts from PCG research
-- including possibility spaces, expressive range analysis, and generative
pipelines -- to key concepts in TTRPG design. We then discuss the implications
of these relationships and suggest directions for future work uniting research
in TTRPGs and PCG.
",Tabletop Roleplaying Games as Procedural Content Generators
"  Domain reduction is an essential tool for solving the constraint satisfaction
problem (CSP). In the binary CSP, neighbourhood substitution consists in
eliminating a value if there exists another value which can be substituted for
it in each constraint. We show that the notion of neighbourhood substitution
can be strengthened in two distinct ways without increasing time complexity. We
also show the theoretical result that, unlike neighbourhood substitution,
finding an optimal sequence of these new operations is NP-hard.
",Strengthening neighbourhood substitution
"  Inspired by e-participation systems, in this paper we propose a new model to
represent human debates and methods to obtain collective conclusions from them.
This model overcomes drawbacks of existing approaches by allowing users to
introduce new pieces of information into the discussion, to relate them to
existing pieces, and also to express their opinion on the pieces proposed by
other users. In addition, our model does not assume that users' opinions are
rational in order to extract information from it, an assumption that
significantly limits current approaches. Instead, we define a weaker notion of
rationality that characterises coherent opinions, and we consider different
scenarios based on the coherence of individual opinions and the level of
consensus that users have on the debate structure. Considering these two
factors, we analyse the outcomes of different opinion aggregation functions
that compute a collective decision based on the individual opinions and the
debate structure. In particular, we demonstrate that aggregated opinions can be
coherent even if there is a lack of consensus and individual opinions are not
coherent. We conclude our analysis with a computational evaluation
demonstrating that collective opinions can be computed efficiently for
real-sized debates.
","A model to support collective reasoning: Formalization, analysis and
  computational assessment"
"  Videogames are nowadays one of the biggest entertainment industries in the
world. Being part of this industry means competing against lots of other
companies and developers, thus, making fanbases of vital importance. They are a
group of clients that constantly support your company because your video games
are fun. Videogames are most entertaining when the difficulty level is a good
match for the player's skill, increasing the player engagement. However, not
all players are equally proficient, so some kind of difficulty selection is
required. In this paper, we will present Dynamic Difficulty Adjustment (DDA), a
recently arising research topic, which aims to develop an automated difficulty
selection mechanism that keeps the player engaged and properly challenged,
neither bored nor overwhelmed. We will present some recent research addressing
this issue, as well as an overview of how to implement it. Satisfactorily
solving the DDA problem directly affects the player's experience when playing
the game, making it of high interest to any game developer, from independent
ones, to 100 billion dollar businesses, because of the potential impacts in
player retention and monetization.
",Exploring Dynamic Difficulty Adjustment in Videogames
"  Predicting undesirable events during the execution of a business process
instance provides the process participants with an opportunity to intervene and
keep the process aligned with its goals. Few approaches for tackling this
challenge consider a multi-perspective view, where the flow perspective of the
process is combined with its surrounding context. Given the many sources of
data in today's world, context can vary widely and have various meanings. This
paper addresses the issue of context being cause or effect of the next event
and its impact on next event prediction. We leverage previous work on
probabilistic models to develop a Dynamic Bayesian Network technique.
Probabilistic models are considered comprehensible and they allow the end-user
and his or her understanding of the domain to be involved in the prediction.
Our technique models context attributes that have either a cause or effect
relationship towards the event. We evaluate our technique with two real-life
data sets and benchmark it with other techniques from the field of predictive
process monitoring. The results show that our solution achieves superior
prediction results if context information is correctly introduced into the
model.
","Cause vs. Effect in Context-Sensitive Prediction of Business Process
  Instances"
"  In this paper, we focus on qualitative temporal sequences of topological
information. We firstly consider the context of topological temporal sequences
of length greater than 3 describing the evolution of regions at consecutive
time points. We show that there is no Cartesian subclass containing all the
basic relations and the universal relation for which the algebraic closure
decides satisfiability. However, we identify some tractable subclasses, by
giving up the relations containing the non-tangential proper part relation and
not containing the tangential proper part relation. We then formalize an
alternative semantics for temporal sequences. We place ourselves in the context
of the topological temporal sequences describing the evolution of regions on a
partition of time (i.e. an alternation of instants and intervals). In this
context, we identify large tractable fragments.
",Tractable Fragments of Temporal Sequences of Topological Information
"  In recent years, Semantic Web technologies have been increasingly adopted by
researchers, industry and public institutions to describe and link data on the
Web, create web annotations and consume large knowledge graphs like Wikidata
and DBPedia. However, there is still a knowledge gap between ontology
engineers, who design, populate and create knowledge graphs; and web
developers, who need to understand, access and query these knowledge graphs but
are not familiar with ontologies, RDF or SPARQL. In this paper we describe the
Ontology-Based APIs framework (OBA), our approach to automatically create REST
APIs from ontologies while following RESTful API best practices. Given an
ontology (or ontology network) OBA uses standard technologies familiar to web
developers (OpenAPI Specification, JSON) and combines them with W3C standards
(OWL, JSON-LD frames and SPARQL) to create maintainable APIs with
documentation, units tests, automated validation of resources and clients (in
Python, Javascript, etc.) for non Semantic Web experts to access the contents
of a target knowledge graph. We showcase OBA with three examples that
illustrate the capabilities of the framework for different ontologies.
","OBA: An Ontology-Based Framework for Creating REST APIs for Knowledge
  Graphs"
"  Tracking a car or a person in a city is crucial for urban safety management.
How can we complete the task with minimal number of spatiotemporal searches
from massive camera records? This paper proposes a strategy named IHMs
(Intermediate Searching at Heuristic Moments): each step we figure out which
moment is the best to search according to a heuristic indicator, then at that
moment search locations one by one in descending order of predicted appearing
probabilities, until a search hits; iterate this step until we get the object's
current location. Five searching strategies are compared in experiments, and
IHMs is validated to be most efficient, which can save up to 1/3 total costs.
This result provides an evidence that ""searching at intermediate moments can
save cost"".
",Object Tracking by Least Spatiotemporal Searches
"  As the current trend of artificial intelligence is shifting towards
self-supervised learning, conventional norms such as highly curated
domain-specific data, application-specific learning models, extrinsic reward
based learning policies etc. might not provide with the suitable ground for
such developments. In this paper, we introduce SEDRo, a Simulated Environment
for Developmental Robotics which allows a learning agent to have similar
experiences that a human infant goes through from the fetus stage up to 12
months. A series of simulated tests based on developmental psychology will be
used to evaluate the progress of a learning model.
",An Open-World Simulated Environment for Developmental Robotics
"  Recent advances in methods focused on the grounding problem have resulted in
techniques that can be used to construct a symbolic language associated with a
specific domain. Inspired by how humans communicate complex ideas through
language, we developed a generalized Symbolic Semantic ($\text{S}^2$) framework
for interpretable segmentation. Unlike adversarial models (e.g., GANs), we
explicitly model cooperation between two agents, a Sender and a Receiver, that
must cooperate to achieve a common goal. The Sender receives information from a
high layer of a segmentation network and generates a symbolic sentence derived
from a categorical distribution. The Receiver obtains the symbolic sentences
and co-generates the segmentation mask. In order for the model to converge, the
Sender and Receiver must learn to communicate using a private language. We
apply our architecture to segment tumors in the TCGA dataset. A UNet-like
architecture is used to generate input to the Sender network which produces a
symbolic sentence, and a Receiver network co-generates the segmentation mask
based on the sentence. Our Segmentation framework achieved similar or better
performance compared with state-of-the-art segmentation methods. In addition,
our results suggest direct interpretation of the symbolic sentences to
discriminate between normal and tumor tissue, tumor morphology, and other image
characteristics.
","Towards Emergent Language Symbolic Semantic Segmentation and Model
  Interpretability"
"  Assistance games (also known as cooperative inverse reinforcement learning
games) have been proposed as a model for beneficial AI, wherein a robotic agent
must act on behalf of a human principal but is initially uncertain about the
humans payoff function. This paper studies multi-principal assistance games,
which cover the more general case in which the robot acts on behalf of N humans
who may have widely differing payoffs. Impossibility theorems in social choice
theory and voting theory can be applied to such games, suggesting that
strategic behavior by the human principals may complicate the robots task in
learning their payoffs. We analyze in particular a bandit apprentice game in
which the humans act first to demonstrate their individual preferences for the
arms and then the robot acts to maximize the sum of human payoffs. We explore
the extent to which the cost of choosing suboptimal arms reduces the incentive
to mislead, a form of natural mechanism design. In this context we propose a
social choice method that uses shared control of a system to combine preference
inference with social welfare optimization.
",Multi-Principal Assistance Games
"  Advances in hardware technology have facilitated more integration of
sophisticated software toward augmenting the development of Unmanned Vehicles
(UVs) and mitigating constraints for onboard intelligence. As a result, UVs can
operate in complex missions where continuous trans-formation in environmental
condition calls for a higher level of situational responsiveness and autonomous
decision making. This book is a research monograph that aims to provide a
comprehensive survey of UVs autonomy and its related properties in internal and
external situation awareness to-ward robust mission planning in severe
conditions. An advance level of intelligence is essential to minimize the
reliance on the human supervisor, which is a main concept of autonomy. A
self-controlled system needs a robust mission management strategy to push the
boundaries towards autonomous structures, and the UV should be aware of its
internal state and capabilities to assess whether current mission goal is
achievable or find an alternative solution. In this book, the AUVs will become
the major case study thread but other cases/types of vehicle will also be
considered. In-deed the research monograph, the review chapters and the new
approaches we have developed would be appropriate for use as a reference in
upper years or postgraduate degrees for its coverage of literature and
algorithms relating to Robot/Vehicle planning, tasking, routing, and trust.
","Autonomy and Unmanned Vehicles Augmented Reactive Mission-Motion
  Planning Architecture for Autonomous Vehicles"
"  Recent work has demonstrated the promise of combining local explanations with
active learning for understanding and supervising black-box models. Here we
show that, under specific conditions, these algorithms may misrepresent the
quality of the model being learned. The reason is that the machine illustrates
its beliefs by predicting and explaining the labels of the query instances: if
the machine is unaware of its own mistakes, it may end up choosing queries on
which it performs artificially well. This biases the ""narrative"" presented by
the machine to the user.We address this narrative bias by introducing
explanatory guided learning, a novel interactive learning strategy in which: i)
the supervisor is in charge of choosing the query instances, while ii) the
machine uses global explanations to illustrate its overall behavior and to
guide the supervisor toward choosing challenging, informative instances. This
strategy retains the key advantages of explanatory interaction while avoiding
narrative bias and compares favorably to active learning in terms of sample
complexity. An initial empirical evaluation with a clustering-based prototype
highlights the promise of our approach.
","Toward Machine-Guided, Human-Initiated Explanatory Interactive Learning"
"  We tackle the challenge of in-session attribution for on-site search engines
in eCommerce. We phrase the problem as a causal counterfactual inference, and
contrast the approach with rule-based systems from industry settings and
prediction models from the multi-touch attribution literature. We approach
counterfactuals in analogy with treatments in formal semantics, explicitly
modeling possible outcomes through alternative shopper timelines; in
particular, we propose to learn a generative browsing model over a target shop,
leveraging the latent space induced by prod2vec embeddings; we show how natural
language queries can be effectively represented in the same space and how
""search intervention"" can be performed to assess causal contribution. Finally,
we validate the methodology on a synthetic dataset, mimicking important
patterns emerged in customer interviews and qualitative analysis, and we
present preliminary findings on an industry dataset from a partnering shop.
","Shopping in the Multiverse: A Counterfactual Approach to In-Session
  Attribution"
"  To provide a foundation for conceptual modeling, ontologies have been
introduced to specify the entities, the existences of which are acknowledged in
the model. Ontologies are essential components as mechanisms to model a portion
of reality in software engineering. In this context, a model refers to a
description of objects and processes that populate a system. Developing such a
description constrains and directs the design, development, and use of the
corresponding system, thus avoiding such difficulties as conflicts and lack of
a common understanding. In this cross-area research between modeling and
ontology, there has been a growing interest in the development and use of
domain ontologies (e.g., Resource Description Framework, Ontology Web
Language). This paper contributes to the establishment of a broad ontological
foundation for conceptual modeling in a specific domain through proposing a
workable ontology (abbreviated as TM). A TM is a one-category ontology called a
thimac (things/machines) that is used to elaborate the design and analysis of
ontological presumptions. The focus of the study is on such notions as change,
event, and time. Several current ontological difficulties are reviewed and
remodeled in the TM. TM modeling is also contrasted with time representation in
SysML. The results demonstrate that a TM is a useful tool for addressing these
ontological problems.
",Conceptual Modeling of Time for Computational Ontologies
"  Agricultural production has become a complex business that requires the
accumulation and integration of knowledge, in addition to information from many
different sources. To remain competitive, the modern farmer often relies on
agricultural specialists and advisors who provide them with information for
decision making in their crops. But unfortunately, the help of the agricultural
specialist is not always available when the farmer needs it. To alleviate this
problem, expert systems have become a powerful instrument that has great
potential within agriculture. This paper presents an Expert System for the
diagnosis of diseases and pests in rice, tobacco, tomato, pepper, corn,
cucumber and bean crops. For the development of this Expert System, SWI-Prolog
was used to create the knowledge base, so it works with predicates and allows
the system to be based on production rules. This system allows a fast and
reliable diagnosis of pests and diseases that affect these crops.
","Sistema experto para el diagn\'ostico de enfermedades y plagas en los
  cultivos del arroz, tabaco, tomate, pimiento, ma\'iz, pepino y frijol"
"  The quality of opponent Artificial Intelligence (AI) in fighting videogames
is crucial. Some other game genres can rely on their story or visuals, but
fighting games are all about the adversarial experience. In this paper, we will
introduce standard behavior algorithms in videogames, such as Finite-State
Machines and Behavior Trees, as well as more recent developments, such as
Monte-Carlo Tree Search. We will also discuss the existing and potential
combinations of these algorithms, and how they might be used in fighting games.
Since we are at the financial peak of fighting games, both for casual players
and in tournaments, it is important to build and expand on fighting game AI, as
it is one of the pillars of this growing market.
",Introduction to Behavior Algorithms for Fighting Games
"  The current reward learning from human preferences could be used to resolve
complex reinforcement learning (RL) tasks without access to a reward function
by defining a single fixed preference between pairs of trajectory segments.
However, the judgement of preferences between trajectories is not dynamic and
still requires human input over thousands of iterations. In this study, we
proposed a weak human preference supervision framework, for which we developed
a human preference scaling model that naturally reflects the human perception
of the degree of weak choices between trajectories and established a
human-demonstration estimator via supervised learning to generate the predicted
preferences for reducing the number of human inputs. The proposed weak human
preference supervision framework can effectively solve complex RL tasks and
achieve higher cumulative rewards in simulated robot locomotion -- MuJoCo games
-- relative to the single fixed human preferences. Furthermore, our established
human-demonstration estimator requires human feedback only for less than 0.01\%
of the agent's interactions with the environment and significantly reduces the
cost of human inputs by up to 30\% compared with the existing approaches. To
present the flexibility of our approach, we released a video
(https://youtu.be/jQPe1OILT0M) showing comparisons of the behaviours of agents
trained on different types of human input. We believe that our naturally
inspired human preferences with weakly supervised learning are beneficial for
precise reward learning and can be applied to state-of-the-art RL systems, such
as human-autonomy teaming systems.
",Weak Human Preference Supervision For Deep Reinforcement Learning
"  This paper will focus on the process of 'fusing' several observations or
models of uncertainty into a single resultant model. Many existing approaches
to fusion use subjective quantities such as 'strengths of belief' and process
these quantities with heuristic algorithms. This paper argues in favor of
quantities that can be objectively measured, as opposed to the subjective
'strength of belief' values. This paper will focus on probability
distributions, and more importantly, structures that denote sets of probability
distributions known as 'credal sets'. The novel aspect of this paper will be a
taxonomy of models of fusion that use specific types of credal sets, namely
probability interval distributions and Dempster-Shafer models. An objective
requirement for information fusion algorithms is provided, and is satisfied by
all models of fusion presented in this paper. Dempster's rule of combination is
shown to not satisfy this requirement. This paper will also assess the
computational challenges involved for the proposed fusion approaches.
",Information Fusion on Belief Networks
"  In this survey, we study how recent advances in machine intelligence are
disrupting the world of business processes. Over the last decade, there has
been steady progress towards the automation of business processes under the
umbrella of ``robotic process automation'' (RPA). However, we are currently at
an inflection point in this evolution, as a new paradigm called ``Intelligent
Process Automation'' (IPA) emerges, bringing machine learning (ML) and
artificial intelligence (AI) technologies to bear in order to improve business
process outcomes. The purpose of this paper is to provide a survey of this
emerging theme and identify key open research challenges at the intersection of
AI and business processes. We hope that this emerging theme will spark engaging
conversations at the RPA Forum.
","From Robotic Process Automation to Intelligent Process Automation:
  Emerging Trends"
"  We propose a novel solution to challenging sparse-reward, continuous control
problems that require hierarchical planning at multiple levels of abstraction.
Our solution, dubbed AlphaNPI-X, involves three separate stages of learning.
First, we use off-policy reinforcement learning algorithms with experience
replay to learn a set of atomic goal-conditioned policies, which can be easily
repurposed for many tasks. Second, we learn self-models describing the effect
of the atomic policies on the environment. Third, the self-models are harnessed
to learn recursive compositional programs with multiple levels of abstraction.
The key insight is that the self-models enable planning by imagination,
obviating the need for interaction with the world when learning higher-level
compositional programs. To accomplish the third stage of learning, we extend
the AlphaNPI algorithm, which applies AlphaZero to learn recursive neural
programmer-interpreters. We empirically show that AlphaNPI-X can effectively
learn to tackle challenging sparse manipulation tasks, such as stacking
multiple blocks, where powerful model-free baselines fail.
",Learning Compositional Neural Programs for Continuous Control
"  Enterprise information systems have adopted Web-based foundations for
exchanges between heterogeneous programmes. These programs provide and consume
via Web APIs some resources identified by URIs, whose representations are
transmitted via HTTP. Furthermore HTTP remains at the heart of all Web
developments (Semantic Web, linked data, IoT...). Thus, situations where a
program must be able to reason about HTTP interactions (request-response) are
multiplying. This requires an explicit formal specification of a shared
conceptualization of those interactions. A proposal for an RDF vocabulary
exists, developed with a view to carrying out web application conformity tests
and record the tests outputs. This vocabulary has already been reused. In this
paper we propose to adapt and extend it for making it more reusable.
",Towards an ontology of HTTP interactions
"  We introduce a new incremental preference elicitation procedure able to deal
with noisy responses of a Decision Maker (DM). The originality of the
contribution is to propose a Bayesian approach for determining a preferred
solution in a multiobjective decision problem involving a combinatorial set of
alternatives. We assume that the preferences of the DM are represented by an
aggregation function whose parameters are unknown and that the uncertainty
about them is represented by a density function on the parameter space.
Pairwise comparison queries are used to reduce this uncertainty (by Bayesian
revision). The query selection strategy is based on the solution of a mixed
integer linear program with a combinatorial set of variables and constraints,
which requires to use columns and constraints generation methods. Numerical
tests are provided to show the practicability of the approach.
","Bayesian preference elicitation for multiobjective combinatorial
  optimization"
"  The Boolean Satisfiability problem (SAT) is important on artificial
intelligence community and the impact of its solving on complex problems.
Recently, great breakthroughs have been made respectively on stochastic local
search (SLS) algorithms for uniform random k-SAT resulting in several
state-of-the-art SLS algorithms Score2SAT, YalSAT, ProbSAT, CScoreSAT and on a
hybrid algorithm for hard random SAT (HRS) resulting in one state-of-the-art
hybrid algorithm SparrowToRiss. However, there is no an algorithm which can
effectively solve both uniform random k-SAT and HRS. In this paper, we present
a new SLS algorithm named SelectNTS for uniform random k-SAT and HRS. SelectNTS
is an improved probability selecting based local search algorithm for SAT
problem. The core of SelectNTS relies on new clause and variable selection
heuristics. The new clause selection heuristic uses a new clause weighting
scheme and a biased random walk. The new variable selection heuristic uses a
probability selecting strategy with the variation of CC strategy based on a new
variable weighting scheme. Extensive experimental results on the well-known
random benchmarks instances from the SAT Competitions in 2017 and 2018, and on
randomly generated problems, show that our algorithm outperforms
state-of-the-art random SAT algorithms, and our SelectNTS can effectively solve
both uniform random k-SAT and HRS.
",Improving probability selecting based weights for Satisfiability Problem
"  Social choice is the theory about collective decision towards social welfare
starting from individual opinions, preferences, interests or welfare. The field
of Computational Social Welfare is somewhat recent and it is gaining impact in
the Artificial Intelligence Community. Classical literature makes the
assumption of single-peaked preferences, i.e. there exist a order in the
preferences and there is a global maximum in this order. This year some
theoretical results were published about Two-stage Approval Voting Systems
(TAVs), Multi-winner Selection Rules (MWSR) and Incomplete (IPs) and Circular
Preferences (CPs). The purpose of this paper is three-fold: Firstly, I want to
introduced Social Choice Optimisation as a generalisation of TAVs where there
is a max stage and a min stage implementing thus a Minimax, well-known
Artificial Intelligence decision-making rule to minimize hindering towards a
(Social) Goal. Secondly, I want to introduce, following my Open Standardization
and Open Integration Theory (in refinement process) put in practice in my
dissertation, the Open Standardization of Social Inclusion, as a global social
goal of Social Choice Optimization.
",Social Choice Optimization
"  Recent advances in Artificial Intelligence have produced agents that can beat
human world champions at games like Go, Starcraft, and Dota2. However, most of
these models do not seem to play in a human-like manner: People infer others'
intentions from their behaviour, and use these inferences in scheming and
strategizing. Here, using a Bayesian Theory of Mind (ToM) approach, we
investigated how much an explicit representation of others' intentions improves
performance in a cooperative game. We compared the performance of humans
playing with optimal-planning agents with and without ToM, in a cooperative
game where players have to flexibly cooperate to achieve joint goals. We find
that teams with ToM agents significantly outperform non-ToM agents when
collaborating with all types of partners: non-ToM, ToM, as well as human
players, and that the benefit of ToM increases the more ToM agents there are.
These findings have implications for designing better cooperative agents.
",Improving Multi-Agent Cooperation using Theory of Mind
"  A structural causal model is made of endogenous (manifest) and exogenous
(latent) variables. We show that endogenous observations induce linear
constraints on the probabilities of the exogenous variables. This allows to
exactly map a causal model into a credal network. Causal inferences, such as
interventions and counterfactuals, can consequently be obtained by standard
algorithms for the updating of credal nets. These natively return sharp values
in the identifiable case, while intervals corresponding to the exact bounds are
produced for unidentifiable queries. A characterization of the causal models
that allow the map above to be compactly derived is given, along with a
discussion about the scalability for general models. This contribution should
be regarded as a systematic approach to represent structural causal models by
credal networks and hence to systematically compute causal inferences. A number
of demonstrative examples is presented to clarify our methodology. Extensive
experiments show that approximate algorithms for credal networks can
immediately be used to do causal inference in real-size problems.
",Structural Causal Models Are (Solvable by) Credal Networks
"  In this paper, several techniques for learning game state evaluation
functions by reinforcement are proposed. The first is a generalization of tree
bootstrapping (tree learning): it is adapted to the context of reinforcement
learning without knowledge based on non-linear functions. With this technique,
no information is lost during the reinforcement learning process. The second is
a modification of minimax with unbounded depth extending the best sequences of
actions to the terminal states. This modified search is intended to be used
during the learning process. The third is to replace the classic gain of a game
(+1 / -1) with a reinforcement heuristic. We study particular reinforcement
heuristics such as: quick wins and slow defeats ; scoring ; mobility or
presence. The four is another variant of unbounded minimax, which plays the
safest action instead of playing the best action. This modified search is
intended to be used after the learning process. The five is a new action
selection distribution. The conducted experiments suggest that these techniques
improve the level of play. Finally, we apply these different techniques to
design program-players to the game of Hex (size 11 and 13) surpassing the level
of Mohex 3HNN with reinforcement learning from self-play without knowledge.
",Learning to Play Two-Player Perfect-Information Games without Knowledge
"  The paper describes an ongoing effort in developing a declarative system for
supporting operators in the Nuclear Power Plant (NPP) control room. The focus
is on two modules: diagnosis and explanation of events that happened in NPPs.
We describe an Answer Set Programming (ASP) representation of an NPP, which
consists of declarations of state variables, components, their connections, and
rules encoding the plant behavior. We then show how the ASP program can be used
to explain the series of events that occurred in the Three Mile Island, Unit 2
(TMI-2) NPP accident, the most severe accident in the USA nuclear power plant
operating history. We also describe an explanation module aimed at addressing
answers to questions such as ``why an event occurs?'' or ``what should be
done?'' given the collected data.
  This paper is *under consideration* for acceptance in TPLP Journal.
","An Application of ASP in Nuclear Engineering: Explaining the Three Mile
  Island Nuclear Accident Scenario"
"  Cooperation among constraint solvers is difficult because different solving
paradigms have different theoretical foundations. Recent works have shown that
abstract interpretation can provide a unifying theory for various constraint
solvers. In particular, it relies on abstract domains which capture constraint
languages as ordered structures. The key insight of this paper is viewing
cooperation schemes as abstract domains combinations. We propose a modular
framework in which solvers and cooperation schemes can be seamlessly added and
combined. This differs from existing approaches such as SMT where the
cooperation scheme is usually fixed (e.g., Nelson-Oppen). We contribute to two
new cooperation schemes: (i) interval propagators completion that allows
abstract domains to exchange bound constraints, and (ii) delayed product which
exchanges over-approximations of constraints between two abstract domains.
Moreover, the delayed product is based on delayed goal of logic programming,
and it shows that abstract domains can also capture control aspects of
constraint solving. Finally, to achieve modularity, we propose the shared
product to combine abstract domains and cooperation schemes. Our approach has
been fully implemented, and we provide various examples on the flexible job
shop scheduling problem. Under consideration for acceptance in TPLP.
",Modular Constraint Solver Cooperation via Abstract Interpretation
"  Distributed linguistic representations are powerful tools for modelling the
uncertainty and complexity of preference information in linguistic decision
making. To provide a comprehensive perspective on the development of
distributed linguistic representations in decision making, we present the
taxonomy of existing distributed linguistic representations. Then, we review
the key elements of distributed linguistic information processing in decision
making, including the distance measurement, aggregation methods, distributed
linguistic preference relations, and distributed linguistic multiple attribute
decision making models. Next, we provide a discussion on ongoing challenges and
future research directions from the perspective of data science and explainable
artificial intelligence.
","Distributed Linguistic Representations in Decision Making: Taxonomy, Key
  Elements and Applications, and Challenges in Data Science and Explainable
  Artificial Intelligence"
"  Recently, there has been increasing interest in transparency and
interpretability in Deep Reinforcement Learning (DRL) systems. Verbal
explanations, as the most natural way of communication in our daily life,
deserve more attention, since they allow users to gain a better understanding
of the system which ultimately could lead to a high level of trust and smooth
collaboration. This paper reports a novel work in generating verbal
explanations for DRL behaviors agent. A rule-based model is designed to
construct explanations using a series of rules which are predefined with prior
knowledge. A learning model is then proposed to expand the implicit logic of
generating verbal explanation to general situations by employing rule-based
explanations as training data. The learning model is shown to have better
flexibility and generalizability than the static rule-based model. The
performance of both models is evaluated quantitatively through objective
metrics. The results show that verbal explanation generated by both models
improve subjective satisfaction of users towards the interpretability of DRL
systems. Additionally, seven variants of the learning model are designed to
illustrate the contribution of input channels, attention mechanism, and
proposed encoder in improving the quality of verbal explanation.
","Explanation of Reinforcement Learning Model in Dynamic Multi-Agent
  System"
"  Qualitative reasoning involves expressing and deriving knowledge based on
qualitative terms such as natural language expressions, rather than strict
mathematical quantities. Well over 40 qualitative calculi have been proposed so
far, mostly in the spatial and temporal domains, with several practical
applications such as naval traffic monitoring, warehouse process optimisation
and robot manipulation. Even if a number of specialised qualitative reasoning
tools have been developed so far, an important barrier to the wider adoption of
these tools is that only qualitative reasoning is supported natively, when
real-world problems most often require a combination of qualitative and other
forms of reasoning. In this work, we propose to overcome this barrier by using
ASP as a unifying formalism to tackle problems that require qualitative
reasoning in addition to non-qualitative reasoning. A family of ASP encodings
is proposed which can handle any qualitative calculus with binary relations.
These encodings are experimentally evaluated using a real-world dataset based
on a case study of determining optimal coverage of telecommunication antennas,
and compared with the performance of two well-known dedicated reasoners.
Experimental results show that the proposed encodings outperform one of the two
reasoners, but fall behind the other, an acceptable trade-off given the added
benefits of handling any type of reasoning as well as the interpretability of
logic programs. This paper is under consideration for acceptance in TPLP.
","A Generalised Approach for Encoding and Reasoning with Qualitative
  Theories in Answer Set Programming"
"  In recent years, Reinforcement Learning (RL), has become a popular field of
study as well as a tool for enterprises working on cutting-edge artificial
intelligence research. To this end, many researchers have built RL frameworks
such as openAI Gym and KerasRL for ease of use. While these works have made
great strides towards bringing down the barrier of entry for those new to RL,
we propose a much simpler framework called EasyRL, by providing an interactive
graphical user interface for users to train and evaluate RL agents. As it is
entirely graphical, EasyRL does not require programming knowledge for training
and testing simple built-in RL agents. EasyRL also supports custom RL agents
and environments, which can be highly beneficial for RL researchers in
evaluating and comparing their RL models.
",EasyRL: A Simple and Extensible Reinforcement Learning Framework
"  Purpose: AI in radiology is hindered chiefly by: 1) Requiring large annotated
data sets. 2) Non-generalizability that limits deployment to new scanners /
institutions. And 3) Inadequate explainability and interpretability. We believe
that reinforcement learning can address all three shortcomings, with robust and
intuitive algorithms trainable on small datasets. To the best of our knowledge,
reinforcement learning has not been directly applied to computer vision tasks
for radiological images. In this proof-of-principle work, we train a deep
reinforcement learning network to predict brain tumor location.
  Materials and Methods: Using the BraTS brain tumor imaging database, we
trained a deep Q network on 70 post-contrast T1-weighted 2D image slices. We
did so in concert with image exploration, with rewards and punishments designed
to localize lesions. To compare with supervised deep learning, we trained a
keypoint detection convolutional neural network on the same 70 images. We
applied both approaches to a separate 30 image testing set.
  Results: Reinforcement learning predictions consistently improved during
training, whereas those of supervised deep learning quickly diverged.
Reinforcement learning predicted testing set lesion locations with 85%
accuracy, compared to roughly 7% accuracy for the supervised deep network.
  Conclusion: Reinforcement learning predicted lesions with high accuracy,
which is unprecedented for such a small training set. We believe that
reinforcement learning can propel radiology AI well past the inherent
limitations of supervised deep learning, with more clinician-driven research
and finally toward true clinical applicability.
","Deep reinforcement learning to detect brain lesions on MRI: a
  proof-of-concept application of reinforcement learning to medical images"
"  We propose a novel ranking-based semantics for Dung-style argumentation
frameworks with the help of conditional logics. Using an intuitive translation
for an argumentation framework to generate conditionals, we can apply
nonmonotonic inference systems to generate a ranking on possible worlds. With
this ranking we construct a ranking for our arguments. With a small extension
to this ranking-based semantics we already satisfy some desirable properties
for a ranking over arguments.
","Towards Ranking-based Semantics for Abstract Argumentation using
  Conditional Logic Semantics"
"  Designing agents that reason and act upon the world has always been one of
the main objectives of the Artificial Intelligence community. While for
planning in ""simple"" domains the agents can solely rely on facts about the
world, in several contexts, e.g., economy, security, justice and politics, the
mere knowledge of the world could be insufficient to reach a desired goal. In
these scenarios, epistemic reasoning, i.e., reasoning about agents' beliefs
about themselves and about other agents' beliefs, is essential to design
winning strategies.
  This paper addresses the problem of reasoning in multi-agent epistemic
settings exploiting declarative programming techniques. In particular, the
paper presents an actual implementation of a multi-shot Answer Set
Programming-based planner that can reason in multi-agent epistemic settings,
called PLATO (ePistemic muLti-agent Answer seT programming sOlver). The ASP
paradigm enables a concise and elegant design of the planner, w.r.t. other
imperative implementations, facilitating the development of formal verification
of correctness.
  The paper shows how the planner, exploiting an ad-hoc epistemic state
representation and the efficiency of ASP solvers, has competitive performance
results on benchmarks collected from the literature. It is under consideration
for acceptance in TPLP.
",Modelling Multi-Agent Epistemic Planning in ASP
"  Generalising and re-using knowledge learned while solving one problem
instance has been neglected by state-of-the-art answer set solvers. We suggest
a new approach that generalises learned nogoods for re-use to speed-up the
solving of future problem instances. Our solution combines well-known ASP
solving techniques with deductive logic-based machine learning. Solving
performance can be improved by adding learned non-ground constraints to the
original program. We demonstrate the effects of our method by means of
realistic examples, showing that our approach requires low computational cost
to learn constraints that yield significant performance benefits in our test
cases. These benefits can be seen with ground-and-solve systems as well as
lazy-grounding systems. However, ground-and-solve systems suffer from
additional grounding overheads, induced by the additional constraints in some
cases. By means of conflict minimization, non-minimal learned constraints can
be reduced. This can result in significant reductions of grounding and solving
efforts, as our experiments show. (Under consideration for acceptance in TPLP.)
","Conflict Generalisation in ASP: Learning Correct and Effective
  Non-Ground Constraints"
"  Efficient decision-making over continuously changing data is essential for
many application domains such as cyber-physical systems, industry
digitalization, etc. Modern stream reasoning frameworks allow one to model and
solve various real-world problems using incremental and continuous evaluation
of programs as new data arrives in the stream. Applied techniques use, e.g.,
Datalog-like materialization or truth maintenance algorithms to avoid costly
re-computations, thus ensuring low latency and high throughput of a stream
reasoner. However, the expressiveness of existing approaches is quite limited
and, e.g., they cannot be used to encode problems with constraints, which often
appear in practice. In this paper, we suggest a novel approach that uses the
Conflict-Driven Constraint Learning (CDCL) to efficiently update legacy
solutions by using intelligent management of learned constraints. In
particular, we study the applicability of reinforcement learning to
continuously assess the utility of learned constraints computed in previous
invocations of the solving algorithm for the current one. Evaluations conducted
on real-world reconfiguration problems show that providing a CDCL algorithm
with relevant learned constraints from previous iterations results in
significant performance improvements of the algorithm in stream reasoning
scenarios.
  Under consideration for acceptance in TPLP.
","Managing caching strategies for stream reasoning with reinforcement
  learning"
"  This work is inspired by recent advances in hierarchical reinforcement
learning (HRL) (Barto and Mahadevan 2003; Hengst 2010), and improvements in
learning efficiency from heuristic-based subgoal selection, experience replay
(Lin 1993; Andrychowicz et al. 2017), and task-based curriculum learning
(Bengio et al. 2009; Zaremba and Sutskever 2014). We propose a new method to
integrate HRL, experience replay and effective subgoal selection through an
implicit curriculum design based on human expertise to support sample-efficient
learning and enhance interpretability of the agent's behavior. Human expertise
remains indispensable in many areas such as medicine (Buch, Ahmed, and
Maruthappu 2018) and law (Cath 2018), where interpretability, explainability
and transparency are crucial in the decision making process, for ethical and
legal reasons. Our method simplifies the complex task sets for achieving the
overall objectives by decomposing them into subgoals at different levels of
abstraction. Incorporating relevant subjective knowledge also significantly
reduces the computational resources spent in exploration for RL, especially in
high speed, changing, and complex environments where the transition dynamics
cannot be effectively learned and modelled in a short time. Experimental
results in two StarCraft II (SC2) (Vinyals et al. 2017) minigames demonstrate
that our method can achieve better sample efficiency than flat and end-to-end
RL methods, and provides an effective method for explaining the agent's
performance.
","Hierarchical Reinforcement Learning in StarCraft II with Human Expertise
  in Subgoals Selection"
"  Pre-departure flight plan scheduling for Urban Air Mobility (UAM) and cargo
delivery drones will require on-demand scheduling of large numbers of aircraft.
We examine the scalability of an algorithm known as FastMDP which was shown to
perform well in deconflicting many dozens of aircraft in a dense airspace
environment with terrain. We show that the algorithm can adapted to perform
first-come-first-served pre-departure flight plan scheduling where conflict
free flight plans are generated on demand. We demonstrate a parallelized
implementation of the algorithm on a Graphics Processor Unit (GPU) which we
term FastMDP-GPU and show the level of performance and scaling that can be
achieved. Our results show that on commodity GPU hardware we can perform flight
plan scheduling against 2000-3000 known flight plans and with server-class
hardware the performance can be higher. We believe the results show promise for
implementing a large scale UAM scheduler capable of performing on-demand flight
scheduling that would be suitable for both a centralized or distributed flight
planning system
","Scalable FastMDP for Pre-departure Airspace Reservation and Strategic
  De-conflict"
"  Wikidata constraints, albeit useful, are represented and processed in an
incomplete, ad hoc fashion. Constraint declarations do not fully express their
meaning, and thus do not provide a precise, unambiguous basis for constraint
specification, or a logical foundation for constraint-checking implementations.
In prior work we have proposed a logical framework for Wikidata as a whole,
based on multi-attributed relational structures (MARS) and related logical
languages. In this paper we explain how constraints are handled in the proposed
framework, and show that nearly all of Wikidata's existing property constraints
can be completely characterized in it, in a natural and economical fashion. We
also give characterizations for several proposed property constraints, and show
that a variety of non-property constraints can be handled in the same
framework.
",Wikidata Constraints on MARS (Extended Technical Report)
"  Planimation is a modular and extensible open source framework to visualise
sequential solutions of planning problems specified in PDDL. We introduce a
preliminary declarative PDDL-like animation profile specification, expressive
enough to synthesise animations of arbitrary initial states and goals of a
benchmark with just a single profile.
",Planimation
"  This article discusses some trends and concepts in developing new generation
of future Artificial General Intelligence (AGI) systems which relate to complex
facets and different types of human intelligence, especially social, emotional,
attentional and ethical intelligence. We describe various aspects of multiple
human intelligences and learning styles, which may impact on a variety of AI
problem domains. Using the concept of 'multiple intelligences' rather than a
single type of intelligence, we categorize and provide working definitions of
various AGI depending on their cognitive skills or capacities. Future AI
systems will be able not only to communicate with human users and each other,
but also to efficiently exchange knowledge and wisdom with abilities of
cooperation, collaboration and even co-creating something new and valuable and
have meta-learning capacities. Multi-agent systems such as these can be used to
solve problems that would be difficult to solve by any individual intelligent
agent.
  Key words: Artificial General Intelligence (AGI), multiple intelligences,
learning styles, physical intelligence, emotional intelligence, social
intelligence, attentional intelligence, moral-ethical intelligence, responsible
decision making, creative-innovative intelligence, cognitive functions,
meta-learning of AI systems.
","Future Trends for Human-AI Collaboration: A Comprehensive Taxonomy of
  AI/AGI Using Multiple Intelligences and Learning Styles"
"  Ortus is a simple virtual organism that also serves as an initial framework
for investigating and developing biologically-based artificial intelligence.
Born from a goal to create complex virtual intelligence and an initial attempt
to model C. elegans, Ortus implements a number of mechanisms observed in
organic nervous systems, and attempts to fill in unknowns based upon plausible
biological implementations and psychological observations. Implemented
mechanisms include excitatory and inhibitory chemical synapses, bidirectional
gap junctions, and Hebbian learning with its Stentian extension. We present an
initial experiment that showcases Ortus' fundamental principles; specifically,
a cyclic respiratory circuit, and emotionally-driven associative learning with
respect to an input stimulus. Finally, we discuss the implications and future
directions for Ortus and similar systems.
","Ortus: an Emotion-Driven Approach to (artificial) Biological
  Intelligence"
"  OWL ontologies are nowadays a quite popular way to describe structured
knowledge in terms of classes, relations among classes and class instances. In
this paper, given a target class T of an OWL ontology, we address the problem
of learning fuzzy concept inclusion axioms that describe sufficient conditions
for being an individual instance of T. To do so, we present Fuzzy OWL-BOOST
that relies on the Real AdaBoost boosting algorithm adapted to the (fuzzy) OWL
case. We illustrate its effectiveness by means of an experimentation. An
interesting feature is that the learned rules can be represented directly into
Fuzzy OWL 2. As a consequence, any Fuzzy OWL 2 reasoner can then be used to
automatically determine/classify (and to which degree) whether an individual
belongs to the target class T.
","Fuzzy OWL-BOOST: Learning Fuzzy Concept Inclusions via Real-Valued
  Boosting"
"  This paper studies the deception applied on agent in a partially observable
Markov decision process. We introduce deceptive kernel function (the kernel)
applied to agent's observations in a discrete POMDP. Based on value iteration,
value function approximation and POMCP three characteristic algorithms used by
agent, we analyze its belief being misled by falsified observations as the
kernel's outputs and anticipate its probable threat on agent's reward and
potentially other performance. We validate our expectation and explore more
detrimental effects of the deception by experimenting on two POMDP problems.
The result shows that the kernel applied on agent's observation can affect its
belief and substantially lower its resulting rewards; meantime certain
implementation of the kernel could induce other abnormal behaviors by the
agent.
",Deceptive Kernel Function on Observations of Discrete POMDP
"  With the rapid prevalence and explosive development of MOBA esports
(Multiplayer Online Battle Arena electronic sports), much research effort has
been devoted to automatically predicting game results (win predictions). While
this task has great potential in various applications, such as esports live
streaming and game commentator AI systems, previous studies fail to investigate
the methods to interpret these win predictions. To mitigate this issue, we
collected a large-scale dataset that contains real-time game records with rich
input features of the popular MOBA game Honor of Kings. For interpretable
predictions, we proposed a Two-Stage Spatial-Temporal Network (TSSTN) that can
not only provide accurate real-time win predictions but also attribute the
ultimate prediction results to the contributions of different features for
interpretability. Experiment results and applications in real-world live
streaming scenarios showed that the proposed TSSTN model is effective both in
prediction accuracy and interpretability.
","Interpretable Real-Time Win Prediction for Honor of Kings, a Popular
  Mobile MOBA Esport"
"  Multi-attributed relational structures (MARSs) have been proposed as a formal
data model for generalized property graphs, along with multi-attributed
rule-based predicate logic (MARPL) as a useful rule-based logic in which to
write inference rules over property graphs. Wikidata can be modelled in an
extended MARS that adds the (imprecise) datatypes of Wikidata. The rules of
inference for the Wikidata ontology can be modelled as a MARPL ontology, with
extensions to handle the Wikidata datatypes and functions over these datatypes.
Because many Wikidata qualifiers should participate in most inference rules in
Wikidata a method of implicitly handling qualifier values on a per-qualifier
basis is needed to make this modelling useful. The meaning of Wikidata is then
the extended MARS that is the closure of running these rules on the Wikidata
data model. Wikidata constraints can be modelled as multi-attributed predicate
logic (MAPL) formulae, again extended with datatypes, that are evaluated over
this extended MARS. The result models Wikidata in a way that fixes several of
its major problems.
",Wikidata on MARS
"  A large set of the explainable Artificial Intelligence (XAI) literature is
emerging on feature relevance techniques to explain a deep neural network (DNN)
output or explaining models that ingest image source data. However, assessing
how XAI techniques can help understand models beyond classification tasks, e.g.
for reinforcement learning (RL), has not been extensively studied. We review
recent works in the direction to attain Explainable Reinforcement Learning
(XRL), a relatively new subfield of Explainable Artificial Intelligence,
intended to be used in general public applications, with diverse audiences,
requiring ethical, responsible and trustable algorithms. In critical situations
where it is essential to justify and explain the agent's behaviour, better
explainability and interpretability of RL models could help gain scientific
insight on the inner workings of what is still considered a black box. We
evaluate mainly studies directly linking explainability to RL, and split these
into two categories according to the way the explanations are generated:
transparent algorithms and post-hoc explainaility. We also review the most
prominent XAI works from the lenses of how they could potentially enlighten the
further deployment of the latest advances in RL, in the demanding present and
future of everyday problems.
",Explainability in Deep Reinforcement Learning
"  This paper investigates the feasibility of automated reasoning over temporal
DL-Lite (TDL-Lite) knowledge bases (KBs). We test the usage of off-the-shelf
LTL reasoners to check satisfiability of TDL-Lite KBs. In particular, we test
the robustness and the scalability of reasoners when dealing with TDL-Lite
TBoxes paired with a temporal ABox. We conduct various experiments to analyse
the performance of different reasoners by randomly generating TDL-Lite KBs and
then measuring the running time and the size of the translations. Furthermore,
in an effort to make the usage of TDL-Lite KBs a reality, we present a fully
fledged tool with a graphical interface to design them. Our interface is based
on conceptual modelling principles and it is integrated with our translation
tool and a temporal reasoner.
",Automated Reasoning in Temporal DL-Lite
"  We study the problem of policy repair for learning-based control policies in
safety-critical settings. We consider an architecture where a high-performance
learning-based control policy (e.g. one trained as a neural network) is paired
with a model-based safety controller. The safety controller is endowed with the
abilities to predict whether the trained policy will lead the system to an
unsafe state, and take over control when necessary. While this architecture can
provide added safety assurances, intermittent and frequent switching between
the trained policy and the safety controller can result in undesirable
behaviors and reduced performance. We propose to reduce or even eliminate
control switching by `repairing' the trained policy based on runtime data
produced by the safety controller in a way that deviates minimally from the
original policy. The key idea behind our approach is the formulation of a
trajectory optimization problem that allows the joint reasoning of policy
update and safety constraints. Experimental results demonstrate that our
approach is effective even when the system model in the safety controller is
unknown and only approximated.
",Runtime-Safety-Guided Policy Repair
"  Wikidata and Wikipedia have been proven useful for reason-ing in natural
language applications, like question answering or entitylinking. Yet, no
existing work has studied the potential of Wikidata for commonsense reasoning.
This paper investigates whether Wikidata con-tains commonsense knowledge which
is complementary to existing commonsense sources. Starting from a definition of
common sense, we devise three guiding principles, and apply them to generate a
commonsense subgraph of Wikidata (Wikidata-CS). Within our approach, we map the
relations of Wikidata to ConceptNet, which we also leverage to integrate
Wikidata-CS into an existing consolidated commonsense graph. Our experiments
reveal that: 1) albeit Wikidata-CS represents a small portion of Wikidata, it
is an indicator that Wikidata contains relevant commonsense knowledge, which
can be mapped to 15 ConceptNet relations; 2) the overlap between Wikidata-CS
and other commonsense sources is low, motivating the value of knowledge
integration; 3) Wikidata-CS has been evolving over time at a slightly slower
rate compared to the overall Wikidata, indicating a possible lack of focus on
commonsense knowledge. Based on these findings, we propose three recommended
actions to improve the coverage and quality of Wikidata-CS further.
",Commonsense Knowledge in Wikidata
"  Probabilistic sentential decision diagrams are logic circuits where the
inputs of disjunctive gates are annotated by probability values. They allow for
a compact representation of joint probability mass functions defined over sets
of Boolean variables, that are also consistent with the logical constraints
defined by the circuit. The probabilities in such a model are usually learned
from a set of observations. This leads to overconfident and prior-dependent
inferences when data are scarce, unreliable or conflicting. In this work, we
develop the credal sentential decision diagrams, a generalisation of their
probabilistic counterpart that allows for replacing the local probabilities
with (so-called credal) sets of mass functions. These models induce a joint
credal set over the set of Boolean variables, that sharply assigns probability
zero to states inconsistent with the logical constraints. Three inference
algorithms are derived for these models, these allow to compute: (i) the lower
and upper probabilities of an observation for an arbitrary number of variables;
(ii) the lower and upper conditional probabilities for the state of a single
variable given an observation; (iii) whether or not all the probabilistic
sentential decision diagrams compatible with the credal specification have the
same most probable explanation of a given set of variables given an observation
of the other variables. These inferences are tractable, as all the three
algorithms, based on bottom-up traversal with local linear programming tasks on
the disjunctive gates, can be solved in polynomial time with respect to the
circuit size. For a first empirical validation, we consider a simple
application based on noisy seven-segment display images. The credal models are
observed to properly distinguish between easy and hard-to-detect instances and
outperform other generative models not able to cope with logical constraints.
",Tractable Inference in Credal Sentential Decision Diagrams
"  Reasoning with declarative knowledge (RDK) and sequential decision-making
(SDM) are two key research areas in artificial intelligence. RDK methods reason
with declarative domain knowledge, including commonsense knowledge, that is
either provided a priori or acquired over time, while SDM methods
(probabilistic planning and reinforcement learning) seek to compute action
policies that maximize the expected cumulative utility over a time horizon;
both classes of methods reason in the presence of uncertainty. Despite the rich
literature in these two areas, researchers have not fully explored their
complementary strengths. In this paper, we survey algorithms that leverage RDK
methods while making sequential decisions under uncertainty. We discuss
significant developments, open problems, and directions for future work.
",A Survey of Knowledge-based Sequential Decision Making under Uncertainty
"  ""Mathematics is not a careful march down a well-cleared highway, but a
journey into a strange wilderness, where the explorers often get lost. Rigour
should be a signal to the historian that the maps have been made, and the real
explorers have gone elsewhere."" W.S. Anglin, the Mathematical Intelligencer, 4
(4), 1982.
",Adventures in Mathematical Reasoning
"  Online electronic coupon (e-coupon) is becoming a primary tool for e-commerce
platforms to attract users to place orders. E-coupons are the digital
equivalent of traditional paper coupons which provide customers with discounts
or gifts. One of the fundamental problems related is how to deliver e-coupons
with minimal cost while users' willingness to place an order is maximized. We
call this problem the coupon allocation problem. This is a non-trivial problem
since the number of regular users on a mature e-platform often reaches hundreds
of millions and the types of e-coupons to be allocated are often multiple. The
policy space is extremely large and the online allocation has to satisfy a
budget constraint. Besides, one can never observe the responses of one user
under different policies which increases the uncertainty of the policy making
process. Previous work fails to deal with these challenges. In this paper, we
decompose the coupon allocation task into two subtasks: the user intent
detection task and the allocation task. Accordingly, we propose a two-stage
solution: at the first stage (detection stage), we put forward a novel
Instantaneous Intent Detection Network (IIDN) which takes the user-coupon
features as input and predicts user real-time intents; at the second stage
(allocation stage), we model the allocation problem as a Multiple-Choice
Knapsack Problem (MCKP) and provide a computational efficient allocation method
using the intents predicted at the detection stage. We conduct extensive online
and offline experiments and the results show the superiority of our proposed
framework, which has brought great profits to the platform and continues to
function online.
","Spending Money Wisely: Online Electronic Coupon Allocation based on
  Real-Time User Intent Detection"
"  The architecture of the neural networks used in Deep Reinforcement Learning
programs such as Alpha Zero or Polygames has been shown to have a great impact
on the performances of the resulting playing engines. For example the use of
residual networks gave a 600 ELO increase in the strength of Alpha Go. This
paper proposes to evaluate the interest of Mobile Network for the game of Go
using supervised learning as well as the use of a policy head and a value head
different from the Alpha Zero heads. The accuracy of the policy, the mean
squared error of the value, the efficiency of the networks with the number of
parameters, the playing speed and strength of the trained networks are
evaluated.
",Mobile Networks for Computer Go
"  Understanding data and reaching valid conclusions are of paramount importance
in the present era of big data. Machine learning and probability theory methods
have widespread application for this purpose in different fields. One
critically important yet less explored aspect is how data and model
uncertainties are captured and analyzed. Proper quantification of uncertainty
provides valuable information for optimal decision making. This paper reviewed
related studies conducted in the last 30 years (from 1991 to 2020) in handling
uncertainties in medical data using probability theory and machine learning
techniques. Medical data is more prone to uncertainty due to the presence of
noise in the data. So, it is very important to have clean medical data without
any noise to get accurate diagnosis. The sources of noise in the medical data
need to be known to address this issue. Based on the medical data obtained by
the physician, diagnosis of disease, and treatment plan are prescribed. Hence,
the uncertainty is growing in healthcare and there is limited knowledge to
address these problems. We have little knowledge about the optimal treatment
methods as there are many sources of uncertainty in medical science. Our
findings indicate that there are few challenges to be addressed in handling the
uncertainty in medical raw data and new models. In this work, we have
summarized various methods employed to overcome this problem. Nowadays,
application of novel deep learning techniques to deal such uncertainties have
significantly increased.
","Handling of uncertainty in medical data using machine learning and
  probability theory techniques: A review of 30 years (1991-2020)"
"  Task planning in a probabilistic belief state domains allows generating
complex and robust execution policies in those domains affected by state
uncertainty. The performance of a task planner relies on the belief state
representation. However, current belief state representation becomes easily
intractable as the number of variables and execution time grows. To address
this problem, we developed a novel belief state representation based on
cartesian product and union operations over belief substates. These two
operations and single variable assignment nodes form And-Or directed acyclic
graph of Belief State (AOBS). We show how to apply actions with probabilistic
outcomes and measure the probability of conditions holding over belief state.
We evaluated AOBS performance in simulated forward state space exploration. We
compared the size of AOBS with the size of Binary Decision Diagrams (BDD) that
were previously used to represent belief state. We show that AOBS
representation is not only much more compact than a full belief state but it
also scales better than BDD for most of the cases.
",Compact Belief State Representation for Task Planning
"  We present several completely general diversity metrics to quantify the
problem-solving capacity of any public policy decision making process. This is
performed by modelling the policy process using a declarative process paradigm
in conjunction with constraints modelled by expressions in linear temporal
logic. We introduce a class of traces, called first-passage traces, to
represent the different executions of the declarative processes. Heuristics of
what properties a diversity measure of such processes ought to satisfy are used
to derive two different metrics for these processes in terms of the set of
first-passage traces. These metrics turn out to have formulations in terms of
the entropies of two different random variables on the set of traces of the
processes. In addition, we introduce a measure of `goodness' whereby a trace is
termed {\it good} if it satisfies some prescribed linear temporal logic
expression. This allows for comparisons of policy processes with respect to the
prescribed notion of `goodness'.
",Combinatorial diversity metrics for the analysis of policy processes
"  This paper proposes environment design in the life simulation game The Sims
as a novel platform and challenge for testing divergent search algorithms. In
this domain, which includes a minimal viability criterion, the goal is to
furnish a house with objects that satisfy the physical needs of a simulated
agent. Importantly, the large number of objects available to the player
(whether human or automated) affords a wide variety of solutions to the
underlying design problem. Empirical studies in a novel open source simulator
called SimSim investigate the ability of novelty-based evolutionary algorithms
to effectively generate viable environment designs.
","Say ""Sul Sul!"" to SimSim, A Sims-Inspired Platform for Sandbox Game AI"
"  Can artificial intelligence systems exhibit superhuman performance, but in
critical ways, lack the intelligence of even a single-celled organism? The
answer is clearly 'yes' for narrow AI systems. Animals, plants, and even
single-celled organisms learn to reliably avoid danger and move towards food.
This is accomplished via a physical knowledge preserving metamodel that
autonomously generates useful models of the world. We posit that preserving the
structure of knowledge is critical for higher intelligences that manage
increasingly higher levels of abstraction, be they human or artificial. This is
the key lesson learned from applying AGI subsystems to complex real-world
problems that require continuous learning and adaptation. In this paper, we
introduce the Deep Fusion Reasoning Engine (DFRE), which implements a
knowledge-preserving metamodel and framework for constructing applied AGI
systems. The DFRE metamodel exhibits some important fundamental knowledge
preserving properties such as clear distinctions between symmetric and
antisymmetric relations, and the ability to create a hierarchical knowledge
representation that clearly delineates between levels of abstraction. The DFRE
metamodel, which incorporates these capabilities, demonstrates how this
approach benefits AGI in specific ways such as managing combinatorial explosion
and enabling cumulative, distributed and federated learning. Our experiments
show that the proposed framework achieves 94% accuracy on average on
unsupervised object detection and recognition. This work is inspired by the
state-of-the-art approaches to AGI, recent AGI-aspiring work, the granular
computing community, as well as Alfred Korzybski's general semantics.
",A Metamodel and Framework for AGI
"  We propose a novel simulation model that is able to predict the per-level
churn and pass rates of Angry Birds Dream Blast, a popular mobile free-to-play
game. Our primary contribution is to combine AI gameplay using Deep
Reinforcement Learning (DRL) with a simulation of how the player population
evolves over the levels. The AI players predict level difficulty, which is used
to drive a player population model with simulated skill, persistence, and
boredom. This allows us to model, e.g., how less persistent and skilled players
are more sensitive to high difficulty, and how such players churn early, which
makes the player population and the relation between difficulty and churn
evolve level by level. Our work demonstrates that player behavior predictions
produced by DRL gameplay can be significantly improved by even a very simple
population-level simulation of individual player differences, without requiring
costly retraining of agents or collecting new DRL gameplay data for each
simulated player.
",Predicting Game Difficulty and Churn Without Players
"  Consumer electronic devices such as mobile handsets, goods tagged with RFID
labels, location and position sensors are continuously generating a vast amount
of location enriched data called geospatial data. Conventionally such
geospatial data is used for military applications. In recent times, many useful
civilian applications have been designed and deployed around such geospatial
data. For example, a recommendation system to suggest restaurants or places of
attraction to a tourist visiting a particular locality. At the same time, civic
bodies are harnessing geospatial data generated through remote sensing devices
to provide better services to citizens such as traffic monitoring, pothole
identification, and weather reporting. Typically such applications are
leveraged upon non-hierarchical machine learning techniques such as Naive-Bayes
Classifiers, Support Vector Machines, and decision trees. Recent advances in
the field of deep-learning showed that Neural Network-based techniques
outperform conventional techniques and provide effective solutions for many
geospatial data analysis tasks such as object recognition, image
classification, and scene understanding. The chapter presents a survey on the
current state of the applications of deep learning techniques for analyzing
geospatial data.
  The chapter is organized as below: (i) A brief overview of deep learning
algorithms. (ii)Geospatial Analysis: a Data Science Perspective (iii)
Deep-learning techniques for Remote Sensing data analytics tasks (iv)
Deep-learning techniques for GPS data analytics(iv) Deep-learning techniques
for RFID data analytics.
",Deep Learning Techniques for Geospatial Data Analysis
"  A Bayesian network is a widely used probabilistic graphical model with
applications in knowledge discovery and prediction. Learning a Bayesian network
(BN) from data can be cast as an optimization problem using the well-known
score-and-search approach. However, selecting a single model (i.e., the best
scoring BN) can be misleading or may not achieve the best possible accuracy. An
alternative to committing to a single model is to perform some form of Bayesian
or frequentist model averaging, where the space of possible BNs is sampled or
enumerated in some fashion. Unfortunately, existing approaches for model
averaging either severely restrict the structure of the Bayesian network or
have only been shown to scale to networks with fewer than 30 random variables.
In this paper, we propose a novel approach to model averaging inspired by
performance guarantees in approximation algorithms. Our approach has two
primary advantages. First, our approach only considers credible models in that
they are optimal or near-optimal in score. Second, our approach is more
efficient and scales to significantly larger Bayesian networks than existing
approaches.
",Learning All Credible Bayesian Network Structures for Model Averaging
"  RDF2vec is an embedding technique for representing knowledge graph entities
in a continuous vector space. In this paper, we investigate the effect of
materializing implicit A-box axioms induced by subproperties, as well as
symmetric and transitive properties. While it might be a reasonable assumption
that such a materialization before computing embeddings might lead to better
embeddings, we conduct a set of experiments on DBpedia which demonstrate that
the materialization actually has a negative effect on the performance of
RDF2vec. In our analysis, we argue that despite the huge body of work devoted
on completing missing information in knowledge graphs, such missing implicit
information is actually a signal, not a defect, and we show examples
illustrating that assumption.
","More is not Always Better: The Negative Impact of A-box Materialization
  on RDF2vec Knowledge Graph Embeddings"
"  In this document, we introduce PYCSP$3$, a Python library that allows us to
write models of combinatorial constrained problems in a simple and declarative
way. Currently, with PyCSP$3$, you can write models of constraint satisfaction
and optimization problems. More specifically, you can build CSP (Constraint
Satisfaction Problem) and COP (Constraint Optimization Problem) models.
Importantly, there is a complete separation between modeling and solving
phases: you write a model, you compile it (while providing some data) in order
to generate an XCSP3 instance (file), and you solve that problem instance by
means of a constraint solver. In this document, you will find all that you need
to know about PYCSP$3$, with more than 40 illustrative models.
",PYCSP3: Modeling Combinatorial Constrained Problems in Python
"  This paper surveys the state-of-the-art in machine ethics, that is,
considerations of how to implement ethical behaviour in robots, unmanned
autonomous vehicles, or software systems. The emphasis is on covering the
breadth of ethical theories being considered by implementors, as well as the
implementation techniques being used. There is no consensus on which ethical
theory is best suited for any particular domain, nor is there any agreement on
which technique is best placed to implement a particular theory. Another
unresolved problem in these implementations of ethical theories is how to
objectively validate the implementations. The paper discusses the dilemmas
being used as validating 'whetstones' and whether any alternative validation
mechanism exists. Finally, it speculates that an intermediate step of creating
domain-specific ethics might be a possible stepping stone towards creating
machines that exhibit ethical behaviour.
",Landscape of Machine Implemented Ethics
"  In this document, we introduce XCSP3-core, a subset of XCSP3 that allows us
to represent constraint satisfaction/optimization problems. The interest of
XCSP3-core is multiple: (i) focusing on the most popular frameworks (CSP and
COP) and constraints, (ii) facilitating the parsing process by means of
dedicated XCSP3-core parsers written in Java and C++ (using callback
functions), (iii) and defining a core format for comparisons (competitions) of
constraint solvers.
","XCSP3-core: A Format for Representing Constraint
  Satisfaction/Optimization Problems"
"  Game agents such as opponents, non-player characters, and teammates are
central to player experiences in many modern games. As the landscape of AI
techniques used in the games industry evolves to adopt machine learning (ML)
more widely, it is vital that the research community learn from the best
practices cultivated within the industry over decades creating agents. However,
although commercial game agent creation pipelines are more mature than those
based on ML, opportunities for improvement still abound. As a foundation for
shared progress identifying research opportunities between researchers and
practitioners, we interviewed seventeen game agent creators from AAA studios,
indie studios, and industrial research labs about the challenges they
experienced with their professional workflows. Our study revealed several open
challenges ranging from design to implementation and evaluation. We compare
with literature from the research community that address the challenges
identified and conclude by highlighting promising directions for future
research supporting agent creation in the games industry.
","""It's Unwieldy and It Takes a Lot of Time."" Challenges and Opportunities
  for Creating Agents in Commercial Games"
"  Drafting in Magic the Gathering is a sub-game within a larger trading card
game, where several players progressively build decks by picking cards from a
common pool. Drafting poses an interesting problem for game and AI research due
to its large search space, mechanical complexity, multiplayer nature, and
hidden information. Despite this, drafting remains understudied, in part due to
a lack of high-quality, public datasets. To rectify this problem, we present a
dataset of over 100,000 simulated, anonymized human drafts collected from
Draftsim.com. We also propose four diverse strategies for drafting agents,
including a primitive heuristic agent, an expert-tuned complex heuristic agent,
a Naive Bayes agent, and a deep neural network agent. We benchmark their
ability to emulate human drafting, and show that the deep neural network agent
outperforms other agents, while the Naive Bayes and expert-tuned agents
outperform simple heuristics. We analyze the accuracy of AI agents across the
timeline of a draft, and describe unique strengths and weaknesses for each
approach. This work helps to identify next steps in the creation of humanlike
drafting agents, and can serve as a benchmark for the next generation of
drafting bots.
",AI solutions for drafting in Magic: the Gathering
"  Clustering techniques have been proved highly suc-cessful for Takagi-Sugeno
(T-S) fuzzy model identification. Inparticular, fuzzyc-regression clustering
based on type-2 fuzzyset has been shown the remarkable results on non-sparse
databut their performance degraded on sparse data. In this paper, aninnovative
architecture for fuzzyc-regression model is presentedand a novel
student-tdistribution based membership functionis designed for sparse data
modelling. To avoid the overfitting,we have adopted a Bayesian approach for
incorporating aGaussian prior on the regression coefficients. Additional
noveltyof our approach lies in type-reduction where the final output iscomputed
using Karnik Mendel algorithm and the consequentparameters of the model are
optimized using Stochastic GradientDescent method. As detailed experimentation,
the result showsthat proposed approach outperforms on standard datasets
incomparison of various state-of-the-art methods.
","A Bayesian Approach with Type-2 Student-tMembership Function for T-S
  Model Identification"
"  We define a modular multi-concept extension of the lexicographic closure
semantics for defeasible description logics with typicality. The idea is that
of distributing the defeasible properties of concepts into different modules,
according to their subject, and of defining a notion of preference for each
module based on the lexicographic closure semantics. The preferential semantics
of the knowledge base can then be defined as a combination of the preferences
of the single modules. The range of possibilities, from fine grained to coarse
grained modules, provides a spectrum of alternative semantics.
",A framework for a modular multi-concept lexicographic closure semantics
"  Highly regulated domains such as finance have long favoured the use of
machine learning algorithms that are scalable, transparent, robust and yield
better performance. One of the most prominent examples of such an algorithm is
XGBoost. Meanwhile, there is also a growing interest in building fair and
unbiased models in these regulated domains and numerous bias-mitigation
algorithms have been proposed to this end. However, most of these
bias-mitigation methods are restricted to specific model families such as
logistic regression or support vector machine models, thus leaving modelers
with a difficult decision of choosing between fairness from the bias-mitigation
algorithms and scalability, transparency, performance from algorithms such as
XGBoost. We aim to leverage the best of both worlds by proposing a fair variant
of XGBoost that enjoys all the advantages of XGBoost, while also matching the
levels of fairness from the state-of-the-art bias-mitigation algorithms.
Furthermore, the proposed solution requires very little in terms of changes to
the original XGBoost library, thus making it easy for adoption. We provide an
empirical analysis of our proposed method on standard benchmark datasets used
in the fairness community.
",FairXGBoost: Fairness-aware Classification in XGBoost
"  To drive purchase in online advertising, it is of the advertiser's great
interest to optimize the sequential advertising strategy whose performance and
interpretability are both important. The lack of interpretability in existing
deep reinforcement learning methods makes it not easy to understand, diagnose
and further optimize the strategy. In this paper, we propose our Deep Intents
Sequential Advertising (DISA) method to address these issues. The key part of
interpretability is to understand a consumer's purchase intent which is,
however, unobservable (called hidden states). In this paper, we model this
intention as a latent variable and formulate the problem as a Partially
Observable Markov Decision Process (POMDP) where the underlying intents are
inferred based on the observable behaviors. Large-scale industrial offline and
online experiments demonstrate our method's superior performance over several
baselines. The inferred hidden states are analyzed, and the results prove the
rationality of our inference.
",Learning to Infer User Hidden States for Online Sequential Advertising
"  In recent years, chat-bot has become a new type of intelligent terminal to
guide users to consume services. However, it is criticized most that the
services it provides are not what users expect or most expect. This defect
mostly dues to two problems, one is that the incompleteness and uncertainty of
user's requirement expression caused by the information asymmetry, the other is
that the diversity of service resources leads to the difficulty of service
selection. Conversational bot is a typical mesh device, so the guided
multi-rounds Q$\&$A is the most effective way to elicit user requirements.
Obviously, complex Q$\&$A with too many rounds is boring and always leads to
bad user experience. Therefore, we aim to obtain user requirements as
accurately as possible in as few rounds as possible. To achieve this, a user
intention recognition method based on Knowledge Graph (KG) was developed for
fuzzy requirement inference, and a requirement elicitation method based on
Granular Computing was proposed for dialog policy generation. Experimental
results show that these two methods can effectively reduce the number of
conversation rounds, and can quickly and accurately identify the user
intention.
","User Intention Recognition and Requirement Elicitation Method for
  Conversational AI Services"
"  The widespread availability of superhuman AI engines is changing how we play
the ancient game of Go. The open-source software packages developed after the
AlphaGo series shifted focus from producing strong playing entities to
providing tools for analyzing games. Here we describe two ways of how the
innovations of the second generation engines (e.g.~score estimates, variable
komi) can be used for defining new metrics that help deepen our understanding
of the game. First, we study how much information the search component
contributes in addition to the raw neural network policy output. This gives an
intrinsic strength measurement for the neural network. Second, we define the
effect of a move by the difference in score estimates. This gives a
fine-grained, move-by-move performance evaluation of a player. We use this in
combating the new challenge of detecting online cheating.
","Derived metrics for the game of Go -- intrinsic network strength
  assessment and cheat-detection"
"  Even with impressive advances in application-specific models, we still lack
knowledge about how to build a model that can learn in a human-like way and do
multiple tasks. To learn in a human-like way, we need to provide a diverse
experience that is comparable to humans. In this paper, we introduce our
ongoing effort to build a simulated environment for developmental robotics
(SEDRo). SEDRo provides diverse human experiences ranging from those of a fetus
to a 12th-month-old. A series of simulated tests based on developmental
psychology will be used to evaluate the progress of a learning model. We
anticipate SEDRo to lower the cost of entry and facilitate research in the
developmental robotics community.
",SEDRo: A Simulated Environment for Developmental Robotics
"  A method of a fusion of fuzzy inference and policy gradient reinforcement
learning has been proposed that directly learns, as maximizes the expected
value of the reward per episode, parameters in a policy function represented by
fuzzy rules with weights. A study has applied this method to a task of speed
control of an automobile and has obtained correct policies, some of which
control speed of the automobile appropriately but many others generate
inappropriate vibration of speed. In general, the policy is not desirable that
causes sudden time change or vibration in the output value, and there would be
many cases where the policy giving smooth time change in the output value is
desirable. In this paper, we propose a fusion method using the objective
function, that introduces defuzzification with the center of gravity model
weighted stochastically and a constraint term for smoothness of time change, as
an improvement measure in order to suppress sudden change of the output value
of the fuzzy controller. Then we show the learning rule in the fusion, and also
consider the effect by reward functions on the fluctuation of the output value.
As experimental results of an application of our method on speed control of an
automobile, it was confirmed that the proposed method has the effect of
suppressing the undesirable fluctuation in time-series of the output value.
Moreover, it was also showed that the difference between reward functions might
adversely affect the results of learning.
","Policy Gradient Reinforcement Learning for Policy Represented by Fuzzy
  Rules: Application to Simulations of Speed Control of an Automobile"
"  Optimizing a partially observable Markov decision process (POMDP) policy is
challenging. The policy graph improvement (PGI) algorithm for POMDPs represents
the policy as a fixed size policy graph and improves the policy monotonically.
Due to the fixed policy size, computation time for each improvement iteration
is known in advance. Moreover, the method allows for compact understandable
policies. This report describes the technical details of the PGI [1] and
particle based PGI [2] algorithms for POMDPs in a more accessible way than [1]
or [2] allowing practitioners and students to understand and implement the
algorithms.
",Technical Report: The Policy Graph Improvement Algorithm
"  Training a model to detect patterns of interrelated events that form
situations of interest can be a complex problem: such situations tend to be
uncommon, and only sparse data is available. We propose a hybrid neuro-symbolic
architecture based on Event Calculus that can perform Complex Event Processing
(CEP). It leverages both a neural network to interpret inputs and logical rules
that express the pattern of the complex event. Our approach is capable of
training with much fewer labelled data than a pure neural network approach, and
to learn to classify individual events even when training in an end-to-end
manner. We demonstrate this comparing our approach against a pure neural
network approach on a dataset based on Urban Sounds 8K.
",A Hybrid Neuro-Symbolic Approach for Complex Event Processing
"  Current applied intelligent systems have crucial shortcomings either in
reasoning the gathered knowledge, or representation of comprehensive integrated
information. To address these limitations, we develop a formal transition
system which is applied to the common artificial intelligence (AI) systems, to
reason about the findings. The developed model was created by combining the
Public Announcement Logic (PAL) and the Linear Temporal Logic (LTL), which will
be done to analyze both single-framed data and the following time-series data.
To do this, first, the achieved knowledge by an AI-based system (i.e.,
classifiers) for an individual time-framed data, will be taken, and then, it
would be modeled by a PAL. This leads to developing a unified representation of
knowledge, and the smoothness in the integration of the gathered and external
experiences. Therefore, the model could receive the classifier's predefined --
or any external -- knowledge, to assemble them in a unified manner. Alongside
the PAL, all the timed knowledge changes will be modeled, using a temporal
logic transition system. Later, following by the translation of natural
language questions into the temporal formulas, the satisfaction leads the model
to answer that question. This interpretation integrates the information of the
recognized input data, rules, and knowledge. Finally, we suggest a mechanism to
reduce the investigated paths for the performance improvements, which results
in a partial correction for an object-detection system.
","Linear Temporal Public Announcement Logic: a new perspective for
  reasoning the knowledge of multi-classifiers"
"  The previous works on formalizing enterprise application integration (EAI)
scenarios showed an emerging need for setting up formal foundations for
integration patterns, the EAI building blocks, in order to facilitate the
model-driven development and ensure its correctness. So far, the formalization
requirements were focusing on more ""conventional"" integration scenarios, in
which control-flow, transactional persistent data and time aspects were
considered. However, none of these works took into consideration another
arising EAI trend that covers social and multimedia computing. In this work we
propose a Petri net-based formalism that addresses requirements arising from
the multimedia domain. We also demonstrate realizations of one of the most
frequently used multimedia patterns and discuss which implications our formal
proposal may bring into the area of the multimedia EAI development.
",Formalizing Integration Patterns with Multimedia Data (Extended Version)
"  In explainable artificial intelligence, there is increasing interest in
understanding the behaviour of autonomous agents to build trust and validate
performance. Modern agent architectures, such as those trained by deep
reinforcement learning, are currently so lacking in interpretable structure as
to effectively be black boxes, but insights may still be gained from an
external, behaviourist perspective. Inspired by conceptual spaces theory, we
suggest that a versatile first step towards general understanding is to
discretise the state space into convex regions, jointly capturing similarities
over the agent's action, value function and temporal dynamics within a dataset
of observations. We create such a representation using a novel variant of the
CART decision tree algorithm, and demonstrate how it facilitates practical
understanding of black box agents through prediction, visualisation and
rule-based explanation.
","TripleTree: A Versatile Interpretable Representation of Black Box Agents
  and their Environments"
"  Recently, qualitative uncertainty in abstract argumentation has received much
attention. The first works on this topic introduced uncertainty about the
presence of attacks, then about the presence of arguments, and finally combined
both kinds of uncertainty. This results in the Incomplete Argumentation
Framework (IAFs). But another kind of uncertainty was introduced in the context
of Control Argumentation Frameworks (CAFs): it consists in a conflict relation
with uncertain orientation, i.e. we are sure that there is an attack between
two arguments, but the actual direction of the attack is unknown. Here, we
formally define Rich IAFs, that combine the three different kinds of
uncertainty that were previously introduced in IAFs and CAFs. We show that this
new model, although strictly more expressive than IAFs, does not suffer from a
blow up of computational complexity. Also, the existing computational approach
based on SAT can be easily adapted to the new framework.
",A Note on Rich Incomplete Argumentation Frameworks
"  The recent Control Argumentation Framework (CAF) is a generalization of
Dung's Argumentation Framework which handles argumentation dynamics under
uncertainty; especially it can be used to model the behavior of an agent which
can anticipate future changes in the environment. Here we provide new insights
on this model by defining the notion of possible controllability of a CAF. We
study the complexity of this new form of reasoning for the four classical
semantics, and we provide a logical encoding for reasoning with this framework.
","Possible Controllability of Control Argumentation Frameworks -- Extended
  Version"
"  DL^N is a recent approach that extends description logics with defeasible
reasoning capabilities. In this paper we provide an overview on DL^N,
illustrating the underlying knowledge engineering requirements as well as the
characteristic features that preserve DL^N from some recurrent semantic and
computational drawbacks. We also compare DL^N with some alternative
nonmonotonic semantics, enlightening the relationships between the KLM
postulates and DL^N.
",Defeasible reasoning in Description Logics: an overview on DL^N
"  We introduce multi-goal multi agent path finding (MAPF$^{MG}$) which
generalizes the standard discrete multi-agent path finding (MAPF) problem.
While the task in MAPF is to navigate agents in an undirected graph from their
starting vertices to one individual goal vertex per agent, MAPF$^{MG}$ assigns
each agent multiple goal vertices and the task is to visit each of them at
least once. Solving MAPF$^{MG}$ not only requires finding collision free paths
for individual agents but also determining the order of visiting agent's goal
vertices so that common objectives like the sum-of-costs are optimized. We
suggest two novel algorithms using different paradigms to address MAPF$^{MG}$:
a heuristic search-based search algorithm called Hamiltonian-CBS (HCBS) and a
compilation-based algorithm built using the SMT paradigm, called
SMT-Hamiltonian-CBS (SMT-HCBS). Experimental comparison suggests limitations of
compilation-based approach.
","Multi-Goal Multi-Agent Path Finding via Decoupled and Integrated Goal
  Vertex Ordering"
"  During the first step of practical reasoning, i.e. deliberation, an
intelligent agent generates a set of pursuable goals and then selects which of
them he commits to achieve. An intelligent agent may in general generate
multiple pursuable goals, which may be incompatible among them. In this paper,
we focus on the definition, identification and resolution of these
incompatibilities. The suggested approach considers the three forms of
incompatibility introduced by Castelfranchi and Paglieri, namely the terminal
incompatibility, the instrumental or resources incompatibility and the
superfluity. We characterise computationally these forms of incompatibility by
means of arguments that represent the plans that allow an agent to achieve his
goals. Thus, the incompatibility among goals is defined based on the conflicts
among their plans, which are represented by means of attacks in an
argumentation framework. We also work on the problem of goals selection; we
propose to use abstract argumentation theory to deal with this problem, i.e. by
applying argumentation semantics. We use a modified version of the ""cleaner
world"" scenario in order to illustrate the performance of our proposal.
","An Argumentation-based Approach for Identifying and Dealing with
  Incompatibilities among Procedural Goals"
"  Stratega, a general strategy games framework, has been designed to foster
research on computational intelligence for strategy games. In contrast to other
strategy game frameworks, Stratega allows to create a wide variety of
turn-based and real-time strategy games using a common API for agent
development. While the current version supports the development of turn-based
strategy games and agents, we will add support for real-time strategy games in
future updates. Flexibility is achieved by utilising YAML-files to configure
tiles, units, actions, and levels. Therefore, the user can design and run a
variety of games to test developed agents without specifically adjusting it to
the game being generated. The framework has been built with a focus of
statistical forward planning (SFP) agents. For this purpose, agents can access
and modify game-states and use the forward model to simulate the outcome of
their actions. While SFP agents have shown great flexibility in general
game-playing, their performance is limited in case of complex state and
action-spaces. Finally, we hope that the development of this framework and its
respective agents helps to better understand the complex decision-making
process in strategy games. Stratega can be downloaded at:
https://github.research.its.qmul.ac.uk/eecsgameai/Stratega
","The Design Of ""Stratega"": A General Strategy Games Framework"
"  One of the ambitions of artificial intelligence is to root artificial
intelligence deeply in basic science while developing brain-inspired artificial
intelligence platforms that will promote new scientific discoveries. The
challenges are essential to push artificial intelligence theory and applied
technologies research forward. This paper presents the grand challenges of
artificial intelligence research for the next 20 years which include:~(i) to
explore the working mechanism of the human brain on the basis of understanding
brain science, neuroscience, cognitive science, psychology and data science;
(ii) how is the electrical signal transmitted by the human brain? What is the
coordination mechanism between brain neural electrical signals and human
activities? (iii)~to root brain-computer interface~(BCI) and brain-muscle
interface~(BMI) technologies deeply in science on human behaviour; (iv)~making
research on knowledge-driven visual commonsense reasoning~(VCR), develop a new
inference engine for cognitive network recognition~(CNR); (v)~to develop
high-precision, multi-modal intelligent perceptrons; (vi)~investigating
intelligent reasoning and fast decision-making systems based on knowledge
graph~(KG). We believe that the frontier theory innovation of AI,
knowledge-driven modeling methodologies for commonsense reasoning,
revolutionary innovation and breakthroughs of the novel algorithms and new
technologies in AI, and developing responsible AI should be the main research
strategies of AI scientists in the future.
","To Root Artificial Intelligence Deeply in Basic Science for a New
  Generation of AI"
"  Accurate demand forecasting of different public transport modes(e.g., buses
and light rails) is essential for public service operation.However, the
development level of various modes often varies sig-nificantly, which makes it
hard to predict the demand of the modeswith insufficient knowledge and sparse
station distribution (i.e.,station-sparse mode). Intuitively, different public
transit modes mayexhibit shared demand patterns temporally and spatially in a
city.As such, we propose to enhance the demand prediction of station-sparse
modes with the data from station-intensive mode and designaMemory-Augmented
Multi-taskRecurrent Network (MATURE)to derive the transferable demand patterns
from each mode andboost the prediction of station-sparse modes through
adaptingthe relevant patterns from the station-intensive mode.
Specifically,MATUREcomprises three components: 1) a memory-augmentedrecurrent
network for strengthening the ability to capture the long-short term
information and storing temporal knowledge of eachtransit mode; 2) a knowledge
adaption module to adapt the rele-vant knowledge from a station-intensive
source to station-sparsesources; 3) a multi-task learning framework to
incorporate all theinformation and forecast the demand of multiple modes
jointly.The experimental results on a real-world dataset covering four pub-lic
transport modes demonstrate that our model can promote thedemand forecasting
performance for the station-sparse modes.
","Knowledge Adaption for Demand Prediction based on Multi-task Memory
  Neural Network"
"  Applying automated reasoning tools for decision support and analysis in law
has the potential to make court decisions more transparent and objective. Since
there is often uncertainty about the accuracy and relevance of evidence,
non-classical reasoning approaches are required. Here, we investigate
probabilistic epistemic argumentation as a tool for automated reasoning about
legal cases. We introduce a general scheme to model legal cases as
probabilistic epistemic argumentation problems, explain how evidence can be
modeled and sketch how explanations for legal decisions can be generated
automatically. Our framework is easily interpretable, can deal with cyclic
structures and imprecise probabilities and guarantees polynomial-time
probabilistic reasoning in the worst-case.
","Explainable Automated Reasoning in Law using Probabilistic Epistemic
  Argumentation"
"  Explainable Artificial Intelligence (XAI) systems, including intelligent
agents, must be able to explain their internal decisions, behaviours and
reasoning that produce their choices to the humans (or other systems) with
which they interact. In this paper, we focus on how an extended model of BDI
(Beliefs-Desires-Intentions) agents can be able to generate explanations about
their reasoning, specifically, about the goals he decides to commit to. Our
proposal is based on argumentation theory, we use arguments to represent the
reasons that lead an agent to make a decision and use argumentation semantics
to determine acceptable arguments (reasons). We propose two types of
explanations: the partial one and the complete one. We apply our proposal to a
scenario of rescue robots.
",Argumentation-based Agents that Explain their Decisions
"  An intelligent agent may in general pursue multiple procedural goals
simultaneously, which may lead to arise some conflicts (incompatibilities)
among them. In this paper, we focus on the incompatibilities that emerge due to
resources limitations. Thus, the contribution of this article is twofold. On
one hand, we give an algorithm for identifying resource incompatibilities from
a set of pursued goals and, on the other hand, we propose two ways for
selecting those goals that will continue to be pursued: (i) the first is based
on abstract argumentation theory, and (ii) the second based on two algorithms
developed by us. We illustrate our proposal using examples throughout the
article.
",Resolving Resource Incompatibilities in Intelligent Agents
"  Knowledge Graph Embedding (KGE) is a popular method for KG reasoning and
usually a higher dimensional one ensures better reasoning capability. However,
high-dimensional KGEs pose huge challenges to storage and computing resources
and are not suitable for resource-limited or time-constrained applications, for
which faster and cheaper reasoning is necessary. To address this problem, we
propose DistilE, a knowledge distillation method to build low-dimensional
student KGE from pre-trained high-dimensional teacher KGE. We take the original
KGE loss as hard label loss and design specific soft label loss for different
KGEs in DistilE. We also propose a two-stage distillation approach to make the
student and teacher adapt to each other and further improve the reasoning
capability of the student. Our DistilE is general enough to be applied to
various KGEs. Experimental results of link prediction show that our method
successfully distills a good student which performs better than a same
dimensional one directly trained, and sometimes even better than the teacher,
and it can achieve 2 times - 8 times embedding compression rate and more than
10 times faster inference speed than the teacher with a small performance loss.
We also experimentally prove the effectiveness of our two-stage training
proposal via ablation study.
","DistilE: Distiling Knowledge Graph Embeddings for Faster and Cheaper
  Reasoning"
"  Skin cancer is by far in top-3 of the world's most common cancer. Among
different skin cancer types, melanoma is particularly dangerous because of its
ability to metastasize. Early detection is the key to success in skin cancer
treatment. However, skin cancer diagnosis is still a challenge, even for
experienced dermatologists, due to strong resemblances between benign and
malignant lesions. To aid dermatologists in skin cancer diagnosis, we developed
a deep learning system that can effectively and automatically classify skin
lesions into one of the seven classes: (1) Actinic Keratoses, (2) Basal Cell
Carcinoma, (3) Benign Keratosis, (4) Dermatofibroma, (5) Melanocytic nevi, (6)
Melanoma, (7) Vascular Skin Lesion. The HAM10000 dataset was used to train the
system. An end-to-end deep learning process, transfer learning technique,
utilizing multiple pre-trained models, combining with class-weighted and focal
loss were applied for the classification process. The result was that our
ensemble of modified ResNet50 models can classify skin lesions into one of the
seven classes with top-1, top-2 and top-3 accuracy 93%, 97% and 99%,
respectively. This deep learning system can potentially be integrated into
computer-aided diagnosis systems that support dermatologists in skin cancer
diagnosis.
","Transfer learning with class-weighted and focal loss function for
  automatic skin cancer classification"
"  With the rapid development in online education, knowledge tracing (KT) has
become a fundamental problem which traces students' knowledge status and
predicts their performance on new questions. Questions are often numerous in
online education systems, and are always associated with much fewer skills.
However, the previous literature fails to involve question information together
with high-order question-skill correlations, which is mostly limited by data
sparsity and multi-skill problems. From the model perspective, previous models
can hardly capture the long-term dependency of student exercise history, and
cannot model the interactions between student-questions, and student-skills in
a consistent way. In this paper, we propose a Graph-based Interaction model for
Knowledge Tracing (GIKT) to tackle the above probems. More specifically, GIKT
utilizes graph convolutional network (GCN) to substantially incorporate
question-skill correlations via embedding propagation. Besides, considering
that relevant questions are usually scattered throughout the exercise history,
and that question and skill are just different instantiations of knowledge,
GIKT generalizes the degree of students' master of the question to the
interactions between the student's current state, the student's history related
exercises, the target question, and related skills. Experiments on three
datasets demonstrate that GIKT achieves the new state-of-the-art performance,
with at least 1% absolute AUC improvement.
",GIKT: A Graph-based Interaction Model for Knowledge Tracing
"  Real-time ridesharing systems such as UberPool, Lyft Line, GrabShare have
become hugely popular as they reduce the costs for customers, improve per trip
revenue for drivers and reduce traffic on the roads by grouping customers with
similar itineraries. The key challenge in these systems is to group the ""right""
requests to travel together in the ""right"" available vehicles in real-time, so
that the objective (e.g., requests served, revenue or delay) is optimized. This
challenge has been addressed in existing work by: (i) generating as many
relevant feasible (with respect to the available delay for customers)
combinations of requests as possible in real-time; and then (ii) optimizing
assignment of the feasible request combinations to vehicles. Since the number
of request combinations increases exponentially with the increase in vehicle
capacity and number of requests, unfortunately, such approaches have to employ
ad hoc heuristics to identify a subset of request combinations for assignment.
Our key contribution is in developing approaches that employ zone (abstraction
of individual locations) paths instead of request combinations. Zone paths
allow for generation of significantly more ""relevant"" combinations (in
comparison to ad hoc heuristics) in real-time than competing approaches due to
two reasons: (i) Each zone path can typically represent multiple request
combinations; (ii) Zone paths are generated using a combination of offline and
online methods. Specifically, we contribute both myopic (ridesharing assignment
focussed on current requests only) and non-myopic (ridesharing assignment
considers impact on expected future requests) approaches that employ zone
paths. In our experimental results, we demonstrate that our myopic approach
outperforms (with respect to both objective and runtime) the current best
myopic approach for ridesharing on both real-world and synthetic datasets.
","Zone pAth Construction (ZAC) based Approaches for Effective Real-Time
  Ridesharing"
"  Due to advances in machine learning and artificial intelligence (AI), a new
role is emerging for machines as intelligent assistants to radiologists in
their clinical workflows. But what systematic clinical thought processes are
these machines using? Are they similar enough to those of radiologists to be
trusted as assistants? A live demonstration of such a technology was conducted
at the 2016 Scientific Assembly and Annual Meeting of the Radiological Society
of North America (RSNA). The demonstration was presented in the form of a
question-answering system that took a radiology multiple choice question and a
medical image as inputs. The AI system then demonstrated a cognitive workflow,
involving text analysis, image analysis, and reasoning, to process the question
and generate the most probable answer. A post demonstration survey was made
available to the participants who experienced the demo and tested the question
answering system. Of the reported 54,037 meeting registrants, 2,927 visited the
demonstration booth, 1,991 experienced the demo, and 1,025 completed a
post-demonstration survey. In this paper, the methodology of the survey is
shown and a summary of its results are presented. The results of the survey
show a very high level of receptiveness to cognitive computing technology and
artificial intelligence among radiologists.
","Receptivity of an AI Cognitive Assistant by the Radiology Community: A
  Report on Data Collected at RSNA"
"  Most knowledge graph use cases are data-centric, focusing on representing
data entities and their semantic relationships. There are no published success
stories to represent large-scale complicated business logic with knowledge
graph technologies. In this paper, we will share our innovative and practical
approach to representing complicated U.S. and Canadian income tax compliance
logic (calculations and rules) via a large-scale knowledge graph. We will cover
how the Tax Knowledge Graph is constructed and automated, how it is used to
calculate tax refunds, reasoned to find missing info, and navigated to explain
the calculated results. The Tax Knowledge Graph has helped transform Intuit's
flagship TurboTax product into a smart and personalized experience,
accelerating and automating the tax preparation process while instilling
confidence for millions of customers.
",Tax Knowledge Graph for a Smarter and More Personalized TurboTax
"  During the first step of practical reasoning, i.e. deliberation or goals
selection, an intelligent agent generates a set of pursuable goals and then
selects which of them he commits to achieve. Explainable Artificial
Intelligence (XAI) systems, including intelligent agents, must be able to
explain their internal decisions. In the context of goals selection, agents
should be able to explain the reasoning path that leads them to select (or not)
a certain goal. In this article, we use an argumentation-based approach for
generating explanations about that reasoning path. Besides, we aim to enrich
the explanations with information about emerging conflicts during the selection
process and how such conflicts were resolved. We propose two types of
explanations: the partial one and the complete one and a set of explanatory
schemes to generate pseudo-natural explanations. Finally, we apply our proposal
to the cleaner world scenario.
","An Argumentation-based Approach for Explaining Goal Selection in
  Intelligent Agents"
"  Topology optimization (TO) is a common technique used in free-form designs.
However, conventional TO-based design approaches suffer from high computational
cost due to the need for repetitive forward calculations and/or sensitivity
analysis, which are typically done using high-dimensional simulations such as
Finite Element Analysis (FEA). In this work, neural networks are used as
efficient surrogate models for forward and sensitivity calculations in order to
greatly accelerate the design process of topology optimization. To improve the
accuracy of sensitivity analyses, dual-model neural networks that are trained
with both forward and sensitivity data are constructed and are integrated into
the Solid Isotropic Material with Penalization (SIMP) method to replace FEA.
The performance of the accelerated SIMP method is demonstrated on two benchmark
design problems namely minimum compliance design and metamaterial design. The
efficiency gained in the problem with size of 64x64 is 137 times in forward
calculation and 74 times in sensitivity analysis. In addition, effective data
generation methods suitable for TO designs are investigated and developed,
which lead to a great saving in training time. In both benchmark design
problems, a design accuracy of 95% can be achieved with only around 2000
training data.
","Accelerating gradient-based topology optimization design with dual-model
  neural networks"
"  The possible risk that AI systems could promote discrimination by reproducing
and enforcing unwanted bias in data has been broadly discussed in research and
society. Many current legal standards demand to remove sensitive attributes
from data in order to achieve ""fairness through unawareness"". We argue that
this approach is obsolete in the era of big data where large datasets with
highly correlated attributes are common. In the contrary, we propose the active
use of sensitive attributes with the purpose of observing and controlling any
kind of discrimination, and thus leading to fair results.
",Active Fairness Instead of Unawareness
"  This chapter describes how the SP System, meaning the SP Theory of
Intelligence, and its realisation as the SP Computer Model, may promote
transparency and granularity in AI, and some other areas of application. The
chapter describes how transparency in the workings and output of the SP
Computer Model may be achieved via three routes: 1) the program provides a very
full audit trail for such processes as recognition, reasoning, analysis of
language, and so on. There is also an explicit audit trail for the unsupervised
learning of new knowledge; 2) knowledge from the system is likely to be
granular and easy for people to understand; and 3) there are seven principles
for the organisation of knowledge which are central in the workings of the SP
System and also very familiar to people (eg chunking-with-codes, part-whole
hierarchies, and class-inclusion hierarchies), and that kind of familiarity in
the way knowledge is structured by the system, is likely to be important in the
interpretability, explainability, and transparency of that knowledge. Examples
from the SP Computer Model are shown throughout the chapter.
","Transparency and granularity in the SP Theory of Intelligence and its
  realisation in the SP Computer Model"
"  Machine learning has recently enabled large advances in artificial
intelligence, but these results can be highly centralized. The large datasets
required are generally proprietary; predictions are often sold on a per-query
basis; and published models can quickly become out of date without effort to
acquire more data and maintain them. Published proposals to provide models and
data for free for certain tasks include Microsoft Research's Decentralized and
Collaborative AI on Blockchain. The framework allows participants to
collaboratively build a dataset and use smart contracts to share a continuously
updated model on a public blockchain. The initial proposal gave an overview of
the framework omitting many details of the models used and the incentive
mechanisms in real world scenarios. In this work, we evaluate the use of
several models and configurations in order to propose best practices when using
the Self-Assessment incentive mechanism so that models can remain accurate and
well-intended participants that submit correct data have the chance to profit.
We have analyzed simulations for each of three models: Perceptron, Na\""ive
Bayes, and a Nearest Centroid Classifier, with three different datasets:
predicting a sport with user activity from Endomondo, sentiment analysis on
movie reviews from IMDB, and determining if a news article is fake. We compare
several factors for each dataset when models are hosted in smart contracts on a
public blockchain: their accuracy over time, balances of a good and bad user,
and transaction costs (or gas) for deploying, updating, collecting refunds, and
collecting rewards. A free and open source implementation for the Ethereum
blockchain and simulations written in Python is provided at
https://github.com/microsoft/0xDeCA10B. This version has updated gas costs
using newer optimizations written after the original publication.
",Analysis of Models for Decentralized and Collaborative AI on Blockchain
"  In our previous work we have shown how Bayesian networks can be used for
adaptive testing of student skills. Later, we have taken the advantage of
monotonicity restrictions in order to learn models fitting data better. This
article provides a synergy between these two phases as it evaluates Bayesian
network models used for computerized adaptive testing and learned with a
recently proposed monotonicity gradient algorithm. This learning method is
compared with another monotone method, the isotonic regression EM algorithm.
The quality of methods is empirically evaluated on a large data set of the
Czech National Mathematics Exam. Besides advantages of adaptive testing
approach we observed also advantageous behavior of monotonic methods,
especially for small learning data set sizes. Another novelty of this work is
the use of the reliability interval of the score distribution, which is used to
predict student's final score and grade. In the experiments we have clearly
shown we can shorten the test while keeping its reliability. We have also shown
that the monotonicity increases the prediction quality with limited training
data sets. The monotone model learned by the gradient method has a lower
question prediction quality than unrestricted models but it is better in the
main target of this application, which is the student score prediction. It is
an important observation that a mere optimization of the model likelihood or
the prediction accuracy do not necessarily lead to a model that describes best
the student.
",Monotonicity in practice of adaptive testing
"  According to GHO (Global Health Observatory (GHO), the high prevalence of a
large variety of diseases such as Ischaemic heart disease, stroke, lung cancer
disease and lower respiratory infections have remained the top killers during
the past decade.
  The growth in the number of mortalities caused by these disease is due to the
very delayed symptoms'detection. Since in the early stages, the symptoms are
insignificant and similar to those of benign diseases (e.g. the flu ), and we
can only detect the disease at an advanced stage.
  In addition, The high frequency of improper practices that are harmful to
health, the hereditary factors, and the stressful living conditions can
increase the death rates.
  Many researches dealt with these fatal disease, and most of them applied
advantage machine learning models to deal with image diagnosis. However the
drawback is that imagery permit only to detect disease at a very delayed stage
and then patient can hardly be saved.
  In this Paper we present our new approach ""DeepLCP"" to predict fatal diseases
that threaten people's lives. It's mainly based on raw and heterogeneous data
of the concerned (or under-tested) person. ""DeepLCP"" results of a combination
combination of the Natural Language Processing (NLP) and the deep learning
paradigm.The experimental results of the proposed model in the case of Lung
cancer prediction have approved high accuracy and a low loss data rate during
the validation of the disease prediction.
",General DeepLCP model for disease prediction : Case of Lung Cancer
"  Some abstract argumentation approaches consider that arguments have a degree
of uncertainty, which impacts on the degree of uncertainty of the extensions
obtained from a abstract argumentation framework (AAF) under a semantics. In
these approaches, both the uncertainty of the arguments and of the extensions
are modeled by means of precise probability values. However, in many real life
situations the exact probabilities values are unknown and sometimes there is a
need for aggregating the probability values of different sources. In this
paper, we tackle the problem of calculating the degree of uncertainty of the
extensions considering that the probability values of the arguments are
imprecise. We use credal sets to model the uncertainty values of arguments and
from these credal sets, we calculate the lower and upper bounds of the
extensions. We study some properties of the suggested approach and illustrate
it with an scenario of decision making.
","An Imprecise Probability Approach for Abstract Argumentation based on
  Credal Sets"
"  Job Title Benchmarking (JTB) aims at matching job titles with similar
expertise levels across various companies. JTB could provide precise guidance
and considerable convenience for both talent recruitment and job seekers for
position and salary calibration/prediction. Traditional JTB approaches mainly
rely on manual market surveys, which is expensive and labor-intensive.
Recently, the rapid development of Online Professional Graph has accumulated a
large number of talent career records, which provides a promising trend for
data-driven solutions. However, it is still a challenging task since (1) the
job title and job transition (job-hopping) data is messy which contains a lot
of subjective and non-standard naming conventions for the same position (e.g.,
Programmer, Software Development Engineer, SDE, Implementation Engineer), (2)
there is a large amount of missing title/transition information, and (3) one
talent only seeks limited numbers of jobs which brings the incompleteness and
randomness modeling job transition patterns. To overcome these challenges, we
aggregate all the records to construct a large-scale Job Title Benchmarking
Graph (Job-Graph), where nodes denote job titles affiliated with specific
companies and links denote the correlations between jobs. We reformulate the
JTB as the task of link prediction over the Job-Graph that matched job titles
should have links. Along this line, we propose a collective multi-view
representation learning method (Job2Vec) by examining the Job-Graph jointly in
(1) graph topology view, (2)semantic view, (3) job transition balance view, and
(4) job transition duration view. We fuse the multi-view representations in the
encode-decode paradigm to obtain a unified optimal representation for the task
of link prediction. Finally, we conduct extensive experiments to validate the
effectiveness of our proposed method.
","Job2Vec: Job Title Benchmarking with Collective Multi-View
  Representation Learning"
"  Guilt aversion induces experience of a utility loss in people if they believe
they have disappointed others, and this promotes cooperative behaviour in
human. In psychological game theory, guilt aversion necessitates modelling of
agents that have theory about what other agents think, also known as Theory of
Mind (ToM). We aim to build a new kind of affective reinforcement learning
agents, called Theory of Mind Agents with Guilt Aversion (ToMAGA), which are
equipped with an ability to think about the wellbeing of others instead of just
self-interest. To validate the agent design, we use a general-sum game known as
Stag Hunt as a test bed. As standard reinforcement learning agents could learn
suboptimal policies in social dilemmas like Stag Hunt, we propose to use
belief-based guilt aversion as a reward shaping mechanism. We show that our
belief-based guilt averse agents can efficiently learn cooperative behaviours
in Stag Hunt Games.
","Theory of Mind with Guilt Aversion Facilitates Cooperative Reinforcement
  Learning"
"  Numerical reasoning over texts, such as addition, subtraction, sorting and
counting, is a challenging machine reading comprehension task, since it
requires both natural language understanding and arithmetic computation. To
address this challenge, we propose a heterogeneous graph representation for the
context of the passage and question needed for such reasoning, and design a
question directed graph attention network to drive multi-step numerical
reasoning over this context graph.
","Question Directed Graph Attention Network for Numerical Reasoning over
  Text"
"  Logical forgetting is NP-complete even in the simple case of propositional
Horn formulae, and may exponentially increase their size. A way to forget is to
replace each variable to forget with the body of each clause whose head is the
variable. It takes polynomial time in the single-head case: each variable is at
most the head of a clause. Some formulae are not single-head but can be made so
to simplify forgetting. They are single-head equivalent. The first contribution
of this article is the study of a semantical characterization of single-head
equivalence. Two necessary conditions are given. They are sufficient when the
formula is inequivalent: it makes two sets of variables equivalent only if they
are also equivalent to their intersection. All acyclic formulae are
inequivalent. The second contribution of this article is an incomplete
algorithm for turning a formula single-head. In case of success, forgetting
becomes possible in polynomial time and produces a polynomial-size formula,
none of which is otherwise guaranteed. The algorithm is complete on
inequivalent formulae.
","One head is better than two: a polynomial restriction for propositional
  definite Horn forgetting"
"  The Causal Bandit is a variant of the classic Bandit problem where an agent
must identify the best action in a sequential decision-making process, where
the reward distribution of the actions displays a non-trivial dependence
structure that is governed by a causal model. All methods proposed thus far in
the literature rely on exact prior knowledge of the causal model to obtain
improved estimators for the reward. We formulate a new causal bandit algorithm
that is the first to no longer rely on explicit prior causal knowledge and
instead uses the output of causal discovery algorithms. This algorithm relies
on a new estimator based on separating sets, a causal structure already known
in causal discovery literature. We show that given a separating set, this
estimator is unbiased, and has lower variance compared to the sample mean. We
derive a concentration bound and construct a UCB-type algorithm based on this
bound, as well as a Thompson sampling variant. We compare our algorithms with
traditional bandit algorithms on simulation data. On these problems, our
algorithms show a significant boost in performance.
",Causal Discovery for Causal Bandits utilizing Separating Sets
"  Sepsis is one of the leading causes of death in Intensive Care Units (ICU).
The strategy for treating sepsis involves the infusion of intravenous (IV)
fluids and administration of antibiotics. Determining the optimal quantity of
IV fluids is a challenging problem due to the complexity of a patient's
physiology. In this study, we develop a data-driven optimization solution that
derives the optimal quantity of IV fluids for individual patients. The proposed
method minimizes the probability of severe outcomes by controlling the
prescribed quantity of IV fluids and utilizes human-in-the-loop artificial
intelligence. We demonstrate the performance of our model on 1122 ICU patients
with sepsis diagnosis extracted from the MIMIC-III dataset. The results show
that, on average, our model can reduce mortality by 22%. This study has the
potential to help physicians synthesize optimal, patient-specific treatment
strategies.
","Optimal Sepsis Patient Treatment using Human-in-the-loop Artificial
  Intelligence"
"  Traffic forecasting is an important prerequisite for the application of
intelligent transportation systems in urban traffic networks. The existing
works adopted RNN and CNN/GCN, among which GCRN is the state of art work, to
characterize the temporal and spatial correlation of traffic flows. However, it
is hard to apply GCRN to the large scale road networks due to high
computational complexity. To address this problem, we propose to abstract the
road network into a geometric graph and build a Fast Graph Convolution
Recurrent Neural Network (FastGCRNN) to model the spatial-temporal dependencies
of traffic flow. Specifically, We use FastGCN unit to efficiently capture the
topological relationship between the roads and the surrounding roads in the
graph with reducing the computational complexity through importance sampling,
combine GRU unit to capture the temporal dependency of traffic flow, and embed
the spatiotemporal features into Seq2Seq based on the Encoder-Decoder
framework. Experiments on large-scale traffic data sets illustrate that the
proposed method can greatly reduce computational complexity and memory
consumption while maintaining relatively high accuracy.
",Urban Traffic Flow Forecast Based on FastGCRNN
"  The increasing importance of robots and automation creates a demand for
learnable controllers which can be obtained through various approaches such as
Evolutionary Algorithms (EAs) or Reinforcement Learning (RL). Unfortunately,
these two families of algorithms have mainly developed independently and there
are only a few works comparing modern EAs with deep RL algorithms. We show that
Multidimensional Archive of Phenotypic Elites (MAP-Elites), which is a modern
EA, can deliver better-performing solutions than one of the state-of-the-art RL
methods, Proximal Policy Optimization (PPO) in the generation of locomotion
controllers for a simulated hexapod robot. Additionally, extensive
hyper-parameter tuning shows that MAP-Elites displays greater robustness across
seeds and hyper-parameter sets. Generally, this paper demonstrates that EAs
combined with modern computational resources display promising characteristics
and have the potential to contribute to the state-of-the-art in controller
learning.
","Competitiveness of MAP-Elites against Proximal Policy Optimization on
  locomotion tasks in deterministic simulations"
"  Conditional sequence generation aims to instruct the generation procedure by
conditioning the model with additional context information, which is a
self-supervised learning issue (a form of unsupervised learning with
supervision information from data itself). Unfortunately, the current
state-of-the-art generative models have limitations in sequence generation with
multiple attributes. In this paper, we propose a novel conditional hybrid GAN
(C-Hybrid-GAN) to solve this issue. Discrete sequence with triplet attributes
are separately generated when conditioned on the same context. Most
importantly, relational reasoning technique is exploited to model not only the
dependency inside each sequence of the attribute during the training of the
generator but also the consistency among the sequences of attributes during the
training of the discriminator. To avoid the non-differentiability problem in
GANs encountered during discrete data generation, we exploit the Gumbel-Softmax
technique to approximate the distribution of discrete-valued sequences.Through
evaluating the task of generating melody (associated with note, duration, and
rest) from lyrics, we demonstrate that the proposed C-Hybrid-GAN outperforms
the existing methods in context-conditioned discrete-valued sequence
generation.
",Conditional Hybrid GAN for Sequence Generation
"  Many researchers and developers are exploring for adopting Deep Reinforcement
Learning (DRL) techniques in their applications. They however often find such
an adoption challenging. Existing DRL libraries provide poor support for
prototyping DRL agents (i.e., models), customising the agents, and comparing
the performance of DRL agents. As a result, the developers often report low
efficiency in developing DRL agents. In this paper, we introduce RLzoo, a new
DRL library that aims to make the development of DRL agents efficient. RLzoo
provides developers with (i) high-level yet flexible APIs for prototyping DRL
agents, and further customising the agents for best performance, (ii) a model
zoo where users can import a wide range of DRL agents and easily compare their
performance, and (iii) an algorithm that can automatically construct DRL agents
with custom components (which are critical to improve agent's performance in
custom applications). Evaluation results show that RLzoo can effectively reduce
the development cost of DRL agents, while achieving comparable performance with
existing DRL libraries.
",Efficient Reinforcement Learning Development with RLzoo
"  Knowledge graph completion aims to predict the new links in given entities
among the knowledge graph (KG). Most mainstream embedding methods focus on fact
triplets contained in the given KG, however, ignoring the rich background
information provided by logic rules driven from knowledge base implicitly. To
solve this problem, in this paper, we propose a general framework, named
EM-RBR(embedding and rule-based reasoning), capable of combining the advantages
of reasoning based on rules and the state-of-the-art models of embedding.
EM-RBR aims to utilize relational background knowledge contained in rules to
conduct multi-relation reasoning link prediction rather than superficial vector
triangle linkage in embedding models. By this way, we can explore relation
between two entities in deeper context to achieve higher accuracy. In
experiments, we demonstrate that EM-RBR achieves better performance compared
with previous models on FB15k, WN18 and our new dataset FB15k-R, especially the
new dataset where our model perform futher better than those state-of-the-arts.
We make the implementation of EM-RBR available at
https://github.com/1173710224/link-prediction-with-rule-based-reasoning.
","EM-RBR: a reinforced framework for knowledge graph completion from
  reasoning perspective"
"  This paper presents TotalBotWar, a new pseudo real-time multi-action
challenge for game AI, as well as some initial experiments that benchmark the
framework with different agents. The game is based on the real-time battles of
the popular TotalWar games series where players manage an army to defeat the
opponent's one. In the proposed game, a turn consists of a set of orders to
control the units. The number and specific orders that can be performed in a
turn vary during the progression of the game. One interesting feature of the
game is that if a particular unit does not receive an order in a turn, it will
continue performing the action specified in a previous turn. The turn-wise
branching factor becomes overwhelming for traditional algorithms and the
partial observability of the game state makes the proposed game an interesting
platform to test modern AI algorithms.
","TotalBotWar: A New Pseudo Real-time Multi-action Game Challenge and
  Competition for AI"
"  We propose a novel approach to understanding the decision making of complex
machine learning models (e.g., deep neural networks) using a combination of
probably approximately correct learning (PAC) and a logic inference methodology
called syntax-guided synthesis (SyGuS). We prove that our framework produces
explanations that with a high probability make only few errors and show
empirically that it is effective in generating small, human-interpretable
explanations.
","Probably Approximately Correct Explanations of Machine Learning Models
  via Syntax-Guided Synthesis"
"  By considering rational agents, we focus on the problem of selecting goals
out of a set of incompatible ones. We consider three forms of incompatibility
introduced by Castelfranchi and Paglieri, namely the terminal, the instrumental
(or based on resources), and the superfluity. We represent the agent's plans by
means of structured arguments whose premises are pervaded with uncertainty. We
measure the strength of these arguments in order to determine the set of
compatible goals. We propose two novel ways for calculating the strength of
these arguments, depending on the kind of incompatibility that exists between
them. The first one is the logical strength value, it is denoted by a
three-dimensional vector, which is calculated from a probabilistic interval
associated with each argument. The vector represents the precision of the
interval, the location of it, and the combination of precision and location.
This type of representation and treatment of the strength of a structured
argument has not been defined before by the state of the art. The second way
for calculating the strength of the argument is based on the cost of the plans
(regarding the necessary resources) and the preference of the goals associated
with the plans. Considering our novel approach for measuring the strength of
structured arguments, we propose a semantics for the selection of plans and
goals that is based on Dung's abstract argumentation theory. Finally, we make a
theoretical evaluation of our proposal.
",Dealing with Incompatibilities among Procedural Goals under Uncertainty
"  Recent progress in Game AI has demonstrated that given enough data from human
gameplay, or experience gained via simulations, machines can rival or surpass
the most skilled human players in classic games such as Go, or commercial
computer games such as Starcraft. We review the current state-of-the-art
through the lens of wargaming, and ask firstly what features of wargames
distinguish them from the usual AI testbeds, and secondly which recent AI
advances are best suited to address these wargame-specific features.
",AI and Wargaming
"  Commonsense knowledge graph (CKG) is a special type of knowledge graph (KG),
where entities are composed of free-form text. However, most existing CKG
completion methods focus on the setting where all the entities are presented at
training time. Although this setting is standard for conventional KG
completion, it has limitations for CKG completion. At test time, entities in
CKGs can be unseen because they may have unseen text/names and entities may be
disconnected from the training graph, since CKGs are generally very sparse.
Here, we propose to study the inductive learning setting for CKG completion
where unseen entities may present at test time. We develop a novel learning
framework named InductivE. Different from previous approaches, InductiveE
ensures the inductive learning capability by directly computing entity
embeddings from raw entity attributes/text. InductiveE consists of a free-text
encoder, a graph encoder, and a KG completion decoder. Specifically, the
free-text encoder first extracts the textual representation of each entity
based on the pre-trained language model and word embedding. The graph encoder
is a gated relational graph convolutional neural network that learns from a
densified graph for more informative entity representation learning. We develop
a method that densifies CKGs by adding edges among semantic-related entities
and provide more supportive information for unseen entities, leading to better
generalization ability of entity embedding for unseen entities. Finally,
inductiveE employs Conv-TransE as the CKG completion decoder. Experimental
results show that InductiveE significantly outperforms state-of-the-art
baselines in both standard and inductive settings on ATOMIC and ConceptNet
benchmarks. InductivE performs especially well on inductive scenarios where it
achieves above 48% improvement over present methods.
",Inductive Learning on Commonsense Knowledge Graph Completion
"  Path finding problems involve identification of a plan for conflict free
movement of agents over a common road network. Most approaches to this problem
handle the agents as point objects, wherein the size of the agent is
significantly smaller than the road on which it travels. In this paper, we
consider spatially extended agents which have a size comparable to the length
of the road on which they travel. An optimal multi agent path finding approach
for spatially-extended agents was proposed in the eXtended Conflict Based
Search (XCBS) algorithm. As XCBS resolves only a pair of conflicts at a time,
it results in deeper search trees in case of cascading or multiple (more than
two agent) conflicts at a given location. This issue is addressed in eXtended
Conflict Based Search with Awareness (XCBS-A) in which an agent uses awareness
of other agents' plans to make its own plan. In this paper, we explore XCBS-A
in greater detail, we theoretically prove its completeness and empirically
demonstrate its performance with other algorithms in terms of variances in road
characteristics, agent characteristics and plan characteristics. We demonstrate
the distributive nature of the algorithm by evaluating its performance when
distributed over multiple machines. XCBS-A generates a huge search space
impacting its efficiency in terms of memory; to address this we propose an
approach for memory-efficiency and empirically demonstrate the performance of
the algorithm. The nature of XCBS-A is such that it may lead to suboptimal
solutions, hence the final contribution of this paper is an enhanced approach,
XCBS-Local Awareness (XCBS-LA) which we prove will be optimal and complete.
",Multi Agent Path Finding with Awareness for Spatially Extended Agents
"  Augmenting a base constraint model with additional constraints can strengthen
the inferences made by a solver and therefore reduce search effort. We focus on
the automatic addition of streamliner constraints, derived from the types
present in an abstract Essence specification of a problem class of interest,
which trade completeness for potentially very significant reduction in search.
The refinement of streamlined Essence specifications into constraint models
suitable for input to constraint solvers gives rise to a large number of
modelling choices in addition to those required for the base Essence
specification. Previous automated streamlining approaches have been limited in
evaluating only a single default model for each streamlined specification. In
this paper we explore the effect of model selection in the context of
streamlined specifications. We propose a new best-first search method that
generates a portfolio of Pareto Optimal streamliner-model combinations by
evaluating for each streamliner a portfolio of models to search and explore the
variability in performance and find the optimal model. Various forms of racing
are utilised to constrain the computational cost of training.
","Towards Portfolios of Streamlined Constraint Models: A Case Study with
  the Balanced Academic Curriculum Problem"
"  Many of the core disciplines of artificial intelligence have sets of standard
benchmark problems well known and widely used by the community when developing
new algorithms. Constraint programming and automated planning are examples of
these areas, where the behaviour of a new algorithm is measured by how it
performs on these instances. Typically the efficiency of each solving method
varies not only between problems, but also between instances of the same
problem. Therefore, having a diverse set of instances is crucial to be able to
effectively evaluate a new solving method. Current methods for automatic
generation of instances for Constraint Programming problems start with a
declarative model and search for instances with some desired attributes, such
as hardness or size. We first explore the difficulties of adapting this
approach to generate instances starting from problem specifications written in
PDDL, the de-facto standard language of the automated planning community. We
then propose a new approach where the whole planning problem description is
modelled using Essence, an abstract modelling language that allows expressing
high-level structures without committing to a particular low level
representation in PDDL.
",Exploring Instance Generation for Automated Planning
"  Making decisions freely presupposes that there is some indeterminacy in the
environment and in the decision making engine. The former is reflected on the
behavioral changes due to communicating: few changes indicate rigid
environments; productive changes manifest a moderate indeterminacy, but a large
communicating effort with few productive changes characterize a chaotic
environment. Hence, communicating, effective decision making and productive
behavioral changes are related. The entropy measures the indeterminacy of the
environment, and there is an entropy range in which communicating supports
effective decision making. This conjecture is referred to here as the The
Potential Productivity of Decisions.
  The computing engine that is causal to decision making should also have some
indeterminacy. However, computations performed by standard Turing Machines are
predetermined. To overcome this limitation an entropic mode of computing that
is called here Relational-Indeterminate is presented. Its implementation in a
table format has been used to model an associative memory. The present theory
and experiment suggest the Entropy Trade-off: There is an entropy range in
which computing is effective but if the entropy is too low computations are too
rigid and if it is too high computations are unfeasible. The entropy trade-off
of computing engines corresponds to the potential productivity of decisions of
the environment.
  The theory is referred to an Interaction-Oriented Cognitive Architecture.
Memory, perception, action and thought involve a level of indeterminacy and
decision making may be free in such degree. The overall theory supports an
ecological view of rationality. The entropy of the brain has been measured in
neuroscience studies and the present theory supports that the brain is an
entropic machine. The paper is concluded with a number of predictions that may
be tested empirically.
","Entropy, Computing and Rationality"
"  Hybrid Answer Set Programming (Hybrid ASP) is an extension of Answer Set
Programming (ASP) that allows ASP-like rules to interact with outside sources.
The Splitting Set Theorem is an important and extensively used result for ASP.
The paper introduces the Splitting Set Theorem for Hybrid ASP, which is for
Hybrid ASP the equivalent of the Splitting Set Theorem, and shows how it can be
applied to simplify computing answer sets for Hybrid ASP programs most relevant
for practical applications.
",Splitting a Hybrid ASP Program
"  Problems affecting the transport of people or goods are plentiful in industry
and commerce and they also appear to be at the origin of much more complex
problems. In recent years, the logistics and transport sector keeps growing
supported by technological progress, i.e. companies to be competitive are
resorting to innovative technologies aimed at efficiency and effectiveness.
This is why companies are increasingly using technologies such as Artificial
Intelligence (AI), Blockchain and Internet of Things (IoT). Artificial
intelligence, in particular, is often used to solve optimization problems in
order to provide users with the most efficient ways to exploit available
resources. In this work we present an overview of our current research
activities concerning the development of new algorithms, based on CLP
techniques, for route planning problems exploiting the geometric information
intrinsically present in many of them or in some of their variants. The
research so far has focused in particular on the Euclidean Traveling
Salesperson Problem (Euclidean TSP) with the aim to exploit the results
obtained also to other problems of the same category, such as the Euclidean
Vehicle Routing Problem (Euclidean VRP), in the future.
","Constraint Programming Algorithms for Route Planning Exploiting
  Geometrical Information"
"  The integration of low-level perception with high-level reasoning is one of
the oldest problems in Artificial Intelligence. Recently, several proposals
were made to implement the reasoning process in complex neural network
architectures. While these works aim at extending neural networks with the
capability of reasoning, a natural question that we consider is: can we extend
answer set programs with neural networks to allow complex and high-level
reasoning on neural network outputs? As a preliminary result, we propose
NeurASP -- a simple extension of answer set programs by embracing neural
networks where neural network outputs are treated as probability distributions
over atomic facts in answer set programs. We show that NeurASP can not only
improve the perception accuracy of a pre-trained neural network, but also help
to train a neural network better by giving restrictions through logic rules.
However, training with NeurASP would take much more time than pure neural
network training due to the internal use of a symbolic reasoning engine. For
future work, we plan to investigate the potential ways to solve the scalability
issue of NeurASP. One potential way is to embed logic programs directly in
neural networks. On this route, we plan to first design a SAT solver using
neural networks, then extend such a solver to allow logic programs.
",Extending Answer Set Programs with Neural Networks
"  Lately there has been a lot of discussion about why deep learning algorithms
perform better than we would theoretically suspect. To get insight into this
question, it helps to improve our understanding of how learning works. We
explore the core problem of generalization and show that long-accepted Occam's
razor and parsimony principles are insufficient to ground learning. Instead, we
derive and demonstrate a set of relativistic principles that yield clearer
insight into the nature and dynamics of learning. We show that concepts of
simplicity are fundamentally contingent, that all learning operates relative to
an initial guess, and that generalization cannot be measured or strongly
inferred, but that it can be expected given enough observation. Using these
principles, we reconstruct our understanding in terms of distributed learning
systems whose components inherit beliefs and update them. We then apply this
perspective to elucidate the nature of some real world inductive processes
including deep learning.
",The Relativity of Induction
"  To be capable of lifelong learning in a real-life environment, robots have to
tackle multiple challenges. Being able to relate physical properties they may
observe in their environment to possible interactions they may have is one of
them. This skill, named affordance learning, is strongly related to embodiment
and is mastered through each person's development: each individual learns
affordances differently through their own interactions with their surroundings.
Current methods for affordance learning usually use either fixed actions to
learn these affordances or focus on static setups involving a robotic arm to be
operated. In this article, we propose an algorithm using intrinsic motivation
to guide the learning of affordances for a mobile robot. This algorithm is
capable to autonomously discover, learn and adapt interrelated affordances
without pre-programmed actions. Once learned, these affordances may be used by
the algorithm to plan sequences of actions in order to perform tasks of various
difficulties. We then present one experiment and analyse our system before
comparing it with other approaches from reinforcement learning and affordance
learning.
",Hierarchical Affordance Discovery using Intrinsic Motivation
"  In various scenarios, a single phase of modelling and solving is either not
sufficient or not feasible to solve the problem at hand. A standard approach to
solving AI planning problems, for example, is to incrementally extend the
planning horizon and solve the problem of trying to find a plan of a particular
length. Indeed, any optimization problem can be solved as a sequence of
decision problems in which the objective value is incrementally updated.
Another example is constraint dominance programming (CDP), in which search is
organized into a sequence of levels. The contribution of this work is to enable
a native interaction between SAT solvers and the automated modelling system
Savile Row to support efficient incremental modelling and solving. This allows
adding new decision variables, posting new constraints and removing existing
constraints (via assumptions) between incremental steps. Two additional
benefits of the native coupling of modelling and solving are the ability to
retain learned information between SAT solver calls and to enable SAT
assumptions, further improving flexibility and efficiency. Experiments on one
optimisation problem and five pattern mining tasks demonstrate that the native
interaction between the modelling system and SAT solver consistently improves
performance significantly.
",Efficient Incremental Modelling and Solving
"  A common issue for companies is that the volume of product orders may at
times exceed the production capacity. We formally introduce two novel problems
dealing with the question which orders to discard or postpone in order to meet
certain (timeliness) goals, and try to approach them by means of model-based
diagnosis. In thorough analyses, we identify many similarities of the
introduced problems to diagnosis problems, but also reveal crucial
idiosyncracies and outline ways to handle or leverage them. Finally, a
proof-of-concept evaluation on industrial-scale problem instances from a
well-known scheduling benchmark suite demonstrates that one of the two
formalized problems can be well attacked by out-of-the-box model-based
diagnosis tools.
","The Scheduling Job-Set Optimization Problem: A Model-Based Diagnosis
  Approach"
"  This paper deals with belief base revision that is a form of belief change
consisting of the incorporation of new facts into an agent's beliefs
represented by a finite set of propositional formulas. In the aim to guarantee
more reliability and rationality for real applications while performing
revision, we propose the idea of credible belief base revision yielding to
define two new formula-based revision operators using the suitable tools
offered by evidence theory. These operators, uniformly presented in the same
spirit of others in [9], stem from consistent subbases maximal with respect to
credibility instead of set inclusion and cardinality. Moreover, in between
these two extremes operators, evidence theory let us shed some light on a
compromise operator avoiding losing initial beliefs to the maximum extent
possible. Its idea captures maximal consistent sets stemming from all possible
intersections of maximal consistent subbases. An illustration of all these
operators and a comparison with others are inverstigated by examples.
",On the use of evidence theory in belief base revision
"  This document describes the design and implementation of the Tabletop Games
framework (TAG), a Java-based benchmark for developing modern board games for
AI research. TAG provides a common skeleton for implementing tabletop games
based on a common API for AI agents, a set of components and classes to easily
add new games and an import module for defining data in JSON format. At
present, this platform includes the implementation of seven different tabletop
games that can also be used as an example for further developments.
Additionally, TAG also incorporates logging functionality that allows the user
to perform a detailed analysis of the game, in terms of action space, branching
factor, hidden information, and other measures of interest for Game AI
research. The objective of this document is to serve as a central point where
the framework can be described at length. TAG can be downloaded at:
https://github.com/GAIGResearch/TabletopGames
",Design and Implementation of TAG: A Tabletop Games Framework
"  Statistical samples, in order to be representative, have to be drawn from a
population in a random and unbiased way. Nevertheless, it is common practice in
the field of model-based diagnosis to make estimations from (biased) best-first
samples. One example is the computation of a few most probable possible fault
explanations for a defective system and the use of these to assess which aspect
of the system, if measured, would bring the highest information gain.
  In this work, we scrutinize whether these statistically not well-founded
conventions, that both diagnosis researchers and practitioners have adhered to
for decades, are indeed reasonable. To this end, we empirically analyze various
sampling methods that generate fault explanations. We study the
representativeness of the produced samples in terms of their estimations about
fault explanations and how well they guide diagnostic decisions, and we
investigate the impact of sample size, the optimal trade-off between sampling
efficiency and effectivity, and how approximate sampling techniques compare to
exact ones.
",Do We Really Sample Right In Model-Based Diagnosis?
"  Various model-based diagnosis scenarios require the computation of the most
preferred fault explanations. Existing algorithms that are sound (i.e., output
only actual fault explanations) and complete (i.e., can return all
explanations), however, require exponential space to achieve this task. As a
remedy, to enable successful diagnosis on memory-restricted devices and for
memory-intensive problem cases, we propose RBF-HS, a diagnostic search method
based on Korf's well-known RBFS algorithm. RBF-HS can enumerate an arbitrary
fixed number of fault explanations in best-first order within linear space
bounds, without sacrificing the desirable soundness or completeness properties.
Evaluations using real-world diagnosis cases show that RBF-HS, when used to
compute minimum-cardinality fault explanations, in most cases saves substantial
space (up to 98 %) while requiring only reasonably more or even less time than
Reiter's HS-Tree, a commonly used and as generally applicable sound, complete
and best-first diagnosis search.
","Sound, Complete, Linear-Space, Best-First Diagnosis Search"
"  Using a weightless neural network architecture WiSARD we propose a
straightforward graph to retina codification to represent business process
graph flows avoiding kernels, and we present how WiSARD outperforms the
classification performance with small training sets in the process mining
context.
",Process mining classification with a weightless neural network
"  There are situations in which an agent should receive rewards only after
having accomplished a series of previous tasks, that is, rewards are
non-Markovian. One natural and quite general way to represent history-dependent
rewards is via a Mealy machine, a finite state automaton that produces output
sequences from input sequences. In our formal setting, we consider a Markov
decision process (MDP) that models the dynamics of the environment in which the
agent evolves and a Mealy machine synchronized with this MDP to formalize the
non-Markovian reward function. While the MDP is known by the agent, the reward
function is unknown to the agent and must be learned.
  Our approach to overcome this challenge is to use Angluin's $L^*$ active
learning algorithm to learn a Mealy machine representing the underlying
non-Markovian reward machine (MRM). Formal methods are used to determine the
optimal strategy for answering so-called membership queries posed by $L^*$.
  Moreover, we prove that the expected reward achieved will eventually be at
least as much as a given, reasonable value provided by a domain expert. We
evaluate our framework on three problems. The results show that using $L^*$ to
learn an MRM in a non-Markovian reward decision process is effective.
",Online Learning of Non-Markovian Reward Models
"  The worldwide growth of e-commerce has created new challenges for logistics
companies, one of which is being able to deliver products quickly and at low
cost, which reflects directly in the way of sorting packages, needing to
eliminate steps such as storage and batch creation. Our work presents a
multi-agent system that uses trajectory data mining techniques to extract
territorial patterns and use them in the dynamic creation of last-mile routes.
The problem can be modeled as a Dynamic Capacitated Vehicle Routing Problem
(VRP) with Stochastic Customer, being therefore NP-HARD, what makes its
implementation unfeasible for many packages. The work's main contribution is to
solve this problem only depending on the Warehouse system configurations and
not on the number of packages processed, which is appropriate for Big Data
scenarios commonly present in the delivery of e-commerce products.
Computational experiments were conducted for single and multi depot instances.
Due to its probabilistic nature, the proposed approach presented slightly lower
performances when compared to the static VRP algorithm. However, the
operational gains that our solution provides making it very attractive for
situations in which the routes must be set dynamically.
","A Multi-Agent System for Solving the Dynamic Capacitated Vehicle Routing
  Problem with Stochastic Customers using Trajectory Data Mining"
"  Monte Carlo Tree Search (MCTS) is a relatively new sampling method with
multiple variants in the literature. They can be applied to a wide variety of
challenging domains including board games, video games, and energy-based
problems to mention a few. In this work, we explore the use of the vanilla MCTS
and the MCTS with Rapid Action Value Estimation (MCTS-RAVE) in the game of
Carcassonne, a stochastic game with a deceptive scoring system where limited
research has been conducted. We compare the strengths of the MCTS-based methods
with the Star2.5 algorithm, previously reported to yield competitive results in
the game of Carcassonne when a domain-specific heuristic is used to evaluate
the game states. We analyse the particularities of the strategies adopted by
the algorithms when they share a common reward system. The MCTS-based methods
consistently outperformed the Star2.5 algorithm given their ability to find and
follow long-term strategies, with the vanilla MCTS exhibiting a more robust
game-play than the MCTS-RAVE.
",Playing Carcassonne with Monte Carlo Tree Search
"  Beginning with a simple semantics for propositions, based on counting
observations, it is shown that probabilistic and fuzzy logic correspond to two
different heuristic assumptions regarding the combination of propositions whose
evidence bases are not currently available. These two different heuristic
assumptions lead to two different sets of formulas for propagating quantitative
truth values through lattice operations. It is shown that these two sets of
formulas provide a natural grounding for the multiplicative and additive
operator-sets in linear logic. The standard rules of linear logic then emerge
as consequences of the underlying semantics. The concept of linear logic as a
``logic of resources"" is manifested here via the principle of ``conservation of
evidence"" -- the restrictions to weakening and contraction in linear logic
serve to avoid double-counting of evidence (beyond any double-counting incurred
via use of heuristic truth value functions).
",Uncertain Linear Logic via Fibring of Probabilistic and Fuzzy Logic
"  Natural memories are associative, declarative and distributed. Symbolic
computing memories resemble natural memories in their declarative character,
and information can be stored and recovered explicitly; however, they lack the
associative and distributed properties of natural memories. Sub-symbolic
memories developed within the connectionist or artificial neural networks
paradigm are associative and distributed, but are unable to express symbolic
structure and information cannot be stored and retrieved explicitly; hence,
they lack the declarative property. To address this dilemma, we use
Relational-Indeterminate Computing to model associative memory registers that
hold distributed representations of individual objects. This mode of computing
has an intrinsic computing entropy which measures the indeterminacy of
representations. This parameter determines the operational characteristics of
the memory. Associative registers are embedded in an architecture that maps
concrete images expressed in modality-specific buffers into abstract
representations, and vice versa, and the memory system as a whole fulfills the
three properties of natural memories. The system has been used to model a
visual memory holding the representations of hand-written digits, and
recognition and recall experiments show that there is a range of entropy
values, not too low and not too high, in which associative memory registers
have a satisfactory performance. The similarity between the cue and the object
recovered in memory retrieve operations depends on the entropy of the memory
register holding the representation of the corresponding object. The
experiments were implemented in a simulation using a standard computer, but a
parallel architecture may be built where the memory operations would take a
very reduced number of computing steps.
",An Entropic Associative Memory
"  Within intelligent tutoring systems, considerable research has investigated
hints, including how to generate data-driven hints, what hint content to
present, and when to provide hints for optimal learning outcomes. However, less
attention has been paid to how hints are presented. In this paper, we propose a
new hint delivery mechanism called ""Assertions"" for providing unsolicited hints
in a data-driven intelligent tutor. Assertions are partially-worked example
steps designed to appear within a student workspace, and in the same format as
student-derived steps, to show students a possible subgoal leading to the
solution. We hypothesized that Assertions can help address the well-known hint
avoidance problem. In systems that only provide hints upon request, hint
avoidance results in students not receiving hints when they are needed. Our
unsolicited Assertions do not seek to improve student help-seeking, but rather
seek to ensure students receive the help they need. We contrast Assertions with
Messages, text-based, unsolicited hints that appear after student inactivity.
Our results show that Assertions significantly increase unsolicited hint usage
compared to Messages. Further, they show a significant aptitude-treatment
interaction between Assertions and prior proficiency, with Assertions leading
students with low prior proficiency to generate shorter (more efficient)
posttest solutions faster. We also present a clustering analysis that shows
patterns of productive persistence among students with low prior knowledge when
the tutor provides unsolicited help in the form of Assertions. Overall, this
work provides encouraging evidence that hint presentation can significantly
impact how students use them and using Assertions can be an effective way to
address help avoidance.
","Avoiding Help Avoidance: Using Interface Design Changes to Promote
  Unsolicited Hint Usage in an Intelligent Tutor"
"  In this work, we propose a novel cross Q-learning algorithm, aim at
alleviating the well-known overestimation problem in value-based reinforcement
learning methods, particularly in the deep Q-networks where the overestimation
is exaggerated by function approximation errors. Our algorithm builds on double
Q-learning, by maintaining a set of parallel models and estimate the Q-value
based on a randomly selected network, which leads to reduced overestimation
bias as well as the variance. We provide empirical evidence on the advantages
of our method by evaluating on some benchmark environment, the experimental
results demonstrate significant improvement of performance in reducing the
overestimation bias and stabilizing the training, further leading to better
derived policies.
",Cross Learning in Deep Q-Networks
"  Explainability has been a challenge in AI for as long as AI has existed. With
the recently increased use of AI in society, it has become more important than
ever that AI systems would be able to explain the reasoning behind their
results also to end-users in situations such as being eliminated from a
recruitment process or having a bank loan application refused by an AI system.
Especially if the AI system has been trained using Machine Learning, it tends
to contain too many parameters for them to be analysed and understood, which
has caused them to be called `black-box' systems. Most Explainable AI (XAI)
methods are based on extracting an interpretable model that can be used for
producing explanations. However, the interpretable model does not necessarily
map accurately to the original black-box model. Furthermore, the
understandability of interpretable models for an end-user remains questionable.
The notions of Contextual Importance and Utility (CIU) presented in this paper
make it possible to produce human-like explanations of black-box outcomes
directly, without creating an interpretable model. Therefore, CIU explanations
map accurately to the black-box model itself. CIU is completely model-agnostic
and can be used with any black-box system. In addition to feature importance,
the utility concept that is well-known in Decision Theory provides a new
dimension to explanations compared to most existing XAI methods. Finally, CIU
can produce explanations at any level of abstraction and using different
vocabularies and other means of interaction, which makes it possible to adjust
explanations and interaction according to the context and to the target users.
",Explainable AI without Interpretable Model
"  Existing exploration strategies in reinforcement learning (RL) often either
ignore the history or feedback of search, or are complicated to implement.
There is also a very limited literature showing their effectiveness over
diverse domains. We propose an algorithm based on the idea of reannealing, that
aims at encouraging exploration only when it is needed, for example, when the
algorithm detects that the agent is stuck in a local optimum. The approach is
simple to implement. We perform an illustrative case study showing that it has
potential to both accelerate training and obtain a better policy.
","Reannealing of Decaying Exploration Based On Heuristic Measure in Deep
  Q-Network"
"  Toolpath optimization of metal-based additive manufacturing processes is
currently hampered by the high-dimensionality of its design space. In this
work, a reinforcement learning platform is proposed that dynamically learns
toolpath strategies to build an arbitrary part. To this end, three prominent
model-free reinforcement learning formulations are investigated to design
additive manufacturing toolpaths and demonstrated for two cases of dense and
sparse reward structures. The results indicate that this learning-based
toolpath design approach achieves high scores, especially when a dense reward
structure is present.
","Toolpath design for additive manufacturing using deep reinforcement
  learning"
"  How can we effectively regularize BERT? Although BERT proves its
effectiveness in various downstream natural language processing tasks, it often
overfits when there are only a small number of training instances. A promising
direction to regularize BERT is based on pruning its attention heads based on a
proxy score for head importance. However, heuristic-based methods are usually
suboptimal since they predetermine the order by which attention heads are
pruned. In order to overcome such a limitation, we propose AUBER, an effective
regularization method that leverages reinforcement learning to automatically
prune attention heads from BERT. Instead of depending on heuristics or
rule-based policies, AUBER learns a pruning policy that determines which
attention heads should or should not be pruned for regularization. Experimental
results show that AUBER outperforms existing pruning methods by achieving up to
10% better accuracy. In addition, our ablation study empirically demonstrates
the effectiveness of our design choices for AUBER.
",AUBER: Automated BERT Regularization
"  The strong impulse to digitize processes and operations in companies and
enterprises have resulted in the creation and automatic recording of an
increasingly large amount of process data in information systems. These are
made available in the form of event logs. Process mining techniques enable the
process-centric analysis of data, including automatically discovering process
models and checking if event data conform to a given model. In this paper, we
analyze the previously unexplored setting of uncertain event logs. In such
event logs uncertainty is recorded explicitly, i.e., the time, activity and
case of an event may be unclear or imprecise. In this work, we define a
taxonomy of uncertain event logs and models, and we examine the challenges that
uncertainty poses on process discovery and conformance checking. Finally, we
show how upper and lower bounds for conformance can be obtained by aligning an
uncertain trace onto a regular process model.
",Conformance Checking over Uncertain Event Data
"  Online Social Networks (OSNs) provide a platform for users to share their
thoughts and opinions with their community of friends or to the general public.
In order to keep the platform safe for all users, as well as to keep it
compliant with local laws, OSNs typically create a set of community standards
organized into policy groups, and use Machine Learning (ML) models to identify
and remove content that violates any of the policies. However, out of the
billions of content that is uploaded on a daily basis only a small fraction is
so unambiguously violating that it can be removed by the automated models.
Prevalence estimation is the task of estimating the fraction of violating
content in the residual items by sending a small sample of these items to human
labelers to get ground truth labels. This task is exceedingly hard because even
though we can easily get the ML scores or features for all of the billions of
items we can only get ground truth labels on a few thousands of these items due
to practical considerations. Indeed the prevalence can be so low that even
after a judicious choice of items to be labeled there can be many days in which
not even a single item is labeled violating. A pragmatic choice for such low
prevalence, $10^{-4}$ to $10^{-5}$, regimes is to report the upper bound, or
$97.5\%$ confidence interval, prevalence (UBP) that takes the uncertainties of
the sampling and labeling processes into account and gives a smoothed estimate.
In this work we present two novel techniques Bucketed-Beta-Binomial and a
Bucketed-Gaussian Process for this UBP task and demonstrate on real and
simulated data that it has much better coverage than the commonly used
bootstrapping technique.
","Uncertainty Estimation For Community Standards Violation In Online
  Social Networks"
"  Solution concepts of traditional game theory assume entirely rational
players; therefore, their ability to exploit subrational opponents is limited.
One type of subrationality that describes human behavior well is the quantal
response. While there exist algorithms for computing solutions against quantal
opponents, they either do not scale or may provide strategies that are even
worse than the entirely-rational Nash strategies. This paper aims to analyze
and propose scalable algorithms for computing effective and robust strategies
against a quantal opponent in normal-form and extensive-form games. Our
contributions are: (1) we define two different solution concepts related to
exploiting quantal opponents and analyze their properties; (2) we prove that
computing these solutions is computationally hard; (3) therefore, we evaluate
several heuristic approximations based on scalable counterfactual regret
minimization (CFR); and (4) we identify a CFR variant that exploits the bounded
opponents better than the previously used variants while being less exploitable
by the worst-case perfectly-rational opponent.
","Complexity and Algorithms for Exploiting Quantal Opponents in Large
  Two-Player Games"
"  Static knowledge graph (SKG) embedding (SKGE) has been studied intensively in
the past years. Recently, temporal knowledge graph (TKG) embedding (TKGE) has
emerged. In this paper, we propose a Recursive Temporal Fact Embedding (RTFE)
framework to transplant SKGE models to TKGs and to enhance the performance of
existing TKGE models for TKG completion. Different from previous work which
ignores the continuity of states of TKG in time evolution, we treat the
sequence of graphs as a Markov chain, which transitions from the previous state
to the next state. RTFE takes the SKGE to initialize the embeddings of TKG.
Then it recursively tracks the state transition of TKG by passing updated
parameters/features between timestamps. Specifically, at each timestamp, we
approximate the state transition as the gradient update process. Since RTFE
learns each timestamp recursively, it can naturally transit to future
timestamps. Experiments on five TKG datasets show the effectiveness of RTFE.
","RTFE: A Recursive Temporal Fact Embedding Framework for Temporal
  Knowledge Graph Completion"
"  Semantic embedding of knowledge graphs has been widely studied and used for
prediction and statistical analysis tasks across various domains such as
Natural Language Processing and the Semantic Web. However, less attention has
been paid to developing robust methods for embedding OWL (Web Ontology
Language) ontologies which can express a much wider range of semantics than
knowledge graphs and have been widely adopted in domains such as
bioinformatics. In this paper, we propose a random walk and word embedding
based ontology embedding method named OWL2Vec*, which encodes the semantics of
an OWL ontology by taking into account its graph structure, lexical information
and logical constructors. Our empirical evaluation with three real world
datasets suggests that OWL2Vec* benefits from these three different aspects of
an ontology in class membership prediction and class subsumption prediction
tasks. Furthermore, OWL2Vec* often significantly outperforms the
state-of-the-art methods in our experiments.
",OWL2Vec*: Embedding of OWL Ontologies
"  We explore unconstrained natural language feedback as a learning signal for
artificial agents. Humans use rich and varied language to teach, yet most prior
work on interactive learning from language assumes a particular form of input
(e.g., commands). We propose a general framework which does not make this
assumption, using aspect-based sentiment analysis to decompose feedback into
sentiment about the features of a Markov decision process. We then perform an
analogue of inverse reinforcement learning, regressing the sentiment on the
features to infer the teacher's latent reward function. To evaluate our
approach, we first collect a corpus of teaching behavior in a cooperative task
where both teacher and learner are human. We implement three artificial
learners: sentiment-based ""literal"" and ""pragmatic"" models, and an inference
network trained end-to-end to predict latent rewards. We then repeat our
initial experiment and pair them with human teachers. All three successfully
learn from interactive human feedback. The sentiment models outperform the
inference network, with the ""pragmatic"" model approaching human performance.
Our work thus provides insight into the information structure of naturalistic
linguistic feedback as well as methods to leverage it for reinforcement
learning.
",Learning Rewards from Linguistic Feedback
"  Neural Module Network (NMN) is a machine learning model for solving the
visual question answering tasks. NMN uses programs to encode modules'
structures, and its modularized architecture enables it to solve logical
problems more reasonably. However, because of the non-differentiable procedure
of module selection, NMN is hard to be trained end-to-end. To overcome this
problem, existing work either included ground-truth program into training data
or applied reinforcement learning to explore the program. However, both of
these methods still have weaknesses. In consideration of this, we proposed a
new learning framework for NMN. Graph-based Heuristic Search is the algorithm
we proposed to discover the optimal program through a heuristic search on the
data structure named Program Graph. Our experiments on FigureQA and CLEVR
dataset show that our methods can realize the training of NMN without
ground-truth programs and achieve superior efficiency over existing
reinforcement learning methods in program exploration.
","Graph-based Heuristic Search for Module Selection Procedure in Neural
  Module Network"
"  Current discussions of ""Explainable AI"" (XAI) do not much consider the role
of abduction in explanatory reasoning (see Mueller, et al., 2018). It might be
worthwhile to pursue this, to develop intelligent systems that allow for the
observation and analysis of abductive reasoning and the assessment of abductive
reasoning as a learnable skill. Abductive inference has been defined in many
ways. For example, it has been defined as the achievement of insight. Most
often abduction is taken as a single, punctuated act of syllogistic reasoning,
like making a deductive or inductive inference from given premises. In
contrast, the originator of the concept of abduction---the American
scientist/philosopher Charles Sanders Peirce---regarded abduction as an
exploratory activity. In this regard, Peirce's insights about reasoning align
with conclusions from modern psychological research. Since abduction is often
defined as ""inferring the best explanation,"" the challenge of implementing
abductive reasoning and the challenge of automating the explanation process are
closely linked. We explore these linkages in this report. This analysis
provides a theoretical framework for understanding what the XAI researchers are
already doing, it explains why some XAI projects are succeeding (or might
succeed), and it leads to design advice.
",Explaining AI as an Exploratory Process: The Peircean Abduction Model
"  Over the past decades, research in cognitive and affective neuroscience has
emphasized that emotion is crucial for human intelligence and in fact
inseparable from cognition. Concurrently, there has been a significantly
growing interest in simulating and modeling emotion in robots and artificial
agents. Yet, existing models of emotion and their integration in cognitive
architectures remain quite limited and frequently disconnected from
neuroscientific evidence. We argue that a stronger integration of emotion in
robot models is critical for the design of intelligent machines capable of
tackling real world problems. Drawing from current neuroscientific knowledge,
we provide a set of guidelines for future research in artificial emotion and
intelligent machines more generally.
",Emotion in Future Intelligent Machines
"  This paper exploits extended Bayesian networks for uncertainty reasoning on
Petri nets, where firing of transitions is probabilistic. In particular,
Bayesian networks are used as symbolic representations of probability
distributions, modelling the observer's knowledge about the tokens in the net.
The observer can study the net by monitoring successful and failed steps.
  An update mechanism for Bayesian nets is enabled by relaxing some of their
restrictions, leading to modular Bayesian nets that can conveniently be
represented and modified. As for every symbolic representation, the question is
how to derive information - in this case marginal probability distributions -
from a modular Bayesian net. We show how to do this by generalizing the known
method of variable elimination.
  The approach is illustrated by examples about the spreading of diseases (SIR
model) and information diffusion in social networks. We have implemented our
approach and provide runtime results.
",Uncertainty Reasoning for Probabilistic Petri Nets via Bayesian Networks
"  In this work, we focus on decomposing large multi-agent path planning
problems with global temporal logic goals (common to all agents) into smaller
sub-problems that can be solved and executed independently. Crucially, the
sub-problems' solutions must jointly satisfy the common global mission
specification. The agents' missions are given as Capability Temporal Logic
(CaTL) formulas, a fragment of signal temporal logic, that can express
properties over tasks involving multiple agent capabilities (sensors, e.g.,
camera, IR, and effectors, e.g., wheeled, flying, manipulators) under strict
timing constraints. The approach we take is to decompose both the temporal
logic specification and the team of agents. We jointly reason about the
assignment of agents to subteams and the decomposition of formulas using a
satisfiability modulo theories (SMT) approach. The output of the SMT is then
distributed to subteams and leads to a significant speed up in planning time.
We include computational results to evaluate the efficiency of our solution, as
well as the trade-offs introduced by the conservative nature of the SMT
encoding.
","Fast Decomposition of Temporal Logic Specifications for Heterogeneous
  Teams"
"  We propose a new class of ""grand challenge"" AI problems that we call creative
captioning---generating clever, interesting, or abstract captions for images,
as well as understanding such captions. Creative captioning draws on core AI
research areas of vision, natural language processing, narrative reasoning, and
social reasoning, and across all these areas, it requires sophisticated uses of
common sense and cultural knowledge. In this paper, we analyze several specific
research problems that fall under creative captioning, using the popular board
game Dixit as both inspiration and proposed testing ground. We expect that
Dixit could serve as an engaging and motivating benchmark for creative
captioning across numerous AI research communities for the coming 1-2 decades.
",Creative Captioning: An AI Grand Challenge Based on the Dixit Board Game
"  Vector Symbolic Architectures belong to a family of related cognitive
modeling approaches that encode symbols and structures in high-dimensional
vectors. Similar to human subjects, whose capacity to process and store
information or concepts in short-term memory is subject to numerical
restrictions,the capacity of information that can be encoded in such vector
representations is limited and one way of modeling the numerical restrictions
to cognition. In this paper, we analyze these limits regarding information
capacity of distributed representations. We focus our analysis on simple
superposition and more complex, structured representations involving
convolutive powers to encode spatial information. In two experiments, we find
upper bounds for the number of concepts that can effectively be stored in a
single vector.
","Analyzing the Capacity of Distributed Vector Representations to Encode
  Spatial Information"
"  This paper describes an initial dataset and automatic natural language
processing (NLP) method for extracting concepts related to precision oncology
from biomedical research articles. We extract five concept types: Cancer,
Mutation, Population, Treatment, Outcome. A corpus of 250 biomedical abstracts
were annotated with these concepts following standard double-annotation
procedures. We then experiment with BERT-based models for concept extraction.
The best-performing model achieved a precision of 63.8%, a recall of 71.9%, and
an F1 of 67.1. Finally, we propose additional directions for research for
improving extraction performance and utilizing the NLP system in downstream
precision oncology applications.
","Extracting Concepts for Precision Oncology from the Biomedical
  Literature"
"  Graph Neural Networks (GNNs) have been increasingly deployed in a multitude
of different applications that involve node-wise and graph-level tasks. The
existing literature usually studies these questions independently while they
are inherently correlated. We propose in this work a unified model, Adaptive
Multi-grained GNN (AdamGNN), to learn node and graph level representation
interactively. Compared with the existing GNN models and pooling methods,
AdamGNN enhances node representation with multi-grained semantics and avoids
node feature and graph structure information loss during pooling. More
specifically, a differentiable pooling operator in AdamGNN is used to obtain a
multi-grained structure that involves node-wise and meso/macro level semantic
information. The unpooling and flyback aggregators in AdamGNN is to leverage
the multi-grained semantics to enhance node representation. The updated node
representation can further enrich the generated graph representation in the
next iteration. Experimental results on twelve real-world graphs demonstrate
the effectiveness of AdamGNN on multiple tasks, compared with several competing
methods. In addition, the ablation and empirical studies confirm the
effectiveness of different components in AdamGNN.
",Adaptive Multi-grained Graph Neural Networks
"  The development of rigorous quality assessment model relies on the collection
of reliable subjective data, where the perceived quality of visual multimedia
is rated by the human observers. Different subjective assessment protocols can
be used according to the objectives, which determine the discriminability and
accuracy of the subjective data.
  Single stimulus methodology, e.g., the Absolute Category Rating (ACR) has
been widely adopted due to its simplicity and efficiency. However, Pair
Comparison (PC) is of significant advantage over ACR in terms of
discriminability. In addition, PC avoids the influence of observers' bias
regarding their understanding of the quality scale. Nevertheless, full pair
comparison is much more time-consuming. In this study, we therefore 1) employ a
generic model to bridge the pair comparison data and ACR data, where the
variance term could be recovered and the obtained information is more complete;
2) propose a fusion strategy to boost pair comparisons by utilizing the ACR
results as initialization information; 3) develop a novel active batch sampling
strategy based on Minimum Spanning Tree (MST) for PC. In such a way, the
proposed methodology could achieve the same accuracy of pair comparison but
with the compelxity as low as ACR. Extensive experimental results demonstrate
the efficiency and accuracy of the proposed approach, which outperforms the
state of the art approaches.
","Strategy for Boosting Pair Comparison and Improving Quality Assessment
  Accuracy"
"  Combinatorial problems which have been proven to be NP-hard are faced in
Higher Education Institutions and researches have extensively investigated some
of the well-known combinatorial problems such as the timetabling and student
project allocation problems. However, NP-hard problems faced in Higher
Education Institutions are not only confined to these categories of
combinatorial problems. The majority of NP-hard problems faced in institutions
involve grouping students and/or resources, albeit with each problem having its
own unique set of constraints. Thus, it can be argued that techniques to solve
NP-hard problems in Higher Education Institutions can be transferred across the
different problem categories. As no method is guaranteed to outperform all
others in all problems, it is necessary to investigate heuristic techniques for
solving lesser-known problems in order to guide stakeholders or software
developers to the most appropriate algorithm for each unique class of NP-hard
problems faced in Higher Education Institutions. To this end, this study
described an optimization problem faced in a real university that involved
grouping students for the presentation of semester results. Ordering based
heuristics, genetic algorithm and the ant colony optimization algorithm
implemented in Python programming language were used to find feasible solutions
to this problem, with the ant colony optimization algorithm performing better
or equal in 75% of the test instances and the genetic algorithm producing
better or equal results in 38% of the test instances.
","Meta-Heuristic Solutions to a Student Grouping Optimization Problem
  faced in Higher Education Institutions"
"  Procedural Content Generation via Machine Learning (PCGML) refers to a group
of methods for creating game content (e.g. platformer levels, game maps, etc.)
using machine learning models. PCGML approaches rely on black box models, which
can be difficult to understand and debug by human designers who do not have
expert knowledge about machine learning. This can be even more tricky in
co-creative systems where human designers must interact with AI agents to
generate game content. In this paper we present an approach to explainable
artificial intelligence in which certain training instances are offered to
human users as an explanation for the AI agent's actions during a co-creation
process. We evaluate this approach by approximating its ability to provide
human users with the explanations of AI agent's actions and helping them to
more efficiently cooperate with the AI agent.
",Explainability via Responsibility
"  Procedural content generation via machine learning (PCGML) has shown success
at producing new video game content with machine learning. However, the
majority of the work has focused on the production of static game content,
including game levels and visual elements. There has been much less work on
dynamic game content, such as game mechanics. One reason for this is the lack
of a consistent representation for dynamic game content, which is key for a
number of statistical machine learning approaches. We present an autoencoder
for deriving what we call ""entity embeddings"", a consistent way to represent
different dynamic entities across multiple games in the same representation. In
this paper we introduce the learned representation, along with some evidence
towards its quality and future utility.
",Entity Embedding as Game Representation
"  In AI research, synthesizing a plan of action has typically used descriptive
models of the actions that abstractly specify what might happen as a result of
an action, and are tailored for efficiently computing state transitions.
However, executing the planned actions has needed operational models, in which
rich computational control structures and closed-loop online decision-making
are used to specify how to perform an action in a complex execution context,
react to events and adapt to an unfolding situation. Deliberative actors, which
integrate acting and planning, have typically needed to use both of these
models together -- which causes problems when attempting to develop the
different models, verify their consistency, and smoothly interleave acting and
planning.
  As an alternative, we define and implement an integrated acting-and-planning
system in which both planning and acting use the same operational models. These
rely on hierarchical task-oriented refinement methods offering rich control
structures. The acting component, called Reactive Acting Engine (RAE), is
inspired by the well-known PRS system. At each decision step, RAE can get
advice from a planner for a near-optimal choice with respect to a utility
function. The anytime planner uses a UCT-like Monte Carlo Tree Search
procedure, called UPOM, (UCT Procedure for Operational Models), whose rollouts
are simulations of the actor's operational models. We also present learning
strategies for use with RAE and UPOM that acquire, from online acting
experiences and/or simulated planning results, a mapping from decision contexts
to method instances as well as a heuristic function to guide UPOM. We
demonstrate the asymptotic convergence of UPOM towards optimal methods in
static domains, and show experimentally that UPOM and the learning strategies
significantly improve the acting efficiency and robustness.
","Deliberative Acting, Online Planning and Learning with Hierarchical
  Operational Models"
"  Recent advances in the development of artificial intelligence, technological
progress acceleration, long-term trends of macroeconomic dynamics increase the
relevance of technological singularity hypothesis. In this paper, we build a
model of finite-time technological singularity assuming that artificial
intelligence will replace humans for artificial intelligence engineers after
some point in time when it is developed enough. This model implies the
following: let A be the level of development of artificial intelligence. Then,
the moment of technological singularity n is defined as the point in time where
artificial intelligence development function approaches infinity. Thus, it
happens in finite time. Although infinite level of development of artificial
intelligence cannot be reached practically, this approximation is useful for
several reasons, firstly because it allows modeling a phase transition or a
change of regime. In the model, intelligence growth function appears to be
hyperbolic function under relatively broad conditions which we list and
compare. Subsequently, we also add a stochastic term (Brownian motion) to the
model and investigate the changes in its behavior. The results can be applied
for the modeling of dynamics of various processes characterized by
multiplicative growth.
","A Finite-Time Technological Singularity Model With Artificial
  Intelligence Self-Improvement"
"  There is currently a rapid increase in the number of challenge problem,
benchmarking datasets and algorithmic optimization tests for evaluating AI
systems. However, there does not currently exist an objective measure to
determine the complexity between these newly created domains. This lack of
cross-domain examination creates an obstacle to effectively research more
general AI systems. We propose a theory for measuring the complexity between
varied domains. This theory is then evaluated using approximations by a
population of neural network based AI systems. The approximations are compared
to other well known standards and show it meets intuitions of complexity. An
application of this measure is then demonstrated to show its effectiveness as a
tool in varied situations. The experimental results show this measure has
promise as an effective tool for aiding in the evaluation of AI systems. We
propose the future use of such a complexity metric for use in computing an AI
system's intelligence.
",Measuring the Complexity of Domains Used to Evaluate AI Systems
"  Societal rules, as exemplified by norms, aim to provide a degree of
behavioural stability to multi-agent societies. Norms regulate a society using
the deontic concepts of permissions, obligations and prohibitions to specify
what can, must and must not occur in a society. Many implementations of
normative systems assume various combinations of the following assumptions:
that the set of norms is static and defined at design time; that agents joining
a society are instantly informed of the complete set of norms; that the set of
agents within a society does not change; and that all agents are aware of the
existing norms. When any one of these assumptions is dropped, agents need a
mechanism to identify the set of norms currently present within a society, or
risk unwittingly violating the norms. In this paper, we develop a norm
identification mechanism that uses a combination of parsing-based plan
recognition and Hierarchical Task Network (HTN) planning mechanisms, which
operates by analysing the actions performed by other agents. While our basic
mechanism cannot learn in situations where norm violations take place, we
describe an extension which is able to operate in the presence of violations.
",Norm Identification through Plan Recognition
"  To reduce the danger of powerful super-intelligent AIs, we might make the
first such AIs oracles that can only send and receive messages. This paper
proposes a possibly practical means of using machine learning to create two
classes of narrow AI oracles that would provide chess advice: those aligned
with the player's interest, and those that want the player to lose and give
deceptively bad advice. The player would be uncertain which type of oracle it
was interacting with. As the oracles would be vastly more intelligent than the
player in the domain of chess, experience with these oracles might help us
prepare for future artificial general intelligence oracles.
",Chess as a Testing Grounds for the Oracle Approach to AI Safety
"  Online solvers for partially observable Markov decision processes have
difficulty scaling to problems with large action spaces. Monte Carlo tree
search with progressive widening attempts to improve scaling by sampling from
the action space to construct a policy search tree. The performance of
progressive widening search is dependent upon the action sampling policy, often
requiring problem-specific samplers. In this work, we present a general method
for efficient action sampling based on Bayesian optimization. The proposed
method uses a Gaussian process to model a belief over the action-value function
and selects the action that will maximize the expected improvement in the
optimal action value. We implement the proposed approach in a new online tree
search algorithm called Bayesian Optimized Monte Carlo Planning (BOMCP).
Several experiments show that BOMCP is better able to scale to large action
space POMDPs than existing state-of-the-art tree search solvers.
",Bayesian Optimized Monte Carlo Planning
"  Various model-based diagnosis scenarios require the computation of most
preferred fault explanations. Existing algorithms that are sound (i.e., output
only actual fault explanations) and complete (i.e., can return all
explanations), however, require exponential space to achieve this task. As a
remedy, we propose two novel diagnostic search algorithms, called RBF-HS
(Recursive Best-First Hitting Set Search) and HBF-HS (Hybrid Best-First Hitting
Set Search), which build upon tried and tested techniques from the heuristic
search domain. RBF-HS can enumerate an arbitrary predefined finite number of
fault explanations in best-first order within linear space bounds, without
sacrificing the desirable soundness or completeness properties. The idea of
HBF-HS is to find a trade-off between runtime optimization and a restricted
space consumption that does not exceed the available memory.
  In extensive experiments on real-world diagnosis cases we compared our
approaches to Reiter's HS-Tree, a state-of-the-art method that gives the same
theoretical guarantees and is as general(ly applicable) as the suggested
algorithms. For the computation of minimum-cardinality fault explanations, we
find that (1) RBF-HS reduces memory requirements substantially in most cases by
up to several orders of magnitude, (2) in more than a third of the cases, both
memory savings and runtime savings are achieved, and (3) given the runtime
overhead is significant, using HBF-HS instead of RBF-HS reduces the runtime to
values comparable with HS-Tree while keeping the used memory reasonably
bounded. When computing most probable fault explanations, we observe that
RBF-HS tends to trade memory savings more or less one-to-one for runtime
overheads. Again, HBF-HS proves to be a reasonable remedy to cut down the
runtime while complying with practicable memory bounds.
",Memory-Limited Model-Based Diagnosis
"  In this paper, we are dealing with the fundamental concepts of Bi-logic
proposed by Chilean psychoanalyst Ignacio Matte Blanco in the context of
quantum logic, founded by Gareth Birkhoff and John Von Neumann. The main
purpose of this paper is to present how a quantum-logical model, represented by
the lattice of a closed subspace of Hilbert space, can be used as a
computational framework for concepts that are originally described by Sigmund
Freud as the fundamental properties of the unconscious psyche.
","Quantum Computational Psychoanalysis -- Quantum logic approach to
  Bi-logic"
"  Counterfactual explanations are a prominent example of post-hoc
interpretability methods in the explainable Artificial Intelligence research
domain. They provide individuals with alternative scenarios and a set of
recommendations to achieve a sought-after machine learning model outcome.
Recently, the literature has identified desiderata of counterfactual
explanations, such as feasibility, actionability and sparsity that should
support their applicability in real-world contexts. However, we show that the
literature has neglected the problem of the time dependency of counterfactual
explanations. We argue that, due to their time dependency and because of the
provision of recommendations, even feasible, actionable and sparse
counterfactual explanations may not be appropriate in real-world applications.
This is due to the possible emergence of what we call ""unfortunate
counterfactual events."" These events may occur due to the retraining of machine
learning models whose outcomes have to be explained via counterfactual
explanation. Series of unfortunate counterfactual events frustrate the efforts
of those individuals who successfully implemented the recommendations of
counterfactual explanations. This negatively affects people's trust in the
ability of institutions to provide machine learning-supported decisions
consistently. We introduce an approach to address the problem of the emergence
of unfortunate counterfactual events that makes use of histories of
counterfactual explanations. In the final part of the paper we propose an
ethical analysis of two distinct strategies to cope with the challenge of
unfortunate counterfactual events. We show that they respond to an ethically
responsible imperative to preserve the trustworthiness of credit lending
organizations, the decision models they employ, and the social-economic
function of credit lending.
","A Series of Unfortunate Counterfactual Events: the Role of Time in
  Counterfactual Explanations"
"  In this paper, we propose a novel approach to generate images (or other
artworks) by using neural cellular automatas (NCAs). Rather than training NCAs
based on single images one by one, we combined the idea with variational
autoencoders (VAEs), and hence explored some applications, such as image
restoration and style fusion. The code for model implementation is available
online.
",Image Generation With Neural Cellular Automatas
"  Deep neural networks (DNNs) demonstrate great success in classification
tasks. However, they act as black boxes and we don't know how they make
decisions in a particular classification task. To this end, we propose to
distill the knowledge from a DNN into a fuzzy inference system (FIS), which is
Takagi-Sugeno-Kang (TSK)-type in this paper. The model has the capability to
express the knowledge acquired by a DNN based on fuzzy rules, thus explaining a
particular decision much easier. Knowledge distillation (KD) is applied to
create a TSK-type FIS that generalizes better than one directly from the
training data, which is guaranteed through experiments in this paper. To
further improve the performances, we modify the baseline method of KD and
obtain good results.
","Distilling a Deep Neural Network into a Takagi-Sugeno-Kang Fuzzy
  Inference System"
"  The recent advances in artificial intelligence namely in machine learning and
deep learning, have boosted the performance of intelligent systems in several
ways. This gave rise to human expectations, but also created the need for a
deeper understanding of how intelligent systems think and decide. The concept
of explainability appeared, in the extent of explaining the internal system
mechanics in human terms. Recommendation systems are intelligent systems that
support human decision making, and as such, they have to be explainable in
order to increase user trust and improve the acceptance of recommendations. In
this work, we focus on a context-aware recommendation system for energy
efficiency and develop a mechanism for explainable and persuasive
recommendations, which are personalized to user preferences and habits. The
persuasive facts either emphasize on the economical saving prospects (Econ) or
on a positive ecological impact (Eco) and explanations provide the reason for
recommending an energy saving action. Based on a study conducted using a
Telegram bot, different scenarios have been validated with actual data and
human feedback. Current results show a total increase of 19\% on the
recommendation acceptance ratio when both economical and ecological persuasive
facts are employed. This revolutionary approach on recommendation systems,
demonstrates how intelligent recommendations can effectively encourage energy
saving behavior.
","The emergence of Explainability of Intelligent Systems: Delivering
  Explainable and Personalised Recommendations for Energy Efficiency"
"  We investigate a deep reinforcement learning (RL) architecture that supports
explaining why a learned agent prefers one action over another. The key idea is
to learn action-values that are directly represented via human-understandable
properties of expected futures. This is realized via the embedded
self-prediction (ESP)model, which learns said properties in terms of human
provided features. Action preferences can then be explained by contrasting the
future properties predicted for each action. To address cases where there are a
large number of features, we develop a novel method for computing minimal
sufficient explanations from anESP. Our case studies in three domains,
including a complex strategy game, show that ESP models can be effectively
learned and support insightful explanations.
","Contrastive Explanations for Reinforcement Learning via Embedded Self
  Predictions"
"  A variety of strategies have been proposed for overcoming local optimality in
metaheuristic search. This paper examines characteristics of moves that can be
exploited to make good decisions about steps that lead away from a local
optimum and then lead toward a new local optimum. We introduce strategies to
identify and take advantage of useful features of solution history with an
adaptive memory metaheuristic, to provide rules for selecting moves that offer
promise for discovering improved local optima.
  Our approach uses a new type of adaptive memory based on a construction
called exponential extrapolation. The memory operates by means of threshold
inequalities that ensure selected moves will not lead to a specified number of
most recently encountered local optima. Associated thresholds are embodied in
choice rule strategies that further exploit the exponential extrapolation
concept. Together these produce a threshold based Alternating Ascent (AA)
algorithm that opens a variety of research possibilities for exploration.
",Exploiting Local Optimality in Metaheuristic Search
"  As progress in AI continues to advance, it is crucial to know how advanced
systems will make choices and in what ways they may fail. Machines can already
outsmart humans in some domains, and understanding how to safely build ones
which may have capabilities at or above the human level is of particular
concern. One might suspect that artificially generally intelligent (AGI) and
artificially superintelligent (ASI) systems should be modeled as as something
which humans, by definition, can't reliably outsmart. As a challenge to this
assumption, this paper presents the Achilles Heel hypothesis which states that
even a potentially superintelligent system may nonetheless have stable
decision-theoretic delusions which cause them to make obviously irrational
decisions in adversarial settings. In a survey of relevant dilemmas and
paradoxes from the decision theory literature, a number of these potential
Achilles Heels are discussed in context of this hypothesis. Several novel
contributions are made toward understanding the ways in which these weaknesses
might be implanted into a system.
",Achilles Heels for AGI/ASI via Decision Theoretic Adversaries
"  This paper shows a novel fuzzy approximate reasoning method based on the
least common multiple (LCM). Its fundamental idea is to obtain a new fuzzy
reasoning result by the extended distance measure based on LCM between the
antecedent fuzzy set and the consequent one in discrete SISO fuzzy system. The
proposed method is called LCM one. And then this paper analyzes its some
properties, i.e., the reductive property, information loss occurred in
reasoning process, and the convergence of fuzzy control. Theoretical and
experimental research results highlight that proposed method meaningfully
improve the reductive property and information loss and controllability than
the previous fuzzy reasoning methods.
","Fuzzy Approximate Reasoning Method based on Least Common Multiple and
  its Property Analysis"
"  The manuscript presented an analysis of the work in the field of eye-tracking
over the past ten years in the low-cost filed. We researched in detail the
methods, algorithms, and developed hardware. To realization, this task we
considered the commercial eye-tracking systems with hardware and software and
Free software. Additionally, the manuscript considered advances in the neural
network fields for eye-tracking tasks and problems which hold back the
development of the low-cost eye-tracking system. special attention in the
manuscript is given to recommendations for further research in the field of
eye-tracking devices in the low-cost field.
",A review of the low-cost eye-tracking systems for 2010-2020
"  This paper proposes a research direction to advance AI which draws
inspiration from cognitive theories of human decision making. The premise is
that if we gain insights about the causes of some human capabilities that are
still lacking in AI (for instance, adaptability, generalizability, common
sense, and causal reasoning), we may obtain similar capabilities in an AI
system by embedding these causal components. We hope that the high-level
description of our vision included in this paper, as well as the several
research questions that we propose to consider, can stimulate the AI research
community to define, try and evaluate new methodologies, frameworks, and
evaluation metrics, in the spirit of achieving a better understanding of both
human and machine intelligence.
",Thinking Fast and Slow in AI
"  The control of a tail-sitter aircraft is a challenging task, especially
during transition maneuver where the lift and drag forces are highly nonlinear.
In this work, we implement a Neural Network (NN) capable of estimate such
nonlinearities. Once they are estimated, one can propose a control scheme where
these forces can correctly feed-forwarded. Our implementation of the NN has
been programmed in C++ on the PX4 Autopilot an open-source autopilot for
drones. To ensure that this implementation does not considerably affect the
autopilot's performance, the coded NN must be of a light computational load.
With the aim to test our approach, we have carried out a series of realistic
simulations in the Software in The Loop (SITL) using the PX4 Autopilot. These
experiments demonstrate that the implemented NN can be used to estimate the
tail-sitter aerodynamic forces, and can be used to improve the control
algorithms during all the flight phases of the tail-sitter aircraft: hover,
cruise flight, and transition.
","Implementation of a neural network for non-linearities estimation in a
  tail-sitter aircraft"
"  Virtual coaching has rapidly evolved into a foundational component of modern
clinical practice. At a time when healthcare professionals are in short supply
and the demand for low-cost treatments is ever-increasing, virtual health
coaches (VHCs) offer intervention-on-demand for those limited by finances or
geographic access to care. More recently, AI-powered virtual coaches have
become a viable complement to human coaches. However, the push for AI-powered
coaching systems raises several important issues for researchers, designers,
clinicians, and patients. In this paper, we present a novel framework to guide
the design and development of virtual coaching systems. This framework augments
a traditional data science pipeline with four key guiding goals: reliability,
fairness, engagement, and ethics.
","A Framework for Addressing the Risks and Opportunities In AI-Supported
  Virtual Health Coaches"
"  A Random Graph is a random object which take its values in the space of
graphs. We take advantage of the expressibility of graphs in order to model the
uncertainty about the existence of causal relationships within a given set of
variables. We adopt a Bayesian point of view in order to capture a causal
structure via interaction and learning with a causal environment. We test our
method over two different scenarios, and the experiments mainly confirm that
our technique can learn a causal structure. Furthermore, the experiments and
results presented for the first test scenario demonstrate the usefulness of our
method to learn a causal structure as well as the optimal action. On the other
hand the second experiment, shows that our proposal manages to learn the
underlying causal structure of several tasks with different sizes and different
causal structures.
",Causal Structure Learning: a Bayesian approach based on random graphs
"  Temporal collaborative filtering (TCF) methods aim at modelling non-static
aspects behind recommender systems, such as the dynamics in users' preferences
and social trends around items. State-of-the-art TCF methods employ recurrent
neural networks (RNNs) to model such aspects. These methods deploy
matrix-factorization-based (MF-based) approaches to learn the user and item
representations. Recently, graph-neural-network-based (GNN-based) approaches
have shown improved performance in providing accurate recommendations over
traditional MF-based approaches in non-temporal CF settings. Motivated by this,
we propose a novel TCF method that leverages GNNs to learn user and item
representations, and RNNs to model their temporal dynamics. A challenge with
this method lies in the increased data sparsity, which negatively impacts
obtaining meaningful quality representations with GNNs. To overcome this
challenge, we train a GNN model at each time step using a set of observed
interactions accumulated time-wise. Comprehensive experiments on real-world
data show the improved performance obtained by our method over several
state-of-the-art temporal and non-temporal CF models.
","Temporal Collaborative Filtering with Graph Convolutional Neural
  Networks"
"  Recent advancements in procedural content generation via machine learning
enable the generation of video-game levels that are aesthetically similar to
human-authored examples. However, the generated levels are often unplayable
without additional editing. We propose a generate-then-repair framework for
automatic generation of playable levels adhering to specific styles. The
framework constructs levels using a generative adversarial network (GAN)
trained with human-authored examples and repairs them using a mixed-integer
linear program (MIP) with playability constraints. A key component of the
framework is computing minimum cost edits between the GAN generated level and
the solution of the MIP solver, which we cast as a minimum cost network flow
problem. Results show that the proposed framework generates a diverse range of
playable levels, that capture the spatial relationships between objects
exhibited in the human-authored levels.
",Video Game Level Repair via Mixed Integer Linear Programming
"  Human creativity is often described as the mental process of combining
associative elements into a new form, but emerging computational creativity
algorithms may not operate in this manner. Here we develop an inverse problem
formulation to deconstruct the products of combinatorial and compositional
creativity into associative chains as a form of post-hoc interpretation that
matches the human creative process. In particular, our formulation is
structured as solving a traveling salesman problem through a knowledge graph of
associative elements. We demonstrate our approach using an example in
explaining culinary computational creativity where there is an explicit
semantic structure, and two examples in language generation where we either
extract explicit concepts that map to a knowledge graph or we consider
distances in a word embedding space. We close by casting the length of an
optimal traveling salesman path as a measure of novelty in creativity.
",Explaining Creative Artifacts
"  Emergency response to incidents such as accidents, crimes, and fires is a
major problem faced by communities. Emergency response management comprises of
several stages and sub-problems like forecasting, resource allocation, and
dispatch. The design of principled approaches to tackle each problem is
necessary to create efficient emergency response management (ERM) pipelines.
Over the last six years, we have worked with several first responder
organizations to design ERM pipelines. In this paper, we highlight some of the
challenges that we have identified and lessons that we have learned through our
experience in this domain. Such challenges are particularly relevant for
practitioners and researchers, and are important considerations even in the
design of response strategies to mitigate disasters like floods and
earthquakes.
",Designing Emergency Response Pipelines : Lessons and Challenges
"  Extracting entity pairs along with relation types from unstructured texts is
a fundamental subtask of information extraction. Most existing joint models
rely on fine-grained labeling scheme or focus on shared embedding parameters.
These methods directly model the joint probability of multi-labeled triplets,
which suffer from extracting redundant triplets with all relation types.
However, each sentence may contain very few relation types. In this paper, we
first model the final triplet extraction result as a three-order tensor of
word-to-word pairs enriched with each relation type. And in order to obtain the
sentence contained relations, we introduce an independent but joint training
relation classification module. The tensor decomposition strategy is finally
utilized to decompose the triplet tensor with predicted relational components
which omits the calculations for unpredicted relation types. According to
effective decomposition methods, we propose the Tensor Decomposition based
Relation Extraction (TDRE) approach which is able to extract overlapping
triplets and avoid detecting unnecessary entity pairs. Experiments on benchmark
datasets NYT, CoNLL04 and ADE datasets demonstrate that the proposed method
outperforms existing strong baselines.
",TDRE: A Tensor Decomposition Based Approach for Relation Extraction
"  Online Social Media (OSM) platforms such as Twitter, Facebook are extensively
exploited by the users of these platforms for spreading the (mis)information to
a large audience effortlessly at a rapid pace. It has been observed that the
misinformation can cause panic, fear, and financial loss to society. Thus, it
is important to detect and control the misinformation in such platforms before
it spreads to the masses. In this work, we focus on rumors, which is one type
of misinformation (other types are fake news, hoaxes, etc). One way to control
the spread of the rumors is by identifying users who are possibly the rumor
spreaders, that is, users who are often involved in spreading the rumors. Due
to the lack of availability of rumor spreaders labeled dataset (which is an
expensive task), we use publicly available PHEME dataset, which contains rumor
and non-rumor tweets information, and then apply a weak supervised learning
approach to transform the PHEME dataset into rumor spreaders dataset. We
utilize three types of features, that is, user, text, and ego-network features,
before applying various supervised learning approaches. In particular, to
exploit the inherent network property in this dataset (user-user reply graph),
we explore Graph Convolutional Network (GCN), a type of Graph Neural Network
(GNN) technique. We compare GCN results with the other approaches: SVM, RF, and
LSTM. Extensive experiments performed on the rumor spreaders dataset, where we
achieve up to 0.864 value for F1-Score and 0.720 value for AUC-ROC, shows the
effectiveness of our methodology for identifying possible rumor spreaders using
the GCN technique.
","Identifying Possible Rumor Spreaders on Twitter: A Weak Supervised
  Learning Approach"
"  The development of domain-independent planners within the AI Planning
community is leading to ""off-the-shelf"" technology that can be used in a wide
range of applications. Moreover, it allows a modular approach --in which
planners and domain knowledge are modules of larger software applications--
that facilitates substitutions or improvements of individual modules without
changing the rest of the system. This approach also supports the use of
reformulation and configuration techniques, which transform how a model is
represented in order to improve the efficiency of plan generation.
  In this article, we investigate how the performance of domain-independent
planners is affected by domain model configuration, i.e., the order in which
elements are ordered in the model, particularly in the light of planner
comparisons. We then introduce techniques for the online and offline
configuration of domain models, and we analyse the impact of domain model
configuration on other reformulation approaches, such as macros.
","On the Importance of Domain Model Configuration for Automated Planning
  Engines"
"  We propose a spurious region guided refinement approach for robustness
verification of deep neural networks. Our method starts with applying the
DeepPoly abstract domain to analyze the network. If the robustness property
cannot be verified, the result is inconclusive. Due to the over-approximation,
the computed region in the abstraction may be spurious in the sense that it
does not contain any true counterexample. Our goal is to identify such spurious
regions and use them to guide the abstraction refinement. The core idea is to
make use of the obtained constraints of the abstraction to infer new bounds for
the neurons. This is achieved by linear programming techniques. With the new
bounds, we iteratively apply DeepPoly, aiming to eliminate spurious regions. We
have implemented our approach in a prototypical tool DeepSRGR. Experimental
results show that a large amount of regions can be identified as spurious, and
as a result, the precision of DeepPoly can be significantly improved. As a side
contribution, we show that our approach can be applied to verify quantitative
robustness properties.
","Improving Neural Network Verification through Spurious Region Guided
  Refinement"
"  Robotic process automation (RPA) and its next evolutionary stage, intelligent
process automation, promise to drive improvements in efficiencies and process
outcomes. However, how can business leaders evaluate how to integrate
intelligent automation into business processes? What is an appropriate division
of labor between humans and machines? How should combined human-AI teams be
evaluated? For RPA, often the human labor cost and the robotic labor cost are
directly compared to make an automation decision. In this position paper, we
argue for a broader view that incorporates the potential for multiple levels of
autonomy and human involvement, as well as a wider range of metrics beyond
productivity when integrating digital workers into a business process
",Do's and Don'ts for Human and Digital Worker Integration
"  The development of artificial intelligent composition has resulted in the
increasing popularity of machine-generated pieces, with frequent copyright
disputes consequently emerging. There is an insufficient amount of research on
the judgement of artificial and machine-generated works; the creation of a
method to identify and distinguish these works is of particular importance.
Starting from the essence of the music, the article constructs a
music-rule-identifying algorithm through extracting modes, which will identify
the stability of the mode of machine-generated music, to judge whether it is
artificial intelligent. The evaluation datasets used are provided by the
Conference on Sound and Music Technology(CSMT). Experimental results
demonstrate the algorithm to have a successful distinguishing ability between
datasets with different source distributions. The algorithm will also provide
some technological reference to the benign development of the music copyright
and artificial intelligent music.
",Research on AI Composition Recognition Based on Music Rules
"  One major task of spoken language understanding (SLU) in modern personal
assistants is to extract semantic concepts from an utterance, called slot
filling. Although existing slot filling models attempted to improve extracting
new concepts that are not seen in training data, the performance in practice is
still not satisfied. Recent research collected question and answer annotated
data to learn what is unknown and should be asked, yet not practically scalable
due to the heavy data collection effort. In this paper, we incorporate
softmax-based slot filling neural architectures to model the sequence
uncertainty without question supervision. We design a Dirichlet Prior RNN to
model high-order uncertainty by degenerating as softmax layer for RNN model
training. To further enhance the uncertainty modeling robustness, we propose a
novel multi-task training to calibrate the Dirichlet concentration parameters.
We collect unseen concepts to create two test datasets from SLU benchmark
datasets Snips and ATIS. On these two and another existing Concept Learning
benchmark datasets, we show that our approach significantly outperforms
state-of-the-art approaches by up to 8.18%. Our method is generic and can be
applied to any RNN or Transformer based slot filling models with a softmax
layer.
","Modeling Token-level Uncertainty to Learn Unknown Concepts in SLU via
  Calibrated Dirichlet Prior RNN"
"  The work reported here addresses the capacity of psychophysiological sensors
and measures using Electroencephalogram (EEG) and Galvanic Skin Response (GSR)
to detect levels of trust for humans using AI-supported Human-Machine
Interaction (HMI). Improvements to the analysis of EEG and GSR data may create
models that perform as well, or better than, traditional tools. A challenge to
analyzing the EEG and GSR data is the large amount of training data required
due to a large number of variables in the measurements. Researchers have
routinely used standard machine-learning classifiers like artificial neural
networks (ANN), support vector machines (SVM), and K-nearest neighbors (KNN).
Traditionally, these have provided few insights into which features of the EEG
and GSR data facilitate the more and least accurate predictions - thus making
it harder to improve the HMI and human-machine trust relationship. A key
ingredient to applying trust-sensor research results to practical situations
and monitoring trust in work environments is the understanding of which key
features are contributing to trust and then reducing the amount of data needed
for practical applications. We used the Local Interpretable Model-agnostic
Explanations (LIME) model as a process to reduce the volume of data required to
monitor and enhance trust in HMI systems - a technology that could be valuable
for governmental and public sector applications. Explainable AI can make HMI
systems transparent and promote trust. From customer service in government
agencies and community-level non-profit public service organizations to
national military and cybersecurity institutions, many public sector
organizations are increasingly concerned to have effective and ethical HMI with
services that are trustworthy, unbiased, and free of unintended negative
consequences.
","Monitoring Trust in Human-Machine Interactions for Public Sector
  Applications"
"  Multimodal sentiment analysis utilizes multiple heterogeneous modalities for
sentiment classification. The recent multimodal fusion schemes customize LSTMs
to discover intra-modal dynamics and design sophisticated attention mechanisms
to discover the inter-modal dynamics from multimodal sequences. Although
powerful, these schemes completely rely on attention mechanisms which is
problematic due to two major drawbacks 1) deceptive attention masks, and 2)
training dynamics. Nevertheless, strenuous efforts are required to optimize
hyperparameters of these consolidate architectures, in particular their
custom-designed LSTMs constrained by attention schemes. In this research, we
first propose a common network to discover both intra-modal and inter-modal
dynamics by utilizing basic LSTMs and tensor based convolution networks. We
then propose unique networks to encapsulate temporal-granularity among the
modalities which is essential while extracting information within asynchronous
sequences. We then integrate these two kinds of information via a fusion layer
and call our novel multimodal fusion scheme as Deep-HOSeq (Deep network with
higher order Common and Unique Sequence information). The proposed Deep-HOSeq
efficiently discovers all-important information from multimodal sequences and
the effectiveness of utilizing both types of information is empirically
demonstrated on CMU-MOSEI and CMU-MOSI benchmark datasets. The source code of
our proposed Deep-HOSeq is and available at
https://github.com/sverma88/Deep-HOSeq--ICDM-2020.
","Deep-HOSeq: Deep Higher Order Sequence Fusion for Multimodal Sentiment
  Analysis"
"  The recent series of innovations in deep learning (DL) have shown enormous
potential to impact individuals and society, both positively and negatively.
The DL models utilizing massive computing power and enormous datasets have
significantly outperformed prior historical benchmarks on increasingly
difficult, well-defined research tasks across technology domains such as
computer vision, natural language processing, signal processing, and
human-computer interactions. However, the Black-Box nature of DL models and
their over-reliance on massive amounts of data condensed into labels and dense
representations poses challenges for interpretability and explainability of the
system. Furthermore, DLs have not yet been proven in their ability to
effectively utilize relevant domain knowledge and experience critical to human
understanding. This aspect is missing in early data-focused approaches and
necessitated knowledge-infused learning and other strategies to incorporate
computational knowledge. This article demonstrates how knowledge, provided as a
knowledge graph, is incorporated into DL methods using knowledge-infused
learning, which is one of the strategies. We then discuss how this makes a
fundamental difference in the interpretability and explainability of current
approaches, and illustrate it with examples from natural language processing
for healthcare and education applications.
","Semantics of the Black-Box: Can knowledge graphs help make deep learning
  systems more interpretable and explainable?"
"  A generally intelligent agent requires an open-scope world model: one rich
enough to tackle any of the wide range of tasks it may be asked to solve over
its operational lifetime. Unfortunately, planning to solve any specific task
using such a rich model is computationally intractable - even for
state-of-the-art methods - due to the many states and actions that are
necessarily present in the model but irrelevant to that problem. We propose
task scoping: a method that exploits knowledge of the initial condition, goal
condition, and transition-dynamics structure of a task to automatically and
efficiently prune provably irrelevant factors and actions from a planning
problem, which can dramatically decrease planning time. We prove that task
scoping never deletes relevant factors or actions, characterize its
computational complexity, and characterize the planning problems for which it
is especially useful. Finally, we empirically evaluate task scoping on a
variety of domains and demonstrate that using it as a pre-planning step can
reduce the state-action space of various planning problems by orders of
magnitude and speed up planning. When applied to a complex Minecraft domain,
our approach speeds up a state-of-the-art planner by 30 times, including the
time required for task scoping itself.
",Task Scoping: Generating Task-Specific Abstractions for Planning
"  Neural nets, one of the oldest architectures for AI programming, are loosely
based on biological neurons and their properties. Recent work on language
applications has made the AI code closer to biological reality in several ways.
This commentary examines this convergence and, in light of what is known of
neocortical structure, addresses the question of whether ``general AI'' looks
attainable with these tools.
",The Convergence of AI code and Cortical Functioning -- a Commentary
"  Groundbreaking successes have been achieved by Deep Reinforcement Learning
(DRL) in solving practical decision-making problems. Robotics, in particular,
can involve high-cost hardware and human interactions. Hence, scrupulous
evaluations of trained models are required to avoid unsafe behaviours in the
operational environment. However, designing metrics to measure the safety of a
neural network is an open problem, since standard evaluation parameters (e.g.,
total reward) are not informative enough. In this paper, we present a
semi-formal verification approach for decision-making tasks, based on interval
analysis, that addresses the computational demanding of previous verification
frameworks and design metrics to measure the safety of the models. Our method
obtains comparable results over standard benchmarks with respect to formal
verifiers, while drastically reducing the computation time. Moreover, our
approach allows to efficiently evaluate safety properties for decision-making
models in practical applications such as mapless navigation for mobile robots
and trajectory generation for manipulators.
","Evaluating the Safety of Deep Reinforcement Learning Models using
  Semi-Formal Verification"
"  Process Mining has recently gained popularity in healthcare due to its
potential to provide a transparent, objective and data-based view on processes.
Conformance checking is a sub-discipline of process mining that has the
potential to answer how the actual process executions deviate from existing
guidelines. In this work, we analyze a medical training process for a surgical
procedure. Ten students were trained to install a Central Venous Catheters
(CVC) with ultrasound. Event log data was collected directly after instruction
by the supervisors during a first test run and additionally after a subsequent
individual training phase. In order to provide objective performance measures,
we formulate an optimal, global sequence alignment problem inspired by
approaches in bioinformatics. Therefore, we use the Petri net model
representation of the medical process guideline to simulate a representative
set of guideline conform sequences. Next, we calculate the optimal, global
sequence alignment of the recorded and simulated event logs. Finally, the
output measures and visualization of aligned sequences are provided for
objective feedback.
","Conformance Checking for a Medical Training Process Using Petri net
  Simulation and Sequence Alignment"
"  A number of Multiple Criteria Decision Analysis (MCDA) methods have been
developed to rank alternatives based on several decision criteria. Usually,
MCDA methods deal with the criteria value at the time the decision is made
without considering their evolution over time. However, it may be relevant to
consider the criteria' time series since providing essential information for
decision-making (e.g., an improvement of the criteria). To deal with this
issue, we propose a new approach to rank the alternatives based on the criteria
time-series features (tendency, variance, etc.). In this novel approach, the
data is structured in three dimensions, which require a more complex data
structure, as the \textit{tensors}, instead of the classical matrix
representation used in MCDA. Consequently, we propose an extension for the
TOPSIS method to handle a tensor rather than a matrix. Computational results
reveal that it is possible to rank the alternatives from a new perspective by
considering meaningful decision-making information.
","A study of the Multicriteria decision analysis based on the time-series
  features and a TOPSIS method proposal for a tensorial approach"
"  In barter exchanges, participants swap goods with one another without
exchanging money; exchanges are often facilitated by a central clearinghouse,
with the goal of maximizing the aggregate quality (or number) of swaps. Barter
exchanges are subject to many forms of uncertainty--in participant preferences,
the feasibility and quality of various swaps, and so on. Our work is motivated
by kidney exchange, a real-world barter market in which patients in need of a
kidney transplant swap their willing living donors, in order to find a better
match. Modern exchanges include 2- and 3-way swaps, making the kidney exchange
clearing problem NP-hard. Planned transplants often fail for a variety of
reasons--if the donor organ is refused by the recipient's medical team, or if
the donor and recipient are found to be medically incompatible. Due to 2- and
3-way swaps, failed transplants can ""cascade"" through an exchange; one US-based
exchange estimated that about 85% of planned transplants failed in 2019. Many
optimization-based approaches have been designed to avoid these failures;
however most exchanges cannot implement these methods due to legal and policy
constraints. Instead we consider a setting where exchanges can query the
preferences of certain donors and recipients--asking whether they would accept
a particular transplant. We characterize this as a two-stage decision problem,
in which the exchange program (a) queries a small number of transplants before
committing to a matching, and (b) constructs a matching according to fixed
policy. We show that selecting these edges is a challenging combinatorial
problem, which is non-monotonic and non-submodular, in addition to being
NP-hard. We propose both a greedy heuristic and a Monte Carlo tree search,
which outperforms previous approaches, using experiments on both synthetic data
and real kidney exchange data from the United Network for Organ Sharing.
",Improving Policy-Constrained Kidney Exchange via Pre-Screening
"  Balancing group teaching and individual mentoring is an important issue in
education area. The nature behind this issue is to explore common
characteristics shared by multiple students and individual characteristics for
each student. Biclustering methods have been proved successful for detecting
meaningful patterns with the goal of driving group instructions based on
students' characteristics. However, these methods ignore the individual
characteristics of students as they only focus on common characteristics of
students. In this article, we propose a framework to detect both group
characteristics and individual characteristics of students simultaneously. We
assume that the characteristics matrix of students' is composed of two parts:
one is a low-rank matrix representing the common characteristics of students;
the other is a sparse matrix representing individual characteristics of
students. Thus, we treat the balancing issue as a matrix recovering problem.
The experiment results show the effectiveness of our method. Firstly, it can
detect meaningful biclusters that are comparable with the state-of-the-art
biclutering algorithms. Secondly, it can identify individual characteristics
for each student simultaneously. Both the source code of our algorithm and the
real datasets are available upon request.
","Exploring Common and Individual Characteristics of Students via Matrix
  Recovering"
"  We propose a novel framework and algorithm for hierarchical planning based on
the principle of delegation. This framework, the Markov Intent Process,
features a collection of skills which are each specialised to perform a single
task well. Skills are aware of their intended effects and are able to analyse
planning goals to delegate planning to the best-suited skill. This principle
dynamically creates a hierarchy of plans, in which each skill plans for
sub-goals for which it is specialised. The proposed planning method features
on-demand execution---skill policies are only evaluated when needed. Plans are
only generated at the highest level, then expanded and optimised when the
latest state information is available. The high-level plan retains the initial
planning intent and previously computed skills, effectively reducing the
computation needed to adapt to environmental changes. We show this planning
approach is experimentally very competitive to classic planning and
reinforcement learning techniques on a variety of domains, both in terms of
solution length and planning time.
",Robust Hierarchical Planning with Policy Delegation
"  Temporal planning offers numerous advantages when based on an expressive
representation. Timelines have been known to provide the required
expressiveness but at the cost of search efficiency. We propose here a temporal
planner, called FAPE, which supports many of the expressive temporal features
of the ANML modeling language without loosing efficiency.
  FAPE's representation coherently integrates flexible timelines with
hierarchical refinement methods that can provide efficient control knowledge. A
novel reachability analysis technique is proposed and used to develop causal
networks to constrain the search space. It is employed for the design of
informed heuristics, inference methods and efficient search strategies.
Experimental results on common benchmarks in the field permit to assess the
components and search strategies of FAPE, and to compare it to IPC planners.
The results show the proposed approach to be competitive with less expressive
planners and often superior when hierarchical control knowledge is provided.
FAPE, a freely available system, provides other features, not covered here,
such as the integration of planning with acting, and the handling of sensing
actions in partially observable environments.
","FAPE: a Constraint-based Planner for Generative and Hierarchical
  Temporal Planning"
"  The AutoSpeech challenge calls for automated machine learning (AutoML)
solutions to automate the process of applying machine learning to speech
processing tasks. These tasks, which cover a large variety of domains, will be
shown to the automated system in a random order. Each time when the tasks are
switched, the information of the new task will be hinted with its corresponding
training set. Thus, every submitted solution should contain an adaptation
routine which adapts the system to the new task. Compared to the first edition,
the 2020 edition includes advances of 1) more speech tasks, 2) noisier data in
each task, 3) a modified evaluation metric. This paper outlines the challenge
and describe the competition protocol, datasets, evaluation metric, starting
kit, and baseline systems.
","AutoSpeech 2020: The Second Automated Machine Learning Challenge for
  Speech Classification"
"  With rapid progress in artificial intelligence (AI), popularity of generative
art has grown substantially. From creating paintings to generating novel art
styles, AI based generative art has showcased a variety of applications.
However, there has been little focus concerning the ethical impacts of AI based
generative art. In this work, we investigate biases in the generative art AI
pipeline right from those that can originate due to improper problem
formulation to those related to algorithm design. Viewing from the lens of art
history, we discuss the socio-cultural impacts of these biases. Leveraging
causal models, we highlight how current methods fall short in modeling the
process of art creation and thus contribute to various types of biases. We
illustrate the same through case studies, in particular those related to style
transfer. To the best of our knowledge, this is the first extensive analysis
that investigates biases in the generative art AI pipeline from the perspective
of art history. We hope our work sparks interdisciplinary discussions related
to accountability of generative art.
",Biases in Generative Art -- A Causal Look from the Lens of Art History
"  Literature analysis facilitates researchers to acquire a good understanding
of the development of science and technology. The traditional literature
analysis focuses largely on the literature metadata such as topics, authors,
abstracts, keywords, references, etc., and little attention was paid to the
main content of papers. In many scientific domains such as science, computing,
engineering, etc., the methods and datasets involved in the scientific papers
published in those domains carry important information and are quite useful for
domain analysis as well as algorithm and dataset recommendation. In this paper,
we propose a novel entity recognition model, called MDER, which is able to
effectively extract the method and dataset entities from the main textual
content of scientific papers. The model utilizes rule embedding and adopts a
parallel structure of CNN and Bi-LSTM with the self-attention mechanism. We
evaluate the proposed model on datasets which are constructed from the
published papers of four research areas in computer science, i.e., NLP, CV,
Data Mining and AI. The experimental results demonstrate that our model
performs well in all the four areas and it features a good learning capacity
for cross-area learning and recognition. We also conduct experiments to
evaluate the effectiveness of different building modules within our model which
indicate that the importance of different building modules in collectively
contributing to the good entity recognition performance as a whole. The data
augmentation experiments on our model demonstrated that data augmentation
positively contributes to model training, making our model much more robust in
dealing with the scenarios where only small number of training samples are
available. We finally apply our model on PAKDD papers published from 2009-2019
to mine insightful results from scientific papers published in a longer time
span.
","Method and Dataset Entity Mining in Scientific Literature: A CNN +
  Bi-LSTM Model with Self-attention"
"  Query-based document summarization aims to extract or generate a summary of a
document which directly answers or is relevant to the search query. It is an
important technique that can be beneficial to a variety of applications such as
search engines, document-level machine reading comprehension, and chatbots.
Currently, datasets designed for query-based summarization are short in numbers
and existing datasets are also limited in both scale and quality. Moreover, to
the best of our knowledge, there is no publicly available dataset for Chinese
query-based document summarization. In this paper, we present QBSUM, a
high-quality large-scale dataset consisting of 49,000+ data samples for the
task of Chinese query-based document summarization. We also propose multiple
unsupervised and supervised solutions to the task and demonstrate their
high-speed inference and superior performance via both offline experiments and
online A/B tests. The QBSUM dataset is released in order to facilitate future
advancement of this research field.
","QBSUM: a Large-Scale Query-Based Document Summarization Dataset from
  Real-world Applications"
"  Generating asset-specific trading signals based on the financial conditions
of the assets is one of the challenging problems in automated trading. Various
asset trading rules are proposed experimentally based on different technical
analysis techniques. However, these kind of trading strategies are profitable,
extracting new asset-specific trading rules from vast historical data to
increase total return and decrease the risk of portfolios is difficult for
human experts. Recently, various deep reinforcement learning (DRL) methods are
employed to learn the new trading rules for each asset. In this paper, a novel
DRL model with various feature extraction modules is proposed. The effect of
different input representations on the performance of the models is
investigated and the performance of DRL-based models in different markets and
asset situations is studied. The proposed model in this work outperformed the
other state-of-the-art models in learning single asset-specific trading rules
and obtained a total return of almost 262% in two years on a specific asset
while the best state-of-the-art model get 78% on the same asset in the same
time period.
","Learning Financial Asset-Specific Trading Rules via Deep Reinforcement
  Learning"
"  This paper presents the participation of NetEase Game AI Lab team for the
ClariQ challenge at Search-oriented Conversational AI (SCAI) EMNLP workshop in
2020. The challenge asks for a complete conversational information retrieval
system that can understanding and generating clarification questions. We
propose a clarifying question selection system which consists of response
understanding, candidate question recalling and clarifying question ranking. We
fine-tune a RoBERTa model to understand user's responses and use an enhanced
BM25 model to recall the candidate questions. In clarifying question ranking
stage, we reconstruct the training dataset and propose two models based on
ELECTRA. Finally we ensemble the models by summing up their output
probabilities and choose the question with the highest probability as the
clarification question. Experiments show that our ensemble ranking model
outperforms in the document relevance task and achieves the best recall@[20,30]
metrics in question relevance task. And in multi-turn conversation evaluation
in stage2, our system achieve the top score of all document relevance metrics.
","A Clarifying Question Selection System from NTES_ALONG in Convai3
  Challenge"
"  The personnel rostering problem is the problem of finding an optimal way to
assign employees to shifts, subject to a set of hard constraints which all
valid solutions must follow, and a set of soft constraints which define the
relative quality of valid solutions. The problem has received significant
attention in the literature and is addressed by a large number of exact and
metaheuristic methods. In order to make the complex and costly design of
heuristics for the personnel rostering problem automatic, we propose a new
method combined Deep Neural Network and Tree Search. By treating schedules as
matrices, the neural network can predict the distance between the current
solution and the optimal solution. It can select solution strategies by
analyzing existing (near-)optimal solutions to personnel rostering problem
instances. Combined with branch and bound, the network can give every node a
probability which indicates the distance between it and the optimal one, so
that a well-informed choice can be made on which branch to choose next and to
prune the search tree.
","Neural Networked Assisted Tree Search for the Personnel Rostering
  Problem"
"  General value functions (GVFs) in the reinforcement learning (RL) literature
are long-term predictive summaries of the outcomes of agents following specific
policies in the environment. Affordances as perceived action possibilities with
specific valence may be cast into predicted policy-relative goodness and
modelled as GVFs. A systematic explication of this connection shows that GVFs
and especially their deep learning embodiments (1) realize affordance
prediction as a form of direct perception, (2) illuminate the fundamental
connection between action and perception in affordance, and (3) offer a
scalable way to learn affordances using RL methods. Through an extensive review
of existing literature on GVF applications and representative affordance
research in robotics, we demonstrate that GVFs provide the right framework for
learning affordances in real-world applications. In addition, we highlight a
few new avenues of research opened up by the perspective of ""affordance as
GVF"", including using GVFs for orchestrating complex behaviors.
",Affordance as general value function: A computational model
"  Services for Cyber-Physical Systems based on Artificial Intelligence and
Machine Learning require a virtual representation of the physical. To reduce
modeling efforts and to synchronize results, for each system, a common and
unique virtual representation used by all services during the whole system
life-cycle is needed, i.e. a DigitalTwin. In this paper such a DigitalTwin,
namely the AI reference model AITwin, is defined. This reference model is
verified by using a running example from process industry and by analyzing the
work done in recent projects.
",The DigitalTwin from an Artificial Intelligence Perspective
"  We present an experimentation platform for coalition situational
understanding research that highlights capabilities in explainable artificial
intelligence/machine learning (AI/ML) and integration of symbolic and
subsymbolic AI/ML approaches for event processing. The Situational
Understanding Explorer (SUE) platform is designed to be lightweight, to easily
facilitate experiments and demonstrations, and open. We discuss our
requirements to support coalition multi-domain operations with emphasis on
asset interoperability and ad hoc human-machine teaming in a dense urban
terrain setting. We describe the interface functionality and give examples of
SUE applied to coalition situational understanding tasks.
","An Experimentation Platform for Explainable Coalition Situational
  Understanding"
"  Artificial Intelligence (AI) has been improving the performance of systems
for a diverse set of tasks and introduced a more interactive generation of
personal agents. Despite the current trend of applying AI for a great amount of
areas, we have not seen the same quantity of work being developed for the
tourism sector. This paper reports on the main applications of AI systems
developed for tourism and the current state of the art for this sector. The
paper also provides an up-to-date survey of this field regarding several key
works and systems that are applied to tourism, like Personal Agents, for
providing a more interactive experience. We also carried out an in-depth
research on systems for predicting traffic human flow, more accurate
recommendation systems and even how geospatial is trying to display tourism
data in a more informative way and prevent problems before they arise.
",Artificial Intelligence Systems applied to tourism: A Survey
"  Neural networks play an important role in crop-weed classification have high
accuracy more than 95%. Manually choosing models and fine-tuning are laborious,
yet it is indispensable in most traditional practices and researches. Moreover,
classic training metric are not thoroughly compatible with farming tasks, that
a model still have a noticeable chance of miss classifying crop to weed while
it reach higher accuracy even more than 99%. In this paper we demonstrate a
methodology of weed-crop classification based on autonomous models choosing and
ensemble that could make models choosing and tunning automatically, and improve
the prediction with high accuracy(>99% for both data set) in specific class
with low risk in incorrect predicting.
","A methodology of weed-crop classification based on autonomous models
  choosing and ensemble"
"  For humans in a teaming scenario, context switching between reasoning about a
teammate's behavior and thinking about thier own task can slow us down,
especially if the cognitive cost of predicting the teammate's actions is high.
So if we can make the prediction of a robot-teammate's actions quicker, then
the human can be more productive. In this paper we present an approach to
constrain the actions of a robot so as to increase predictability (specifically
online predictability) while keeping the plan costs of the robot within
acceptable limits. Existing works on human-robot interaction do not consider
the computational cost for predictability, which we consider in our approach.
We approach this problem from the perspective of directed graph minimization,
and we connect the concept of predictability to the out-degree of vertices. We
present an algorithm to minimize graphs for predictability, and contrast this
with minimization for legibility (goal inference) and optimality.
",Model Minimization For Online Predictability
"  In this paper we perform an analytic comparison of a number of techniques
used to detect fake and deceptive online reviews. We apply a number machine
learning approaches found to be effective, and introduce our own approach by
fine-tuning state of the art contextualised embeddings. The results we obtain
show the potential of contextualised embeddings for fake review detection, and
lay the groundwork for future research in this area.
",Fact or Factitious? Contextualized Opinion Spam Detection
"  The 9th International Workshop on Theorem-Proving Components for Educational
Software (ThEdu'20) was scheduled to happen on June 29 as a satellite of the
IJCAR-FSCD 2020 joint meeting, in Paris. The COVID-19 pandemic came by
surprise, though, and the main conference was virtualised. Fearing that an
online meeting would not allow our community to fully reproduce the usual
face-to-face networking opportunities of the ThEdu initiative, the Steering
Committee of ThEdu decided to cancel our workshop. Given that many of us had
already planned and worked for that moment, we decided that ThEdu'20 could
still live in the form of an EPTCS volume. The EPTCS concurred with us,
recognising this very singular situation, and accepted our proposal of
organising a special issue with papers submitted to ThEdu'20. An open call for
papers was then issued, and attracted five submissions, all of which have been
accepted by our reviewers, who produced three careful reports on each of the
contributions. The resulting revised papers are collected in the present
volume. We, the volume editors, hope that this collection of papers will help
further promoting the development of theorem-proving-based software, and that
it will collaborate to improve the mutual understanding between computer
mathematicians and stakeholders in education. With some luck, we would actually
expect that the very special circumstances set up by the worst sanitary crisis
in a century will happen to reinforce the need for the application of certified
components and of verification methods for the production of educational
software that would be available even when the traditional on-site learning
experiences turn out not to be recommendable.
","Proceedings 9th International Workshop on Theorem Proving Components for
  Educational Software"
"  The ""Thinking, Fast and Slow"" paradigm of Kahneman proposes that we use two
different styles of thinking -- a fast and intuitive System 1 for certain
tasks, along with a slower but more analytical System 2 for others. While the
idea of using this two-system style of thinking is gaining popularity in AI and
robotics, our work considers how to interleave the two styles of
decision-making, i.e., how System 1 and System 2 should be used together. For
this, we propose a novel and general framework which includes a new System 0 to
oversee Systems 1 and 2. At every point when a decision needs to be made,
System 0 evaluates the situation and quickly hands over the decision-making
process to either System 1 or System 2. We evaluate such a framework on a
modified version of the classic Pac-Man game, with an already-trained RL
algorithm for System 1, a Monte-Carlo tree search for System 2, and several
different possible strategies for System 0. As expected, arbitrary switches
between Systems 1 and 2 do not work, but certain strategies do well. With
System 0, an agent is able to perform better than one that uses only System 1
or System 2.
",Interleaving Fast and Slow Decision Making
"  In this paper, we propose and prove the theorem regarding the stability of
attributes in a decision system. Based on the theorem, we propose the LRA
framework for accelerating rough set algorithms. It is a general-purpose
framework which can be applied to almost all rough set methods significantly .
Theoretical analysis guarantees high efficiency. Note that the enhancement of
efficiency will not lead to any decrease of the classification accuracy.
Besides, we provide a simpler prove for the positive approximation acceleration
framework.
","LRA: an accelerated rough set framework based on local redundancy of
  attribute for feature selection"
"  Generally, negation-limited inverters problem is known as a puzzle of
constructing an inverter with AND gates and OR gates and a few inverters. In
this paper, we introduce a curious new result about the effectiveness of two
powerful ATP (Automated Theorem Proving) strategies on tackling negation
limited inverter problem. Two resolution strategies are UR (Unit Resulting)
resolution and hyper-resolution. In experiment, we come two kinds of automated
circuit construction: 3 input/output inverters and 4 input/output BCD Counter
Circuit. Both circuits are constructed with a few limited inverters. Curiously,
it has been turned out that UR resolution is drastically faster than
hyper-resolution in the measurement of the size of SOS (Set of Support).
Besides, we discuss the syntactic and semantic criteria which might causes
considerable difference of computation cost between UR resolution and
hyper-resolution.
","A Curious New Result of Resolution Strategies in Negation-Limited
  Inverters Problem"
"  We propose a novel non-randomized anytime orienteering algorithm for finding
k-optimal goals that maximize reward on a specialized graph with budget
constraints. This specialized graph represents a real-world scenario which is
analogous to an orienteering problem of finding k-most optimal goal states.
","Searching k-Optimal Goals for an Orienteering Problem on a Specialized
  Graph with Budget Constraints"
"  The ability to hypothesise, develop abstract concepts based on concrete
observations and apply these hypotheses to justify future actions has been
paramount in human development. An existing line of research in outfitting
intelligent machines with abstract reasoning capabilities revolves around the
Raven's Progressive Matrices (RPM). There have been many breakthroughs in
supervised approaches to solving RPM in recent years. However, this process
requires external assistance, and thus it cannot be claimed that machines have
achieved reasoning ability comparable to humans. Namely, humans can solve RPM
problems without supervision or prior experience once the RPM rule that
relations can only exist row/column-wise is properly introduced. In this paper,
we introduce a pairwise relations discriminator (PRD), a technique to develop
unsupervised models with sufficient reasoning abilities to tackle an RPM
problem. PRD reframes the RPM problem into a relation comparison task, which we
can solve without requiring the labelling of the RPM problem. We can identify
the optimal candidate by adapting the application of PRD to the RPM problem.
Our approach, the PRD, establishes a new state-of-the-art unsupervised learning
benchmark with an accuracy of 55.9% on the I-RAVEN, presenting a significant
improvement and a step forward in equipping machines with abstract reasoning.
","Pairwise Relations Discriminator for Unsupervised Raven's Progressive
  Matrices"
"  We study the novel problem of blackbox optimization of multiple objectives
via multi-fidelity function evaluations that vary in the amount of resources
consumed and their accuracy. The overall goal is to approximate the true Pareto
set of solutions by minimizing the resources consumed for function evaluations.
For example, in power system design optimization, we need to find designs that
trade-off cost, size, efficiency, and thermal tolerance using multi-fidelity
simulators for design evaluations. In this paper, we propose a novel approach
referred as Multi-Fidelity Output Space Entropy Search for Multi-objective
Optimization (MF-OSEMO) to solve this problem. The key idea is to select the
sequence of candidate input and fidelity-vector pairs that maximize the
information gained about the true Pareto front per unit resource cost. Our
experiments on several synthetic and real-world benchmark problems show that
MF-OSEMO, with both approximations, significantly improves over the
state-of-the-art single-fidelity algorithms for multi-objective optimization.
","Multi-Fidelity Multi-Objective Bayesian Optimization: An Output Space
  Entropy Search Approach"
"  Sequential decision making techniques hold great promise to improve the
performance of many real-world systems, but computational complexity hampers
their principled application. Influence-based abstraction aims to gain leverage
by modeling local subproblems together with the 'influence' that the rest of
the system exerts on them. While computing exact representations of such
influence might be intractable, learning approximate representations offers a
promising approach to enable scalable solutions. This paper investigates the
performance of such approaches from a theoretical perspective. The primary
contribution is the derivation of sufficient conditions on approximate
influence representations that can guarantee solutions with small value loss.
In particular we show that neural networks trained with cross entropy are well
suited to learn approximate influence representations. Moreover, we provide a
sample based formulation of the bounds, which reduces the gap to applications.
Finally, driven by our theoretical insights, we propose approximation error
estimators, which empirically reveal to correlate well with the value loss.
",Loss Bounds for Approximate Influence-Based Abstraction
"  Many business applications involve adversarial relationships in which both
sides adapt their strategies to optimize their opposing benefits. One of the
key characteristics of these applications is the wide range of strategies that
an adversary may choose as they adapt their strategy dynamically to sustain
benefits and evade authorities. In this paper, we present a novel way of
approaching these types of applications, in particular in the context of
Anti-Money Laundering. We provide a mechanism through which diverse, realistic
and new unobserved behavior may be generated to discover potential unobserved
adversarial actions to enable organizations to preemptively mitigate these
risks. In this regard, we make three main contributions. (a) Propose a novel
behavior-based model as opposed to individual transactions-based models
currently used by financial institutions. We introduce behavior traces as
enriched relational representation to represent observed human behavior. (b) A
modelling approach that observes these traces and is able to accurately infer
the goals of actors by classifying the behavior into money laundering or
standard behavior despite significant unobserved activity. And (c) a synthetic
behavior simulator that can generate new previously unseen traces. The
simulator incorporates a high level of flexibility in the behavioral parameters
so that we can challenge the detection algorithm. Finally, we provide
experimental results that show that the learning module (automated
investigator) that has only partial observability can still successfully infer
the type of behavior, and thus the simulated goals, followed by customers based
on traces - a key aspiration for many applications today.
","Simulating and classifying behavior in adversarial environments based on
  action-state traces: an application to money laundering"
"  Goal recognition aims at predicting human intentions from a trace of
observations. This ability allows people or organizations to anticipate future
actions and intervene in a positive (collaborative) or negative (adversarial)
way. Goal recognition has been successfully used in many domains, but it has
been seldom been used by financial institutions. We claim the techniques are
ripe for its wide use in finance-related tasks. The main two approaches to
perform goal recognition are model-based (planning-based) and model-free
(learning-based). In this paper, we adapt state-of-the-art learning techniques
to goal recognition, and compare model-based and model-free approaches in
different domains. We analyze the experimental data to understand the
trade-offs of using both types of methods. The experiments show that
planning-based approaches are ready for some goal-recognition finance tasks.
",Goal recognition via model-based and model-free techniques
"  This paper updates the cognitive model, firstly by creating two systems and
then unifying them over the same structure. It represents information at the
semantic level only, where labelled patterns are aggregated into a
'type-set-match' form. It is described that the aggregations can be used to
match across regions with potentially different functionality and therefore
give the structure a required amount of flexibility. The theory is that if the
model stores information which can be transposed in consistent ways, then that
will result in knowledge and some level of intelligence. As part of the design,
patterns have to become distinct and that is realised by unique paths through
shared aggregated structures. An ensemble-hierarchy relation also helps to
define uniqueness through local feedback that may even be an action potential.
The earlier models are still consistent in terms of their proposed
functionality, but some of the architecture boundaries have been moved to match
them up more closely. After pattern optimisation and tree-like aggregations,
the two main models differ only in their upper, more intelligent level. One
provides a propositional logic for mutually inclusive or exclusive pattern
groups and sequences, while the other provides a behaviour script that is
constructed from node types. It can be seen that these two views are
complimentary and would allow some control over behaviours, as well as
memories, that might get selected.
",New Ideas for Brain Modelling 7
"  Structural causal models are the fundamental modelling unit in Pearl's causal
theory; in principle they allow us to solve any causal inference query, such as
causal effects or counterfactuals. But they most often contain latent variables
that limit their application to special settings. In this paper we introduce
the causal EM algorithm that aims at reconstructing the uncertainty about the
latent variables; based on this, causal inference can approximately be solved
via standard algorithms for Bayesian networks. The result is, for the first
time, a completely general method to solve causal inference queries, be they
identifiable or not (in which case we deliver bounds), on semi-Markovian
structural causal models with categorical variables. We show empirically that
the approximation we provide becomes accurate in a fair number of EM runs. We
discuss the application of the causal EM to a real medical problem. Finally, we
show that causal inference is NP-hard also in models characterised by
polytree-shaped graphs; this supports developing approximate approaches to
causal inference.
",Causal Expectation-Maximisation
"  Financial institutions mostly deal with people. Therefore, characterizing
different kinds of human behavior can greatly help institutions for improving
their relation with customers and with regulatory offices. In many of such
interactions, humans have some internal goals, and execute some actions within
the financial system that lead them to achieve their goals. In this paper, we
tackle these tasks as a behavior-traces classification task. An observer agent
tries to learn characterizing other agents by observing their behavior when
taking actions in a given environment. The other agents can be of several types
and the goal of the observer is to identify the type of the other agent given a
trace of observations. We present CABBOT, a learning technique that allows the
agent to perform on-line classification of the type of planning agent whose
behavior is observing. In this work, the observer agent has partial and noisy
observability of the environment (state and actions of the other agents). In
order to evaluate the performance of the learning technique, we have generated
a domain-independent goal-based simulator of agents. We present experiments in
several (both financial and non-financial) domains with promising results.
",Domain-independent generation and classification of behavior traces
"  Dynamic Uncertain Causality Graph(DUCG) is a recently proposed model for
diagnoses of complex systems. It performs well for industry system such as
nuclear power plants, chemical system and spacecrafts. However, the variable
state combination explosion in some cases is still a problem that may result in
inefficiency or even disability in DUCG inference. In the situation of clinical
diagnoses, when a lot of intermediate causes are unknown while the downstream
results are known in a DUCG graph, the combination explosion may appear during
the inference computation. Monte Carlo sampling is a typical algorithm to solve
this problem. However, we are facing the case that the occurrence rate of the
case is very small, e.g. $10^{-20}$, which means a huge number of samplings are
needed. This paper proposes a new scheme based on conditional stochastic
simulation which obtains the final result from the expectation of the
conditional probability in sampling loops instead of counting the sampling
frequency, and thus overcomes the problem. As a result, the proposed algorithm
requires much less time than the DUCG recursive inference algorithm presented
earlier. Moreover, a simple analysis of convergence rate based on a designed
example is given to show the advantage of the proposed method. % In addition,
supports for logic gate, logic cycles, and parallelization, which exist in
DUCG, are also addressed in this paper. The new algorithm reduces the time
consumption a lot and performs 3 times faster than old one with 2.7% error
ratio in a practical graph for Viral Hepatitis B.
","A New Inference algorithm of Dynamic Uncertain Causality Graph based on
  Conditional Sampling Method for Complex Cases"
"  With consideration to behavior trees and their relevance to planning and
control, within and without game development, the distinction between stateful
and stateless models is discussed; a three-valued logic bridging traditional
control flow with behavior trees is introduced, and a C# implementation is
presented.
",Implementing Behavior Trees using Three-Valued Logic
"  We explore using T5 (Raffel et al. (2019)) to directly translate natural
language questions into SQL statements. General purpose natural language that
interfaces to information stored within databases requires flexibly translating
natural language questions into database queries. The best performing
text-to-SQL systems approach this task by first converting questions into an
intermediate logical form (LF) (Lyu et al. (2020)). While LFs provide a
convenient intermediate representation and simplify query generation, they
introduce an additional layer of complexity and annotation requirements.
However, weakly supervised modeling that directly converts questions to SQL
statements has proven more difficult without the scaffolding provided by LFs
(Min et al. (2019)). We approach direct conversion of questions to SQL
statements using T5 (Raffel et al. (2019)), a pre-trained textto-text
generation model, modified to support pointer-generator style decoding (See et
al. (2017)). We explore using question augmentation with table schema
information and the use of automatically generated silver training data. The
resulting model achieves 90.5% execution accuracy on the WikiSQL (Zhong et al.
(2017)) test data set, a new state-of-the-art on weakly supervised SQL
generation. The performance improvement is 6.6% absolute over the prior
state-of-the-art (Min et al. (2019)) and approaches the performance of
state-ofthe-art systems making use of LFs.
","SeqGenSQL -- A Robust Sequence Generation Model for Structured Query
  Language"
"  We propose a novel genetic-algorithm technique that generates black-box
adversarial examples which successfully fool neural network based text
classifiers. We perform a genetic search with multi-objective optimization
guided by deep learning based inferences and Seq2Seq mutation to generate
semantically similar but imperceptible adversaries. We compare our approach
with DeepWordBug (DWB) on SST and IMDB sentiment datasets by attacking three
trained models viz. char-LSTM, word-LSTM and elmo-LSTM. On an average, we
achieve an attack success rate of 65.67% for SST and 36.45% for IMDB across the
three models showing an improvement of 49.48% and 101% respectively.
Furthermore, our qualitative study indicates that 94% of the time, the users
were not able to distinguish between an original and adversarial sample.
","Adversarial Black-Box Attacks On Text Classifiers Using Multi-Objective
  Genetic Optimization Guided By Deep Networks"
"  This paper is dedicated to the application of reinforcement learning combined
with neural networks to the general formulation of user scheduling problem. Our
simulator resembles real world problems by means of stochastic changes in
environment. We applied Q-learning based method to the number of dynamic
simulations and outperformed analytical greedy-based solution in terms of total
reward, the aim of which is to get the lowest possible penalty throughout
simulation.
",Reinforcement Learning for Assignment problem
"  Long-term forecasting involves predicting a horizon that is far ahead of the
last observation. It is a problem of high practical relevance, for instance for
companies in order to decide upon expensive long-term investments. Despite the
recent progress and success of Gaussian processes (GPs) based on spectral
mixture kernels, long-term forecasting remains a challenging problem for these
kernels because they decay exponentially at large horizons. This is mainly due
to their use of a mixture of Gaussians to model spectral densities.
Characteristics of the signal important for long-term forecasting can be
unravelled by investigating the distribution of the Fourier coefficients of
(the training part of) the signal, which is non-smooth, heavy-tailed, sparse,
and skewed. The heavy tail and skewness characteristics of such distributions
in the spectral domain allow to capture long-range covariance of the signal in
the time domain. Motivated by these observations, we propose to model spectral
densities using a skewed Laplace spectral mixture (SLSM) due to the skewness of
its peaks, sparsity, non-smoothness, and heavy tail characteristics. By
applying the inverse Fourier Transform to this spectral density we obtain a new
GP kernel for long-term forecasting. In addition, we adapt the lottery ticket
method, originally developed to prune weights of a neural network, to GPs in
order to automatically select the number of kernel components. Results of
extensive experiments, including a multivariate time series, show the
beneficial effect of the proposed SLSM kernel for long-term extrapolation and
robustness to the choice of the number of mixture components.
","Gaussian Processes with Skewed Laplace Spectral Mixture Kernels for
  Long-term Forecasting"
"  Because radio spectrum is a finite resource, its usage and sharing is
regulated by government agencies. These agencies define policies to manage
spectrum allocation and assignment across multiple organizations, systems, and
devices. With more portions of the radio spectrum being licensed for commercial
use, the importance of providing an increased level of automation when
evaluating such policies becomes crucial for the efficiency and efficacy of
spectrum management. We introduce our Dynamic Spectrum Access Policy Framework
for supporting the United States government's mission to enable both federal
and non-federal entities to compatibly utilize available spectrum. The DSA
Policy Framework acts as a machine-readable policy repository providing policy
management features and spectrum access request evaluation. The framework
utilizes a novel policy representation using OWL and PROV-O along with a
domain-specific reasoning implementation that mixes GeoSPARQL, OWL reasoning,
and knowledge graph traversal to evaluate incoming spectrum access requests and
explain how applicable policies were used. The framework is currently being
used to support live, over-the-air field exercises involving a diverse set of
federal and commercial radios, as a component of a prototype spectrum
management system.
","A Semantic Framework for Enabling Radio Spectrum Policy Management and
  Evaluation"
"  The booming online e-commerce platforms demand highly accurate approaches to
segment queries that carry the product requirements of consumers. Recent works
have shown that the supervised methods, especially those based on deep
learning, are attractive for achieving better performance on the problem of
query segmentation. However, the lack of labeled data is still a big challenge
for training a deep segmentation network, and the problem of Out-of-Vocabulary
(OOV) also adversely impacts the performance of query segmentation. Different
from query segmentation task in an open domain, e-commerce scenario can provide
external documents that are closely related to these queries. Thus, to deal
with the two challenges, we employ the idea of distant supervision and design a
novel method to find contexts in external documents and extract features from
these contexts. In this work, we propose a BiLSTM-CRF based model with an
attention module to encode external features, such that external contexts
information, which can be utilized naturally and effectively to help query
segmentation. Experiments on two datasets show the effectiveness of our
approach compared with several kinds of baselines.
","Distant Supervision for E-commerce Query Segmentation via Attention
  Network"
"  In practice, it is quite common to face combinatorial optimization problems
which contain uncertainty along with non-determinism and dynamicity. These
three properties call for appropriate algorithms; reinforcement learning (RL)
is dealing with them in a very natural way. Today, despite some efforts, most
real-life combinatorial optimization problems remain out of the reach of
reinforcement learning algorithms.
  In this paper, we propose a reinforcement learning approach to solve a
realistic scheduling problem, and apply it to an algorithm commonly executed in
the high performance computing community, the Cholesky factorization. On the
contrary to static scheduling, where tasks are assigned to processors in a
predetermined ordering before the beginning of the parallel execution, our
method is dynamic: task allocations and their execution ordering are decided at
runtime, based on the system state and unexpected events, which allows much
more flexibility. To do so, our algorithm uses graph neural networks in
combination with an actor-critic algorithm (A2C) to build an adaptive
representation of the problem on the fly.
  We show that this approach is competitive with state-of-the-art heuristics
used in high-performance computing runtime systems. Moreover, our algorithm
does not require an explicit model of the environment, but we demonstrate that
extra knowledge can easily be incorporated and improves performance. We also
exhibit key properties provided by this RL approach, and study its transfer
abilities to other instances.
",Geometric Deep Reinforcement Learning for Dynamic DAG Scheduling
"  Solving multiagent problems can be an uphill task due to uncertainty in the
environment, partial observability, and scalability of the problem at hand.
Especially in an urban setting, there are more challenges since we also need to
maintain safety for all users while minimizing congestion of the agents as well
as their travel times. To this end, we tackle the problem of multiagent
pathfinding under uncertainty and partial observability where the agents are
tasked to move from their starting points to ending points while also
satisfying some constraints, e.g., low congestion, and model it as a multiagent
reinforcement learning problem. We compile the domain constraints using
propositional logic and integrate them with the RL algorithms to enable fast
simulation for RL.
","Combining Propositional Logic Based Decision Diagrams with Decision
  Making in Urban Systems"
"  The rise of online labor markets (e.g., Freelancer, Guru and Upwork) has
ignited a lot of research on team formation, where experts acquiring different
skills form teams to complete tasks. The core idea in this line of work has
been the strict requirement that the team of experts assigned to complete a
given task should contain a superset of the skills required by the task.
However, in many applications the required skills are often a wishlist of the
entity that posts the task and not all of the skills are absolutely necessary.
Thus, in our setting we relax the complete coverage requirement and we allow
for tasks to be partially covered by the formed teams, assuming that the
quality of task completion is proportional to the fraction of covered skills
per task. At the same time, we assume that when multiple tasks need to be
performed, the less the load of an expert the better the performance. We
combine these two high-level objectives into one and define the BalancedTA
problem. We also consider a generalization of this problem where each task
consists of required and optional skills. In this setting, our objective is the
same under the constraint that all required skills should be covered. From the
technical point of view, we show that the BalancedTA problem (and its variant)
is NP-hard and design efficient heuristics for solving it in practice. Using
real datasets from three online market places, Freelancer, Guru and Upwork we
demonstrate the efficiency of our methods and the practical utility of our
framework.
",Finding teams that balance expert load and task coverage
"  Proceedings of the AAAI Fall Symposium on Artificial Intelligence in
Government and Public Sector, Washington, DC, USA, November 13-14, 2020
","AAAI FSS-20: Artificial Intelligence in Government and Public Sector
  Proceedings"
"  Experiments in classical conditioning show that animals such as rabbits,
pigeons, and dogs can make long temporal associations that enable multi-step
prediction. To replicate this remarkable ability, an agent must construct an
internal state representation that summarizes its interaction history.
Recurrent neural networks can automatically construct state and learn temporal
associations. But the current training methods are prohibitively expensive for
online prediction -- continual learning on every time step -- which is the
focus of this paper. To facilitate research in online prediction, we present
three new diagnostic prediction problems inspired by classical-conditioning
experiments. The proposed problems test the learning capabilities that animals
readily exhibit and highlight the current recurrent learning methods'
limitations. While the proposed problems are nontrivial, they are still
amenable to extensive testing and analysis in the small-compute regime, thereby
enabling researchers to study issues in isolation carefully, ultimately
accelerating progress towards scalable online representation learning methods.
","From Eye-blinks to State Construction: Diagnostic Benchmarks for Online
  Representation Learning"
"  Social recommendation system is to predict unobserved user-item rating values
by taking advantage of user-user social relation and user-item ratings.
However, user/item diversities in social recommendations are not well utilized
in the literature. Especially, inter-factor (social and rating factors)
relations and distinct rating values need taking into more consideration. In
this paper, we propose an attentive social recommendation system (ASR) to
address this issue from two aspects. First, in ASR, Rec-conv graph network
layers are proposed to extract the social factor, user-rating and item-rated
factors and then automatically assign contribution weights to aggregate these
factors into the user/item embedding vectors. Second, a disentangling strategy
is applied for diverse rating values. Extensive experiments on benchmarks
demonstrate the effectiveness and advantages of our ASR.
",Attentive Social Recommendation: Towards User And Item Diversities
"  In this paper, we propose a system-level approach for verifying the safety of
neural network controlled systems, combining a continuous-time physical system
with a discrete-time neural network based controller. We assume a generic model
for the controller that can capture both simple and complex behaviours
involving neural networks. Based on this model, we perform a reachability
analysis that soundly approximates the reachable states of the overall system,
allowing to achieve a formal proof of safety. To this end, we leverage both
validated simulation to approximate the behaviour of the physical system and
abstract interpretation to approximate the behaviour of the controller. We
evaluate the applicability of our approach using a real-world use case.
Moreover, we show that our approach can provide valuable information when the
system cannot be proved totally safe.
",Safety Verification of Neural Network Controlled Systems
"  Reinforcement learning has successfully learned to play challenging board and
video games. However, its generalization ability remains under-explored. The
General Video Game AI Learning Competition aims at designing agents that are
capable of learning to play different games levels that were unseen during
training. This paper presents the games, entries and results of the 2020
General Video Game AI Learning Competition, held at the Sixteenth International
Conference on Parallel Problem Solving from Nature and the 2020 IEEE Conference
on Games. Three new games with sparse, periodic and dense rewards,
respectively, were designed for this competition and the test levels were
generated by adding minor perturbations to training levels or combining
training levels. In this paper, we also design a reinforcement learning agent,
called Arcane, for general video game playing. We assume that it is more likely
to observe similar local information in different levels rather than global
information. Therefore, instead of directly inputting a single, raw pixel-based
screenshot of current game screen, Arcane takes the encoded, transformed global
and local observations of the game screen as two simultaneous inputs, aiming at
learning local information for playing new levels. Two versions of Arcane,
using a stochastic or deterministic policy for decision-making during test,
both show robust performance on the game set of the 2020 General Video Game AI
Learning Competition.
",Robust Reinforcement Learning for General Video Game Playing
"  Training a multimodal network is challenging and it requires complex
architectures to achieve reasonable performance. We show that one reason for
this phenomena is the difference between the convergence rate of various
modalities. We address this by pre-training modality-specific sub-networks in
multimodal architectures independently before end-to-end training of the entire
network. Furthermore, we show that the addition of an attention mechanism
between sub-networks after pre-training helps identify the most important
modality during ambiguous scenarios boosting the performance. We demonstrate
that by performing these two tricks a simple network can achieve similar
performance to a complicated architecture that is significantly more expensive
to train on multiple tasks including sentiment analysis, emotion recognition,
and speaker trait recognition.
","Improving Multimodal Accuracy Through Modality Pre-training and
  Attention"
"  In this comprehensive review, we describe a new mathematical problem in
artificial intelligence (AI) from a mathematical modeling perspective,
following the philosophy stated by Rudolf E. Kalman that ""Once you get the
physics right, the rest is mathematics"". The new problem is called ""Generalized
Constraints (GCs)"", and we adopt GCs as a general term to describe any type of
prior information in modelings. To understand better about GCs to be a general
problem, we compare them with the conventional constraints (CCs) and list their
extra challenges over CCs. In the construction of AI machines, we basically
encounter more often GCs for modeling, rather than CCs with well-defined forms.
Furthermore, we discuss the ultimate goals of AI and redefine transparent,
interpretable, and explainable AI in terms of comprehension levels about
machines. We review the studies in relation to the GC problems although most of
them do not take the notion of GCs. We demonstrate that if AI machines are
simplified by a coupling with both knowledge-driven submodel and data-driven
submodel, GCs will play a critical role in a knowledge-driven submodel as well
as in the coupling form between the two submodels. Examples are given to show
that the studies in view of a generalized constraint problem will help us
perceive and explore novel subjects in AI, or even in mathematics, such as
generalized constraint learning (GCL).
","Generalized Constraints as A New Mathematical Problem in Artificial
  Intelligence: A Review and Perspective"
"  In this paper, we propose a new mixed-integer linear programming (MILP) model
ontology and a novel constraint typology of MILP formulations. MILP is a
commonly used mathematical programming technique for modelling and solving
real-life scheduling, routing, planning, resource allocation, and timetabling
optimization problems providing optimized business solutions for industry
sectors such as manufacturing, agriculture, defence, healthcare, medicine,
energy, finance, and transportation. Despite the numerous real-life
Combinatorial Optimization Problems found and solved and millions yet to be
discovered and formulated, the number of types of constraints (the building
blocks of a MILP) is relatively small. In the search for a suitable
machine-readable knowledge representation structure for MILPs, we propose an
optimization modelling tree built based upon an MILP model ontology that can be
used as a guide for automated systems to elicit an MILP model from end-users on
their combinatorial business optimization problems. Our ultimate aim is to
develop a machine-readable knowledge representation for MILP that allows us to
map an end-user's natural language description of the business optimization
problem to an MILP formal specification as a first step towards automated
mathematical modelling.
",A Knowledge Representation Approach to Automated Mathematical Modelling
"  In recent years, there have been immense breakthroughs in Game AI research,
particularly with Reinforcement Learning (RL). Despite their success, the
underlying games are usually implemented with their own preset environments and
game mechanics, thus making it difficult for researchers to prototype different
game environments. However, testing the RL agents against a variety of game
environments is critical for recent effort to study generalization in RL and
avoid the problem of overfitting that may otherwise occur. In this paper, we
present Griddly as a new platform for Game AI research that provides a unique
combination of highly configurable games, different observer types and an
efficient C++ core engine. Additionally, we present a series of baseline
experiments to study the effect of different observation configurations and
generalization ability of RL agents.
",Griddly: A platform for AI research in games
"  For strategic problems, intelligent systems based on Deep Reinforcement
Learning (DRL) have demonstrated an impressive ability to learn advanced
solutions that can go far beyond human capabilities, especially when dealing
with complex scenarios. While this creates new opportunities for the
development of intelligent assistance systems with groundbreaking
functionalities, applying this technology to real-world problems carries
significant risks and therefore requires trust in their transparency and
reliability. With superhuman strategies being non-intuitive and complex by
definition and real-world scenarios prohibiting a reliable performance
evaluation, the key components for trust in these systems are difficult to
achieve. Explainable AI (XAI) has successfully increased transparency for
modern AI systems through a variety of measures, however, XAI research has not
yet provided approaches enabling domain level insights for expert users in
strategic situations. In this paper, we discuss the existence of superhuman
DRL-based strategies, their properties, the requirements and challenges for
transforming them into real-world environments, and the implications for trust
through explainability as a key technology.
","Domain-Level Explainability -- A Challenge for Creating Trust in
  Superhuman AI Strategies"
"  The extremely sensitive and highly nonlinear search space of interplanetary
transfer trajectory design bring about big challenges on global optimization.
As a representative, the current known best solution of the global trajectory
optimization problem (GTOP) designed by the European space agency (ESA) is very
hard to be found. To deal with this difficulty, a powerful differential
evolution-based optimization tool named COoperative Differential Evolution
(CODE) is proposed in this paper. CODE employs a two-stage evolutionary
process, which concentrates on learning global structure in the earlier
process, and tends to self-adaptively learn the structures of different local
spaces. Besides, considering the spatial distribution of global optimum on
different problems and the gradient information on different variables, a
multiple boundary check technique has been employed. Also, Covariance Matrix
Adaptation Evolutionary Strategies (CMA-ES) is used as a local optimizer. The
previous studies have shown that a specific swarm intelligent optimization
algorithm usually can solve only one or two GTOP problems. However, the
experimental test results show that CODE can find the current known best
solutions of Cassini1 and Sagas directly, and the cooperation with CMA-ES can
solve Cassini2, GTOC1, Messenger (reduced) and Rosetta. For the most
complicated Messenger (full) problem, even though CODE cannot find the current
known best solution, the found best solution with objective function equaling
to 3.38 km/s is still a level that other swarm intelligent algorithms cannot
easily reach.
","A differential evolution-based optimization tool for interplanetary
  transfer trajectory design"
"  We present DeepMind Lab2D, a scalable environment simulator for artificial
intelligence research that facilitates researcher-led experimentation with
environment design. DeepMind Lab2D was built with the specific needs of
multi-agent deep reinforcement learning researchers in mind, but it may also be
useful beyond that particular subfield.
",DeepMind Lab2D
"  Neurons in real brains are enormously complex computational units. Among
other things, they're responsible for transforming inbound electro-chemical
vectors into outbound action potentials, updating the strengths of intermediate
synapses, regulating their own internal states, and modulating the behavior of
other nearby neurons. One could argue that these cells are the only things
exhibiting any semblance of real intelligence. It is odd, therefore, that the
machine learning community has, for so long, relied upon the assumption that
this complexity can be reduced to a simple sum and fire operation. We ask,
might there be some benefit to substantially increasing the computational power
of individual neurons in artificial systems? To answer this question, we
introduce Deep Artificial Neurons (DANs), which are themselves realized as deep
neural networks. Conceptually, we embed DANs inside each node of a traditional
neural network, and we connect these neurons at multiple synaptic sites,
thereby vectorizing the connections between pairs of cells. We demonstrate that
it is possible to meta-learn a single parameter vector, which we dub a neuronal
phenotype, shared by all DANs in the network, which facilitates a
meta-objective during deployment. Here, we isolate continual learning as our
meta-objective, and we show that a suitable neuronal phenotype can endow a
single network with an innate ability to update its synapses with minimal
forgetting, using standard backpropagation, without experience replay, nor
separate wake/sleep phases. We demonstrate this ability on sequential
non-linear regression tasks.
",Continual Learning with Deep Artificial Neurons
"  Class Scheduling is a highly constrained task. Educational institutes spend a
lot of resources, in the form of time and manual computation, to find a
satisficing schedule that fulfills all the requirements. A satisficing class
schedule accommodates all the students to all their desired courses at
convenient timing. The scheduler also needs to take into account the
availability of course teachers on the given slots. With the added limitation
of available classrooms, the number of solutions satisfying all constraints in
this huge search-space, further decreases.
  This paper proposes an efficient system to generate class schedules that can
fulfill every possible need of a typical university. Though it is primarily a
fixed-credit scheduler, it can be adjusted for open-credit systems as well. The
model is designed in MiniZinc and solved using various off-the-shelf solvers.
The proposed scheduling system can find a balanced schedule for a
moderate-sized educational institute in less than a minute.
",Automated Large-scale Class Scheduling in MiniZinc
"  Ill-managed intersections are the primary reasons behind the increasing
traffic problem in urban areas, leading to nonoptimal traffic-flow and
unnecessary deadlocks. In this paper, we propose an automated intersection
management system that extracts data from a well-defined grid of sensors and
optimizes traffic flow by controlling traffic signals. The data extraction
mechanism is independent of the optimization algorithm and this paper primarily
emphasizes the later one. We have used MiniZinc modeling language to define our
system as a constraint satisfaction problem which can be solved using any
off-the-shelf solver. The proposed system performs much better than the systems
currently in use. Our system reduces the mean waiting time and standard
deviation of the waiting time of vehicles and avoids deadlocks.
",Automated Intersection Management with MiniZinc
"  Quality of life assessment represents a key process of deciding treatment
success and viability. As such, patients' perceptions of their functional
status and well-being are important inputs for impairment assessment. Given
that patient completed questionnaires are often used to assess patient status
and determine future treatment options, it is important to know the level of
agreement of the words used by patients and different groups of medical
professionals. In this paper, we propose a measure called the Agreement Ratio
which provides a ratio of overall agreement when modelling words through Fuzzy
Sets (FSs). The measure has been specifically designed for assessing this
agreement in fuzzy sets which are generated from data such as patient
responses. The measure relies on using the Jaccard Similarity Measure for
comparing the different levels of agreement in the FSs generated.
","Measuring agreement on linguistic expressions in medical treatment
  scenarios"
"  Knowledge graphs have been demonstrated to be an effective tool for numerous
intelligent applications. However, a large amount of valuable knowledge still
exists implicitly in the knowledge graphs. To enrich the existing knowledge
graphs, recent years witness that many algorithms for link prediction and
knowledge graphs embedding have been designed to infer new facts. But most of
these studies focus on the static knowledge graphs and ignore the temporal
information that reflects the validity of knowledge. Developing the model for
temporal knowledge graphs completion is an increasingly important task. In this
paper, we build a new tensor decomposition model for temporal knowledge graphs
completion inspired by the Tucker decomposition of order 4 tensor. We
demonstrate that the proposed model is fully expressive and report
state-of-the-art results for several public benchmarks. Additionally, we
present several regularization schemes to improve the strategy and study their
impact on the proposed model. Experimental studies on three temporal datasets
(i.e. ICEWS2014, ICEWS2005-15, GDELT) justify our design and demonstrate that
our model outperforms baselines with an explicit margin on link prediction
task.
",Tucker decomposition-based Temporal Knowledge Graph Completion
"  Knowledge graphs (KGs) are a useful source of background knowledge to
(dis)prove facts of the form (s, p, o). Finding paths between s and o is the
cornerstone of several fact-checking approaches. While paths are useful to
(visually) explain why a given fact is true or false, it is not completely
clear how to identify paths that are most relevant to a fact, encode them and
weigh their importance. The goal of this paper is to present the Fact Checking
via path Embedding and Aggregation (FEA) system. FEA starts by carefully
collecting the paths between s and o that are most semantically related to the
domain of p. However, instead of directly working with this subset of all
paths, it learns vectorized path representations, aggregates them according to
different strategies, and use them to finally (dis)prove a fact. We conducted a
large set of experiments on a variety of KGs and found that our hybrid solution
brings some benefits in terms of performance.
",Fact Checking via Path Embedding and Aggregation
"  This contribution reviews critically the existing entropy measures for
probabilistic hesitant fuzzy sets (PHFSs), and demonstrates that these entropy
measures fail to effectively distinguish a variety of different PHFSs in some
cases. In the sequel, we develop a new axiomatic framework of entropy measures
for probabilistic hesitant fuzzy elements (PHFEs) by considering two facets of
uncertainty associated with PHFEs which are known as fuzziness and
nonspecificity. Respect to each kind of uncertainty, a number of formulae are
derived to permit flexible selection of PHFE entropy measures. Moreover, based
on the proposed PHFE entropy measures, we introduce some entropy-based distance
measures which are used in the portion of comparative analysis.
","Uncertainty measures for probabilistic hesitant fuzzy sets in multiple
  criteria decision making"
"  Generally, the criteria involved in a decision making problem are interactive
or inter-dependent, and therefore aggregating them by the use of traditional
operators which are based on additive measures is not logical. This verifies
that we have to implement fuzzy measures for modelling the interaction
phenomena among the criteria.On the other hand, based on the recent extension
of hesitant fuzzy set, called higher order hesitant fuzzy set (HOHFS) which
allows the membership of a given element to be defined in forms of several
possible generalized types of fuzzy set, we encourage to propose the higher
order hesitant fuzzy (HOHF) Choquet integral operator. This concept not only
considers the importance of the higher order hesitant fuzzy arguments, but also
it can reflect the correlations among those arguments. Then,a detailed
discussion on the aggregation properties of the HOHF Choquet integral operator
will be presented.To enhance the application of HOHF Choquet integral operator
in decision making, we first assess the appropriate energy policy for the
socio-economic development. Then, the efficiency of the proposed HOHF Choquet
integral operator-based technique over a number of exiting techniques is
further verified by employing another decision making problem associated with
the technique of TODIM (an acronym in Portuguese of Interactive and
Multicriteria Decision Making).
","Higher order hesitant fuzzy Choquet integral operator and its
  application to multiple criteria decision making"
"  Understanding the reasoning behind the behavior of an automated scheduling
system is essential to ensure that it will be trusted and consequently used to
its full capabilities in critical applications. In cases where a scheduler
schedules activities in an invalid location, it is usually easy for the user to
infer the missing constraint by inspecting the schedule with the invalid
activity to determine the missing constraint. If a scheduler fails to schedule
activities because constraints could not be satisfied, determining the cause
can be more challenging. In such cases it is important to understand which
constraints caused the activities to fail to be scheduled and how to alter
constraints to achieve the desired schedule. In this paper, we describe such a
scheduling system for NASA's Mars 2020 Perseverance Rover, as well as
Crosscheck, an explainable scheduling tool that explains the scheduler
behavior. The scheduling system and Crosscheck are the baseline for operational
use to schedule activities for the Mars 2020 rover. As we describe, the
scheduler generates a schedule given a set of activities and their constraints
and Crosscheck: (1) provides a visual representation of the generated schedule;
(2) analyzes and explains why activities failed to schedule given the
constraints provided; and (3) provides guidance on potential constraint
relaxations to enable the activities to schedule in future scheduler runs.
",Using Explainable Scheduling for the Mars 2020 Rover Mission
"  In this paper, we build upon notions from knowledge representation and
reasoning (KR) to expand a preliminary logic-based framework that characterizes
the model reconciliation problem for explainable planning. We also provide a
detailed exposition on the relationship between similar KR techniques, such as
abductive explanations and belief change, and their applicability to
explainable planning.
",On the Relationship Between KR Approaches for Explainable Planning
"  We introduce factorize sum split product networks (FSPNs), a new class of
probabilistic graphical models (PGMs). FSPNs are designed to overcome the
drawbacks of existing PGMs in terms of estimation accuracy and inference
efficiency. Specifically, Bayesian networks (BNs) have low inference speed and
performance of tree structured sum product networks(SPNs) significantly
degrades in presence of highly correlated variables. FSPNs absorb their
advantages by adaptively modeling the joint distribution of variables according
to their dependence degree, so that one can simultaneously attain the two
desirable goals: high estimation accuracy and fast inference speed. We present
efficient probability inference and structure learning algorithms for FSPNs,
along with a theoretical analysis and extensive evaluation evidence. Our
experimental results on synthetic and benchmark datasets indicate the
superiority of FSPN over other PGMs.
",FSPN: A New Class of Probabilistic Graphical Model
"  In this chapter we propose Generic Ontology Design Patterns, GODPs, as a
methodology for representing and instantiating ontology design patterns in a
way that is adaptable, and allows domain experts (and other users) to safely
use them without cluttering their ontologies.
",Generic Ontology Design Patterns: Roles and Change over Time
"  Most recently developed approaches to cooperative multi-agent reinforcement
learning in the \emph{centralized training with decentralized execution}
setting involve estimating a centralized, joint value function. In this paper,
we demonstrate that, despite its various theoretical shortcomings, Independent
PPO (IPPO), a form of independent learning in which each agent simply estimates
its local value function, can perform just as well as or better than
state-of-the-art joint learning approaches on popular multi-agent benchmark
suite SMAC with little hyperparameter tuning. We also compare IPPO to several
variants; the results suggest that IPPO's strong performance may be due to its
robustness to some forms of environment non-stationarity.
","Is Independent Learning All You Need in the StarCraft Multi-Agent
  Challenge?"
"  Empowering decision support systems with automated planning has received
significant recognition in the planning community. The central idea for such
systems is to augment the capabilities of the human-in-the-loop with automated
planning techniques and provide timely support to enhance the decision-making
experience. In addition to this, an effective decision support system must be
able to provide intuitive explanations based on specific queries on proposed
decisions to its end users. This makes decision-support systems an ideal
test-bed to study the effectiveness of various XAIP techniques being developed
in the community. To this end, we present our decision support system RADAR-X
that extends RADAR (Grover et al. 2020) by allowing the user to participate in
an interactive explanatory dialogue with the system. Specifically, we allow the
user to ask for contrastive explanations, wherein the user can try to
understand why a specific plan was chosen over an alternative (referred to as
the foil). Furthermore, we use the foil raised as evidence for unspecified user
preferences and use it to further refine plan suggestions.
","RADAR-X: An Interactive Interface Pairing Contrastive Explanations with
  Revised Plan Suggestions"
"  Applications like personal assistants need to be aware ofthe user's context,
e.g., where they are, what they are doing, and with whom. Context information
is usually inferred from sensor data, like GPS sensors and accelerometers on
the user's smartphone. This prediction task is known as context recognition. A
well-defined context model is fundamental for successful recognition. Existing
models, however, have two major limitations. First, they focus on few aspects,
like location or activity, meaning that recognition methods based onthem can
only compute and leverage few inter-aspect correlations. Second, existing
models typically assume that context is objective, whereas in most applications
context is best viewed from the user's perspective. Neglecting these factors
limits the usefulness of the context model and hinders recognition. We present
a novel ontological context model that captures five dimensions, namely time,
location, activity, social relations and object. Moreover, our model defines
three levels of description(objective context, machine context and subjective
context) that naturally support subjective annotations and reasoning.An initial
context recognition experiment on real-world data hints at the promise of our
model.
",Multi-Modal Subjective Context Modelling and Recognition
"  In a variety of application settings, the user preference for a planning task
- the precise optimization objective - is difficult to elicit. One possible
remedy is planning as an iterative process, allowing the user to iteratively
refine and modify example plans. A key step to support such a process are
explanations, answering user questions about the current plan. In particular, a
relevant kind of question is ""Why does the plan you suggest not satisfy $p$?"",
where p is a plan property desirable to the user. Note that such a question
pertains to plan space, i.e., the set of possible alternative plans. Adopting
the recent approach to answer such questions in terms of plan-property
dependencies, here we implement a tool and user interface for human-guided
iterative planning including plan-space explanations. The tool runs in standard
Web browsers, and provides simple user interfaces for both developers and
users. We conduct a first user study, whose outcome indicates the usefulness of
plan-property dependency explanations in iterative planning.
",Iterative Planning with Plan-Space Explanations: A Tool and User Study
"  Finding good solutions for Multi-objective Optimization (MOPs) Problems is
considered a hard problem, especially when considering MOPs with constraints.
Thus, most of the works in the context of MOPs do not explore in-depth how
different constraints affect the performance of MOP solvers. Here, we focus on
exploring the effects of different Constraint Handling Techniques (CHTs) on
MOEA/D, a commonly used MOP solver when solving complex real-world MOPs.
Moreover, we introduce a simple and effective CHT focusing on the exploration
of the decision space, the Three Stage Penalty. We explore each of these CHTs
in MOEA/D on two simulated MOPs and six analytic MOPs (eight in total). The
results of this work indicate that while the best CHT is problem-dependent, our
new proposed Three Stage Penalty achieves competitive results and remarkable
performance in terms of hypervolume values in the hard simulated car design
MOP.
","Exploring Constraint Handling Techniques in Real-world Problems on
  MOEA/D with Limited Budget of Evaluations"
"  Structure learning algorithms that learn the graph of a Bayesian network from
observational data often do so by assuming the data correctly reflect the true
distribution of the variables. However, this assumption does not hold in the
presence of measurement error, which can lead to spurious edges. This is one of
the reasons why the synthetic performance of these algorithms often
overestimates real-world performance. This paper describes an algorithm that
can be added as an additional learning phase at the end of any structure
learning algorithm, and serves as a correction learning phase that removes
potential false positive edges. The results show that the proposed correction
algorithm successfully improves the graphical score of four well-established
structure learning algorithms spanning different classes of learning in the
presence of measurement error.
","Improving Bayesian Network Structure Learning in the Presence of
  Measurement Error"
"  The quest to understand consciousness, once the purview of philosophers and
theologians, is now actively pursued by scientists of many stripes. This paper
studies consciousness from the perspective of theoretical computer science. It
formalizes the Global Workspace Theory (GWT) originated by cognitive
neuroscientist Bernard Baars and further developed by him, Stanislas Dehaene,
and others. Our major contribution lies in the precise formal definition of a
Conscious Turing Machine (CTM), also called a Conscious AI. We define the CTM
in the spirit of Alan Turing's simple yet powerful definition of a computer,
the Turing Machine (TM). We are not looking for a complex model of the brain
nor of cognition but for a simple model of (the admittedly complex concept of)
consciousness. After formally defining CTM, we give a formal definition of
consciousness in CTM. We then suggest why the CTM has the feeling of
consciousness. The reasonableness of the definitions and explanations can be
judged by how well they agree with commonly accepted intuitive concepts of
human consciousness, the breadth of related concepts that the model explains
easily and naturally, and the extent of its agreement with scientific evidence.
",A Theoretical Computer Science Perspective on Consciousness
"  We investigate the decidability and computational complexity of conservative
extensions and the related notions of inseparability and entailment in Horn
description logics (DLs) with inverse roles. We consider both query
conservative extensions, defined by requiring that the answers to all
conjunctive queries are left unchanged, and deductive conservative extensions,
which require that the entailed concept inclusions, role inclusions, and
functionality assertions do not change. Upper bounds for query conservative
extensions are particularly challenging because characterizations in terms of
unbounded homomorphisms between universal models, which are the foundation of
the standard approach to establishing decidability, fail in the presence of
inverse roles. We resort to a characterization that carefully mixes unbounded
and bounded homomorphisms and enables a decision procedure that combines tree
automata and a mosaic technique. Our main results are that query conservative
extensions are 2ExpTime-complete in all DLs between ELI and Horn-ALCHIF and
between Horn-ALC and Horn-ALCHIF, and that deductive conservative extensions
are 2ExpTime-complete in all DLs between ELI and ELHIF_\bot. The same results
hold for inseparability and entailment.
",Conservative Extensions in Horn Description Logics with Inverse Roles
"  Stochastic service network designs with uncertain demand represented by a set
of scenarios can be modelled as a large-scale two-stage stochastic
mixed-integer program (SMIP). The progressive hedging algorithm (PHA) is a
decomposition method for solving the resulting SMIP. The computational
performance of the PHA can be greatly enhanced by decomposing according to
scenario bundles instead of individual scenarios. At the heart of bundle-based
decomposition is the method for grouping the scenarios into bundles. In this
paper, we present a fuzzy c-means-based scenario bundling method to address
this problem. Rather than full membership of a bundle, which is typically the
case in existing scenario bundling strategies such as k-means, a scenario has
partial membership in each of the bundles and can be assigned to more than one
bundle in our method.
","Fuzzy C-means-based scenario bundling for stochastic service network
  design"
"  This paper studies the scheduling of jobs of different families on parallel
machines with qualification constraints. Originating from semiconductor
manufacturing, this constraint imposes a time threshold between the execution
of two jobs of the same family. Otherwise, the machine becomes disqualified for
this family. The goal is to minimize both the flow time and the number of
disqualifications. Recently, an efficient constraint programming model has been
proposed. However, when priority is given to the flow time objective, the
efficiency of the model can be improved. This paper uses a polynomial-time
algorithm which minimize the flow time for a single machine relaxation where
disqualifications are not considered. Using this algorithm one can derived
filtering rules on different variables of the model. Experimental results are
presented showing the effectiveness of these rules. They improve the
competitiveness with the mixed integer linear program of the literature.
","Filtering Rules for Flow Time Minimization in a Parallel Machine
  Scheduling Problem"
"  A new fuzzy method is developed using triangular/trapezoidal fuzzy numbers
for evaluating a group's mean performance, when qualitative grades instead of
numerical scores are used for assessing its members' individual performance.
Also, a new technique is developed for solving Linear Programming problems with
fuzzy coefficients and everyday life applications are presented to illustrate
our results.
",Assessment and Linear Programming under Fuzzy Conditions
"  Artificial Intelligence (AI) governance regulates the exercise of authority
and control over the management of AI. It aims at leveraging AI through
effective use of data and minimization of AI-related cost and risk. While
topics such as AI governance and AI ethics are thoroughly discussed on a
theoretical, philosophical, societal and regulatory level, there is limited
work on AI governance targeted to companies and corporations. This work views
AI products as systems, where key functionality is delivered by machine
learning (ML) models leveraging (training) data. We derive a conceptual
framework by synthesizing literature on AI and related fields such as ML. Our
framework decomposes AI governance into governance of data, (ML) models and
(AI) systems along four dimensions. It relates to existing IT and data
governance frameworks and practices. It can be adopted by practitioners and
academics alike. For practitioners the synthesis of mainly research papers, but
also practitioner publications and publications of regulatory bodies provides a
valuable starting point to implement AI governance, while for academics the
paper highlights a number of areas of AI governance that deserve more
attention.
",AI Governance for Businesses
"  A new design of an AI assistant that has become increasingly popular is that
of an ""aggregated assistant"" -- realized as an orchestrated composition of
several individual skills or agents that can each perform atomic tasks. In this
paper, we will talk about the role of planning in the automated composition of
such assistants and explore how concepts in automated planning can help to
establish transparency of the inner workings of the assistant to the end-user.
",Explainable Composition of Aggregated Assistants
"  Adversarial attacks have proved to be the major impediment in the progress on
research towards reliable machine learning solutions. Carefully crafted
perturbations, imperceptible to human vision, can be added to images to force
misclassification by an otherwise high performing neural network. To have a
better understanding of the key contributors of such structured attacks, we
searched for and studied spatially co-located patterns in the distribution of
pixels in the input space. In this paper, we propose a framework for
segregating and isolating regions within an input image which are particularly
critical towards either classification (during inference), or adversarial
vulnerability or both. We assert that during inference, the trained model looks
at a specific region in the image, which we call Region of Importance (RoI);
and the attacker looks at a region to alter/modify, which we call Region of
Attack (RoA). The success of this approach could also be used to design a
post-hoc adversarial defence method, as illustrated by our observations. This
uses the notion of blocking out (we call neutralizing) that region of the image
which is highly vulnerable to adversarial attacks but is not important for the
task of classification. We establish the theoretical setup for formalising the
process of segregation, isolation and neutralization and substantiate it
through empirical analysis on standard benchmarking datasets. The findings
strongly indicate that mapping features into the input space preserves the
significant patterns typically observed in the feature-space while adding major
interpretability and therefore simplifies potential defensive mechanisms.
",Spatially Correlated Patterns in Adversarial Images
"  Binary Neural Networks (BNNs) have received significant attention due to
their promising efficiency. Currently, most BNN studies directly adopt
widely-used CNN architectures, which can be suboptimal for BNNs. This paper
proposes a novel Binary ARchitecture Search (BARS) flow to discover superior
binary architecture in a large design space. Specifically, we analyze the
information bottlenecks that are related to both the topology and layout
architecture design choices. And we propose to automatically search for the
optimal information flow. To achieve that, we design a two-level (Macro &
Micro) search space tailored for BNNs and apply a differentiable neural
architecture search (NAS) to explore this search space efficiently. The
macro-level search space includes width and depth decisions, which is required
for better balancing the model performance and complexity. We also design the
micro-level search space to strengthen the information flow for BNN. %A notable
challenge of BNN architecture search lies in that binary operations exacerbate
the ""collapse"" problem of differentiable NAS, for which we incorporate various
search and derive strategies to stabilize the search process. On CIFAR-10, BARS
achieves 1.5% higher accuracy with 2/3 binary operations and 1/10
floating-point operations comparing with existing BNN NAS studies. On ImageNet,
with similar resource consumption, BARS-discovered architecture achieves a 6%
accuracy gain than hand-crafted binary ResNet-18 architectures and outperforms
other binary architectures while fully binarizing the architecture backbone.
","BARS: Joint Search of Cell Topology and Layout for Accurate and
  Efficient Binary ARchitectures"
"  In this paper we present a scalable deep learning framework for finding
Markovian Nash Equilibria in multi-agent stochastic games using fictitious
play. The motivation is inspired by theoretical analysis of Forward Backward
Stochastic Differential Equations (FBSDE) and their implementation in a deep
learning setting, which is the source of our algorithm's sample efficiency
improvement. By taking advantage of the permutation-invariant property of
agents in symmetric games, the scalability and performance is further enhanced
significantly. We showcase superior performance of our framework over the
state-of-the-art deep fictitious play algorithm on an inter-bank
lending/borrowing problem in terms of multiple metrics. More importantly, our
approach scales up to 3000 agents in simulation, a scale which, to the best of
our knowledge, represents a new state-of-the-art. We also demonstrate the
applicability of our framework in robotics on a belief space autonomous racing
problem.
",Large-Scale Multi-Agent Deep FBSDEs
"  Existing approaches for the design of interpretable agent behavior consider
different measures of interpretability in isolation. In this paper we posit
that, in the design and deployment of human-aware agents in the real world,
notions of interpretability are just some among many considerations; and the
techniques developed in isolation lack two key properties to be useful when
considered together: they need to be able to 1) deal with their mutually
competing properties; and 2) an open world where the human is not just there to
interpret behavior in one specific form. To this end, we consider three
well-known instances of interpretable behavior studied in existing literature
-- namely, explicability, legibility, and predictability -- and propose a
revised model where all these behaviors can be meaningfully modeled together.
We will highlight interesting consequences of this unified model and motivate,
through results of a user study, why this revision is necessary.
","A Bayesian Account of Measures of Interpretability in Human-AI
  Interaction"
"  Word embeddings are a core component of modern natural language processing
systems, making the ability to thoroughly evaluate them a vital task. We
describe DiaLex, a benchmark for intrinsic evaluation of dialectal Arabic word
embedding. DiaLex covers five important Arabic dialects: Algerian, Egyptian,
Lebanese, Syrian, and Tunisian. Across these dialects, DiaLex provides a
testbank for six syntactic and semantic relations, namely male to female,
singular to dual, singular to plural, antonym, comparative, and genitive to
past tense. DiaLex thus consists of a collection of word pairs representing
each of the six relations in each of the five dialects. To demonstrate the
utility of DiaLex, we use it to evaluate a set of existing and new Arabic word
embeddings that we developed. Our benchmark, evaluation code, and new word
embedding models will be publicly available.
",DiaLex: A Benchmark for Evaluating Multidialectal Arabic Word Embeddings
"  The concept of disinformation is to use fake messages to confuse people in
order to protect the real information. This strategy can be adapted into data
science to protect valuable private and sensitive data. Huge amount of private
data are being generated from personal devices such as smart phone and wearable
in recent years. Being able to utilize these personal data will bring big
opportunities to design personalized products, conduct precision healthcare and
many other tasks that were impossible in the past. However, due to privacy,
safety and regulation reasons, it is often difficult to transfer or store data
in its original form while keeping them safe. Building a secure data transfer
and storage infrastructure to preserving privacy is costly in most cases and
there is always a concern of data security due to human errors. In this study,
we propose a method, named FakeSafe, to provide human level data protection
using generative adversarial network with cycle consistency and conducted
experiments using both benchmark and real world data sets to illustrate
potential applications of FakeSafe.
","FakeSafe: Human Level Data Protection by Disinformation Mapping using
  Cycle-consistent Adversarial Network"
"  The brain is a highly reconfigurable machine capable of task-specific
adaptations. The brain continually rewires itself for a more optimal
configuration to solve problems. We propose a novel strategic synthesis
algorithm for feedforward networks that draws directly from the brain's
behaviours when learning. The proposed approach analyses the network and ranks
weights based on their magnitude. Unlike existing approaches that advocate
random selection, we select highly performing nodes as starting points for new
edges and exploit the Gaussian distribution over the weights to select
corresponding endpoints. The strategy aims only to produce useful connections
and result in a smaller residual network structure. The approach is
complemented with pruning to further the compression. We demonstrate the
techniques to deep feedforward networks. The residual sub-networks that are
formed from the synthesis approaches in this work form common sub-networks with
similarities up to ~90%. Using pruning as a complement to the strategic
synthesis approach, we observe improvements in compression.
","Synthesis and Pruning as a Dynamic Compression Strategy for Efficient
  Deep Neural Networks"
"  Cyber-Physical Systems (CPS) play a crucial role in the era of the
4thIndustrial Revolution. Recently, the application of the CPS to industrial
manufacturing leads to a specialization of them referred as Cyber-Physical
Production Systems (CPPS). Among other challenges, CPS and CPPS should be able
to address interoperability issues, since one of their intrinsic requirement is
the capability to interface and cooperate with other systems. On the other
hand, to fully realize theIndustry 4.0 vision, it is required to address
horizontal, vertical, and end-to-end integration enabling a complete awareness
through the entire supply chain. In this context, Semantic Web standards and
technologies may have a promising role to represent manufacturing knowledge in
a machine-interpretable way for enabling communications among heterogeneous
Industrial assets. This paper proposes an integration of Semantic Web models
available at state of the art for implementing a5C architecture mainly targeted
to collect and process semantic data stream in a way that would unlock the
potentiality of data yield in a smart manufacturing environment. The analysis
of key industrial ontologies and semantic technologies allows us to instantiate
an example scenario for monitoring Overall Equipment Effectiveness(OEE). The
solution uses the SOSA ontology for representing the semantic datastream. Then,
C-SPARQL queries are defined for periodically carrying out useful KPIs to
address the proposed aim.
",Semantic CPPS in Industry 4.0
"  This paper introduces an information-theoretic constraint on learned policy
complexity in the Multi-Agent Deep Deterministic Policy Gradient (MADDPG)
reinforcement learning algorithm. Previous research with a related approach in
continuous control experiments suggests that this method favors learning
policies that are more robust to changing environment dynamics. The multi-agent
game setting naturally requires this type of robustness, as other agents'
policies change throughout learning, introducing a nonstationary environment.
For this reason, recent methods in continual learning are compared to our
approach, termed Capacity-Limited MADDPG. Results from experimentation in
multi-agent cooperative and competitive tasks demonstrate that the
capacity-limited approach is a good candidate for improving learning
performance in these environments.
","Consolidation via Policy Information Regularization in Deep RL for
  Multi-Agent Games"
"  The future will be replete with scenarios where humans are robots will be
working together in complex environments. Teammates interact, and the robot's
interaction has to be about getting useful information about the human's
(teammate's) model. There are many challenges before a robot can interact, such
as incorporating the structural differences in the human's model, ensuring
simpler responses, etc. In this paper, we investigate how a robot can interact
to localize the human model from a set of models. We show how to generate
questions to refine the robot's understanding of the teammate's model. We
evaluate the method in various planning domains. The evaluation shows that
these questions can be generated offline, and can help refine the model through
simple answers.
",Model Elicitation through Direct Questioning
"  This paper presents a new approach to visual zero-shot slot filling. The
approach extends previous approaches by reformulating the slot filling task as
Question Answering. Slot tags are converted to rich natural language questions
that capture the semantics of visual information and lexical text on the GUI
screen. These questions are paired with the user's utterance and slots are
extracted from the utterance using a state-of-the-art ALBERT-based Question
Answering system trained on the Stanford Question Answering dataset (SQuaD2).
An approach to further refine the model with multi-task training is presented.
The multi-task approach facilitates the incorporation of a large number of
successive refinements and transfer learning across similar tasks. A new Visual
Slot dataset and a visual extension of the popular ATIS dataset is introduced
to support research and experimentation on visual slot filling. Results show F1
scores between 0.52 and 0.60 on the Visual Slot and ATIS datasets with no
training data (zero-shot).
",Zero-Shot Visual Slot Filling as Question Answering
"  Machine learning has the potential to aid in mitigating the human effects of
climate change. Previous applications of machine learning to tackle the human
effects in climate change include approaches like informing individuals of
their carbon footprint and strategies to reduce it. For these methods to be the
most effective they must consider relevant social-psychological factors for
each individual. Of social-psychological factors at play in climate change,
affect has been previously identified as a key element in perceptions and
willingness to engage in mitigative behaviours. In this work, we propose an
investigation into how affect could be incorporated to enhance machine learning
based interventions for climate change. We propose using affective agent-based
modelling for climate change as well as the use of a simulated climate change
social dilemma to explore the potential benefits of affective machine learning
interventions. Behavioural and informational interventions can be a powerful
tool in helping humans adopt mitigative behaviours. We expect that utilizing
affective ML can make interventions an even more powerful tool and help
mitigative behaviours become widely adopted.
","The Human Effect Requires Affect: Addressing Social-Psychological
  Factors of Climate Change with Machine Learning"
"  Planning - the ability to analyze the structure of a problem in the large and
decompose it into interrelated subproblems - is a hallmark of human
intelligence. While deep reinforcement learning (RL) has shown great promise
for solving relatively straightforward control tasks, it remains an open
problem how to best incorporate planning into existing deep RL paradigms to
handle increasingly complex environments. One prominent framework, Model-Based
RL, learns a world model and plans using step-by-step virtual rollouts. This
type of world model quickly diverges from reality when the planning horizon
increases, thus struggling at long-horizon planning. How can we learn world
models that endow agents with the ability to do temporally extended reasoning?
In this work, we propose to learn graph-structured world models composed of
sparse, multi-step transitions. We devise a novel algorithm to learn latent
landmarks that are scattered (in terms of reachability) across the goal space
as the nodes on the graph. In this same graph, the edges are the reachability
estimates distilled from Q-functions. On a variety of high-dimensional
continuous control tasks ranging from robotic manipulation to navigation, we
demonstrate that our method, named L3P, significantly outperforms prior work,
and is oftentimes the only method capable of leveraging both the robustness of
model-free RL and generalization of graph-search algorithms. We believe our
work is an important step towards scalable planning in reinforcement learning.
",World Model as a Graph: Learning Latent Landmarks for Planning
"  In this work, we analyze the happiness levels of countries using an unbiased
emotion detector, artificial intelligence (AI). To date, researchers proposed
many factors that may affect happiness such as wealth, health and safety. Even
though these factors all seem relevant, there is no clear consensus between
sociologists on how to interpret these, and the models to estimate the cost of
these utilities include some assumptions. Researchers in social sciences have
been working on determination of the happiness levels in society and
exploration of the factors correlated with it through polls and different
statistical methods. In our work, by using artificial intelligence, we
introduce a different and relatively unbiased approach to this problem. By
using AI, we make no assumption about what makes a person happy, and leave the
decision to AI to detect the emotions from the faces of people collected from
publicly available street footages. We analyzed the happiness levels in eight
different cities around the world through available footage on the Internet and
found out that there is no statistically significant difference between
countries in terms of happiness.
",Measuring Happiness Around the World Through Artificial Intelligence
"  Mitigating the new user cold-start problem has been critical in the
recommendation system for online service providers to influence user experience
in decision making which can ultimately affect the intention of users to use a
particular service. Previous studies leveraged various side information from
users and items; however, it may be impractical due to privacy concerns. In
this paper, we present ColdGAN, an end-to-end GAN based model with no use of
side information to resolve this problem. The main idea of the proposed model
is to train a network that learns the rating distributions of experienced users
given their cold-start distributions. We further design a time-based function
to restore the preferences of users to cold-start states. With extensive
experiments on two real-world datasets, the results show that our proposed
method achieves significantly improved performance compared with the
state-of-the-art recommenders.
","ColdGAN: Resolving Cold Start User Recommendation by using Generative
  Adversarial Networks"
"  Ontology reuse aims to foster interoperability and facilitate knowledge
reuse. Several approaches are typically evaluated by ontology engineers when
bootstrapping a new project. However, current practices are often motivated by
subjective, case-by-case decisions, which hamper the definition of a
recommended behaviour. In this chapter we argue that to date there are no
effective solutions for supporting developers' decision-making process when
deciding on an ontology reuse strategy. The objective is twofold: (i) to survey
current approaches to ontology reuse, presenting motivations, strategies,
benefits and limits, and (ii) to analyse two representative approaches and
discuss their merits.
",The Landscape of Ontology Reuse Approaches
"  We discuss conceptual limitations of generic learning algorithms pursuing
adversarial goals in competitive environments, and prove that they are subject
to limitations that are analogous to the constraints on knowledge imposed by
the famous theorems of G\""odel and Turing. These limitations are shown to be
related to intransitivity, which is commonly present in competitive
environments.
",On limitations of learning algorithms in competitive environments
"  Cable trees are used in industrial products to transmit energy and
information between different product parts. To this date, they are mostly
assembled by humans and only few automated manufacturing solutions exist using
complex robotic machines. For these machines, the wiring plan has to be
translated into a wiring sequence of cable plugging operations to be followed
by the machine. In this paper, we study and formalize the problem of deriving
the optimal wiring sequence for a given layout of a cable tree. We summarize
our investigations to model this cable tree wiring Problem (CTW) as a traveling
salesman problem with atomic, soft atomic, and disjunctive precedence
constraints as well as tour-dependent edge costs such that it can be solved by
state-of-the-art constraint programming (CP), Optimization Modulo Theories
(OMT), and mixed-integer programming (MIP) solvers. It is further shown, how
the CTW problem can be viewed as a soft version of the coupled tasks scheduling
problem. We discuss various modeling variants for the problem, prove its
NP-hardness, and empirically compare CP, OMT, and MIP solvers on a benchmark
set of 278 instances. The complete benchmark set with all models and instance
data is available on github and is accepted for inclusion in the MiniZinc
challenge 2020.
","Cable Tree Wiring -- Benchmarking Solvers on a Real-World Scheduling
  Problem with a Variety of Precedence Constraints"
"  Artificial intelligence (AI) is already part of our daily lives and is
playing a key role in defining the economic and social shape of the future. In
2018, the European Commission introduced its AI strategy able to compete in the
next years with world powers such as China and US, but relying on the respect
of European values and fundamental rights. As a result, most of the Member
States have published their own National Strategy with the aim to work on a
coordinated plan for Europe. In this paper, we present an ongoing study on how
European countries are approaching the field of Artificial Intelligence, with
its promises and risks, through the lens of their national AI strategies. In
particular, we aim to investigate how European countries are investing in AI
and to what extent the stated plans can contribute to the benefit of the whole
society. This paper reports the main findings of a qualitative analysis of the
investment plans reported in 15 European National Strategies
",European Strategy on AI: Are we truly fostering social good?
"  A conceptual system with rich connotation is key to improving the performance
of knowledge-based artificial intelligence systems. While a conceptual system,
which has abundant concepts and rich semantic relationships, and is
developable, evolvable, and adaptable to multi-task environments, its actual
construction is not only one of the major challenges of knowledge engineering,
but also the fundamental goal of research on knowledge and conceptualization.
Finding a new method to represent concepts and construct a conceptual system
will therefore greatly improve the performance of many intelligent systems.
Fortunately the core of human cognition is a system with relatively complete
concepts and a mechanism that ensures the establishment and development of the
system. The human conceptual system can not be achieved immediately, but rather
must develop gradually. Developmental psychology carefully observes the process
of concept acquisition in humans at the behavioral level, and along with
cognitive psychology has proposed some rough explanations of those
observations. However, due to the lack of research in aspects such as
representation, systematic models, algorithm details and realization, many of
the results of developmental psychology have not been applied directly to the
building of artificial conceptual systems. For example, Karmiloff-Smith's
Representation Redescription (RR) supposition reflects a concept-acquisition
process that re-describes a lower level representation of a concept to a higher
one. This paper is inspired by this developmental psychology viewpoint. We use
an object-oriented approach to re-explain and materialize RR supposition from
the formal semantic perspective, because the OO paradigm is a natural way to
describe the outside world, and it also has strict grammar regulations.
",The Evolution of Concept-Acquisition based on Developmental Psychology
"  Remarkable success of modern image-based AI methods and the resulting
interest in their applications in critical decision-making processes has led to
a surge in efforts to make such intelligent systems transparent and
explainable. The need for explainable AI does not stem only from ethical and
moral grounds but also from stricter legislation around the world mandating
clear and justifiable explanations of any decision taken or assisted by AI.
Especially in the medical context where Computer-Aided Diagnosis can have a
direct influence on the treatment and well-being of patients, transparency is
of utmost importance for safe transition from lab research to real world
clinical practice. This paper provides a comprehensive overview of current
state-of-the-art in explaining and interpreting Deep Learning based algorithms
in applications of medical research and diagnosis of diseases. We discuss early
achievements in development of explainable AI for validation of known disease
criteria, exploration of new potential biomarkers, as well as methods for the
subsequent correction of AI models. Various explanation methods like visual,
textual, post-hoc, ante-hoc, local and global have been thoroughly and
critically analyzed. Subsequently, we also highlight some of the remaining
challenges that stand in the way of practical applications of AI as a clinical
decision support tool and provide recommendations for the direction of future
research.
","Achievements and Challenges in Explaining Deep Learning based
  Computer-Aided Diagnosis Systems"
"  A gossip protocol is a procedure for sharing secrets in a network. The basic
action in a gossip protocol is a telephone call wherein the calling agents
exchange all the secrets they know. An agent who knows all secrets is an
expert. The usual termination condition is that all agents are experts.
  Instead, we explore protocols wherein the termination condition is that all
agents know that all agents are experts. We call such agents super experts.
Additionally, we model that agents who are super experts do not make and do not
answer calls. Such agents are called engaged agents. We also model that such
gossip protocols are common knowledge among the agents. We investigate
conditions under which protocols terminate, both in the synchronous case, where
there is a global clock, and in the asynchronous case, where there is not. We
show that a commonly known protocol with engaged agents may terminate faster
than the same protocol without engaged agents.
",Everyone Knows that Everyone Knows: Gossip Protocols for Super Experts
"  This paper presents new approach based on grammar induction called AMLSI
Action Model Learning with State machine Interactions. The AMLSI approach does
not require a training dataset of plan traces to work. AMLSI proceeds by trial
and error: it queries the system to learn with randomly generated action
sequences, and it observes the state transitions of the system, then AMLSI
returns a PDDL domain corresponding to the system. A key issue for domain
learning is the ability to plan with the learned domains. It often happens that
a small learning error leads to a domain that is unusable for planning. Unlike
other algorithms, we show that AMLSI is able to lift this lock by learning
domains from partial and noisy observations with sufficient accuracy to allow
planners to solve new problems.
",AMLSI: A Novel Accurate Action Model Learning Algorithm
"  In this paper, we outline the implementation of the TFD (Totally Ordered Fast
Downward) and the PFD (Partially ordered Fast Downward) hierarchical planners
that participated in the first HTN IPC competition in 2020. These two planners
are based on forward-chaining task decomposition coupled with a compact
grounding of actions, methods, tasks and HTN problems.
",Totally and Partially Ordered Hierarchical Planners in PDDL4J Library
"  Meta-learning, or learning to learn, has gained renewed interest in recent
years within the artificial intelligence community. However, meta-learning is
incredibly prevalent within nature, has deep roots in cognitive science and
psychology, and is currently studied in various forms within neuroscience. The
aim of this review is to recast previous lines of research in the study of
biological intelligence within the lens of meta-learning, placing these works
into a common framework. More recent points of interaction between AI and
neuroscience will be discussed, as well as interesting new directions that
arise under this perspective.
",Meta-learning in natural and artificial intelligence
"  In this paper, we study the task of multimodal sequence analysis which aims
to draw inferences from visual, language and acoustic sequences. A majority of
existing works generally focus on aligned fusion, mostly at word level, of the
three modalities to accomplish this task, which is impractical in real-world
scenarios. To overcome this issue, we seek to address the task of multimodal
sequence analysis on unaligned modality sequences which is still relatively
underexplored and also more challenging. Recurrent neural network (RNN) and its
variants are widely used in multimodal sequence analysis, but they are
susceptible to the issues of gradient vanishing/explosion and high time
complexity due to its recurrent nature. Therefore, we propose a novel model,
termed Multimodal Graph, to investigate the effectiveness of graph neural
networks (GNN) on modeling multimodal sequential data. The graph-based
structure enables parallel computation in time dimension and can learn longer
temporal dependency in long unaligned sequences. Specifically, our Multimodal
Graph is hierarchically structured to cater to two stages, i.e., intra- and
inter-modal dynamics learning. For the first stage, a graph convolutional
network is employed for each modality to learn intra-modal dynamics. In the
second stage, given that the multimodal sequences are unaligned, the commonly
considered word-level fusion does not pertain. To this end, we devise a graph
pooling fusion network to automatically learn the associations between various
nodes from different modalities. Additionally, we define multiple ways to
construct the adjacency matrix for sequential data. Experimental results
suggest that our graph-based model reaches state-of-the-art performance on two
benchmark datasets.
","Analyzing Unaligned Multimodal Sequence via Graph Convolution and Graph
  Pooling Fusion"
"  This paper investigates the issues of combination and normalization of
interval-valued belief structures within the framework of Dempster-Shafer
theory of evidence. Existing approaches are reviewed and thoroughly analyzed.
The advantages and drawbacks of previous approach are presented. A new
optimality approach based on uncertainty measure is developed, where the
problem of combining interval-valued belief structures degenerates into
combining basic probability assignments. Numerical examples are provided to
illustrate the rationality of the proposed approach.
",Combination of interval-valued belief structures based on belief entropy
"  In this paper we present a system capable of collecting and annotating, human
performed, robot understandable, everyday activities from virtual environments.
The human movements are mapped in the simulated world using off-the-shelf
virtual reality devices with full body, and eye tracking capabilities. All the
interactions in the virtual world are physically simulated, thus movements and
their effects are closely relatable to the real world. During the activity
execution, a subsymbolic data logger is recording the environment and the human
gaze on a per-frame basis, enabling offline scene reproduction and replays.
Coupled with the physics engine, online monitors (symbolic data loggers) are
parsing (using various grammars) and recording events, actions, and their
effects in the simulated world.
","Automated acquisition of structured, semantic models of manipulation
  activities from human VR demonstration"
"  Knowledge compilation studies the trade-off between succinctness and
efficiency of different representation languages. For many languages, there are
known strong lower bounds on the representation size, but recent work shows
that, for some languages, one can bypass these bounds using approximate
compilation. The idea is to compile an approximation of the knowledge for which
the number of errors can be controlled. We focus on circuits in deterministic
decomposable negation normal form (d-DNNF), a compilation language suitable in
contexts such as probabilistic reasoning, as it supports efficient model
counting and probabilistic inference. Moreover, there are known size lower
bounds for d-DNNF which by relaxing to approximation one might be able to
avoid. In this paper we formalize two notions of approximation: weak
approximation which has been studied before in the decision diagram literature
and strong approximation which has been used in recent algorithmic results. We
then show lower bounds for approximation by d-DNNF, complementing the positive
results from the literature.
",Lower Bounds for Approximate Knowledge Compilation
"  Cognitive control, the ability of a system to adapt to the demands of a task,
is an integral part of cognition. A widely accepted fact about cognitive
control is that it is context-sensitive: Adults and children alike infer
information about a task's demands from contextual cues and use these
inferences to learn from ambiguous cues. However, the precise way in which
people use contextual cues to guide adaptation to a new task remains poorly
understood. This work connects the context-sensitive nature of cognitive
control to a method for meta-learning with context-conditioned adaptation. We
begin by identifying an essential difference between human learning and current
approaches to meta-learning: In contrast to humans, existing meta-learning
algorithms do not make use of task-specific contextual cues but instead rely
exclusively on online feedback in the form of task-specific labels or rewards.
To remedy this, we introduce a framework for using contextual information about
a task to guide the initialization of task-specific models before adaptation to
online feedback. We show how context-conditioned meta-learning can capture
human behavior in a cognitive task and how it can be scaled to improve the
speed of learning in various settings, including few-shot classification and
low-sample reinforcement learning. Our work demonstrates that guiding
meta-learning with task information can capture complex, human-like behavior,
thereby deepening our understanding of cognitive control.
",Connecting Context-specific Adaptation in Humans to Meta-learning
"  Human interaction relies on a wide range of signals, including non-verbal
cues. In order to develop effective Explainable Planning (XAIP) agents it is
important that we understand the range and utility of these communication
channels. Our starting point is existing results from joint task interaction
and their study in cognitive science. Our intention is that these lessons can
inform the design of interaction agents -- including those using planning
techniques -- whose behaviour is conditioned on the user's response, including
affective measures of the user (i.e., explicitly incorporating the user's
affective state within the planning model). We have identified several concepts
at the intersection of plan-based agent behaviour and joint task interaction
and have used these to design two agents: one reactive and the other partially
predictive. We have designed an experiment in order to examine human behaviour
and response as they interact with these agents. In this paper we present the
designed study and the key questions that are being investigated. We also
present the results from an empirical analysis where we examined the behaviour
of the two agents for simulated users.
","Investigating Human Response, Behaviour, and Preference in Joint-Task
  Interaction"
"  We introduce a human-compatible reinforcement-learning approach to a
cooperative game, making use of a third-party hand-coded human-compatible bot
to generate initial training data and to perform initial evaluation. Our
learning approach consists of imitation learning, search, and policy iteration.
Our trained agents achieve a new state-of-the-art for bridge bidding in three
settings: an agent playing in partnership with a copy of itself; an agent
partnering a pre-existing bot; and an agent partnering a human player.
",Human-Agent Cooperation in Bridge Bidding
"  This work seeks to study the beneficial properties that an autonomous agent
can obtain by implementing a cognitive architecture similar to the one of
conscious beings. Along this document, a conscious model of autonomous agent
based in a global workspace architecture is presented. We describe how this
agent is viewed from different perspectives of philosophy of mind, being
inspired by their ideas. The goal of this model is to create autonomous agents
able to navigate within an environment composed of multiple independent
magnitudes, adapting to its surroundings in order to find the best possible
position in base of its inner preferences. The purpose of the model is to test
the effectiveness of many cognitive mechanisms that are incorporated, such as
an attention mechanism for magnitude selection, pos-session of inner feelings
and preferences, usage of a memory system to storage beliefs and past
experiences, and incorporating a global workspace which controls and integrates
information processed by all the subsystem of the model. We show in a large
experiment set how an autonomous agent can benefit from having a cognitive
architecture such as the one described.
","An Artificial Consciousness Model and its relations with Philosophy of
  Mind"
"  Beyond representing the external world, humans also represent their own
cognitive processes. In the context of perception, this metacognition helps us
identify unreliable percepts, such as when we recognize that we are seeing an
illusion. Here we propose MetaGen, a model for the unsupervised learning of
metacognition. In MetaGen, metacognition is expressed as a generative model of
how a perceptual system produces noisy percepts. Using basic principles of how
the world works (such as object permanence, part of infants' core knowledge),
MetaGen jointly infers the objects in the world causing the percepts and a
representation of its own perceptual system. MetaGen can then use this
metacognition to infer which objects are actually present in the world. On
simulated data, we find that MetaGen quickly learns a metacognition and
improves overall accuracy, outperforming models that lack a metacognition.
",Learning a metacognition for object perception
"  Nowadays, human resource is an important part of various resources of
enterprises. For enterprises, high-loyalty and high-quality talented persons
are often the core competitiveness of enterprises. Therefore, it is of great
practical significance to predict whether employees leave and reduce the
turnover rate of employees. First, this paper established a multi-layer
perceptron predictive model of employee turnover rate. A model based on Sarsa
which is a kind of reinforcement learning algorithm is proposed to
automatically generate a set of strategies to reduce the employee turnover
rate. These strategies are a collection of strategies that can reduce the
employee turnover rate the most and cost less from the perspective of the
enterprise, and can be used as a reference plan for the enterprise to optimize
the employee system. The experimental results show that the algorithm can
indeed improve the efficiency and accuracy of the specific strategy.
","Obtain Employee Turnover Rate and Optimal Reduction Strategy Based On
  Neural Network and Reinforcement Learning"
"  In this contribution we extend an ontology for modelling agents and their
interactions, called Ontology for Agents, Systems, and Integration of Services
(in short, OASIS), with conditionals and ontological smart contracts (in short,
OSCs). OSCs are ontological representations of smart contracts that allow to
establish responsibilities and authorizations among agents and set agreements,
whereas conditionals allow one to restrict and limit agent interactions, define
activation mechanisms that trigger agent actions, and define constraints and
contract terms on OSCs. Conditionals and OSCs, as defined in OASIS, are applied
to extend with ontological capabilities digital public ledgers such as the
blockchain and smart contracts implemented on it. We will also sketch the
architecture of a framework based on the OASIS definition of OSCs that exploits
the Ethereum platform and the Interplanetary File System.
","Ontological Smart Contracts in OASIS: Ontology for Agents, Systems, and
  Integration of Services (Extended Version)"
"  In this study, we propose a multicriteria group decision making (MCGDM)
algorithm under uncertainty where data is collected as intervals. The proposed
MCGDM algorithm aggregates the data, determines the optimal weights for
criteria and ranks alternatives with no further input. The intervals give
flexibility to experts in assessing alternatives against criteria and provide
an opportunity to gain maximum information. We also propose a novel method to
aggregate expert judgements using cloud models. We introduce an experimental
approach to check the validity of the aggregation method. After that, we use
the aggregation method for an MCGDM problem. Here, we find the optimal weights
for each criterion by proposing a bilevel optimisation model. Then, we extend
the technique for order of preference by similarity to ideal solution (TOPSIS)
for data based on cloud models to prioritise alternatives. As a result, the
algorithm can gain information from decision makers with different levels of
uncertainty and examine alternatives with no more information from
decision-makers. The proposed MCGDM algorithm is implemented on a case study of
a cybersecurity problem to illustrate its feasibility and effectiveness. The
results verify the robustness and validity of the proposed MCGDM using
sensitivity analysis and comparison with other existing algorithms.
","Multicriteria Group Decision-Making Under Uncertainty Using Interval
  Data and Cloud Models"
"  This paper primarily presents two methods of ranking aggregated fuzzy numbers
from intervals using the Interval Agreement Approach (IAA). The two proposed
ranking methods within this study contain the combination and application of
previously proposed similarity measures, along with attributes novel to that of
aggregated fuzzy numbers from interval-valued data. The shortcomings of
previous measures, along with the improvements of the proposed methods, are
illustrated using both a synthetic and real-world application. The real-world
application regards the Technique for Order of Preference by Similarity to
Ideal Solution (TOPSIS) algorithm, modified to include both the previous and
newly proposed methods.
","Methods of ranking for aggregated fuzzy numbers from interval-valued
  data"
"  Images encode both the state of the world and its content. The former is
useful for tasks such as planning and control, and the latter for
classification. The automatic extraction of this information is challenging
because of the high-dimensionality and entangled encoding inherent to the image
representation. This article introduces two theoretical approaches aimed at the
resolution of these challenges. The approaches allow for the interpolation and
extrapolation of images from an image sequence by joint estimation of the image
representation and the generators of the sequence dynamics. In the first
approach, the image representations are learned using probabilistic PCA
\cite{tipping1999probabilistic}. The linear-Gaussian conditional distributions
allow for a closed form analytical description of the latent distributions but
assumes the underlying image manifold is a linear subspace. In the second
approach, the image representations are learned using probabilistic nonlinear
PCA which relieves the linear manifold assumption at the cost of requiring a
variational approximation of the latent distributions. In both approaches, the
underlying dynamics of the image sequence are modelled explicitly to
disentangle them from the image representations. The dynamics themselves are
modelled with Lie group structure which enforces the desirable properties of
smoothness and composability of inter-image transformations.
",Joint Estimation of Image Representations and their Lie Invariants
"  In recent years, data-intensive AI, particularly the domain of natural
language processing and understanding, has seen significant progress driven by
the advent of large datasets and deep neural networks that have sidelined more
classic AI approaches to the field. These systems can apparently demonstrate
sophisticated linguistic understanding or generation capabilities, but often
fail to transfer their skills to situations they have not encountered before.
We argue that computational situated grounding provides a solution to some of
these learning challenges by creating situational representations that both
serve as a formal model of the salient phenomena, and contain rich amounts of
exploitable, task-appropriate data for training new, flexible computational
models. Our model reincorporates some ideas of classic AI into a framework of
neurosymbolic intelligence, using multimodal contextual modeling of interactive
situations, events, and object properties. We discuss how situated grounding
provides diverse data and multiple levels of modeling for a variety of AI
learning challenges, including learning how to interact with object
affordances, learning semantics for novel structures and configurations, and
transferring such learned knowledge to new objects and situations.
",Neurosymbolic AI for Situated Language Understanding
"  Given the pressing need for assuring algorithmic transparency, Explainable AI
(XAI) has emerged as one of the key areas of AI research. In this paper, we
develop a novel Bayesian extension to the LIME framework, one of the most
widely used approaches in XAI -- which we call BayLIME. Compared to LIME,
BayLIME exploits prior knowledge and Bayesian reasoning to improve both the
consistency in repeated explanations of a single prediction and the robustness
to kernel settings. BayLIME also exhibits better explanation fidelity than the
state-of-the-art (LIME, SHAP and GradCAM) by its ability to integrate prior
knowledge from, e.g., a variety of other XAI techniques, as well as
verification and validation (V&V) methods. We demonstrate the desirable
properties of BayLIME through both theoretical analysis and extensive
experiments.
",BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations
"  We describe a SAT solver using both the GPU (CUDA) and the CPU with a new
clause exchange strategy. The CPU runs a classic multithreaded CDCL SAT solver.
EachCPU thread exports all the clauses it learns to the GPU. The GPU makes a
heavy usage of bitwise operations. It notices when a clause would have been
used by a CPU thread and notifies that thread, in which case it imports that
clause. This relies on the GPU repeatedly testing millions of clauses against
hundreds of assignments. All the clauses are tested independantly from each
other (which allows the GPU massively parallel approach), but against all the
assignments at once, using bitwise operations. This allows CPU threads to only
import clauses which would have been useful for them. Our solver is based upon
glucose-syrup. Experiments show that this leads to a strong performance
improvement, with 22 more instances solved on the SAT 2020 competition than
glucose-syrup.
",GpuShareSat: a SAT solver using the GPU for clause sharing
"  An ideal safe workplace is described as a place where staffs fulfill
responsibilities in a well-organized order, potential hazardous events are
being monitored in real-time, as well as the number of accidents and relevant
damages are minimized. However, occupational-related death and injury are still
increasing and have been highly attended in the last decades due to the lack of
comprehensive safety management. A smart safety management system is therefore
urgently needed, in which the staffs are instructed to fulfill responsibilities
as well as automating risk evaluations and alerting staffs and departments when
needed. In this paper, a smart system for safety management in the workplace
based on responsibility big data analysis and the internet of things (IoT) are
proposed. The real world implementation and assessment demonstrate that the
proposed systems have superior accountability performance and improve the
responsibility fulfillment through real-time supervision and self-reminder.
",A Data-driven Human Responsibility Management System
"  The development of deep reinforcement learning (DRL) has benefited from the
emergency of a variety type of game environments where new challenging problems
are proposed and new algorithms can be tested safely and quickly, such as Board
games, RTS, FPS, and MOBA games. However, many existing environments lack
complexity and flexibility and assume the actions are synchronously executed in
multi-agent settings, which become less valuable. We introduce the Fever
Basketball game, a novel reinforcement learning environment where agents are
trained to play basketball game. It is a complex and challenging environment
that supports multiple characters, multiple positions, and both the
single-agent and multi-agent player control modes. In addition, to better
simulate real-world basketball games, the execution time of actions differs
among players, which makes Fever Basketball a novel asynchronized environment.
We evaluate commonly used multi-agent algorithms of both independent learners
and joint-action learners in three game scenarios with varying difficulties,
and heuristically propose two baseline methods to diminish the extra
non-stationarity brought by asynchronism in Fever Basketball Benchmarks.
Besides, we propose an integrated curricula training (ICT) framework to better
handle Fever Basketball problems, which includes several game-rule based
cascading curricula learners and a coordination curricula switcher focusing on
enhancing coordination within the team. The results show that the game remains
challenging and can be used as a benchmark environment for studies like
long-time horizon, sparse rewards, credit assignment, and non-stationarity,
etc. in multi-agent settings.
","Fever Basketball: A Complex, Flexible, and Asynchronized Sports Game
  Environment for Multi-agent Reinforcement Learning"
"  In this paper, the publicly available dataset of condition based maintenance
of combined diesel-electric and gas (CODLAG) propulsion system for ships has
been utilized to obtain symbolic expressions which could estimate gas turbine
shaft torque and fuel flow using genetic programming (GP) algorithm. The entire
dataset consists of 11934 samples that was divided into training and testing
portions of dataset in an 80:20 ratio. The training dataset used to train the
GP algorithm to obtain symbolic expressions for gas turbine shaft torque and
fuel flow estimation consisted of 9548 samples. The best symbolic expressions
obtained for gas turbine shaft torque and fuel flow estimation were obtained
based on their $R^2$ score generated as a result of the application of the
testing portion of the dataset on the aforementioned symbolic expressions. The
testing portion of the dataset consisted of 2386 samples. The three best
symbolic expressions obtained for gas turbine shaft torque estimation generated
$R^2$ scores of 0.999201, 0.999296, and 0.999374, respectively. The three best
symbolic expressions obtained for fuel flow estimation generated $R^2$ scores
of 0.995495, 0.996465, and 0.996487, respectively.
","Estimation of Gas Turbine Shaft Torque and Fuel Flow of a CODLAG
  Propulsion System Using Genetic Programming Algorithm"
"  Combinatorial problems stated as Constraint Satisfaction Problems (CSP) are
examined. It is shown by example that any algorithm designed for the original
CSP, and involving the AllDifferent constraint, has at least the same level of
efficacy when simultaneously applied to both the original and its complementary
problem. The 1-to-1 mapping employed to transform a CSP to its complementary
problem, which is also a CSP, is introduced. This ""Dual CSP"" method and its
application are outlined. The analysis of several random problem instances
demonstrate the benefits of this method for variable domain reduction compared
to the standard approach to CSP. Extensions to additional constraints other
than AllDifferent, as well as the use of hybrid algorithms, are proposed as
candidates for this Dual CSP method.
","Improving Constraint Satisfaction Algorithm Efficiency for the
  AllDifferent Constraint"
"  This paper presents a method to compute the degree of similarity between two
aggregated fuzzy numbers from intervals using the Interval Agreement Approach
(IAA). The similarity measure proposed within this study contains several
features and attributes, of which are novel to aggregated fuzzy numbers. The
attributes completely redefined or modified within this study include area,
perimeter, centroids, quartiles and the agreement ratio. The recommended
weighting for each feature has been learned using Principal Component Analysis
(PCA). Furthermore, an illustrative example is provided to detail the
application and potential future use of the similarity measure.
","Similarity measure for aggregated fuzzy numbers from interval-valued
  data"
"  How should we decide which fairness criteria or definitions to adopt in
machine learning systems? To answer this question, we must study the fairness
preferences of actual users of machine learning systems. Stringent parity
constraints on treatment or impact can come with trade-offs, and may not even
be preferred by the social groups in question (Zafar et al., 2017). Thus it
might be beneficial to elicit what the group's preferences are, rather than
rely on a priori defined mathematical fairness constraints. Simply asking for
self-reported rankings of users is challenging because research has shown that
there are often gaps between people's stated and actual preferences(Bernheim et
al., 2013).
  This paper outlines a research program and experimental designs for
investigating these questions. Participants in the experiments are invited to
perform a set of tasks in exchange for a base payment--they are told upfront
that they may receive a bonus later on, and the bonus could depend on some
combination of output quantity and quality. The same group of workers then
votes on a bonus payment structure, to elicit preferences. The voting is
hypothetical (not tied to an outcome) for half the group and actual (tied to
the actual payment outcome) for the other half, so that we can understand the
relation between a group's actual preferences and hypothetical (stated)
preferences. Connections and lessons from fairness in machine learning are
explored.
","Fairness Preferences, Actual and Hypothetical: A Study of Crowdworker
  Incentives"
"  Learning pseudo-Boolean (PB) constraints in PB solvers exploiting cutting
planes based inference is not as well understood as clause learning in
conflict-driven clause learning solvers. In this paper, we show that PB
constraints derived using cutting planes may contain \emph{irrelevant
literals}, i.e., literals whose assigned values (whatever they are) never
change the truth value of the constraint. Such literals may lead to infer
constraints that are weaker than they should be, impacting the size of the
proof built by the solver, and thus also affecting its performance. This
suggests that current implementations of PB solvers based on cutting planes
should be reconsidered to prevent the generation of irrelevant literals.
Indeed, detecting and removing irrelevant literals is too expensive in practice
to be considered as an option (the associated problem is NP-hard.
",On Irrelevant Literals in Pseudo-Boolean Constraint Learning
"  Anticipating what might happen as a result of an action is an essential
ability humans have in order to perform tasks effectively. On the other hand,
robots capabilities in this regard are quite lacking. While machine learning is
used to increase the ability of prospection it is still limiting for novel
situations. A possibility to improve the prospection ability of robots is
through simulation of imagined motions and the physical results of these
actions. Therefore, we present URoboSim, a robot simulator that allows robots
to perform tasks as mental simulation before performing this task in reality.
We show the capabilities of URoboSim in form of mental simulations, generating
data for machine learning and the usage as belief state for a real robot.
","URoboSim -- An Episodic Simulation Framework for Prospective Reasoning
  in Robotic Agents"
"  The parameters for a Markov Decision Process (MDP) often cannot be specified
exactly. Uncertain MDPs (UMDPs) capture this model ambiguity by defining sets
which the parameters belong to. Minimax regret has been proposed as an
objective for planning in UMDPs to find robust policies which are not overly
conservative. In this work, we focus on planning for Stochastic Shortest Path
(SSP) UMDPs with uncertain cost and transition functions. We introduce a
Bellman equation to compute the regret for a policy. We propose a dynamic
programming algorithm that utilises the regret Bellman equation, and show that
it optimises minimax regret exactly for UMDPs with independent uncertainties.
For coupled uncertainties, we extend our approach to use options to enable a
trade off between computation and solution quality. We evaluate our approach on
both synthetic and real-world domains, showing that it significantly
outperforms existing baselines.
","Minimax Regret Optimisation for Robust Planning in Uncertain Markov
  Decision Processes"
"  This paper introduces EvoCraft, a framework for Minecraft designed to study
open-ended algorithms. We introduce an API that provides an open-source Python
interface for communicating with Minecraft to place and track blocks. In
contrast to previous work in Minecraft that focused on learning to play the
game, the grand challenge we pose here is to automatically search for
increasingly complex artifacts in an open-ended fashion. Compared to other
environments used to study open-endedness, Minecraft allows the construction of
almost any kind of structure, including actuated machines with circuits and
mechanical components. We present initial baseline results in evolving simple
Minecraft creations through both interactive and automated evolution. While
evolution succeeds when tasked to grow a structure towards a specific target,
it is unable to find a solution when rewarded for creating a simple machine
that moves. Thus, EvoCraft offers a challenging new environment for automated
search methods (such as evolution) to find complex artifacts that we hope will
spur the development of more open-ended algorithms. A Python implementation of
the EvoCraft framework is available at:
https://github.com/real-itu/Evocraft-py.
",EvoCraft: A New Challenge for Open-Endedness
"  In model serving, having one fixed model during the entire often life-long
inference process is usually detrimental to model performance, as data
distribution evolves over time, resulting in lack of reliability of the model
trained on historical data. It is important to detect changes and retrain the
model in time. The existing methods generally have three weaknesses: 1) using
only classification error rate as signal, 2) assuming ground truth labels are
immediately available after features from samples are received and 3) unable to
decide what data to use to retrain the model when change occurs. We address the
first problem by utilizing six different signals to capture a wide range of
characteristics of data, and we address the second problem by allowing lag of
labels, where labels of corresponding features are received after a lag in
time. For the third problem, our proposed method automatically decides what
data to use to retrain based on the signals. Extensive experiments on
structured and unstructured data for different type of data changes establish
that our method consistently outperforms the state-of-the-art methods by a
large margin.
",Concept Drift and Covariate Shift Detection Ensemble with Lagged Labels
"  In previous work with Joost Vennekens I proposed a definition of actual
causation that is based on certain plausible principles, thereby allowing the
debate on causation to shift away from its heavy focus on examples towards a
more systematic analysis. This paper contributes to that analysis in two ways.
First, I show that our definition is in fact a formalization of Wright's famous
NESS definition of causation combined with a counterfactual difference-making
condition. This means that our definition integrates two highly influential
approaches to causation that are claimed to stand in opposition to each other.
Second, I modify our definition to offer a substantial improvement: I weaken
the difference-making condition in such a way that it avoids the problematic
analysis of cases of preemption. The resulting Counterfactual NESS definition
of causation forms a natural compromise between counterfactual approaches and
the NESS approach.
",The Counterfactual NESS Definition of Causation
"  The aim of this paper is to offer the first systematic exploration and
definition of equivalent causal models in the context where both models are not
made up of the same variables. The idea is that two models are equivalent when
they agree on all ""essential"" causal information that can be expressed using
their common variables. I do so by focussing on the two main features of causal
models, namely their structural relations and their functional relations. In
particular, I define several relations of causal ancestry and several relations
of causal sufficiency, and require that the most general of these relations are
preserved across equivalent models.
",Equivalent Causal Models
"  Despite the recent, widespread focus on eXplainable AI (XAI), explanations
computed by XAI methods tend to provide little insight into the functioning of
Neural Networks (NNs). We propose a novel framework for obtaining (local)
explanations from NNs while providing transparency about their inner workings,
and show how to deploy it for various neural architectures and tasks. We refer
to our novel explanations collectively as Deep Argumentative eXplanations (DAXs
in short), given that they reflect the deep structure of the underlying NNs and
that they are defined in terms of notions from computational argumentation, a
form of symbolic AI offering useful reasoning abstractions for explanation. We
evaluate DAXs empirically showing that they exhibit deep fidelity and low
computational cost. We also conduct human experiments indicating that DAXs are
comprehensible to humans and align with their judgement, while also being
competitive, in terms of user acceptance, with some existing approaches to XAI
that also have an argumentative spirit.
",Deep Argumentative Explanations
"  One of the most pressing issues in AI in recent years has been the need to
address the lack of explainability of many of its models. We focus on
explanations for discrete Bayesian network classifiers (BCs), targeting greater
transparency of their inner workings by including intermediate variables in
explanations, rather than just the input and output variables as is standard
practice. The proposed influence-driven explanations (IDXs) for BCs are
systematically generated using the causal relationships between variables
within the BC, called influences, which are then categorised by logical
requirements, called relation properties, according to their behaviour. These
relation properties both provide guarantees beyond heuristic explanation
methods and allow the information underpinning an explanation to be tailored to
a particular context's and user's requirements, e.g., IDXs may be dialectical
or counterfactual. We demonstrate IDXs' capability to explain various forms of
BCs, e.g., naive or multi-label, binary or categorical, and also integrate
recent approaches to explanations for BCs from the literature. We evaluate IDXs
with theoretical and empirical analyses, demonstrating their considerable
advantages when compared with existing explanation methods.
",Influence-Driven Explanations for Bayesian Network Classifiers
"  Extreme multi-label text classification (XMTC) aims to tag a text instance
with the most relevant subset of labels from an extremely large label set. XMTC
has attracted much recent attention due to massive label sets yielded by modern
applications, such as news annotation and product recommendation. The main
challenges of XMTC are the data scalability and sparsity, thereby leading to
two issues: i) the intractability to scale to the extreme label setting, ii)
the presence of long-tailed label distribution, implying that a large fraction
of labels have few positive training instances. To overcome these problems, we
propose GNN-XML, a scalable graph neural network framework tailored for XMTC
problems. Specifically, we exploit label correlations via mining their
co-occurrence patterns and build a label graph based on the correlation matrix.
We then conduct the attributed graph clustering by performing graph convolution
with a low-pass graph filter to jointly model label dependencies and label
features, which induces semantic label clusters. We further propose a
bilateral-branch graph isomorphism network to decouple representation learning
and classifier learning for better modeling tail labels. Experimental results
on multiple benchmark datasets show that GNN-XML significantly outperforms
state-of-the-art methods while maintaining comparable prediction efficiency and
model size.
","GNN-XML: Graph Neural Networks for Extreme Multi-label Text
  Classification"
"  Efficient automated scheduling of trains remains a major challenge for modern
railway systems. The underlying vehicle rescheduling problem (VRSP) has been a
major focus of Operations Research (OR) since decades. Traditional approaches
use complex simulators to study VRSP, where experimenting with a broad range of
novel ideas is time consuming and has a huge computational overhead. In this
paper, we introduce a two-dimensional simplified grid environment called
""Flatland"" that allows for faster experimentation. Flatland does not only
reduce the complexity of the full physical simulation, but also provides an
easy-to-use interface to test novel approaches for the VRSP, such as
Reinforcement Learning (RL) and Imitation Learning (IL). In order to probe the
potential of Machine Learning (ML) research on Flatland, we (1) ran a first
series of RL and IL experiments and (2) design and executed a public Benchmark
at NeurIPS 2020 to engage a large community of researchers to work on this
problem. Our own experimental results, on the one hand, demonstrate that ML has
potential in solving the VRSP on Flatland. On the other hand, we identify key
topics that need further research. Overall, the Flatland environment has proven
to be a robust and valuable framework to investigate the VRSP for railway
networks. Our experiments provide a good starting point for further research
and for the participants of the NeurIPS 2020 Flatland Benchmark. All of these
efforts together have the potential to have a substantial impact on shaping the
mobility of the future.
",Flatland-RL : Multi-Agent Reinforcement Learning on Trains
"  Abstract dialectical frameworks (ADFs) have been introduced as a formalism
for modeling and evaluating argumentation allowing general logical satisfaction
conditions. Different criteria used to settle the acceptance of arguments are
called semantics. Semantics of ADFs have so far mainly been defined based on
the concept of admissibility. However, the notion of strongly admissible
semantics studied for abstract argumentation frameworks has not yet been
introduced for ADFs. In the current work we present the concept of strong
admissibility of interpretations for ADFs. Further, we show that strongly
admissible interpretations of ADFs form a lattice with the grounded
interpretation as top element.
",Strong Admissibility for Abstract Dialectical Frameworks
"  Our title alludes to the three Christmas ghosts encountered by Ebenezer
Scrooge in \textit{A Christmas Carol}, who guide Ebenezer through the past,
present, and future of Christmas holiday events. Similarly, our article will
take readers through a journey of the past, present, and future of medical AI.
In doing so, we focus on the crux of modern machine learning: the reliance on
powerful but intrinsically opaque models. When applied to the healthcare
domain, these models fail to meet the needs for transparency that their
clinician and patient end-users require. We review the implications of this
failure, and argue that opaque models (1) lack quality assurance, (2) fail to
elicit trust, and (3) restrict physician-patient dialogue. We then discuss how
upholding transparency in all aspects of model design and model validation can
help ensure the reliability of medical AI.
",The Three Ghosts of Medical AI: Can the Black-Box Present Deliver?
"  Conflict-Based Search (CBS) is a state-of-the-art algorithm for multi-agent
path finding. At the high level, CBS repeatedly detects conflicts and resolves
one of them by splitting the current problem into two subproblems. Previous
work chooses the conflict to resolve by categorizing the conflict into three
classes and always picking a conflict from the highest-priority class. In this
work, we propose an oracle for conflict selection that results in smaller
search tree sizes than the one used in previous work. However, the computation
of the oracle is slow. Thus, we propose a machine-learning framework for
conflict selection that observes the decisions made by the oracle and learns a
conflict-selection strategy represented by a linear ranking function that
imitates the oracle's decisions accurately and quickly. Experiments on
benchmark maps indicate that our method significantly improves the success
rates, the search tree sizes and runtimes over the current state-of-the-art CBS
solver.
","Learning to Resolve Conflicts for Multi-Agent Path Finding with
  Conflict-Based Search"
"  This paper presents an intelligent price suggestion system for online
second-hand listings based on their uploaded images and text descriptions. The
goal of price prediction is to help sellers set effective and reasonable prices
for their second-hand items with the images and text descriptions uploaded to
the online platforms. Specifically, we design a multi-modal price suggestion
system which takes as input the extracted visual and textual features along
with some statistical item features collected from the second-hand item
shopping platform to determine whether the image and text of an uploaded
second-hand item are qualified for reasonable price suggestion with a binary
classification model, and provide price suggestions for second-hand items with
qualified images and text descriptions with a regression model. To satisfy
different demands, two different constraints are added into the joint training
of the classification model and the regression model. Moreover, a customized
loss function is designed for optimizing the regression model to provide price
suggestions for second-hand items, which can not only maximize the gain of the
sellers but also facilitate the online transaction. We also derive a set of
metrics to better evaluate the proposed price suggestion system. Extensive
experiments on a large real-world dataset demonstrate the effectiveness of the
proposed multi-modal price suggestion system.
",Price Suggestion for Online Second-hand Items with Texts and Images
"  The role of verbal and non-verbal cues towards great public speaking has been
a topic of exploration for many decades. We identify a commonality across
present theories, the element of ""variety or heterogeneity"" in channels or
modes of communication (e.g. resorting to stories, scientific facts, emotional
connections, facial expressions etc.) which is essential for effectively
communicating information. We use this observation to formalize a novel
HEterogeneity Metric, HEM, that quantifies the quality of a talk both in the
verbal and non-verbal domain (transcript and facial gestures). We use TED talks
as an input repository of public speeches because it consists of speakers from
a diverse community besides having a wide outreach. We show that there is an
interesting relationship between HEM and the ratings of TED talks given to
speakers by viewers. It emphasizes that HEM inherently and successfully
represents the quality of a talk based on ""variety or heterogeneity"". Further,
we also discover that HEM successfully captures the prevalent bias in ratings
with respect to race and gender, that we call sensitive attributes (because
prediction based on these might result in unfair outcome). We incorporate the
HEM metric into the loss function of a neural network with the goal to reduce
unfairness in rating predictions with respect to race and gender. Our results
show that the modified loss function improves fairness in prediction without
considerably affecting prediction accuracy of the neural network. Our work ties
together a novel metric for public speeches in both verbal and non-verbal
domain with the computational power of a neural network to design a fair
prediction system for speakers.
","Fairness in Rating Prediction by Awareness of Verbal and Gesture Quality
  of Public Speeches"
"  Research on notable accomplishments and important events in the life of
people of public interest usually requires close reading of long encyclopedic
or biographical sources, which is a tedious and time-consuming task. Whereas
semantic reference sources, such as the EventKG knowledge graph, provide
structured representations of relevant facts, they often include hundreds of
events and temporal relations for particular entities. In this paper, we
present EventKG+BT - a timeline generation system that creates concise and
interactive spatio-temporal representations of biographies from a knowledge
graph using distant supervision.
","EventKG+BT: Generation of Interactive Biography Timelines from a
  Knowledge Graph"
"  Many natural optimization problems are NP-hard, which implies that they are
probably hard to solve exactly in the worst-case. However, it suffices to get
reasonably good solutions for all (or even most) instances in practice. This
paper presents a new algorithm for computing approximate solutions in
${\Theta(N})$ for the Maximum Exact 3-Satisfiability (MAX-E-$3$-SAT) problem by
using deep learning methodology. This methodology allows us to create a
learning algorithm able to fix Boolean variables by using local information
obtained by the Survey Propagation algorithm. By performing an accurate
analysis, on random CNF instances of the MAX-E-$3$-SAT with several Boolean
variables, we show that this new algorithm, avoiding any decimation strategy,
can build assignments better than a random one, even if the convergence of the
messages is not found. Although this algorithm is not competitive with
state-of-the-art Maximum Satisfiability (MAX-SAT) solvers, it can solve
substantially larger and more complicated problems than it ever saw during
training.
",Learning from Survey Propagation: a Neural Network for MAX-E-$3$-SAT
"  Modelling realistic human behaviours in simulation is an ongoing challenge
that resides between several fields like social sciences, philosophy, and
artificial intelligence. Human movement is a special type of behaviour driven
by intent (e.g. to get groceries) and the surrounding environment (e.g.
curiosity to see new interesting places). Services available online and offline
do not normally consider the environment when planning a path, which is
decisive especially on a leisure trip. Two novel algorithms have been presented
to generate human-like trajectories based on environmental features. The
Attraction-Based A* algorithm includes in its computation information from the
environmental features meanwhile, the Feature-Based A* algorithm also injects
information from the real trajectories in its computation. The human-likeness
aspect has been tested by a human expert judging the final generated
trajectories as realistic. This paper presents a comparison between the two
approaches in some key metrics like efficiency, efficacy, and hyper-parameters
sensitivity. We show how, despite generating trajectories that are closer to
the real one according to our predefined metrics, the Feature-Based A*
algorithm fall short in time efficiency compared to the Attraction-Based A*
algorithm, hindering the usability of the model in the real world.
","Generating Human-Like Movement: A Comparison Between Two Approaches
  Based on Environmental Features"
"  The purpose of this paper is to discuss the possibilities for computing
machinery, or AI agents, to know and to possess knowledge. This is done mainly
from a virtue epistemology perspective and definition of knowledge. However,
this inquiry also shed light on the human condition, what it means for a human
to know, and to possess knowledge. The paper argues that it is possible for an
AI agent to know and examines this from both current state-of-the-art in
artificial intelligence as well as from the perspective of what the future AI
development might bring in terms of superintelligent AI agents.
",Computing Machinery and Knowledge
"  Multiagent systems can use commitments as the core of a general coordination
infrastructure, supporting both cooperative and non-cooperative interactions.
Agents whose objectives are aligned, and where one agent can help another
achieve greater reward by sacrificing some of its own reward, should choose a
cooperative commitment to maximize their joint reward. We present a solution to
the problem of how cooperative agents can efficiently find an (approximately)
optimal commitment by querying about carefully-selected commitment choices. We
prove structural properties of the agents' values as functions of the
parameters of the commitment specification, and develop a greedy method for
composing a query with provable approximation bounds, which we empirically show
can find nearly optimal commitments in a fraction of the time methods that lack
our insights require.
",Efficient Querying for Cooperative Probabilistic Commitments
"  As from time to time it is impractical to ask agents to provide linear orders
over all alternatives, for these partial rankings it is necessary to conduct
preference completion. Specifically, the personalized preference of each agent
over all the alternatives can be estimated with partial rankings from
neighboring agents over subsets of alternatives. However, since the agents'
rankings are nondeterministic, where they may provide rankings with noise, it
is necessary and important to conduct the trustworthy preference completion.
Hence, in this paper firstly, a trust-based anchor-kNN algorithm is proposed to
find $k$-nearest trustworthy neighbors of the agent with trust-oriented
Kendall-Tau distances, which will handle the cases when an agent exhibits
irrational behaviors or provides only noisy rankings. Then, for alternative
pairs, a bijection can be built from the ranking space to the preference space,
and its certainty and conflict can be evaluated based on a well-built
statistical measurement Probability-Certainty Density Function. Therefore, a
certain common voting rule for the first $k$ trustworthy neighboring agents
based on certainty and conflict can be taken to conduct the trustworthy
preference completion. The properties of the proposed certainty and conflict
have been studied empirically, and the proposed approach has been
experimentally validated compared to state-of-arts approaches with several data
sets.
",Trustworthy Preference Completion in Social Choice
"  Recognition in planning seeks to find agent intentions, goals or activities
given a set of observations and a knowledge library (e.g. goal states, plans or
domain theories). In this work we introduce the problem of Online Action
Recognition. It consists in recognizing, in an open world, the planning action
that best explains a partially observable state transition from a knowledge
library of first-order STRIPS actions, which is initially empty. We frame this
as an optimization problem, and propose two algorithms to address it: Action
Unification (AU) and Online Action Recognition through Unification (OARU). The
former builds on logic unification and generalizes two input actions using
weighted partial MaxSAT. The latter looks for an action within the library that
explains an observed transition. If there is such action, it generalizes it
making use of AU, building in this way an AU hierarchy. Otherwise, OARU inserts
a Trivial Grounded Action (TGA) in the library that explains just that
transition. We report results on benchmarks from the International Planning
Competition and PDDLGym, where OARU recognizes actions accurately with respect
to expert knowledge, and shows real-time performance.
",Online Action Recognition
"  It has been observed that in many of the benchmark planning domains, atomic
goals can be reached with a simple polynomial exploration procedure, called IW,
that runs in time exponential in the problem width. Such problems have indeed a
bounded width: a width that does not grow with the number of problem variables
and is often no greater than two. Yet, while the notion of width has become
part of the state-of-the-art planning algorithms like BFWS, there is still no
good explanation for why so many benchmark domains have bounded width. In this
work, we address this question by relating bounded width and serialized width
to ideas of generalized planning, where general policies aim to solve multiple
instances of a planning problem all at once. We show that bounded width is a
property of planning domains that admit optimal general policies in terms of
features that are explicitly or implicitly represented in the domain encoding.
The results are extended to much larger class of domains with bounded
serialized width where the general policies do not have to be optimal. The
study leads also to a new simple, meaningful, and expressive language for
specifying domain serializations in the form of policy sketches which can be
used for encoding domain control knowledge by hand or for learning it from
traces. The use of sketches and the meaning of the theoretical results are all
illustrated through a number of examples.
","General Policies, Serializations, and Planning Width"
"  The recent success of Bayesian methods in neuroscience and artificial
intelligence gives rise to the hypothesis that the brain is a Bayesian machine.
Since logic and learning are both practices of the human brain, it leads to
another hypothesis that there is a Bayesian interpretation underlying both
logical reasoning and machine learning. In this paper, we introduce a
generative model of logical consequence relations. It formalises the process of
how the truth value of a sentence is probabilistically generated from the
probability distribution over states of the world. We show that the generative
model characterises a classical consequence relation, paraconsistent
consequence relation and nonmonotonic consequence relation. In particular, the
generative model gives a new consequence relation that outperforms them in
reasoning with inconsistent knowledge. We also show that the generative model
gives a new classification algorithm that outperforms several representative
algorithms in predictive accuracy and complexity on the Kaggle Titanic dataset.
","Bayes Meets Entailment and Prediction: Commonsense Reasoning with
  Non-monotonicity, Paraconsistency and Predictive Accuracy"
"  Recent advances in Artificial Intelligence (AI) have revived the quest for
agents able to acquire an open-ended repertoire of skills. However, although
this ability is fundamentally related to the characteristics of human
intelligence, research in this field rarely considers the processes that may
have guided the emergence of complex cognitive capacities during the evolution
of the species.
  Research in Human Behavioral Ecology (HBE) seeks to understand how the
behaviors characterizing human nature can be conceived as adaptive responses to
major changes in the structure of our ecological niche. In this paper, we
propose a framework highlighting the role of environmental complexity in
open-ended skill acquisition, grounded in major hypotheses from HBE and recent
contributions in Reinforcement learning (RL). We use this framework to
highlight fundamental links between the two disciplines, as well as to identify
feedback loops that bootstrap ecological complexity and create promising
research directions for AI researchers.
",Grounding Artificial Intelligence in the Origins of Human Behavior
"  Ontology is the explicit and formal representation of the concepts in a
domain and relations among them. Transportation science is a wide domain
dealing with mobility over various complex and interconnected transportation
systems, such as land, aviation, and maritime transport, and can take
considerable advantage from ontology development. While several studies can be
found in the recent literature, there exists a large potential to improve and
develop a comprehensive smart mobility ontology. The current chapter aims to
present different aspects of ontology development in general, such as ontology
development methods, languages, tools, and software. Subsequently, it presents
the currently available mobility-related ontologies developed across different
domains, such as transportation, smart cities, goods mobility, sensors. Current
gaps in the available ontologies are identified, and future directions
regarding ontology development are proposed that can incorporate the
forthcoming autonomous and connected vehicles, mobility as a service (MaaS),
and other disruptive transportation technologies and services.
",Smart Mobility Ontology: Current Trends and Future Directions
"  The Travelling Thief Problem (TTP) is a challenging combinatorial
optimization problem that attracts many scholars. The TTP interconnects two
well-known NP-hard problems: the Travelling Salesman Problem (TSP) and the 0-1
Knapsack Problem (KP). Increasingly algorithms have been proposed for solving
this novel problem that combines two interdependent sub-problems. In this
paper, TTP is investigated theoretically and empirically. An algorithm based on
the score value calculated by our proposed formulation in picking items and
sorting items in the reverse order in the light of the scoring value is
proposed to solve the problem. Different approaches for solving the TTP are
compared and analyzed; the experimental investigations suggest that our
proposed approach is very efficient in meeting or beating current
state-of-the-art heuristic solutions on a comprehensive set of benchmark TTP
instances.
","Solving the Travelling Thief Problem based on Item Selection Weight and
  Reverse Order Allocation"
"  Relation prediction for knowledge graphs aims at predicting missing
relationships between entities. Despite the importance of inductive relation
prediction, most previous works are limited to a transductive setting and
cannot process previously unseen entities. The recent proposed subgraph-based
relation reasoning models provided alternatives to predict links from the
subgraph structure surrounding a candidate triplet inductively. However, we
observe that these methods often neglect the directed nature of the extracted
subgraph and weaken the role of relation information in the subgraph modeling.
As a result, they fail to effectively handle the asymmetric/anti-symmetric
triplets and produce insufficient embeddings for the target triplets. To this
end, we introduce a \textbf{C}\textbf{o}mmunicative \textbf{M}essage
\textbf{P}assing neural network for \textbf{I}nductive re\textbf{L}ation
r\textbf{E}asoning, \textbf{CoMPILE}, that reasons over local directed subgraph
structures and has a vigorous inductive bias to process entity-independent
semantic relations. In contrast to existing models, CoMPILE strengthens the
message interactions between edges and entitles through a communicative kernel
and enables a sufficient flow of relation information. Moreover, we demonstrate
that CoMPILE can naturally handle asymmetric/anti-symmetric relations without
the need for explosively increasing the number of model parameters by
extracting the directed enclosing subgraphs. Extensive experiments show
substantial performance gains in comparison to state-of-the-art methods on
commonly used benchmark datasets with variant inductive settings.
",Communicative Message Passing for Inductive Relation Reasoning
"  Knowledge graphs in manufacturing and production aim to make production lines
more efficient and flexible with higher quality output. This makes knowledge
graphs attractive for companies to reach Industry 4.0 goals. However, existing
research in the field is quite preliminary, and more research effort on
analyzing how knowledge graphs can be applied in the field of manufacturing and
production is needed. Therefore, we have conducted a systematic literature
review as an attempt to characterize the state-of-the-art in this field, i.e.,
by identifying exiting research and by identifying gaps and opportunities for
further research. To do that, we have focused on finding the primary studies in
the existing literature, which were classified and analyzed according to four
criteria: bibliometric key facts, research type facets, knowledge graph
characteristics, and application scenarios. Besides, an evaluation of the
primary studies has also been carried out to gain deeper insights in terms of
methodology, empirical evidence, and relevance. As a result, we can offer a
complete picture of the domain, which includes such interesting aspects as the
fact that knowledge fusion is currently the main use case for knowledge graphs,
that empirical research and industrial application are still missing to a large
extent, that graph embeddings are not fully exploited, and that technical
literature is fast-growing but seems to be still far from its peak.
","Knowledge Graphs in Manufacturing and Production: A Systematic
  Literature Review"
"  The multiplayer online battle arena (MOBA) games have become increasingly
popular in recent years. Consequently, many efforts have been devoted to
providing pre-game or in-game predictions for them. However, these works are
limited in the following two aspects: 1) the lack of sufficient in-game
features; 2) the absence of interpretability in the prediction results. These
two limitations greatly restrict the practical performance and industrial
application of the current works. In this work, we collect and release a
large-scale dataset containing rich in-game features for the popular MOBA game
Honor of Kings. We then propose to predict four types of important events in an
interpretable way by attributing the predictions to the input features using
two gradient-based attribution methods: Integrated Gradients and SmoothGrad. To
evaluate the explanatory power of different models and attribution methods, a
fidelity-based evaluation metric is further proposed. Finally, we evaluate the
accuracy and Fidelity of several competitive methods on the collected dataset
to assess how well machines predict events in MOBA games.
","Predicting Events in MOBA Games: Dataset, Attribution, and Evaluation"
"  Cognitive Psychology and related disciplines have identified several critical
mechanisms that enable intelligent biological agents to learn to solve complex
problems. There exists pressing evidence that the cognitive mechanisms that
enable problem-solving skills in these species build on hierarchical mental
representations. Among the most promising computational approaches to provide
comparable learning-based problem-solving abilities for artificial agents and
robots is hierarchical reinforcement learning. However, so far the existing
computational approaches have not been able to equip artificial agents with
problem-solving abilities that are comparable to intelligent animals, including
human and non-human primates, crows, or octopuses. Here, we first survey the
literature in Cognitive Psychology, and related disciplines, and find that many
important mental mechanisms involve compositional abstraction, curiosity, and
forward models. We then relate these insights with contemporary hierarchical
reinforcement learning methods, and identify the key machine intelligence
approaches that realise these mechanisms. As our main result, we show that all
important cognitive mechanisms have been implemented independently in isolated
computational architectures, and there is simply a lack of approaches that
integrate them appropriately. We expect our results to guide the development of
more sophisticated cognitively inspired hierarchical methods, so that future
artificial agents achieve a problem-solving performance on the level of
intelligent animals.
",Hierarchical principles of embodied reinforcement learning: A review
"  Hero drafting is essential in MOBA game playing as it builds the team of each
side and directly affects the match outcome. State-of-the-art drafting methods
fail to consider: 1) drafting efficiency when the hero pool is expanded; 2) the
multi-round nature of a MOBA 5v5 match series, i.e., two teams play best-of-N
and the same hero is only allowed to be drafted once throughout the series. In
this paper, we formulate the drafting process as a multi-round combinatorial
game and propose a novel drafting algorithm based on neural networks and
Monte-Carlo tree search, named JueWuDraft. Specifically, we design a long-term
value estimation mechanism to handle the best-of-N drafting case. Taking Honor
of Kings, one of the most popular MOBA games at present, as a running case, we
demonstrate the practicality and effectiveness of JueWuDraft when compared to
state-of-the-art drafting methods.
","Which Heroes to Pick? Learning to Draft in MOBA Games with Neural
  Networks and Tree Search"
"  Ranking vertices of multidimensional networks is crucial in many areas of
research, including selecting and determining the importance of decisions. Some
decisions are significantly more important than others, and their weight
categorization is also imortant. This paper defines a completely new method for
determining the weight decisions using artificial intelligence for importance
ranking of three-dimensional network vertices, improving the existing Ordered
Statistics Vertex Extraction and Tracking Algorithm (OSVETA) based on
modulation of quantized indices (QIM) and error correction codes. The technique
we propose in this paper offers significant improvements the efficiency of
determination the importance of network vertices in relation to statistical
OSVETA criteria, replacing heuristic methods with methods of precise prediction
of modern neural networks. The new artificial intelligence technique enables a
significantly better definition of the 3D meshes and a better assessment of
their topological features. The new method contributions result in a greater
precision in defining stable vertices, significantly reducing the probability
of deleting mesh vertices.
",Artificial Intelligence ordered 3D vertex importance
"  Belief propagation is an algorithm that is known from statistical physics and
computer science. It provides an efficient way of calculating marginals that
involve large sums of products which are efficiently rearranged into nested
products of sums to approximate the marginals. It allows a reliable estimation
of the state and its variance of power grids that is needed for the control and
forecast of power grid management. At prototypical examples of IEEE-grids we
show that belief propagation not only scales linearly with the grid size for
the state estimation itself, but also facilitates and accelerates the retrieval
of missing data and allows an optimized positioning of measurement units. Based
on belief propagation, we give a criterion for how to assess whether other
algorithms, using only local information, are adequate for state estimation for
a given grid. We also demonstrate how belief propagation can be utilized for
coarse-graining power grids towards representations that reduce the
computational effort when the coarse-grained version is integrated into a
larger grid. It provides a criterion for partitioning power grids into areas in
order to minimize the error of flow estimates between different areas.
",State Estimation of Power Flows for Smart Grids via Belief Propagation
"  Condition-based monitoring (CBM) has been widely utilised in the wind
industry for monitoring operational inconsistencies and failures in turbines,
with techniques ranging from signal processing and vibration analysis to
artificial intelligence (AI) models using Supervisory Control & Acquisition
(SCADA) data. However, existing studies do not present a concrete basis to
facilitate explainable decision support in operations and maintenance (O&M),
particularly for automated decision support through recommendation of
appropriate maintenance action reports corresponding to failures predicted by
CBM techniques. Knowledge graph databases (KGs) model a collection of
domain-specific information and have played an intrinsic role for real-world
decision support in domains such as healthcare and finance, but have seen very
limited attention in the wind industry. We propose XAI4Wind, a multimodal
knowledge graph for explainable decision support in real-world operational
turbines and demonstrate through experiments several use-cases of the proposed
KG towards O&M planning through interactive query and reasoning and providing
novel insights using graph data science algorithms. The proposed KG combines
multimodal knowledge like SCADA parameters and alarms with natural language
maintenance actions, images etc. By integrating our KG with an Explainable AI
model for anomaly prediction, we show that it can provide effective
human-intelligible O&M strategies for predicted operational inconsistencies in
various turbine sub-components. This can help instil better trust and
confidence in conventionally black-box AI models. We make our KG publicly
available and envisage that it can serve as the building ground for providing
autonomous decision support in the wind industry.
","XAI4Wind: A Multimodal Knowledge Graph Database for Explainable Decision
  Support in Operations & Maintenance of Wind Turbines"
"  After a few decades of development, computational argumentation has become
one of the active realms in AI. This paper considers extension-based concrete
and abstract semantics of argumentation. For concrete ones, based on Grossi and
Modgil's recent work, this paper considers some issues on graded
extension-based semantics of abstract argumentation framework (AAF, for short).
First, an alternative fundamental lemma is given, which generalizes the
corresponding result due to Grossi and Modgil by relaxing the constraint on
parameters. This lemma provides a new sufficient condition for preserving
conflict-freeness and brings a Galois adjunction between admissible sets and
complete extensions, which is of vital importance in constructing some special
extensions in terms of iterations of the defense function. Applying such a
lemma, some flaws in Grossi and Modgil's work are corrected, and the structural
property and universal definability of various extension-based semantics are
given. Second, an operator so-called reduced meet modulo an ultrafilter is
presented, which is a simple but powerful tool in exploring infinite AAFs. The
neutrality function and the defense function, which play central roles in
Dung's abstract argumentation theory, are shown to be distributive over reduced
meets modulo any ultrafilter. A variety of fundamental semantics of AAFs,
including conflict-free, admissible, complete and stable semantics, etc, are
shown to be closed under this operator. Based on this fact, a number of
applications of such operators are considered. In particular, we provide a
simple and uniform method to prove the universal definability of a family of
range related semantics. Since all graded concrete semantics considered in this
paper are generalizations of corresponding non-graded ones, all results about
them obtained in this paper also hold in the traditional situation.
",More on extension-based semantics of argumentation
"  Deep Reinforcement Learning (DRL) reaches a superhuman level of play in many
complete information games. The state of the art search algorithm used in
combination with DRL is Monte Carlo Tree Search (MCTS). We take another
approach to DRL using a Minimax algorithm instead of MCTS and learning only the
evaluation of states, not the policy. We show that for multiple games it is
competitive with the state of the art DRL for the learning performances and for
the confrontations.
",Minimax Strikes Back
"  In this paper, we propose a novel method named Biomedical Confident Itemsets
Explanation (BioCIE), aiming at post-hoc explanation of black-box machine
learning models for biomedical text classification. Using sources of domain
knowledge and a confident itemset mining method, BioCIE discretizes the
decision space of a black-box into smaller subspaces and extracts semantic
relationships between the input text and class labels in different subspaces.
Confident itemsets discover how biomedical concepts are related to class labels
in the black-box's decision space. BioCIE uses the itemsets to approximate the
black-box's behavior for individual predictions. Optimizing fidelity,
interpretability, and coverage measures, BioCIE produces class-wise
explanations that represent decision boundaries of the black-box. Results of
evaluations on various biomedical text classification tasks and black-box
models demonstrated that BioCIE can outperform perturbation-based and decision
set methods in terms of producing concise, accurate, and interpretable
explanations. BioCIE improved the fidelity of instance-wise and class-wise
explanations by 11.6% and 7.5%, respectively. It also improved the
interpretability of explanations by 8%. BioCIE can be effectively used to
explain how a black-box biomedical text classification model semantically
relates input texts to class labels. The source code and supplementary material
are available at https://github.com/mmoradi-iut/BioCIE.
",Explaining Black-box Models for Biomedical Text Classification
"  Given a system that does not work as expected, Sequential Diagnosis (SD) aims
at suggesting a series of system measurements to isolate the true explanation
for the system's misbehavior from a potentially exponential set of possible
explanations. To reason about the best next measurement, SD methods usually
require a sample of possible fault explanations at each step of the iterative
diagnostic process. The computation of this sample can be accomplished by
various diagnostic search algorithms. Among those, Reiter's HS-Tree is one of
the most popular due its desirable properties and general applicability.
Usually, HS-Tree is used in a stateless fashion throughout the SD process to
(re)compute a sample of possible fault explanations in each iteration, each
time given the latest (updated) system knowledge including all so-far collected
measurements. At this, the built search tree is discarded between two
iterations, although often large parts of the tree have to be rebuilt in the
next iteration, involving redundant operations and calls to costly reasoning
services.
  As a remedy to this, we propose DynamicHS, a variant of HS-Tree that
maintains state throughout the diagnostic session and additionally embraces
special strategies to minimize the number of expensive reasoner invocations. In
this vein, DynamicHS provides an answer to a longstanding question posed by
Raymond Reiter in his seminal paper from 1987.
  Extensive evaluations on real-world diagnosis problems prove the
reasonability of the DynamicHS and testify its clear superiority to HS-Tree
wrt. computation time. More specifically, DynamicHS outperformed HS-Tree in 96%
of the executed sequential diagnosis sessions and, per run, the latter required
up to 800% the time of the former. Remarkably, DynamicHS achieves these
performance improvements while preserving all desirable properties as well as
the general applicability of HS-Tree.
","DynamicHS: Streamlining Reiter's Hitting-Set Tree for Sequential
  Diagnosis"
"  Flooding results in 8 billion dollars of damage annually in the US and causes
the most deaths of any weather related event. Due to climate change scientists
expect more heavy precipitation events in the future. However, no current
datasets exist that contain both hourly precipitation and river flow data. We
introduce a novel hourly river flow and precipitation dataset and a second
subset of flash flood events with damage estimates and injury counts. Using
these datasets we create two challenges (1) general stream flow forecasting and
(2) flash flood damage estimation. We have created several publicly available
benchmarks and an easy to use package. Additionally, in the future we aim to
augment our dataset with snow pack data and soil index moisture data to improve
predictions.
","FlowDB a large scale precipitation, river, and flash flood dataset"
"  In the era of MOOCs, online exams are taken by millions of candidates, where
scoring short answers is an integral part. It becomes intractable to evaluate
them by human graders. Thus, a generic automated system capable of grading
these responses should be designed and deployed. In this paper, we present a
fast, scalable, and accurate approach towards automated Short Answer Scoring
(SAS). We propose and explain the design and development of a system for SAS,
namely AutoSAS. Given a question along with its graded samples, AutoSAS can
learn to grade that prompt successfully. This paper further lays down the
features such as lexical diversity, Word2Vec, prompt, and content overlap that
plays a pivotal role in building our proposed model. We also present a
methodology for indicating the factors responsible for scoring an answer. The
trained model is evaluated on an extensively used public dataset, namely
Automated Student Assessment Prize Short Answer Scoring (ASAP-SAS). AutoSAS
shows state-of-the-art performance and achieves better results by over 8% in
some of the question prompts as measured by Quadratic Weighted Kappa (QWK),
showing performance comparable to humans.
","Get It Scored Using AutoSAS -- An Automated System for Scoring Short
  Answers"
"  Commonsense question-answering (QA) tasks, in the form of benchmarks, are
constantly being introduced for challenging and comparing commonsense QA
systems. The benchmarks provide question sets that systems' developers can use
to train and test new models before submitting their implementations to
official leaderboards. Although these tasks are created to evaluate systems in
identified dimensions (e.g. topic, reasoning type), this metadata is limited
and largely presented in an unstructured format or completely not present.
Because machine common sense is a fast-paced field, the problem of fully
assessing current benchmarks and systems with regards to these evaluation
dimensions is aggravated. We argue that the lack of a common vocabulary for
aligning these approaches' metadata limits researchers in their efforts to
understand systems' deficiencies and in making effective choices for future
tasks. In this paper, we first discuss this MCS ecosystem in terms of its
elements and their metadata. Then, we present how we are supporting the
assessment of approaches by initially focusing on commonsense benchmarks. We
describe our initial MCS Benchmark Ontology, an extensible common vocabulary
that formalizes benchmark metadata, and showcase how it is supporting the
development of a Benchmark tool that enables benchmark exploration and
analysis.
",Exploring and Analyzing Machine Commonsense Benchmarks
"  We propose a novel Transformer encoder-based architecture with syntactical
knowledge encoded for intent detection and slot filling. Specifically, we
encode syntactic knowledge into the Transformer encoder by jointly training it
to predict syntactic parse ancestors and part-of-speech of each token via
multi-task learning. Our model is based on self-attention and feed-forward
layers and does not require external syntactic information to be available at
inference time. Experiments show that on two benchmark datasets, our models
with only two Transformer encoder layers achieve state-of-the-art results.
Compared to the previously best performed model without pre-training, our
models achieve absolute F1 score and accuracy improvement of 1.59% and 0.85%
for slot filling and intent detection on the SNIPS dataset, respectively. Our
models also achieve absolute F1 score and accuracy improvement of 0.1% and
0.34% for slot filling and intent detection on the ATIS dataset, respectively,
over the previously best performed model. Furthermore, the visualization of the
self-attention weights illustrates the benefits of incorporating syntactic
information during training.
","Encoding Syntactic Knowledge in Transformer Encoder for Intent Detection
  and Slot Filling"
"  Providing explanations is considered an imperative ability for an AI agent in
a human-robot teaming framework. The right explanation provides the rationale
behind an AI agent's decision-making. However, to maintain the human teammate's
cognitive demand to comprehend the provided explanations, prior works have
focused on providing explanations in a specific order or intertwining the
explanation generation with plan execution. Moreover, these approaches do not
consider the degree of details required to share throughout the provided
explanations. In this work, we argue that the agent-generated explanations,
especially the complex ones, should be abstracted to be aligned with the level
of details the human teammate desires to maintain the recipient's cognitive
load. Therefore, learning a hierarchical explanations model is a challenging
task. Moreover, the agent needs to follow a consistent high-level policy to
transfer the learned teammate preferences to a new scenario while lower-level
detailed plans are different. Our evaluation confirmed the process of
understanding an explanation, especially a complex and detailed explanation, is
hierarchical. The human preference that reflected this aspect corresponded
exactly to creating and employing abstraction for knowledge assimilation hidden
deeper in our cognitive process. We showed that hierarchical explanations
achieved better task performance and behavior interpretability while reduced
cognitive load. These results shed light on designing explainable agents
utilizing reinforcement learning and planning across various domains.
","Are We On The Same Page? Hierarchical Explanation Generation for
  Planning Tasks in Human-Robot Teaming using Reinforcement Learning"
"  Convolutional neural networks (CNNs) are vulnerable to adversarial examples,
and studies show that increasing the model capacity of an architecture topology
(e.g., width expansion) can bring consistent robustness improvements. This
reveals a clear robustness-efficiency trade-off that should be considered in
architecture design. In this paper, considering scenarios with capacity budget,
we aim to discover adversarially robust architecture at targeted capacities.
Recent studies employed one-shot neural architecture search (NAS) to discover
robust architectures. However, since the capacities of different topologies
cannot be aligned in the search process, one-shot NAS methods favor topologies
with larger capacities in the supernet. And the discovered topology might be
suboptimal when augmented to the targeted capacity. We propose a novel
multi-shot NAS method to address this issue and explicitly search for robust
architectures at targeted capacities. At the targeted FLOPs of 2000M, the
discovered MSRobNet-2000 outperforms the recent NAS-discovered architecture
RobNet-large under various criteria by a large margin of 4%-7%. And at the
targeted FLOPs of 1560M, MSRobNet-1560 surpasses another NAS-discovered
architecture RobNet-free by 2.3% and 1.3% in the clean and PGD-7 accuracies,
respectively. All codes are available at https://github.com/walkerning/aw\_nas.
","Discovering Robust Convolutional Architecture at Targeted Capacity: A
  Multi-Shot Approach"
"  One of the grand challenges discussed during the Dagstuhl Seminar ""Knowledge
Graphs: New Directions for Knowledge Representation on the Semantic Web"" and
described in its report is that of a: ""Public FAIR Knowledge Graph of
Everything: We increasingly see the creation of knowledge graphs that capture
information about the entirety of a class of entities. [...] This grand
challenge extends this further by asking if we can create a knowledge graph of
""everything"" ranging from common sense concepts to location based entities.
This knowledge graph should be ""open to the public"" in a FAIR manner
democratizing this mass amount of knowledge."" Although linked open data (LOD)
is one knowledge graph, it is the closest realisation (and probably the only
one) to a public FAIR Knowledge Graph (KG) of everything. Surely, LOD provides
a unique testbed for experimenting and evaluating research hypotheses on open
and FAIR KG. One of the most neglected FAIR issues about KGs is their ongoing
evolution and long term preservation. We want to investigate this problem, that
is to understand what preserving and supporting the evolution of KGs means and
how these problems can be addressed. Clearly, the problem can be approached
from different perspectives and may require the development of different
approaches, including new theories, ontologies, metrics, strategies,
procedures, etc. This document reports a collaborative effort performed by 9
teams of students, each guided by a senior researcher as their mentor,
attending the International Semantic Web Research School (ISWS 2019). Each team
provides a different perspective to the problem of knowledge graph evolution
substantiated by a set of research questions as the main subject of their
investigation. In addition, they provide their working definition for KG
preservation and evolution.
","Knowledge Graphs Evolution and Preservation -- A Technical Report from
  ISWS 2019"
"  Developing link prediction models to automatically complete knowledge graphs
has recently been the focus of significant research interest. The current
methods for the link prediction taskhavetwonaturalproblems:1)the relation
distributions in KGs are usually unbalanced, and 2) there are many unseen
relations that occur in practical situations. These two problems limit the
training effectiveness and practical applications of the existing link
prediction models. We advocate a holistic understanding of KGs and we propose
in this work a unified Generalized Relation Learning framework GRL to address
the above two problems, which can be plugged into existing link prediction
models. GRL conducts a generalized relation learning, which is aware of
semantic correlations between relations that serve as a bridge to connect
semantically similar relations. After training with GRL, the closeness of
semantically similar relations in vector space and the discrimination of
dissimilar relations are improved. We perform comprehensive experiments on six
benchmarks to demonstrate the superior capability of GRL in the link prediction
task. In particular, GRL is found to enhance the existing link prediction
models making them insensitive to unbalanced relation distributions and capable
of learning unseen relations.
","Generalized Relation Learning with Semantic Correlation Awareness for
  Link Prediction"
"  We consider learning to play multiplayer imperfect-information games with
simultaneous moves and large state-action spaces. Previous attempts to tackle
such challenging games have largely focused on model-free learning methods,
often requiring hundreds of years of experience to produce competitive agents.
Our approach is based on model-based planning. We tackle the problem of partial
observability by first building an (oracle) planner that has access to the full
state of the environment and then distilling the knowledge of the oracle to a
(follower) agent which is trained to play the imperfect-information game by
imitating the oracle's choices. We experimentally show that planning with naive
Monte Carlo tree search does not perform very well in large combinatorial
action spaces. We therefore propose planning with a fixed-depth tree search and
decoupled Thompson sampling for action selection. We show that the planner is
able to discover efficient playing strategies in the games of Clash Royale and
Pommerman and the follower policy successfully learns to implement them by
training on a few hundred battles.
","Learning to Play Imperfect-Information Games by Imitating an Oracle
  Planner"
"  Expert networks are formed by a group of expert-professionals with different
specialties to collaboratively resolve specific queries posted to the network.
In such networks, when a query reaches an expert who does not have sufficient
expertise, this query needs to be routed to other experts for further
processing until it is completely solved; therefore, query answering efficiency
is sensitive to the underlying query routing mechanism being used. Among all
possible query routing mechanisms, decentralized search, operating purely on
each expert's local information without any knowledge of network global
structure, represents the most basic and scalable routing mechanism, which is
applicable to any network scenarios even in dynamic networks. However, there is
still a lack of fundamental understanding of the efficiency of decentralized
search in expert networks. In this regard, we investigate decentralized search
by quantifying its performance under a variety of network settings. Our key
findings reveal the existence of network conditions, under which decentralized
search can achieve significantly short query routing paths (i.e., between
$O(\log n)$ and $O(\log^2 n)$ hops, $n$: total number of experts in the
network). Based on such theoretical foundation, we further study how the unique
properties of decentralized search in expert networks is related to the
anecdotal small-world phenomenon. In addition, we demonstrate that
decentralized search is robust against estimation errors introduced by
misinterpreting the required expertise levels. To the best of our knowledge,
this is the first work studying fundamental behaviors of decentralized search
in expert networks. The developed performance bounds, confirmed by real
datasets, are able to assist in predicting network performance and designing
complex expert networks.
",Query Answering via Decentralized Search
"  Recently, we have seen a rapid rise in usage of online educational platforms.
The personalized education became crucially important in future learning
environments. Knowledge tracing (KT) refers to the detection of students'
knowledge states and predict future performance given their past outcomes for
providing adaptive solution to Intelligent Tutoring Systems (ITS). Bayesian
Knowledge Tracing (BKT) is a model to capture mastery level of each skill with
psychologically meaningful parameters and widely used in successful tutoring
systems. However, it is unable to detect learning transfer across skills
because each skill model is learned independently and shows lower efficiency in
student performance prediction. While recent KT models based on deep neural
networks shows impressive predictive power but it came with a price. Ten of
thousands of parameters in neural networks are unable to provide
psychologically meaningful interpretation that reflect to cognitive theory. In
this paper, we proposed an efficient student model called BKT-LSTM. It contains
three meaningful components: individual \textit{skill mastery} assessed by BKT,
\textit{ability profile} (learning transfer across skills) detected by k-means
clustering and \textit{problem difficulty}. All these components are taken into
account in student's future performance prediction by leveraging predictive
power of LSTM. BKT-LSTM outperforms state-of-the-art student models in
student's performance prediction by considering these meaningful features
instead of using binary values of student's past interaction in DKT. We also
conduct ablation studies on each of BKT-LSTM model components to examine their
value and each component shows significant contribution in student's
performance prediction. Thus, it has potential for providing adaptive and
personalized instruction in real-world educational systems.
","BKT-LSTM: Efficient Student Modeling for knowledge tracing and student
  performance prediction"
"  Artificial intelligence (AI) has been used to advance different fields, such
as education, healthcare, and finance. However, the application of AI in the
field of project management (PM) has not progressed equally. This paper reports
on a systematic review of the published studies used to investigate the
application of AI in PM. This systematic review identified relevant papers
using Web of Science, Science Direct, and Google Scholar databases. Of the 652
articles found, 58 met the predefined criteria and were included in the review.
Included papers were classified per the following dimensions: PM knowledge
areas, PM processes, and AI techniques. The results indicated that the
application of AI in PM was in its early stages and AI models have not applied
for multiple PM processes especially in processes groups of project stakeholder
management, project procurements management, and project communication
management. However, the most popular PM processes among included papers were
project effort prediction and cost estimation, and the most popular AI
techniques were support vector machines, neural networks, and genetic
algorithms.
",The Last State of Artificial Intelligence in Project Management
"  In this work we propose a goal reasoning method which learns to select
subgoals with Deep Q-Learning in order to decrease the load of a planner when
faced with scenarios with tight time restrictions, such as online execution
systems. We have designed a CNN-based goal selection module and trained it on a
standard video game environment, testing it on different games (planning
domains) and levels (planning problems) to measure its generalization
abilities. When comparing its performance with a satisfying planner, the
results obtained show both approaches are able to find plans of good quality,
but our method greatly decreases planning time. We conclude our approach can be
successfully applied to different types of domains (games), and shows good
generalization properties when evaluated on new levels (problems) of the same
game (domain).
",Goal Reasoning by Selecting Subgoals with Deep Q-Learning
"  The notion of stability in a structured argumentation setup characterizes
situations where the acceptance status associated with a given literal will not
be impacted by any future evolution of this setup. In this paper, we abstract
away from the logical structure of arguments, and we transpose this notion of
stability to the context of Dungean argumentation frameworks. In particular, we
show how this problem can be translated into reasoning with Argument-Incomplete
AFs. Then we provide preliminary complexity results for stability under four
prominent semantics, in the case of both credulous and skeptical reasoning.
Finally, we illustrate to what extent this notion can be useful with an
application to argument-based negotiation.
",Stability in Abstract Argumentation
"  In recent years, deep learning has become more and more mature, and as a
commonly used algorithm in deep learning, convolutional neural networks have
been widely used in various visual tasks. In the past, research based on deep
learning algorithms mainly relied on hardware such as GPUs and CPUs. However,
with the increasing development of FPGAs, both field programmable logic gate
arrays, it has become the main implementation hardware platform that combines
various neural network deep learning algorithms This article is a review
article, which mainly introduces the related theories and algorithms of
convolution. It summarizes the application scenarios of several existing FPGA
technologies based on convolutional neural networks, and mainly introduces the
application of accelerators. At the same time, it summarizes some accelerators'
under-utilization of logic resources or under-utilization of memory bandwidth,
so that they can't get the best performance.
","Overview of FPGA deep learning acceleration based on convolutional
  neural network"
"  Partially Observable Monte-Carlo Planning (POMCP) is a powerful online
algorithm able to generate approximate policies for large Partially Observable
Markov Decision Processes. The online nature of this method supports
scalability by avoiding complete policy representation. The lack of an explicit
representation however hinders interpretability. In this work, we propose a
methodology based on Satisfiability Modulo Theory (SMT) for analyzing POMCP
policies by inspecting their traces, namely sequences of
belief-action-observation triplets generated by the algorithm. The proposed
method explores local properties of policy behavior to identify unexpected
decisions. We propose an iterative process of trace analysis consisting of
three main steps, i) the definition of a question by means of a parametric
logical formula describing (probabilistic) relationships between beliefs and
actions, ii) the generation of an answer by computing the parameters of the
logical formula that maximize the number of satisfied clauses (solving a
MAX-SMT problem), iii) the analysis of the generated logical formula and the
related decision boundaries for identifying unexpected decisions made by POMCP
with respect to the original question. We evaluate our approach on Tiger, a
standard benchmark for POMDPs, and a real-world problem related to mobile robot
navigation. Results show that the approach can exploit human knowledge on the
domain, outperforming state-of-the-art anomaly detection methods in identifying
unexpected decisions. An improvement of the Area Under Curve up to 47\% has
been achieved in our tests.
","Identification of Unexpected Decisions in Partially Observable
  Monte-Carlo Planning: a Rule-Based Approach"
"  Recently, deep reinforcement learning (DRL)-based approach has shown
promisein solving complex decision and control problems in power engineering
domain.In this paper, we present an in-depth analysis of DRL-based voltage
control fromaspects of algorithm selection, state space representation, and
reward engineering.To resolve observed issues, we propose a novel imitation
learning-based approachto directly map power grid operating points to effective
actions without any interimreinforcement learning process. The performance
results demonstrate that theproposed approach has strong generalization ability
with much less training time.The agent trained by imitation learning is
effective and robust to solve voltagecontrol problem and outperforms the former
RL agents.
",Rethink AI-based Power Grid Control: Diving Into Algorithm Design
"  Symbolic planning models allow decision-making agents to sequence actions in
arbitrary ways to achieve a variety of goals in dynamic domains. However, they
are typically handcrafted and tend to require precise formulations that are not
robust to human error. Reinforcement learning (RL) approaches do not require
such models, and instead learn domain dynamics by exploring the environment and
collecting rewards. However, RL approaches tend to require millions of episodes
of experience and often learn policies that are not easily transferable to
other tasks. In this paper, we address one aspect of the open problem of
integrating these approaches: how can decision-making agents resolve
discrepancies in their symbolic planning models while attempting to accomplish
goals? We propose an integrated framework named SPOTTER that uses RL to augment
and support (""spot"") a planning agent by discovering new operators needed by
the agent to accomplish goals that are initially unreachable for the agent.
SPOTTER outperforms pure-RL approaches while also discovering transferable
symbolic knowledge and does not require supervision, successful plan traces or
any a priori knowledge about the missing planning operator.
","SPOTTER: Extending Symbolic Planning Operators through Targeted
  Reinforcement Learning"
"  Automatic evaluation metrics hold a fundamental importance in the development
and fine-grained analysis of captioning systems. While current evaluation
metrics tend to achieve an acceptable correlation with human judgements at the
system level, they fail to do so at the caption level. In this work, we propose
a neural network-based learned metric to improve the caption-level caption
evaluation. To get a deeper insight into the parameters which impact a learned
metrics performance, this paper investigates the relationship between different
linguistic features and the caption-level correlation of the learned metrics.
We also compare metrics trained with different training examples to measure the
variations in their evaluation. Moreover, we perform a robustness analysis,
which highlights the sensitivity of learned and handcrafted metrics to various
sentence perturbations. Our empirical analysis shows that our proposed metric
not only outperforms the existing metrics in terms of caption-level correlation
but it also shows a strong system-level correlation against human assessments.
",LCEval: Learned Composite Metric for Caption Evaluation
"  The paper relies on the clinical data of a previously published study. We
identify two very questionable assumptions of said work, namely confusing
evidence of absence and absence of evidence, and neglecting the ordinal nature
of attributes' domains. We then show that using an adequate ordinal methodology
such as the dominance-based rough sets approach (DRSA) can significantly
improve the predictive accuracy of the expert system, resulting in almost
complete accuracy for a dataset of 100 instances. Beyond the performance of
DRSA in solving the diagnosis problem at hand, these results suggest the
inadequacy and triviality of the underlying dataset. We provide links to open
data from the UCI machine learning repository to allow for an easy
verification/refutation of the claims made in this paper.
",Predicting Seminal Quality with the Dominance-Based Rough Sets Approach
"  A classical problem in city-scale cyber-physical systems (CPS) is resource
allocation under uncertainty. Typically, such problems are modeled as Markov
(or semi-Markov) decision processes. While online, offline, and decentralized
approaches have been applied to such problems, they have difficulty scaling to
large decision problems. We present a general approach to hierarchical planning
that leverages structure in city-level CPS problems for resource allocation
under uncertainty. We use the emergency response as a case study and show how a
large resource allocation problem can be split into smaller problems. We then
create a principled framework for solving the smaller problems and tackling the
interaction between them. Finally, we use real-world data from Nashville,
Tennessee, a major metropolitan area in the United States, to validate our
approach. Our experiments show that the proposed approach outperforms
state-of-the-art approaches used in the field of emergency response.
","Hierarchical Planning for Resource Allocation in Emergency Response
  Systems"
"  Portfolio-based algorithm selection has seen tremendous practical success
over the past two decades. This algorithm configuration procedure works by
first selecting a portfolio of diverse algorithm parameter settings, and then,
on a given problem instance, using an algorithm selector to choose a parameter
setting from the portfolio with strong predicted performance. Oftentimes, both
the portfolio and the algorithm selector are chosen using a training set of
typical problem instances from the application domain at hand. In this paper,
we provide the first provable guarantees for portfolio-based algorithm
selection. We analyze how large the training set should be to ensure that the
resulting algorithm selector's average performance over the training set is
close to its future (expected) performance. This involves analyzing three key
reasons why these two quantities may diverge: 1) the learning-theoretic
complexity of the algorithm selector, 2) the size of the portfolio, and 3) the
learning-theoretic complexity of the algorithm's performance as a function of
its parameters. We introduce an end-to-end learning-theoretic analysis of the
portfolio construction and algorithm selection together. We prove that if the
portfolio is large, overfitting is inevitable, even with an extremely simple
algorithm selector. With experiments, we illustrate a tradeoff exposed by our
theoretical analysis: as we increase the portfolio size, we can hope to include
a well-suited parameter setting for every possible problem instance, but it
becomes impossible to avoid overfitting.
",Generalization in portfolio-based algorithm selection
"  Exploring the tremendous amount of data efficiently to make a decision,
similar to answering a complicated question, is challenging with many
real-world application scenarios. In this context, automatic summarization has
substantial importance as it will provide the foundation for big data analytic.
Traditional summarization approaches optimize the system to produce a short
static summary that fits all users that do not consider the subjectivity aspect
of summarization, i.e., what is deemed valuable for different users, making
these approaches impractical in real-world use cases. This paper proposes an
interactive concept-based summarization model, called Adaptive Summaries, that
helps users make their desired summary instead of producing a single inflexible
summary. The system learns from users' provided information gradually while
interacting with the system by giving feedback in an iterative loop. Users can
choose either reject or accept action for selecting a concept being included in
the summary with the importance of that concept from users' perspectives and
confidence level of their feedback. The proposed approach can guarantee
interactive speed to keep the user engaged in the process. Furthermore, it
eliminates the need for reference summaries, which is a challenging issue for
summarization tasks. Evaluations show that Adaptive Summaries helps users make
high-quality summaries based on their preferences by maximizing the
user-desired content in the generated summaries.
","Adaptive Summaries: A Personalized Concept-based Summarization Approach
  by Learning from Users' Feedback"
"  Online reviews are a vital source of information when purchasing a service or
a product. Opinion spammers manipulate these reviews, deliberately altering the
overall perception of the service. Though there exists a corpus of online
reviews, only a few have been labeled as spam or non-spam, making it difficult
to train spam detection models. We propose an adversarial training mechanism
leveraging the capabilities of Generative Pre-Training 2 (GPT-2) for
classifying opinion spam with limited labeled data and a large set of unlabeled
data. Experiments on TripAdvisor and YelpZip datasets show that the proposed
model outperforms state-of-the-art techniques by at least 7% in terms of
accuracy when labeled data is limited. The proposed model can also generate
synthetic spam/non-spam reviews with reasonable perplexity, thereby, providing
additional labeled data during training.
","Leveraging GPT-2 for Classifying Spam Reviews with Limited Labeled Data
  via Adversarial Training"
"  It is argued that 4-valued paraconsistent truth values (called here ""p-bits"")
can serve as a conceptual, mathematical and practical foundation for highly
AI-relevant forms of probabilistic logic and probabilistic programming and
concept formation.
  First it is shown that appropriate averaging-across-situations and
renormalization of 4-valued p-bits operating in accordance with Constructible
Duality (CD) logic yields PLN (Probabilistic Logic Networks)
strength-and-confidence truth values. Then variations on the Curry-Howard
correspondence are used to map these paraconsistent and probabilistic logics
into probabilistic types suitable for use within dependent type based
programming languages.
  Zach Weber's paraconsistent analysis of the sorites paradox is extended to
form a paraconsistent / probabilistic / fuzzy analysis of concept boundaries;
and a paraconsistent version of concept formation via Formal Concept Analysis
is presented, building on a definition of fuzzy property-value degrees in terms
of relative entropy on paraconsistent probability distributions.
  These general points are fleshed out via reference to the realization of
probabilistic reasoning and programming and concept formation in the OpenCog
AGI framework which is centered on collaborative multi-algorithm updating of a
common knowledge metagraph.
","Paraconsistent Foundations for Probabilistic Reasoning, Programming and
  Concept Formation"
"  Constraint Satisfaction Problems (CSPs) play a central role in many
applications in Artificial Intelligence and Operations Research. In general,
solving CSPs is NP-complete. The structure of CSPs is best described by
hypergraphs. Therefore, various forms of hypergraph decompositions have been
proposed in the literature to identify tractable fragments of CSPs. However,
also the computation of a concrete hypergraph decomposition is a challenging
task in itself. In this paper, we report on recent progress in the study of
hypergraph decompositions and we outline several directions for future
research.
","The HyperTrac Project: Recent Progress and Future Research Directions on
  Hypergraph Decompositions"
"  We explore using the Suggested Upper Merged Ontology (SUMO) to develop a
semantic simulation. We provide two proof-of-concept demonstrations modeling
transitions in a simulated gasoline engine using a general-purpose programming
language. Rather than focusing on computationally highly intensive techniques,
we explore a less computationally intensive approach related to familiar
software engineering testing procedures. In addition, we propose structured
representations of terms based on linguistic approaches to lexicography.
",Semantic Modeling with SUMO
"  The goal of Inductive Logic Programming (ILP) is to learn a program that
explains a set of examples. Until recently, most research on ILP targeted
learning Prolog programs. The ILASP system instead learns Answer Set Programs
(ASP). Learning such expressive programs widens the applicability of ILP
considerably; for example, enabling preference learning, learning common-sense
knowledge, including defaults and exceptions, and learning non-deterministic
theories.
  Early versions of ILASP can be considered meta-level ILP approaches, which
encode a learning task as a logic program and delegate the search to an ASP
solver. More recently, ILASP has shifted towards a new method, inspired by
conflict-driven SAT and ASP solvers. The fundamental idea of the approach,
called Conflict-driven ILP (CDILP), is to iteratively interleave the search for
a hypothesis with the generation of constraints which explain why the current
hypothesis does not cover a particular example. These coverage constraints
allow ILASP to rule out not just the current hypothesis, but an entire class of
hypotheses that do not satisfy the coverage constraint.
  This paper formalises the CDILP approach and presents the ILASP3 and ILASP4
systems for CDILP, which are demonstrated to be more scalable than previous
ILASP systems, particularly in the presence of noise.
",Conflict-driven Inductive Logic Programming
"  In this paper I present an argument and a general schema which can be used to
construct a problem case for any decision theory, in a way that could be taken
to show that one cannot formulate a decision theory that is never outperformed
by any other decision theory. I also present and discuss a number of possible
responses to this argument. One of these responses raises the question of what
it means for two decision problems to be ""equivalent"" in the relevant sense,
and gives an answer to this question which would invalidate the first argument.
However, this position would have further consequences for how we compare
different decision theories in decision problems already discussed in the
literature (including e.g. Newcomb's problem).
",A General Counterexample to Any Decision Theory and Some Responses
"  In this paper, we present an Ontology Design Pattern for representing
situations that recur at regular periods and share some invariant factors,
which unify them conceptually: we refer to this set of recurring situations as
recurrent situation series. The proposed pattern appears to be foundational,
since it can be generalised for modelling the top-level domain-independent
concept of recurrence, which is strictly associated with invariance. The
pattern reuses other foundational patterns such as Collection, Description and
Situation, Classification, Sequence. Indeed, a recurrent situation series is
formalised as both a collection of situations occurring regularly over time and
unified according to some properties that are common to all the members, and a
situation itself, which provides a relational context to its members that
satisfy a reference description. Besides including some exemplifying instances
of this pattern, we show how it has been implemented and specialised to model
recurrent cultural events and ceremonies in ArCo, the Knowledge Graph of
Italian cultural heritage.
",An Ontology Design Pattern for representing Recurrent Situations
"  The applicability of common sentiment analysis models to open domain human
robot interaction is investigated within this paper. The models are used on a
dataset specific to user interaction with the Alana system (a Alexa prize
system) in order to determine which would be more appropriate for the task of
identifying sentiment when a user interacts with a non-human driven socialbot.
With the identification of a model, various improvements are attempted and
detailed prior to integration into the Alana system. The study showed that a
Random Forest Model with 25 trees trained on the dataset specific to user
interaction with the Alana system combined with the dataset present in NLTK
Vader outperforms other models. The new system (called 'Rob') matches it's
output utterance sentiment with the user's utterance sentiment. This method is
expected to improve user experience because it builds upon the overall
sentiment detection which makes it seem that new system sympathises with user
feelings. Furthermore, the results obtained from the user feedback confirms our
expectation.
",Sentiment Analysis for Open Domain Conversational Agent
"  Generalized planning is concerned with the computation of general policies
that solve multiple instances of a planning domain all at once. It has been
recently shown that these policies can be computed in two steps: first, a
suitable abstraction in the form of a qualitative numerical planning problem
(QNP) is learned from sample plans, then the general policies are obtained from
the learned QNP using a planner. In this work, we introduce an alternative
approach for computing more expressive general policies which does not require
sample plans or a QNP planner. The new formulation is very simple and can be
cast in terms that are more standard in machine learning: a large but finite
pool of features is defined from the predicates in the planning examples using
a general grammar, and a small subset of features is sought for separating
""good"" from ""bad"" state transitions, and goals from non-goals. The problems of
finding such a ""separating surface"" while labeling the transitions as ""good"" or
""bad"" are jointly addressed as a single combinatorial optimization problem
expressed as a Weighted Max-SAT problem. The advantage of looking for the
simplest policy in the given feature space that solves the given examples,
possibly non-optimally, is that many domains have no general, compact policies
that are optimal. The approach yields general policies for a number of
benchmark domains.
",Learning General Policies from Small Examples Without Supervision
"  Open-domain Question Answering (OpenQA) is an important task in Natural
Language Processing (NLP), which aims to answer a question in the form of
natural language based on large-scale unstructured documents. Recently, there
has been a surge in the amount of research literature on OpenQA, particularly
on techniques that integrate with neural Machine Reading Comprehension (MRC).
While these research works have advanced performance to new heights on
benchmark datasets, they have been rarely covered in existing surveys on QA
systems. In this work, we review the latest research trends in OpenQA, with
particular attention to systems that incorporate neural MRC techniques.
Specifically, we begin with revisiting the origin and development of OpenQA
systems. We then introduce modern OpenQA architecture named ""Retriever-Reader""
and analyze the various systems that follow this architecture as well as the
specific techniques adopted in each of the components. We then discuss key
challenges to developing OpenQA systems and offer an analysis of benchmarks
that are commonly used. We hope our work would enable researchers to be
informed of the recent advancement and also the open challenges in OpenQA
research, so as to stimulate further progress in this field.
","Retrieving and Reading: A Comprehensive Survey on Open-domain Question
  Answering"
"  This short paper describes an ongoing research project that requires the
automated self-play learning and evaluation of a large number of board games in
digital form. We describe the approach we are taking to determine relevant
features, for biasing MCTS playouts for arbitrary games played on arbitrary
geometries. Benefits of our approach include efficient implementation, the
potential to transfer learnt knowledge to new contexts, and the potential to
explain strategic knowledge embedded in features in human-comprehensible terms.
",Strategic Features for General Games
"  In various cases of decision analysis we use two popular methods: Analytical
Hierarchical Process (AHP) and Fuzzy based AHP or Fuzzy AHP. Both the methods
deal with stochastic data and can determine decision result through Multi
Criteria Decision Making (MCDM) process. Obviously resulting values of the two
methods are not same though same set of data is fed into them. In this research
work, we have tried to observe similarities and dissimilarities between two
methods outputs. Almost same trend or fluctuations in outputs have been seen
for both methods for same set of input data which are not consistent. Both
method outputs ups and down fluctuations are same for fifty percent cases.
",A Comparative Study of AHP and Fuzzy AHP Method for Inconsistent Data
"  Semantic parsing, as an important approach to question answering over
knowledge bases (KBQA), transforms a question into the complete query graph for
further generating the correct logical query. Existing semantic parsing
approaches mainly focus on relations matching with paying less attention to the
underlying internal structure of questions (e.g., the dependencies and
relations between all entities in a question) to select the query graph. In
this paper, we present a relational graph convolutional network (RGCN)-based
model gRGCN for semantic parsing in KBQA. gRGCN extracts the global semantics
of questions and their corresponding query graphs, including structure
semantics via RGCN and relational semantics (label representation of relations
between entities) via a hierarchical relation attention mechanism. Experiments
evaluated on benchmarks show that our model outperforms off-the-shelf models.
",Modeling Global Semantics for Question Answering over Knowledge Bases
"  With the growing capabilities of intelligent systems, the integration of
robots in our everyday life is increasing. However, when interacting in such
complex human environments, the occasional failure of robotic systems is
inevitable. The field of explainable AI has sought to make complex-decision
making systems more interpretable but most existing techniques target domain
experts. On the contrary, in many failure cases, robots will require recovery
assistance from non-expert users. In this work, we introduce a new type of
explanation, that explains the cause of an unexpected failure during an agent's
plan execution to non-experts. In order for error explanations to be
meaningful, we investigate what types of information within a set of
hand-scripted explanations are most helpful to non-experts for failure and
solution identification. Additionally, we investigate how such explanations can
be autonomously generated, extending an existing encoder-decoder model, and
generalized across environments. We investigate such questions in the context
of a robot performing a pick-and-place manipulation task in the home
environment. Our results show that explanations capturing the context of a
failure and history of past actions, are the most effective for failure and
solution identification among non-experts. Furthermore, through a second user
evaluation, we verify that our model-generated explanations can generalize to
an unseen office environment, and are just as effective as the hand-scripted
explanations.
","Explainable AI for Robot Failures: Generating Explanations that Improve
  User Assistance in Fault Recovery"
"  Meta-reinforcement learning (RL) addresses the problem of sample inefficiency
in deep RL by using experience obtained in past tasks for a new task to be
solved.
  However, most meta-RL methods require partially or fully on-policy data,
i.e., they cannot reuse the data collected by past policies, which hinders the
improvement of sample efficiency.
  To alleviate this problem, we propose a novel off-policy meta-RL method,
embedding learning and evaluation of uncertainty (ELUE).
  An ELUE agent is characterized by the learning of a feature embedding space
shared among tasks.
  It learns a belief model over the embedding space and a belief-conditional
policy and Q-function.
  Then, for a new task, it collects data by the pretrained policy, and updates
its belief based on the belief model.
  Thanks to the belief update, the performance can be improved with a small
amount of data.
  In addition, it updates the parameters of the neural networks to adjust the
pretrained relationships when there are enough data.
  We demonstrate that ELUE outperforms state-of-the-art meta RL methods through
experiments on meta-RL benchmarks.
",Off-Policy Meta-Reinforcement Learning Based on Feature Embedding Spaces
"  Two major considerations when encoding pseudo-Boolean (PB) constraints into
SAT are the size of the encoding and its propagation strength, that is, the
guarantee that it has a good behaviour under unit propagation. Several
encodings with propagation strength guarantees rely upon prior compilation of
the constraints into DNNF (decomposable negation normal form), BDD (binary
decision diagram), or some other sub-variants. However it has been shown that
there exist PB-constraints whose ordered BDD (OBDD) representations, and thus
the inferred CNF encodings, all have exponential size. Since DNNFs are more
succinct than OBDDs, preferring encodings via DNNF to avoid size explosion
seems a legitimate choice. Yet in this paper, we prove the existence of
PB-constraints whose DNNFs all require exponential size.
",A Lower Bound on DNNF Encodings of Pseudo-Boolean Constraints
"  In this paper, we release an open-source library, called TextBox, to provide
a unified, modularized, and extensible text generation framework. TextBox aims
to support a broad set of text generation tasks and models. In our library, we
implement 21 text generation models on 9 benchmark datasets, covering the
categories of VAE, GAN, and pretrained language models. Meanwhile, our library
maintains sufficient modularity and extensibility by properly decomposing the
model architecture, inference, and learning process into highly reusable
modules, which allows users to easily incorporate new models into our
framework. The above features make TextBox specially suitable for researchers
and practitioners to quickly reproduce baseline models and develop new models.
TextBox is implemented based on PyTorch, and released under Apache License 2.0
at https://github.com/RUCAIBox/TextBox.
","TextBox: A Unified, Modularized, and Extensible Framework for Text
  Generation"
"  This technical report outlines the fundamental workings of the game logic
behind Ludii, a general game system, that can be used to play a wide variety of
games. Ludii is a program developed for the ERC-funded Digital Ludeme Project,
in which mathematical and computational approaches are used to study how games
were played, and spread, throughout history. This report explains how general
game states and equipment are represented in Ludii, and how the rule ludemes
dictating play are implemented behind the scenes, giving some insight into the
core game logic behind the Ludii general game player. This guide is intended to
help game designers using the Ludii game description language to understand it
more completely and make fuller use of its features when describing their
games.
",Ludii Game Logic Guide
"  In this study I proposed a filtering beliefs method for improving performance
of Partially Observable Markov Decision Processes(POMDPs), which is a method
wildly used in autonomous robot and many other domains concerning control
policy. My method search and compare every similar belief pair. Because a
similar belief have insignificant influence on control policy, the belief is
filtered out for reducing training time. The empirical results show that the
proposed method outperforms the point-based approximate POMDPs in terms of the
quality of training results as well as the efficiency of the method.
","Improving Training Result of Partially Observable Markov Decision
  Process by Filtering Beliefs"
"  A short review of the literature on measurement and detection of artificial
general intelligence is made. Proposed benchmarks and tests for artificial
general intelligence are critically evaluated against multiple criteria. Based
on the findings, the most promising approaches are identified and some useful
directions for future work are proposed.
",The case for psychometric artificial general intelligence
"  In real time electricity markets, the objective of generation companies while
bidding is to maximize their profit. The strategies for learning optimal
bidding have been formulated through game theoretical approaches and stochastic
optimization problems. Similar studies in reactive power markets have not been
reported so far because the network voltage operating conditions have an
increased impact on reactive power markets than on active power markets.
Contrary to active power markets, the bids of rivals are not directly related
to fuel costs in reactive power markets. Hence, the assumption of a suitable
probability distribution function is unrealistic, making the strategies adopted
in active power markets unsuitable for learning optimal bids in reactive power
market mechanisms. Therefore, a bidding strategy is to be learnt from market
observations and experience in imperfect oligopolistic competition-based
markets. In this paper, a pioneer work on learning optimal bidding strategies
from observation and experience in a three-stage reactive power market is
reported.
","Neural Fitted Q Iteration based Optimal Bidding Strategy in Real Time
  Reactive Power Market_1"
"  Most typical click models assume that the probability of a document to be
examined by users only depends on position, such as PBM and UBM. It works well
in various kinds of search engines. However, in a search engine where massive
candidate documents display images as responses to the query, the examination
probability should not only depend on position. The visual appearance of an
image-oriented document also plays an important role in its opportunity to be
examined. In this paper, we assume that vision bias exists in an image-oriented
search engine as another crucial factor affecting the examination probability
aside from position. Specifically, we apply this assumption to classical click
models and propose an extended model, to better capture the examination
probabilities of documents. We use regression-based EM algorithm to predict the
vision bias given the visual features extracted from candidate documents.
Empirically, we evaluate our model on a dataset developed from a real-world
online image-oriented search engine, and demonstrate that our proposed model
can achieve significant improvements over its baseline model in data fitness
and sparsity handling.
","Incorporating Vision Bias into Click Models for Image-oriented Search
  Engine"
"  In this paper, we study the problem of mobile user profiling, which is a
critical component for quantifying users' characteristics in the human mobility
modeling pipeline. Human mobility is a sequential decision-making process
dependent on the users' dynamic interests. With accurate user profiles, the
predictive model can perfectly reproduce users' mobility trajectories. In the
reverse direction, once the predictive model can imitate users' mobility
patterns, the learned user profiles are also optimal. Such intuition motivates
us to propose an imitation-based mobile user profiling framework by exploiting
reinforcement learning, in which the agent is trained to precisely imitate
users' mobility patterns for optimal user profiles. Specifically, the proposed
framework includes two modules: (1) representation module, which produces state
combining user profiles and spatio-temporal context in real-time; (2) imitation
module, where Deep Q-network (DQN) imitates the user behavior (action) based on
the state that is produced by the representation module. However, there are two
challenges in running the framework effectively. First, epsilon-greedy strategy
in DQN makes use of the exploration-exploitation trade-off by randomly pick
actions with the epsilon probability. Such randomness feeds back to the
representation module, causing the learned user profiles unstable. To solve the
problem, we propose an adversarial training strategy to guarantee the
robustness of the representation module. Second, the representation module
updates users' profiles in an incremental manner, requiring integrating the
temporal effects of user profiles. Inspired by Long-short Term Memory (LSTM),
we introduce a gated mechanism to incorporate new and old user characteristics
into the user profile.
","Reinforced Imitative Graph Representation Learning for Mobile User
  Profiling: An Adversarial Training Perspective"
"  Artificial Intelligence (AI) is being increasingly deployed in practical
applications. However, there is a major concern whether AI systems will be
trusted by humans. In order to establish trust in AI systems, there is a need
for users to understand the reasoning behind their solutions. Therefore,
systems should be able to explain and justify their output. In this paper, we
propose an argument scheme-based approach to provide explanations in the domain
of AI planning. We present novel argument schemes to create arguments that
explain a plan and its key elements; and a set of critical questions that allow
interaction between the arguments and enable the user to obtain further
information regarding the key elements of the plan. Furthermore, we present a
novel dialogue system using the argument schemes and critical questions for
providing interactive dialectical explanations.
",Argument Schemes and Dialogue for Explainable Planning
"  Artificial Intelligence (AI) is a discipline of computer science that deals
with machine intelligence. It is essential to bring AI into the context of
learning because it helps in analysing the enormous amounts of data that is
collected from individual students, teachers and academic staff. The major
priorities of implementing AI in education are making innovative use of
existing digital technologies for learning, and teaching practices that
significantly improve traditional educational methods. The main problem with
traditional learning is that it cannot be suited to every student in class.
Some students may grasp the concepts well, while some may have difficulties in
understanding them and some may be more auditory or visual learners. The World
Bank report on education has indicated that the learning gap created by this
problem causes many students to drop out (World Development Report, 2018).
Personalised learning has been able to solve this grave problem.
",Artificial Intelligence enabled Smart Learning
"  Despite years of research into patient falls in hospital rooms, falls and
related injuries remain a serious concern to patient safety. In this work, we
formulate a gradient-free constrained optimization problem to generate and
reconfigure the hospital room interior layout to minimize the risk of falls. We
define a cost function built on a hospital room fall model that takes into
account the supportive or hazardous effect of the patient's surrounding
objects, as well as simulated patient trajectories inside the room. We define a
constraint set that ensures the functionality of the generated room layouts in
addition to conforming to architectural guidelines. We solve this problem
efficiently using a variant of simulated annealing. We present results for two
real-world hospital room types and demonstrate a significant improvement of 18%
on average in patient fall risk when compared with a traditional hospital room
layout and 41% when compared with randomly generated layouts.
",Optimizing Hospital Room Layout to Reduce the Risk of Patient Falls
"  Nested Rollout Policy Adaptation (NRPA) is a Monte Carlo search algorithm for
single player games. In this paper we propose to modify NRPA in order to
improve the stability of the algorithm. Experiments show it improves the
algorithm for different application domains: SameGame, Traveling Salesman with
Time Windows and Expression Discovery.
",Stabilized Nested Rollout Policy Adaptation
"  We investigate a learning decision support system for vehicle routing, where
the routing engine learns implicit preferences that human planners have when
manually creating route plans (or routings). The goal is to use these learned
subjective preferences on top of the distance-based objective criterion in
vehicle routing systems. This is an alternative to the practice of
distinctively formulating a custom VRP for every company with its own routing
requirements. Instead, we assume the presence of past vehicle routing solutions
over similar sets of customers, and learn to make similar choices. The learning
approach is based on the concept of learning a Markov model, which corresponds
to a probabilistic transition matrix, rather than a deterministic distance
matrix. This nevertheless allows us to use existing arc routing VRP software in
creating the actual routings, and to optimize over both distances and
preferences at the same time. For the learning, we explore different schemes to
construct the probabilistic transition matrix that can co-evolve with changing
preferences over time. Our results on a use-case with a small transportation
company show that our method is able to generate results that are close to the
manually created solutions, without needing to characterize all constraints and
sub-objectives explicitly. Even in the case of changes in the customer sets,
our method is able to find solutions that are closer to the actual routings
than when using only distances, and hence, solutions that require fewer manual
changes when transformed into practical routings.
",Learn-n-Route: Learning implicit preferences for vehicle routing
"  We present DEGARI (Dynamic Emotion Generator And ReclassIfier), an
explainable system for emotion attribution and recommendation. This system
relies on a recently introduced commonsense reasoning framework, the TCL logic,
which is based on a human-like procedure for the automatic generation of novel
concepts in a Description Logics knowledge base. Starting from an ontological
formalization of emotions based on the Plutchik model, known as ArsEmotica, the
system exploits the logic TCL to automatically generate novel commonsense
semantic representations of compound emotions (e.g. Love as derived from the
combination of Joy and Trust according to Plutchik). The generated emotions
correspond to prototypes, i.e. commonsense representations of given concepts,
and have been used to reclassify emotion-related contents in a variety of
artistic domains, ranging from art datasets to the editorial contents available
in RaiPlay, the online platform of RAI Radiotelevisione Italiana (the Italian
public broadcasting company). We show how the reported results (evaluated in
the light of the obtained reclassifications, the user ratings assigned to such
reclassifications, and their explainability) are encouraging, and pave the way
to many further research directions.
","A Commonsense Reasoning Framework for Explanatory Emotion Attribution,
  Generation and Re-classification"
"  Commonsense knowledge is essential for many AI applications, including those
in natural language processing, visual processing, and planning. Consequently,
many sources that include commonsense knowledge have been designed and
constructed over the past decades. Recently, the focus has been on large
text-based sources, which facilitate easier integration with neural (language)
models and application to textual tasks, typically at the expense of the
semantics of the sources and their harmonization. Efforts to consolidate
commonsense knowledge have yielded partial success, with no clear path towards
a comprehensive solution. We aim to organize these sources around a common set
of dimensions of commonsense knowledge. We survey a wide range of popular
commonsense sources with a special focus on their relations. We consolidate
these relations into 13 knowledge dimensions. This consolidation allows us to
unify the separate sources and to compute indications of their coverage,
overlap, and gaps with respect to the knowledge dimensions. Moreover, we
analyze the impact of each dimension on downstream reasoning tasks that require
commonsense knowledge, observing that the temporal and desire/goal dimensions
are very beneficial for reasoning on current downstream tasks, while
distinctness and lexical knowledge have little impact. These results reveal
preferences for some dimensions in current evaluation, and potential neglect of
others.
",Dimensions of Commonsense Knowledge
"  Meta-Interpretive Learners, like most ILP systems, learn by searching for a
correct hypothesis in the hypothesis space, the powerset of all constructible
clauses. We show how this exponentially-growing search can be replaced by the
construction of a Top program: the set of clauses in all correct hypotheses
that is itself a correct hypothesis. We give an algorithm for Top program
construction and show that it constructs a correct Top program in polynomial
time and from a finite number of examples. We implement our algorithm in Prolog
as the basis of a new MIL system, Louise, that constructs a Top program and
then reduces it by removing redundant clauses. We compare Louise to the
state-of-the-art search-based MIL system Metagol in experiments on grid world
navigation, graph connectedness and grammar learning datasets and find that
Louise improves on Metagol's predictive accuracy when the hypothesis space and
the target theory are both large, or when the hypothesis space does not include
a correct hypothesis because of ""classification noise"" in the form of
mislabelled examples. When the hypothesis space or the target theory are small,
Louise and Metagol perform equally well.
","Top Program Construction and Reduction for polynomial time
  Meta-Interpretive Learning"
"  The notion of concept has been studied for centuries, by philosophers,
linguists, cognitive scientists, and researchers in artificial intelligence
(Margolis & Laurence, 1999). There is a large literature on formal,
mathematical models of concepts, including a whole sub-field of AI -- Formal
Concept Analysis -- devoted to this topic (Ganter & Obiedkov, 2016). Recently,
researchers in machine learning have begun to investigate how methods from
representation learning can be used to induce concepts from raw perceptual data
(Higgins, Sonnerat, et al., 2018). The goal of this report is to provide a
formal account of concepts which is compatible with this latest work in deep
learning.
  The main technical goal of this report is to show how techniques from
representation learning can be married with a lattice-theoretic formulation of
conceptual spaces. The mathematics of partial orders and lattices is a standard
tool for modelling conceptual spaces (Ch.2, Mitchell (1997), Ganter and
Obiedkov (2016)); however, there is no formal work that we are aware of which
defines a conceptual lattice on top of a representation that is induced using
unsupervised deep learning (Goodfellow et al., 2016). The advantages of
partially-ordered lattice structures are that these provide natural mechanisms
for use in concept discovery algorithms, through the meets and joins of the
lattice.
",Formalising Concepts as Grounded Abstractions
"  Computer modeling of human decision making is of large importance for, e.g.,
sustainable transport, urban development, and online recommendation systems. In
this paper we present a model for predicting the behavior of an individual
during a binary game under different amounts of risk, gain, and time pressure.
The model is based on Quantum Decision Theory (QDT), which has been shown to
enable modeling of the irrational and subjective aspects of the decision
making, not accounted for by the classical Cumulative Prospect Theory (CPT).
Experiments on two different datasets show that our QDT-based approach
outperforms both a CPT-based approach and data driven approaches such as
feed-forward neural networks and random forests.
","A Subjective Model of Human Decision Making Based on Quantum Decision
  Theory"
"  This paper explores the state of the art on to methods to verify and validate
navigation algorithms for autonomous surface ships. We perform a systematic
mapping study to find research works published in the last 10 years proposing
new algorithms for autonomous navigation and collision avoidance and we have
extracted what verification and validation approaches have been applied on
these algorithms. We observe that most research works use simulations to
validate their algorithms. However, these simulations often involve just a few
scenarios designed manually. This raises the question if the algorithms have
been validated properly. To remedy this, we propose the use of a systematic
scenario-based testing approach to validate navigation algorithms extensively.
",On the Verification and Validation of AI Navigation Algorithms
"  Width-based search methods have demonstrated state-of-the-art performance in
a wide range of testbeds, from classical planning problems to image-based
simulators such as Atari games. These methods scale independently of the size
of the state-space, but exponentially in the problem width. In practice,
running the algorithm with a width larger than 1 is computationally
intractable, prohibiting IW from solving higher width problems. In this paper,
we present a hierarchical algorithm that plans at two levels of abstraction. A
high-level planner uses abstract features that are incrementally discovered
from low-level pruning decisions. We illustrate this algorithm in classical
planning PDDL domains as well as in pixel-based simulator domains. In classical
planning, we show how IW(1) at two levels of abstraction can solve problems of
width 2. For pixel-based domains, we show how in combination with a learned
policy and a learned value function, the proposed hierarchical IW can
outperform current flat IW-based planners in Atari games with sparse rewards.
",Hierarchical Width-Based Planning and Learning
"  Knowledge tracing (KT) is the problem of modeling each student's mastery of
knowledge concepts (KCs) as (s)he engages with a sequence of learning
activities. It is an active research area to help provide learners with
personalized feedback and materials. Various deep learning techniques have been
proposed for solving KT. Recent release of large-scale student performance
dataset \cite{choi2019ednet} motivates the analysis of performance of deep
learning approaches that have been proposed to solve KT. Our analysis can help
understand which method to adopt when large dataset related to student
performance is available. We also show that incorporating contextual
information such as relation between exercises and student forget behavior
further improves the performance of deep learning models.
","An Empirical Comparison of Deep Learning Models for Knowledge Tracing on
  Large-Scale Dataset"
"  With the rapid development of biomedical software and hardware, a large
amount of relational data interlinking genes, proteins, chemical components,
drugs, diseases, and symptoms has been collected for modern biomedical
research. Many graph-based learning methods have been proposed to analyze such
type of data, giving a deeper insight into the topology and knowledge behind
the biomedical data, which greatly benefit to both academic research and
industrial application for human healthcare. However, the main difficulty is
how to handle high dimensionality and sparsity of the biomedical graphs.
Recently, graph embedding methods provide an effective and efficient way to
address the above issues. It converts graph-based data into a low dimensional
vector space where the graph structural properties and knowledge information
are well preserved. In this survey, we conduct a literature review of recent
developments and trends in applying graph embedding methods for biomedical
data. We also introduce important applications and tasks in the biomedical
domain as well as associated public biomedical datasets.
","A Literature Review of Recent Graph Embedding Techniques for Biomedical
  Data"
"  Current Artificial Intelligence (AI) methods, most based on deep learning,
have facilitated progress in several fields, including computer vision and
natural language understanding. The progress of these AI methods is measured
using benchmarks designed to solve challenging tasks, such as visual question
answering. A question remains of how much understanding is leveraged by these
methods and how appropriate are the current benchmarks to measure understanding
capabilities. To answer these questions, we have analysed existing benchmarks
and their understanding capabilities, defined by a set of understanding
capabilities, and current research streams. We show how progress has been made
in benchmark development to measure understanding capabilities of AI methods
and we review as well how current methods develop understanding capabilities.
",Understanding in Artificial Intelligence
"  With the powerful learning ability of deep convolutional networks, deep
clustering methods can extract the most discriminative information from
individual data and produce more satisfactory clustering results. However,
existing deep clustering methods usually ignore the relationship between the
data. Fortunately, the graph convolutional network can handle such
relationship, opening up a new research direction for deep clustering. In this
paper, we propose a cross-attention based deep clustering framework, named
Cross-Attention Fusion based Enhanced Graph Convolutional Network (CaEGCN),
which contains four main modules: the cross-attention fusion module which
innovatively concatenates the Content Auto-encoder module (CAE) relating to the
individual data and Graph Convolutional Auto-encoder module (GAE) relating to
the relationship between the data in a layer-by-layer manner, and the
self-supervised model that highlights the discriminative information for
clustering tasks. While the cross-attention fusion module fuses two kinds of
heterogeneous representation, the CAE module supplements the content
information for the GAE module, which avoids the over-smoothing problem of GCN.
In the GAE module, two novel loss functions are proposed that reconstruct the
content and relationship between the data, respectively. Finally, the
self-supervised module constrains the distributions of the middle layer
representations of CAE and GAE to be consistent. Experimental results on
different types of datasets prove the superiority and robustness of the
proposed CaEGCN.
","CaEGCN: Cross-Attention Fusion based Enhanced Graph Convolutional
  Network for Clustering"
"  Behavioural symptoms and urinary tract infections (UTI) are among the most
common problems faced by people with dementia. One of the key challenges in the
management of these conditions is early detection and timely intervention in
order to reduce distress and avoid unplanned hospital admissions. Using in-home
sensing technologies and machine learning models for sensor data integration
and analysis provides opportunities to detect and predict clinically
significant events and changes in health status. We have developed an
integrated platform to collect in-home sensor data and performed an
observational study to apply machine learning models for agitation and UTI risk
analysis. We collected a large dataset from 88 participants with a mean age of
82 and a standard deviation of 6.5 (47 females and 41 males) to evaluate a new
deep learning model that utilises attention and rational mechanism. The
proposed solution can process a large volume of data over a period of time and
extract significant patterns in a time-series data (i.e. attention) and use the
extracted features and patterns to train risk analysis models (i.e. rational).
The proposed model can explain the predictions by indicating which time-steps
and features are used in a long series of time-series data. The model provides
a recall of 91\% and precision of 83\% in detecting the risk of agitation and
UTIs. This model can be used for early detection of conditions such as UTIs and
managing of neuropsychiatric symptoms such as agitation in association with
initial treatment and early intervention approaches. In our study we have
developed a set of clinical pathways for early interventions using the alerts
generated by the proposed model and a clinical monitoring team has been set up
to use the platform and respond to the alerts according to the created
intervention plans.
","An attention model to analyse the risk of agitation and urinary tract
  infections in people with dementia"
"  The information describing the conditions of a system or a person is
constantly evolving and may become obsolete and contradict other information. A
database, therefore, must be consistently updated upon the acquisition of new
valid observations that contradict obsolete ones contained in the database. In
this paper, we propose a novel approach for dealing with the information
obsolescence problem. Our approach aims to detect, in real-time, contradictions
between observations and then identify the obsolete ones, given a
representation model. Since we work within an uncertain environment
characterized by the lack of information, we choose to use a Bayesian network
as our representation model and propose a new approximate concept,
$\epsilon$-Contradiction. The new concept is parameterised by a confidence
level of having a contradiction in a set of observations. We propose a
polynomial-time algorithm for detecting obsolete information. We show that the
resulting obsolete information is better represented by an AND-OR tree than a
simple set of observations. Finally, we demonstrate the effectiveness of our
approach on a real elderly fall-prevention database and showcase how this tree
can be used to give reliable recommendations to doctors. Our experiments give
systematically and substantially very good results.
","Data Obsolescence Detection in the Light of Newly Acquired Valid
  Observations"
"  Recently, hetero-functional graph theory (HFGT) has developed as a means to
mathematically model the structure of large flexible engineering systems. In
that regard, it intellectually resembles a fusion of network science and
model-based systems engineering. With respect to the former, it relies on
multiple graphs as data structures so as to support matrix-based quantitative
analysis. In the meantime, HFGT explicitly embodies the heterogeneity of
conceptual and ontological constructs found in model-based systems engineering
including system form, system function, and system concept. At their
foundation, these disparate conceptual constructs suggest multi-dimensional
rather than two-dimensional relationships. This paper provides the first
tensor-based treatment of some of the most important parts of hetero-functional
graph theory. In particular, it addresses the ""system concept"", the
hetero-functional adjacency matrix, and the hetero-functional incidence tensor.
The tensor-based formulation described in this work makes a stronger tie
between HFGT and its ontological foundations in MBSE. Finally, the tensor-based
formulation facilitates an understanding of the relationships between HFGT and
multi-layer networks.
",A Tensor-Based Formulation of Hetero-functional Graph Theory
"  Complex machine learning models are deployed in several critical domains
including healthcare and autonomous vehicles nowadays, albeit as functional
black boxes. Consequently, there has been a recent surge in interpreting
decisions of such complex models in order to explain their actions to humans.
Models that correspond to human interpretation of a task are more desirable in
certain contexts and can help attribute liability, build trust, expose biases
and in turn build better models. It is, therefore, crucial to understand how
and which models conform to human understanding of tasks. In this paper, we
present a large-scale crowdsourcing study that reveals and quantifies the
dissonance between human and machine understanding, through the lens of an
image classification task. In particular, we seek to answer the following
questions: Which (well-performing) complex ML models are closer to humans in
their use of features to make accurate predictions? How does task difficulty
affect the feature selection capability of machines in comparison to humans?
Are humans consistently better at selecting features that make image
recognition more accurate? Our findings have important implications on
human-machine collaboration, considering that a long term goal in the field of
artificial intelligence is to make machines capable of learning and reasoning
like humans.
",Dissonance Between Human and Machine Understanding
"  It is argued that a fuzzy version of 4-truth-valued paraconsistent logic
(with truth values corresponding to True, False, Both and Neither) can be
approximately isomorphically mapped into the complex-number algebra of quantum
probabilities. I.e., p-bits (paraconsistent bits) can be transformed into close
approximations of qubits. The approximation error can be made arbitrarily
small, at least in a formal sense, and can be related to the degree of
irreducible ""evidential error"" assumed to plague an observer's observations.
This logical correspondence manifests itself in program space via an
approximate mapping between probabilistic and quantum types in programming
languages.
",Paraconsistent Foundations for Quantum Probability
"  For some scientific questions, empirical data are essential to develop
reliable simulation models. These data usually come from different sources with
diverse and heterogeneous formats. The design of complex data-driven models is
often shaped by the structure of the data available in research projects.
Hence, applying such models to other case studies requires either to get
similar data or to transform new data to fit the model inputs. It is the case
of agent-based models (ABMs) that use advanced data structures such as
Geographic Information Systems data. We faced this problem in the LittoSIM-GEN
project when generalizing our participatory flooding model (LittoSIM) to new
territories. From this experience, we provide a mapping approach to structure,
describe, and automatize the integration of geospatial data into ABMs.
","Mapping and Describing Geospatial Data to Generalize Complex Mapping and
  Describing Geospatial Data to Generalize Complex Models: The Case of
  LittoSIM-GEN Models"
"  Contributions: The Chinese University of Hong Kong (CUHK)-Jockey Club AI for
the Future Project (AI4Future) co-created an AI curriculum for pre-tertiary
education and evaluated its efficacy. While AI is conventionally taught in
tertiary level education, our co-creation process successfully developed the
curriculum that has been used in secondary school teaching in Hong Kong and
received positive feedback. Background: AI4Future is a cross-sector project
that engages five major partners - CUHK Faculty of Engineering and Faculty of
Education, Hong Kong secondary schools, the government and the AI industry. A
team of 14 professors with expertise in engineering and education collaborated
with 17 principals and teachers from 6 secondary schools to co-create the
curriculum. This team formation bridges the gap between researchers in
engineering and education, together with practitioners in education context.
Research Questions: What are the main features of the curriculum content
developed through the co-creation process? Would the curriculum significantly
improve the students perceived competence in, as well as attitude and
motivation towards AI? What are the teachers perceptions of the co-creation
process that aims to accommodate and foster teacher autonomy? Methodology: This
study adopted a mix of quantitative and qualitative methods and involved 335
student participants. Findings: 1) two main features of learning resources, 2)
the students perceived greater competence, and developed more positive attitude
to learn AI, and 3) the co-creation process generated a variety of resources
which enhanced the teachers knowledge in AI, as well as fostered teachers
autonomy in bringing the subject matter into their classrooms.
","Creation and Evaluation of a Pre-tertiary Artificial Intelligence (AI)
  Curriculum"
"  Logical theories in the form of ontologies and similar artefacts in computing
and IT are used for structuring, annotating, and querying data, among others,
and therewith influence data analytics regarding what is fed into the
algorithms. Algorithmic bias is a well-known notion, but what does bias mean in
the context of ontologies that provide a structuring mechanism for an
algorithm's input? What are the sources of bias there and how would they
manifest themselves in ontologies? We examine and enumerate types of bias
relevant for ontologies, and whether they are explicit or implicit. These eight
types are illustrated with examples from extant production-level ontologies and
samples from the literature. We then assessed three concurrently developed
COVID-19 ontologies on bias and detected different subsets of types of bias in
each one, to a greater or lesser extent. This first characterisation aims
contribute to a sensitisation of ethical aspects of ontologies primarily
regarding representation of information and knowledge.
",Bias in ontologies -- a preliminary assessment
"  Deep reinforcement learning (DRL) is applied in safety-critical domains such
as robotics and autonomous driving. It achieves superhuman abilities in many
tasks, however whether DRL agents can be shown to act safely is an open
problem. Atari games are a simple yet challenging exemplar for evaluating the
safety of DRL agents and feature a diverse portfolio of game mechanics. The
safety of neural agents has been studied before using methods that either
require a model of the system dynamics or an abstraction; unfortunately, these
are unsuitable to Atari games because their low-level dynamics are complex and
hidden inside their emulator. We present the first exact method for analysing
and ensuring the safety of DRL agents for Atari games. Our method only requires
access to the emulator. First, we give a set of 43 properties that characterise
""safe behaviour"" for 30 games. Second, we develop a method for exploring all
traces induced by an agent and a game and consider a variety of sources of game
non-determinism. We observe that the best available DRL agents reliably satisfy
only very few properties; several critical properties are violated by all
agents. Finally, we propose a countermeasure that combines a bounded
explicit-state exploration with shielding. We demonstrate that our method
improves the safety of all agents over multiple properties.
",Shielding Atari Games with Bounded Prescience
"  Autonomous trading robots have been studied in ar-tificial intelligence area
for quite some time. Many AI techniqueshave been tested in finance field
including recent approaches likeconvolutional neural networks and deep
reinforcement learning.There are many reported cases, where the developers are
suc-cessful in creating robots with great performance when executingwith
historical price series, so called backtesting. However, whenthese robots are
used in real markets or data not used intheir training or evaluation frequently
they present very poorperformance in terms of risks and return. In this paper,
wediscussed some fundamental aspects of modelling autonomoustraders and the
complex environment that is the financialworld. Furthermore, we presented a
framework that helps thedevelopment and testing of autonomous traders. It may
also beused in real or simulated operation in financial markets. Finally,we
discussed some open problems in the area and pointed outsome interesting
technologies that may contribute to advancein such task. We believe that mt5b3
may also contribute todevelopment of new autonomous traders.
",mt5b3: A Framework for Building AutonomousTraders
"  There have been several research works proposing new Explainable AI (XAI)
methods designed to generate model explanations having specific properties, or
desiderata, such as fidelity, robustness, or human-interpretability. However,
explanations are seldom evaluated based on their true practical impact on
decision-making tasks. Without that assessment, explanations might be chosen
that, in fact, hurt the overall performance of the combined system of ML model
+ end-users. This study aims to bridge this gap by proposing XAI Test, an
application-grounded evaluation methodology tailored to isolate the impact of
providing the end-user with different levels of information. We conducted an
experiment following XAI Test to evaluate three popular post-hoc explanation
methods -- LIME, SHAP, and TreeInterpreter -- on a real-world fraud detection
task, with real data, a deployed ML model, and fraud analysts. During the
experiment, we gradually increased the information provided to the fraud
analysts in three stages: Data Only, i.e., just transaction data without access
to model score nor explanations, Data + ML Model Score, and Data + ML Model
Score + Explanations. Using strong statistical analysis, we show that, in
general, these popular explainers have a worse impact than desired. Some of the
conclusion highlights include: i) showing Data Only results in the highest
decision accuracy and the slowest decision time among all variants tested, ii)
all the explainers improve accuracy over the Data + ML Model Score variant but
still result in lower accuracy when compared with Data Only; iii) LIME was the
least preferred by users, probably due to its substantially lower variability
of explanations from case to case.
","How can I choose an explainer? An Application-grounded Evaluation of
  Post-hoc Explanations"
"  The primary objective of this work is to develop a Neural Network based on
LSTM to predict stock market movements using tweets. Word embeddings, used in
the LSTM network, are initialised using Stanford's GloVe embeddings, pretrained
specifically on 2 billion tweets. To overcome the limited size of the dataset,
an augmentation strategy is proposed to split each input sequence into 150
subsets. To achieve further improvements in the original configuration,
hyperparameter optimisation is performed. The effects of variation in
hyperparameters such as dropout rate, batch size, and LSTM hidden state output
size are assessed individually. Furthermore, an exhaustive set of parameter
combinations is examined to determine the optimal model configuration. The best
performance on the validation dataset is achieved by hyperparameter combination
0.4,8,100 for the dropout, batch size, and hidden units respectively. The final
testing accuracy of the model is 76.14%.
",Artificial intelligence prediction of stock prices using social media
"  The partially observable card game Hanabi has recently been proposed as a new
AI challenge problem due to its dependence on implicit communication
conventions and apparent necessity of theory of mind reasoning for efficient
play. In this work, we propose a mechanism for imbuing Reinforcement Learning
agents with a theory of mind to discover efficient cooperative strategies in
Hanabi. The primary contributions of this work are threefold: First, a formal
definition of a computationally tractable mechanism for computing hand
probabilities in Hanabi. Second, an extension to conventional Deep
Reinforcement Learning that introduces reasoning over finitely nested theory of
mind belief hierarchies. Finally, an intrinsic reward mechanism enabled by
theory of mind that incentivizes agents to share strategically relevant private
knowledge with their teammates. We demonstrate the utility of our algorithm
against Rainbow, a state-of-the-art Reinforcement Learning agent.
",Theory of Mind for Deep Reinforcement Learning in Hanabi
"  Attribute reduction is one of the most important research topics in the
theory of rough sets, and many rough sets-based attribute reduction methods
have thus been presented. However, most of them are specifically designed for
dealing with either labeled data or unlabeled data, while many real-world
applications come in the form of partial supervision. In this paper, we propose
a rough sets-based semi-supervised attribute reduction method for partially
labeled data. Particularly, with the aid of prior class distribution
information about data, we first develop a simple yet effective strategy to
produce the proxy labels for unlabeled data. Then the concept of information
granularity is integrated into the information-theoretic measure, based on
which, a novel granular conditional entropy measure is proposed, and its
monotonicity is proved in theory. Furthermore, a fast heuristic algorithm is
provided to generate the optimal reduct of partially labeled data, which could
accelerate the process of attribute reduction by removing irrelevant examples
and excluding redundant attributes simultaneously. Extensive experiments
conducted on UCI data sets demonstrate that the proposed semi-supervised
attribute reduction method is promising and even compares favourably with the
supervised methods on labeled data and unlabeled data with true labels in terms
of classification performance.
","Granular conditional entropy-based attribute reduction for partially
  labeled data with proxy labels"
"  Combinations of Monte-Carlo tree search and Deep Neural Networks, trained
through self-play, have produced state-of-the-art results for automated
game-playing in many board games. The training and search algorithms are not
game-specific, but every individual game that these approaches are applied to
still requires domain knowledge for the implementation of the game's rules, and
constructing the neural network's architecture -- in particular the shapes of
its input and output tensors. Ludii is a general game system that already
contains over 500 different games, which can rapidly grow thanks to its
powerful and user-friendly game description language. Polygames is a framework
with training and search algorithms, which has already produced superhuman
players for several board games. This paper describes the implementation of a
bridge between Ludii and Polygames, which enables Polygames to train and
evaluate models for games that are implemented and run through Ludii. We do not
require any game-specific domain knowledge anymore, and instead leverage our
domain knowledge of the Ludii system and its abstract state and move
representations to write functions that can automatically determine the
appropriate shapes for input and output tensors for any game implemented in
Ludii. We describe experimental results for short training runs in a wide
variety of different board games, and discuss several open problems and avenues
for future research.
",Deep Learning for General Game Playing with Ludii and Polygames
"  Sampling is a popular method for approximate inference when exact inference
is impractical. Generally, sampling algorithms do not exploit context-specific
independence (CSI) properties of probability distributions. We introduce
context-specific likelihood weighting (CS-LW), a new sampling methodology,
which besides exploiting the classical conditional independence properties,
also exploits CSI properties. Unlike the standard likelihood weighting, CS-LW
is based on partial assignments of random variables and requires fewer samples
for convergence due to the sampling variance reduction. Furthermore, the speed
of generating samples increases. Our novel notion of contextual assignments
theoretically justifies CS-LW. We empirically show that CS-LW is competitive
with state-of-the-art algorithms for approximate inference in the presence of a
significant amount of CSIs.
",Context-Specific Likelihood Weighting
"  Many complex activities of production cycles, such as quality control or
fault analysis, require highly experienced specialists to perform various
operations on (semi)finished products using different tools. In practical
scenarios, the selection of a next operation is complicated, since each expert
has only a local view on the total set of operations to be performed. As a
result, decisions made by the specialists are suboptimal and might cause
significant costs. In this paper, we consider a Multi-resource Partial-ordering
Flexible Job-shop Scheduling (MPF-JSS) problem where partially-ordered
sequences of operations must be scheduled on multiple required resources, such
as tools and specialists. The resources are flexible and can perform one or
more operations depending on their properties. The problem is modeled using
Answer Set Programming (ASP) in which the time assignments are efficiently done
using Difference Logic. Moreover, we suggest two multi-shot solving strategies
aiming at the identification of the time bounds allowing for a solution of the
schedule optimization problem. Experiments conducted on a set of instances
extracted from a medium-sized semiconductor fault analysis lab indicate that
our approach can find schedules for 87 out of 91 considered real-world
instances.
","Solving a Multi-resource Partial-ordering Flexible Variant of the
  Job-shop Scheduling Problem with Hybrid ASP"
"  When human cognition is modeled in Philosophy and Cognitive Science, there is
a pervasive idea that humans employ mental representations in order to navigate
the world and make predictions about outcomes of future actions. By
understanding how these representational structures work, we not only
understand more about human cognition but also gain a better understanding for
how humans rationalise and explain decisions. This has an influencing effect on
explainable AI, where the goal is to provide explanations of computer
decision-making for a human audience. We show that the Contextual Importance
and Utility method for XAI share an overlap with the current new wave of
action-oriented predictive representational structures, in ways that makes CIU
a reliable tool for creating explanations that humans can relate to and trust.
",Cognitive Perspectives on Context-based Decisions and Explanations
"  In many problem settings, most notably in game playing, an agent receives a
possibly delayed reward for its actions. Often, those rewards are handcrafted
and not naturally given. Even simple terminal-only rewards, like winning equals
one and losing equals minus one, can not be seen as an unbiased statement,
since these values are chosen arbitrarily, and the behavior of the learner may
change with different encodings. It is hard to argue about good rewards and the
performance of an agent often depends on the design of the reward signal. In
particular, in domains where states by nature only have an ordinal ranking and
where meaningful distance information between game state values is not
available, a numerical reward signal is necessarily biased. In this paper we
take a look at MCTS, a popular algorithm to solve MDPs, highlight a reoccurring
problem concerning its use of rewards, and show that an ordinal treatment of
the rewards overcomes this problem. Using the General Video Game Playing
framework we show dominance of our newly proposed ordinal MCTS algorithm over
other MCTS variants, based on a novel bandit algorithm that we also introduce
and test versus UCB.
",Ordinal Monte Carlo Tree Search
"  Natural selection drives species to develop brains, with sizes that increase
with the complexity of the tasks to be tackled. Our goal is to investigate the
balance between the metabolic costs of larger brains compared to the advantage
they provide in solving general and combinatorial problems. Defining advantage
as the performance relative to competitors, a two-player game based on the
knapsack problem is used. Within this framework, two opponents compete over
shared resources, with the goal of collecting more resources than the opponent.
Neural nets of varying sizes are trained using a variant of the AlphaGo Zero
algorithm. A surprisingly simple relation, $N_A/(N_A+N_B)$, is found for the
relative win rate of a net with $N_A$ neurons against one with $N_B$. Success
increases linearly with investments in additional resources when the networks
sizes are very different, i.e. when $N_A \ll N_B$, with returns diminishing
when both networks become comparable in size.
",Investment vs. reward in a competitive knapsack problem
"  Artificial Intelligence (AI), and in particular, the explainability thereof,
has gained phenomenal attention over the last few years. Whilst we usually do
not question the decision-making process of these systems in situations where
only the outcome is of interest, we do however pay close attention when these
systems are applied in areas where the decisions directly influence the lives
of humans. It is especially noisy and uncertain observations close to the
decision boundary which results in predictions which cannot necessarily be
explained that may foster mistrust among end-users. This drew attention to AI
methods for which the outcomes can be explained. Bayesian networks are
probabilistic graphical models that can be used as a tool to manage
uncertainty. The probabilistic framework of a Bayesian network allows for
explainability in the model, reasoning and evidence. The use of these methods
is mostly ad hoc and not as well organised as explainability methods in the
wider AI research field. As such, we introduce a taxonomy of explainability in
Bayesian networks. We extend the existing categorisation of explainability in
the model, reasoning or evidence to include explanation of decisions. The
explanations obtained from the explainability methods are illustrated by means
of a simple medical diagnostic scenario. The taxonomy introduced in this paper
has the potential not only to encourage end-users to efficiently communicate
outcomes obtained, but also support their understanding of how and, more
importantly, why certain predictions were made.
",A Taxonomy of Explainable Bayesian Networks
"  Persuasion is an important and yet complex aspect of human intelligence. When
undertaken through dialogue, the deployment of good arguments, and therefore
counterarguments, clearly has a significant effect on the ability to be
successful in persuasion. Two key dimensions for determining whether an
argument is good in a particular dialogue are the degree to which the intended
audience believes the argument and counterarguments, and the impact that the
argument has on the concerns of the intended audience. In this paper, we
present a framework for modelling persuadees in terms of their beliefs and
concerns, and for harnessing these models in optimizing the choice of move in
persuasion dialogues. Our approach is based on the Monte Carlo Tree Search
which allows optimization in real-time. We provide empirical results of a study
with human participants showing that our automated persuasion system based on
this technology is superior to a baseline system that does not take the beliefs
and concerns into account in its strategy.
","Strategic Argumentation Dialogues for Persuasion: Framework and
  Experiments Based on Modelling the Beliefs and Concerns of the Persuadee"
"  In 2011, Hibbard suggested an intelligence measure for agents who compete in
an adversarial sequence prediction game. We argue that Hibbard's idea should
actually be considered as two separate ideas: first, that the intelligence of
such agents can be measured based on the growth rates of the runtimes of the
competitors that they defeat; and second, one specific (somewhat arbitrary)
method for measuring said growth rates. Whereas Hibbard's intelligence measure
is based on the latter growth-rate-measuring method, we survey other methods
for measuring function growth rates, and exhibit the resulting Hibbard-like
intelligence measures and taxonomies. Of particular interest, we obtain
intelligence taxonomies based on Big-O and Big-Theta notation systems, which
taxonomies are novel in that they challenge conventional notions of what an
intelligence measure should look like. We discuss how intelligence measurement
of sequence predictors can indirectly serve as intelligence measurement for
agents with Artificial General Intelligence (AGIs).
","Measuring Intelligence and Growth Rate: Variations on Hibbard's
  Intelligence Measure"
"  $\alpha\mu$ is a search algorithm which repairs two defaults of Perfect
Information Monte Carlo search: strategy fusion and non locality. In this paper
we optimize $\alpha\mu$ for the game of Bridge, avoiding useless computations.
The proposed optimizations are general and apply to other imperfect information
turn-based games. We define multiple optimizations involving Pareto fronts, and
show that these optimizations speed up the search. Some of these optimizations
are cuts that stop the search at a node, while others keep track of which
possible worlds have become redundant, avoiding unnecessary, costly
evaluations. We also measure the benefits of parallelizing the double dummy
searches at the leaves of the $\alpha\mu$ search tree.
",Optimizing $\alpha\mu$
"  In online advertising, recommender systems try to propose items from a list
of products to potential customers according to their interests. Such systems
have been increasingly deployed in E-commerce due to the rapid growth of
information technology and availability of large datasets. The ever-increasing
progress in the field of artificial intelligence has provided powerful tools
for dealing with such real-life problems. Deep reinforcement learning (RL) that
deploys deep neural networks as universal function approximators can be viewed
as a valid approach for design and implementation of recommender systems. This
paper provides a comparative study between value-based and policy-based deep RL
algorithms for designing recommender systems for online advertising. The
RecoGym environment is adopted for training these RL-based recommender systems,
where the long short term memory (LSTM) is deployed to build value and policy
networks in these two approaches, respectively. LSTM is used to take account of
the key role that order plays in the sequence of item observations by users.
The designed recommender systems aim at maximising the click-through rate (CTR)
for the recommended items. Finally, guidelines are provided for choosing proper
RL algorithms for different scenarios that the recommender system is expected
to handle.
","Deep Reinforcement Learning-Based Product Recommender for Online
  Advertising"
"  In Maurice Merleau-Ponty's phenomenology of perception, analysis of
perception accounts for an element of intentionality, and in effect therefore,
perception and action cannot be viewed as distinct procedures. In the same line
of thinking, Alva No\""{e} considers perception as a thoughtful activity that
relies on capacities for action and thought. Here, by looking into psychology
as a source of inspiration, we propose a computational model for the action
involved in visual perception based on the notion of equilibrium as defined by
Jean Piaget. In such a model, Piaget's equilibrium reflects the mind's status,
which is used to control the observation process. The proposed model is built
around a modified version of convolutional neural networks (CNNs) with enhanced
filter performance, where characteristics of filters are adaptively adjusted
via a high-level control signal that accounts for the thoughtful activity in
perception. While the CNN plays the role of the visual system, the control
signal is assumed to be a product of mind.
","Enacted Visual Perception: A Computational Model based on Piaget
  Equilibrium"
"  Previous post-processing bias mitigation algorithms on both group and
individual fairness don't work on regression models and datasets with
multi-class numerical labels. We propose a priority-based post-processing bias
mitigation on both group and individual fairness with the notion that similar
individuals should get similar outcomes irrespective of socio-economic factors
and more the unfairness, more the injustice. We establish this proposition by a
case study on tariff allotment in a smart grid. Our novel framework establishes
it by using a user segmentation algorithm to capture the consumption strategy
better. This process ensures priority-based fair pricing for group and
individual facing the maximum injustice. It upholds the notion of fair tariff
allotment to the entire population taken into consideration without modifying
the in-built process for tariff calculation. We also validate our method and
show superior performance to previous work on a real-world dataset in criminal
sentencing.
","Priority-based Post-Processing Bias Mitigation for Individual and Group
  Fairness"
"  To make good decisions in the real world people need efficient planning
strategies because their computational resources are limited. Knowing which
planning strategies would work best for people in different situations would be
very useful for understanding and improving human decision-making. But our
ability to compute those strategies used to be limited to very small and very
simple planning tasks. To overcome this computational bottleneck, we introduce
a cognitively-inspired reinforcement learning method that can overcome this
limitation by exploiting the hierarchical structure of human behavior. The
basic idea is to decompose sequential decision problems into two sub-problems:
setting a goal and planning how to achieve it. This hierarchical decomposition
enables us to discover optimal strategies for human planning in larger and more
complex tasks than was previously possible. The discovered strategies
outperform existing planning algorithms and achieve a super-human level of
computational efficiency. We demonstrate that teaching people to use those
strategies significantly improves their performance in sequential
decision-making tasks that require planning up to eight steps ahead. By
contrast, none of the previous approaches was able to improve human performance
on these problems. These findings suggest that our cognitively-informed
approach makes it possible to leverage reinforcement learning to improve human
decision-making in complex sequential decision-problems. Future work can
leverage our method to develop decision support systems that improve human
decision making in the real world.
","Improving Human Decision-Making by Discovering Efficient Strategies for
  Hierarchical Planning"
"  Capacitated vehicle routing problem (CVRP) is being one of the most common
optimization problems in our days, considering the wide usage of routing
algorithms in multiple fields such as transportation domain, food delivery,
network routing, ... Capacitated vehicle routing problem is classified as an
NP-Hard problem, hence normal optimization algorithm can’t solve it. In our
paper, we discuss a new way to solve the mentioned problem, using a recursive
approach of the most known clustering algorithm “K-Means”, one of the known
shortest path algorithm “Dijkstra”, and some mathematical operations. In this
paper, we will show how to implement those methods together in order to get the
nearest solution of the optimal route, since research and development are still
on go, this research paper may be extended with another one, that will involve
the implementational results of this thoric side.
",Using Recursive KMeans and Dijkstra Algorithm to Solve CVRP
"  Endeavors for designing robots with human-level cognitive abilities have led
to different categories of learning machines. According to Skinner's theory,
reinforcement learning (RL) plays a key role in human intuition and cognition.
Majority of the state-of-the-art methods including deep RL algorithms are
strongly influenced by the connectionist viewpoint. Such algorithms can
significantly benefit from theories of mind and learning in other disciplines.
This paper entertains the idea that theories such as language of thought
hypothesis (LOTH), script theory, and Piaget's cognitive development theory
provide complementary approaches, which will enrich the RL field. Following
this line of thinking, a general computational building block is proposed for
Piaget's schema theory that supports the notions of productivity,
systematicity, and inferential coherence as described by Fodor in contrast with
the connectionism theory. Abstraction in the proposed method is completely upon
the system itself and is not externally constrained by any predefined
architecture. The whole process matches the Neisser's perceptual cycle model.
Performed experiments on three typical control problems followed by behavioral
analysis confirm the interpretability of the proposed method and its
competitiveness compared to the state-of-the-art algorithms. Hence, the
proposed framework can be viewed as a step towards achieving human-like
cognition in artificial intelligent systems.
","Interpretable Reinforcement Learning Inspired by Piaget's Theory of
  Cognitive Development"
"  People hope automated driving technology is always in a stable and
controllable state; specifically, it can be divided into controllable planning,
controllable responsibility, and controllable information. When this
controllability is undermined, it brings about the problems, e.g., trolley
dilemma, responsibility attribution, information leakage, and security. This
article discusses these three types of issues separately and clarifies the
misunderstandings.
","The Controllability of Planning, Responsibility, and Security in
  Automatic Driving Technology"
"  We present counterfactual planning as a design approach for creating a range
of safety mechanisms that can be applied in hypothetical future AI systems
which have Artificial General Intelligence.
  The key step in counterfactual planning is to use an AGI machine learning
system to construct a counterfactual world model, designed to be different from
the real world the system is in. A counterfactual planning agent determines the
action that best maximizes expected utility in this counterfactual planning
world, and then performs the same action in the real world.
  We use counterfactual planning to construct an AGI agent emergency stop
button, and a safety interlock that will automatically stop the agent before it
undergoes an intelligence explosion. We also construct an agent with an input
terminal that can be used by humans to iteratively improve the agent's reward
function, where the incentive for the agent to manipulate this improvement
process is suppressed. As an example of counterfactual planning in a non-agent
AGI system, we construct a counterfactual oracle.
  As a design approach, counterfactual planning is built around the use of a
graphical notation for defining mathematical counterfactuals. This two-diagram
notation also provides a compact and readable language for reasoning about the
complex types of self-referencing and indirect representation which are
typically present inside machine learning agents.
",Counterfactual Planning in AGI Systems
"  Generating an image from its textual description requires both a certain
level of language understanding and common sense knowledge about the spatial
relations of the physical entities being described. In this work, we focus on
inferring the spatial relation between entities, a key step in the process of
composing scenes based on text. More specifically, given a caption containing a
mention to a subject and the location and size of the bounding box of that
subject, our goal is to predict the location and size of an object mentioned in
the caption. Previous work did not use the caption text information, but a
manually provided relation holding between the subject and the object. In fact,
the used evaluation datasets contain manually annotated ontological triplets
but no captions, making the exercise unrealistic: a manual step was required;
and systems did not leverage the richer information in captions. Here we
present a system that uses the full caption, and Relations in Captions
(REC-COCO), a dataset derived from MS-COCO which allows to evaluate spatial
relation inference from captions directly. Our experiments show that: (1) it is
possible to infer the size and location of an object with respect to a given
subject directly from the caption; (2) the use of full text allows to place the
object better than using a manually annotated relation. Our work paves the way
for systems that, given a caption, decide which entities need to be depicted
and their respective location and sizes, in order to then generate the final
image.
",Inferring spatial relations from textual descriptions of images
"  Computer-based modelling and simulation have become useful tools to
facilitate humans to understand systems in different domains, such as physics,
astrophysics, chemistry, biology, economics, engineering and social science. A
complex system is featured with a large number of interacting components
(agents, processes, etc.), whose aggregate activities are nonlinear and
self-organized. Complex systems are hard to be simulated or modelled by using
traditional computational approaches due to complex relationships among system
components, distributed features of resources, and dynamics of environments.
Meanwhile, smart systems such as multi-agent systems have demonstrated
advantages and great potentials in modelling and simulating complex systems.
","The 4th International Workshop on Smart Simulation and Modelling for
  Complex Systems"
"  The pythagorean fuzzy set (PFS) which is developed based on intuitionistic
fuzzy set, is more efficient in elaborating and disposing uncertainties in
indeterminate situations, which is a very reason of that PFS is applied in
various kinds of fields. How to measure the distance between two pythagorean
fuzzy sets is still an open issue. Mnay kinds of methods have been proposed to
present the of the question in former reaserches. However, not all of existing
methods can accurately manifest differences among pythagorean fuzzy sets and
satisfy the property of similarity. And some other kinds of methods neglect the
relationship among three variables of pythagorean fuzzy set. To addrees the
proplem, a new method of measuring distance is proposed which meets the
requirements of axiom of distance measurement and is able to indicate the
degree of distinction of PFSs well. Then some numerical examples are offered to
to verify that the method of measuring distances can avoid the situation that
some counter? intuitive and irrational results are produced and is more
effective, reasonable and advanced than other similar methods. Besides, the
proposed method of measuring distances between PFSs is applied in a real
environment of application which is the medical diagnosis and is compared with
other previous methods to demonstrate its superiority and efficiency. And the
feasibility of the proposed method in handling uncertainties in practice is
also proved at the same time.
","A new distance measure of Pythagorean fuzzy sets based on matrix and and
  its application in medical diagnosis"
"  The ahadith, prophetic traditions for the Muslims around the world, are
narrations originating from the sayings and the deeds of Prophet Muhammad
(pbuh). They are considered one of the fundamental sources of Islamic
legislation along with the Quran. The list of persons involved in the narration
of each hadith is carefully scrutinized by scholars studying the hadith, with
respect to their reputation and authenticity of the hadith. This is due to the
its legislative importance in Islamic principles. There were many narrators who
contributed to this responsibility of preserving prophetic narrations over the
centuries. But to date, no systematic and comprehensive study, based on the
social network, has been adapted to understand the contribution of early hadith
narrators and the propagation of hadith across generations. In this study, we
represented the chain of narrators of the hadith collection from Sahih Bukhari
as a social graph. Based on social network analysis (SNA) on this graph, we
found that the network of narrators is a scale-free network. We identified a
list of influential narrators from the companions as well as the narrators from
the second and third-generation who contribute significantly in the propagation
of hadith collected in Sahih Bukhari. We discovered sixteen communities from
the narrators of Sahih Bukhari. In each of these communities, there are other
narrators who contributed significantly to the propagation of prophetic
narrations. We also found that most narrators were centered in Makkah and
Madinah in the era of companions and, then, gradually the center of hadith
narrators shifted towards Kufa, Baghdad and central Asia over a period of time.
To the best of our knowledge, this the first comprehensive and systematic study
based on SNA, representing the narrators as a social graph to analyze their
contribution to the preservation and propagation of hadith.
",Social Network Analysis of Hadith Narrators from Sahih Bukhari
"  Global optimization solves real-world problems numerically or analytically by
minimizing their objective functions. Most of the analytical algorithms are
greedy and computationally intractable. Metaheuristics are nature-inspired
optimization algorithms. They numerically find a near-optimal solution for
optimization problems in a reasonable amount of time. We propose a novel
metaheuristic algorithm for global optimization. It is based on the shooting
and jumping behaviors of the archerfish for hunting aerial insects. We name it
the Archerfish Hunting Optimizer (AHO). We Perform two sorts of comparisons to
validate the proposed algorithm's performance. First, AHO is compared to the 12
recent metaheuristic algorithms (the accepted algorithms for the 2020's
competition on single objective bound-constrained numerical optimization) on
ten test functions of the benchmark CEC 2020 for unconstrained optimization.
Second, the performance of AHO and 3 recent metaheuristic algorithms, is
evaluated using five engineering design problems taken from the benchmark CEC
2020 for non-convex constrained optimization. The experimental results are
evaluated using the Wilcoxon signed-rank and the Friedman tests. The
statistical indicators illustrate that the Archerfish Hunting Optimizer has an
excellent ability to accomplish higher performance in competition with the
well-established optimizers.
","The Archerfish Hunting Optimizer: a novel metaheuristic algorithm for
  global optimization"
"  Pearl opened the door to formally defining actual causation using causal
models. His approach rests on two strategies: first, capturing the widespread
intuition that X=x causes Y=y iff X=x is a Necessary Element of a Sufficient
Set for Y=y, and second, showing that his definition gives intuitive answers on
a wide set of problem cases. This inspired dozens of variations of his
definition of actual causation, the most prominent of which are due to Halpern
& Pearl. Yet all of them ignore Pearl's first strategy, and the second strategy
taken by itself is unable to deliver a consensus. This paper offers a way out
by going back to the first strategy: it offers six formal definitions of causal
sufficiency and two interpretations of necessity. Combining the two gives
twelve new definitions of actual causation. Several interesting results about
these definitions and their relation to the various Halpern & Pearl definitions
are presented. Afterwards the second strategy is evaluated as well. In order to
maximize neutrality, the paper relies mostly on the examples and intuitions of
Halpern & Pearl. One definition comes out as being superior to all others, and
is therefore suggested as a new definition of actual causation.
",Causal Sufficiency and Actual Causation
"  Egalitarian considerations play a central role in many areas of social choice
theory. Applications of egalitarian principles range from ensuring everyone
gets an equal share of a cake when deciding how to divide it, to guaranteeing
balance with respect to gender or ethnicity in committee elections. Yet, the
egalitarian approach has received little attention in judgment aggregation -- a
powerful framework for aggregating logically interconnected issues. We make the
first steps towards filling that gap. We introduce axioms capturing two
classical interpretations of egalitarianism in judgment aggregation and situate
these within the context of existing axioms in the pertinent framework of
belief merging. We then explore the relationship between these axioms and
several notions of strategyproofness from social choice theory at large.
Finally, a novel egalitarian judgment aggregation rule stems from our analysis;
we present complexity results concerning both outcome determination and
strategic manipulation for that rule.
",Egalitarian Judgment Aggregation
"  Conversational systems enable numerous valuable applications, and
question-answering is an important component underlying many of these. However,
conversational question-answering remains challenging due to the lack of
realistic, domain-specific training data. Inspired by this bottleneck, we focus
on conversational question generation as a means to generate synthetic
conversations for training and evaluation purposes. We present a number of
novel strategies to improve conversational flow and accommodate varying
question types and overall fluidity. Specifically, we design ChainCQG as a
two-stage architecture that learns question-answer representations across
multiple dialogue turns using a flow propagation training strategy.ChainCQG
significantly outperforms both answer-aware and answer-unaware SOTA baselines
(e.g., up to 48% BLEU-1 improvement). Additionally, our model is able to
generate different types of questions, with improved fluidity and coreference
alignment.
",ChainCQG: Flow-Aware Conversational Question Generation
"  There has been an increasing interest in harnessing deep learning to tackle
combinatorial optimization (CO) problems in recent years. Typical CO deep
learning approaches leverage the problem structure in the model architecture.
Nevertheless, the model selection is still mainly based on the conventional
machine learning setting. Due to the discrete nature of CO problems, a single
model is unlikely to learn the problem entirely. We introduce ZTop, which
stands for Zero Training Overhead Portfolio, a simple yet effective model
selection and ensemble mechanism for learning to solve combinatorial problems.
ZTop is inspired by algorithm portfolios, a popular CO ensembling strategy,
particularly restart portfolios, which periodically restart a randomized CO
algorithm, de facto exploring the search space with different heuristics. We
have observed that well-trained models acquired in the same training
trajectory, with similar top validation performance, perform well on very
different validation instances. Following this observation, ZTop ensembles a
set of well-trained models, each providing a unique heuristic with zero
training overhead, and applies them, sequentially or in parallel, to solve the
test instances. We show how ZTopping, i.e., using a ZTop ensemble strategy with
a given deep learning approach, can significantly improve the performance of
the current state-of-the-art deep learning approaches on three prototypical CO
domains, the hardest unique-solution Sudoku instances, challenging routing
problems, and the graph maximum cut problem, as well as on multi-label
classification, a machine learning task with a large combinatorial label space.
","Zero Training Overhead Portfolios for Learning to Solve Combinatorial
  Problems"
"  Balancing safety and efficiency when planning in dense traffic is
challenging. Interactive behavior planners incorporate prediction uncertainty
and interactivity inherent to these traffic situations. Yet, their use of
single-objective optimality impedes interpretability of the resulting safety
goal. Safety envelopes which restrict the allowed planning region yield
interpretable safety under the presence of behavior uncertainty, yet, they
sacrifice efficiency in dense traffic due to conservative driving. Studies show
that humans balance safety and efficiency in dense traffic by accepting a
probabilistic risk of violating the safety envelope. In this work, we adopt
this safety objective for interactive planning. Specifically, we formalize this
safety objective, present the Risk-Constrained Robust Stochastic Bayesian Game
modeling interactive decisions satisfying a maximum risk of violating a safety
envelope under uncertainty of other traffic participants' behavior and solve it
using our variant of Multi-Agent Monte Carlo Tree Search. We demonstrate in
simulation that our approach outperforms baselines approaches, and by reaching
the specified violation risk level over driven simulation time, provides an
interpretable and tunable safety objective for interactive planning.
","Risk-Constrained Interactive Safety under Behavior Uncertainty for
  Autonomous Driving"
"  With Artificial Intelligence on the rise, human interaction with autonomous
agents becomes more frequent. Effective human-agent collaboration requires that
the human understands the agent's behavior, as failing to do so may lead to
reduced productiveness, misuse, frustration and even danger. Agent strategy
summarization methods are used to describe the strategy of an agent to its
destined user through demonstration. The summary's purpose is to maximize the
user's understanding of the agent's aptitude by showcasing its behaviour in a
set of world states, chosen by some importance criteria. While shown to be
useful, we show that these methods are limited in supporting the task of
comparing agent behavior, as they independently generate a summary for each
agent. In this paper, we propose a novel method for generating contrastive
summaries that highlight the differences between agent's policies by
identifying and ranking states in which the agents disagree on the best course
of action. We conduct a user study in which participants face an agent
selection task. Our results show that the novel disagreement-based summaries
lead to improved user performance compared to summaries generated using
HIGHLIGHTS, a previous strategy summarization algorithm.
","""I Don't Think So"": Disagreement-Based Policy Summaries for Comparing
  Agents"
"  For highly automated driving above SAE level~3, behavior generation
algorithms must reliably consider the inherent uncertainties of the traffic
environment, e.g. arising from the variety of human driving styles. Such
uncertainties can generate ambiguous decisions, requiring the algorithm to
appropriately balance low-probability hazardous events, e.g. collisions, and
high-probability beneficial events, e.g. quickly crossing the intersection.
State-of-the-art behavior generation algorithms lack a distributional treatment
of decision outcome. This impedes a proper risk evaluation in ambiguous
situations, often encouraging either unsafe or conservative behavior. Thus, we
propose a two-step approach for risk-sensitive behavior generation combining
offline distribution learning with online risk assessment. Specifically, we
first learn an optimal policy in an uncertain environment with Deep
Distributional Reinforcement Learning. During execution, the optimal
risk-sensitive action is selected by applying established risk criteria, such
as the Conditional Value at Risk, to the learned state-action return
distributions. In intersection crossing scenarios, we evaluate different risk
criteria and demonstrate that our approach increases safety, while maintaining
an active driving style. Our approach shall encourage further studies about the
benefits of risk-sensitive approaches for self-driving vehicles.
","Addressing Inherent Uncertainty: Risk-Sensitive Behavior Generation for
  Automated Driving using Distributional Reinforcement Learning"
"  The standard for Deep Reinforcement Learning in games, following Alpha Zero,
is to use residual networks and to increase the depth of the network to get
better results. We propose to improve mobile networks as an alternative to
residual networks and experimentally show the playing strength of the networks
according to both their width and their depth. We also propose a generalization
of the PUCT search algorithm that improves on PUCT.
",Improving Model and Search for Computer Go
"  Vampire has been for a long time the strongest first-order automatic theorem
prover, widely used for hammer-style proof automation in ITPs such as Mizar,
Isabelle, HOL, and Coq. In this work, we considerably improve the performance
of Vampire in hammering over the full Mizar library by enhancing its saturation
procedure with efficient neural guidance. In particular, we employ a recently
proposed recursive neural network classifying the generated clauses based only
on their derivation history. Compared to previous neural methods based on
considering the logical content of the clauses, our architecture makes
evaluating a single clause much less time consuming. The resulting system shows
good learning capability and improves on the state-of-the-art performance on
the Mizar library, while proving many theorems that the related ENIGMA system
could not prove in a similar hammering evaluation.
",Vampire With a Brain Is a Good ITP Hammer
"  We present a heuristic algorithm for solving the problem of scheduling plans
of tasks. The plans are ordered vectors of tasks, and tasks are basic
operations carried out by resources. Plans are tied by temporal, precedence and
resource constraints that makes the scheduling problem hard to solve in
polynomial time. The proposed heuristic, that has a polynomial worst-case time
complexity, searches for a feasible schedule that maximize the number of plans
scheduled, along a fixed time window, with respect to temporal, precedence and
resource constraints.
",Scheduling Plans of Tasks
"  AI systems often rely on two key components: a specified goal or reward
function and an optimization algorithm to compute the optimal behavior for that
goal. This approach is intended to provide value for a principal: the user on
whose behalf the agent acts. The objectives given to these agents often refer
to a partial specification of the principal's goals. We consider the cost of
this incompleteness by analyzing a model of a principal and an agent in a
resource constrained world where the $L$ attributes of the state correspond to
different sources of utility for the principal. We assume that the reward
function given to the agent only has support on $J < L$ attributes. The
contributions of our paper are as follows: 1) we propose a novel model of an
incomplete principal-agent problem from artificial intelligence; 2) we provide
necessary and sufficient conditions under which indefinitely optimizing for any
incomplete proxy objective leads to arbitrarily low overall utility; and 3) we
show how modifying the setup to allow reward functions that reference the full
state or allowing the principal to update the proxy objective over time can
lead to higher utility solutions. The results in this paper argue that we
should view the design of reward functions as an interactive and dynamic
process and identifies a theoretical scenario where some degree of
interactivity is desirable.
",Consequences of Misaligned AI
"  State-of-the-art deep-learning systems use decision rules that are
challenging for humans to model. Explainable AI (XAI) attempts to improve human
understanding but rarely accounts for how people typically reason about
unfamiliar agents. We propose explicitly modeling the human explainee via
Bayesian Teaching, which evaluates explanations by how much they shift
explainees' inferences toward a desired goal. We assess Bayesian Teaching in a
binary image classification task across a variety of contexts. Absent
intervention, participants predict that the AI's classifications will match
their own, but explanations generated by Bayesian Teaching improve their
ability to predict the AI's judgements by moving them away from this prior
belief. Bayesian Teaching further allows each case to be broken down into
sub-examples (here saliency maps). These sub-examples complement whole examples
by improving error detection for familiar categories, whereas whole examples
help predict correct AI judgements of unfamiliar cases.
","Mitigating belief projection in explainable artificial intelligence via
  Bayesian Teaching"
"  Compositional generalization is the capacity to recognize and imagine a large
amount of novel combinations from known components. It is a key in human
intelligence, but current neural networks generally lack such ability. This
report connects a series of our work for compositional generalization, and
summarizes an approach. The first part contains concepts and properties. The
second part looks into a machine learning approach. The approach uses
architecture design and regularization to regulate information of
representations. This report focuses on basic ideas with intuitive and
illustrative explanations. We hope this work would be helpful to clarify
fundamentals of compositional generalization and lead to advance artificial
intelligence.
","Concepts, Properties and an Approach for Compositional Generalization"
"  Explainable Artificial Intelligence (XAI) has re-emerged in response to the
development of modern AI and ML systems. These systems are complex and
sometimes biased, but they nevertheless make decisions that impact our lives.
XAI systems are frequently algorithm-focused; starting and ending with an
algorithm that implements a basic untested idea about explainability. These
systems are often not tested to determine whether the algorithm helps users
accomplish any goals, and so their explainability remains unproven. We propose
an alternative: to start with human-focused principles for the design, testing,
and implementation of XAI systems, and implement algorithms to serve that
purpose. In this paper, we review some of the basic concepts that have been
used for user-centered XAI systems over the past 40 years of research. Based on
these, we describe the ""Self-Explanation Scorecard"", which can help developers
understand how they can empower users by enabling self-explanation. Finally, we
present a set of empirically-grounded, user-centered design principles that may
guide developers to create successful explainable systems.
",Principles of Explanation in Human-AI Systems
"  Disruption management during the airline scheduling process can be
compartmentalized into proactive and reactive processes depending upon the time
of schedule execution. The state of the art for decision-making in airline
disruption management involves a heuristic human-centric approach that does not
categorically study uncertainty in proactive and reactive processes for
managing airline schedule disruptions. Hence, this paper introduces an
uncertainty transfer function model (UTFM) framework that characterizes
uncertainty for proactive airline disruption management before schedule
execution, reactive airline disruption management during schedule execution,
and proactive airline disruption management after schedule execution to enable
the construction of quantitative tools that can allow an intelligent agent to
rationalize complex interactions and procedures for robust airline disruption
management. Specifically, we use historical scheduling and operations data from
a major U.S. airline to facilitate the development and assessment of the UTFM,
defined by hidden Markov models (a special class of probabilistic graphical
models) that can efficiently perform pattern learning and inference on portions
of large data sets.
","Uncertainty Quantification and Propagation for Airline Disruption
  Management"
"  This paper introduces a new metamodel-based knowledge representation that
significantly improves autonomous learning and adaptation. While interest in
hybrid machine learning / symbolic AI systems leveraging, for example,
reasoning and knowledge graphs, is gaining popularity, we find there remains a
need for both a clear definition of knowledge and a metamodel to guide the
creation and manipulation of knowledge. Some of the benefits of the metamodel
we introduce in this paper include a solution to the symbol grounding problem,
cumulative learning, and federated learning. We have applied the metamodel to
problems ranging from time series analysis, computer vision, and natural
language understanding and have found that the metamodel enables a wide variety
of learning mechanisms ranging from machine learning, to graph network analysis
and learning by reasoning engines to interoperate in a highly synergistic way.
Our metamodel-based projects have consistently exhibited unprecedented
accuracy, performance, and ability to generalize. This paper is inspired by the
state-of-the-art approaches to AGI, recent AGI-aspiring work, the granular
computing community, as well as Alfred Korzybski's general semantics. One
surprising consequence of the metamodel is that it not only enables a new level
of autonomous learning and optimal functioning for machine intelligences, but
may also shed light on a path to better understanding how to improve human
cognition.
","A Metamodel and Framework for Artificial General Intelligence From
  Theory to Practice"
"  Knowledge base completion (KBC) methods aim at inferring missing facts from
the information present in a knowledge base (KB) by estimating the likelihood
of candidate facts. In the prevailing evaluation paradigm, models do not
actually decide whether a new fact should be accepted or not but are solely
judged on the position of true facts in a likelihood ranking with other
candidates. We argue that consideration of binary predictions is essential to
reflect the actual KBC quality, and propose a novel evaluation paradigm,
designed to provide more transparent model selection criteria for a realistic
scenario. We construct the data set FB14k-QAQ where instead of single facts, we
use KB queries, i.e., facts where one entity is replaced with a variable, and
construct corresponding sets of entities that are correct answers. We randomly
remove some of these correct answers from the data set, simulating the
realistic scenario of real-world entities missing from a KB. This way, we can
explicitly measure a model's ability to handle queries that have more correct
answers in the real world than in the KB, including the special case of queries
without any valid answer. The latter especially contrasts the ranking setting.
We evaluate a number of state-of-the-art KB embeddings models on our new
benchmark. The differences in relative performance between ranking-based and
classification-based evaluation that we observe in our experiments confirm our
hypothesis that good performance on the ranking task does not necessarily
translate to good performance on the actual completion task. Our results
motivate future work on KB embedding models with better prediction separability
and, as a first step in that direction, we propose a simple variant of TransE
that encourages thresholding and achieves a significant improvement in
classification F1 score relative to the original TransE.
",Ranking vs. Classifying: Measuring Knowledge Base Completion Quality
"  This paper outlines the ideas behind developing a graph-based
heuristic-driven routing algorithm designed for a particular instance of a
goods transportation problem with a single good type. The proposed algorithm
solves the optimization problem of satisfying the demand of goods on a given
undirected transportation graph with minimizing the estimated cost for each
traversed segment of the delivery path. The operation of the routing algorithm
is discussed and overall evaluation of the proposed problem solving technique
is given.
",Goods Transportation Problem Solving via Routing Algorithm
"  Fairness has emerged as an important concern in automated decision-making in
recent years, especially when these decisions affect human welfare. In this
work, we study fairness in temporally extended decision-making settings,
specifically those formulated as Markov Decision Processes (MDPs). Our proposed
notion of fairness ensures that each state's long-term visitation frequency is
more than a specified fraction. In an average-reward MDP (AMDP) setting, we
formulate the problem as a bilinear saddle point program and, for a generative
model, solve it using a Stochastic Mirror Descent (SMD) based algorithm. The
proposed solution guarantees a simultaneous approximation on the expected
average-reward and the long-term state-visitation frequency. We validate our
theoretical results with experiments on synthetic data.
",State-Visitation Fairness in Average-Reward MDPs
"  As the potential applications of Artificial Intelligence (AI) in the
financial sector increases, ethical issues become gradually latent. The
distrust of individuals, social groups, and governments about the risks arising
from Fintech's activities is growing. Due to this scenario, the preparation of
recommendations and Ethics Guidelines is increasing and the risks of being
chosen the principles and ethical values most appropriate to companies are
high. Thus, this exploratory research aims to analyze the benefits of the
application of the stakeholder theory and the idea of Social License to build
an environment of trust and for the realization of ethical principles by
Fintech. The formation of a Fintech association for the creation of a Social
License will allow early-stage Fintech to participate from the beginning of its
activities in the elaboration of a dynamic ethical code and with the
participation of stakeholders.
","Why Talking about ethics is not enough: a proposal for Fintech's AI
  ethics"
"  The safety management is critically important in the workplace.
Unfortunately, responsibility issues therein such as inefficient supervision,
poor evaluation and inadequate perception have not been properly addressed. To
this end, in this paper, we deploy the Internet of Responsibilities (IoR) for
responsibility management. Through the building of IoR framework, hierarchical
responsibility management, automated responsibility evaluation at all level and
efficient responsibility perception are achieved. The practical deployment of
IoR system showed its effective responsibility management capability in various
workplaces.
",Responsibility Management through Responsibility Networks
"  In this chapter we argue that discourses on AI must transcend the language of
'ethics' and engage with power and political economy in order to constitute
'Good Data'. In particular, we must move beyond the depoliticised language of
'ethics' currently deployed (Wagner 2018) in determining whether AI is 'good'
given the limitations of ethics as a frame through which AI issues can be
viewed. In order to circumvent these limits, we use instead the language and
conceptualisation of 'Good Data', as a more expansive term to elucidate the
values, rights and interests at stake when it comes to AI's development and
deployment, as well as that of other digital technologies. Good Data
considerations move beyond recurring themes of data protection/privacy and the
FAT (fairness, transparency and accountability) movement to include explicit
political economy critiques of power. Instead of yet more ethics principles
(that tend to say the same or similar things anyway), we offer four 'pillars'
on which Good Data AI can be built: community, rights, usability and politics.
Overall we view AI's 'goodness' as an explicly political (economy) question of
power and one which is always related to the degree which AI is created and
used to increase the wellbeing of society and especially to increase the power
of the most marginalized and disenfranchised. We offer recommendations and
remedies towards implementing 'better' approaches towards AI. Our strategies
enable a different (but complementary) kind of evaluation of AI as part of the
broader socio-technical systems in which AI is built and deployed.
",AI Ethics Needs Good Data
"  Zero-shot Learning (ZSL), which aims to predict for those classes that have
never appeared in the training data, has arisen hot research interests. The key
of implementing ZSL is to leverage the prior knowledge of classes which builds
the semantic relationship between classes and enables the transfer of the
learned models (e.g., features) from training classes (i.e., seen classes) to
unseen classes. However, the priors adopted by the existing methods are
relatively limited with incomplete semantics. In this paper, we explore richer
and more competitive prior knowledge to model the inter-class relationship for
ZSL via ontology-based knowledge representation and semantic embedding.
Meanwhile, to address the data imbalance between seen classes and unseen
classes, we developed a generative ZSL framework with Generative Adversarial
Networks (GANs). Our main findings include: (i) an ontology-enhanced ZSL
framework that can be applied to different domains, such as image
classification (IMGC) and knowledge graph completion (KGC); (ii) a
comprehensive evaluation with multiple zero-shot datasets from different
domains, where our method often achieves better performance than the
state-of-the-art models. In particular, on four representative ZSL baselines of
IMGC, the ontology-based class semantics outperform the previous priors e.g.,
the word embeddings of classes by an average of 12.4 accuracy points in the
standard ZSL across two example datasets (see Figure 4).
",OntoZSL: Ontology-enhanced Zero-shot Learning
"  In this paper, we introduce the first large vocabulary speech recognition
system (LVSR) for the Central Kurdish language, named Jira. The Kurdish
language is an Indo-European language spoken by more than 30 million people in
several countries, but due to the lack of speech and text resources, there is
no speech recognition system for this language. To fill this gap, we introduce
the first speech corpus and pronunciation lexicon for the Kurdish language.
Regarding speech corpus, we designed a sentence collection in which the ratio
of di-phones in the collection resembles the real data of the Central Kurdish
language. The designed sentences are uttered by 576 speakers in a controlled
environment with noise-free microphones (called AsoSoft Speech-Office) and in
Telegram social network environment using mobile phones (denoted as AsoSoft
Speech-Crowdsourcing), resulted in 43.68 hours of speech. Besides, a test set
including 11 different document topics is designed and recorded in two
corresponding speech conditions (i.e., Office and Crowdsourcing). Furthermore,
a 60K pronunciation lexicon is prepared in this research in which we faced
several challenges and proposed solutions for them. The Kurdish language has
several dialects and sub-dialects that results in many lexical variations. Our
methods for script standardization of lexical variations and automatic
pronunciation of the lexicon tokens are presented in detail. To setup the
recognition engine, we used the Kaldi toolkit. A statistical tri-gram language
model that is extracted from the AsoSoft text corpus is used in the system.
Several standard recipes including HMM-based models (i.e., mono, tri1, tr2,
tri2, tri3), SGMM, and DNN methods are used to generate the acoustic model.
These methods are trained with AsoSoft Speech-Office and AsoSoft
Speech-Crowdsourcing and a combination of them. The best performance achieved
by the SGMM acoustic model which results in 13.9% of the average word error
rate (on different document topics) and 4.9% for the general topic.
","Jira: a Kurdish Speech Recognition System Designing and Building Speech
  Corpus and Pronunciation Lexicon"
"  Even though Afaan Oromo is the most widely spoken language in the Cushitic
family by more than fifty million people in the Horn and East Africa, it is
surprisingly resource-scarce from a technological point of view. The increasing
amount of various useful documents written in English language brings to
investigate the machine that can translate those documents and make it easily
accessible for local language. The paper deals with implementing a translation
of English to Afaan Oromo and vice versa using Neural Machine Translation. But
the implementation is not very well explored due to the limited amount and
diversity of the corpus. However, using a bilingual corpus of just over 40k
sentence pairs we have collected, this study showed a promising result. About a
quarter of this corpus is collected via Community Engagement Platform (CEP)
that was implemented to enrich the parallel corpus through crowdsourcing
translations.
","Crowdsourcing Parallel Corpus for English-Oromo Neural Machine
  Translation using Community Engagement Platform"
"  Understanding the principles of real-world biological multi-agent behaviors
is a current challenge in various scientific and engineering fields. The rules
regarding the real-world biological multi-agent behaviors such as team sports
are often largely unknown due to their inherently higher-order interactions,
cognition, and body dynamics. Estimation of the rules from data, i.e.,
data-driven approaches such as machine learning, provides an effective way for
the analysis of such behaviors. Although most data-driven models have
non-linear structures and high prediction performances, it is sometimes hard to
interpret them. This survey focuses on data-driven analysis for quantitative
understanding of invasion team sports behaviors such as basketball and
football, and introduces two main approaches for understanding such multi-agent
behaviors: (1) extracting easily interpretable features or rules from data and
(2) generating and controlling behaviors in visually-understandable ways. The
first approach involves the visualization of learned representations and the
extraction of mathematical structures behind the behaviors. The second approach
can be used to test hypotheses by simulating and controlling future and
counterfactual behaviors. Lastly, the potential practical applications of
extracted rules, features, and generated behaviors are discussed. These
approaches can contribute to a better understanding of multi-agent behaviors in
the real world.
",Data-driven Analysis for Understanding Team Sports Behaviors
"  Human is able to conduct 3D recognition by a limited number of haptic
contacts between the target object and his/her fingers without seeing the
object. This capability is defined as `haptic glance' in cognitive
neuroscience. Most of the existing 3D recognition models were developed based
on dense 3D data. Nonetheless, in many real-life use cases, where robots are
used to collect 3D data by haptic exploration, only a limited number of 3D
points could be collected. In this study, we thus focus on solving the
intractable problem of how to obtain cognitively representative 3D key-points
of a target object with limited interactions between the robot and the object.
A novel reinforcement learning based framework is proposed, where the haptic
exploration procedure (the agent iteratively predicts the next position for the
robot to explore) is optimized simultaneously with the objective 3D recognition
with actively collected 3D points. As the model is rewarded only when the 3D
object is accurately recognized, it is driven to find the sparse yet efficient
haptic-perceptual 3D representation of the object. Experimental results show
that our proposed model outperforms the state of the art models.
","Seeing by haptic glance: reinforcement learning-based 3D object
  Recognition"
"  Symbiotic Autonomous Systems (SAS) are advanced intelligent and cognitive
systems exhibiting autonomous collective intelligence enabled by coherent
symbiosis of human-machine interactions in hybrid societies. Basic research in
the emerging field of SAS has triggered advanced general AI technologies
functioning without human intervention or hybrid symbiotic systems synergizing
humans and intelligent machines into coherent cognitive systems. This work
presents a theoretical framework of SAS underpinned by the latest advances in
intelligence, cognition, computer, and system sciences. SAS are characterized
by the composition of autonomous and symbiotic systems that adopt
bio-brain-social-inspired and heterogeneously synergized structures and
autonomous behaviors. This paper explores their cognitive and mathematical
foundations. The challenge to seamless human-machine interactions in a hybrid
environment is addressed. SAS-based collective intelligence is explored in
order to augment human capability by autonomous machine intelligence towards
the next generation of general AI, autonomous computers, and trustworthy
mission-critical intelligent systems. Emerging paradigms and engineering
applications of SAS are elaborated via an autonomous knowledge learning system
that symbiotically works between humans and cognitive robots.
","On the Philosophical, Cognitive and Mathematical Foundations of
  Symbiotic Autonomous Systems (SAS)"
"  Globally operating enterprises selling large and complex products and
services often have to deal with situations where variability models are
locally developed to take into account the requirements of local markets. For
example, cars sold on the U.S. market are represented by variability models in
some or many aspects different from European ones. In order to support global
variability management processes, variability models and the underlying
knowledge bases often need to be integrated. This is a challenging task since
an integrated knowledge base should not produce results which are different
from those produced by the individual knowledge bases. In this paper, we
introduce an approach to variability model integration that is based on the
concepts of contextual modeling and conflict detection. We present the
underlying concepts and the results of a corresponding performance analysis.
",Consistency-based Merging of Variability Models
"  Quantum mass function has been applied in lots of fields because of its
efficiency and validity of managing uncertainties in the form of quantum which
can be regarded as an extension of classical Dempster-Shafer (D-S) evidence
theory. However, how to handle uncertainties in the form of quantum is still an
open issue. In this paper, a new method is proposed to dispose uncertain
quantum information, which is called two-dimensional quantum mass function
(TDQMF). A TDQMF is consist of two elements, TQ = (Qoriginal, Qindicative),
both of the Qs are quantum mass functions, in which the Qindicative is an
indicator of the reliability on Qoriginal. More flexibility and effectiveness
are offered in handling uncertainty in the field of quantum by the proposed
method compared with primary quantum mass function. Besides, some numerical
examples are provided and some practical applications are given to verify its
correctness and validity
",TDQMF: Two-dimensional quantum mass function
"  Reinforcement learning in complex environments may require supervision to
prevent the agent from attempting dangerous actions. As a result of supervisor
intervention, the executed action may differ from the action specified by the
policy. How does this affect learning? We present the Modified-Action Markov
Decision Process, an extension of the MDP model that allows actions to differ
from the policy. We analyze the asymptotic behaviours of common reinforcement
learning algorithms in this setting and show that they adapt in different ways:
some completely ignore modifications while others go to various lengths in
trying to avoid action modifications that decrease reward. By choosing the
right algorithm, developers can prevent their agents from learning to
circumvent interruptions or constraints, and better control agent responses to
other kinds of action modification, like self-damage.
",How RL Agents Behave When Their Actions Are Modified
"  The task of learning to rank has been widely studied by the machine learning
community, mainly due to its use and great importance in information retrieval,
data mining, and natural language processing. Therefore, ranking accurately and
learning to rank are crucial tasks. Context-Based Information Retrieval systems
have been of great importance to reduce the effort of finding relevant data.
Such systems have evolved by using machine learning techniques to improve their
results, but they are mainly dependent on user feedback. Although information
retrieval has been addressed in different works along with classifiers based on
Optimum-Path Forest (OPF), these have so far not been applied to the learning
to rank task. Therefore, the main contribution of this work is to evaluate
classifiers based on Optimum-Path Forest, in such a context. Experiments were
performed considering the image retrieval and ranking scenarios, and the
performance of OPF-based approaches was compared to the well-known SVM-Rank
pairwise technique and a baseline based on distance calculation. The
experiments showed competitive results concerning precision and outperformed
traditional techniques in terms of computational load.
",Information Ranking Using Optimum-Path Forest
"  Automatic Music Generation (AMG) has become an interesting research topic for
many scientists in artificial intelligence, who are also interested in the
music industry. One of the main challenges in AMG is that there is no clear
objective evaluation criterion that can measure the music grammar, structural
rules, and audience satisfaction. Also, original music contains different
elements that should work together, such as melody, harmony, and rhythm; but in
the most of previous works, AMG works only for one element (e.g., melody).
Therefore, in this paper, we propose a Multi-Objective Genetic Algorithm
(MO-GA) to generate polyphonic music pieces, considering grammar and listener
satisfaction. In this method, we use three objective functions. The first
objective function is the accuracy of the generated music piece, based on music
theory; and the other two objective functions are modeled scores provided by
music experts and ordinary listeners. The scoring of experts and listeners
separately are modeled using Bi-directional Long Short-Term Memory (Bi-LSTM)
neural networks. The proposed music generation system tries to maximize
mentioned objective functions to generate a new piece of music, including
melody and harmony. The results show that the proposed method can generate
pleasant pieces with desired styles and lengths, along with harmonic sounds
that follow the grammar.
","A Combination of Multi-Objective Genetic Algorithm and Deep Learning for
  Music Harmony Generation"
"  Recent advances in Reinforcement Learning (RL) have surpassed human-level
performance in many simulated environments. However, existing reinforcement
learning techniques are incapable of explicitly incorporating already known
domain-specific knowledge into the learning process. Therefore, the agents have
to explore and learn the domain knowledge independently through a trial and
error approach, which consumes both time and resources to make valid responses.
Hence, we adapt the Deep Deterministic Policy Gradient (DDPG) algorithm to
incorporate an adviser, which allows integrating domain knowledge in the form
of pre-learned policies or pre-defined relationships to enhance the agent's
learning process. Our experiments on OpenAi Gym benchmark tasks show that
integrating domain knowledge through advisers expedites the learning and
improves the policy towards better optima.
",Transferring Domain Knowledge with an Adviser in Continuous Tasks
"  This paper describes the design and development of a prototype technique for
artificial intelligence based on the fusion of genetic algorithm, neural
network and fuzzy logic. It starts by establishing a relationship between the
neural network and fuzzy logic. Then, it combines the genetic algorithm with
them. Information fusions are at the confidence level, where matching scores
can be reported and discussed. The technique is called the Genetic Neuro-Fuzzy
(GNF). It can be used for high accuracy real-time environments.
","Design a Technology Based on the Fusion of Genetic Algorithm, Neural
  network and Fuzzy logic"
"  Unstructured pruning reduces the memory footprint in deep neural networks
(DNNs). Recently, researchers proposed different types of structural pruning
intending to reduce also the computation complexity. In this work, we first
suggest a new measure called mask-diversity which correlates with the expected
accuracy of the different types of structural pruning. We focus on the recently
suggested N:M fine-grained block sparsity mask, in which for each block of M
weights, we have at least N zeros. While N:M fine-grained block sparsity allows
acceleration in actual modern hardware, it can be used only to accelerate the
inference phase. In order to allow for similar accelerations in the training
phase, we suggest a novel transposable fine-grained sparsity mask, where the
same mask can be used for both forward and backward passes. Our transposable
mask guarantees that both the weight matrix and its transpose follow the same
sparsity pattern; thus, the matrix multiplication required for passing the
error backward can also be accelerated. We formulate the problem of finding the
optimal transposable-mask as a minimum-cost flow problem. Additionally, to
speed up the minimum-cost flow computation, we also introduce a fast
linear-time approximation that can be used when the masks dynamically change
during training. Our experiments suggest a 2x speed-up in the matrix
multiplications with no accuracy degradation over vision and language models.
Finally, to solve the problem of switching between different structure
constraints, we suggest a method to convert a pre-trained model with
unstructured sparsity to an N:M fine-grained block sparsity model with little
to no training. A reference implementation can be found at
https://github.com/papers-submission/structured_transposable_masks.
","Accelerated Sparse Neural Training: A Provable and Efficient Method to
  Find N:M Transposable Masks"
"  Argumentation provides a representation of arguments and attacks between
these arguments. Argumentation can be used to represent a reasoning process
over evidence to reach conclusions. Within such a reasoning process,
understanding the value of information can improve the quality of decision
making based on the output of the reasoning process. The value of an item of
information is inherently dependent on the available evidence and the question
being answered by the reasoning. In this paper we introduce a value of
information on argument frameworks to identify the most valuable arguments
within the finite set of arguments in the framework, and the arguments and
attacks which could be added to change the output of an evaluation. We
demonstrate the value of information within an argument framework representing
an intelligence analysis in the maritime domain. Understanding the value of
information in an intelligence analysis will allow analysts to balance the
value against the costs and risks of collection, to effectively request further
collection of intelligence to increase the confidence in the analysis of
hypotheses.
",Value of Information for Argumentation based Intelligence Analysis
"  In large-scale systems there are fundamental challenges when centralised
techniques are used for task allocation. The number of interactions is limited
by resource constraints such as on computation, storage, and network
communication. We can increase scalability by implementing the system as a
distributed task-allocation system, sharing tasks across many agents. However,
this also increases the resource cost of communications and synchronisation,
and is difficult to scale.
  In this paper we present four algorithms to solve these problems. The
combination of these algorithms enable each agent to improve their task
allocation strategy through reinforcement learning, while changing how much
they explore the system in response to how optimal they believe their current
strategy is, given their past experience. We focus on distributed agent systems
where the agents' behaviours are constrained by resource usage limits, limiting
agents to local rather than system-wide knowledge. We evaluate these algorithms
in a simulated environment where agents are given a task composed of multiple
subtasks that must be allocated to other agents with differing capabilities, to
then carry out those tasks. We also simulate real-life system effects such as
networking instability. Our solution is shown to solve the task allocation
problem to 6.7% of the theoretical optimal within the system configurations
considered. It provides 5x better performance recovery over no-knowledge
retention approaches when system connectivity is impacted, and is tested
against systems up to 100 agents with less than a 9% impact on the algorithms'
performance.
",Dynamic neighbourhood optimisation for task allocation using multi-agent
"  Resource allocation and task prioritisation are key problem domains in the
fields of autonomous vehicles, networking, and cloud computing. The challenge
in developing efficient and robust algorithms comes from the dynamic nature of
these systems, with many components communicating and interacting in complex
ways. The multi-group resource allocation optimisation (MG-RAO) algorithm we
present uses multiple function approximations of resource demand over time,
alongside reinforcement learning techniques, to develop a novel method of
optimising resource allocation in these multi-agent systems. This method is
applicable where there are competing demands for shared resources, or in task
prioritisation problems. Evaluation is carried out in a simulated environment
containing multiple competing agents. We compare the new algorithm to an
approach where child agents distribute their resources uniformly across all the
tasks they can be allocated. We also contrast the performance of the algorithm
where resource allocation is modelled separately for groups of agents, as to
being modelled jointly over all agents. The MG-RAO algorithm shows a 23 - 28%
improvement over fixed resource allocation in the simulated environments.
Results also show that, in a volatile system, using the MG-RAO algorithm
configured so that child agents model resource allocation for all agents as a
whole has 46.5% of the performance of when it is set to model multiple groups
of agents. These results demonstrate the ability of the algorithm to solve
resource allocation problems in multi-agent systems and to perform well in
dynamic environments.
",Resource allocation in dynamic multiagent systems
"  Employing multiple workers to label data for machine learning models has
become increasingly important in recent years with greater demand to collect
huge volumes of labelled data to train complex models while mitigating the risk
of incorrect and noisy labelling. Whether it is large scale data gathering on
popular crowd-sourcing platforms or smaller sets of workers in high-expertise
labelling exercises, there are various methods recommended to gather a
consensus from employed workers and establish ground-truth labels. However,
there is very little research on how the various parameters of a labelling task
can impact said methods. These parameters include the number of workers, worker
expertise, number of labels in a taxonomy and sample size. In this paper,
Majority Vote, CrowdTruth and Binomial Expectation Maximisation are
investigated against the permutations of these parameters in order to provide
better understanding of the parameter settings to give an advantage in
ground-truth inference. Findings show that both Expectation Maximisation and
CrowdTruth are only likely to give an advantage over majority vote under
certain parameter conditions, while there are many cases where the methods can
be shown to have no major impact. Guidance is given as to what parameters
methods work best under, while the experimental framework provides a way of
testing other established methods and also testing new methods that can attempt
to provide advantageous performance where the methods in this paper did not. A
greater level of understanding regarding optimal crowd-sourcing parameters is
also achieved.
","Finding the Ground-Truth from Multiple Labellers: Why Parameters of the
  Task Matter"
"  During Multi-Agent Path Finding (MAPF) problems, agents can be delayed by
unexpected events. To address such situations recent work describes k-Robust
Conflict-BasedSearch (k-CBS): an algorithm that produces coordinated and
collision-free plan that is robust for up to k delays. In this work we
introducing a variety of pairwise symmetry breaking constraints, specific to
k-robust planning, that can efficiently find compatible and optimal paths for
pairs of conflicting agents. We give a thorough description of the new
constraints and report large improvements to success rate ina range of domains
including: (i) classic MAPF benchmarks;(ii) automated warehouse domains and;
(iii) on maps from the 2019 Flatland Challenge, a recently introduced railway
domain where k-robust planning can be fruitfully applied to schedule trains.
",Symmetry Breaking for k-Robust Multi-Agent Path Finding
"  Approximate computing frameworks configure applications so they can operate
at a range of points in an accuracy-performance trade-off space. Prior work has
introduced many frameworks to create approximate programs. As approximation
frameworks proliferate, it is natural to ask how they can be compared and
combined to create even larger, richer trade-off spaces. We address these
questions by presenting VIPER and BOA. VIPER compares trade-off spaces induced
by different approximation frameworks by visualizing performance improvements
across the full range of possible accuracies. BOA is a family of exploration
techniques that quickly locate Pareto-efficient points in the immense trade-off
space produced by the combination of two or more approximation frameworks. We
use VIPER and BOA to compare and combine three different approximation
frameworks from across the system stack, including: one that changes numerical
precision, one that skips loop iterations, and one that manipulates existing
application parameters. Compared to simply looking at Pareto-optimal curves, we
find VIPER's visualizations provide a quicker and more convenient way to
determine the best approximation technique for any accuracy loss. Compared to a
state-of-the-art evolutionary algorithm, we find that BOA explores 14x fewer
configurations yet locates 35% more Pareto-efficient points.
",Comparing and Combining Approximate Computing Frameworks
"  The application of remaining useful life (RUL) prediction has taken great
importance in terms of energy optimization, cost-effectiveness, and risk
mitigation. The existing RUL prediction algorithms mostly constitute deep
learning frameworks. In this paper, we implement LSTM and GRU models and
compare the obtained results with a proposed genetically trained neural
network. The current models solely depend on Adam and SGD for optimization and
learning. Although the models have worked well with these optimizers, even
little uncertainties in prognostics prediction can result in huge losses. We
hope to improve the consistency of the predictions by adding another layer of
optimization using Genetic Algorithms. The hyper-parameters - learning rate and
batch size are optimized beyond manual capacity. These models and the proposed
architecture are tested on the NASA Turbofan Jet Engine dataset. The optimized
architecture can predict the given hyper-parameters autonomously and provide
superior results.
",Genetically Optimized Prediction of Remaining Useful Life
"  Constraint sets can become inconsistent in different contexts. For example,
during a configuration session the set of customer requirements can become
inconsistent with the configuration knowledge base. Another example is the
engineering phase of a configuration knowledge base where the underlying
constraints can become inconsistent with a set of test cases. In such
situations we are in the need of techniques that support the identification of
minimal sets of faulty constraints that have to be deleted in order to restore
consistency. In this paper we introduce a divide-and-conquer based diagnosis
algorithm (FastDiag) which identifies minimal sets of faulty constraints in an
over-constrained problem. This algorithm is specifically applicable in
scenarios where the efficient identification of leading (preferred) diagnoses
is crucial. We compare the performance of FastDiag with the conflict-directed
calculation of hitting sets and present an in-depth performance analysis that
shows the advantages of our approach.
",An Efficient Diagnosis Algorithm for Inconsistent Constraint Sets
"  Grid cells enable the brain to model the physical space of the world and
navigate effectively via path integration, updating self-position using
information from self-movement. Recent proposals suggest that the brain might
use similar mechanisms to understand the structure of objects in diverse
sensory modalities, including vision. In machine vision, object recognition
given a sequence of sensory samples of an image, such as saccades, is a
challenging problem when the sequence does not follow a consistent, fixed
pattern - yet this is something humans do naturally and effortlessly. We
explore how grid cell-based path integration in a cortical network can support
reliable recognition of objects given an arbitrary sequence of inputs. Our
network (GridCellNet) uses grid cell computations to integrate visual
information and make predictions based on movements. We use local Hebbian
plasticity rules to learn rapidly from a handful of examples (few-shot
learning), and consider the task of recognizing MNIST digits given only a
sequence of image feature patches. We compare GridCellNet to k-Nearest
Neighbour (k-NN) classifiers as well as recurrent neural networks (RNNs), both
of which lack explicit mechanisms for handling arbitrary sequences of input
samples. We show that GridCellNet can reliably perform classification,
generalizing to both unseen examples and completely novel sequence
trajectories. We further show that inference is often successful after sampling
a fraction of the input space, enabling the predictive GridCellNet to
reconstruct the rest of the image given just a few movements. We propose that
dynamically moving agents with active sensors can use grid cell representations
not only for navigation, but also for efficient recognition and feature
prediction of seen objects.
",Grid Cell Path Integration For Movement-Based Visual Object Recognition
"  Bag-of-Visual Words (BoVW) and deep learning techniques have been widely used
in several domains, which include computer-assisted medical diagnoses. In this
work, we are interested in developing tools for the automatic identification of
Parkinson's disease using machine learning and the concept of BoVW. The
proposed approach concerns a hierarchical-based learning technique to design
visual dictionaries through the Deep Optimum-Path Forest classifier. The
proposed method was evaluated in six datasets derived from data collected from
individuals when performing handwriting exams. Experimental results showed the
potential of the technique, with robust achievements.
",Hierarchical Learning Using Deep Optimum-Path Forest
"  Drug discovery and development is an extremely complex process, with high
attrition contributing to the costs of delivering new medicines to patients.
Recently, various machine learning approaches have been proposed and
investigated to help improve the effectiveness and speed of multiple stages of
the drug discovery pipeline. Among these techniques, it is especially those
using Knowledge Graphs that are proving to have considerable promise across a
range of tasks, including drug repurposing, drug toxicity prediction and target
gene-disease prioritisation. In such a knowledge graph-based representation of
drug discovery domains, crucial elements including genes, diseases and drugs
are represented as entities or vertices, whilst relationships or edges between
them indicate some level of interaction. For example, an edge between a disease
and drug entity might represent a successful clinical trial, or an edge between
two drug entities could indicate a potentially harmful interaction.
  In order to construct high-quality and ultimately informative knowledge
graphs however, suitable data and information is of course required. In this
review, we detail publicly available primary data sources containing
information suitable for use in constructing various drug discovery focused
knowledge graphs. We aim to help guide machine learning and knowledge graph
practitioners who are interested in applying new techniques to the drug
discovery field, but who may be unfamiliar with the relevant data sources.
Overall we hope this review will help motivate more machine learning
researchers to explore combining knowledge graphs and machine learning to help
solve key and emerging questions in the drug discovery domain.
","A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge
  Graph Perspective"
"  We present a new concept called Game Mechanic Alignment theory as a way to
organize game mechanics through the lens of systemic rewards and agential
motivations. By disentangling player and systemic influences, mechanics may be
better identified for use in an automated tutorial generation system, which
could tailor tutorials for a particular playstyle or player. Within, we apply
this theory to several well-known games to demonstrate how designers can
benefit from it, we describe a methodology for how to estimate ""mechanic
alignment"", and we apply this methodology on multiple games in the GVGAI
framework. We discuss how effectively this estimation captures agential
motivations and systemic rewards and how our theory could be used as an
alternative way to find mechanics for tutorial generation.
",Game Mechanic Alignment Theory and Discovery
"  It is argued that a broad class of AGI-relevant algorithms can be expressed
in a common formal framework, via specifying Galois connections linking search
and optimization processes on directed metagraphs whose edge targets are
labeled with probabilistic dependent types, and then showing these connections
are fulfilled by processes involving metagraph chronomorphisms. Examples are
drawn from the core cognitive algorithms used in the OpenCog AGI framework:
Probabilistic logical inference, evolutionary program learning, pattern mining,
agglomerative clustering, pattern mining and nonlinear-dynamical attention
allocation.
  The analysis presented involves representing these cognitive algorithms as
recursive discrete decision processes involving optimizing functions defined
over metagraphs, in which the key decisions involve sampling from probability
distributions over metagraphs and enacting sets of combinatory operations on
selected sub-metagraphs. The mutual associativity of the combinatory operations
involved in a cognitive process is shown to often play a key role in enabling
the decomposition of the process into folding and unfolding operations; a
conclusion that has some practical implications for the particulars of
cognitive processes, e.g. militating toward use of reversible logic and
reversible program execution. It is also observed that where this mutual
associativity holds, there is an alignment between the hierarchy of subgoals
used in recursive decision process execution and a hierarchy of subpatterns
definable in terms of formal pattern theory.
","Patterns of Cognition: Cognitive Algorithms as Galois Connections
  Fulfilled by Chronomorphisms On Probabilistically Typed Metagraphs"
"  Conceptual abstraction and analogy-making are key abilities underlying
humans' abilities to learn, reason, and robustly adapt their knowledge to new
domains. Despite of a long history of research on constructing AI systems with
these abilities, no current AI system is anywhere close to a capability of
forming humanlike abstractions or analogies. This paper reviews the advantages
and limitations of several approaches toward this goal, including symbolic
methods, deep learning, and probabilistic program induction. The paper
concludes with several proposals for designing challenge tasks and evaluation
measures in order to make quantifiable and generalizable progress in this area.
",Abstraction and Analogy-Making in Artificial Intelligence
"  When collaborating with an AI system, we need to assess when to trust its
recommendations. If we mistakenly trust it in regions where it is likely to
err, catastrophic failures may occur, hence the need for Bayesian approaches
for probabilistic reasoning in order to determine the confidence (or epistemic
uncertainty) in the probabilities in light of the training data. We propose an
approach to overcome the independence assumption behind most of the approaches
dealing with a large class of probabilistic reasoning that includes Bayesian
networks as well as several instances of probabilistic logic. We provide an
algorithm for Bayesian learning from sparse, albeit complete, observations, and
for deriving inferences and their confidences keeping track of the dependencies
between variables when they are manipulated within the unifying computational
formalism provided by probabilistic circuits. Each leaf of such circuits is
labelled with a beta-distributed random variable that provides us with an
elegant framework for representing uncertain probabilities. We achieve better
estimation of epistemic uncertainty than state-of-the-art approaches, including
highly engineered ones, while being able to handle general circuits and with
just a modest increase in the computational effort compared to using point
probabilities.
",Handling Epistemic and Aleatory Uncertainties in Probabilistic Circuits
"  A key challenge for reinforcement learning is solving long-horizon planning
problems. Recent work has leveraged programs to guide reinforcement learning in
these settings. However, these approaches impose a high manual burden on the
user since they must provide a guiding program for every new task. Partially
observed environments further complicate the programming task because the
program must implement a strategy that correctly, and ideally optimally,
handles every possible configuration of the hidden regions of the environment.
We propose a new approach, model predictive program synthesis (MPPS), that uses
program synthesis to automatically generate the guiding programs. It trains a
generative model to predict the unobserved portions of the world, and then
synthesizes a program based on samples from this model in a way that is robust
to its uncertainty. In our experiments, we show that our approach significantly
outperforms non-program-guided approaches on a set of challenging benchmarks,
including a 2D Minecraft-inspired environment where the agent must complete a
complex sequence of subtasks to achieve its goal, and achieves a similar
performance as using handcrafted programs to guide the agent. Our results
demonstrate that our approach can obtain the benefits of program-guided
reinforcement learning without requiring the user to provide a new guiding
program for every new task.
","Program Synthesis Guided Reinforcement Learning for Partially Observed
  Environments"
"  Faced with an ever-increasing complexity of their domains of application,
artificial learning agents are now able to scale up in their ability to process
an overwhelming amount of information coming from their interaction with an
environment. However, this process of scaling does come with a cost of encoding
and processing an increasing amount of redundant information that is not
necessarily beneficial to the learning process itself. This work exploits the
properties of the learning systems defined over partially observable domains by
selectively focusing on the specific type of information that is more likely to
express the causal interaction among the transitioning states of the
environment. Adaptive masking of the observation space based on the
$\textit{temporal difference displacement}$ criterion enabled a significant
improvement in convergence of temporal difference algorithms defined over a
partially observable Markov process.
","Uncertainty Maximization in Partially Observable Domains: A Cognitive
  Perspective"
"  Individual behavior and decisions are substantially influenced by their
contexts, such as location, environment, and time. Changes along these
dimensions can be readily observed in Multiplayer Online Battle Arena games
(MOBA), where players face different in-game settings for each match and are
subject to frequent game patches. Existing methods utilizing contextual
information generalize the effect of a context over the entire population, but
contextual information tailored to each individual can be more effective. To
achieve this, we present the Neural Individualized Context-aware Embeddings
(NICE) model for predicting user performance and game outcomes. Our proposed
method identifies individual behavioral differences in different contexts by
learning latent representations of users and contexts through non-negative
tensor factorization. Using a dataset from the MOBA game League of Legends, we
demonstrate that our model substantially improves the prediction of winning
outcome, individual user performance, and user engagement.
","Individualized Context-Aware Tensor Factorization for Online Games
  Predictions"
"  The integration of reasoning, learning, and decision-making is key to build
more general AI systems. As a step in this direction, we propose a novel
neural-logic architecture that can solve both inductive logic programming (ILP)
and deep reinforcement learning (RL) problems. Our architecture defines a
restricted but expressive continuous space of first-order logic programs by
assigning weights to predicates instead of rules. Therefore, it is fully
differentiable and can be efficiently trained with gradient descent. Besides,
in the deep RL setting with actor-critic algorithms, we propose a novel
efficient critic architecture. Compared to state-of-the-art methods on both ILP
and RL problems, our proposition achieves excellent performance, while being
able to provide a fully interpretable solution and scaling much better,
especially during the testing phase.
",Differentiable Logic Machines
"  Recent approaches to goal recognition have leveraged planning landmarks to
achieve high-accuracy with low runtime cost. These approaches, however, lack a
probabilistic interpretation. Furthermore, while most probabilistic models to
goal recognition assume that the recognizer has access to a prior probability
representing, for example, an agent's preferences, virtually no goal
recognition approach actually uses the prior in practice, simply assuming a
uniform prior. In this paper, we provide a model to both extend landmark-based
goal recognition with a probabilistic interpretation and allow the estimation
of such prior probability and its usage to compute posterior probabilities
after repeated interactions of observed agents. We empirically show that our
model can not only recognize goals effectively but also successfully infer the
correct prior probability distribution representing an agent's preferences.
","Inferring Agents Preferences as Priors for Probabilistic Goal
  Recognition"
"  We consider the problem of selecting a set of individuals from a candidate
population in order to maximise utility. When the utility function is defined
over sets, this raises the question of how to define meritocracy. We define and
analyse an appropriate notion of meritocracy derived from the utility function.
We introduce the notion of expected marginal contributions of individuals and
analyse its links to the underlying optimisation problem, our notion of
meritocracy, and other notions of fairness such as the Shapley value. We also
experimentally analyse the effect of different policy structures on the utility
and meritocracy in a simulated college admission setting including constraints
on statistical parity.
",On Meritocracy in Optimal Set Selection
"  Specially customised Entropies are widely applied in measuring the degree of
uncertainties existing in the frame of discernment. However, all of these
entropies regard the frame as a whole that has already been determined which
dose not conform to actual situations. In real life, everything comes in an
order, so how to measure uncertainties of the dynamic process of determining
sequence of propositions contained in a frame of discernment is still an open
issue and no related research has been proceeded. Therefore, a novel ordinal
entropy to measure uncertainties of the frame of discernment considering the
order of confirmation of propositions is proposed in this paper. Compared with
traditional entropies, it manifests effects on degree of uncertainty brought by
orders of propositions existing in a frame of discernment. Besides, some
numerical examples are provided to verify the correctness and validity of the
proposed entropy in this paper.
",Ordinal relative belief entropy
"  Finding exact circuit size is a notorious optimization problem in practice.
Whereas modern computers and algorithmic techniques allow to find a circuit of
size seven in blink of an eye, it may take more than a week to search for a
circuit of size thirteen. One of the reasons of this behavior is that the
search space is enormous: the number of circuits of size $s$ is
$s^{\Theta(s)}$, the number of Boolean functions on $n$ variables is $2^{2^n}$.
  In this paper, we explore the following natural heuristic idea for decreasing
the size of a given circuit: go through all its subcircuits of moderate size
and check whether any of them can be improved by reducing to SAT. This may be
viewed as a local search approach: we search for a smaller circuit in a ball
around a given circuit. We report the results of experiments with various
symmetric functions.
",SAT-based Circuit Local Improvement
"  Combining the closed-world reasoning of answer set programming (ASP) with the
open-world reasoning of ontologies broadens the space of applications of
reasoners. Disjunctive hybrid MKNF knowledge bases succinctly extend ASP and in
some cases without increasing the complexity of reasoning tasks. However, in
many cases, solver development is lagging behind. As the result, the only known
method of solving disjunctive hybrid MKNF knowledge bases is based on
guess-and-verify, as formulated by Motik and Rosati in their original work. A
main obstacle is understanding how constraint propagation may be performed by a
solver, which, in the context of ASP, centers around the computation of
\textit{unfounded atoms}, the atoms that are false given a partial
interpretation. In this work, we build towards improving solvers for hybrid
MKNF knowledge bases with disjunctive rules: We formalize a notion of unfounded
sets for these knowledge bases, identify lower complexity bounds, and
demonstrate how we might integrate these developments into a solver. We discuss
challenges introduced by ontologies that are not present in the development of
solvers for disjunctive logic programs, which warrant some deviations from
traditional definitions of unfounded sets. We compare our work with prior
definitions of unfounded sets.
",Unfounded Sets for Disjunctive Hybrid MKNF Knowledge Bases
"  We aim to investigate the potential impacts of smart homes on human behavior.
To this end, we simulate a series of human models capable of performing various
activities inside a reinforcement learning-based smart home. We then
investigate the possibility of human behavior being altered as a result of the
smart home and the human model adapting to one-another. We design a semi-Markov
decision process human task interleaving model based on hierarchical
reinforcement learning that learns to make decisions to either pursue or leave
an activity. We then integrate our human model in the smart home which is based
on Q-learning. We show that a smart home trained on a generic human model is
able to anticipate and learn the thermal preferences of human models with
intrinsic rewards similar to the generic model. The hierarchical human model
learns to complete each activity and set optimal thermal settings for maximum
comfort. With the smart home, the number of time steps required to change the
thermal settings are reduced for the human models. Interestingly, we observe
that small variations in the human model reward structures can lead to the
opposite behavior in the form of unexpected switching between activities which
signals changes in human behavior due to the presence of the smart home.
","Potential Impacts of Smart Homes on Human Behavior: A Reinforcement
  Learning Approach"
"  In this paper, we show that coherent sets of gambles and coherent lower and
upper previsions can be embedded into the algebraic structure of information
algebra. This leads firstly, to a new perspective of the algebraic and logical
structure of desirability and imprecise probabilities and secondly, it connects
imprecise probabilities to other formalism in computer science sharing the same
underlying structure. Both the domain free and the labeled view of the
resulting information algebras are presented, considering product possibility
spaces. Moreover, it is shown that both are atomistic and therefore they can be
embedded in set algebras.
",Information algebras in the theory of imprecise probabilities
"  We re-examine the topic of machine-learned clause selection guidance in
saturation-based theorem provers. The central idea, recently popularized by the
ENIGMA system, is to learn a classifier for recognizing clauses that appeared
in previously discovered proofs. In subsequent runs, clauses classified
positively are prioritized for selection. We propose several improvements to
this approach and experimentally confirm their viability. For the
demonstration, we use a recursive neural network to classify clauses based on
their derivation history and the presence or absence of automatically supplied
theory axioms therein. The automatic theorem prover Vampire guided by the
network achieves a 41% improvement on a relevant subset of SMT-LIB in a real
time evaluation.
",Improving ENIGMA-Style Clause Selection While Learning From History
"  Current deep learning based disease diagnosis systems usually fall short in
catastrophic forgetting, i.e., directly fine-tuning the disease diagnosis model
on new tasks usually leads to abrupt decay of performance on previous tasks.
What is worse, the trained diagnosis system would be fixed once deployed but
collecting training data that covers enough diseases is infeasible, which
inspires us to develop a lifelong learning diagnosis system. In this work, we
propose to adopt attention to combine medical entities and context, embedding
episodic memory and consolidation to retain knowledge, such that the learned
model is capable of adapting to sequential disease-diagnosis tasks. Moreover,
we establish a new benchmark, named Jarvis-40, which contains clinical notes
collected from various hospitals. Our experiments show that the proposed method
can achieve state-of-the-art performance on the proposed benchmark.
",Lifelong Learning based Disease Diagnosis on Clinical Notes
"  In recent years, research on Physarum polycephalum has become more popular
after Nakagaki et al. (2000) performed their famous experiment showing that
Physarum was able to find the shortest route through a maze. Subsequent
researches have confirmed the ability of Physarum-inspired algorithms to solve
a wide range of NP-hard problems. In contrast to previous reviews that either
focus on biological aspects or bio-inspired applications, here we present a
comprehensive review that highlights recent Physarum polycephalum biological
aspects, mathematical models, and Physarum bio-inspired algorithms and their
applications. The novelty of this review stems from our exploration of Physarum
intelligent behaviour in competition settings. Further, we have presented our
new model to simulate Physarum in competition, where multiple Physarum interact
with each other and with their environments. The bio-inspired Physarum in
competition algorithms proved to have great potentials for future research.
","A Survey on Physarum Polycephalum Intelligent Foraging Behaviour and
  Bio-Inspired Applications"
"  In this report, we present results reproductions for several core algorithms
implemented in the OpenSpiel framework for learning in games. The primary
contribution of this work is a validation of OpenSpiel's re-implemented search
and Reinforcement Learning algorithms against the results reported in their
respective originating works. Additionally, we provide complete documentation
of hyperparameters and source code required to reproduce these experiments
easily and exactly.
",Multi-agent Reinforcement Learning in OpenSpiel: A Reproduction Report
"  Auxiliary information attracts more and more attention in the area of machine
learning. Attempts so far to include such auxiliary information in
state-of-the-art learning process have often been based on simply appending
these auxiliary features to the data level or feature level. In this paper, we
intend to propose a novel training method with new options and architectures.
Siamese labels, which were used in the training phase as auxiliary modules.
While in the testing phase, the auxiliary module should be removed. Siamese
label module makes it easier to train and improves the performance in testing
process. In general, the main contributions can be summarized as, 1) Siamese
Labels are firstly proposed as auxiliary information to improve the learning
efficiency; 2) We establish a new architecture, Siamese Labels Auxiliary
Network (SilaNet), which is to assist the training of the model; 3) Siamese
Labels Auxiliary Network is applied to compress the model parameters by 50% and
ensure the high accuracy at the same time. For the purpose of comparison, we
tested the network on CIFAR-10 and CIFAR100 using some common models. The
proposed SilaNet performs excellent efficiency both on the accuracy and
robustness.
",Siamese Labels Auxiliary Network(SiLaNet)
"  Markov Decision Process (MDP) is the underlying model for optimal planning
for decision-theoretic agents in stochastic environments. Although much
research focuses on solving MDP problems both in tabular form or using factored
representations, none focused on tensor decomposition methods. Solving MDPs
using tensor algebra offers the prospect of leveraging advances in tensor-based
computations to further increase solver efficiency. In this paper, we develop
an MDP solver for a multidimensional problem using a tensor decomposition
method to compress the transition models and optimize the value iteration and
policy iteration algorithms. We empirically evaluate our approach against
tabular methods and show our approach can compute much larger problems using
substantially less memory, opening up new possibilities for tensor-based
approaches in stochastic planning
","CP-MDP: A CANDECOMP-PARAFAC Decomposition Approach to Solve a Markov
  Decision Process Multidimensional Problem"
"  There has been a paradigm-shift in urban logistic services in the last years;
demand for real-time, instant mobility and delivery services grows. This poses
new challenges to logistic service providers as the underlying stochastic
dynamic vehicle routing problems (SDVRPs) require anticipatory real-time
routing actions. Searching the combinatorial action space for efficient routing
actions is by itself a complex task of mixed-integer programming (MIP)
well-known by the operations research community. This complexity is now
multiplied by the challenge of evaluating such actions with respect to their
effectiveness given future dynamism and uncertainty, a potentially ideal case
for reinforcement learning (RL) well-known by the computer science community.
For solving SDVRPs, joint work of both communities is needed, but as we show,
essentially non-existing. Both communities focus on their individual strengths
leaving potential for improvement. Our survey paper highlights this potential
in research originating from both communities. We point out current obstacles
in SDVRPs and guide towards joint approaches to overcome them.
","Where the Action is: Let's make Reinforcement Learning for Stochastic
  Dynamic Vehicle Routing Problems work!"
"  Machine intelligence is very successful at standard recognition tasks when
having high-quality training data. There is still a significant gap between
machine-level pattern recognition and human-level concept learning. Humans can
learn under uncertainty from only a few examples and generalize these concepts
to solve new problems. The growing interest in explainable machine
intelligence, requires experimental environments and diagnostic tests to
analyze weaknesses in existing approaches to drive progress in the field. In
this paper, we discuss existing diagnostic tests and test data sets such as
CLEVR, CLEVERER, CLOSURE, CURI, Bongard-LOGO, V-PROM, and present our own
experimental environment: The KANDINSKYPatterns, named after the Russian artist
Wassily Kandinksy, who made theoretical contributions to compositivity, i.e.
that all perceptions consist of geometrically elementary individual components.
This was experimentally proven by Hubel &Wiesel in the 1960s and became the
basis for machine learning approaches such as the Neocognitron and the even
later Deep Learning. While KANDINSKYPatterns have computationally controllable
properties on the one hand, bringing ground truth, they are also easily
distinguishable by human observers, i.e., controlled patterns can be described
by both humans and algorithms, making them another important contribution to
international research in machine intelligence.
","KANDINSKYPatterns -- An experimental exploration environment for Pattern
  Analysis and Machine Intelligence"
"  We address scaling up equilibrium computation in Mean Field Games (MFGs)
using Online Mirror Descent (OMD). We show that continuous-time OMD provably
converges to a Nash equilibrium under a natural and well-motivated set of
monotonicity assumptions. This theoretical result nicely extends to
multi-population games and to settings involving common noise. A thorough
experimental investigation on various single and multi-population MFGs shows
that OMD outperforms traditional algorithms such as Fictitious Play (FP). We
empirically show that OMD scales up and converges significantly faster than FP
by solving, for the first time to our knowledge, examples of MFGs with hundreds
of billions states. This study establishes the state-of-the-art for learning in
large-scale multi-agent and multi-population games.
",Scaling up Mean Field Games with Online Mirror Descent
"  Despite many proposed algorithms to provide robustness to deep learning (DL)
models, DL models remain susceptible to adversarial attacks. We hypothesize
that the adversarial vulnerability of DL models stems from two factors. The
first factor is data sparsity which is that in the high dimensional data space,
there are large regions outside the support of the data distribution. The
second factor is the existence of many redundant parameters in the DL models.
Owing to these factors, different models are able to come up with different
decision boundaries with comparably high prediction accuracy. The appearance of
the decision boundaries in the space outside the support of the data
distribution does not affect the prediction accuracy of the model. However,
they make an important difference in the adversarial robustness of the model.
We propose that the ideal decision boundary should be as far as possible from
the support of the data distribution.\par In this paper, we develop a training
framework for DL models to learn such decision boundaries spanning the space
around the class distributions further from the data points themselves.
Semi-supervised learning was deployed to achieve this objective by leveraging
unlabeled data generated in the space outside the support of the data
distribution. We measure adversarial robustness of the models trained using
this training framework against well-known adversarial attacks We found that
our results, other regularization methods and adversarial training also support
our hypothesis of data sparcity. We show that the unlabeled data generated by
noise using our framework is almost as effective as unlabeled data, sourced
from existing data sets or generated by synthesis algorithms, on adversarial
robustness. Our code is available at
https://github.com/MahsaPaknezhad/AdversariallyRobustTraining.
",Explaining Adversarial Vulnerability with a Data Sparsity Hypothesis
"  Robust and accurate detection of small moving targets in cluttered moving
backgrounds is a significant and challenging problem for robotic visual systems
to perform search and tracking tasks. Inspired by the neural circuitry of
elementary motion vision in the mammalian retina, this paper proposes a
bioinspired retinal neural network based on a new neurodynamics-based temporal
filtering and multiform 2-D spatial Gabor filtering. This model can estimate
motion direction accurately via only two perpendicular spatiotemporal filtering
signals, and respond to small targets of different sizes and velocities by
adjusting the dendrite field size of the spatial filter. Meanwhile, an
algorithm of directionally selective inhibition is proposed to suppress the
target-like features in the moving background, which can reduce the influence
of background motion effectively. Extensive synthetic and real-data experiments
show that the proposed model works stably for small targets of a wider size and
velocity range, and has better detection performance than other bioinspired
models. Additionally, it can also extract the information of motion direction
and motion energy accurately and rapidly.
","A Bioinspired Retinal Neural Network for Accurately Extracting
  Small-Target Motion Information in Cluttered Backgrounds"
"  To improve the detection accuracy and generalization of steganalysis, this
paper proposes the Steganalysis Contrastive Framework (SCF) based on
contrastive learning. The SCF improves the feature representation of
steganalysis by maximizing the distance between features of samples of
different categories and minimizing the distance between features of samples of
the same category. To decrease the computing complexity of the contrastive loss
in supervised learning, we design a novel Steganalysis Contrastive Loss
(StegCL) based on the equivalence and transitivity of similarity. The StegCL
eliminates the redundant computing in the existing contrastive loss. The
experimental results show that the SCF improves the generalization and
detection accuracy of existing steganalysis DNNs, and the maximum promotion is
2% and 3% respectively. Without decreasing the detection accuracy, the training
time of using the StegCL is 10% of that of using the contrastive loss in
supervised learning.
","Using contrastive learning to improve the performance of steganalysis
  schemes"
"  In this report, we investigate (element-based) inconsistency measures for
multisets of business rule bases. Currently, related works allow to assess
individual rule bases, however, as companies might encounter thousands of such
instances daily, studying not only individual rule bases separately, but rather
also their interrelations becomes necessary, especially in regard to
determining suitable re-modelling strategies. We therefore present an approach
to induce multiset-measures from arbitrary (traditional) inconsistency
measures, propose new rationality postulates for a multiset use-case, and
investigate the complexity of various aspects regarding multi-rule base
inconsistency measurement.
",Measuring Inconsistency over Sequences of Business Rule Cases
"  A desirable goal for autonomous agents is to be able to coordinate on the fly
with previously unknown teammates. Known as ""ad hoc teamwork"", enabling such a
capability has been receiving increasing attention in the research community.
One of the central challenges in ad hoc teamwork is quickly recognizing the
current plans of other agents and planning accordingly. In this paper, we focus
on the scenario in which teammates can communicate with one another, but only
at a cost. Thus, they must carefully balance plan recognition based on
observations vs. that based on communication. This paper proposes a new metric
for evaluating how similar are two policies that a teammate may be following -
the Expected Divergence Point (EDP). We then present a novel planning algorithm
for ad hoc teamwork, determining which query to ask and planning accordingly.
We demonstrate the effectiveness of this algorithm in a range of increasingly
general communication in ad hoc teamwork problems.
",Expected Value of Communication for Planning in Ad Hoc Teamwork
"  We propose a causal framework to explain the catastrophic forgetting in
Class-Incremental Learning (CIL) and then derive a novel distillation method
that is orthogonal to the existing anti-forgetting techniques, such as data
replay and feature/label distillation. We first 1) place CIL into the
framework, 2) answer why the forgetting happens: the causal effect of the old
data is lost in new training, and then 3) explain how the existing techniques
mitigate it: they bring the causal effect back. Based on the framework, we find
that although the feature/label distillation is storage-efficient, its causal
effect is not coherent with the end-to-end feature learning merit, which is
however preserved by data replay. To this end, we propose to distill the
Colliding Effect between the old and the new data, which is fundamentally
equivalent to the causal effect of data replay, but without any cost of replay
storage. Thanks to the causal effect analysis, we can further capture the
Incremental Momentum Effect of the data stream, removing which can help to
retain the old effect overwhelmed by the new data effect, and thus alleviate
the forgetting of the old class in testing. Extensive experiments on three CIL
benchmarks: CIFAR-100, ImageNet-Sub&Full, show that the proposed causal effect
distillation can improve various state-of-the-art CIL methods by a large margin
(0.72%--9.06%).
",Distilling Causal Effect of Data in Class-Incremental Learning
"  In this paper we study a simple extension of the total weighted flowtime
minimization problem for single and identical parallel machines. While the
standard problem simply defines a set of jobs with their processing times and
weights and assumes that all jobs have release date 0 and have no deadline, we
assume that the release date of each job is a decision variable that is only
constrained by a single global latest arrival deadline. To our knowledge, this
simple yet practically highly relevant extension has never been studied. Our
main contribution is that we show the NP- completeness of the problem even for
the single machine case and provide an exhaustive empirical study of different
typical approaches including genetic algorithms, tree search, and constraint
programming.
",Single and Parallel Machine Scheduling with Variable Release Dates
"  Many amputees throughout the world are left with limited options to
personally own a prosthetic arm due to the expensive cost, mechanical system
complexity, and lack of availability. The three main control methods of
prosthetic hands are: (1) body-powered control, (2) extrinsic mechanical
control, and (3) myoelectric control. These methods can perform well under a
controlled situation but will often break down in clinical and everyday use due
to poor robustness, weak adaptability, long-term training, and heavy mental
burden during use. This paper lays the complete outline of the design process
of an affordable and easily accessible novel prosthetic arm that reduces the
cost of prosthetics from $10,000 to $700 on average. The 3D printed prosthetic
arm is equipped with a depth camera and closed-loop off-policy deep learning
algorithm to help form grasps to the object in view. Current work in
reinforcement learning masters only individual skills and is heavily focused on
parallel jaw grippers for in-hand manipulation. In order to create
generalization, which better performs real-world manipulation, the focus is
specifically on using the general framework of Markov Decision Process (MDP)
through scalable learning with off-policy algorithms such as deep deterministic
policy gradient (DDPG) and to study this question in the context of grasping a
prosthetic arm. We were able to achieve a 78% grasp success rate on previously
unseen objects and generalize across multiple objects for manipulation tasks.
This work will make prosthetics cheaper, easier to use and accessible globally
for amputees. Future work includes applying similar approaches to other medical
assistive devices where a human is interacting with a machine to complete a
task.
","Design of an Affordable Prosthetic Arm Equipped with Deep Learning
  Vision-Based Manipulation"
"  Sentiment analysis is the basis of intelligent human-computer interaction. As
one of the frontier research directions of artificial intelligence, it can help
computers better identify human intentions and emotional states so that provide
more personalized services. However, as human present sentiments by spoken
words, gestures, facial expressions and others which involve variable forms of
data including text, audio, video, etc., it poses many challenges to this
study. Due to the limitations of unimodal sentiment analysis, recent research
has focused on the sentiment analysis of videos containing time series data of
multiple modalities. When analyzing videos with multimodal data, the key
problem is how to fuse these heterogeneous data. In consideration that the
contribution of each modality is different, current fusion methods tend to
extract the important information of single modality prior to fusion, which
ignores the consistency and complementarity of bimodal interaction and has
influences on the final decision. To solve this problem, a video sentiment
analysis method using multi-head attention with bimodal information augmented
is proposed. Based on bimodal interaction, more important bimodal features are
assigned larger weights. In this way, different feature representations are
adaptively assigned corresponding attention for effective multimodal fusion.
Extensive experiments were conducted on both Chinese and English public
datasets. The results show that our approach outperforms the existing methods
and can give an insight into the contributions of bimodal interaction among
three modalities.
","Video Sentiment Analysis with Bimodal Information-augmented Multi-Head
  Attention"
"  Conventional deep reinforcement learning methods are sample-inefficient and
usually require a large number of training trials before convergence. Since
such methods operate on an unconstrained action set, they can lead to useless
actions. A recent neuro-symbolic framework called the Logical Neural Networks
(LNNs) can simultaneously provide key-properties of both neural networks and
symbolic logic. The LNNs functions as an end-to-end differentiable network that
minimizes a novel contradiction loss to learn interpretable rules. In this
paper, we utilize LNNs to define an inference graph using basic logical
operations, such as AND and NOT, for faster convergence in reinforcement
learning. Specifically, we propose an integrated method that enables model-free
reinforcement learning from external knowledge sources in an LNNs-based logical
constrained framework such as action shielding and guide. Our results
empirically demonstrate that our method converges faster compared to a
model-free reinforcement learning method that doesn't have such logical
constraints.
","Reinforcement Learning with External Knowledge by using Logical Neural
  Networks"
"  Advances in unmanned aerial vehicle (UAV) design have opened up applications
as varied as surveillance, firefighting, cellular networks, and delivery
applications. Additionally, due to decreases in cost, systems employing fleets
of UAVs have become popular. The uniqueness of UAVs in systems creates a novel
set of trajectory or path planning and coordination problems. Environments
include many more points of interest (POIs) than UAVs, with obstacles and
no-fly zones. We introduce REPlanner, a novel multi-agent reinforcement
learning algorithm inspired by economic transactions to distribute tasks
between UAVs. This system revolves around an economic theory, in particular an
auction mechanism where UAVs trade assigned POIs. We formulate the path
planning problem as a multi-agent economic game, where agents can cooperate and
compete for resources. We then translate the problem into a Partially
Observable Markov decision process (POMDP), which is solved using a
reinforcement learning (RL) model deployed on each agent. As the system
computes task distributions via UAV cooperation, it is highly resilient to any
change in the swarm size. Our proposed network and economic game architecture
can effectively coordinate the swarm as an emergent phenomenon while
maintaining the swarm's operation. Evaluation results prove that REPlanner
efficiently outperforms conventional RL-based trajectory search.
",Efficient UAV Trajectory-Planning using Economic Reinforcement Learning
"  Multiplayer Online Battle Area (MOBA) games are a recent huge success both in
the video game industry and the international eSports scene. These games
encourage team coordination and cooperation, short and long-term planning,
within a real-time combined action and strategy gameplay.
  Artificial Intelligence and Computational Intelligence in Games research
competitions offer a wide variety of challenges regarding the study and
application of AI techniques to different game genres. These events are widely
accepted by the AI/CI community as a sort of AI benchmarking that strongly
influences many other research areas in the field.
  This paper presents and describes in detail the Dota 2 Bot competition and
the Dota 2 AI framework that supports it. This challenge aims to join both,
MOBAs and AI/CI game competitions, inviting participants to submit AI
controllers for the successful MOBA \textit{Defense of the Ancients 2} (Dota 2)
to play in 1v1 matches, which aims for fostering research on AI techniques for
real-time games. The Dota 2 AI framework makes use of the actual Dota 2 game
modding capabilities to enable to connect external AI controllers to actual
Dota 2 game matches using the original Free-to-Play game.se of the actual Dota
2 game modding capabilities to enable to connect external AI controllers to
actual Dota 2 game matches using the original Free-to-Play game.
",The Dota 2 Bot Competition
"  In this short paper, we will introduce a simple model for quantifying
philosophical vagueness. There is growing interest in this endeavor to quantify
vague concepts of consciousness, agency, etc. We will then discuss some of the
implications of this model including the conditions under which the
quantification of `nifty' leads to pan-nifty-ism. Understanding this leads to
an interesting insight - the reason a framework to quantify consciousness like
Integrated Information Theory implies (forms of) panpsychism is because there
is favorable structure already implicitly encoded in the construction of the
quantification metric.
",From Quantifying Vagueness To Pan-niftyism
"  The widespread use of deep neural networks has achieved substantial success
in many tasks. However, there still exists a huge gap between the operating
mechanism of deep learning models and human-understandable decision making, so
that humans cannot fully trust the predictions made by these models. To date,
little work has been done on how to align the behaviors of deep learning models
with human perception in order to train a human-understandable model. To fill
this gap, we propose a new framework to train a deep neural network by
incorporating the prior of human perception into the model learning process.
Our proposed model mimics the process of perceiving conceptual parts from
images and assessing their relative contributions towards the final
recognition. The effectiveness of our proposed model is evaluated on two
classical visual recognition tasks. The experimental results and analysis
confirm our model is able to provide interpretable explanations for its
predictions, but also maintain competitive recognition accuracy.
",Human-Understandable Decision Making for Visual Recognition
"  Increased adoption of artificial intelligence (AI) systems into scientific
workflows will result in an increasing technical debt as the distance between
the data scientists and engineers who develop AI system components and
scientists, researchers and other users grows. This could quickly become
problematic, particularly where guidance or regulations change and
once-acceptable best practice becomes outdated, or where data sources are later
discredited as biased or inaccurate. This paper presents a novel method for
deriving a quantifiable metric capable of ranking the overall transparency of
the process pipelines used to generate AI systems, such that users, auditors
and other stakeholders can gain confidence that they will be able to validate
and trust the data sources and contributors in the AI systems that they rely
on. The methodology for calculating the metric, and the type of criteria that
could be used to make judgements on the visibility of contributions to systems
are evaluated through models published at ModelHub and PyTorch Hub, popular
archives for sharing science resources, and is found to be helpful in driving
consideration of the contributions made to generating AI systems and approaches
towards effective documentation and improving transparency in machine learning
assets shared within scientific communities.
","A framework for fostering transparency in shared artificial intelligence
  models by increasing visibility of contributions"
"  Many social sciences such as psychology and economics try to learn the
behaviour of complex agents such as humans, organisations and countries. The
current statistical methods used for learning this behaviour try to infer
generally valid behaviour, but can only learn from one type of study at a time.
Furthermore, only data from carefully designed studies can be used, as the
phenomenon of interest has to be isolated and confounding factors accounted
for. These restrictions limit the robustness and accuracy of insights that can
be gained from social/economic systems. Here we present the online platform
TreeOfKnowledge which implements a new methodology specifically designed for
learning complex behaviours from complex systems: agent-based behaviour
learning. With agent-based behaviour learning it is possible to gain more
accurate and robust insights as it does not have the restriction of
conventional statistics. It learns agent behaviour from many heterogenous
datasets and can learn from these datasets even if the phenomenon of interest
is not directly observed, but appears deep within complex systems. This new
methodology shows how the internet and advances in computational power allow
for more accurate and powerful mathematical models.
","Tree of Knowledge: an Online Platform for Learning the Behaviour of
  Complex Systems"
"  A major challenge in applying machine learning to automated theorem proving
is the scarcity of training data, which is a key ingredient in training
successful deep learning models. To tackle this problem, we propose an approach
that relies on training purely with synthetically generated theorems, without
any human data aside from axioms. We use these theorems to train a
neurally-guided saturation-based prover. Our neural prover outperforms the
state-of-the-art E-prover on this synthetic data in both time and search steps,
and shows significant transfer to the unseen human-written theorems from the
TPTP library, where it solves 72\% of first-order problems without equality.
",Training a First-Order Theorem Prover from Synthetic Data
"  In this short paper, we outline nine classical benchmarks submitted to the
first hierarchical planning track of the International Planning competition in
2020. All of these benchmarks are based on the HDDL language. The choice of the
benchmarks was based on a questionnaire sent to the HTN community. They are the
following: Barman, Childsnack, Rover, Satellite, Blocksworld, Depots, Gripper,
and Hiking. In the rest of the paper we give a short description of these
benchmarks. All are totally ordered.
","From Classical to Hierarchical: benchmarks for the HTN Track of the
  International Planning Competition"
"  The discipline of process mining aims to study processes in a data-driven
manner by analyzing historical process executions, often employing Petri nets.
Event data, extracted from information systems (e.g. SAP), serve as the
starting point for process mining. Recently, novel types of event data have
gathered interest among the process mining community, including uncertain event
data. Uncertain events, process traces and logs contain attributes that are
characterized by quantified imprecisions, e.g., a set of possible attribute
values. The PROVED tool helps to explore, navigate and analyze such uncertain
event data by abstracting the uncertain information using behavior graphs and
nets, which have Petri nets semantics. Based on these constructs, the tool
enables discovery and conformance checking.
","PROVED: A Tool for Graph Representation and Analysis of Uncertain Event
  Data"
"  There hardly exists a general solver that is efficient for scheduling
problems due to their diversity and complexity. In this study, we develop a
two-stage framework, in which reinforcement learning (RL) and traditional
operations research (OR) algorithms are combined together to efficiently deal
with complex scheduling problems. The scheduling problem is solved in two
stages, including a finite Markov decision process (MDP) and a mixed-integer
programming process, respectively. This offers a novel and general paradigm
that combines RL with OR approaches to solving scheduling problems, which
leverages the respective strengths of RL and OR: The MDP narrows down the
search space of the original problem through an RL method, while the
mixed-integer programming process is settled by an OR algorithm. These two
stages are performed iteratively and interactively until the termination
criterion has been met. Under this idea, two implementation versions of the
combination methods of RL and OR are put forward. The agile Earth observation
satellite scheduling problem is selected as an example to demonstrate the
effectiveness of the proposed scheduling framework and methods. The convergence
and generalization capability of the methods are verified by the performance of
training scenarios, while the efficiency and accuracy are tested in 50
untrained scenarios. The results show that the proposed algorithms could stably
and efficiently obtain satisfactory scheduling schemes for agile Earth
observation satellite scheduling problems. In addition, it can be found that
RL-based optimization algorithms have stronger scalability than non-learning
algorithms. This work reveals the advantage of combining reinforcement learning
methods with heuristic methods or mathematical programming methods for solving
complex combinatorial optimization problems.
","A Two-stage Framework and Reinforcement Learning-based Optimization
  Algorithms for Complex Scheduling Problems"
"  Biological agents have adopted the principle of attention to limit the rate
of incoming information from the environment. One question that arises is if an
artificial agent has access to only a limited view of its surroundings, how can
it control its attention to effectively solve tasks? We propose an approach for
learning how to control a hard attention window by maximizing the mutual
information between the environment state and the attention location at each
step. The agent employs an internal world model to make predictions about its
state and focuses attention towards where the predictions may be wrong.
Attention is trained jointly with a dynamic memory architecture that stores
partial observations and keeps track of the unobserved state. We demonstrate
that our approach is effective in predicting the full state from a sequence of
partial observations. We also show that the agent's internal representation of
the surroundings, a live mental map, can be used for control in two partially
observable reinforcement learning tasks. Videos of the trained agent can be
found at https://sites.google.com/view/hard-attention-control.
",Hard Attention Control By Mutual Information Maximization
"  In this paper, we demonstrate a Symbolic Reinforcement Learning (SRL)
architecture for safe control in Radio Access Network (RAN) applications. In
our automated tool, a user can select a high-level safety specifications
expressed in Linear Temporal Logic (LTL) to shield an RL agent running in a
given cellular network with aim of optimizing network performance, as measured
through certain Key Performance Indicators (KPIs). In the proposed
architecture, network safety shielding is ensured through model-checking
techniques over combined discrete system models (automata) that are abstracted
through reinforcement learning. We demonstrate the user interface (UI) helping
the user set intent specifications to the architecture and inspect the
difference in allowed and blocked actions.
",Symbolic Reinforcement Learning for Safe RAN Control
"  In this paper we establish a link between preferential semantics for
description logics and self-organising maps, which have been proposed as
possible candidates to explain the psychological mechanisms underlying category
generalisation. In particular, we show that a concept-wise multipreference
semantics, which takes into account preferences with respect to different
concepts and has been recently proposed for defeasible description logics, can
be used to to provide a logical interpretation of SOMs. We also provide a
logical interpretation of SOMs in terms of a fuzzy description logic as well as
a probabilistic account.
","A conditional, a fuzzy and a probabilistic interpretation of
  self-organising maps"
"  Self-adaptive systems continuously adapt to changes in their execution
environment. Capturing all possible changes to define suitable behaviour
beforehand is unfeasible, or even impossible in the case of unknown changes,
hence human intervention may be required. We argue that adapting to unknown
situations is the ultimate challenge for self-adaptive systems. Learning-based
approaches are used to learn the suitable behaviour to exhibit in the case of
unknown situations, to minimize or fully remove human intervention. While such
approaches can, to a certain extent, generalize existing adaptations to new
situations, there is a number of breakthroughs that need to be achieved before
systems can adapt to general unknown and unforeseen situations. We posit the
research directions that need to be explored to achieve unanticipated
adaptation from the perspective of learning-based self-adaptive systems. At
minimum, systems need to define internal representations of previously unseen
situations on-the-fly, extrapolate the relationship to the previously
encountered situations to evolve existing adaptations, and reason about the
feasibility of achieving their intrinsic goals in the new set of conditions. We
close discussing whether, even when we can, we should indeed build systems that
define their own behaviour and adapt their goals, without involving a human
supervisor.
","Adaptation to Unknown Situations as the Holy Grail of Learning-Based
  Self-Adaptive Systems: Research Directions"
"  Quality-of-Service prediction of web service is an integral part of services
computing due to its diverse applications in the various facets of a service
life cycle, such as service composition, service selection, service
recommendation. One of the primary objectives of designing a QoS prediction
algorithm is to achieve satisfactory prediction accuracy. However, accuracy is
not the only criteria to meet while developing a QoS prediction algorithm. The
algorithm has to be faster in terms of prediction time so that it can be
integrated into a real-time recommendation or composition system. The other
important factor to consider while designing the prediction algorithm is
scalability to ensure that the prediction algorithm can tackle large-scale
datasets. The existing algorithms on QoS prediction often compromise on one
goal while ensuring the others. In this paper, we propose a semi-offline QoS
prediction model to achieve three important goals simultaneously: higher
accuracy, faster prediction time, scalability. Here, we aim to predict the QoS
value of service that varies across users. Our framework consists of
multi-phase prediction algorithms: preprocessing-phase prediction, online
prediction, and prediction using the pre-trained model. In the preprocessing
phase, we first apply multi-level clustering on the dataset to obtain
correlated users and services. We then preprocess the clusters using
collaborative filtering to remove the sparsity of the given QoS invocation log
matrix. Finally, we create a two-staged, semi-offline regression model using
neural networks to predict the QoS value of service to be invoked by a user in
real-time. Our experimental results on four publicly available WS-DREAM
datasets show the efficiency in terms of accuracy, scalability, fast
responsiveness of our framework as compared to the state-of-the-art methods.
",FES: A Fast Efficient Scalable QoS Prediction Framework
"  In this paper, we resort to the TensorFlow framework to investigate the
benefits of applying data vectorization and fitness caching methods to domain
evaluation in Genetic Programming. For this purpose, an independent engine was
developed, TensorGP, along with a testing suite to extract comparative timing
results across different architectures and amongst both iterative and
vectorized approaches. Our performance benchmarks demonstrate that by
exploiting the TensorFlow eager execution model, performance gains of up to two
orders of magnitude can be achieved on a parallel approach running on dedicated
hardware when compared to a standard iterative approach.
",TensorGP -- Genetic Programming Engine in TensorFlow
"  Clinicians often do not sufficiently adhere to evidence-based clinical
guidelines in a manner sensitive to the context of each patient. It is
important to detect such deviations, typically including redundant or missing
actions, even when the detection is performed retrospectively, so as to inform
both the attending clinician and policy makers. Furthermore, it would be
beneficial to detect such deviations in a manner proportional to the level of
the deviation, and not to simply use arbitrary cut-off values. In this study,
we introduce a new approach for automated guideline-based quality assessment of
the care process, the bidirectional knowledge-based assessment of compliance
(BiKBAC) method. Our BiKBAC methodology assesses the degree of compliance when
applying clinical guidelines, with respect to multiple different aspects of the
guideline (e.g., the guideline's process and outcome objectives). The
assessment is performed through a highly detailed, automated quality-assessment
retrospective analysis, which compares a formal representation of the guideline
and of its process and outcome intentions (we use the Asbru language for that
purpose) with the longitudinal electronic medical record of its continuous
application over a significant time period, using both a top-down and a
bottom-up approach, which we explain in detail. Partial matches of the data to
the process and to the outcome objectives are resolved using fuzzy temporal
logic. We also introduce the DiscovErr system, which implements the BiKBAC
approach, and present its detailed architecture. The DiscovErr system was
evaluated in a separate study in the type 2 diabetes management domain, by
comparing its performance to a panel of three clinicians, with highly
encouraging results with respect to the completeness and correctness of its
comments.
","A Methodology for Bi-Directional Knowledge-Based Assessment of
  Compliance to Continuous Application of Clinical Guidelines"
"  Heterogeneous graph is a kind of data structure widely existing in real life.
Nowadays, the research of graph neural network on heterogeneous graph has
become more and more popular. The existing heterogeneous graph neural network
algorithms mainly have two ideas, one is based on meta-path and the other is
not. The idea based on meta-path often requires a lot of manual preprocessing,
at the same time it is difficult to extend to large scale graphs. In this
paper, we proposed the general heterogeneous message passing paradigm and
designed R-GSN that does not need meta-path, which is much improved compared to
the baseline R-GCN. Experiments have shown that our R-GSN algorithm achieves
the state-of-the-art performance on the ogbn-mag large scale heterogeneous
graph dataset.
",R-GSN: The Relation-based Graph Similar Network for Heterogeneous Graph
"  Applications of reinforcement learning (RL) are popular in autonomous driving
tasks. That being said, tuning the performance of an RL agent and guaranteeing
the generalization performance across variety of different driving scenarios is
still largely an open problem. In particular, getting good performance on
complex road and weather conditions require exhaustive tuning and computation
time. Curriculum RL, which focuses on solving simpler automation tasks in order
to transfer knowledge to complex tasks, is attracting attention in RL
community. The main contribution of this paper is a systematic study for
investigating the value of curriculum reinforcement learning in autonomous
driving applications. For this purpose, we setup several different driving
scenarios in a realistic driving simulator, with varying road complexity and
weather conditions. Next, we train and evaluate performance of RL agents on
different sequences of task combinations and curricula. Results show that
curriculum RL can yield significant gains in complex driving tasks, both in
terms of driving performance and sample complexity. Results also demonstrate
that different curricula might enable different benefits, which hints future
research directions for automated curriculum training.
","Investigating Value of Curriculum Reinforcement Learning in Autonomous
  Driving Under Diverse Road and Weather Conditions"
"  We combine ideas from uni-directional and bi-directional heuristic search,
and approximation algorithms for the Traveling Salesman Problem, to develop a
novel framework for a Multi-Goal Path Finding (MGPF) problem that provides a
2-approximation guarantee. MGPF aims to find a least-cost path from an origin
to a destination such that each node in a given set of goals is visited at
least once along the path. We present numerical results to illustrate the
advantages of our framework over conventional alternates in terms of the number
of expanded nodes and run time.
","S$^*$: A Heuristic Information-Based Approximation Framework for
  Multi-Goal Path Finding"
"  Building a humanlike integrative artificial cognitive system, that is, an
artificial general intelligence, is one of the goals in artificial intelligence
and developmental robotics. Furthermore, a computational model that enables an
artificial cognitive system to achieve cognitive development will be an
excellent reference for brain and cognitive science. This paper describes the
development of a cognitive architecture using probabilistic generative models
(PGMs) to fully mirror the human cognitive system. The integrative model is
called a whole-brain PGM (WB-PGM). It is both brain-inspired and PGMbased. In
this paper, the process of building the WB-PGM and learning from the human
brain to build cognitive architectures is described.
","Whole brain Probabilistic Generative Model toward Realizing Cognitive
  Architecture for Developmental Robots"
"  Recent progress in deep reinforcement learning (DRL) can be largely
attributed to the use of neural networks. However, this black-box approach
fails to explain the learned policy in a human understandable way. To address
this challenge and improve the transparency, we propose a Neural Symbolic
Reinforcement Learning framework by introducing symbolic logic into DRL. This
framework features a fertilization of reasoning and learning modules, enabling
end-to-end learning with prior symbolic knowledge. Moreover, interpretability
is achieved by extracting the logical rules learned by the reasoning module in
a symbolic rule space. The experimental results show that our framework has
better interpretability, along with competing performance in comparison to
state-of-the-art approaches.
",Learning Symbolic Rules for Interpretable Deep Reinforcement Learning
"  This paper proposes a meta-learning approach to evolving a parametrized loss
function, which is called Meta-Loss Network (MLN), for training the image
classification learning on small datasets. In our approach, the MLN is embedded
in the framework of classification learning as a differentiable objective
function. The MLN is evolved with the Evolutionary Strategy algorithm (ES) to
an optimized loss function, such that a classifier, which optimized to minimize
this loss, will achieve a good generalization effect. A classifier learns on a
small training dataset to minimize MLN with Stochastic Gradient Descent (SGD),
and then the MLN is evolved with the precision of the small-dataset-updated
classifier on a large validation dataset. In order to evaluate our approach,
the MLN is trained with a large number of small sample learning tasks sampled
from FashionMNIST and tested on validation tasks sampled from FashionMNIST and
CIFAR10. Experiment results demonstrate that the MLN effectively improved
generalization compared to classical cross-entropy error and mean squared
error.
","Evolving parametrized Loss for Image Classification Learning on Small
  Datasets"
"  We consider the problem of reaching a propositional goal condition in
fully-observable non-deterministic (FOND) planning under a general class of
fairness assumptions that are given explicitly. The fairness assumptions are of
the form A/B and say that state trajectories that contain infinite occurrences
of an action a from A in a state s and finite occurrence of actions from B,
must also contain infinite occurrences of action a in s followed by each one of
its possible outcomes. The infinite trajectories that violate this condition
are deemed as unfair, and the solutions are policies for which all the fair
trajectories reach a goal state. We show that strong and strong-cyclic FOND
planning, as well as QNP planning, a planning model introduced recently for
generalized planning, are all special cases of FOND planning with fairness
assumptions of this form which can also be combined. FOND+ planning, as this
form of planning is called, combines the syntax of FOND planning with some of
the versatility of LTL for expressing fairness constraints. A new planner is
implemented by reducing FOND+ planning to answer set programs, and the
performance of the planner is evaluated in comparison with FOND and QNP
planners, and LTL synthesis tools.
",Flexible FOND Planning with Explicit Fairness Assumptions
"  Security attacks present unique challenges to self-adaptive system design due
to the adversarial nature of the environment. However, modeling the system as a
single player, as done in prior works in security domain, is insufficient for
the system under partial compromise and for the design of fine-grained
defensive strategies where the rest of the system with autonomy can cooperate
to mitigate the impact of attacks. To deal with such issues, we propose a new
self-adaptive framework incorporating Bayesian game and model the defender
(i.e., the system) at the granularity of components in system architecture. The
system architecture model is translated into a Bayesian multi-player game,
where each component is modeled as an independent player while security attacks
are encoded as variant types for the components. The defensive strategy for the
system is dynamically computed by solving the pure equilibrium to achieve the
best possible system utility, improving the resiliency of the system against
security attacks.
",System Component-Level Self-Adaptations for Security via Bayesian Games
"  We evaluated the DiscovErr system, in which we had previously implemented a
new methodology for assessment of compliance to continuous application of
evidence-based clinical guidelines, based on a bidirectional search from the
guideline objectives to the patient's longitudinal data, and vice versa. We
compared the system comments on 1584 transactions regarding the management,
over a mean of 5.23 years, of 10 randomly selected Type 2 diabetes patients, to
those of two diabetes experts and a senior family practitioner. After providing
their own comments, the experts assessed both the correctness (precision) and
the importance of each of the DiscovErr system comments. The completeness
(recall or coverage) of the system was computed by comparing its comments to
those made by the experts. The system made 279 comments. The experts made 181
unique comments. The completeness of the system was 91% compared to comments
made by at least two experts, and 98% when compared to comments made by all
three. 172 comments were evaluated by the experts for correctness and
importance: All 114 medication-related comments, and a random 35% of the 165
monitoring-related comments. The system's correctness was 81% compared to
comments judged as correct by both diabetes experts, and 91% compared to
comments judged as correct by a diabetes expert and at least as partially
correct by the other. 89% of the comments were judged as important by both
diabetes experts, 8% were judged as important by one expert, 3% were judged as
less important by both experts. The completeness scores of the three experts
(compared to the comments of all experts plus the validated system comments)
were 75%, 60%, and 55%; the experts' correctness scores (compared to their
majority) were respectively 99%, 91%, and 88%. Conclusion: Systems such as
DiscovErr can assess the quality of continuous guideline-based care.
","Evaluation of a Bi-Directional Methodology for Automated Assessment of
  Compliance to Continuous Application of Clinical Guidelines, in the Type 2
  Diabetes-Management Domain"
"  This paper proposes a novel ternary hash encoding for learning to hash
methods, which provides a principled more efficient coding scheme with
performances better than those of the state-of-the-art binary hashing
counterparts. Two kinds of axiomatic ternary logic, Kleene logic and
{\L}ukasiewicz logic are adopted to calculate the Ternary Hamming Distance
(THD) for both the learning/encoding and testing/querying phases. Our work
demonstrates that, with an efficient implementation of ternary logic on
standard binary machines, the proposed ternary hashing is compared favorably to
the binary hashing methods with consistent improvements of retrieval mean
average precision (mAP) ranging from 1\% to 5.9\% as shown in CIFAR10, NUS-WIDE
and ImageNet100 datasets.
",Ternary Hashing
"  With the development of measurement technology, data on the movements of
actual games in various sports are available and are expected to be used for
planning and evaluating the tactics and strategy. In particular, defense in
team sports is generally difficult to be evaluated because of the lack of
statistical data. Conventional evaluation methods based on predictions of
scores are considered unreliable and predict rare events throughout the entire
game, and it is difficult to evaluate various plays leading up to a score. On
the other hand, evaluation methods based on certain plays that lead to scoring
and dominant regions are sometimes unsuitable to evaluate the performance
(e.g., goals scored) of players and teams. In this study, we propose a method
to evaluate team defense from a comprehensive perspective related to team
performance based on the prediction of ball recovery and being attacked, which
occur more frequently than goals, using player actions and positional data of
all players and the ball. Using data from 45 soccer matches, we examined the
relationship between the proposed index and team performance in actual matches
and throughout a season. Results show that the proposed classifiers more
accurately predicted the true events than the existing classifiers which were
based on rare events (i.e., goals). Also, the proposed index had a moderate
correlation with the long-term outcomes of the season. These results suggest
that the proposed index might be a more reliable indicator rather than winning
or losing with the inclusion of accidental factors.
","Evaluation of soccer team defense based on prediction models of ball
  recovery and being attacked"
"  This paper proposes a new distance metric function, called Z distance, for
KNN classification. The Z distance function is not a geometric direct-line
distance between two data points. It gives a consideration to the class
attribute of a training dataset when measuring the affinity between data
points. Concretely speaking, the Z distance of two data points includes their
class center distance and real distance. And its shape looks like ""Z"". In this
way, the affinity of two data points in the same class is always stronger than
that in different classes. Or, the intraclass data points are always closer
than those interclass data points. We evaluated the Z distance with
experiments, and demonstrated that the proposed distance function achieved
better performance in KNN classification.
",Z Distance Function for KNN Classification
"  In this paper, we aim at providing a comprehensive outline of the different
threads of work in human-AI collaboration. By highlighting various aspects of
works on the human-AI team such as the flow of complementing, task horizon,
model representation, knowledge level, and teaming goal, we make a taxonomy of
recent works according to these dimensions. We hope that the survey will
provide a more clear connection between the works in the human-AI team and
guidance to new researchers in this area.
",Human-AI Symbiosis: A Survey of Current Approaches
"  Scanned receipts OCR and key information extraction (SROIE) represent the
processeses of recognizing text from scanned receipts and extracting key texts
from them and save the extracted tests to structured documents. SROIE plays
critical roles for many document analysis applications and holds great
commercial potentials, but very little research works and advances have been
published in this area. In recognition of the technical challenges, importance
and huge commercial potentials of SROIE, we organized the ICDAR 2019
competition on SROIE. In this competition, we set up three tasks, namely,
Scanned Receipt Text Localisation (Task 1), Scanned Receipt OCR (Task 2) and
Key Information Extraction from Scanned Receipts (Task 3). A new dataset with
1000 whole scanned receipt images and annotations is created for the
competition. In this report we will presents the motivation, competition
datasets, task definition, evaluation protocol, submission statistics,
performance of submitted methods and results analysis.
",ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction
"  The partial Latin square extension problem is to fill as many as possible
empty cells of a partially filled Latin square. This problem is a useful model
for a wide range of relevant applications in diverse domains. This paper
presents the first massively parallel hybrid search algorithm for this
computationally challenging problem based on a transformation of the problem to
partial graph coloring. The algorithm features the following original elements.
Based on a very large population (with more than $10^4$ individuals) and modern
graphical processing units, the algorithm performs many local searches in
parallel to ensure an intensified exploitation of the search space. It employs
a dedicated crossover with a specific parent matching strategy to create a
large number of diversified and information-preserving offspring at each
generation. Extensive experiments on 1800 benchmark instances show a high
competitiveness of the algorithm compared with the current best performing
methods. Competitive results are also reported on the related Latin square
completion problem. Analyses are performed to shed lights on the understanding
of the main algorithmic components. The code of the algorithm will be made
publicly available.
","Massively parallel hybrid search for the partial Latin square extension
  problem"
"  Conformance checking is a key process mining task for comparing the expected
behavior captured in a process model and the actual behavior recorded in a log.
While this problem has been extensively studied for pure control-flow
processes, conformance checking with multi-perspective processes is still at
its infancy. In this paper, we attack this challenging problem by considering
processes that combine the data and control-flow dimensions. In particular, we
adopt data Petri nets (DPNs) as the underlying reference formalism, and show
how solid, well-established automated reasoning techniques can be effectively
employed for computing conformance metrics and data-aware alignments. We do so
by introducing the CoCoMoT (Computing Conformance Modulo Theories) framework,
with a fourfold contribution. First, we show how SAT-based encodings studied in
the pure control-flow setting can be lifted to our data-aware case, using SMT
as the underlying formal and algorithmic framework. Second, we introduce a
novel preprocessing technique based on a notion of property-preserving
clustering, to speed up the computation of conformance checking outputs. Third,
we provide a proof-of-concept implementation that uses a state-of-the-art SMT
solver and report on preliminary experiments. Finally, we discuss how CoCoMoT
directly lends itself to a number of further tasks, like multi- and
anti-alignments, log analysis by clustering, and model repair.
","CoCoMoT: Conformance Checking of Multi-Perspective Processes via SMT
  (Extended Version)"
"  In recent years, the world has witnessed various primitives pertaining to the
complexity of human behavior. Identifying an event in the presence of
insufficient, incomplete, or tentative premises along with the constraints on
resources such as time, data and memory is a vital aspect of an intelligent
system. Data explosion presents one of the most challenging research issues for
intelligent systems; to optimally represent and store this heterogeneous and
voluminous data semantically to provide human behavior. There is a requirement
of intelligent but personalized human behavior subject to constraints on
resources and priority of the user. Knowledge, when represented in the form of
an ontology, procures an intelligent response to a query posed by users; but it
does not offer content in accordance with the user context. To this aim, we
propose a model to quantify the user context and provide semantic contextual
reasoning. A diagnostic belief algorithm (DBA) is also presented that
identifies a given event and also computes the confidence of the decision as a
function of available resources, premises, exceptions, and desired specificity.
We conduct an empirical study in the domain of day-to-day routine queries and
the experimental results show that the answer to queries and also its
confidence varies with user context.
",Semantic Contextual Reasoning to Provide Human Behavior
"  The terminological landscape is rather cluttered when referring to autonomous
driving or vehicles. A plethora of terms are used interchangeably, leading to
misuse and confusion. With its technological, social and legal progress, it is
increasingly imperative to establish a clear terminology that allows each
concept to be placed in its corresponding place.
","From driving automation systems to autonomous vehicles: clarifying the
  terminology"
"  Case Management has been gradually evolving to support Knowledge-intensive
business process management, which resulted in developing different modeling
languages, e.g., Declare, Dynamic Condition Response (DCR), and Case Management
Model and Notation (CMMN). A language will die if users do not accept and use
it in practice - similar to extinct human languages. Thus, it is important to
evaluate how users perceive languages to determine if there is a need for
improvement. Although some studies have investigated how the process designers
perceived Declare and DCR, there is a lack of research on how they perceive
CMMN. Therefore, this study investigates how the process designers perceive the
usefulness and ease of use of CMMN and DCR based on the Technology Acceptance
Model. DCR is included to enable comparing the study result with previous ones.
The study is performed by educating master level students with these languages
over eight weeks by giving feedback on their assignments to reduce perceptions
biases. The students' perceptions are collected through questionnaires before
and after sending feedback on their final practice in the exam. Thus, the
result shows how the perception of participants can change by receiving
feedback - despite being well trained. The reliability of responses is tested
using Cronbach's alpha, and the result indicates that both languages have an
acceptable level for both perceived usefulness and ease of use.
",Evaluating Perceived Usefulness and Ease of Use of CMMN and DCR
"  In this article, we discuss how to solve information-gathering problems
expressed as rho-POMDPs, an extension of Partially Observable Markov Decision
Processes (POMDPs) whose reward rho depends on the belief state. Point-based
approaches used for solving POMDPs have been extended to solving rho-POMDPs as
belief MDPs when its reward rho is convex in B or when it is
Lipschitz-continuous. In the present paper, we build on the POMCP algorithm to
propose a Monte Carlo Tree Search for rho-POMDPs, aiming for an efficient
on-line planner which can be used for any rho function. Adaptations are
required due to the belief-dependent rewards to (i) propagate more than one
state at a time, and (ii) prevent biases in value estimates. An asymptotic
convergence proof to epsilon-optimal values is given when rho is continuous.
Experiments are conducted to analyze the algorithms at hand and show that they
outperform myopic approaches.
",Monte Carlo Information-Oriented Planning
"  Goal Recognition is the task of discerning the correct intended goal that an
agent aims to achieve, given a set of possible goals, a domain model, and a
sequence of observations as a sample of the plan being executed in the
environment. Existing approaches assume that the possible goals are formalized
as a conjunction in deterministic settings. In this paper, we develop a novel
approach that is capable of recognizing temporally extended goals in Fully
Observable Non-Deterministic (FOND) planning domain models, focusing on goals
on finite traces expressed in Linear Temporal Logic (LTLf) and (Pure) Past
Linear Temporal Logic (PLTLf). We empirically evaluate our goal recognition
approach using different LTLf and PLTLf goals over six common FOND planning
domain models, and show that our approach is accurate to recognize temporally
extended goals at several levels of observability.
","Recognizing LTLf/PLTLf Goals in Fully Observable Non-Deterministic
  Domain Models"
"  This work provides a starting point for researchers interested in gaining a
deeper understanding of the big picture of artificial intelligence (AI). To
this end, a narrative is conveyed that allows the reader to develop an
objective view on current developments that is free from false promises that
dominate public communication. An essential takeaway for the reader is that AI
must be understood as an umbrella term encompassing a plethora of different
methods, schools of thought, and their respective historical movements.
Consequently, a bottom-up strategy is pursued in which the field of AI is
introduced by presenting various aspects that are characteristic of the
subject. This paper is structured in three parts: (i) Discussion of current
trends revealing false public narratives, (ii) an introduction to the history
of AI focusing on recurring patterns and main characteristics, and (iii) a
critical discussion on the limitations of current methods in the context of the
potential emergence of a strong(er) AI. It should be noted that this work does
not cover any of these aspects holistically; rather, the content addressed is a
selection made by the author and subject to a didactic strategy.
","Artificial Intelligence Narratives: An Objective Perspective on Current
  Developments"
"  We present a new algorithm A*+BFHS for solving hard problems where A* and
IDA* fail due to memory limitations and/or the existence of many short cycles.
A*+BFHS is based on A* and breadth-first heuristic search (BFHS). A*+BFHS
combines advantages from both algorithms, namely A*'s node ordering, BFHS's
memory savings, and both algorithms' duplicate detection. On easy problems,
A*+BFHS behaves the same as A*. On hard problems, it is slower than A* but
saves a large amount of memory. Compared to BFIDA*, A*+BFHS reduces the search
time and/or memory requirement by several times on a variety of planning
domains.
",A*+BFHS: A Hybrid Heuristic Search Algorithm
"  Actionable Cognitive Twins are the next generation Digital Twins enhanced
with cognitive capabilities through a knowledge graph and artificial
intelligence models that provide insights and decision-making options to the
users. The knowledge graph describes the domain-specific knowledge regarding
entities and interrelationships related to a manufacturing setting. It also
contains information on possible decision-making options that can assist
decision-makers, such as planners or logisticians. In this paper, we propose a
knowledge graph modeling approach to construct actionable cognitive twins for
capturing specific knowledge related to demand forecasting and production
planning in a manufacturing plant. The knowledge graph provides semantic
descriptions and contextualization of the production lines and processes,
including data identification and simulation or artificial intelligence
algorithms and forecasts used to support them. Such semantics provide ground
for inferencing, relating different knowledge types: creative, deductive,
definitional, and inductive. To develop the knowledge graph models for
describing the use case completely, systems thinking approach is proposed to
design and verify the ontology, develop a knowledge graph and build an
actionable cognitive twin. Finally, we evaluate our approach in two use cases
developed for a European original equipment manufacturer related to the
automotive industry as part of the European Horizon 2020 project FACTLOG.
",Actionable Cognitive Twins for Decision Making in Manufacturing
"  Automating the derivation of published results is a challenge, in part due to
the informal use of mathematics by physicists, compared to that of
mathematicians. Following demand, we describe a method for converting informal
hand-written derivations into datasets, and present an example dataset crafted
from a contemporary result in condensed matter. We define an equation
reconstruction task completed by rederiving an unknown intermediate equation
posed as a state, taken from three consecutive equational states within a
derivation. Derivation automation is achieved by applying string-based
CAS-reliant actions to states, which mimic mathematical operations and induce
state transitions. We implement a symbolic similarity-based heuristic search to
solve the equation reconstruction task as an early step towards multi-hop
equational inference in physics.
",Similarity-Based Equational Inference in Physics
"  We discuss how over the last 30 to 50 years, Artificial Intelligence (AI)
systems that focused only on data have been handicapped, and how knowledge has
been critical in developing smarter, intelligent, and more effective systems.
In fact, the vast progress in AI can be viewed in terms of the three waves of
AI as identified by DARPA. During the first wave, handcrafted knowledge has
been at the center-piece, while during the second wave, the data-driven
approaches supplanted knowledge. Now we see a strong role and resurgence of
knowledge fueling major breakthroughs in the third wave of AI underpinning
future intelligent systems as they attempt human-like decision making, and seek
to become trusted assistants and companions for humans. We find a wider
availability of knowledge created from diverse sources, using manual to
automated means both by repurposing as well as by extraction. Using knowledge
with statistical learning is becoming increasingly indispensable to help make
AI systems more transparent and auditable. We will draw a parallel with the
role of knowledge and experience in human intelligence based on cognitive
science, and discuss emerging neuro-symbolic or hybrid AI systems in which
knowledge is the critical enabler for combining capabilities of the
data-intensive statistical AI systems with those of symbolic AI systems,
resulting in more capable AI systems that support more human-like intelligence.
",The Duality of Data and Knowledge Across the Three Waves of AI
"  Weighted model counting (WMC) is a popular framework to perform probabilistic
inference with discrete random variables. Recently, WMC has been extended to
weighted model integration (WMI) in order to additionally handle continuous
variables. At their core, WMI problems consist of computing integrals and sums
over weighted logical formulas. From a theoretical standpoint, WMI has been
formulated by patching the sum over weighted formulas, which is already present
in WMC, with Riemann integration. A more principled approach to integration,
which is rooted in measure theory, is Lebesgue integration. Lebesgue
integration allows one to treat discrete and continuous variables on equal
footing in a principled fashion. We propose a theoretically sound measure
theoretic formulation of weighted model integration, which naturally reduces to
weighted model counting in the absence of continuous variables. Instead of
regarding weighted model integration as an extension of weighted model
counting, WMC emerges as a special case of WMI in our formulation.
",Measure Theoretic Weighted Model Integration
"  Although heuristic search is one of the most successful approaches to
classical planning, this planning paradigm does not apply straightforwardly to
Generalized Planning (GP). Planning as heuristic search traditionally addresses
the computation of sequential plans by searching in a grounded state-space. On
the other hand GP aims at computing algorithm-like plans, that can branch and
loop, and that generalize to a (possibly infinite) set of classical planning
instances. This paper adapts the planning as heuristic search paradigm to the
particularities of GP, and presents the first native heuristic search approach
to GP. First, the paper defines a novel GP solution space that is independent
of the number of planning instances in a GP problem, and the size of these
instances. Second, the paper defines different evaluation and heuristic
functions for guiding a combinatorial search in our GP solution space. Lastly
the paper defines a GP algorithm, called Best-First Generalized Planning
(BFGP), that implements a best-first search in the solution space guided by our
evaluation/heuristic functions.
",Generalized Planning as Heuristic Search
"  Recent knowledge graph embedding (KGE) models based on hyperbolic geometry
have shown great potential in a low-dimensional embedding space. However, the
necessity of hyperbolic space in KGE is still questionable, because the
calculation based on hyperbolic geometry is much more complicated than
Euclidean operations. In this paper, based on the state-of-the-art
hyperbolic-based model RotH, we develop two lightweight Euclidean-based models,
called RotL and Rot2L. The RotL model simplifies the hyperbolic operations
while keeping the flexible normalization effect. Utilizing a novel two-layer
stacked transformation and based on RotL, the Rot2L model obtains an improved
representation capability, yet costs fewer parameters and calculations than
RotH. The experiments on link prediction show that Rot2L achieves the
state-of-the-art performance on two widely-used datasets in low-dimensional
knowledge graph embeddings. Furthermore, RotL achieves similar performance as
RotH but only requires half of the training time.
","Hyperbolic Geometry is Not Necessary: Lightweight Euclidean-Based Models
  for Low-Dimensional Knowledge Graph Embeddings"
"  This article outlines what we learned from the first year of the AI
Settlement Generation Competition in Minecraft, a competition about producing
AI programs that can generate interesting settlements in Minecraft for an
unseen map. This challenge seeks to focus research into adaptive and holistic
procedural content generation. Generating Minecraft towns and villages given
existing maps is a suitable task for this, as it requires the generated content
to be adaptive, functional, evocative and aesthetic at the same time. Here, we
present the results from the first iteration of the competition. We discuss the
evaluation methodology, present the different technical approaches by the
competitors, and outline the open problems.
",The AI Settlement Generation Challenge in Minecraft: First Year Report
"  Recently it was introduced a negation of a probability distribution. The need
for such negation arises when a knowledge-based system can use the terms like
NOT HIGH, where HIGH is represented by a probability distribution (pd). For
example, HIGH PROFIT or HIGH PRICE can be considered. The application of this
negation in Dempster-Shafer theory was considered in many works. Although
several negations of probability distributions have been proposed, it was not
clear how to construct other negations. In this paper, we consider negations of
probability distributions as point-by-point transformations of pd using
decreasing functions defined on [0,1] called negators. We propose the general
method of generation of negators and corresponding negations of pd, and study
their properties. We give a characterization of linear negators as a convex
combination of Yager and uniform negators.
",Generating Negations of Probability Distributions
"  In the last few years, the interest in knowledge bases has grown
exponentially in both the research community and the industry due to their
essential role in AI applications. Entity alignment is an important task for
enriching knowledge bases. This paper provides a comprehensive tutorial-type
survey on representative entity alignment techniques that use the new approach
of representation learning. We present a framework for capturing the key
characteristics of these techniques, propose two datasets to address the
limitation of existing benchmark datasets, and conduct extensive experiments
using the proposed datasets. The framework gives a clear picture of how the
techniques work. The experiments yield important results about the empirical
performance of the techniques and how various factors affect the performance.
One important observation not stressed by previous work is that techniques
making good use of attribute triples and relation predicates as features stand
out as winners.
","A Comprehensive Survey on Knowledge Graph Entity Alignment via
  Representation Learning"
"  A multi-decade exploration into the theoretical foundations of artificial and
natural general intelligence, which has been expressed in a series of books and
papers and used to guide a series of practical and research-prototype software
systems, is reviewed at a moderate level of detail. The review covers
underlying philosophies (patternist philosophy of mind, foundational
phenomenological and logical ontology), formalizations of the concept of
intelligence, and a proposed high level architecture for AGI systems partly
driven by these formalizations and philosophies. The implementation of specific
cognitive processes such as logical reasoning, program learning, clustering and
attention allocation in the context and language of this high level
architecture is considered, as is the importance of a common (e.g. typed
metagraph based) knowledge representation for enabling ""cognitive synergy""
between the various processes. The specifics of human-like cognitive
architecture are presented as manifestations of these general principles, and
key aspects of machine consciousness and machine ethics are also treated in
this context. Lessons for practical implementation of advanced AGI in
frameworks such as OpenCog Hyperon are briefly considered.
","The General Theory of General Intelligence: A Pragmatic Patternist
  Perspective"
"  Trained AI systems and expert decision makers can make errors that are often
difficult to identify and understand. Determining the root cause for these
errors can improve future decisions. This work presents Generative Error Model
(GEM), a generative model for inferring representational errors based on
observations of an actor's behavior (either simulated agent, robot, or human).
The model considers two sources of error: those that occur due to
representational limitations -- ""blind spots"" -- and non-representational
errors, such as those caused by noise in execution or systematic errors present
in the actor's policy. Disambiguating these two error types allows for targeted
refinement of the actor's policy (i.e., representational errors require
perceptual augmentation, while other errors can be reduced through methods such
as improved training or attention support). We present a Bayesian inference
algorithm for GEM and evaluate its utility in recovering representational
errors on multiple domains. Results show that our approach can recover blind
spots of both reinforcement learning agents as well as human users.
",A Bayesian Approach to Identifying Representational Errors
"  AI has surpassed humans across a variety of tasks such as image
classification, playing games (e.g., go, ""Starcraft"" and poker), and protein
structure prediction. However, at the same time, AI is also bearing serious
controversies. Many researchers argue that little substantial progress has been
made for AI in recent decades. In this paper, the author (1) explains why
controversies about AI exist; (2) discriminates two paradigms of AI research,
termed ""weak AI"" and ""strong AI"" (a.k.a. artificial general intelligence); (3)
clarifies how to judge which paradigm a research work should be classified
into; (4) discusses what is the greatest value of ""weak AI"" if it has no chance
to develop into ""strong AI"".
","""Weak AI"" is Likely to Never Become ""Strong AI"", So What is its Greatest
  Value for us?"
"  Seeking the equivalent entities among multi-source Knowledge Graphs (KGs) is
the pivotal step to KGs integration, also known as \emph{entity alignment}
(EA). However, most existing EA methods are inefficient and poor in
scalability. A recent summary points out that some of them even require several
days to deal with a dataset containing 200,000 nodes (DWY100K). We believe
over-complex graph encoder and inefficient negative sampling strategy are the
two main reasons. In this paper, we propose a novel KG encoder -- Dual
Attention Matching Network (Dual-AMN), which not only models both intra-graph
and cross-graph information smartly, but also greatly reduces computational
complexity. Furthermore, we propose the Normalized Hard Sample Mining Loss to
smoothly select hard negative samples with reduced loss shift. The experimental
results on widely used public datasets indicate that our method achieves both
high accuracy and high efficiency. On DWY100K, the whole running process of our
method could be finished in 1,100 seconds, at least 10* faster than previous
work. The performances of our method also outperform previous works across all
datasets, where Hits@1 and MRR have been improved from 6% to 13%.
","Boosting the Speed of Entity Alignment 10*: Dual Attention Matching
  Network with Normalized Hard Sample Mining"
"  The dream of building machines that can do science has inspired scientists
for decades. Remarkable advances have been made recently; however, we are still
far from achieving this goal. In this paper, we focus on the scientific
discovery process where a high level of reasoning and remarkable
problem-solving ability are required. We review different machine learning
techniques used in scientific discovery with their limitations. We survey and
discuss the main principles driving the scientific discovery process. These
principles are used in different fields and by different scientists to solve
problems and discover new knowledge. We provide many examples of the use of
these principles in different fields such as physics, mathematics, and biology.
We also review AI systems that attempt to implement some of these principles.
We argue that building science discovery machines should be guided by these
principles as an alternative to the dominant approach of current AI systems
that focuses on narrow objectives. Building machines that fully incorporate
these principles in an automated way might open the doors for many
advancements.
",Toward Building Science Discovery Machines
"  Along with the development of modern computing technology and social
sciences, both theoretical research and practical applications of social
computing have been continuously extended. In particular with the boom of
artificial intelligence (AI), social computing is significantly influenced by
AI. However, the conventional technologies of AI have drawbacks in dealing with
more complicated and dynamic problems. Such deficiency can be rectified by
hybrid human-artificial intelligence (H-AI) which integrates both human
intelligence and AI into one unity, forming a new enhanced intelligence. H-AI
in dealing with social problems shows the advantages that AI can not surpass.
This paper firstly introduces the concept of H-AI. AI is the intelligence in
the transition stage of H-AI, so the latest research progresses of AI in social
computing are reviewed. Secondly, it summarizes typical challenges faced by AI
in social computing, and makes it possible to introduce H-AI to solve these
challenges. Finally, the paper proposes a holistic framework of social
computing combining with H-AI, which consists of four layers: object layer,
base layer, analysis layer, and application layer. It represents H-AI has
significant advantages over AI in solving social problems.
",A Survey of Hybrid Human-Artificial Intelligence for Social Computing
"  Deep neural networks are vulnerable to adversarial examples that mislead the
models with imperceptible perturbations. Though adversarial attacks have
achieved incredible success rates in the white-box setting, most existing
adversaries often exhibit weak transferability in the black-box setting,
especially under the scenario of attacking models with defense mechanisms. In
this work, we propose a new method called variance tuning to enhance the class
of iterative gradient based attack methods and improve their attack
transferability. Specifically, at each iteration for the gradient calculation,
instead of directly using the current gradient for the momentum accumulation,
we further consider the gradient variance of the previous iteration to tune the
current gradient so as to stabilize the update direction and escape from poor
local optima. Empirical results on the standard ImageNet dataset demonstrate
that our method could significantly improve the transferability of
gradient-based adversarial attacks. Besides, our method could be used to attack
ensemble models or be integrated with various input transformations.
Incorporating variance tuning with input transformations on iterative
gradient-based attacks in the multi-model setting, the integrated method could
achieve an average success rate of 90.1% against nine advanced defense methods,
improving the current best attack performance significantly by 85.1% . Code is
available at https://github.com/JHL-HUST/VT.
","Enhancing the Transferability of Adversarial Attacks through Variance
  Tuning"
"  In automated planning, the need for explanations arises when there is a
mismatch between a proposed plan and the user's expectation. We frame
Explainable AI Planning in the context of the plan negotiation problem, in
which a succession of hypothetical planning problems are generated and solved.
The object of the negotiation is for the user to understand and ultimately
arrive at a satisfactory plan. We present the results of a user study that
demonstrates that when users ask questions about plans, those questions are
contrastive, i.e. ""why A rather than B?"". We use the data from this study to
construct a taxonomy of user questions that often arise during plan
negotiation. We formally define our approach to plan negotiation through model
restriction as an iterative process. This approach generates hypothetical
problems and contrastive plans by restricting the model through constraints
implied by user questions. We formally define model-based compilations in
PDDL2.1 of each constraint derived from a user question in the taxonomy, and
empirically evaluate the compilations in terms of computational complexity. The
compilations were implemented as part of an explanation framework that employs
iterative model restriction. We demonstrate its benefits in a second user
study.
",Contrastive Explanations of Plans Through Model Restrictions
"  The article provides a review of the publications on the current trends and
developments in Dempster-Shafer theory and its different applications in
science, engineering, and technologies. The review took account of the
following provisions with a focus on some specific aspects of the theory.
Firstly, the article considers the research directions whose results are known
not only in scientific and academic community but understood by a wide circle
of potential designers and developers of advanced engineering solutions and
technologies. Secondly, the article shows the theory applications in some
important areas of human activity such as manufacturing systems, diagnostics of
technological processes, materials and products, building and construction,
product quality control, economic and social systems. The particular attention
is paid to the current state of research in the domains under consideration
and, thus, the papers published, as a rule, in recent years and presenting the
achievements of modern research on Dempster-Shafer theory and its application
are selected and analyzed.
",Current Trends and Applications of Dempster-Shafer Theory (Review)
"  Ethics is sometimes considered to be too abstract to be meaningfully
implemented in artificial intelligence (AI). In this paper, we reflect on other
aspects of computing that were previously considered to be very abstract. Yet,
these are now accepted as being done very well by computers. These tasks have
ranged from multiple aspects of software engineering to mathematics to
conversation in natural language with humans. This was done by automating the
simplest possible step and then building on it to perform more complex tasks.
We wonder if ethical AI might be similarly achieved and advocate the process of
automation as key step in making AI take ethical decisions. The key
contribution of this paper is to reflect on how automation was introduced into
domains previously considered too abstract for computers.
",Automation: An Essential Component Of Ethical AI?
"  In this paper we focus on artificial intelligence (AI) for governance, not
governance for AI, and on just one aspect of governance, namely ethics audit.
Different kinds of ethical audit bots are possible, but who makes the choices
and what are the implications? In this paper, we do not provide
ethical/philosophical solutions, but rather focus on the technical aspects of
what an AI-based solution for validating the ethical soundness of a target
system would be like. We propose a system that is able to conduct an ethical
audit of a target system, given certain socio-technical conditions. To be more
specific, we propose the creation of a bot that is able to support
organisations in ensuring that their software development lifecycles contain
processes that meet certain ethical standards.
",Towards An Ethics-Audit Bot
"  Opioid and substance misuse is rampant in the United States today, with the
phenomenon known as the opioid crisis. The relationship between substance use
and mental health has been extensively studied, with one possible relationship
being substance misuse causes poor mental health. However, the lack of evidence
on the relationship has resulted in opioids being largely inaccessible through
legal means. This study analyzes the substance misuse posts on social media
with the opioids being sold through crypto market listings. We use the Drug
Abuse Ontology, state-of-the-art deep learning, and BERT-based models to
generate sentiment and emotion for the social media posts to understand user
perception on social media by investigating questions such as, which synthetic
opioids people are optimistic, neutral, or negative about or what kind of drugs
induced fear and sorrow or what kind of drugs people love or thankful about or
which drug people think negatively about or which opioids cause little to no
sentimental reaction. We also perform topic analysis associated with the
generated sentiments and emotions to understand which topics correlate with
people's responses to various drugs. Our findings can help shape policy to help
isolate opioid use cases where timely intervention may be required to prevent
adverse consequences, prevent overdose-related deaths, and worsen the epidemic.
","eDarkTrends: Harnessing Social Media Trends in Substance use disorders
  for Opioid Listings on Cryptomarket"
"  We introduce Platform for Situated Intelligence, an open-source framework
created to support the rapid development and study of multimodal,
integrative-AI systems. The framework provides infrastructure for sensing,
fusing, and making inferences from temporal streams of data across different
modalities, a set of tools that enable visualization and debugging, and an
ecosystem of components that encapsulate a variety of perception and processing
technologies. These assets jointly provide the means for rapidly constructing
and refining multimodal, integrative-AI systems, while retaining the efficiency
and performance characteristics required for deployment in open-world settings.
",Platform for Situated Intelligence
"  A dozen papers have considered the concept of negation of probability
distributions (pd) introduced by Yager. Usually, such negations are generated
point-by-point by functions defined on a set of probability values and called
here negators. Recently it was shown that Yager negator plays a crucial role in
the definition of pd-independent linear negators: any linear negator is a
function of Yager negator. Here, we prove that the sequence of multiple
negations of pd generated by a linear negator converges to the uniform
distribution with maximal entropy. We show that any pd-independent negator is
non-involutive, and any non-trivial linear negator is strictly contracting.
Finally, we introduce an involutive negator in the class of pd-dependent
negators that generates an involutive negation of probability distributions.
",Contracting and Involutive Negations of Probability Distributions
"  A general approach for building a smart assistant that guides a user from a
forecast generated by a machine learning model through a sequence of
decision-making steps is presented. We develop a methodology to build such a
system. The system is demonstrated on a demand forecasting use case in
manufacturing. The methodology can be extended to several use cases in
manufacturing. The system provides means for knowledge acquisition, gathering
data from users. We envision active learning can be used to get data labels
where labeled data is scarce.
",Towards Active Learning Based Smart Assistant for Manufacturing
"  We discuss the interconnections between AO*, adversarial game-searching
algorithms, e.g., proof number search and minimax search. The former was
developed in the context of a general AND/OR graph model, while the latter were
mostly presented in game-trees which are sometimes modeled using AND/OR trees.
It is thus worth investigating to what extent these algorithms are related and
how they are connected. In this paper, we explicate the interconnections
between these search paradigms. We argue that generalized proof number search
might be regarded as a more informed replacement of AO* for solving arbitrary
AND/OR graphs, and the minimax principle might also extended to use dual
heuristics.
","On AO*, Proof Number Search and Minimax Search"
"  The human ability to flexibly reason using analogies with domain-general
content depends on mechanisms for identifying relations between concepts, and
for mapping concepts and their relations across analogs. Building on a recent
model of how semantic relations can be learned from non-relational word
embeddings, we present a new computational model of mapping between two
analogs. The model adopts a Bayesian framework for probabilistic graph
matching, operating on semantic relation networks constructed from distributed
representations of individual concepts and of relations between concepts.
Through comparisons of model predictions with human performance in a novel
mapping task requiring integration of multiple relations, as well as in several
classic studies, we demonstrate that the model accounts for a broad range of
phenomena involving analogical mapping by both adults and children. We also
show the potential for extending the model to deal with analog retrieval. Our
approach demonstrates that human-like analogical mapping can emerge from
comparison mechanisms applied to rich semantic representations of individual
concepts and relations.
",Probabilistic Analogical Mapping with Semantic Relation Networks
"  The damage and the impact of natural disasters are becoming more destructive
with the increase of urbanization. Today's metropolitan cities are not
sufficiently prepared for the pre and post-disaster situations. Digital Twin
technology can provide a solution. A virtual copy of the physical city could be
created by collecting data from sensors of the Internet of Things (IoT) devices
and stored on the cloud infrastructure. This virtual copy is kept current and
up to date with the continuous flow of the data coming from the sensors. We
propose a disaster management system utilizing machine learning called DT-DMS
is used to support decision-making mechanisms. This study aims to show how to
educate and prepare emergency center staff by simulating potential disaster
situations on the virtual copy. The event of a disaster will be simulated
allowing emergency center staff to make decisions and depicting the potential
outcomes of these decisions. A rescue operation after an earthquake is
simulated. Test results are promising and the simulation scope is planned to be
extended.
",Digital Twin Based Disaster Management System Proposal: DT-DMS
"  Solving planning and scheduling problems for multiple tasks with highly
coupled state and temporal constraints is notoriously challenging. An appealing
approach to effectively decouple the problem is to judiciously order the events
such that decisions can be made over sequences of tasks. As many problems
encountered in practice are over-constrained, we must instead find relaxed
solutions in which certain requirements are dropped. This motivates a
formulation of optimality with respect to the costs of relaxing constraints and
the problem of finding an optimal ordering under which this relaxing cost is
minimum. In this paper, we present Generalized Conflict-directed Ordering
(GCDO), a branch-and-bound ordering method that generates an optimal total
order of events by leveraging the generalized conflicts of both inconsistency
and suboptimality from sub-solvers for cost estimation and solution space
pruning. Due to its ability to reason over generalized conflicts, GCDO is much
more efficient in finding high-quality total orders than the previous
conflict-directed approach CDITO. We demonstrate this by benchmarking on
temporal network configuration problems, which involves managing networks over
time and makes necessary tradeoffs between network flows against CDITO and
Mixed Integer-Linear Programing (MILP). Our algorithm is able to solve two
orders of magnitude more benchmark problems to optimality and twice the
problems compared to CDITO and MILP within a runtime limit, respectively.
",Generalized Conflict-directed Search for Optimal Ordering Problems
"  Predictive business process monitoring is concerned with the prediction how a
running process instance will unfold up to its completion at runtime. Most of
the proposed approaches rely on a wide number of different machine learning
(ML) techniques. In the last years numerous comparative studies, reviews, and
benchmarks of such approaches where published and revealed that they can be
successfully applied for different prediction targets. ML techniques require a
qualitatively and quantitatively sufficient data set. However, there are many
situations in business process management (BPM) where only a quantitatively
insufficient data set is available. The problem of insufficient data in the
context of BPM is still neglected. Hence, none of the comparative studies or
benchmarks investigates the performance of predictive business process
monitoring techniques in environments with small data sets. In this paper an
evaluation framework for comparing existing approaches with regard to their
suitability for small data sets is developed and exemplarily applied to
state-of-the-art approaches in predictive business process monitoring.
","Evaluating Predictive Business Process Monitoring Approaches on Small
  Event Logs"
"  We present AlphaChute: a state-of-the-art algorithm that achieves superhuman
performance in the ancient game of Chutes and Ladders. We prove that our
algorithm converges to the Nash equilibrium in constant time, and therefore is
-- to the best of our knowledge -- the first such formal solution to this game.
Surprisingly, despite all this, our implementation of AlphaChute remains
relatively straightforward due to domain-specific adaptations. We provide the
source code for AlphaChute here in our Appendix.
","Back to Square One: Superhuman Performance in Chutes and Ladders Through
  Deep Neural Networks and Tree Search"
"  Answer set programming (ASP) is a popular nonmonotonic-logic based paradigm
for knowledge representation and solving combinatorial problems. Computing the
answer set of an ASP program is NP-hard in general, and researchers have been
investing significant effort to speed it up. The majority of current ASP
solvers employ SAT solver-like technology to find these answer sets. As a
result, justification for why a literal is in the answer set is hard to
produce. There are dependency graph based approaches to find answer sets, but
due to the representational limitations of dependency graphs, such approaches
are limited. We propose a novel dependency graph-based approach for finding
answer sets in which conjunction of goals is explicitly represented as a node
which allows arbitrary answer set programs to be uniformly represented. Our
representation preserves causal relationships allowing for justification for
each literal in the answer set to be elegantly found. Performance results from
an implementation are also reported. Our work paves the way for computing
answer sets without grounding a program.
",grASP: A Graph Based ASP-Solver and Justification System
"  How to combine uncertain information from different sources has been a hot
topic for years. However, with respect to ordinal quantum evidences contained
in information, there is no any referable work which is able to provide a
solution to this kind of problem. Besides, the method to dispel uncertainty of
quantum information is still an open issue. Therefore, in this paper, a
specially designed method is designed to provide an excellent method which
improves the combination of ordinal quantum evidences reasonably and reduce the
effects brought by uncertainty contained in quantum information simultaneously.
Besides, some actual applications are provided to verify the correctness and
validity of the proposed method.
","Combining conflicting ordinal quantum evidences utilizing individual
  reliability"
"  Big Data analytics supported by AI algorithms can support skills localization
and retrieval in the context of a labor market intelligence problem. We
formulate and solve this problem through specific DataOps models, blending data
sources from administrative and technical partners in several countries into
cooperation, creating shared knowledge to support policy and decision-making.
We then focus on the critical task of skills extraction from resumes and
vacancies featuring state-of-the-art machine learning models. We showcase
preliminary results with applied machine learning on real data from the
employment agencies of the Netherlands and the Flemish region in Belgium. The
final goal is to match these skills to standard ontologies of skills, jobs and
occupations.
","DataOps for Societal Intelligence: a Data Pipeline for Labor Market
  Skills Extraction and Matching"
"  The increasing need for economic, safe, and sustainable smart manufacturing
combined with novel technological enablers, has paved the way for Artificial
Intelligence (AI) and Big Data in support of smart manufacturing. This implies
a substantial integration of AI, Industrial Internet of Things (IIoT),
Robotics, Big data, Blockchain, 5G communications, in support of smart
manufacturing and the dynamical processes in modern industries. In this paper,
we provide a comprehensive overview of different aspects of AI and Big Data in
Industry 4.0 with a particular focus on key applications, techniques, the
concepts involved, key enabling technologies, challenges, and research
perspective towards deployment of Industry 5.0. In detail, we highlight and
analyze how the duo of AI and Big Data is helping in different applications of
Industry 4.0. We also highlight key challenges in a successful deployment of AI
and Big Data methods in smart industries with a particular emphasis on
data-related issues, such as availability, bias, auditing, management,
interpretability, communication, and different adversarial attacks and security
issues. In a nutshell, we have explored the significance of AI and Big data
towards Industry 4.0 applications through panoramic reviews and discussions. We
believe, this work will provide a baseline for future research in the domain.
","The Duo of Artificial Intelligence and Big Data for Industry 4.0: Review
  of Applications, Techniques, Challenges, and Future Research Directions"
"  Medical Cyber-physical Systems (MCPS) are vulnerable to accidental or
malicious faults that can target their controllers and cause safety hazards and
harm to patients. This paper proposes a combined model and data-driven approach
for designing context-aware monitors that can detect early signs of hazards and
mitigate them in MCPS. We present a framework for formal specification of
unsafe system context using Signal Temporal Logic (STL) combined with an
optimization method for patient-specific refinement of STL formulas based on
real or simulated faulty data from the closed-loop system for the generation of
monitor logic. We evaluate our approach in simulation using two
state-of-the-art closed-loop Artificial Pancreas Systems (APS). The results
show the context-aware monitor achieves up to 1.4 times increase in average
hazard prediction accuracy (F1-score) over several baseline monitors, reduces
false-positive and false-negative rates, and enables hazard mitigation with a
54% success rate while decreasing the average risk for patients.
","Data-driven Design of Context-aware Monitors for Hazard Prediction in
  Artificial Pancreas Systems"
"  How to improve the efficiency of routing procedures in CapsNets has been
studied a lot. However, the efficiency of capsule convolutions has largely been
neglected. Capsule convolution, which uses capsules rather than neurons as the
basic computation unit, makes it incompatible with current deep learning
frameworks' optimization solution. As a result, capsule convolutions are
usually very slow with these frameworks. We observe that capsule convolutions
can be considered as the operations of `multiplication of multiple small
matrics' plus tensor-based combination. Based on this observation, we develop
two acceleration schemes with CUDA APIs and test them on a custom CapsNet. The
result shows that our solution achieves a 4X acceleration.
",How to Accelerate Capsule Convolutions in Capsule Networks
"  Skat is a fascinating combinatorial card game, show-casing many of the
intrinsic challenges for modern AI systems such as cooperative and adversarial
behaviors (among the players), randomness (in the deal), and partial knowledge
(due to hidden cards). Given the larger number of tricks and higher degree of
uncertainty, reinforcement learning is less effective compared to classical
board games like Chess and Go. As within the game of Bridge, in Skat we have a
bidding and trick-taking stage. Prior to the trick-taking and as part of the
bidding process, one phase in the game is to select two skat cards, whose
quality may influence subsequent playing performance drastically. This paper
looks into different skat selection strategies. Besides predicting the
probability of winning and other hand strength functions we propose hard
expert-rules and a scoring functions based on refined skat evaluation features.
Experiments emphasize the impact of the refined skat putting algorithm on the
playing performance of the bots, especially for AI bidding and AI game
selection.
",On the Power of Refined Skat Selection
"  Analysis of the popular expected goals (xG) metric in soccer has determined
that a (slightly) smaller number of high-quality attempts will likely yield
more goals than a slew of low-quality ones. This observation has driven a
change in shooting behavior. Teams are passing up on shots from outside the
penalty box, in the hopes of generating a better shot closer to goal later on.
This paper evaluates whether this decrease in long-distance shots is warranted.
Therefore, we propose a novel generic framework to reason about decision-making
in soccer by combining techniques from machine learning and artificial
intelligence (AI). First, we model how a team has behaved offensively over the
course of two seasons by learning a Markov Decision Process (MDP) from event
stream data. Second, we use reasoning techniques arising from the AI literature
on verification to each team's MDP. This allows us to reason about the efficacy
of certain potential decisions by posing counterfactual questions to the MDP.
Our key conclusion is that teams would score more goals if they shot more often
from outside the penalty box in a small number of team-specific locations. The
proposed framework can easily be extended and applied to analyze other aspects
of the game.
",Leaving Goals on the Pitch: Evaluating Decision Making in Soccer
"  Several forms of iterable belief change exist, differing in the kind of
change and its strength: some operators introduce formulae, others remove them;
some add formulae unconditionally, others only as additions to the previous
beliefs; some only relative to the current situation, others in all possible
cases. A sequence of changes may involve several of them: for example, the
first step is a revision, the second a contraction and the third a refinement
of the previous beliefs. The ten operators considered in this article are shown
to be all reducible to three: lexicographic revision, refinement and severe
withdrawal. In turn, these three can be expressed in terms of lexicographic
revision at the cost of restructuring the sequence. This restructuring needs
not to be done explicitly: an algorithm that works on the original sequence is
shown. The complexity of mixed sequences of belief change operators is also
analyzed. Most of them require only a polynomial number of calls to a
satisfiability checker, some are even easier.
",On Mixed Iterated Revisions
"  Climate change poses a major challenge to humanity, especially in its impact
on agriculture, a challenge that a responsible AI should meet. In this paper,
we examine a CBR system (PBI-CBR) designed to aid sustainable dairy farming by
supporting grassland management, through accurate crop growth prediction. As
climate changes, PBI-CBRs historical cases become less useful in predicting
future grass growth. Hence, we extend PBI-CBR using data augmentation, to
specifically handle disruptive climate events, using a counterfactual method
(from XAI). Study 1 shows that historical, extreme climate-events (climate
outlier cases) tend to be used by PBI-CBR to predict grass growth during
climate disrupted periods. Study 2 shows that synthetic outliers, generated as
counterfactuals on a outlier-boundary, improve the predictive accuracy of
PBICBR, during the drought of 2018. This study also shows that an
instance-based counterfactual method does better than a benchmark,
constraint-guided method.
","Handling Climate Change Using Counterfactuals: Using Counterfactuals in
  Data Augmentation to Predict Crop Growth in an Uncertain Climate Future"
"  Making inferences with a deep neural network on a batch of states is much
faster with a GPU than making inferences on one state after another. We build
on this property to propose Monte Carlo Tree Search algorithms using batched
inferences. Instead of using either a search tree or a transposition table we
propose to use both in the same algorithm. The transposition table contains the
results of the inferences while the search tree contains the statistics of
Monte Carlo Tree Search. We also propose to analyze multiple heuristics that
improve the search: the $\mu$ FPU, the Virtual Mean, the Last Iteration and the
Second Move heuristics. They are evaluated for the game of Go using a MobileNet
neural network.
",Batch Monte Carlo Tree Search
"  Among the top approaches of recent years, link prediction using knowledge
graph embedding (KGE) models has gained significant attention for knowledge
graph completion. Various embedding models have been proposed so far, among
which, some recent KGE models obtain state-of-the-art performance on link
prediction tasks by using embeddings with a high dimension (e.g. 1000) which
accelerate the costs of training and evaluation considering the large scale of
KGs. In this paper, we propose a simple but effective performance boosting
strategy for KGE models by using multiple low dimensions in different
repetition rounds of the same model. For example, instead of training a model
one time with a large embedding size of 1200, we repeat the training of the
model 6 times in parallel with an embedding size of 200 and then combine the 6
separate models for testing while the overall numbers of adjustable parameters
are same (6*200=1200) and the total memory footprint remains the same. We show
that our approach enables different models to better cope with their
expressiveness issues on modeling various graph patterns such as symmetric,
1-n, n-1 and n-n. In order to justify our findings, we conduct experiments on
various KGE models. Experimental results on standard benchmark datasets, namely
FB15K, FB15K-237 and WN18RR, show that multiple low-dimensional models of the
same kind outperform the corresponding single high-dimensional models on link
prediction in a certain range and have advantages in training efficiency by
using parallel training while the overall numbers of adjustable parameters are
same.
","Multiple Run Ensemble Learning with Low-Dimensional Knowledge Graph
  Embeddings"
"  This paper discusses the need of an automated system for detecting print
errors and the efficacy of Convolutional Neural Networks in such an
application. We recognise the need of a dataset containing print error samples
and propose a way to generate one artificially. We discuss the algorithms to
generate such data along with the limitaions and advantages of such an
apporach. Our final trained network gives a remarkable accuracy of 99.83\% in
testing. We further evaluate how such efficiency was achieved and what
modifications can be tested to further the results.
",Print Error Detection using Convolutional Neural Networks
"  Traffic forecasting is an indispensable part of Intelligent transportation
systems (ITS), and long-term network-wide accurate traffic speed forecasting is
one of the most challenging tasks. Recently, deep learning methods have become
popular in this domain. As traffic data are physically associated with road
networks, most proposed models treat it as a spatiotemporal graph modeling
problem and use Graph Convolution Network (GCN) based methods. These GCN-based
models highly depend on a predefined and fixed adjacent matrix to reflect the
spatial dependency. However, the predefined fixed adjacent matrix is limited in
reflecting the actual dependence of traffic flow. This paper proposes a novel
model, Traffic Transformer, for spatial-temporal graph modeling and long-term
traffic forecasting to overcome these limitations. Transformer is the most
popular framework in Natural Language Processing (NLP). And by adapting it to
the spatiotemporal problem, Traffic Transformer hierarchically extracts
spatiotemporal features through data dynamically by multi-head attention and
masked multi-head attention mechanism, and fuse these features for traffic
forecasting. Furthermore, analyzing the attention weight matrixes can find the
influential part of road networks, allowing us to learn the traffic networks
better. Experimental results on the public traffic network datasets and
real-world traffic network datasets generated by ourselves demonstrate our
proposed model achieves better performance than the state-of-the-art ones.
","Learning dynamic and hierarchical traffic spatiotemporal features with
  Transformer"
"  Attributed network representation learning aims at learning node embeddings
by integrating network structure and attribute information. It is a challenge
to fully capture the microscopic structure and the attribute semantics
simultaneously, where the microscopic structure includes the one-step, two-step
and multi-step relations, indicating the first-order, second-order and
high-order proximity of nodes, respectively. In this paper, we propose a deep
attributed network representation learning via attribute enhanced neighborhood
(DANRL-ANE) model to improve the robustness and effectiveness of node
representations. The DANRL-ANE model adopts the idea of the autoencoder, and
expands the decoder component to three branches to capture different order
proximity. We linearly combine the adjacency matrix with the attribute
similarity matrix as the input of our model, where the attribute similarity
matrix is calculated by the cosine similarity between the attributes based on
the social homophily. In this way, we preserve the second-order proximity to
enhance the robustness of DANRL-ANE model on sparse networks, and deal with the
topological and attribute information simultaneously. Moreover, the sigmoid
cross-entropy loss function is extended to capture the neighborhood character,
so that the first-order proximity is better preserved. We compare our model
with the state-of-the-art models on five real-world datasets and two network
analysis tasks, i.e., link prediction and node classification. The DANRL-ANE
model performs well on various networks, even on sparse networks or networks
with isolated nodes given the attribute information is sufficient.
","Deep Attributed Network Representation Learning via Attribute Enhanced
  Neighborhood"
"  Patients with Frontotemporal Dementia (FTD) have impaired cognitive
abilities, executive and behavioral traits, loss of language ability, and
decreased memory capabilities. Based on the distinct patterns of cortical
atrophy and symptoms, the FTD spectrum primarily includes three variants:
behavioral variant FTD (bvFTD), non-fluent variant primary progressive aphasia
(nfvPPA), and semantic variant primary progressive aphasia (svPPA). The purpose
of this study is to classify MRI images of every single subject into one of the
spectrums of the FTD in a hierarchical order by applying data-driven techniques
of Artificial Intelligence (AI) on cortical thickness data. This data is
computed by FreeSurfer software. We used the Smallest Univalue Segment
Assimilating Nucleus (SUSAN) technique to minimize the noise in cortical
thickness data. Specifically, we took 204 subjects from the frontotemporal
lobar degeneration neuroimaging initiative (NIFTD) database to validate this
approach, and each subject was diagnosed in one of the diagnostic categories
(bvFTD, svPPA, nfvPPA and cognitively normal). Our proposed automated
classification model yielded classification accuracy of 86.5, 76, and 72.7 with
support vector machine (SVM), linear discriminant analysis (LDA), and Naive
Bayes methods, respectively, in 10-fold cross-validation analysis, which is a
significant improvement on a traditional single multi-class model with an
accuracy of 82.7, 73.4, and 69.2.
","Artificial Intelligence Methods Based Hierarchical Classification of
  Frontotemporal Dementia to Improve Diagnostic Predictability"
"  Today, intelligent systems that offer artificial intelligence capabilities
often rely on machine learning. Machine learning describes the capacity of
systems to learn from problem-specific training data to automate the process of
analytical model building and solve associated tasks. Deep learning is a
machine learning concept based on artificial neural networks. For many
applications, deep learning models outperform shallow machine learning models
and traditional data analysis approaches. In this article, we summarize the
fundamentals of machine learning and deep learning to generate a broader
understanding of the methodical underpinning of current intelligent systems. In
particular, we provide a conceptual distinction between relevant terms and
concepts, explain the process of automated analytical model building through
machine learning and deep learning, and discuss the challenges that arise when
implementing such intelligent systems in the field of electronic markets and
networked business. These naturally go beyond technological aspects and
highlight issues in human-machine interaction and artificial intelligence
servitization.
",Machine learning and deep learning
"  In this paper, We present our approach for IEEEBigMM 2020, Grand Challenge
(BMGC), Identifying senti-ments from tweets related to the MeToo movement. The
modelis based on an ensemble of Convolutional Neural Network,Bidirectional LSTM
and a DNN for final classification. Thispaper is aimed at providing a detailed
analysis of the modeland the results obtained. We have ranked 5th out of 10
teamswith a score of 0.51491
",MeToo Tweets Sentiment Analysis Using Multi Modal frameworks
"  The paper presents a possible solution to the problem of algorithmization for
quantifying inno-vativeness indicators of technical products, inventions and
technologies. The concepts of technological nov-elty, relevance and
implementability as components of product innovation criterion are introduced.
Authors propose a model and algorithm to calculate every of these indicators of
innovativeness under conditions of incompleteness and inaccuracy, and sometimes
inconsistency of the initial information. The paper describes the developed
specialized software that is a promising methodological tool for using interval
estimations in accordance with the theory of evidence. These estimations are
used in the analysis of complex multicomponent systems, aggregations of large
volumes of fuzzy and incomplete data of various structures. Composition and
structure of a multi-agent expert system are presented. The purpose of such
system is to process groups of measurement results and to estimate indicators
values of objects innovativeness. The paper defines active elements of the
system, their functionality, roles, interaction order, input and output
inter-faces, as well as the general software functioning algorithm. It
describes implementation of software modules and gives an example of solving a
specific problem to determine the level of technical products innovation.
","Implementing an expert system to evaluate technical solutions
  innovativeness"
"  How to measure the degree of uncertainty of a given frame of discernment has
been a hot topic for years. A lot of meaningful works have provided some
effective methods to measure the degree properly. However, a crucial factor,
sequence of propositions, is missing in the definition of traditional frame of
discernment. In this paper, a detailed definition of ordinal frame of
discernment has been provided. Besides, an innovative method utilizing a
concept of computer vision to combine the order of propositions and the mass of
them is proposed to better manifest relationships between the two important
element of the frame of discernment. More than that, a specially designed
method covering some powerful tools in indicating the degree of uncertainty of
a traditional frame of discernment is also offered to give an indicator of
level of uncertainty of an ordinal frame of discernment on the level of vector.
","An approach utilizing negation of extended-dimensional vector of
  disposing mass for ordinal evidences combination in a fuzzy environment"
"  This paper proposes \emph{knowledge-based paraonoia search} (KBPS) to find
forced wins during trick-taking in the card game Skat; for some one of the most
interesting card games for three players. It combines efficient partial
information game-tree search with knowledge representation and reasoning. This
worst-case analysis, initiated after a small number of tricks, leads to a
prioritized choice of cards. We provide variants of KBPS for the declarer and
the opponents, and an approximation to find a forced win against most worlds in
the belief space. Replaying thousands of expert games, our evaluation indicates
that the AIs with the new algorithms perform better than humans in their play,
achieving an average score of over 1,000 points in the agreed standard for
evaluating Skat tournaments, the extended Seeger system.
",Knowledge-Based Paranoia Search in Trick-Taking
"  During the past decade, novel Deep Learning (DL) algorithms/workloads and
hardware have been developed to tackle a wide range of problems. Despite the
advances in workload/hardware ecosystems, the programming methodology of DL
systems is stagnant. DL workloads leverage either highly optimized, yet
platform-specific and inflexible kernels from DL libraries, or in the case of
novel operators, reference implementations are built via DL framework
primitives with underwhelming performance. This work introduces the Tensor
Processing Primitives (TPP), a programming abstraction striving for efficient,
portable implementation of DL workloads with high productivity. TPPs define a
compact, yet versatile set of 2D-tensor operators (or a virtual Tensor ISA),
which subsequently can be utilized as building blocks to construct complex
operators on high-dimensional tensors. The TPP specification is
platform-agnostic, thus code expressed via TPPs is portable, whereas the TPP
implementation is highly optimized and platform-specific. We demonstrate the
efficacy of our approach using standalone kernels and end-to-end DL workloads
expressed entirely via TPPs that outperform state-of-the-art implementations on
multiple platforms.
","Tensor Processing Primitives: A Programming Abstraction for Efficiency
  and Portability in Deep Learning Workloads"
"  In this article a surprising result is demonstrated using the neural tangent
kernel. This kernel is defined as the inner product of the vector of the
gradient of an underlying model evaluated at training points. This kernel is
used to perform kernel regression. The surprising thing is that the accuracy of
that regression is independent of the accuracy of the underlying network.
",Gradient Kernel Regression
"  In multi-agent reinforcement learning, the cooperative learning behavior of
agents is very important. In the field of heterogeneous multi-agent
reinforcement learning, cooperative behavior among different types of agents in
a group is pursued. Learning a joint-action set during centralized training is
an attractive way to obtain such cooperative behavior, however, this method
brings limited learning performance with heterogeneous agents. To improve the
learning performance of heterogeneous agents during centralized training,
two-stage heterogeneous centralized training which allows the training of
multiple roles of heterogeneous agents is proposed. During training, two
training processes are conducted in a series. One of the two stages is to
attempt training each agent according to its role, aiming at the maximization
of individual role rewards. The other is for training the agents as a whole to
make them learn cooperative behaviors while attempting to maximize shared
collective rewards, e.g., team rewards. Because these two training processes
are conducted in a series in every timestep, agents can learn how to maximize
role rewards and team rewards simultaneously. The proposed method is applied to
5 versus 5 AI robot soccer for validation. Simulation results show that the
proposed method can train the robots of the robot soccer team effectively,
achieving higher role rewards and higher team rewards as compared to other
approaches that can be used to solve problems of training cooperative
multi-agent.
",Two-stage training algorithm for AI robot soccer
"  In large-scale feature models, feature modeling and configuration processes
are highly expected to be done by a group of stakeholders. In this context,
recommendation techniques can increase the efficiency of feature-model design
and find optimal configurations for groups of stakeholders. Existing studies
show plenty of issues concerning feature model navigation support, group
members' satisfaction, and conflict resolution. This study proposes group
recommendation techniques for feature modeling and configuration on the basis
of addressing the mentioned issues.
",Group Recommendation Techniques for Feature Modeling and Configuration
"  Video game level generation based on machine learning (ML), in particular,
deep generative models, has attracted attention as a technique to automate
level generation. However, applications of existing ML-based level generations
are mostly limited to tile-based level representation. When ML techniques are
applied to game domains with non-tile-based level representation, such as Angry
Birds, where objects in a level are specified by real-valued parameters, ML
often fails to generate playable levels. In this study, we develop a
deep-generative-model-based level generation for the game domain of Angry
Birds. To overcome these drawbacks, we propose a sequential encoding of a level
and process it as text data, whereas existing approaches employ a tile-based
encoding and process it as an image. Experiments show that the proposed level
generator drastically improves the stability and diversity of generated levels
compared with existing approaches. We apply latent variable evolution with the
proposed generator to control the feature of a generated level computed through
an AI agent's play, while keeping the level stable and natural.
","Level Generation for Angry Birds with Sequential VAE and Latent Variable
  Evolution"
"  In this paper, we investigate the computational intelligibility of Boolean
classifiers, characterized by their ability to answer XAI queries in polynomial
time. The classifiers under consideration are decision trees, DNF formulae,
decision lists, decision rules, tree ensembles, and Boolean neural nets. Using
9 XAI queries, including both explanation queries and verification queries, we
show the existence of large intelligibility gap between the families of
classifiers. On the one hand, all the 9 XAI queries are tractable for decision
trees. On the other hand, none of them is tractable for DNF formulae, decision
lists, random forests, boosted decision trees, Boolean multilayer perceptrons,
and binarized neural networks.
",On the Computational Intelligibility of Boolean Classifiers
"  Event schemas encode knowledge of stereotypical structures of events and
their connections. As events unfold, schemas are crucial to act as a
scaffolding. Previous work on event schema induction either focuses on atomic
events or linear temporal event sequences, ignoring the interplay between
events via arguments and argument relations. We introduce the concept of
Temporal Complex Event Schema: a graph-based schema representation that
encompasses events, arguments, temporal connections and argument relations.
Additionally, we propose a Temporal Event Graph Model that models the emergence
of event instances following the temporal complex event schema. To build and
evaluate such schemas, we release a new schema learning corpus containing 6,399
documents accompanied with event graphs, and manually constructed gold schemas.
Intrinsic evaluation by schema matching and instance graph perplexity, prove
the superior quality of our probabilistic graph schema library compared to
linear representations. Extrinsic evaluation on schema-guided event prediction
further demonstrates the predictive power of our event graph model,
significantly surpassing human schemas and baselines by more than 17.8% on
HITS@1.
","Future is not One-dimensional: Graph Modeling based Complex Event Schema
  Induction for Event Prediction"
"  Path finding is a well-studied problem in AI, which is often framed as graph
search. Any-angle path finding is a technique that augments the initial graph
with additional edges to build shorter paths to the goal. Indeed, optimal
algorithms for any-angle path finding in static environments exist. However,
when dynamic obstacles are present and time is the objective to be minimized,
these algorithms can no longer guarantee optimality. In this work, we elaborate
on why this is the case and what techniques can be used to solve the problem
optimally. We present two algorithms, grounded in the same idea, that can
obtain provably optimal solutions to the considered problem. One of them is a
naive algorithm and the other one is much more involved. We conduct a thorough
empirical evaluation showing that, in certain setups, the latter algorithm
might be as fast as the previously-known greedy non-optimal solver while
providing solutions of better quality. In some (rare) cases, the difference in
cost is up to 76%, while on average it is lower than one percent (the same cost
difference is typically observed between optimal and greedy any-angle solvers
in static environments).
",Towards Time-Optimal Any-Angle Path Planning With Dynamic Obstacles
"  Multi-hop reasoning has been widely studied in recent years to obtain more
interpretable link prediction. However, we find in experiments that many paths
given by these models are actually unreasonable, while little works have been
done on interpretability evaluation for them. In this paper, we propose a
unified framework to quantitatively evaluate the interpretability of multi-hop
reasoning models so as to advance their development. In specific, we define
three metrics including path recall, local interpretability, and global
interpretability for evaluation, and design an approximate strategy to
calculate them using the interpretability scores of rules. Furthermore, we
manually annotate all possible rules and establish a Benchmark to detect the
Interpretability of Multi-hop Reasoning (BIMR). In experiments, we run nine
baselines on our benchmark. The experimental results show that the
interpretability of current multi-hop reasoning models is less satisfactory and
is still far from the upper bound given by our benchmark. Moreover, the
rule-based models outperform the multi-hop reasoning models in terms of
performance and interpretability, which points to a direction for future
research, i.e., we should investigate how to better incorporate rule
information into the multi-hop reasoning model. Our codes and datasets can be
obtained from https://github.com/THU-KEG/BIMR.
","Is Multi-Hop Reasoning Really Explainable? Towards Benchmarking
  Reasoning Interpretability"
"  StarCraft II (SC2) is a real-time strategy game, in which players produce and
control multiple units to win. Due to its difficulties, such as huge state
space, various action space, a long time horizon, and imperfect information,
SC2 has been a research highlight in reinforcement learning research. Recently,
an SC2 agent called AlphaStar is proposed which shows excellent performance,
obtaining a high win-rates of 99.8% against Grandmaster level human players. We
implemented a mini-scaled version of it called mini-AlphaStar based on their
paper and the pseudocode they provided. The usage and analysis of it are shown
in this technical report. The difference between AlphaStar and mini-AlphaStar
is that we substituted the hyper-parameters in the former version with much
smaller ones for mini-scale training. The codes of mini-AlphaStar are all
open-sourced. The objective of mini-AlphaStar is to provide a reproduction of
the original AlphaStar and facilitate the future research of RL on large-scale
problems.
",An Introduction of mini-AlphaStar
"  The potential presented by Artificial Intelligence (AI) for healthcare has
long been recognised by the technical community. More recently, this potential
has been recognised by policymakers, resulting in considerable public and
private investment in the development of AI for healthcare across the globe.
Despite this, excepting limited success stories, real-world implementation of
AI systems into front-line healthcare has been limited. There are numerous
reasons for this, but a main contributory factor is the lack of internationally
accepted, or formalised, regulatory standards to assess AI safety and impact
and effectiveness. This is a well-recognised problem with numerous ongoing
research and policy projects to overcome it. Our intention here is to
contribute to this problem-solving effort by seeking to set out a minimally
viable framework for evaluating the safety, acceptability and efficacy of AI
systems for healthcare. We do this by conducting a systematic search across
Scopus, PubMed and Google Scholar to identify all the relevant literature
published between January 1970 and November 2020 related to the evaluation of:
output performance; efficacy; and real-world use of AI systems, and
synthesising the key themes according to the stages of evaluation: pre-clinical
(theoretical phase); exploratory phase; definitive phase; and post-market
surveillance phase (monitoring). The result is a framework to guide AI system
developers, policymakers, and regulators through a sufficient evaluation of an
AI system designed for use in healthcare.
","Towards a framework for evaluating the safety, acceptability and
  efficacy of AI systems for health: an initial synthesis"
"  In hybrid human-AI systems, users need to decide whether or not to trust an
algorithmic prediction while the true error in the prediction is unknown. To
accommodate such settings, we introduce RETRO-VIZ, a method for (i) estimating
and (ii) explaining trustworthiness of regression predictions. It consists of
RETRO, a quantitative estimate of the trustworthiness of a prediction, and VIZ,
a visual explanation that helps users identify the reasons for the (lack of)
trustworthiness of a prediction. We find that RETRO-scores negatively correlate
with prediction error across 117 experimental settings, indicating that RETRO
provides a useful measure to distinguish trustworthy predictions from
untrustworthy ones. In a user study with 41 participants, we find that
VIZ-explanations help users identify whether a prediction is trustworthy or
not: on average, 95.1% of participants correctly select the more trustworthy
prediction, given a pair of predictions. In addition, an average of 75.6% of
participants can accurately describe why a prediction seems to be (not)
trustworthy. Finally, we find that the vast majority of users subjectively
experience RETRO-VIZ as a useful tool to assess the trustworthiness of
algorithmic predictions.
","To Trust or Not to Trust a Regressor: Estimating and Explaining
  Trustworthiness of Regression Predictions"
"  The performance of text classification methods has improved greatly over the
last decade for text instances of less than 512 tokens. This limit has been
adopted by most state-of-the-research transformer models due to the high
computational cost of analyzing longer text instances. To mitigate this problem
and to improve classification for longer texts, researchers have sought to
resolve the underlying causes of the computational cost and have proposed
optimizations for the attention mechanism, which is the key element of every
transformer model. In our study, we are not pursuing the ultimate goal of long
text classification, i.e., the ability to analyze entire text instances at one
time while preserving high performance at a reasonable computational cost.
Instead, we propose a text truncation method called Text Guide, in which the
original text length is reduced to a predefined limit in a manner that improves
performance over naive and semi-naive approaches while preserving low
computational costs. Text Guide benefits from the concept of feature
importance, a notion from the explainable artificial intelligence domain. We
demonstrate that Text Guide can be used to improve the performance of recent
language models specifically designed for long text classification, such as
Longformer. Moreover, we discovered that parameter optimization is the key to
Text Guide performance and must be conducted before the method is deployed.
Future experiments may reveal additional benefits provided by this new method.
","Text Guide: Improving the quality of long text classification by a text
  selection method based on feature importance"
"  Partially Observable Markov Decision Processes (POMDP) is a widely used model
to represent the interaction of an environment and an agent, under state
uncertainty. Since the agent does not observe the environment state, its
uncertainty is typically represented through a probabilistic belief. While the
set of possible beliefs is infinite, making exact planning intractable, the
belief space's complexity (and hence planning complexity) is characterized by
its covering number. Many POMDP solvers uniformly discretize the belief space
and give the planning error in terms of the (typically unknown) covering
number. We instead propose an adaptive belief discretization scheme, and give
its associated planning error. We furthermore characterize the covering number
with respect to the POMDP parameters. This allows us to specify the exact
memory requirements on the planner, needed to bound the value function error.
We then propose a novel, computationally efficient solver using this scheme. We
demonstrate that our algorithm is highly competitive with the state of the art
in a variety of scenarios.
",Adaptive Belief Discretization for POMDP Planning
"  Knowledge graphs that encapsulate personal health information, or personal
health knowledge graphs (PHKG), can help enable personalized health care in
knowledge-driven systems. In this paper we provide a short survey of existing
work surrounding the emerging paradigm of PHKGs and highlight the major
challenges that remain. We find that while some preliminary exploration exists
on the topic of personal knowledge graphs, development of PHKGs remains
under-explored. A range of challenges surrounding the collection, linkage, and
maintenance of personal health knowledge remains to be addressed to fully
realize PHKGs.
",Applying Personal Knowledge Graphs to Health
"  Voting rules based on evaluation inputs rather than preference orders have
been recently proposed, like majority judgement, range voting or approval
voting. Traditionally, probabilistic analysis of voting rules supposes the use
of simulation models to generate preferences data, like the Impartial Culture
(IC) or Impartial and Anonymous Culture (IAC) models. But these simulation
models are not suitable for the analysis of evaluation-based voting rules as
they generate preference orders instead of the needed evaluations. We propose
in this paper several simulation models for generating evaluation-based voting
inputs. These models, inspired by classical ones, are defined, tested and
compared for recommendation purpose.
",A Note on Data Simulations for Voting by Evaluation
"  Reasoning in a temporal knowledge graph (TKG) is a critical task for
information retrieval and semantic search. It is particularly challenging when
the TKG is updated frequently. The model has to adapt to changes in the TKG for
efficient training and inference while preserving its performance on historical
knowledge. Recent work approaches TKG completion (TKGC) by augmenting the
encoder-decoder framework with a time-aware encoding function. However, naively
fine-tuning the model at every time step using these methods does not address
the problems of 1) catastrophic forgetting, 2) the model's inability to
identify the change of facts (e.g., the change of the political affiliation and
end of a marriage), and 3) the lack of training efficiency. To address these
challenges, we present the Time-aware Incremental Embedding (TIE) framework,
which combines TKG representation learning, experience replay, and temporal
regularization. We introduce a set of metrics that characterizes the
intransigence of the model and propose a constraint that associates the deleted
facts with negative labels. Experimental results on Wikidata12k and YAGO11k
datasets demonstrate that the proposed TIE framework reduces training time by
about ten times and improves on the proposed metrics compared to vanilla
full-batch training. It comes without a significant loss in performance for any
traditional measures. Extensive ablation studies reveal performance trade-offs
among different evaluation metrics, which is essential for decision-making
around real-world TKG applications.
","TIE: A Framework for Embedding-based Incremental Temporal Knowledge
  Graph Completion"
"  In model-based reinforcement learning (MBRL), Wan et al. (2019) showed
conditions under which the environment model could produce the expectation of
the next feature vector rather than the full distribution, or a sample thereof,
with no loss in planning performance. Such expectation models are of interest
when the environment is stochastic and non-stationary, and the model is
approximate, such as when it is learned using function approximation. In these
cases a full distribution model may be impractical and a sample model may be
either more expensive computationally or of high variance. Wan et al.
considered only planning for prediction to evaluate a fixed policy. In this
paper, we treat the control case - planning to improve and find a good
approximate policy. We prove that planning with an expectation model must
update a state-value function, not an action-value function as previously
suggested (e.g., Sorg & Singh, 2010). This opens the question of how planning
influences action selections. We consider three strategies for this and present
general MBRL algorithms for each. We identify the strengths and weaknesses of
these algorithms in computational experiments. Our algorithms and experiments
are the first to treat MBRL with expectation models in a general setting.
",Planning with Expectation Models for Control
"  Trust evaluation is an important topic in both research and applications in
sociable environments. This paper presents a model for trust evaluation between
agents by the combination of direct trust, indirect trust through neighbouring
links and the reputation of an agent in the environment (i.e. social network)
to provide the robust evaluation. Our approach is typology independent from
social network structures and in a decentralized manner without a central
controller, so it can be used in broad domains.
","A Robust Model for Trust Evaluation during Interactions between Agents
  in a Sociable Environment"
"  Designing agents that are able to achieve different play-styles while
maintaining a competitive level of play is a difficult task, especially for
games for which the research community has not found super-human performance
yet, like strategy games. These require the AI to deal with large action
spaces, long-term planning and partial observability, among other well-known
factors that make decision-making a hard problem. On top of this, achieving
distinct play-styles using a general algorithm without reducing playing
strength is not trivial. In this paper, we propose Portfolio Monte Carlo Tree
Search with Progressive Unpruning for playing a turn-based strategy game
(Tribes) and show how it can be parameterized so a quality-diversity algorithm
(MAP-Elites) is used to achieve different play-styles while keeping a
competitive level of play. Our results show that this algorithm is capable of
achieving these goals even for an extensive collection of game levels beyond
those used for training.
",Generating Diverse and Competitive Play-Styles for Strategy Games
"  Feature selection (FS) is an important research topic in machine learning.
Usually, FS is modelled as a+ bi-objective optimization problem whose
objectives are: 1) classification accuracy; 2) number of features. One of the
main issues in real-world applications is missing data. Databases with missing
data are likely to be unreliable. Thus, FS performed on a data set missing some
data is also unreliable. In order to directly control this issue plaguing the
field, we propose in this study a novel modelling of FS: we include reliability
as the third objective of the problem. In order to address the modified
problem, we propose the application of the non-dominated sorting genetic
algorithm-III (NSGA-III). We selected six incomplete data sets from the
University of California Irvine (UCI) machine learning repository. We used the
mean imputation method to deal with the missing data. In the experiments,
k-nearest neighbors (K-NN) is used as the classifier to evaluate the feature
subsets. Experimental results show that the proposed three-objective model
coupled with NSGA-III efficiently addresses the FS problem for the six data
sets included in this study.
",Multi-objective Feature Selection with Missing Data in Classification
"  Recently, much attention has been paid to the societal impact of AI,
especially concerns regarding its fairness. A growing body of research has
identified unfair AI systems and proposed methods to debias them, yet many
challenges remain. Representation learning for Heterogeneous Information
Networks (HINs), a fundamental building block used in complex network mining,
has socially consequential applications such as automated career counseling,
but there have been few attempts to ensure that it will not encode or amplify
harmful biases, e.g. sexism in the job market. To address this gap, in this
paper we propose a comprehensive set of de-biasing methods for fair HINs
representation learning, including sampling-based, projection-based, and graph
neural networks (GNNs)-based techniques. We systematically study the behavior
of these algorithms, especially their capability in balancing the trade-off
between fairness and prediction accuracy. We evaluate the performance of the
proposed methods in an automated career counseling application where we
mitigate gender bias in career recommendation. Based on the evaluation results
on two datasets, we identify the most effective fair HINs representation
learning techniques under different conditions.
",Fair Representation Learning for Heterogeneous Information Networks
"  Value functions are central to Dynamic Programming and Reinforcement Learning
but their exact estimation suffers from the curse of dimensionality,
challenging the development of practical value-function (VF) estimation
algorithms. Several approaches have been proposed to overcome this issue, from
non-parametric schemes that aggregate states or actions to parametric
approximations of state and action VFs via, e.g., linear estimators or deep
neural networks. Relevantly, several high-dimensional state problems can be
well-approximated by an intrinsic low-rank structure. Motivated by this and
leveraging results from low-rank optimization, this paper proposes different
stochastic algorithms to estimate a low-rank factorization of the $Q(s, a)$
matrix. This is a non-parametric alternative to VF approximation that
dramatically reduces the computational and sample complexities relative to
classical $Q$-learning methods that estimate $Q(s,a)$ separately for each
state-action pair.
",Low-rank State-action Value-function Approximation
"  Education is the basic step of understanding the truth and the preparation of
the intelligence to reflect. Focused on the rational capacity of the human
being the Cognitive process and knowledge dimensions of Revised Blooms Taxonomy
helps to differentiate the procedure of studying into six types of various
cognitive processes and four types of knowledge dimensions. These types are
synchronized in the increasing level of difficulty. In this paper Software
Engineering courses of B.Tech Computer Engineering and Information Technology
offered by various Universities and Educational Institutes have been
investigated for Revised Blooms Taxonomy RBT. Questions are a very useful
constituent. Knowledge intelligence and strength of the learners can be tested
by applying questions.The fundamental goal of this paper is to create a
relative study of the classification of the summative assessment based on
Revised Blooms Taxonomy using the Convolutional Neural Networks CNN Long
Short-Term Memory LSTM of Deep Learning techniques in an endeavor to attain
significant accomplishment and elevated precision levels.
","Classifications of the Summative Assessment for Revised Blooms Taxonomy
  by using Deep Learning"
"  Task environments developed in Minecraft are becoming increasingly popular
for artificial intelligence (AI) research. However, most of these are currently
constructed manually, thus failing to take advantage of procedural content
generation (PCG), a capability unique to virtual task environments. In this
paper, we present mcg, an open-source library to facilitate implementing PCG
algorithms for voxel-based environments such as Minecraft. The library is
designed with human-machine teaming research in mind, and thus takes a
'top-down' approach to generation, simultaneously generating low and high level
machine-readable representations that are suitable for empirical research.
These can be consumed by downstream AI applications that consider human spatial
cognition. The benefits of this approach include rapid, scalable, and efficient
development of virtual environments, the ability to control the statistics of
the environment at a semantic level, and the ability to generate novel
environments in response to player actions in real time.
",Modular Procedural Generation for Voxel Maps
"  We present an explanation system for applications that leverage Answer Set
Programming (ASP). Given a program P, an answer set A of P, and an atom a in
the program P, our system generates all explanation graphs of a which help
explain why a is true (or false) given the program P and the answer set A. We
illustrate the functionality of the system using some examples from the
literature.
",Generating explanations for answer set programming applications
"  At present, most research on the fairness of recommender systems is conducted
either from the perspective of customers or from the perspective of product (or
service) providers. However, such a practice ignores the fact that when
fairness is guaranteed to one side, the fairness and rights of the other side
are likely to reduce. In this paper, we consider recommendation scenarios from
the perspective of two sides (customers and providers). From the perspective of
providers, we consider the fairness of the providers' exposure in recommender
system. For customers, we consider the fairness of the reduced quality of
recommendation results due to the introduction of fairness measures. We
theoretically analyzed the relationship between recommendation quality,
customers fairness, and provider fairness, and design a two-sided
fairness-aware recommendation model (TFROM) for both customers and providers.
Specifically, we design two versions of TFROM for offline and online
recommendation. The effectiveness of the model is verified on three real-world
data sets. The experimental results show that TFROM provides better two-sided
fairness while still maintaining a higher level of personalization than the
baseline algorithms.
","TFROM: A Two-sided Fairness-Aware Recommendation Model for Both
  Customers and Providers"
"  Categorization is a significant task in decision-making, which is a key part
of human behavior. An interference effect is caused by categorization in some
cases, which breaks the total probability principle. A negation quantum model
(NQ model) is developed in this article to predict the interference. Taking the
advantage of negation to bring more information in the distribution from a
different perspective, the proposed model is a combination of the negation of a
probability distribution and the quantum decision model. Information of the
phase contained in quantum probability and the special calculation method to it
can easily represented the interference effect. The results of the proposed NQ
model is closely to the real experiment data and has less error than the
existed models.
","A Negation Quantum Decision Model to Predict the Interference Effect in
  Categorization"
"  Recently, the research on protecting the intellectual properties (IP) of deep
neural networks (DNN) has attracted serious concerns. A number of DNN copyright
protection methods have been proposed. However, most of the existing
watermarking methods focus on verifying the copyright of the model, which do
not support the authentication and management of users' fingerprints, thus can
not satisfy the requirements of commercial copyright protection. In addition,
the query modification attack which was proposed recently can invalidate most
of the existing backdoor-based watermarking methods. To address these
challenges, in this paper, we propose a method to protect the intellectual
properties of DNN models by using an additional class and steganographic
images. Specifically, we use a set of watermark key samples to embed an
additional class into the DNN, so that the watermarked DNN will classify the
watermark key sample as the predefined additional class in the copyright
verification stage. We adopt the least significant bit (LSB) image
steganography to embed users' fingerprints into watermark key images. Each user
will be assigned with a unique fingerprint image so that the user's identity
can be authenticated later. Experimental results demonstrate that, the proposed
method can protect the copyright of DNN models effectively. On Fashion-MNIST
and CIFAR-10 datasets, the proposed method can obtain 100% watermark accuracy
and 100% fingerprint authentication success rate. In addition, the proposed
method is demonstrated to be robust to the model fine-tuning attack, model
pruning attack, and the query modification attack. Compared with three existing
watermarking methods (the logo-based, noise-based, and adversarial frontier
stitching watermarking methods), the proposed method has better performance on
watermark accuracy and robustness against the query modification attack.
","Protecting the Intellectual Properties of Deep Neural Networks with an
  Additional Class and Steganographic Images"
"  The glissadic overshoot is characterized by an unwanted type of movement
known as glissades. The glissades are a short ocular movement that describe the
failure of the neural programming of saccades to move the eyes in order to
reach a specific target. In this paper we develop a procedure to determine if a
specific saccade have a glissade appended to the end of it. The use of the
third partial sum of the Gauss series as mathematical model, a comparison
between some specific parameters and the RMSE error are the steps made to reach
this goal. Finally a machine learning algorithm is trained, returning expected
responses of the presence or not of this kind of ocular movement.
","Automatic glissade determination through a mathematical model in
  electrooculographic records"
"  Online forums provide a unique opportunity for online users to share comments
and exchange information on a particular topic. Understanding user behaviour is
valuable to organizations and has applications for social and security
strategies, for instance, identifying user opinions within a community or
predicting future behaviour. Discovering the semantic aspects in Incel forums
are the main goal of this research; we apply Natural language processing
techniques based on topic modeling to latent topic discovery and opinion mining
of users from a popular online Incel discussion forum. To prepare the input
data for our study, we extracted the comments from Incels.co. The research
experiments show that Artificial Intelligence (AI) based on NLP models can be
effective for semantic and emotion knowledge discovery and retrieval of useful
information from the Incel community. For example, we discovered
semantic-related words that describe issues within a large volume of Incel
comments, which is difficult with manual methods.
","Semantic Knowledge Discovery and Discussion Mining of Incel Online
  Community: Topic modeling"
"  N-ary relational knowledge bases (KBs) represent knowledge with binary and
beyond-binary relational facts. Especially, in an n-ary relational fact, the
involved entities play different roles, e.g., the ternary relation
PlayCharacterIn consists of three roles, ACTOR, CHARACTER and MOVIE. However,
existing approaches are often directly extended from binary relational KBs,
i.e., knowledge graphs, while missing the important semantic property of role.
Therefore, we start from the role level, and propose a Role-Aware Modeling, RAM
for short, for facts in n-ary relational KBs. RAM explores a latent space that
contains basis vectors, and represents roles by linear combinations of these
vectors. This way encourages semantically related roles to have close
representations. RAM further introduces a pattern matrix that captures the
compatibility between the role and all involved entities. To this end, it
presents a multilinear scoring function to measure the plausibility of a fact
composed by certain roles and entities. We show that RAM achieves both
theoretical full expressiveness and computation efficiency, which also provides
an elegant generalization for approaches in binary relational KBs. Experiments
demonstrate that RAM outperforms representative baselines on both n-ary and
binary relational datasets.
",Role-Aware Modeling for N-ary Relational Knowledge Bases
"  Inefficient traffic control may cause numerous problems such as traffic
congestion and energy waste. This paper proposes a novel multi-agent
reinforcement learning method, named KS-DDPG (Knowledge Sharing Deep
Deterministic Policy Gradient) to achieve optimal control by enhancing the
cooperation between traffic signals. By introducing the knowledge-sharing
enabled communication protocol, each agent can access to the collective
representation of the traffic environment collected by all agents. The proposed
method is evaluated through two experiments respectively using synthetic and
real-world datasets. The comparison with state-of-the-art reinforcement
learning-based and conventional transportation methods demonstrate the proposed
KS-DDPG has significant efficiency in controlling large-scale transportation
networks and coping with fluctuations in traffic flow. In addition, the
introduced communication mechanism has also been proven to speed up the
convergence of the model without significantly increasing the computational
burden.
","Network-wide traffic signal control optimization using a multi-agent
  deep reinforcement learning"
"  The real-time prediction of business processes using historical event data is
an important capability of modern business process monitoring systems. Existing
process prediction methods are able to also exploit the data perspective of
recorded events, in addition to the control-flow perspective. However, while
well-structured numerical or categorical attributes are considered in many
prediction techniques, almost no technique is able to utilize text documents
written in natural language, which can hold information critical to the
prediction task. In this paper, we illustrate the design, implementation, and
evaluation of a novel text-aware process prediction model based on Long
Short-Term Memory (LSTM) neural networks and natural language models. The
proposed model can take categorical, numerical and textual attributes in event
data into account to predict the activity and timestamp of the next event, the
outcome, and the cycle time of a running process instance. Experiments show
that the text-aware model is able to outperform state-of-the-art process
prediction methods on simulated and real-world event logs containing textual
data.
",Text-Aware Predictive Monitoring of Business Processes
"  Knowledge Graph (KG) reasoning that predicts missing facts for incomplete KGs
has been widely explored. However, reasoning over Temporal KG (TKG) that
predicts facts in the future is still far from resolved. The key to predict
future facts is to thoroughly understand the historical facts. A TKG is
actually a sequence of KGs corresponding to different timestamps, where all
concurrent facts in each KG exhibit structural dependencies and temporally
adjacent facts carry informative sequential patterns. To capture these
properties effectively and efficiently, we propose a novel Recurrent Evolution
network based on Graph Convolution Network (GCN), called RE-GCN, which learns
the evolutional representations of entities and relations at each timestamp by
modeling the KG sequence recurrently. Specifically, for the evolution unit, a
relation-aware GCN is leveraged to capture the structural dependencies within
the KG at each timestamp. In order to capture the sequential patterns of all
facts in parallel, the historical KG sequence is modeled auto-regressively by
the gate recurrent components. Moreover, the static properties of entities such
as entity types, are also incorporated via a static graph constraint component
to obtain better entity representations. Fact prediction at future timestamps
can then be realized based on the evolutional entity and relation
representations. Extensive experiments demonstrate that the RE-GCN model
obtains substantial performance and efficiency improvement for the temporal
reasoning tasks on six benchmark datasets. Especially, it achieves up to
11.46\% improvement in MRR for entity prediction with up to 82 times speedup
comparing to the state-of-the-art baseline.
","Temporal Knowledge Graph Reasoning Based on Evolutional Representation
  Learning"
"  Portfolio methods represent a simple but efficient type of action abstraction
which has shown to improve the performance of search-based agents in a range of
strategy games. We first review existing portfolio techniques and propose a new
algorithm for optimization and action-selection based on the Rolling Horizon
Evolutionary Algorithm. Moreover, a series of variants are developed to solve
problems in different aspects. We further analyze the performance of discussed
agents in a general strategy game-playing task. For this purpose, we run
experiments on three different game-modes of the Stratega framework. For the
optimization of the agents' parameters and portfolio sets we study the use of
the N-tuple Bandit Evolutionary Algorithm. The resulting portfolio sets suggest
a high diversity in play-styles while being able to consistently beat the
sample agents. An analysis of the agents' performance shows that the proposed
algorithm generalizes well to all game-modes and is able to outperform other
portfolio methods.
",Portfolio Search and Optimization for General Strategy Game-Playing
"  Recent machine-learning approaches to deterministic search and
domain-independent planning employ policy learning to speed up search.
Unfortunately, when attempting to solve a search problem by successively
applying a policy, no guarantees can be given on solution quality. The problem
of how to effectively use a learned policy within a bounded-suboptimal search
algorithm remains largely as an open question. In this paper, we propose
various ways in which such policies can be integrated into Focal Search,
assuming that the policy is a neural network classifier. Furthermore, we
provide mathematical foundations for some of the resulting algorithms. To
evaluate the resulting algorithms over a number of policies with varying
accuracy, we use synthetic policies which can be generated for a target
accuracy for problems where the search space can be held in memory. We evaluate
our focal search variants over three benchmark domains using our synthetic
approach, and on the 15-puzzle using a neural network learned using 1.5 million
examples. We observe that Discrepancy Focal Search, which we show expands the
node which maximizes an approximation of the probability that its corresponding
path is a prefix of an optimal path, obtains, in general, the best results in
terms of runtime and solution quality.
",Exploiting Learned Policies in Focal Search
"  Existing approaches for generating human-aware agent behaviors have
considered different measures of interpretability in isolation. Further, these
measures have been studied under differing assumptions, thus precluding the
possibility of designing a single framework that captures these measures under
the same assumptions. In this paper, we present a unifying Bayesian framework
that models a human observer's evolving beliefs about an agent and thereby
define the problem of Generalized Human-Aware Planning. We will show that the
definitions of interpretability measures like explicability, legibility and
predictability from the prior literature fall out as special cases of our
general framework. Through this framework, we also bring a previously ignored
fact to light that the human-robot interactions are in effect open-world
problems, particularly as a result of modeling the human's beliefs over the
agent. Since the human may not only hold beliefs unknown to the agent but may
also form new hypotheses about the agent when presented with novel or
unexpected behaviors.
","A Unifying Bayesian Formulation of Measures of Interpretability in
  Human-AI"
"  Automated game design is a rapidly growing area of research, yet many aspects
of game design lie largely unexamined still, as most systems focus on
two-dimensional games with clear objectives and goal-oriented gameplay. This
paper describes several attempts to build an automated game designer for 3D
games more focused on space, atmosphere and experience. We describe our
attempts to build these systems, why they failed, and what steps and future
work we believe would be useful for future attempts by others.
","The Road Less Travelled: Trying And Failing To Generate Walking
  Simulators"
"  Knowledge Graph (KG) is a flexible structure that is able to describe the
complex relationship between data entities. Currently, most KG embedding models
are trained based on negative sampling, i.e., the model aims to maximize some
similarity of the connected entities in the KG, while minimizing the similarity
of the sampled disconnected entities. Negative sampling helps to reduce the
time complexity of model learning by only considering a subset of negative
instances, which may fail to deliver stable model performance due to the
uncertainty in the sampling procedure. To avoid such deficiency, we propose a
new framework for KG embedding -- Efficient Non-Sampling Knowledge Graph
Embedding (NS-KGE). The basic idea is to consider all of the negative instances
in the KG for model learning, and thus to avoid negative sampling. The
framework can be applied to square-loss based knowledge graph embedding models
or models whose loss can be converted to a square loss. A natural side-effect
of this non-sampling strategy is the increased computational complexity of
model learning. To solve the problem, we leverage mathematical derivations to
reduce the complexity of non-sampling loss function, which eventually provides
us both better efficiency and better accuracy in KG embedding compared with
existing models. Experiments on benchmark datasets show that our NS-KGE
framework can achieve a better performance on efficiency and accuracy over
traditional negative sampling based models, and that the framework is
applicable to a large class of knowledge graph embedding models.
",Efficient Non-Sampling Knowledge Graph Embedding
"  Optimization of deep learning algorithms to approach Nash Equilibrium remains
a significant problem in imperfect information games, e.g. StarCraft and poker.
Neural Fictitious Self-Play (NFSP) has provided an effective way to learn
approximate Nash Equilibrium without prior domain knowledge in imperfect
information games. However, optimality gap was left as an optimization problem
of NFSP and by solving the problem, the performance of NFSP could be improved.
In this study, focusing on the optimality gap of NFSP, we have proposed a new
method replacing NFSP's best response computation with regret matching method.
The new algorithm can make the optimality gap converge to zero as it iterates,
thus converge faster than original NFSP. We have conduct experiments on three
typical environments of perfect-information games and imperfect information
games in OpenSpiel and all showed that our new algorithm performances better
than original NFSP.
",Optimize Neural Fictitious Self-Play in Regret Minimization Thinking
"  Zero-shot learning (ZSL) aims to transfer knowledge from seen classes to
semantically related unseen classes, which are absent during training. The
promising strategies for ZSL are to synthesize visual features of unseen
classes conditioned on semantic side information and to incorporate
meta-learning to eliminate the model's inherent bias towards seen classes.
While existing meta generative approaches pursue a common model shared across
task distributions, we aim to construct a generative network adaptive to task
characteristics. To this end, we propose an Attribute-Modulated generAtive
meta-model for Zero-shot learning (AMAZ). Our model consists of an
attribute-aware modulation network, an attribute-augmented generative network,
and an attribute-weighted classifier. Given unseen classes, the modulation
network adaptively modulates the generator by applying task-specific
transformations so that the generative network can adapt to highly diverse
tasks. The weighted classifier utilizes the data quality to enhance the
training procedure, further improving the model performance. Our empirical
evaluations on four widely-used benchmarks show that AMAZ outperforms
state-of-the-art methods by 3.8% and 3.1% in ZSL and generalized ZSL settings,
respectively, demonstrating the superiority of our method. Our experiments on a
zero-shot image retrieval task show AMAZ's ability to synthesize instances that
portray real visual characteristics.
","Attribute-Modulated Generative Meta Learning for Zero-Shot
  Classification"
"  This paper outlines the state of the art in AI. It then describes basic
machine learning and knowledge processing techniques. Based on this, some
possibilities and limitations of future AI developments are discussed.
","K\""unstliche Intelligenz, quo vadis?"
"  This paper explores the use of reinforcement learning (RL) models for
autonomous racing. In contrast to passenger cars, where safety is the top
priority, a racing car aims to minimize the lap-time. We frame the problem as a
reinforcement learning task with a multidimensional input consisting of the
vehicle telemetry, and a continuous action space. To find out which RL methods
better solve the problem and whether the obtained models generalize to driving
on unknown tracks, we put 10 variants of deep deterministic policy gradient
(DDPG) to race in two experiments: i)~studying how RL methods learn to drive a
racing car and ii)~studying how the learning scenario influences the capability
of the models to generalize. Our studies show that models trained with RL are
not only able to drive faster than the baseline open source handcrafted bots
but also generalize to unknown tracks.
","Formula RL: Deep Reinforcement Learning for Autonomous Racing using
  Telemetry Data"
"  Causality analysis is an important problem lying at the heart of science, and
is of particular importance in data science and machine learning. An endeavor
during the past 16 years viewing causality as real physical notion so as to
formulate it from first principles, however, seems to go unnoticed. This study
introduces to the community this line of work, with a long-due generalization
of the information flow-based bivariate time series causal inference to
multivariate series, based on the recent advance in theoretical development.
The resulting formula is transparent, and can be implemented as a
computationally very efficient algorithm for application. It can be normalized,
and tested for statistical significance. Different from the previous work along
this line where only information flows are estimated, here an algorithm is also
implemented to quantify the influence of a unit to itself. While this forms a
challenge in some causal inferences, here it comes naturally, and hence the
identification of self-loops in a causal graph is fulfilled automatically as
the causalities along edges are inferred.
  To demonstrate the power of the approach, presented here are two applications
in extreme situations. The first is a network of multivariate processes buried
in heavy noises (with the noise-to-signal ratio exceeding 100), and the second
a network with nearly synchronized chaotic oscillators. In both graphs,
confounding processes exist. While it seems to be a huge challenge to
reconstruct from given series these causal graphs, an easy application of the
algorithm immediately reveals the desideratum. Particularly, the confounding
processes have been accurately differentiated. Considering the surge of
interest in the community, this study is very timely.
","Normalized multivariate time series causality analysis and causal graph
  reconstruction"
"  In open-domain conversational systems, it is important but challenging to
leverage background knowledge. We can use the incorporation of knowledge to
make the generation of dialogue controllable, and can generate more diverse
sentences that contain real knowledge. In this paper, we combine the knowledge
bases and pre-training model to propose a knowledge-driven conversation system.
The system includes modules such as dialogue topic prediction, knowledge
matching and dialogue generation. Based on this system, we study the
performance factors that maybe affect the generation of knowledge-driven
dialogue: topic coarse recall algorithm, number of knowledge choices,
generation model choices, etc., and finally made the system reach
state-of-the-art. These experimental results will provide some guiding
significance for the future research of this task. As far as we know, this is
the first work to study and analyze the effects of the related factors.
","Prediction, Selection, and Generation: Exploration of Knowledge-Driven
  Conversation System"
"  Due to the fact that basic uncertain information provides a simple form for
decision information with certainty degree, it has been developed to reflect
the quality of observed or subjective assessments. In order to study the
algebra structure and preference relation of basic uncertain information, we
develop some algebra operations for basic uncertain information. The order
relation of such type of information has also been considered. Finally, to
apply the developed algebra operations and order relations, a generalized TODIM
method for multi-attribute decision making with basic uncertain information is
given. The numerical example shows that the developed decision procedure is
valid.
","Generalized-TODIM Method for Multi-criteria Decision Making with Basic
  Uncertain Information and its Application"
"  The emergence of Artificial Intelligence of Things (AIoT) has provided novel
insights for many social computing applications such as group recommender
systems. As distance among people has been greatly shortened, it has been a
more general demand to provide personalized services to groups instead of
individuals. In order to capture group-level preference features from
individuals, existing methods were mostly established via aggregation and face
two aspects of challenges: secure data management workflow is absent, and
implicit preference feedbacks is ignored. To tackle current difficulties, this
paper proposes secure Artificial Intelligence of Things for implicit Group
Recommendations (SAIoT-GR). As for hardware module, a secure IoT structure is
developed as the bottom support platform. As for software module, collaborative
Bayesian network model and non-cooperative game are can be introduced as
algorithms. Such a secure AIoT architecture is able to maximize the advantages
of the two modules. In addition, a large number of experiments are carried out
to evaluate the performance of the SAIoT-GR in terms of efficiency and
robustness.
","Secure Artificial Intelligence of Things for Implicit Group
  Recommendations"
"  This paper presents and evaluates two pruning techniques to reinforce the
efficiency of constraint optimization solvers based on multi-valued
decision-diagrams (MDD). It adopts the branch-and-bound framework proposed by
Bergman et al. in 2016 to solve dynamic programs to optimality. In particular,
our paper presents and evaluates the effectiveness of the local-bound (LocB)
and rough upper-bound pruning (RUB). LocB is a new and effective rule that
leverages the approximate MDD structure to avoid the exploration of
non-interesting nodes. RUB is a rule to reduce the search space during the
development of bounded-width-MDDs. The experimental study we conducted on the
Maximum Independent Set Problem (MISP), Maximum Cut Problem (MCP), Maximum 2
Satisfiability (MAX2SAT) and the Traveling Salesman Problem with Time Windows
(TSPTW) shows evidence indicating that rough-upper-bound and local-bound
pruning have a high impact on optimization solvers based on branch-and-bound
with MDDs. In particular, it shows that RUB delivers excellent results but
requires some effort when defining the model. Also, it shows that LocB provides
a significant improvement automatically; without necessitating any
user-supplied information. Finally, it also shows that rough-upper-bound and
local-bound pruning are not mutually exclusive, and their combined benefit
supersedes the individual benefit of using each technique.
",Improving the filtering of Branch-And-Bound MDD solver (extended)
"  There have been increasing concerns about Artificial Intelligence (AI) due to
its unfathomable potential power. To make AI address ethical challenges and
shun undesirable outcomes, researchers proposed to develop socially responsible
AI (SRAI). One of these approaches is causal learning (CL). We survey
state-of-the-art methods of CL for SRAI. We begin by examining the seven CL
tools to enhance the social responsibility of AI, then review how existing
works have succeeded using these tools to tackle issues in developing SRAI such
as fairness. The goal of this survey is to bring forefront the potentials and
promises of CL for SRAI.
",Causal Learning for Socially Responsible AI
"  Lexical Semantics is concerned with how words encode mental representations
of the world, i.e., concepts . We call this type of concepts, classification
concepts . In this paper, we focus on Visual Semantics , namely on how humans
build concepts representing what they perceive visually. We call this second
type of concepts, substance concepts . As shown in the paper, these two types
of concepts are different and, furthermore, the mapping between them is
many-to-many. In this paper we provide a theory and an algorithm for how to
build substance concepts which are in a one-to-one correspondence with
classifications concepts, thus paving the way to the seamless integration
between natural language descriptions and visual perception. This work builds
upon three main intuitions: (i) substance concepts are modeled as visual
objects , namely sequences of similar frames, as perceived in multiple
encounters ; (ii) substance concepts are organized into a visual subsumption
hierarchy based on the notions of Genus and Differentia ; (iii) the human
feedback is exploited not to name objects, but, rather, to align the hierarchy
of substance concepts with that of classification concepts. The learning
algorithm is implemented for the base case of a hierarchy of depth two. The
experiments, though preliminary, show that the algorithm manages to acquire the
notions of Genus and Differentia with reasonable accuracy, this despite seeing
a small number of examples and receiving supervision on a fraction of them.
",Towards Visual Semantics
"  Since its beginning in the 1950s, the field of artificial intelligence has
cycled several times between periods of optimistic predictions and massive
investment (""AI spring"") and periods of disappointment, loss of confidence, and
reduced funding (""AI winter""). Even with today's seemingly fast pace of AI
breakthroughs, the development of long-promised technologies such as
self-driving cars, housekeeping robots, and conversational companions has
turned out to be much harder than many people expected. One reason for these
repeating cycles is our limited understanding of the nature and complexity of
intelligence itself. In this paper I describe four fallacies in common
assumptions made by AI researchers, which can lead to overconfident predictions
about the field. I conclude by discussing the open questions spurred by these
fallacies, including the age-old challenge of imbuing machines with humanlike
common sense.
",Why AI is Harder Than We Think
"  To inhibit the spread of rumorous information and its severe consequences,
traditional fact checking aims at retrieving relevant evidence to verify the
veracity of a given claim. Fact checking methods typically use knowledge graphs
(KGs) as external repositories and develop reasoning mechanism to retrieve
evidence for verifying the triple claim. However, existing methods only focus
on verifying a single claim. As real-world rumorous information is more complex
and a textual statement is often composed of multiple clauses (i.e. represented
as multiple claims instead of a single one), multiclaim fact checking is not
only necessary but more important for practical applications. Although previous
methods for verifying a single triple can be applied repeatedly to verify
multiple triples one by one, they ignore the contextual information implied in
a multi-claim statement and could not learn the rich semantic information in
the statement as a whole. In this paper, we propose an end-to-end knowledge
enhanced learning and verification method for multi-claim fact checking. Our
method consists of two modules, KG-based learning enhancement and multi-claim
semantic composition. To fully utilize the contextual information, the KG-based
learning enhancement module learns the dynamic context-specific representations
via selectively aggregating relevant attributes of entities. To capture the
compositional semantics of multiple triples, the multi-claim semantic
composition module constructs the graph structure to model claim-level
interactions, and integrates global and salient local semantics with multi-head
attention. Experimental results on a real-world dataset and two benchmark
datasets show the effectiveness of our method for multi-claim fact checking
over KG.
","A Knowledge Enhanced Learning and Semantic Composition Model for
  Multi-Claim Fact Checking"
"  This article reviews the ""Once learning"" mechanism that was proposed 23 years
ago and the subsequent successes of ""One-shot learning"" in image classification
and ""You Only Look Once - YOLO"" in objective detection. Analyzing the current
development of Artificial Intelligence (AI), the proposal is that AI should be
clearly divided into the following categories: Artificial Human Intelligence
(AHI), Artificial Machine Intelligence (AMI), and Artificial Biological
Intelligence (ABI), which will also be the main directions of theory and
application development for AI. As a watershed for the branches of AI, some
classification standards and methods are discussed: 1) Human-oriented,
machine-oriented, and biological-oriented AI R&D; 2) Information input
processed by Dimensionality-up or Dimensionality-reduction; 3) The use of
one/few or large samples for knowledge learning.
","Watershed of Artificial Intelligence: Human Intelligence, Machine
  Intelligence, and Biological Intelligence"
"  Partially Observable Monte-Carlo Planning (POMCP) is a powerful online
algorithm able to generate approximate policies for large Partially Observable
Markov Decision Processes. The online nature of this method supports
scalability by avoiding complete policy representation. The lack of an explicit
representation however hinders policy interpretability and makes policy
verification very complex. In this work, we propose two contributions. The
first is a method for identifying unexpected actions selected by POMCP with
respect to expert prior knowledge of the task. The second is a shielding
approach that prevents POMCP from selecting unexpected actions. The first
method is based on Satisfiability Modulo Theory (SMT). It inspects traces
(i.e., sequences of belief-action-observation triplets) generated by POMCP to
compute the parameters of logical formulas about policy properties defined by
the expert. The second contribution is a module that uses online the logical
formulas to identify anomalous actions selected by POMCP and substitutes those
actions with actions that satisfy the logical formulas fulfilling expert
knowledge. We evaluate our approach on Tiger, a standard benchmark for POMDPs,
and a real-world problem related to velocity regulation in mobile robot
navigation. Results show that the shielded POMCP outperforms the standard POMCP
in a case study in which a wrong parameter of POMCP makes it select wrong
actions from time to time. Moreover, we show that the approach keeps good
performance also if the parameters of the logical formula are optimized using
trajectories containing some wrong actions.
",Rule-based Shielding for Partially Observable Monte-Carlo Planning
"  With populations ageing, the number of people with dementia worldwide is
expected to triple to 152 million by 2050. Seventy percent of cases are due to
Alzheimer's disease (AD) pathology and there is a 10-20 year 'pre-clinical'
period before significant cognitive decline occurs. We urgently need, cost
effective, objective methods to detect AD, and other dementias, at an early
stage. Risk factor modification could prevent 40% of cases and drug trials
would have greater chances of success if participants are recruited at an
earlier stage. Currently, detection of dementia is largely by pen and paper
cognitive tests but these are time consuming and insensitive to pre-clinical
phases. Specialist brain scans and body fluid biomarkers can detect the
earliest stages of dementia but are too invasive or expensive for widespread
use. With the advancement of technology, Artificial Intelligence (AI) shows
promising results in assisting with detection of early-stage dementia. Existing
AI-aided methods and potential future research directions are reviewed and
discussed.
","Applications of Artificial Intelligence to aid detection of dementia: a
  narrative review on current capabilities and future directions"
"  Discovering novel high-level concepts is one of the most important steps
needed for human-level AI. In inductive logic programming (ILP), discovering
novel high-level concepts is known as predicate invention (PI). Although seen
as crucial since the founding of ILP, PI is notoriously difficult and most ILP
systems do not support it. In this paper, we introduce POPPI, an ILP system
that formulates the PI problem as an answer set programming problem. Our
experiments show that (i) PI can drastically improve learning performance when
useful, (ii) PI is not too costly when unnecessary, and (iii) POPPI can
substantially outperform existing ILP systems.
",Predicate Invention by Learning From Failures
"  Recently, it has been proposed that fruitful synergies may exist between Deep
Learning (DL) and Case Based Reasoning (CBR); that there are insights to be
gained by applying CBR ideas to problems in DL (what could be called DeepCBR).
In this paper, we report on a program of research that applies CBR solutions to
the problem of Explainable AI (XAI) in the DL. We describe a series of
twin-systems pairings of opaque DL models with transparent CBR models that
allow the latter to explain the former using factual, counterfactual and
semi-factual explanation strategies. This twinning shows that functional
abstractions of DL (e.g., feature weights, feature importance and decision
boundaries) can be used to drive these explanatory solutions. We also raise the
prospect that this research also applies to the problem of Data Augmentation in
DL, underscoring the fecundity of these DeepCBR ideas.
","Twin Systems for DeepCBR: A Menagerie of Deep Learning and Case-Based
  Reasoning Pairings for Explanation and Data Augmentation"
"  In this paper, we introduce an approach to validate the functional
equivalence of planning domain models. Validating the functional equivalence of
planning domain models is the problem of formally confirming that two planning
domain models can be used to solve the same set of problems. The need for
techniques to validate the functional equivalence of planning domain models has
been highlighted in previous research and has applications in model learning,
development and extension. We prove the soundness and completeness of our
method. We also develop D-VAL, an automatic functional equivalence validation
tool for planning domain models. Empirical evaluation shows that D-VAL
validates the functional equivalence of most examined domains in less than five
minutes. Additionally, we provide a benchmark to evaluate the feasibility and
scalability of this and future related work.
","D-VAL: An automatic functional equivalence validation tool for planning
  domain models"
"  AI has the potential to revolutionize many areas of healthcare. Radiology,
dermatology, and ophthalmology are some of the areas most likely to be impacted
in the near future, and they have received significant attention from the
broader research community. But AI techniques are now also starting to be used
in in vitro fertilization (IVF), in particular for selecting which embryos to
transfer to the woman. The contribution of AI to IVF is potentially
significant, but must be done carefully and transparently, as the ethical
issues are significant, in part because this field involves creating new
people. We first give a brief introduction to IVF and review the use of AI for
embryo selection. We discuss concerns with the interpretation of the reported
results from scientific and practical perspectives. We then consider the
broader ethical issues involved. We discuss in detail the problems that result
from the use of black-box methods in this context and advocate strongly for the
use of interpretable models. Importantly, there have been no published trials
of clinical effectiveness, a problem in both the AI and IVF communities, and we
therefore argue that clinical implementation at this point would be premature.
Finally, we discuss ways for the broader AI community to become involved to
ensure scientifically sound and ethically responsible development of AI in IVF.
","Ethical Implementation of Artificial Intelligence to Select Embryos in
  In Vitro Fertilization"
"  Humans can learn a variety of concepts and skills incrementally over the
course of their lives while exhibiting many desirable properties, such as
continual learning without forgetting, forward transfer and backward transfer
of knowledge, and learning a new concept or task with only a few examples.
Several lines of machine learning research, such as lifelong machine learning,
few-shot learning, and transfer learning attempt to capture these properties.
However, most previous approaches can only demonstrate subsets of these
properties, often by different complex mechanisms. In this work, we propose a
simple yet powerful unified deep learning framework that supports almost all of
these properties and approaches through one central mechanism. Experiments on
toy examples support our claims. We also draw connections between many
peculiarities of human learning (such as memory loss and ""rain man"") and our
framework.
  As academics, we often lack resources required to build and train, deep
neural networks with billions of parameters on hundreds of TPUs. Thus, while
our framework is still conceptual, and our experiment results are surely not
SOTA, we hope that this unified lifelong learning framework inspires new work
towards large-scale experiments and understanding human learning in general.
  This paper is summarized in two short YouTube videos:
https://youtu.be/gCuUyGETbTU (part 1) and https://youtu.be/XsaGI01b-1o (part
2).
",A Deep Learning Framework for Lifelong Machine Learning
"  Given an on-board diagnostics (OBD) dataset and a physics-based emissions
prediction model, this paper aims to develop an accurate and
computational-efficient AI (Artificial Intelligence) method that predicts
vehicle emissions. The problem is of societal importance because vehicular
emissions lead to climate change and impact human health. This problem is
challenging because the OBD data does not contain enough parameters needed by
high-order physics models. Conversely, related work has shown that low-order
physics models have poor predictive accuracy when using available OBD data.
This paper uses a divergent window co-occurrence pattern detection method to
develop a spatiotemporal variability-aware AI model for predicting emission
values from the OBD datasets. We conducted a case study using real-world OBD
data from a local public transportation agency. Results show that the proposed
AI method has approximately 65% improved predictive accuracy than a non-AI
low-order physics model and is approximately 35% more accurate than a baseline
model.
","Vehicle Emissions Prediction with Physics-Aware AI Models: Preliminary
  Results"
"  In recent years, knowledge graphs have been widely applied to organize data
in a uniform way and enhance many tasks that require knowledge, for example,
online shopping which has greatly facilitated people's life. As a backbone for
online shopping platforms, we built a billion-scale e-commerce product
knowledge graph for various item knowledge services such as item
recommendation. However, such knowledge services usually include tedious data
selection and model design for knowledge infusion, which might bring
inappropriate results. Thus, to avoid this problem, we propose a Pre-trained
Knowledge Graph Model (PKGM) for our billion-scale e-commerce product knowledge
graph, providing item knowledge services in a uniform way for embedding-based
models without accessing triple data in the knowledge graph. Notably, PKGM
could also complete knowledge graphs during servicing, thereby overcoming the
common incompleteness issue in knowledge graphs. We test PKGM in three
knowledge-related tasks including item classification, same item
identification, and recommendation. Experimental results show PKGM successfully
improves the performance of each task.
",Billion-scale Pre-trained E-commerce Product Knowledge Graph Model
"  This paper addresses the problem of synthesizing the behavior of an AI agent
that provides proactive task assistance to a human in settings like factory
floors where they may coexist in a common environment. Unlike in the case of
requested assistance, the human may not be expecting proactive assistance and
hence it is crucial for the agent to ensure that the human is aware of how the
assistance affects her task. This becomes harder when there is a possibility
that the human may neither have full knowledge of the AI agent's capabilities
nor have full observability of its activities. Therefore, our \textit{proactive
assistant} is guided by the following three principles: \textbf{(1)} its
activity decreases the human's cost towards her goal; \textbf{(2)} the human is
able to recognize the potential reduction in her cost; \textbf{(3)} its
activity optimizes the human's overall cost (time/resources) of achieving her
goal. Through empirical evaluation and user studies, we demonstrate the
usefulness of our approach.
","Planning for Proactive Assistance in Environments with Partial
  Observability"
"  The problem of measuring sentence similarity is an essential issue in the
natural language processing (NLP) area. It is necessary to measure the
similarity between sentences accurately. There are many approaches to measuring
sentence similarity. Deep learning methodology shows a state-of-the-art
performance in many natural language processing fields and is used a lot in
sentence similarity measurement methods. However, in the natural language
processing field, considering the structure of the sentence or the word
structure that makes up the sentence is also important. In this study, we
propose a methodology combined with both deep learning methodology and a method
considering lexical relationships. Our evaluation metric is the Pearson
correlation coefficient and Spearman correlation coefficient. As a result, the
proposed method outperforms the current approaches on a KorSTS standard
benchmark Korean dataset. Moreover, it performs a maximum of 65% increase than
only using deep learning methodology. Experiments show that our proposed method
generally results in better performance than those with only a deep learning
model.
",A novel hybrid methodology of measuring sentence similarity
"  Building human-like agent, which aims to learn and think like human
intelligence, has long been an important research topic in AI. To train and
test human-like agents, we need an environment that imposes the agent to rich
multimodal perception and allows comprehensive interactions for the agent,
while also easily extensible to develop custom tasks. However, existing
approaches do not support comprehensive interaction with the environment or
lack variety in modalities. Also, most of the approaches are difficult or even
impossible to implement custom tasks. In this paper, we propose a novel
VR-based toolkit, VECA, which enables building fruitful virtual environments to
train and test human-like agents. In particular, VECA provides a humanoid agent
and an environment manager, enabling the agent to receive rich human-like
perception and perform comprehensive interactions. To motivate VECA, we also
provide 24 interactive tasks, which represent (but are not limited to) four
essential aspects in early human development: joint-level locomotion and
control, understanding contexts of objects, multimodal learning, and
multi-agent learning. To show the usefulness of VECA on training and testing
human-like learning agents, we conduct experiments on VECA and show that users
can build challenging tasks for engaging human-like algorithms, and the
features supported by VECA are critical on training human-like agents.
","VECA : A Toolkit for Building Virtual Environments to Train and Test
  Human-like Agents"
"  The current pandemic has introduced substantial uncertainty to traditional
methods for demand planning. These uncertainties stem from the disease
progression, government interventions, economy and consumer behavior. While
most of the emerging literature on the pandemic has focused on disease
progression, a few have focused on consequent regulations and their impact on
individual behavior. The contributions of this paper include a quantitative
behavior model of fear of COVID-19, impact of government interventions on
consumer behavior, and impact of consumer behavior on consumer choice and hence
demand for goods. It brings together multiple models for disease progression,
consumer behavior and demand estimation-thus bridging the gap between disease
progression and consumer demand. We use panel regression to understand the
drivers of demand during the pandemic and Bayesian inference to simplify the
regulation landscape that can help build scenarios for resilient demand
planning. We illustrate this resilient demand planning model using a specific
example of gas retailing. We find that demand is sensitive to fear of COVID-19:
as the number of COVID-19 cases increase over the previous week, the demand for
gas decreases -- though this dissipates over time. Further, government
regulations restrict access to different services, thereby reducing mobility,
which in itself reduces demand.
",Consumer Demand Modeling During COVID-19 Pandemic
"  In this work, we presented a study regarding two important aspects of
evolving feature-based game evaluation functions: the choice of genome
representation and the choice of opponent used to test the model. We compared
three representations. One simpler and more limited, based on a vector of
weights that are used in a linear combination of predefined game features. And
two more complex, based on binary and n-ary trees. On top of this test, we also
investigated the influence of fitness defined as a simulation-based function
that: plays against a fixed weak opponent, plays against a fixed strong
opponent, and plays against the best individual from the previous population.
For a testbed, we have chosen a recently popular domain of digital collectible
card games. We encoded our experiments in a programming game, Legends of Code
and Magic, used in Strategy Card Game AI Competition. However, as the problems
stated are of general nature we are convinced that our observations are
applicable in the other domains as well.
",Evolving Evaluation Functions for Collectible Card Game AI
"  In China, construction accidents have killed more people than any other
industry since 2012. The factors which led to the accident have complex
interaction. Real data about accidents is the key to reveal the mechanism among
these factors. But the data from the questionnaire and interview has inherent
defects. Many behaviors that impact safety are illegal. In China, most of the
cases are from accident investigation reports. Finding out the cause of the
accident and liability affirmation are the core of incident investigation
reports. So the truth of some answers from the respondents is doubtful. With a
series of NLP technologies, in this paper, causal factors of construction
accidents are extracted and organized from Chinese incident case texts.
Finally, three kinds of neglected causal factors are discovered after data
analysis.
",Causal factors discovering from Chinese construction accident cases
"  With the increased use of AI methods to provide recommendations in the
health, specifically in the food dietary recommendation space, there is also an
increased need for explainability of those recommendations. Such explanations
would benefit users of recommendation systems by empowering them with
justifications for following the system's suggestions. We present the Food
Explanation Ontology (FEO) that provides a formalism for modeling explanations
to users for food-related recommendations. FEO models food recommendations,
using concepts from the explanation domain to create responses to user
questions about food recommendations they receive from AI systems such as
personalized knowledge base question answering systems. FEO uses a modular,
extensible structure that lends itself to a variety of explanations while still
preserving important semantic details to accurately represent explanations of
food recommendations. In order to evaluate this system, we used a set of
competency questions derived from explanation types present in literature that
are relevant to food recommendations. Our motivation with the use of FEO is to
empower users to make decisions about their health, fully equipped with an
understanding of the AI recommender systems as they relate to user questions,
by providing reasoning behind their recommendations in the form of
explanations.
",Semantic Modeling for Food Recommendation Explanations
"  Many methods have been proposed to detect concept drift, i.e., the change in
the distribution of streaming data, due to concept drift causes a decrease in
the prediction accuracy of algorithms. However, the most of current detection
methods are based on the assessment of the degree of change in the data
distribution, cannot identify the type of concept drift. In this paper, we
propose Active Drift Detection with Meta learning (Meta-ADD), a novel framework
that learns to classify concept drift by tracking the changed pattern of error
rates. Specifically, in the training phase, we extract meta-features based on
the error rates of various concept drift, after which a meta-detector is
developed via a prototypical neural network by representing various concept
drift classes as corresponding prototypes. In the detection phase, the learned
meta-detector is fine-tuned to adapt to the corresponding data stream via
stream-based active learning. Hence, Meta-ADD uses machine learning to learn to
detect concept drifts and identify their types automatically, which can
directly support drift understand. The experiment results verify the
effectiveness of Meta-ADD.
",Automatic Learning to Detect Concept Drift
"  Process mining has matured as analysis instrument for process-oriented data
in recent years. Manufacturing is a challenging domain that craves for
process-oriented technologies to address digitalization challenges. We found
that process mining creates high expectations, but its implementation and usage
by manufacturing experts such as process supervisors and shopfloor workers
remain unclear to a certain extent. Reason (1) is that even though
manufacturing allows for well-structured processes, the actual workflow is
rarely captured in a process model. Even if a model is available, a software
for orchestrating and logging the execution is often missing. Reason (2) refers
to the work reality in manufacturing: a process instance is started by a
shopfloor worker who then turns to work on other things. Hence continuous
monitoring of the process instances does not happen, i.e., process monitoring
is merely a secondary task, and the shopfloor worker can only react to
problems/errors that have already occurred. (1) and (2) motivate the goals of
this study that is driven by Technical Action Research (TAR). Based on the
experimental artifact TIDATE -- a lightweight process execution and mining
framework -- it is studied how the correct execution of process instances can
be ensured and how a data set suitable for process mining can be generated at
run time in a real-world setting. Secondly, it is investigated whether and how
process mining supports domain experts during process monitoring as a secondary
task. The findings emphasize the importance of online conformance checking in
manufacturing and show how appropriate data sets can be identified and
generated.
","The Role of Time and Data: Online Conformance Checking in the
  Manufacturing Domain"
"  The increasing adoption of artificial intelligence requires accurate
forecasts and means to understand the reasoning of artificial intelligence
models behind such a forecast. Explainable Artificial Intelligence (XAI) aims
to provide cues for why a model issued a certain prediction. Such cues are of
utmost importance to decision-making since they provide insights on the
features that influenced most certain forecasts and let the user decide if the
forecast can be trusted. Though many techniques were developed to explain
black-box models, little research was done on assessing the quality of those
explanations and their influence on decision-making. We propose an ontology and
knowledge graph to support collecting feedback regarding forecasts, forecast
explanations, recommended decision-making options, and user actions. This way,
we provide means to improve forecasting models, explanations, and
recommendations of decision-making options. We tailor the knowledge graph for
the domain of demand forecasting and validate it on real-world data.
","XAI-KG: knowledge graph to support XAI and decision-making in
  manufacturing"
"  In March of 2021, the Santa Fe Institute hosted a workshop as part of its
Foundations of Intelligence in Natural and Artificial Systems project. This
project seeks to advance the field of artificial intelligence by promoting
interdisciplinary research on the nature of intelligence. During the workshop,
speakers from diverse disciplines gathered to develop a taxonomy of
intelligence, articulating their own understanding of intelligence and how
their research has furthered that understanding. In this report, we summarize
the insights offered by each speaker and identify the themes that emerged
during the talks and subsequent discussions.
","Foundations of Intelligence in Natural and Artificial Systems: A
  Workshop Report"
"  The separation assurance task will be extremely challenging for air traffic
controllers in a complex and high density airspace environment. Deep
reinforcement learning (DRL) was used to develop an autonomous separation
assurance framework in our previous work where the learned model advised speed
maneuvers. In order to improve the safety of this model in unseen environments
with uncertainties, in this work we propose a safety module for DRL in
autonomous separation assurance applications. The proposed module directly
addresses both model uncertainty and state uncertainty to improve safety. Our
safety module consists of two sub-modules: (1) the state safety sub-module is
based on the execution-time data augmentation method to introduce state
disturbances in the model input state; (2) the model safety sub-module is a
Monte-Carlo dropout extension that learns the posterior distribution of the DRL
model policy. We demonstrate the effectiveness of the two sub-modules in an
open-source air traffic simulator with challenging environment settings.
Through extensive numerical experiments, our results show that the proposed
sub-safety modules help the DRL agent significantly improve its safety
performance in an autonomous separation assurance task.
","Safety Enhancement for Deep Reinforcement Learning in Autonomous
  Separation Assurance"
"  Advanced communication protocols are critical to enable the coexistence of
autonomous robots with humans. Thus, the development of explanatory
capabilities is an urgent first step toward autonomous robots. This survey
provides an overview of the various types of ""explainability"" discussed in
machine learning research. Then, we discuss the definition of ""explainability""
in the context of autonomous robots (i.e., explainable autonomous robots) by
exploring the question ""what is an explanation?"" We further conduct a research
survey based on this definition and present some relevant topics for future
research.
",Explainable Autonomous Robots: A Survey and Perspective
"  To realize autonomous collaborative robots, it is important to increase the
trust that users have in them. Toward this goal, this paper proposes an
algorithm which endows an autonomous agent with the ability to explain the
transition from the current state to the target state in a Markov decision
process (MDP). According to cognitive science, to generate an explanation that
is acceptable to humans, it is important to present the minimum information
necessary to sufficiently understand an event. To meet this requirement, this
study proposes a framework for identifying important elements in the
decision-making process using a prediction model for the world and generating
explanations based on these elements. To verify the ability of the proposed
method to generate explanations, we conducted an experiment using a grid
environment. It was inferred from the result of a simulation experiment that
the explanation generated using the proposed method was composed of the minimum
elements important for understanding the transition from the current state to
the target state. Furthermore, subject experiments showed that the generated
explanation was a good summary of the process of state transition, and that a
high evaluation was obtained for the explanation of the reason for an action.
",A Framework of Explanation Generation toward Reliable Autonomous Robots
"  Learning disentangled representations of textual data is essential for many
natural language tasks such as fair classification, style transfer and sentence
generation, among others. The existent dominant approaches in the context of
text data {either rely} on training an adversary (discriminator) that aims at
making attribute values difficult to be inferred from the latent code {or rely
on minimising variational bounds of the mutual information between latent code
and the value attribute}. {However, the available methods suffer of the
impossibility to provide a fine-grained control of the degree (or force) of
disentanglement.} {In contrast to} {adversarial methods}, which are remarkably
simple, although the adversary seems to be performing perfectly well during the
training phase, after it is completed a fair amount of information about the
undesired attribute still remains. This paper introduces a novel variational
upper bound to the mutual information between an attribute and the latent code
of an encoder. Our bound aims at controlling the approximation error via the
Renyi's divergence, leading to both better disentangled representations and in
particular, a precise control of the desirable degree of disentanglement {than
state-of-the-art methods proposed for textual data}. Furthermore, it does not
suffer from the degeneracy of other losses in multi-class scenarios. We show
the superiority of this method on fair classification and on textual style
transfer tasks. Additionally, we provide new insights illustrating various
trade-offs in style transfer when attempting to learn disentangled
representations and quality of the generated sentence.
","A Novel Estimator of Mutual Information for Learning to Disentangle
  Textual Representations"
"  Deep reinforcement learning (DRL) has recently shown its success in tackling
complex combinatorial optimization problems. When these problems are extended
to multiobjective ones, it becomes difficult for the existing DRL approaches to
flexibly and efficiently deal with multiple subproblems determined by weight
decomposition of objectives. This paper proposes a concise meta-learning-based
DRL approach. It first trains a meta-model by meta-learning. The meta-model is
fine-tuned with a few update steps to derive submodels for the corresponding
subproblems. The Pareto front is built accordingly. The computational
experiments on multiobjective traveling salesman problems demonstrate the
superiority of our method over most of learning-based and iteration-based
approaches.
","Meta-Learning-based Deep Reinforcement Learning for Multiobjective
  Optimization Problems"
"  We develop a formal framework for automatic reasoning about the obligations
of autonomous cyber-physical systems, including their social and ethical
obligations. Obligations, permissions and prohibitions are distinct from a
system's mission, and are a necessary part of specifying advanced, adaptive
AI-equipped systems. They need a dedicated deontic logic of obligations to
formalize them. Most existing deontic logics lack corresponding algorithms and
system models that permit automatic verification. We demonstrate how a
particular deontic logic, Dominance Act Utilitarianism (DAU), is a suitable
starting point for formalizing the obligations of autonomous systems like
self-driving cars. We demonstrate its usefulness by formalizing a subset of
Responsibility-Sensitive Safety (RSS) in DAU; RSS is an industrial proposal for
how self-driving cars should and should not behave in traffic. We show that
certain logical consequences of RSS are undesirable, indicating a need to
further refine the proposal. We also demonstrate how obligations can change
over time, which is necessary for long-term autonomy. We then demonstrate a
model-checking algorithm for DAU formulas on weighted transition systems, and
illustrate it by model-checking obligations of a self-driving car controller
from the literature.
","Algorithmic Ethics: Formalization and Verification of Autonomous Vehicle
  Obligations"
"  This paper proposes a comprehensive analysis of existing concepts coming from
different disciplines tackling the notion of intelligence, namely psychology
and engineering, and from disciplines aiming to regulate AI innovations, namely
AI ethics and law. The aim is to identify shared notions or discrepancies to
consider for qualifying AI systems. Relevant concepts are integrated into a
matrix intended to help defining more precisely when and how computing tools
(programs or devices) may be qualified as AI while highlighting critical
features to serve a specific technical, ethical and legal assessment of
challenges in AI development. Some adaptations of existing notions of AI
characteristics are proposed. The matrix is a risk-based conceptual model
designed to allow an empirical, flexible and scalable qualification of AI
technologies in the perspective of benefit-risk assessment practices,
technological monitoring and regulatory compliance: it offers a structured
reflection tool for stakeholders in AI development that are engaged in
responsible research and innovation.Pre-print version (achieved on May 2020)
","An interdisciplinary conceptual study of Artificial Intelligence (AI)
  for helping benefit-risk assessment practices: Towards a comprehensive
  qualification matrix of AI programs and devices (pre-print 2020)"
"  In this project, we designed an intelligent assistant player for the
single-player game Space Invaders with the aim to provide a satisfying co-op
experience. The agent behaviour was designed using reinforcement learning
techniques and evaluated based on several criteria. We validate the hypothesis
that an AI-driven computer player can provide a satisfying co-op experience.
","Using reinforcement learning to design an AI assistantfor a satisfying
  co-op experience"
"  The manpower scheduling problem is a critical research field in the resource
management area. Based on the existing studies on scheduling problem solutions,
this paper transforms the manpower scheduling problem into a combinational
optimization problem under multi-constraint conditions from a new perspective.
It also uses logical paradigms to build a mathematical model for problem
solution and an improved multi-dimensional evolution algorithm for solving the
model. Moreover, the constraints discussed in this paper basically cover all
the requirements of human resource coordination in modern society and are
supported by our experiment results. In the discussion part, we compare our
model with other heuristic algorithms or linear programming methods and prove
that the model proposed in this paper makes a 25.7% increase in efficiency and
a 17% increase in accuracy at most. In addition, to the numerical solution of
the manpower scheduling problem, this paper also studies the algorithm for
scheduling task list generation and the method of displaying scheduling
results. As a result, we not only provide various modifications for the basic
algorithm to solve different condition problems but also propose a new
algorithm that increases at least 28.91% in time efficiency by comparing with
different baseline models.
",An Intelligent Model for Solving Manpower Scheduling Problems
"  Scene Rearrangement Planning (SRP) is an interior task proposed recently. The
previous work defines the action space of this task with handcrafted
coarse-grained actions that are inflexible to be used for transforming scene
arrangement and intractable to be deployed in practice. Additionally, this new
task lacks realistic indoor scene rearrangement data to feed popular
data-hungry learning approaches and meet the needs of quantitative evaluation.
To address these problems, we propose a fine-grained action definition for SRP
and introduce a large-scale scene rearrangement dataset. We also propose a
novel learning paradigm to efficiently train an agent through self-playing,
without any prior knowledge. The agent trained via our paradigm achieves
superior performance on the introduced dataset compared to the baseline agents.
We provide a detailed analysis of the design of our approach in our
experiments.
","PEARL: Parallelized Expert-Assisted Reinforcement Learning for Scene
  Rearrangement Planning"
"  Minesweeper is a popular spatial-based decision-making game that works with
incomplete information. As an exemplary NP-complete problem, it is a major area
of research employing various artificial intelligence paradigms. The present
work models this game as Constraint Satisfaction Problem (CSP) and Markov
Decision Process (MDP). We propose a new method named as dependents from the
independent set using deterministic solution search (DSScsp) for the faster
enumeration of all solutions of a CSP based Minesweeper game and improve the
results by introducing heuristics. Using MDP, we implement machine learning
methods on these heuristics. We train the classification model on sparse data
with results from CSP formulation. We also propose a new rewarding method for
applying a modified deep Q-learning for better accuracy and versatile learning
in the Minesweeper game. The overall results have been analyzed for different
kinds of Minesweeper games and their accuracies have been recorded. Results
from these experiments show that the proposed method of MDP based
classification model and deep Q-learning overall is the best methods in terms
of accuracy for games with given mine densities.
","Fast constraint satisfaction problem and learning-based algorithm for
  solving Minesweeper"
"  Credal networks are a popular class of imprecise probabilistic graphical
models obtained as a Bayesian network generalization based on, so-called
credal, sets of probability mass functions. A Java library called CREMA has
been recently released to model, process and query credal networks. Despite the
NP-hardness of the (exact) task, a number of algorithms is available to
approximate credal network inferences. In this paper we present CREPO, an open
repository of synthetic credal networks, provided together with the exact
results of inference tasks on these models. A Python tool is also delivered to
load these data and interact with CREMA, thus making extremely easy to evaluate
and compare existing and novel inference algorithms. To demonstrate such
benchmarking scheme, we propose an approximate heuristic to be used inside
variable elimination schemes to keep a bound on the maximum number of vertices
generated during the combination step. A CREPO-based validation against
approximate procedures based on linearization and exact techniques performed in
CREMA is finally discussed.
",CREPO: An Open Repository to Benchmark Credal Network Algorithms
"  Width-based planning methods deal with conjunctive goals by decomposing
problems into subproblems of low width. Algorithms like SIW thus fail when the
goal is not easily serializable in this way or when some of the subproblems
have a high width. In this work, we address these limitations by using a simple
but powerful language for expressing finer problem decompositions introduced
recently by Bonet and Geffner, called policy sketches. A policy sketch over a
set of Boolean and numerical features is a set of sketch rules that express how
the values of these features are supposed to change. Like general policies,
policy sketches are domain general, but unlike policies, the changes captured
by sketch rules do not need to be achieved in a single step. We show that many
planning domains that cannot be solved by SIW are provably solvable in low
polynomial time with the SIW_R algorithm, the version of SIW that employs
user-provided policy sketches. Policy sketches are thus shown to be a powerful
language for expressing domain-specific knowledge in a simple and compact way
and a convenient alternative to languages such as HTNs or temporal logics.
Furthermore, they make it easy to express general problem decompositions and
prove key properties of them like their width and complexity.
","Expressing and Exploiting the Common Subgoal Structure of Classical
  Planning Domains Using Sketches: Extended Version"
"  This paper introduces MicroRCT, a novel open source simulator inspired by the
theme park sandbox game RollerCoaster Tycoon. The goal in MicroRCT is to place
rides and shops in an amusement park to maximize profit earned from park
guests. Thus, the challenges for game AI include both selecting high-earning
attractions and placing them in locations that are convenient to guests. In
this paper, the MAP-Elites algorithm is used to generate a diversity of park
layouts, exploring two theoretical questions about evolutionary algorithms and
game design: 1) Is there a benefit to starting from a minimal starting point
for evolution and complexifying incrementally? and 2) What are the effects of
resource limitations on creativity and optimization? Results indicate that
building from scratch with no costs results in the widest diversity of
high-performing designs.
",Exploring open-ended gameplay features with Micro RollerCoaster Tycoon
"  Boolean Satisfiability (SAT) is a well-known NP-complete problem. Despite
this theoretical hardness, SAT solvers based on Conflict Driven Clause Learning
(CDCL) can solve large SAT instances from many important domains. CDCL learns
clauses from conflicts, a technique that allows a solver to prune its search
space. The selection heuristics in CDCL prioritize variables that are involved
in recent conflicts. While only a fraction of decisions generate any conflicts,
many generate multiple conflicts.
  In this paper, we study conflict-generating decisions in CDCL in detail. We
investigate the impact of single conflict (sc) decisions, which generate only
one conflict, and multi-conflict (mc) decisions which generate two or more. We
empirically characterize these two types of decisions based on the quality of
the learned clauses produced by each type of decision. We also show an
important connection between consecutive clauses learned within the same mc
decision, where one learned clause triggers the learning of the next one
forming a chain of clauses. This leads to the consideration of similarity
between conflicts, for which we formulate the notion of conflictsproximity as a
similarity measure. We show that conflicts in mc decisions are more closely
related than consecutive conflicts generated from sc decisions. Finally, we
develop Common Reason Variable Reduction (CRVR) as a new decision strategy that
reduces the selection priority of some variables from the learned clauses of mc
decisions. Our empirical evaluation of CRVR implemented in three leading
solvers demonstrates performance gains in benchmarks from the main track of SAT
Competition-2020.
",A Deep Dive into Conflict Generating Decisions
"  Ontologies formalise how the concepts from a given domain are interrelated.
Despite their clear potential as a backbone for explainable AI, existing
ontologies tend to be highly incomplete, which acts as a significant barrier to
their more widespread adoption. To mitigate this issue, we present a mechanism
to infer plausible missing knowledge, which relies on reasoning by analogy. To
the best of our knowledge, this is the first paper that studies analogical
reasoning within the setting of description logic ontologies. After showing
that the standard formalisation of analogical proportion has important
limitations in this setting, we introduce an alternative semantics based on
bijective mappings between sets of features. We then analyse the properties of
analogies under the proposed semantics, and show among others how it enables
two plausible inference patterns: rule translation and rule extrapolation.
",A Description Logic for Analogical Reasoning
"  Probabilistic machine learning models are often insufficient to help with
decisions on interventions because those models find correlations - not causal
relationships. If observational data is only available and experimentation are
infeasible, the correct approach to study the impact of an intervention is to
invoke Pearl's causality framework. Even that framework assumes that the
underlying causal graph is known, which is seldom the case in practice. When
the causal structure is not known, one may use out-of-the-box algorithms to
find causal dependencies from observational data. However, there exists no
method that also accounts for the decision-maker's prior knowledge when
developing the causal structure either. The objective of this paper is to
develop rational approaches for making decisions from observational data in the
presence of causal graph uncertainty and prior knowledge from the
decision-maker. We use ensemble methods like Bayesian Model Averaging (BMA) to
infer set of causal graphs that can represent the data generation process. We
provide decisions by computing the expected value and risk of potential
interventions explicitly. We demonstrate our approach by applying them in
different example contexts.
","Bayesian Model Averaging for Data Driven Decision Making when Causality
  is Partially Known"
"  Algorithmic Information Theory has inspired intractable constructions of
general intelligence (AGI), and undiscovered tractable approximations are
likely feasible. Reinforcement Learning (RL), the dominant paradigm by which an
agent might learn to solve arbitrary solvable problems, gives an agent a
dangerous incentive: to gain arbitrary ""power"" in order to intervene in the
provision of their own reward. We review the arguments that generally
intelligent algorithmic-information-theoretic reinforcement learners such as
Hutter's (2005) AIXI would seek arbitrary power, including over us. Then, using
an information-theoretic exploration schedule, and a setup inspired by causal
influence theory, we present a variant of AIXI which learns to not seek
arbitrary power; we call it ""unambitious"". We show that our agent learns to
accrue reward at least as well as a human mentor, while relying on that mentor
with diminishing probability. And given a formal assumption that we probe
empirically, we show that eventually, the agent's world-model incorporates the
following true fact: intervening in the ""outside world"" will have no effect on
reward acquisition; hence, it has no incentive to shape the outside world.
",Intelligence and Unambitiousness Using Algorithmic Information Theory
"  Artificial Intelligence has been a growth catalyst to our society and is
cosidered across all idustries as a fundamental technology. However, its
development has been limited to the signal processing domain that relies on the
generated and collected data from other sensors. In recent research, concepts
of Digital Artificial Intelligence and Physicial Artifical Intelligence have
emerged and this can be considered a big step in the theoretical development of
Artifical Intelligence. In this paper we explore the concept of Physicial
Artifical Intelligence and propose two subdomains: Integrated Physicial
Artifical Intelligence and Distributed Physicial Artifical Intelligence. The
paper will also examine the trend and governance of Physicial Artifical
Intelligence.
","Physical Artificial Intelligence: The Concept Expansion of
  Next-Generation Artificial Intelligence"
"  Affordances describe the possibilities for an agent to perform actions with
an object. While the significance of the affordance concept has been previously
studied from varied perspectives, such as psychology and cognitive science,
these approaches are not always sufficient to enable direct transfer, in the
sense of implementations, to artificial intelligence (AI)-based systems and
robotics. However, many efforts have been made to pragmatically employ the
concept of affordances, as it represents great potential for AI agents to
effectively bridge perception to action. In this survey, we review and find
common ground amongst different strategies that use the concept of affordances
within robotic tasks, and build on these methods to provide guidance for
including affordances as a mechanism to improve autonomy. To this end, we
outline common design choices for building representations of affordance
relations, and their implications on the generalisation capabilities of an
agent when facing previously unseen scenarios. Finally, we identify and discuss
a range of interesting research directions involving affordances that have the
potential to improve the capabilities of an AI agent.
",Building Affordance Relations for Robotic Agents - A Review
"  One of the most striking features of human cognition is the capacity to plan.
Two aspects of human planning stand out: its efficiency, even in complex
environments, and its flexibility, even in changing environments. Efficiency is
especially impressive because directly computing an optimal plan is
intractable, even for modestly complex tasks, and yet people successfully solve
myriad everyday problems despite limited cognitive resources. Standard accounts
in psychology, economics, and artificial intelligence have suggested this is
because people have a mental representation of a task and then use heuristics
to plan in that representation. However, this approach generally assumes that
mental representations are fixed. Here, we propose that mental representations
can be controlled and that this provides opportunities to adaptively simplify
problems so they can be more easily reasoned about -- a process we refer to as
construal. We construct a formal model of this process and, in a series of
large, pre-registered behavioral experiments, show both that construal is
subject to online cognitive control and that people form value-guided
construals that optimally balance the complexity of a representation and its
utility for planning and acting. These results demonstrate how strategically
perceiving and conceiving problems facilitates the effective use of limited
cognitive resources.
",Control of mental representations in human planning
"  In this paper, we propose a system to estimate heterogeneous concurrent drug
usage effects on overdose estimation, that consists of efficient co-variate
selection, sub-group selection, generation of and heterogeneous causal effect
estimation. Although, there has been several association studies have been
proposed in the state-of-art methods, heterogeneous causal effects have never
been studied in concurrent drug usage and drug overdose problem. We apply our
framework to answer a critical question, ""can concurrent usage of
benzodiazepines and opioids has heterogeneous causal effects on opioid overdose
epidemic?"" Using Truven MarketScan claim data collected from 2001 to 2013 have
shown significant promise of our proposed framework's efficacy. Our efficient
causal inference model estimated that the causal effect is higher (19%) than
the regression studies (15%) to estimate the risks associated with the
concurrent usage of opioid and benzodiazepines on opioid overdose.
",Heterogeneous Causal Effect of Polysubstance Usage on Drug Overdose
"  Evidence theory is that the extension of probability can better deal with
unknowns and inaccurate information. Uncertainty measurement plays a vital role
in both evidence theory and probability theory. Approximate Entropy (ApEn) is
proposed by Pincus to describe the irregularities of complex systems. The more
irregular the time series, the greater the approximate entropy. The ApEn of the
network represents the ability of a network to generate new nodes, or the
possibility of undiscovered nodes. Through the association of network
characteristics and basic probability assignment (BPA) , a measure of the
uncertainty of BPA regarding completeness can be obtained. The main
contribution of paper is to define the integrity of the basic probability
assignment then the approximate entropy of the BPA is proposed to measure the
uncertainty of the integrity of the BPA. The proposed method is based on the
logical network structure to calculate the uncertainty of BPA in evidence
theory. The uncertainty based on the proposed method represents the uncertainty
of integrity of BPA and contributes to the identification of the credibility of
BPA.
","Uncertainty Measurement of Basic Probability Assignment Integrity Based
  on Approximate Entropy in Evidence Theory"
"  Biological infants are naturally curious and try to comprehend their physical
surroundings by interacting, in myriad multisensory ways, with different
objects - primarily macroscopic solid objects - around them. Through their
various interactions, they build hypotheses and predictions, and eventually
learn, infer and understand the nature of the physical characteristics and
behavior of these objects. Inspired thus, we propose a model for
curiosity-driven learning and inference for real-world AI agents. This model is
based on the arousal of curiosity, deriving from observations along
discontinuities in the fundamental macroscopic solid-body physics parameters,
i.e., shape constancy, spatial-temporal continuity, and object permanence. We
use the term body-budget to represent the perceived fundamental properties of
solid objects. The model aims to support the emulation of learning from scratch
followed by substantiation through experience, irrespective of domain, in
real-world AI agents.
",Curiosity-driven Intuitive Physics Learning
"  Neural network architectures are achieving superhuman performance on an
expanding range of tasks. To effectively and safely deploy these systems, their
decision-making must be understandable to a wide range of stakeholders. Methods
to explain AI have been proposed to answer this challenge, but a lack of theory
impedes the development of systematic abstractions which are necessary for
cumulative knowledge gains. We propose Bayesian Teaching as a framework for
unifying explainable AI (XAI) by integrating machine learning and human
learning. Bayesian Teaching formalizes explanation as a communication act of an
explainer to shift the beliefs of an explainee. This formalization decomposes
any XAI method into four components: (1) the inference to be explained, (2) the
explanatory medium, (3) the explainee model, and (4) the explainer model. The
abstraction afforded by Bayesian Teaching to decompose any XAI method
elucidates the invariances among them. The decomposition of XAI systems enables
modular validation, as each of the first three components listed can be tested
semi-independently. This decomposition also promotes generalization through
recombination of components from different XAI systems, which facilitates the
generation of novel variants. These new variants need not be evaluated one by
one provided that each component has been validated, leading to an exponential
decrease in development time. Finally, by making the goal of explanation
explicit, Bayesian Teaching helps developers to assess how suitable an XAI
system is for its intended real-world use case. Thus, Bayesian Teaching
provides a theoretical framework that encourages systematic, scientific
investigation of XAI.
","Abstraction, Validation, and Generalization for Explainable Artificial
  Intelligence"
"  Width-based search algorithms seek plans by prioritizing states according to
a suitably defined measure of novelty, that maps states into a set of novelty
categories. Space and time complexity to evaluate state novelty is known to be
exponential on the cardinality of the set. We present novel methods to obtain
polynomial approximations of novelty and width-based search. First, we
approximate novelty computation via random sampling and Bloom filters, reducing
the runtime and memory footprint. Second, we approximate the best-first search
using an adaptive policy that decides whether to forgo the expansion of nodes
in the open list. These two techniques are integrated into existing width-based
algorithms, resulting in new planners that perform significantly better than
other state-of-the-art planners over benchmarks from the International Planning
Competitions.
",Approximate Novelty Search
"  Existing gradient-based meta-learning approaches to few-shot learning assume
that all tasks have the same input feature space. However, in the real world
scenarios, there are many cases that the input structures of tasks can be
different, that is, different tasks may vary in the number of input modalities
or data types. Existing meta-learners cannot handle the heterogeneous task
distribution (HTD) as there is not only global meta-knowledge shared across
tasks but also type-specific knowledge that distinguishes each type of tasks.
To deal with task heterogeneity and promote fast within-task adaptions for each
type of tasks, in this paper, we propose HetMAML, a task-heterogeneous
model-agnostic meta-learning framework, which can capture both the
type-specific and globally shared knowledge and can achieve the balance between
knowledge customization and generalization. Specifically, we design a
multi-channel backbone module that encodes the input of each type of tasks into
the same length sequence of modality-specific embeddings. Then, we propose a
task-aware iterative feature aggregation network which can automatically take
into account the context of task-specific input structures and adaptively
project the heterogeneous input spaces to the same lower-dimensional embedding
space of concepts. Our experiments on six task-heterogeneous datasets
demonstrate that HetMAML successfully leverages type-specific and globally
shared meta-parameters for heterogeneous tasks and achieves fast within-task
adaptions for each type of tasks.
","HetMAML: Task-Heterogeneous Model-Agnostic Meta-Learning for Few-Shot
  Learning Across Modalities"
"  In real life, lots of information merges from time to time. To appropriately
describe the actual situations, lots of theories have been proposed. Among
them, Dempster-Shafer evidence theory is a very useful tool in managing
uncertain information. To better adapt to complex situations of open world, a
generalized evidence theory is designed. However, everything occurs in sequence
and owns some underlying relationships with each other. In order to further
embody the details of information and better conforms to situations of real
world, a Markov model is introduced into the generalized evidence theory which
helps extract complete information volume from evidence provided. Besides, some
numerical examples is offered to verify the correctness and rationality of the
proposed method.
",MMGET: A Markov model for generalized evidence theory
"  User-generated data on social media contain rich information about who we
are, what we like and how we make decisions. In this paper, we survey
representative work on learning a concise latent user representation (a.k.a.
user embedding) that can capture the main characteristics of a social media
user. The learned user embeddings can later be used to support different
downstream user analysis tasks such as personality modeling, suicidal risk
assessment and purchase decision prediction. The temporal nature of
user-generated data on social media has largely been overlooked in much of the
existing user embedding literature. In this survey, we focus on research that
bridges the gap by incorporating temporal/sequential information in user
representation learning. We categorize relevant papers along several key
dimensions, identify limitations in the current work and suggest future
research directions.
",Learning User Embeddings from Temporal Social Media Data: A Survey
"  We propose a novel reinforcement learning based framework PoBRL for solving
multi-document summarization. PoBRL jointly optimizes over the following three
objectives necessary for a high-quality summary: importance, relevance, and
length. Our strategy decouples this multi-objective optimization into different
subproblems that can be solved individually by reinforcement learning.
Utilizing PoBRL, we then blend each learned policies together to produce a
summary that is a concise and complete representation of the original input.
Our empirical analysis shows state-of-the-art performance on several
multi-document datasets. Human evaluation also shows that our method produces
high-quality output.
","PoBRL: Optimizing Multi-Document Summarization by Blending Reinforcement
  Learning Policies"
"  In this paper, by leveraging abundant observational transaction data, we
propose a novel data-driven and interpretable pricing approach for markdowns,
consisting of counterfactual prediction and multi-period price optimization.
Firstly, we build a semi-parametric structural model to learn individual price
elasticity and predict counterfactual demand. This semi-parametric model takes
advantage of both the predictability of nonparametric machine learning model
and the interpretability of economic model. Secondly, we propose a multi-period
dynamic pricing algorithm to maximize the overall profit of a perishable
product over its finite selling horizon. Different with the traditional
approaches that use the deterministic demand, we model the uncertainty of
counterfactual demand since it inevitably has randomness in the prediction
process. Based on the stochastic model, we derive a sequential pricing strategy
by Markov decision process, and design a two-stage algorithm to solve it. The
proposed algorithm is very efficient. It reduces the time complexity from
exponential to polynomial. Experimental results show the advantages of our
pricing algorithm, and the proposed framework has been successfully deployed to
the well-known e-commerce fresh retail scenario - Freshippo.
","Markdowns in E-Commerce Fresh Retail: A Counterfactual Prediction and
  Multi-Period Optimization Approach"
"  We consider the problem to learn a concept or a query in the presence of an
ontology formulated in the description logic ELr, in Angluin's framework of
active learning that allows the learning algorithm to interactively query an
oracle (such as a domain expert). We show that the following can be learned in
polynomial time: (1) EL-concepts, (2) symmetry-free ELI-concepts, and (3)
conjunctive queries (CQs) that are chordal, symmetry-free, and of bounded
arity. In all cases, the learner can pose to the oracle membership queries
based on ABoxes and equivalence queries that ask whether a given concept/query
from the considered class is equivalent to the target. The restriction to
bounded arity in (3) can be removed when we admit unrestricted CQs in
equivalence queries. We also show that EL-concepts are not polynomial query
learnable in the presence of ELI-ontologies.
",Actively Learning Concepts and Conjunctive Queries under ELr-Ontologies
"  Reconfiguration aims at recovering a system from a fault by automatically
adapting the system configuration, such that the system goal can be reached
again. Classical approaches typically use a set of pre-defined faults for which
corresponding recovery actions are defined manually. This is not possible for
modern hybrid systems which are characterized by frequent changes. Instead,
AI-based approaches are needed which leverage on a model of the non-faulty
system and which search for a set of reconfiguration operations which will
establish a valid behavior again.
  This work presents a novel algorithm which solves three main challenges: (i)
Only a model of the non-faulty system is needed, i.e. the faulty behavior does
not need to be modeled. (ii) It discretizes and reduces the search space which
originally is too large -- mainly due to the high number of continuous system
variables and control signals. (iii) It uses a SAT solver for propositional
logic for two purposes: First, it defines the binary concept of validity.
Second, it implements the search itself -- sacrificing the optimal solution for
a quick identification of an arbitrary solution. It is shown that the approach
is able to reconfigure faults on simulated process engineering systems.
",Reconfiguring Hybrid Systems Using SAT
"  In many real-world scenarios, a team of agents coordinate with each other to
compete against an opponent. The challenge of solving this type of game is that
the team's joint action space grows exponentially with the number of agents,
which results in the inefficiency of the existing algorithms, e.g.,
Counterfactual Regret Minimization (CFR). To address this problem, we propose a
new framework of CFR: CFR-MIX. Firstly, we propose a new strategy
representation that represents a joint action strategy using individual
strategies of all agents and a consistency relationship to maintain the
cooperation between agents. To compute the equilibrium with individual
strategies under the CFR framework, we transform the consistency relationship
between strategies to the consistency relationship between the cumulative
regret values. Furthermore, we propose a novel decomposition method over
cumulative regret values to guarantee the consistency relationship between the
cumulative regret values. Finally, we introduce our new algorithm CFR-MIX which
employs a mixing layer to estimate cumulative regret values of joint actions as
a non-linear combination of cumulative regret values of individual actions.
Experimental results show that CFR-MIX outperforms existing algorithms on
various games significantly.
","CFR-MIX: Solving Imperfect Information Extensive-Form Games with
  Combinatorial Action Space"
"  Link prediction on knowledge graphs (KGs) is a key research topic. Previous
work mainly focused on binary relations, paying less attention to higher-arity
relations although they are ubiquitous in real-world KGs. This paper considers
link prediction upon n-ary relational facts and proposes a graph-based approach
to this task. The key to our approach is to represent the n-ary structure of a
fact as a small heterogeneous graph, and model this graph with edge-biased
fully-connected attention. The fully-connected attention captures universal
inter-vertex interactions, while with edge-aware attentive biases to
particularly encode the graph structure and its heterogeneity. In this fashion,
our approach fully models global and local dependencies in each n-ary fact, and
hence can more effectively capture associations therein. Extensive evaluation
verifies the effectiveness and superiority of our approach. It performs
substantially and consistently better than current state-of-the-art across a
variety of n-ary relational benchmarks. Our code is publicly available.
",Link Prediction on N-ary Relational Facts: A Graph-based Approach
"  Dynamic Algorithm Configuration (DAC) aims to dynamically control a target
algorithm's hyperparameters in order to improve its performance. Several
theoretical and empirical results have demonstrated the benefits of dynamically
controlling hyperparameters in domains like evolutionary computation, AI
Planning or deep learning. Replicating these results, as well as studying new
methods for DAC, however, is difficult since existing benchmarks are often
specialized and incompatible with the same interfaces. To facilitate
benchmarking and thus research on DAC, we propose DACBench, a benchmark library
that seeks to collect and standardize existing DAC benchmarks from different AI
domains, as well as provide a template for new ones. For the design of
DACBench, we focused on important desiderata, such as (i) flexibility, (ii)
reproducibility, (iii) extensibility and (iv) automatic documentation and
visualization. To show the potential, broad applicability and challenges of
DAC, we explore how a set of six initial benchmarks compare in several
dimensions of difficulty.
",DACBench: A Benchmark Library for Dynamic Algorithm Configuration
"  Numeric values associated to edges of a knowledge graph have been used to
represent uncertainty, edge importance, and even out-of-band knowledge in a
growing number of scenarios, ranging from genetic data to social networks.
Nevertheless, traditional knowledge graph embedding models are not designed to
capture such information, to the detriment of predictive power. We propose a
novel method that injects numeric edge attributes into the scoring layer of a
traditional knowledge graph embedding architecture. Experiments with publicly
available numeric-enriched knowledge graphs show that our method outperforms
traditional numeric-unaware baselines as well as the recent UKGE model.
",Learning Embeddings from Knowledge Graphs With Numeric Edge Attributes
"  In real-world multi-agent systems, agents with different capabilities may
join or leave without altering the team's overarching goals. Coordinating teams
with such dynamic composition is challenging: the optimal team strategy varies
with the composition. We propose COPA, a coach-player framework to tackle this
problem. We assume the coach has a global view of the environment and
coordinates the players, who only have partial views, by distributing
individual strategies. Specifically, we 1) adopt the attention mechanism for
both the coach and the players; 2) propose a variational objective to
regularize learning; and 3) design an adaptive communication method to let the
coach decide when to communicate with the players. We validate our methods on a
resource collection task, a rescue game, and the StarCraft micromanagement
tasks. We demonstrate zero-shot generalization to new team compositions. Our
method achieves comparable or better performance than the setting where all
players have a full view of the environment. Moreover, we see that the
performance remains high even when the coach communicates as little as 13% of
the time using the adaptive communication strategy.
","Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team
  Composition"
"  Information management has enter a completely new era, quantum era. However,
there exists a lack of sufficient theory to extract truly useful quantum
information and transfer it to a form which is intuitive and straightforward
for decision making. Therefore, based on the quantum model of mass function, a
fortified dual check system is proposed to ensure the judgment generated
retains enough high accuracy. Moreover, considering the situations in real
life, everything takes place in an observable time interval, then the concept
of time interval is introduced into the frame of the check system. The proposed
model is very helpful in disposing uncertain quantum information in this paper.
And some applications are provided to verify the rationality and correctness of
the proposed method.
","Fortified quantum mass function utilizing ordinal pictorial check based
  on time interval analysis and expertise"
"  In the last few years, AI continues demonstrating its positive impact on
society while sometimes with ethically questionable consequences. Building and
maintaining public trust in AI has been identified as the key to successful and
sustainable innovation. This chapter discusses the challenges related to
operationalizing ethical AI principles and presents an integrated view that
covers high-level ethical AI principles, the general notion of
trust/trustworthiness, and product/process support in the context of
responsible AI, which helps improve both trust and trustworthiness of AI for a
wider set of stakeholders.
",AI and Ethics -- Operationalising Responsible AI
"  Optimal stopping is the problem of deciding the right time at which to take a
particular action in a stochastic system, in order to maximize an expected
reward. It has many applications in areas such as finance, healthcare, and
statistics. In this paper, we employ deep Reinforcement Learning (RL) to learn
optimal stopping policies in two financial engineering applications: namely
option pricing, and optimal option exercise. We present for the first time a
comprehensive empirical evaluation of the quality of optimal stopping policies
identified by three state of the art deep RL algorithms: double deep Q-learning
(DDQN), categorical distributional RL (C51), and Implicit Quantile Networks
(IQN). In the case of option pricing, our findings indicate that in a
theoretical Black-Schole environment, IQN successfully identifies nearly
optimal prices. On the other hand, it is slightly outperformed by C51 when
confronted to real stock data movements in a put option exercise problem that
involves assets from the S&P500 index. More importantly, the C51 algorithm is
able to identify an optimal stopping policy that achieves 8% more out-of-sample
returns than the best of four natural benchmark policies. We conclude with a
discussion of our findings which should pave the way for relevant future
research.
","Deep Reinforcement Learning for Optimal Stopping with Application in
  Financial Engineering"
"  With the rapid adoption of Internet technologies, digital footprints have
become ubiquitous and versatile to revolutionise the financial industry in
digital transformation. This paper takes initiatives to investigate a new
paradigm of the unified credit assessment with the use of federated artificial
intelligence. We conceptualised digital human representation which consists of
social, contextual, financial and technological dimensions to assess the
commercial creditworthiness and social reputation of both banked and unbanked
individuals. A federated artificial intelligence platform is proposed with a
comprehensive set of system design for efficient and effective credit scoring.
The study considerably contributes to the cumulative development of financial
intelligence and social computing. It also provides a number of implications
for academic bodies, practitioners, and developers of financial technologies.
",Federated Artificial Intelligence for Unified Credit Assessment
"  With the rapid development in artificial intelligence, social computing has
evolved beyond social informatics toward the birth of social intelligence
systems. This paper, therefore, takes initiatives to propose a social behaviour
understanding framework with the use of deep neural networks for social and
behavioural analysis. The integration of information fusion, person and object
detection, social signal understanding, behaviour understanding, and context
understanding plays a harmonious role to elicit social behaviours. Three
systems, including depression detection, activity recognition and cognitive
impairment screening, are developed to evidently demonstrate the importance of
social intelligence. The study considerably contributes to the cumulative
development of social computing and health informatics. It also provides a
number of implications for academic bodies, healthcare practitioners, and
developers of socially intelligent agents.
","Social Behaviour Understanding using Deep Neural Networks: Development
  of Social Intelligence Systems"
"  Traffic simulators act as an essential component in the operating and
planning of transportation systems. Conventional traffic simulators usually
employ a calibrated physical car-following model to describe vehicles'
behaviors and their interactions with traffic environment. However, there is no
universal physical model that can accurately predict the pattern of vehicle's
behaviors in different situations. A fixed physical model tends to be less
effective in a complicated environment given the non-stationary nature of
traffic dynamics. In this paper, we formulate traffic simulation as an inverse
reinforcement learning problem, and propose a parameter sharing adversarial
inverse reinforcement learning model for dynamics-robust simulation learning.
Our proposed model is able to imitate a vehicle's trajectories in the real
world while simultaneously recovering the reward function that reveals the
vehicle's true objective which is invariant to different dynamics. Extensive
experiments on synthetic and real-world datasets show the superior performance
of our approach compared to state-of-the-art methods and its robustness to
variant dynamics of traffic.
",Objective-aware Traffic Simulation via Inverse Reinforcement Learning
"  Competency Questions (CQs) are used in many ontology engineering
methodologies to collect requirements and track the completeness and
correctness of an ontology being constructed. Although they are frequently
suggested by ontology engineering methodologies, the publicly available
datasets of CQs and their formalizations in ontology query languages are very
scarce. Since first efforts to automate processes utilizing CQs are being made,
it is of high importance to provide large and diverse datasets to fuel these
solutions. In this paper, we present BigCQ, the biggest dataset of CQ templates
with their formalizations into SPARQL-OWL query templates. BigCQ is created
automatically from a dataset of frequently used axiom shapes. These pairs of CQ
templates and query templates can be then materialized as actual CQs and
SPARQL-OWL queries if filled with resource labels and IRIs from a given
ontology. We describe the dataset in detail, provide a description of the
process leading to the creation of the dataset and analyze how well the dataset
covers real-world examples. We also publish the dataset as well as scripts
transforming axiom shapes into pairs of CQ patterns and SPARQL-OWL templates,
to make engineers able to adapt the process to their particular needs.
","BigCQ: A large-scale synthetic dataset of competency question patterns
  formalized into SPARQL-OWL query templates"
"  Explainable AI has attracted much research attention in recent years with
feature attribution algorithms, which compute ""feature importance"" in
predictions, becoming increasingly popular. However, there is little analysis
of the validity of these algorithms as there is no ""ground truth"" in the
existing datasets to validate their correctness. In this work, we develop a
method to quantitatively evaluate the correctness of XAI algorithms by creating
datasets with known explanation ground truth. To this end, we focus on the
binary classification problems. String datasets are constructed using formal
language derived from a grammar. A string is positive if and only if a certain
property is fulfilled. Symbols serving as explanation ground truth in a
positive string are part of an explanation if and only if they contributes to
fulfilling the property. Two popular feature attribution explainers, Local
Interpretable Model-agnostic Explanations (LIME) and SHapley Additive
exPlanations (SHAP), are used in our experiments.We show that: (1)
classification accuracy is positively correlated with explanation accuracy; (2)
SHAP provides more accurate explanations than LIME; (3) explanation accuracy is
negatively correlated with dataset complexity.
","Evaluating the Correctness of Explainable AI Algorithms for
  Classification"
"  Anaphora and ellipses are two common phenomena in dialogues. Without
resolving referring expressions and information omission, dialogue systems may
fail to generate consistent and coherent responses. Traditionally, anaphora is
resolved by coreference resolution and ellipses by query rewrite. In this work,
we propose a novel joint learning framework of modeling coreference resolution
and query rewriting for complex, multi-turn dialogue understanding. Given an
ongoing dialogue between a user and a dialogue assistant, for the user query,
our joint learning model first predicts coreference links between the query and
the dialogue context, and then generates a self-contained rewritten user query.
To evaluate our model, we annotate a dialogue based coreference resolution
dataset, MuDoCo, with rewritten queries. Results show that the performance of
query rewrite can be substantially boosted (+2.3% F1) with the aid of
coreference modeling. Furthermore, our joint model outperforms the
state-of-the-art coreference resolution model (+2% F1) on this dataset.
",CREAD: Combined Resolution of Ellipses and Anaphora in Dialogues
"  Research in Cognitive Science suggests that humans understand and represent
knowledge of the world through causal relationships. In addition to
observations, they can rely on experimenting and counterfactual reasoning --
i.e. referring to an alternative course of events -- to identify causal
relations and explain atypical situations. Different instances of control
systems, such as smart homes, would benefit from having a similar causal model,
as it would help the user understand the logic of the system and better react
when needed. However, while data-driven methods achieve high levels of
correlation detection, they mainly fall short of finding causal relations,
notably being limited to observations only. Notably, they struggle to identify
the cause from the effect when detecting a correlation between two variables.
This paper introduces a new way to learn causal models from a mixture of
experiments on the environment and observational data. The core of our method
is the use of selected interventions, especially our learning takes into
account the variables where it is impossible to intervene, unlike other
approaches. The causal model we obtain is then used to generate Causal Bayesian
Networks, which can be later used to perform diagnostic and predictive
inference. We use our method on a smart home simulation, a use case where
knowing causal relations pave the way towards explainable systems. Our
algorithm succeeds in generating a Causal Bayesian Network close to the
simulation's ground truth causal interactions, showing encouraging prospects
for application in real-life systems.
",To do or not to do: finding causal relations in smart homes
"  Neural topic models have triggered a surge of interest in extracting topics
from text automatically since they avoid the sophisticated derivations in
conventional topic models. However, scarce neural topic models incorporate the
word relatedness information captured in word embedding into the modeling
process. To address this issue, we propose a novel topic modeling approach,
called Variational Gaussian Topic Model (VaGTM). Based on the variational
auto-encoder, the proposed VaGTM models each topic with a multivariate Gaussian
in decoder to incorporate word relatedness. Furthermore, to address the
limitation that pre-trained word embeddings of topic-associated words do not
follow a multivariate Gaussian, Variational Gaussian Topic Model with
Invertible neural Projections (VaGTM-IP) is extended from VaGTM. Three
benchmark text corpora are used in experiments to verify the effectiveness of
VaGTM and VaGTM-IP. The experimental results show that VaGTM and VaGTM-IP
outperform several competitive baselines and obtain more coherent topics.
",Variational Gaussian Topic Model with Invertible Neural Projections
"  State-of-the-art temporal planners that support continuous numeric effects
typically interweave search with scheduling to ensure temporal consistency. If
such effects are linear, this process often makes use of Linear Programming
(LP) to model the relationship between temporal constraints and conditions on
numeric fluents that are subject to duration-dependent effects. While very
effective on benchmark domains, this approach does not scale well when solving
real-world problems that require long plans. We propose a set of techniques
that allow the planner to compute LP consistency checks lazily where possible,
significantly reducing the computation time required, thus allowing the planner
to solve larger problem instances within an acceptable time-frame. We also
propose an algorithm to perform duration-dependent goal checking more
selectively. Furthermore, we propose an LP formulation with a smaller footprint
that removes linearity restrictions on discrete effects applied within segments
of the plan where a numeric fluent is not duration dependent. The effectiveness
of these techniques is demonstrated on domains that use a mix of discrete and
continuous effects, which is typical of real-world planning problems. The
resultant planner is not only more efficient, but outperforms most
state-of-the-art temporal-numeric and hybrid planners, in terms of both
coverage and scalability.
","Efficient Temporal Piecewise-Linear Numeric Planning with Lazy
  Consistency Checking"
"  In real-time strategy (RTS) game artificial intelligence research, various
multi-agent deep reinforcement learning (MADRL) algorithms are widely and
actively used nowadays. Most of the research is based on StarCraft II
environment because it is the most well-known RTS games in world-wide. In our
proposed MADRL-based algorithm, distributed MADRL is fundamentally used that is
called QMIX. In addition to QMIX-based distributed computation, we consider
state categorization which can reduce computational complexity significantly.
Furthermore, self-attention mechanisms are used for identifying the
relationship among agents in the form of graphs. Based on these approaches, we
propose a categorized state graph attention policy (CSGA-policy). As observed
in the performance evaluation of our proposed CSGA-policy with the most
well-known StarCraft II simulation environment, our proposed algorithm works
well in various settings, as expected.
","Multi-Agent Deep Reinforcement Learning using Attentive Graph Neural
  Architectures for Real-Time Strategy Games"
"  Recently Bonet and Geffner have shown that first-order representations for
planning domains can be learned from the structure of the state space without
any prior knowledge about the action schemas or domain predicates. For this,
the learning problem is formulated as the search for a simplest first-order
domain description D that along with information about instances I_i (number of
objects and initial state) determine state space graphs G(P_i) that match the
observed state graphs G_i where P_i = (D, I_i). The search is cast and solved
approximately by means of a SAT solver that is called over a large family of
propositional theories that differ just in the parameters encoding the possible
number of action schemas and domain predicates, their arities, and the number
of objects. In this work, we push the limits of these learners by moving to an
answer set programming (ASP) encoding using the CLINGO system. The new
encodings are more transparent and concise, extending the range of possible
models while facilitating their exploration. We show that the domains
introduced by Bonet and Geffner can be solved more efficiently in the new
approach, often optimally, and furthermore, that the approach can be easily
extended to handle partial information about the state graphs as well as noise
that prevents some states from being distinguished.
","Learning First-Order Representations for Planning from Black-Box States:
  New Results"
"  Due to the practical importance of vehicle routing problems (VRP), there
exists an ever-growing body of research in algorithms and (meta)heuristics for
solving such problems. However, the diversity of VRP domains creates the
separate problem of modeling such problems -- describing the domain entities
(and, in particular, the planning decisions), the set of valid planning
decisions, and the preferences between different plans. In this paper, we
review the approaches for modeling vehicle routing problems. To make the
comparison more straightforward, we formulate several criteria for evaluating
modeling methods reflecting the practical requirements of the development of
optimization algorithms for such problems. Finally, as a result of this
comparison, we discuss several future research avenues in the field of modeling
VRP domains.
",A review of approaches to modeling applied vehicle routing problems
"  Approximation fixpoint theory (AFT) provides an algebraic framework for the
study of fixpoints of operators on bilattices and has found its applications in
characterizing semantics for various classes of logic programs and nonmonotonic
languages. In this paper, we show one more application of this kind: the
alternating fixpoint operator by Knorr et al. for the study of the well-founded
semantics for hybrid MKNF knowledge bases is in fact an approximator of AFT in
disguise, which, thanks to the power of abstraction of AFT, characterizes not
only the well-founded semantics but also two-valued as well as three-valued
semantics for hybrid MKNF knowledge bases. Furthermore, we show an improved
approximator for these knowledge bases, of which the least stable fixpoint is
information richer than the one formulated from Knorr et al.'s construction.
This leads to an improved computation for the well-founded semantics. This work
is built on an extension of AFT that supports consistent as well as
inconsistent pairs in the induced product bilattice, to deal with
inconsistencies that arise in the context of hybrid MKNF knowledge bases. This
part of the work can be considered generalizing the original AFT from symmetric
approximators to arbitrary approximators.
","Alternating Fixpoint Operator for Hybrid MKNF Knowledge Bases as an
  Approximator of AFT"
"  Probabilistic graphical models have emerged as a powerful modeling tool for
several real-world scenarios where one needs to reason under uncertainty. A
graphical model's partition function is a central quantity of interest, and its
computation is key to several probabilistic reasoning tasks. Given the
#P-hardness of computing the partition function, several techniques have been
proposed over the years with varying guarantees on the quality of estimates and
their runtime behavior. This paper seeks to present a survey of 18 techniques
and a rigorous empirical study of their behavior across an extensive set of
benchmarks. Our empirical study draws up a surprising observation: exact
techniques are as efficient as the approximate ones, and therefore, we conclude
with an optimistic view of opportunities for the design of approximate
techniques with enhanced scalability. Motivated by the observation of an order
of magnitude difference between the Virtual Best Solver and the best performing
tool, we envision an exciting line of research focused on the development of
portfolio solvers.
",Partition Function Estimation: A Quantitative Study
"  Explainable AI (XAI) has been investigated for decades and, together with AI
itself, has witnessed unprecedented growth in recent years. Among various
approaches to XAI, argumentative models have been advocated in both the AI and
social science literature, as their dialectical nature appears to match some
basic desirable features of the explanation activity. In this survey we
overview XAI approaches built using methods from the field of computational
argumentation, leveraging its wide array of reasoning abstractions and
explanation delivery methods. We overview the literature focusing on different
types of explanation (intrinsic and post-hoc), different models with which
argumentation-based explanations are deployed, different forms of delivery, and
different argumentation frameworks they use. We also lay out a roadmap for
future work.
",Argumentative XAI: A Survey
"  In this article we focus on evolving information systems. First a
delimitation of the concept of evolution is provided, resulting in a first
attempt to a general theory for such evolutions. The theory makes a distinction
between the underlying information structure at the conceptual level, its
evolution on the one hand, and the description and semantics of operations on
the information structure and its population on the other hand. Main issues
within this theory are object typing, type relatedness and identification of
objects. In terms of these concepts, we propose some axioms on the
well-formedness of evolution. In this general theory, the underlying data model
is a parameter, making the theory applicable for a wide range of modelling
techniques, including object-role modelling and object oriented techniques.
",A General Theory for the Evolution of Application Models -- Full version
"  Temporal logic inference is the process of extracting formal descriptions of
system behaviors from data in the form of temporal logic formulas. The existing
temporal logic inference methods mostly neglect uncertainties in the data,
which results in limited applicability of such methods in real-world
deployments. In this paper, we first investigate the uncertainties associated
with trajectories of a system and represent such uncertainties in the form of
interval trajectories. We then propose two uncertainty-aware signal temporal
logic (STL) inference approaches to classify the undesired behaviors and
desired behaviors of a system. Instead of classifying finitely many
trajectories, we classify infinitely many trajectories within the interval
trajectories. In the first approach, we incorporate robust semantics of STL
formulas with respect to an interval trajectory to quantify the margin at which
an STL formula is satisfied or violated by the interval trajectory. The second
approach relies on the first learning algorithm and exploits the decision tree
to infer STL formulas to classify behaviors of a given system. The proposed
approaches also work for non-separable data by optimizing the worst-case
robustness in inferring an STL formula. Finally, we evaluate the performance of
the proposed algorithms in two case studies, where the proposed algorithms show
reductions in the computation time by up to four orders of magnitude in
comparison with the sampling-based baseline algorithms (for a dataset with 800
sampled trajectories in total).
",Uncertainty-Aware Signal Temporal Logic Inference
"  Drafting, i.e., the selection of a subset of items from a larger candidate
set, is a key element of many games and related problems. It encompasses team
formation in sports or e-sports, as well as deck selection in many modern card
games. The key difficulty of drafting is that it is typically not sufficient to
simply evaluate each item in a vacuum and to select the best items. The
evaluation of an item depends on the context of the set of items that were
already selected earlier, as the value of a set is not just the sum of the
values of its members - it must include a notion of how well items go together.
  In this paper, we study drafting in the context of the card game Magic: The
Gathering. We propose the use of a contextual preference network, which learns
to compare two possible extensions of a given deck of cards. We demonstrate
that the resulting network is better able to evaluate card decks in this game
than previous attempts.
","Predicting Human Card Selection in Magic: The Gathering with Contextual
  Preference Ranking"
"  In this paper, we show that coherent sets of gambles can be embedded into the
algebraic structure of information algebra. This leads firstly, to a new
perspective of the algebraic and logical structure of desirability and
secondly, it connects desirability, hence imprecise probabilities, to other
formalism in computer science sharing the same underlying structure. Both the
domain-free and the labeled view of the information algebra of coherent sets of
gambles are presented, considering general possibility spaces.
","Information algebras of coherent sets of gambles in general possibility
  spaces"
"  A test is adaptive when its sequence and number of questions is dynamically
tuned on the basis of the estimated skills of the taker. Graphical models, such
as Bayesian networks, are used for adaptive tests as they allow to model the
uncertainty about the questions and the skills in an explainable fashion,
especially when coping with multiple skills. A better elicitation of the
uncertainty in the question/skills relations can be achieved by interval
probabilities. This turns the model into a credal network, thus making more
challenging the inferential complexity of the queries required to select
questions. This is especially the case for the information theoretic quantities
used as scores to drive the adaptive mechanism. We present an alternative
family of scores, based on the mode of the posterior probabilities, and hence
easier to explain. This makes considerably simpler the evaluation in the credal
case, without significantly affecting the quality of the adaptive process.
Numerical tests on synthetic and real-world data are used to support this
claim.
",A New Score for Adaptive Tests in Bayesian and Credal Networks
"  Counterfactual Regret Minimization (CFR)} is the popular method for finding
approximate Nash equilibrium in two-player zero-sum games with imperfect
information. CFR solves games by travsersing the full game tree iteratively,
which limits its scalability in larger games. When applying CFR to solve
large-scale games in previously, large-scale games are abstracted into
small-scale games firstly. Secondly, CFR is used to solve the abstract game.
And finally, the solution strategy is mapped back to the original large-scale
game. However, this process requires considerable expert knowledge, and the
accuracy of abstraction is closely related to expert knowledge. In addition,
the abstraction also loses certain information, which will eventually affect
the accuracy of the solution strategy. Towards this problem, a recent method,
\textit{Deep CFR} alleviates the need for abstraction and expert knowledge by
applying deep neural networks directly to CFR in full games. In this paper, we
introduces \textit{Neural Network Counterfactual Regret Minimization (NNCFR)},
an improved variant of \textit{Deep CFR} that has a faster convergence by
constructing a dueling netwok as the value network. Moreover, an evaluation
module is designed by combining the value network and Monte Carlo, which
reduces the approximation error of the value network. In addition, a new loss
function is designed in the procedure of training policy network in the
proposed \textit{NNCFR}, which can be good to make the policy network more
stable. The extensive experimental tests are conducted to show that the
\textit{NNCFR} converges faster and performs more stable than \textit{Deep
CFR}, and outperforms \textit{Deep CFR} with respect to exploitability and
head-to-head performance on test games.
",NNCFR: Minimize Counterfactual Regret with Neural Networks
"  We present a Satisfiability (SAT)-based approach for building Mixed Covering
Arrays with Constraints of minimum length, referred to as the Covering Array
Number problem. This problem is central in Combinatorial Testing for the
detection of system failures. In particular, we show how to apply Maximum
Satisfiability (MaxSAT) technology by describing efficient encodings for
different classes of complete and incomplete MaxSAT solvers to compute optimal
and suboptimal solutions, respectively. Similarly, we show how to solve through
MaxSAT technology a closely related problem, the Tuple Number problem, which we
extend to incorporate constraints. For this problem, we additionally provide a
new MaxSAT-based incomplete algorithm. The extensive experimental evaluation we
carry out on the available Mixed Covering Arrays with Constraints benchmarks
and the comparison with state-of-the-art tools confirm the good performance of
our approaches.
",Incomplete MaxSAT Approaches for Combinatorial Testing
"  This paper investigates the performance of different general-game-playing
heuristics for games in the Ludii general game system. Based on these results,
we train several regression learning models to predict the performance of these
heuristics based on each game's description file. We also provide a condensed
analysis of the games available in Ludii, and the different ludemes that define
them.
",General Game Heuristic Prediction Based on Ludeme Descriptions
"  The Dynamic Pickup and Delivery Problem (DPDP) is aimed at dynamically
scheduling vehicles among multiple sites in order to minimize the cost when
delivery orders are not known a priori. Although DPDP plays an important role
in modern logistics and supply chain management, state-of-the-art DPDP
algorithms are still limited on their solution quality and efficiency. In
practice, they fail to provide a scalable solution as the numbers of vehicles
and sites become large. In this paper, we propose a data-driven approach,
Spatial-Temporal Aided Double Deep Graph Network (ST-DDGN), to solve
industry-scale DPDP. In our method, the delivery demands are first forecast
using spatial-temporal prediction method, which guides the neural network to
perceive spatial-temporal distribution of delivery demand when dispatching
vehicles. Besides, the relationships of individuals such as vehicles are
modelled by establishing a graph-based value function. ST-DDGN incorporates
attention-based graph embedding with Double DQN (DDQN). As such, it can make
the inference across vehicles more efficiently compared with traditional
methods. Our method is entirely data driven and thus adaptive, i.e., the
relational representation of adjacent vehicles can be learned and corrected by
ST-DDGN from data periodically. We have conducted extensive experiments over
real-world data to evaluate our solution. The results show that ST-DDGN reduces
11.27% number of the used vehicles and decreases 13.12% total transportation
cost on average over the strong baselines, including the heuristic algorithm
deployed in our UAT (User Acceptance Test) environment and a variety of vanilla
DRL methods. We are due to fully deploy our solution into our online logistics
system and it is estimated that millions of USD logistics cost can be saved per
year.
",Learning to Optimize Industry-Scale Dynamic Pickup and Delivery Problems
"  We introduce novel methods for encoding acyclicity and s-t-reachability
constraints for propositional formulas with underlying directed graphs. They
are based on vertex elimination graphs, which makes them suitable for cases
where the underlying graph is sparse. In contrast to solvers with ad hoc
constraint propagators for acyclicity and reachability constraints such as
GraphSAT, our methods encode these constraints as standard propositional
clauses, making them directly applicable with any SAT solver. An empirical
study demonstrates that our methods together with an efficient SAT solver can
outperform both earlier encodings of these constraints as well as GraphSAT,
particularly when underlying graphs are sparse.
","Propositional Encodings of Acyclicity and Reachability by using Vertex
  Elimination"
"  Rapidly changing business environments expose companies to high levels of
uncertainty. This uncertainty manifests itself in significant changes that tend
to occur over the lifetime of a process and possibly affect its performance. It
is important to understand the root causes of such changes since this allows us
to react to change or anticipate future changes. Research in process mining has
so far only focused on detecting, locating and characterizing significant
changes in a process and not on finding root causes of such changes. In this
paper, we aim to close this gap. We propose a framework that adds an
explainability level onto concept drift detection in process mining and
provides insights into the cause-effect relationships behind significant
changes. We define different perspectives of a process, detect concept drifts
in these perspectives and plug the perspectives into a causality check that
determines whether these concept drifts can be causal to each other. We
showcase the effectiveness of our framework by evaluating it on both synthetic
and real event data. Our experiments show that our approach unravels
cause-effect relationships and provides novel insights into executed processes.
",A Framework for Explainable Concept Drift Detection in Process Mining
"  SMT solvers generally tackle quantifiers by instantiating their variables
with tuples of terms from the ground part of the formula. Recent enumerative
approaches for quantifier instantiation consider tuples of terms in some
heuristic order. This paper studies different strategies to order such tuples
and their impact on performance. We decouple the ordering problem into two
parts. First is the order of the sequence of terms to consider for each
quantified variable, and second is the order of the instantiation tuples
themselves. While the most and least preferred tuples, i.e. those with all
variables assigned to the most or least preferred terms, are clear, the
combinations in between allow flexibility in an implementation. We look at
principled strategies of complete enumeration, where some strategies are more
fair, meaning they treat all the variables the same but some strategies may be
more adventurous, meaning that they may venture further down the preference
list. We further describe new techniques for discarding irrelevant
instantiations which are crucial for the performance of these strategies in
practice. These strategies are implemented in the SMT solver cvc5, where they
contribute to the diversification of the solver's configuration space, as shown
by our experimental results.
",Fair and Adventurous Enumeration of Quantifier Instantiations
"  Analysis of large observational data sets generated by a reactive system is a
common challenge in debugging system failures and determining their root cause.
One of the major problems is that these observational data suffer from
survivorship bias. Examples include analyzing traffic logs from networks, and
simulation logs from circuit design. In such applications, users want to detect
non-spurious correlations from observational data and obtain actionable
insights about them. In this paper, we introduce log to Neuro-symbolic
(Log2NS), a framework that combines probabilistic analysis from machine
learning (ML) techniques on observational data with certainties derived from
symbolic reasoning on an underlying formal model. We apply the proposed
framework to network traffic debugging by employing the following steps. To
detect patterns in network logs, we first generate global embedding vector
representations of entities such as IP addresses, ports, and applications.
Next, we represent large log flow entries as clusters that make it easier for
the user to visualize and detect interesting scenarios that will be further
analyzed. To generalize these patterns, Log2NS provides an ability to query
from static logs and correlation engines for positive instances, as well as
formal reasoning for negative and unseen instances. By combining the strengths
of deep learning and symbolic methods, Log2NS provides a very powerful
reasoning and debugging tool for log-based data. Empirical evaluations on a
real internal data set demonstrate the capabilities of Log2NS.
","Log2NS: Enhancing Deep Learning Based Analysis of Logs With Formal to
  Prevent Survivorship Bias"
"  Examining a 20th-century Scandinavian legal theoretical tradition, we can
extract an ontological naturalistic, a logical empiristic, and a modern
idealistic rationale. We introduce the mathematical syntactic figure present in
the `logical empiricism' in a contemporary mathematical logic. A new formal
framework for describing explicit purchase statutes (Sweden) is gradually
developed and subsequently proposed. This new framework is based on a
many-sorted first-order logic (MFOL) approach, where the semantics are grounded
in concrete `physical' objects and situations with a legal relevance.
Specifically, we present a concrete formal syntactic translation of one of the
central statutes of Swedish legislation for the purchase of immovable property.
Additionally, we discuss the potential implications that a subsequent
development of such formalisations would have for constructing artificial
agents (e.g., software) that can be used as `co-creative' legal assistance for
solving highly complex legal issues concerning the transfer of property, among
others.
","Towards a General Many-Sorted Framework for Describing Certain Kinds of
  Legal Statutes with a Potential Computational Realization"
"  This paper proposes various new analysis techniques for Bayes networks in
which conditional probability tables (CPTs) may contain symbolic variables. The
key idea is to exploit scalable and powerful techniques for synthesis problems
in parametric Markov chains. Our techniques are applicable to arbitrarily many,
possibly dependent parameters that may occur in various CPTs. This lifts the
severe restrictions on parameters, e.g., by restricting the number of
parametrized CPTs to one or two, or by avoiding parameter dependencies between
several CPTs, in existing works for parametric Bayes networks (pBNs). We
describe how our techniques can be used for various pBN synthesis problems
studied in the literature such as computing sensitivity functions (and values),
simple and difference parameter tuning, ratio parameter tuning, and minimal
change tuning. Experiments on several benchmarks show that our prototypical
tool built on top of the probabilistic model checker Storm can handle several
hundreds of parameters.
",Fine-Tuning the Odds in Bayesian Networks
"  With the growth of social medias, such as Twitter, plenty of user-generated
data emerge daily. The short texts published on Twitter -- the tweets -- have
earned significant attention as a rich source of information to guide many
decision-making processes. However, their inherent characteristics, such as the
informal, and noisy linguistic style, remain challenging to many natural
language processing (NLP) tasks, including sentiment analysis. Sentiment
classification is tackled mainly by machine learning-based classifiers. The
literature has adopted word representations from distinct natures to transform
tweets to vector-based inputs to feed sentiment classifiers. The
representations come from simple count-based methods, such as bag-of-words, to
more sophisticated ones, such as BERTweet, built upon the trendy BERT
architecture. Nevertheless, most studies mainly focus on evaluating those
models using only a small number of datasets. Despite the progress made in
recent years in language modelling, there is still a gap regarding a robust
evaluation of induced embeddings applied to sentiment analysis on tweets.
Furthermore, while fine-tuning the model from downstream tasks is prominent
nowadays, less attention has been given to adjustments based on the specific
linguistic style of the data. In this context, this study fulfils an assessment
of existing language models in distinguishing the sentiment expressed in tweets
by using a rich collection of 22 datasets from distinct domains and five
classification algorithms. The evaluation includes static and contextualized
representations. Contexts are assembled from Transformer-based autoencoder
models that are also fine-tuned based on the masked language model task, using
a plethora of strategies.
","Sentiment analysis in tweets: an assessment study from classical to
  modern text representation models"
"  Automatic math problem solving has recently attracted increasing attention as
a long-standing AI benchmark. In this paper, we focus on solving geometric
problems, which requires a comprehensive understanding of textual descriptions,
visual diagrams, and theorem knowledge. However, the existing methods were
highly dependent on handcraft rules and were merely evaluated on small-scale
datasets. Therefore, we propose a Geometric Question Answering dataset GeoQA,
containing 5,010 geometric problems with corresponding annotated programs,
which illustrate the solving process of the given problems. Compared with
another publicly available dataset GeoS, GeoQA is 25 times larger, in which the
program annotations can provide a practical testbed for future research on
explicit and explainable numerical reasoning. Moreover, we introduce a Neural
Geometric Solver (NGS) to address geometric problems by comprehensively parsing
multimodal information and generating interpretable programs. We further add
multiple self-supervised auxiliary tasks on NGS to enhance cross-modal semantic
representation. Extensive experiments on GeoQA validate the effectiveness of
our proposed NGS and auxiliary tasks. However, the results are still
significantly lower than human performance, which leaves large room for future
research. Our benchmark and code are released at
https://github.com/chen-judge/GeoQA .
","GeoQA: A Geometric Question Answering Benchmark Towards Multimodal
  Numerical Reasoning"
"  Code generation aims to automatically generate a piece of code given an input
natural language utterance. Currently, among dominant models, it is treated as
a sequence-to-tree task, where a decoder outputs a sequence of actions
corresponding to the pre-order traversal of an Abstract Syntax Tree. However,
such a decoder only exploits the preorder traversal based preceding actions,
which are insufficient to ensure correct action predictions. In this paper, we
first throughly analyze the context modeling difference between neural code
generation models with different traversals based decodings (preorder traversal
vs breadth-first traversal), and then propose to introduce a mutual learning
framework to jointly train these models. Under this framework, we continuously
enhance both two models via mutual distillation, which involves synchronous
executions of two one-to-one knowledge transfers at each training step. More
specifically, we alternately choose one model as the student and the other as
its teacher, and require the student to fit the training data and the action
prediction distributions of its teacher. By doing so, both models can fully
absorb the knowledge from each other and thus could be improved simultaneously.
Experimental results and in-depth analysis on several benchmark datasets
demonstrate the effectiveness of our approach. We release our code at
https://github.com/DeepLearnXMU/CGML.
","Improving Tree-Structured Decoder Training for Code Generation via
  Mutual Learning"
"  This paper discusses a new variant of the Henry Gas Solubility Optimization
(HGSO) Algorithm, called Hybrid HGSO (HHGSO). Unlike its predecessor, HHGSO
allows multiple clusters serving different individual meta-heuristic algorithms
(i.e., with its own defined parameters and local best) to coexist within the
same population. Exploiting the dynamic cluster-to-algorithm mapping via
penalized and reward model with adaptive switching factor, HHGSO offers a novel
approach for meta-heuristic hybridization consisting of Jaya Algorithm, Sooty
Tern Optimization Algorithm, Butterfly Optimization Algorithm, and Owl Search
Algorithm, respectively. The acquired results from the selected two case
studies (i.e., involving team formation problem and combinatorial test suite
generation) indicate that the hybridization has notably improved the
performance of HGSO and gives superior performance against other competing
meta-heuristic and hyper-heuristic algorithms.
","Hybrid Henry Gas Solubility Optimization Algorithm with Dynamic
  Cluster-to-Algorithm Mapping for Search-based Software Engineering Problems"
"  We propose a novel framework to bootstrap the reputation of on-demand service
compositions. On-demand compositions are usually context-aware and have little
or no direct consumer feedback. The reputation bootstrapping of single or
atomic services does not consider the topology of the composition and
relationships among reputation-related factors. We apply Conditional Preference
Networks (CP-nets) of reputation-related factors for component services in a
composition. The reputation of a composite service is bootstrapped by the
composition of CP-nets. We consider the history of invocation among component
services to determine reputation-interdependence in a composition. The
composition rules are constructed using the composition topology and four types
of reputation-influence among component services. A heuristic-based Q-learning
approach is proposed to select the optimal set of reputation-related CP-nets.
Experimental results prove the efficiency of the proposed approach.
",Reputation Bootstrapping for Composite Services using CP-nets
"  Human beings, even small children, quickly become adept at figuring out how
to use applications on their mobile devices. Learning to use a new app is often
achieved via trial-and-error, accelerated by transfer of knowledge from past
experiences with like apps. The prospect of building a smarter smartphone - one
that can learn how to achieve tasks using mobile apps - is tantalizing. In this
paper we explore the use of Reinforcement Learning (RL) with the goal of
advancing this aspiration. We introduce an RL-based framework for learning to
accomplish tasks in mobile apps. RL agents are provided with states derived
from the underlying representation of on-screen elements, and rewards that are
based on progress made in the task. Agents can interact with screen elements by
tapping or typing. Our experimental results, over a number of mobile apps, show
that RL agents can learn to accomplish multi-step tasks, as well as achieve
modest generalization across different apps. More generally, we develop a
platform which addresses several engineering challenges to enable an effective
RL training environment. Our AppBuddy platform is compatible with OpenAI Gym
and includes a suite of mobile apps and benchmark tasks that supports a
diversity of RL research in the mobile app setting.
","AppBuddy: Learning to Accomplish Tasks in Mobile Apps via Reinforcement
  Learning"
"  In general, many dynamic processes are involved with interacting variables,
from physical systems to sociological analysis. The interplay of components in
the system can give rise to confounding dynamic behavior. Many approaches model
temporal sequences holistically ignoring the internal interaction which are
impotent in capturing the protogenic actuation. Differently, our goal is to
represent a system with a part-whole hierarchy and discover the implied
dependencies among intra-system variables: inferring the interactions that
possess causal effects on the sub-system behavior with REcurrent partItioned
Network (REIN). The proposed architecture consists of (i) a perceptive module
that extracts a hierarchical and temporally consistent representation of the
observation at multiple levels, (ii) a deductive module for determining the
relational connection between neurons at each level, and (iii) a statistical
module that can predict the future by conditioning on the temporal
distributional estimation. Our model is demonstrated to be effective in
identifying the componential interactions with limited observation and stable
in long-term future predictions experimented with diverse physical systems.
",Divide and Rule: Recurrent Partitioned Network for Dynamic Processes
"  Predicting student performance is a fundamental task in Intelligent Tutoring
Systems (ITSs), by which we can learn about students' knowledge level and
provide personalized teaching strategies for them. Researchers have made plenty
of efforts on this task. They either leverage educational psychology methods to
predict students' scores according to the learned knowledge proficiency, or
make full use of Collaborative Filtering (CF) models to represent latent
factors of students and exercises. However, most of these methods either
neglect the exercise-specific characteristics (e.g., exercise materials), or
cannot fully explore the high-order interactions between students, exercises,
as well as knowledge concepts. To this end, we propose a Graph-based Exercise-
and Knowledge-Aware Learning Network for accurate student score prediction.
Specifically, we learn students' mastery of exercises and knowledge concepts
respectively to model the two-fold effects of exercises and knowledge concepts.
Then, to model the high-order interactions, we apply graph convolution
techniques in the prediction process. Extensive experiments on two real-world
datasets prove the effectiveness of our proposed Graph-EKLN.
","Graph-based Exercise- and Knowledge-Aware Learning Network for Student
  Performance Prediction"
"  Identifying controllable aspects of the environment has proven to be an
extraordinary intrinsic motivator to reinforcement learning agents. Despite
repeatedly achieving State-of-the-Art results, this approach has only been
studied as a proxy to a reward-based task and has not yet been evaluated on its
own. We show that solutions relying on action-prediction fail to model critical
controlled events. Humans, on the other hand, assign blame to their actions to
decide what they controlled. This work proposes Controlled Effect Network
(CEN), an unsupervised method based on counterfactual measures of blame to
identify effects on the environment controlled by the agent. CEN is evaluated
in a wide range of environments showing that it can accurately identify
controlled effects. Moreover, we demonstrate CEN's capabilities as intrinsic
motivator by integrating it in the state-of-the-art exploration method,
achieving substantially better performance than action-prediction models.
","Did I do that? Blame as a means to identify controlled effects in
  reinforcement learning"
"  Peacefulness is a principal dimension of well-being and is the way out of
inequity and violence. Thus, its measurement has drawn the attention of
researchers, policymakers, and peacekeepers. During the last years, novel
digital data streams have drastically changed the research in this field. The
current study exploits information extracted from a new digital database called
Global Data on Events, Location, and Tone (GDELT) to capture peacefulness
through the Global Peace Index (GPI). Applying predictive machine learning
models, we demonstrate that news media attention from GDELT can be used as a
proxy for measuring GPI at a monthly level. Additionally, we use explainable AI
techniques to obtain the most important variables that drive the predictions.
This analysis highlights each country's profile and provides explanations for
the predictions, and particularly for the errors and the events that drive
these errors. We believe that digital data exploited by researchers,
policymakers, and peacekeepers, with data science tools as powerful as machine
learning, could contribute to maximizing the societal benefits and minimizing
the risks to peacefulness.
",Understanding peacefulness through the world news
"  Temporal Knowledge Graphs (TKGs) have been developed and used in many
different areas. Reasoning on TKGs that predicts potential facts (events) in
the future brings great challenges to existing models. When facing a prediction
task, human beings usually search useful historical information (i.e., clues)
in their memories and then reason for future meticulously. Inspired by this
mechanism, we propose CluSTeR to predict future facts in a two-stage manner,
Clue Searching and Temporal Reasoning, accordingly. Specifically, at the clue
searching stage, CluSTeR learns a beam search policy via reinforcement learning
(RL) to induce multiple clues from historical facts. At the temporal reasoning
stage, it adopts a graph convolution network based sequence method to deduce
answers from clues. Experiments on four datasets demonstrate the substantial
advantages of CluSTeR compared with the state-of-the-art methods. Moreover, the
clues found by CluSTeR further provide interpretability for the results.
","Search from History and Reason for Future: Two-stage Reasoning on
  Temporal Knowledge Graphs"
"  The paper investigates the properties of a fuzzy logic of typicality. The
extension of fuzzy logic with a typicality operator was proposed in recent work
to define a fuzzy multipreference semantics for Multilayer Perceptrons, by
regarding the deep neural network as a conditional knowledge base. In this
paper, we study its properties. First, a monotonic extension of a fuzzy ALC
with typicality is considered (called ALC^FT) and a reformulation the KLM
properties of a preferential consequence relation for this logic is devised.
Most of the properties are satisfied, depending on the reformulation and on the
fuzzy combination functions considered. We then strengthen ALC^FT with a
closure construction by introducing a notion of faithful model of a weighted
knowledge base, which generalizes the notion of coherent model of a conditional
knowledge base previously introduced, and we study its properties.
",On the KLM properties of a fuzzy DL with Typicality
"  Extending transfer learning to cooperative multi-agent reinforcement learning
(MARL) has recently received much attention. In contrast to the single-agent
setting, the coordination indispensable in cooperative MARL constrains each
agent's policy. However, existing transfer methods focus exclusively on agent
policy and ignores coordination knowledge. We propose a new architecture that
realizes robust coordination knowledge transfer through appropriate
decomposition of the overall coordination into several coordination patterns.
We use a novel mixing network named level-adaptive QTransformer
(LA-QTransformer) to realize agent coordination that considers credit
assignment, with appropriate coordination patterns for different agents
realized by a novel level-adaptive Transformer (LA-Transformer) dedicated to
the transfer of coordination knowledge. In addition, we use a novel agent
network named Population Invariant agent with Transformer (PIT) to realize the
coordination transfer in more varieties of scenarios. Extensive experiments in
StarCraft II micro-management show that LA-QTransformer together with PIT
achieves superior performance compared with state-of-the-art baselines.
","Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit
  Assignment"
"  The interpolation of spatial data can be of tremendous value in various
applications, such as forecasting weather from only a few measurements of
meteorological or remote sensing data. Existing methods for spatial
interpolation, such as variants of kriging and spatial autoregressive models,
tend to suffer from at least one of the following limitations: (a) the
assumption of stationarity, (b) the assumption of isotropy, and (c) the
trade-off between modelling local or global spatial interaction. Addressing
these issues in this work, we propose the use of Markov reward processes (MRPs)
as a spatial interpolation method, and we introduce three variants thereof: (i)
a basic static discount MRP (SD-MRP), (ii) an accurate but mostly theoretical
optimised MRP (O-MRP), and (iii) a transferable weight prediction MRP (WP-MRP).
All variants of MRP interpolation operate locally, while also implicitly
accounting for global spatial relationships in the entire system through
recursion. Additionally, O-MRP and WP-MRP no longer assume stationarity and are
robust to anisotropy. We evaluated our proposed methods by comparing the mean
absolute errors of their interpolated grid cells to those of 7 common
baselines, selected from models based on spatial autocorrelation, (spatial)
regression, and deep learning.
  We performed detailed evaluations on two publicly available datasets (local
GDP values, and COVID-19 patient trajectory data). The results from these
experiments clearly show the competitive advantage of MRP interpolation, which
achieved significantly lower errors than the existing methods in 23 out of 40
experimental conditions, or 35 out of 40 when including O-MRP.
",A Markov Reward Process-Based Approach to Spatial Interpolation
"  The paper presents an analysis of the latest developments in the field of
stereo vision in the low-cost segment, both for prototypes and for industrial
designs. We described the theory of stereo vision and presented information
about cameras and data transfer protocols and their compatibility with various
devices. The theory in the field of image processing for stereo vision
processes is considered and the calibration process is described in detail.
Ultimately, we presented the developed stereo vision system and provided the
main points that need to be considered when developing such systems. The final,
we presented software for adjusting stereo vision parameters in real-time in
the python language in the Windows operating system.
",Low-cost Stereovision system (disparity map) for few dollars
"  Information extraction (IE) for visually-rich documents (VRDs) has achieved
SOTA performance recently thanks to the adaptation of Transformer-based
language models, which shows the great potential of pre-training methods. In
this paper, we present a new approach to improve the capability of language
model pre-training on VRDs. Firstly, we introduce a new query-based IE model
that employs span extraction instead of using the common sequence labeling
approach. Secondly, to further extend the span extraction formulation, we
propose a new training task that focuses on modelling the relationships among
semantic entities within a document. This task enables target spans to be
extracted recursively and can be used to pre-train the model or as an IE
downstream task. Evaluation on three datasets of popular business documents
(invoices, receipts) shows that our proposed method achieves significant
improvements compared to existing models. The method also provides a mechanism
for knowledge accumulation from multiple downstream IE tasks.
","A Span Extraction Approach for Information Extraction on Visually-Rich
  Documents"
"  Form understanding is a challenging problem which aims to recognize semantic
entities from the input document and their hierarchical relations. Previous
approaches face significant difficulty dealing with the complexity of the task,
thus treat these objectives separately. To this end, we present a novel deep
neural network to jointly perform both entity detection and link prediction in
an end-to-end fashion. Our model extends the Multi-stage Attentional U-Net
architecture with the Part-Intensity Fields and Part-Association Fields for
link prediction, enriching the spatial information flow with the additional
supervision from entity linking. We demonstrate the effectiveness of the model
on the Form Understanding in Noisy Scanned Documents (FUNSD) dataset, where our
method substantially outperforms the original model and state-of-the-art
baselines in both Entity Labeling and Entity Linking task.
","End-to-End Hierarchical Relation Extraction for Generic Form
  Understanding"
"  With the recent advancements in deep learning, neural solvers have gained
promising results in solving math word problems. However, these SOTA solvers
only generate binary expression trees that contain basic arithmetic operators
and do not explicitly use the math formulas. As a result, the expression trees
they produce are lengthy and uninterpretable because they need to use multiple
operators and constants to represent one single formula. In this paper, we
propose sequence-to-general tree (S2G) that learns to generate interpretable
and executable operation trees where the nodes can be formulas with an
arbitrary number of arguments. With nodes now allowed to be formulas, S2G can
learn to incorporate mathematical domain knowledge into problem-solving, making
the results more interpretable. Experiments show that S2G can achieve a better
performance against strong baselines on problems that require domain knowledge.
",Sequence to General Tree: Knowledge-Guided Geometry Word Problem Solving
"  An improvement of Q-learning is proposed in this paper. It is different from
classic Q-learning in that the similarity between different states and actions
is considered in the proposed method. During the training, a new updating
mechanism is used, in which the Q value of the similar state-action pairs are
updated synchronously. The proposed method can be used in combination with both
tabular Q-learning function and deep Q-learning. And the results of numerical
examples illustrate that compared to the classic Q-learning, the proposed
method has a significantly better performance.
",Smooth Q-learning: Accelerate Convergence of Q-learning Using Similarity
"  In this paper, we describe an open source Python toolkit named Uncertainty
Quantification 360 (UQ360) for the uncertainty quantification of AI models. The
goal of this toolkit is twofold: first, to provide a broad range of
capabilities to streamline as well as foster the common practices of
quantifying, evaluating, improving, and communicating uncertainty in the AI
application development lifecycle; second, to encourage further exploration of
UQ's connections to other pillars of trustworthy AI such as fairness and
transparency through the dissemination of latest research and education
materials. Beyond the Python package (\url{https://github.com/IBM/UQ360}), we
have developed an interactive experience (\url{http://uq360.mybluemix.net}) and
guidance materials as educational tools to aid researchers and developers in
producing and communicating high-quality uncertainties in an effective manner.
","Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and
  Communicating the Uncertainty of AI"
"  The Ebola virus and the disease in effect tend to randomly move individuals
in the population around susceptible, infected, quarantined, hospitalized,
recovered, and dead sub-population. Motivated by the effectiveness in
propagating the disease through the virus, a new bio-inspired and
population-based optimization algorithm is proposed. This paper presents a
novel metaheuristic algorithm named Ebola optimization algorithm (EOSA). To
correctly achieve this, this study models the propagation mechanism of the
Ebola virus disease, emphasising all consistent states of the propagation. The
model was further represented using a mathematical model based on first-order
differential equations. After that, the combined propagation and mathematical
models were adapted for developing the new metaheuristic algorithm. To evaluate
the proposed method's performance and capability compared with other
optimization methods, the underlying propagation and mathematical models were
first investigated to determine how they successfully simulate the EVD.
Furthermore, two sets of benchmark functions consisting of forty-seven (47)
classical and over thirty (30) constrained IEEE CEC-2017 benchmark functions
are investigated numerically. The results indicate that the performance of the
proposed algorithm is competitive with other state-of-the-art optimization
methods based on scalability analysis, convergence analysis, and sensitivity
analysis. Extensive simulation results indicate that the EOSA outperforms other
state-of-the-art popular metaheuristic optimization algorithms such as the
Particle Swarm Optimization Algorithm (PSO), Genetic Algorithm (GA), and
Artificial Bee Colony Algorithm (ABC) on some shifted, high dimensional and
large search range problems.
","Ebola Optimization Search Algorithm (EOSA): A new metaheuristic
  algorithm based on the propagation model of Ebola virus disease"
"  The Angry Birds AI competition has been held over many years to encourage the
development of AI agents that can play Angry Birds game levels better than
human players. Many different agents with various approaches have been employed
over the competition's lifetime to solve this task. Even though the performance
of these agents has increased significantly over the past few years, they still
show major drawbacks in playing deceptive levels. This is because most of the
current agents try to identify the best next shot rather than planning an
effective sequence of shots. In order to encourage advancements in such agents,
we present an automated methodology to generate deceptive game levels for Angry
Birds. Even though there are many existing content generators for Angry Birds,
they do not focus on generating deceptive levels. In this paper, we propose a
procedure to generate deceptive levels for six deception categories that can
fool the state-of-the-art Angry Birds playing AI agents. Our results show that
generated deceptive levels exhibit similar characteristics of human-created
deceptive levels. Additionally, we define metrics to measure the stability,
solvability, and degree of deception of the generated levels.
",Deceptive Level Generation for Angry Birds
"  Conceptual graphs, which is a particular type of Knowledge Graphs, play an
essential role in semantic search. Prior conceptual graph construction
approaches typically extract high-frequent, coarse-grained, and time-invariant
concepts from formal texts. In real applications, however, it is necessary to
extract less-frequent, fine-grained, and time-varying conceptual knowledge and
build taxonomy in an evolving manner. In this paper, we introduce an approach
to implementing and deploying the conceptual graph at Alibaba. Specifically, We
propose a framework called AliCG which is capable of a) extracting fine-grained
concepts by a novel bootstrapping with alignment consensus approach, b) mining
long-tail concepts with a novel low-resource phrase mining approach, c)
updating the graph dynamically via a concept distribution estimation method
based on implicit and explicit user behaviors. We have deployed the framework
at Alibaba UC Browser. Extensive offline evaluation as well as online A/B
testing demonstrate the efficacy of our approach.
","AliCG: Fine-grained and Evolvable Conceptual Graph Construction for
  Semantic Search at Alibaba"
"  Objectively quantifying the value of player actions in football (soccer) is a
challenging problem. To date, studies in football analytics have mainly focused
on the attacking side of the game, while there has been less work on
event-driven metrics for valuing defensive actions (e.g., tackles and
interceptions). Therefore in this paper, we use deep learning techniques to
define a novel metric that values such defensive actions by studying the threat
of passages of play that preceded them. By doing so, we are able to value
defensive actions based on what they prevented from happening in the game. Our
Defensive Action Expected Threat (DAxT) model has been validated using
real-world event-data from the 2017/2018 and 2018/2019 English Premier League
seasons, and we combine our model outputs with additional features to derive an
overall rating of defensive ability for players. Overall, we find that our
model is able to predict the impact of defensive actions allowing us to better
value defenders using event-data.
","What Happened Next? Using Deep Learning to Value Defensive Actions in
  Football Event-Data"
"  In this paper, we present a Symbolic Reinforcement Learning (SRL) based
architecture for safety control of Radio Access Network (RAN) applications. In
particular, we provide a purely automated procedure in which a user can specify
high-level logical safety specifications for a given cellular network topology
in order for the latter to execute optimal safe performance which is measured
through certain Key Performance Indicators (KPIs). The network consists of a
set of fixed Base Stations (BS) which are equipped with antennas, which one can
control by adjusting their vertical tilt angle. The aforementioned process is
called Remote Electrical Tilt (RET) optimization. Recent research has focused
on performing this RET optimization by employing Reinforcement Learning (RL)
strategies due to the fact that they have self-learning capabilities to adapt
in uncertain environments. The term safety refers to particular constraints
bounds of the network KPIs in order to guarantee that when the algorithms are
deployed in a live network, the performance is maintained. In our proposed
architecture the safety is ensured through model-checking techniques over
combined discrete system models (automata) that are abstracted through the
learning process. We introduce a user interface (UI) developed to help a user
set intent specifications to the system, and inspect the difference in agent
proposed actions, and those that are allowed and blocked according to the
safety specification.
",Safe RAN control: A Symbolic Reinforcement Learning Approach
"  The simple gesture of pointing can greatly augment ones ability to comprehend
states of the world based on observations. It triggers additional inferences
relevant to ones task at hand. We model an agents update to its belief of the
world based on individual observations using a partially observable Markov
decision process (POMDP), a mainstream artificial intelligence (AI) model of
how to act rationally according to beliefs formed through observation. On top
of that, we model pointing as a communicative act between agents who have a
mutual understanding that the pointed observation must be relevant and
interpretable. Our model measures relevance by defining a Smithian Value of
Information (SVI) as the utility improvement of the POMDP agent before and
after receiving the pointing. We model that agents calculate SVI by using the
cognitive theory of Smithian helping as a principle of coordinating separate
beliefs for action prediction and action evaluation. We then import SVI into
rational speech act (RSA) as the utility function of an utterance. These lead
us to a pragmatic model of pointing allowing for contextually flexible
interpretations. We demonstrate the power of our Smithian pointing model by
extending the Wumpus world, a classic AI task where a hunter hunts a monster
with only partial observability of the world. We add another agent as a guide
who can only help by marking an observation already perceived by the hunter
with a pointing or not, without providing new observations or offering any
instrumental help. Our results show that this severely limited and overloaded
communication nevertheless significantly improves the hunters performance. The
advantage of pointing is indeed due to a computation of relevance based on
Smithian helping, as it disappears completely when the task is too difficult or
too easy for the guide to help.
","Individual vs. Joint Perception: a Pragmatic Model of Pointing as
  Communicative Smithian Helping"
"  Communication is highly overloaded. Despite this, even young children are
good at leveraging context to understand ambiguous signals. We propose a
computational account of overloaded signaling from a shared agency perspective
which we call the Imagined We for Communication. Under this framework,
communication helps cooperators coordinate their perspectives, allowing them to
act together to achieve shared goals. We assume agents are rational
cooperators, which puts constraints on how signals can be sent and interpreted.
We implement this model in a set of simulations demonstrating this model's
success under increasing ambiguity as well as increasing layers of reasoning.
Our model is capable of improving performance with deeper recursive reasoning;
however, it outperforms comparison baselines at even the shallowest level,
highlighting how shared knowledge and cooperative logic can do much of the
heavy-lifting in language.
",Modeling Communication to Coordinate Perspectives in Cooperation
"  Open-world novelty occurs when the rules of an environment can change
abruptly, such as when a game player encounters ""house rules"". To address
open-world novelty, game playing agents must be able to detect when novelty is
injected, and to quickly adapt to the new rules. We propose a model-based
reinforcement learning approach where game state and rules are represented as
knowledge graphs. The knowledge graph representation of the state and rules
allows novelty to be detected as changes in the knowledge graph, assists with
the training of deep reinforcement learners, and enables imagination-based
re-training where the agent uses the knowledge graph to perform look-ahead.
",Detecting and Adapting to Novelty in Games
"  Thanks to the great progress of machine learning in the last years, several
Artificial Intelligence (AI) techniques have been increasingly moving from the
controlled research laboratory settings to our everyday life. AI is clearly
supportive in many decision-making scenarios, but when it comes to sensitive
areas such as health care, hiring policies, education, banking or justice, with
major impact on individuals and society, it becomes crucial to establish
guidelines on how to design, develop, deploy and monitor this technology.
Indeed the decision rules elaborated by machine learning models are data-driven
and there are multiple ways in which discriminatory biases can seep into data.
Algorithms trained on those data incur the risk of amplifying prejudices and
societal stereotypes by over associating protected attributes such as gender,
ethnicity or disabilities with the prediction task. Starting from the extensive
experience of the National Metrology Institute on measurement standards and
certification roadmaps, and of Politecnico di Torino on machine learning as
well as methods for domain bias evaluation and mastering, we propose a first
joint effort to define the operational steps needed for AI fairness
certification. Specifically we will overview the criteria that should be met by
an AI system before coming into official service and the conformity assessment
procedures useful to monitor its functioning for fair decisions.
",Towards Fairness Certification in Artificial Intelligence
"  Technology companies have produced varied responses to concerns about the
effects of the design of their conversational AI systems. Some have claimed
that their voice assistants are in fact not gendered or human-like -- despite
design features suggesting the contrary. We compare these claims to user
perceptions by analysing the pronouns they use when referring to AI assistants.
We also examine systems' responses and the extent to which they generate output
which is gendered and anthropomorphic. We find that, while some companies
appear to be addressing the ethical concerns raised, in some cases, their
claims do not seem to hold true. In particular, our results show that system
outputs are ambiguous as to the humanness of the systems, and that users tend
to personify and gender them as a result.
","Alexa, Google, Siri: What are Your Pronouns? Gender and Anthropomorphism
  in the Design and Perception of Conversational Assistants"
"  Motivated by the abundance of uncertain event data from multiple sources
including physical devices and sensors, this paper presents the task of
relating a stochastic process observation to a process model that can be
rendered from a dataset. In contrast to previous research that suggested to
transform a stochastically known event log into a less informative uncertain
log with upper and lower bounds on activity frequencies, we consider the
challenge of accommodating the probabilistic knowledge into conformance
checking techniques. Based on a taxonomy that captures the spectrum of
conformance checking cases under stochastic process observations, we present
three types of challenging cases. The first includes conformance checking of a
stochastically known log with respect to a given process model. The second case
extends the first to classify a stochastically known log into one of several
process models. The third case extends the two previous ones into settings in
which process models are only stochastically known. The suggested problem
captures the increasingly growing number of applications in which sensors
provide probabilistic process information.
","Uncertain Process Data with Probabilistic Knowledge: Problem
  Characterization and Challenges"
"  Learning from datasets without interaction with environments (Offline
Learning) is an essential step to apply Reinforcement Learning (RL) algorithms
in real-world scenarios. However, compared with the single-agent counterpart,
offline multi-agent RL introduces more agents with the larger state and action
space, which is more challenging but attracts little attention. We demonstrate
current offline RL algorithms are ineffective in multi-agent systems due to the
accumulated extrapolation error. In this paper, we propose a novel offline RL
algorithm, named Implicit Constraint Q-learning (ICQ), which effectively
alleviates the extrapolation error by only trusting the state-action pairs
given in the dataset for value estimation. Moreover, we extend ICQ to
multi-agent tasks by decomposing the joint-policy under the implicit
constraint. Experimental results demonstrate that the extrapolation error is
successfully controlled within a reasonable range and insensitive to the number
of agents. We further show that ICQ achieves the state-of-the-art performance
in the challenging multi-agent offline tasks (StarCraft II). Our code is public
online at https://github.com/YiqinYang/ICQ.
","Believe What You See: Implicit Constraint Approach for Offline
  Multi-Agent Reinforcement Learning"
"  Metadata vocabularies are used in various domains of study. It provides an
in-depth description of the resources. In this work, we develop Algorithm
Metadata Vocabulary (AMV), a vocabulary for capturing and storing the metadata
about the algorithms (a procedure or a set of rules that is followed
step-by-step to solve a problem, especially by a computer). The snag faced by
the researchers in the current time is the failure of getting relevant results
when searching for algorithms in any search engine. AMV is represented as a
semantic model and produced OWL file, which can be directly used by anyone
interested to create and publish algorithm metadata as a knowledge graph, or to
provide metadata service through SPARQL endpoint. To design the vocabulary, we
propose a well-defined methodology, which considers real issues faced by the
algorithm users and the practitioners. The evaluation shows a promising result.
",AMV : Algorithm Metadata Vocabulary
"  Many AI-related tasks involve the interactions of data in multiple
modalities. It has been a new trend to merge multi-modal information into
knowledge graph(KG), resulting in multi-modal knowledge graphs (MMKG). However,
MMKGs usually suffer from low coverage and incompleteness. To mitigate this
problem, a viable approach is to integrate complementary knowledge from other
MMKGs. To this end, although existing entity alignment approaches could be
adopted, they operate in the Euclidean space, and the resulting Euclidean
entity representations can lead to large distortion of KG's hierarchical
structure. Besides, the visual information has yet not been well exploited. In
response to these issues, in this work, we propose a novel multi-modal entity
alignment approach, Hyperbolic multi-modal entity alignment(HMEA), which
extends the Euclidean representation to hyperboloid manifold. We first adopt
the Hyperbolic Graph Convolutional Networks (HGCNs) to learn structural
representations of entities. Regarding the visual information, we generate
image embeddings using the densenet model, which are also projected into the
hyperbolic space using HGCNs. Finally, we combine the structure and visual
representations in the hyperbolic space and use the aggregated embeddings to
predict potential alignment results. Extensive experiments and ablation studies
demonstrate the effectiveness of our proposed model and its components.
",Multi-modal Entity Alignment in Hyperbolic Space
"  One approach to defining Intention is to use the counterfactual tools
developed to define Causality. Direct Intention is considered the highest level
of intent in the common law, and is a sufficient component for the most serious
crimes to be committed. Loosely defined it is the commission of actions to
bring about a desired or targeted outcome. Direct Intention is not always
necessary for the most serious category of crimes because society has also
found it necessary to develop a theory of intention around side-effects, known
as oblique intent or indirect intent. This is to prevent moral harms from going
unpunished which were not the aim of the actor, but were natural consequences
nevertheless. This paper uses a canonical example of a plane owner, planting a
bomb on their own plane in order to collect insurance, to illustrate how two
accounts of counterfactual intent do not conclude that murder of the plane's
passengers and crew were directly intended. We extend both frameworks to
include a definition of oblique intent developed in Ashton (2021)
",Extending counterfactual accounts of intent to include oblique intent
"  Quality diversity (QD) is a growing branch of stochastic optimization
research that studies the problem of generating an archive of solutions that
maximize a given objective function but are also diverse with respect to a set
of specified measure functions. However, even when these functions are
differentiable, QD algorithms treat them as ""black boxes"", ignoring gradient
information. We present the differentiable quality diversity (DQD) problem, a
special case of QD, where both the objective and measure functions are first
order differentiable. We then present MAP-Elites via a Gradient Arborescence
(MEGA), a DQD algorithm that leverages gradient information to efficiently
explore the joint range of the objective and measure functions. Results in two
QD benchmark domains and in searching the latent space of a StyleGAN show that
MEGA significantly outperforms state-of-the-art QD algorithms, highlighting
DQD's promise for efficient quality diversity optimization when gradient
information is available. Source code is available at
https://github.com/icaros-usc/dqd.
",Differentiable Quality Diversity
"  Overlap functions are a class of aggregation functions that measure the
overlapping degree between two values. Interval-valued overlap functions were
defined as an extension to express the overlapping of interval-valued data, and
they have been usually applied when there is uncertainty regarding the
assignment of membership degrees. The choice of a total order for intervals can
be significant, which motivated the recent developments on interval-valued
aggregation functions and interval-valued overlap functions that are increasing
to a given admissible order, that is, a total order that refines the usual
partial order for intervals. Also, width preservation has been considered on
these recent works, in an intent to avoid the uncertainty increase and
guarantee the information quality, but no deeper study was made regarding the
relation between the widths of the input intervals and the output interval,
when applying interval-valued functions, or how one can control such
uncertainty propagation based on this relation. Thus, in this paper we: (i)
introduce and develop the concepts of width-limited interval-valued functions
and width limiting functions, presenting a theoretical approach to analyze the
relation between the widths of the input and output intervals of bivariate
interval-valued functions, with special attention to interval-valued
aggregation functions; (ii) introduce the concept of $(a,b)$-ultramodular
aggregation functions, a less restrictive extension of one-dimension convexity
for bivariate aggregation functions, which have an important predictable
behaviour with respect to the width when extended to the interval-valued
context; (iii) define width-limited interval-valued overlap functions, taking
into account a function that controls the width of the output interval; (iv)
present and compare three construction methods for these width-limited
interval-valued overlap functions.
","Towards interval uncertainty propagation control in bivariate
  aggregation processes and the introduction of width-limited interval-valued
  overlap functions"
"  Intent modifies an actor's culpability of many types wrongdoing. Autonomous
Algorithmic Agents have the capability of causing harm, and whilst their
current lack of legal personhood precludes them from committing crimes, it is
useful for a number of parties to understand under what type of intentional
mode an algorithm might transgress. From the perspective of the creator or
owner they would like ensure that their algorithms never intend to cause harm
by doing things that would otherwise be labelled criminal if committed by a
legal person. Prosecutors might have an interest in understanding whether the
actions of an algorithm were internally intended according to a transparent
definition of the concept. The presence or absence of intention in the
algorithmic agent might inform the court as to the complicity of its owner.
This article introduces definitions for direct, oblique (or indirect) and
ulterior intent which can be used to test for intent in an algorithmic actor.
",Definitions of intent suitable for algorithms
"  Width-based algorithms search for solutions through a general definition of
state novelty. These algorithms have been shown to result in state-of-the-art
performance in classical planning, and have been successfully applied to
model-based and model-free settings where the dynamics of the problem are given
through simulation engines. Width-based algorithms performance is understood
theoretically through the notion of planning width, providing polynomial
guarantees on their runtime and memory consumption. To facilitate synergies
across research communities, this paper summarizes the area of width-based
planning, and surveys current and future research directions.
","Planning for Novelty: Width-Based Algorithms for Common Problems in
  Control, Planning and Reinforcement Learning"
"  Constraint satisfaction problem (CSP) has been actively used for modeling and
solving a wide range of complex real-world problems. However, it has been
proven that developing efficient methods for solving CSP, especially for large
problems, is very difficult and challenging. Existing complete methods for
problem-solving are in most cases unsuitable. Therefore, proposing hybrid
CSP-based methods for problem-solving has been of increasing interest in the
last decades. This paper aims at proposing a novel approach that combines
incomplete and complete CSP methods for problem-solving. The proposed approach
takes advantage of the group search algorithm (GSO) and the constraint
propagation (CP) methods to solve problems related to the remote sensing field.
To the best of our knowledge, this paper represents the first study that
proposes a hybridization between an improved version of GSO and CP in the
resolution of complex constraint-based problems. Experiments have been
conducted for the resolution of object recognition problems in satellite
images. Results show good performances in terms of convergence and running time
of the proposed CSP-based method compared to existing state-of-the-art methods.
","A Hybrid APM-CPGSO Approach for Constraint Satisfaction Problem Solving:
  Application to Remote Sensing"
"  This article describes an action rule induction algorithm based on a
sequential covering approach. Two variants of the algorithm are presented. The
algorithm allows the action rule induction from a source and a target decision
class point of view. The application of rule quality measures enables the
induction of action rules that meet various quality criteria. The article also
presents a method for recommendation induction. The recommendations indicate
the actions to be taken to move a given test example, representing the source
class, to the target one. The recommendation method is based on a set of
induced action rules. The experimental part of the article presents the results
of the algorithm operation on sixteen data sets. As a result of the conducted
research the Ac-Rules package was made available.
","SCARI: Separate and Conquer Algorithm for Action Rules and
  Recommendations Induction"
"  We tackle the problem of goal-directed graph construction: given a starting
graph, a global objective function (e.g., communication efficiency), and a
budget of modifications, the aim is to find a set of edges whose addition to
the graph maximally improves the objective. This problem emerges in many
networks of great importance for society such as transportation and critical
infrastructure networks. We identify two significant shortcomings with present
methods. Firstly, they focus exclusively on network topology while ignoring
spatial information; however, in many real-world networks, nodes are embedded
in space, which yields different global objectives and governs the range and
density of realizable connections. Secondly, existing RL methods scale poorly
to large networks due to the high cost of training a model and the scaling
factors of the action space and global objectives.
  In this work, we formulate the problem of goal-directed construction of
spatial networks as a deterministic MDP. We adopt the Monte Carlo Tree Search
framework for planning in this domain, prioritizing the optimality of final
solutions over the speed of policy evaluation. We propose several improvements
over the standard UCT algorithm for this family of problems, addressing their
single-agent nature, the trade-off between the costs of edges and their
contribution to the objective, and an action space linear in the number of
nodes. We demonstrate the suitability of this approach for improving the global
efficiency and attack resilience of a variety of synthetic and real-world
networks, including Internet backbone networks and metro systems. We obtain 24%
better solutions on average compared to UCT on the largest networks tested, and
scalability superior to previous methods.
",Planning Spatial Networks
"  Multi-Context Systems (MCS) model in Computational Logic distributed systems
composed of heterogeneous sources, or ""contexts"", interacting via special rules
called ""bridge rules"". In this paper, we consider how to enhance flexibility
and generality in bridge-rules definition and application. In particular, we
introduce and discuss some formal extensions of MCSs useful for a practical use
in dynamic environments, and we try to provide guidelines for implementations
","Multi-Context Systems: Dynamics and Evolution (Pre-Print of
  ""Multi-context systems in dynamic environments"")"
"  Formally verifying Deep Reinforcement Learning (DRL) systems is a challenging
task due to the dynamic continuity of system behaviors and the black-box
feature of embedded neural networks. In this paper, we propose a novel
abstraction-based approach to train DRL systems on finite abstract domains
instead of concrete system states. It yields neural networks whose input states
are finite, making hosting DRL systems directly verifiable using model checking
techniques. Our approach is orthogonal to existing DRL algorithms and
off-the-shelf model checkers. We implement a resulting prototype training and
verification framework and conduct extensive experiments on the
state-of-the-art benchmark. The results show that the systems trained in our
approach can be verified more efficiently while they retain comparable
performance against those that are trained without abstraction.
","Learning on Abstract Domains: A New Approach for Verifiable Guarantee in
  Reinforcement Learning"
"  Consistent alpha generation, i.e., maintaining an edge over the market,
underpins the ability of asset traders to reliably generate profits. Technical
indicators and trading strategies are commonly used tools to determine when to
buy/hold/sell assets, yet these are limited by the fact that they operate on
known values. Over the past decades, multiple studies have investigated the
potential of artificial intelligence in stock trading in conventional markets,
with some success. In this paper, we present RCURRENCY, an RNN-based trading
engine to predict data in the highly volatile digital asset market which is
able to successfully manage an asset portfolio in a live environment. By
combining asset value prediction and conventional trading tools, RCURRENCY
determines whether to buy, hold or sell digital currencies at a given point in
time. Experimental results show that, given the data of an interval $t$, a
prediction with an error of less than 0.5\% of the data at the subsequent
interval $t+1$ can be obtained. Evaluation of the system through backtesting
shows that RCURRENCY can be used to successfully not only maintain a stable
portfolio of digital assets in a simulated live environment using real
historical trading data but even increase the portfolio value over time.
","RCURRENCY: Live Digital Asset Trading Using a Recurrent Neural
  Network-based Forecasting System"
"  This paper presents our research on leveraging social media Big Data and AI
to support hurricane disaster emergency response. The current practice of
hurricane emergency response for rescue highly relies on emergency call
centres. The more recent Hurricane Harvey event reveals the limitations of the
current systems. We use Hurricane Harvey and the associated Houston flooding as
the motivating scenario to conduct research and develop a prototype as a
proof-of-concept of using an intelligent agent as a complementary role to
support emergency centres in hurricane emergency response. This intelligent
agent is used to collect real-time streaming tweets during a natural disaster
event, to identify tweets requesting rescue, to extract key information such as
address and associated geocode, and to visualize the extracted information in
an interactive map in decision supports. Our experiment shows promising
outcomes and the potential application of the research in support of hurricane
emergency response.
","Intelligent Agent for Hurricane Emergency Identification and Text
  Information Extraction from Streaming Social Media Big Data"
"  This study aims at making the architecture search process more adaptive for
one-shot or online training. It is extended from the existing study on
differentiable neural architecture search, and we made the backbone
architecture transformable rather than fixed during the training process. As is
known, differentiable neural architecture search (DARTS) requires a pre-defined
over-parameterized backbone architecture, while its size is to be determined
manually. Also, in DARTS backbone, Hadamard product of two elements is not
introduced, which exists in both LSTM and GRU cells for recurrent nets. This
study introduces a growing mechanism for differentiable neural architecture
search based on network morphism. It enables growing of the cell structures
from small size towards large size ones with one-shot training. Two modes can
be applied in integrating the growing and original pruning process. We also
implement a recently proposed two-input backbone architecture for recurrent
neural networks. Initial experimental results indicate that our approach and
the two-input backbone structure can be quite effective compared with other
baseline architectures including LSTM, in a variety of learning tasks including
multi-variate time series forecasting and language modeling. On the other hand,
we find that dynamic network transformation is promising in improving the
efficiency of differentiable architecture search.
","Differentiable Neural Architecture Search with Morphism-based
  Transformable Backbone Architectures"
"  Computer systems such as storage systems normally require transparent
white-box algorithms that are interpretable for human experts. In this work, we
propose a learning-aided heuristic design method, which automatically generates
human-readable strategies from Deep Reinforcement Learning (DRL) agents. This
method benefits from the power of deep learning but avoids the shortcoming of
its black-box property. Besides the white-box advantage, experiments in our
storage productions resource allocation scenario also show that this solution
outperforms the systems default settings and the elaborately handcrafted
strategy by human experts.
",Learning-Aided Heuristics Design for Storage System
"  Discriminating the matched named entity pairs or identifying the entities'
canonical forms are critical in text mining tasks. More precise named entity
normalization in text mining will benefit other subsequent text analytic
applications. We built the named entity normalization model with a novel Edge
Weight Updating Neural Network. Our proposed model when tested on four
different datasets achieved state-of-the-art results. We, next, verify our
model's performance on NCBI Disease, BC5CDR Disease, and BC5CDR Chemical
databases, which are widely used named entity normalization datasets in the
bioinformatics field. We also tested our model with our own financial named
entity normalization dataset to validate the efficacy for more general
applications. Using the constructed dataset, we differentiate named entity
pairs. Our model achieved the highest named entity normalization performances
in terms of various evaluation metrics.
","Named Entity Normalization Model Using Edge Weight Updating Neural
  Network: Assimilation Between Knowledge-Driven Graph and Data-Driven Graph"
"  While there is evidence that user-adaptive support can greatly enhance the
effectiveness of educational systems, designing such support for exploratory
learning environments (e.g., simulations) is still challenging due to the
open-ended nature of their interaction. In particular, there is little a priori
knowledge of which student's behaviors can be detrimental to learning in such
environments. To address this problem, we focus on a data-driven user-modeling
framework that uses logged interaction data to learn which behavioral or
activity patterns should trigger help during interaction with a specific
learning environment. This framework has been successfully used to provide
adaptive support in interactive learning simulations. Here we present a novel
application of this framework we are working on, namely to Massive Open Online
Courses (MOOCs), a form of exploratory environment that could greatly benefit
from adaptive support due to the large diversity of their users, but typically
lack of such adaptation. We describe an experiment aimed at investigating the
value of our framework to identify student's behaviors that can justify
adapting to, and report some preliminary results.
","A Framework to Counteract Suboptimal User-Behaviors in Exploratory
  Learning Environments: an Application to MOOCs"
"  The Abstraction and Reasoning Corpus (ARC) is a set of tasks that tests an
agent's ability to flexibly solve novel problems. While most ARC tasks are easy
for humans, they are challenging for state-of-the-art AI. How do we build
intelligent systems that can generalize to novel situations and understand
human instructions in domains such as ARC? We posit that the answer may be
found by studying how humans communicate to each other in solving these tasks.
We present LARC, the Language-annotated ARC: a collection of natural language
descriptions by a group of human participants, unfamiliar both with ARC and
with each other, who instruct each other on how to solve ARC tasks. LARC
contains successful instructions for 88\% of the ARC tasks. We analyze the
collected instructions as `natural programs', finding that most natural program
concepts have analogies in typical computer programs. However, unlike how one
precisely programs a computer, we find that humans both anticipate and exploit
ambiguities to communicate effectively. We demonstrate that a state-of-the-art
program synthesis technique, which leverages the additional language
annotations, outperforms its language-free counterpart.
",Communicating Natural Programs to Humans and Machines
"  With the Deep Neural Networks (DNNs) as a powerful function approximator,
Deep Reinforcement Learning (DRL) has been excellently demonstrated on robotic
control tasks. Compared to DNNs with vanilla artificial neurons, the
biologically plausible Spiking Neural Network (SNN) contains a diverse
population of spiking neurons, making it naturally powerful on state
representation with spatial and temporal information. Based on a hybrid
learning framework, where a spike actor-network infers actions from states and
a deep critic network evaluates the actor, we propose a Population-coding and
Dynamic-neurons improved Spiking Actor Network (PDSAN) for efficient state
representation from two different scales: input coding and neuronal coding. For
input coding, we apply population coding with dynamically receptive fields to
directly encode each input state component. For neuronal coding, we propose
different types of dynamic-neurons (containing 1st-order and 2nd-order neuronal
dynamics) to describe much more complex neuronal dynamics. Finally, the PDSAN
is trained in conjunction with deep critic networks using the Twin Delayed Deep
Deterministic policy gradient algorithm (TD3-PDSAN). Extensive experimental
results show that our TD3-PDSAN model achieves better performance than
state-of-the-art models on four OpenAI gym benchmark tasks. It is an important
attempt to improve RL with SNN towards the effective computation satisfying
biological plausibility.
","Population-coding and Dynamic-neurons improved Spiking Actor Network for
  Reinforcement Learning"
"  Artificial Intelligence will significantly impact the work environment of
radiologists. I suggest that up to 50% of a radiologists work in 2021 will be
performed by AI-models in 2025. However, it won't increase beyond that 50%
level, as radiologists remain key for human-centered aspects of their job. I
project that few to no radiologists will be laid off in China due to the
existing supply shortage of radiology services in 2021. The application of AI
in radiology could contribute 1.7 billion USD to China's GDP in 2025. It will
further allow radiologists to start productive work up to four years earlier.
AI in radiology will positively impact the health of patients and radiologists
themselves.
",Diagnosing the Impact of AI on Radiology in China
"  Automated planners are computer tools that allow autonomous agents to make
strategies and decisions by determining a set of actions for the agent that to
take, which will carry a system from a given initial state to the desired goal
state. Many planners are domain-independent, allowing their deployment in a
variety of domains. Such is the broad family of OPTIC planners. These planners
perform Forward Search and call a Linear Programming (LP) solver multiple times
at every state to check for consistency and to set bounds on the numeric
variables. These checks can be computationally costly, especially in real-life
applications. This paper suggests a method for identifying information about
the specific state being evaluated, allowing the formulation of the equations
to facilitate better solver selection and faster LP solving. The usefulness of
the method is demonstrated in six domains and is shown to enhance performance
significantly.
","Improving Search by Utilizing State Information in OPTIC Planners
  Compilation to LP"
"  Clinical notes are unstructured text generated by clinicians during patient
encounters. Clinical notes are usually accompanied by a set of metadata codes
from the International Classification of Diseases(ICD). ICD code is an
important code used in various operations, including insurance, reimbursement,
medical diagnosis, etc. Therefore, it is important to classify ICD codes
quickly and accurately. However, annotating these codes is costly and
time-consuming. So we propose a model based on bidirectional encoder
representations from transformers (BERT) using the sequence attention method
for automatic ICD code assignment. We evaluate our approach on the medical
information mart for intensive care III (MIMIC-III) benchmark dataset. Our
model achieved performance of macro-averaged F1: 0.62898 and micro-averaged F1:
0.68555 and is performing better than a performance of the state-of-the-art
model using the MIMIC-III dataset. The contribution of this study proposes a
method of using BERT that can be applied to documents and a sequence attention
method that can capture important sequence in-formation appearing in documents.
","Medical Code Prediction from Discharge Summary: Document to Sequence
  BERT using Sequence Attention"
"  Node classification is a central task in graph data analysis. Scarce or even
no labeled data of emerging classes is a big challenge for existing methods. A
natural question arises: can we classify the nodes from those classes that have
never been seen? In this paper, we study this zero-shot node classification
(ZNC) problem which has a two-stage nature: (1) acquiring high-quality class
semantic descriptions (CSDs) for knowledge transfer, and (2) designing a well
generalized graph-based learning model. For the first stage, we give a novel
quantitative CSDs evaluation strategy based on estimating the real class
relationships, so as to get the ""best"" CSDs in a completely automatic way. For
the second stage, we propose a novel Decomposed Graph Prototype Network (DGPN)
method, following the principles of locality and compositionality for zero-shot
model generalization. Finally, we conduct extensive experiments to demonstrate
the effectiveness of our solutions.
",Zero-shot Node Classification with Decomposed Graph Prototype Network
"  The use of Artificial Intelligence (AI) for play-testing is still on the
sidelines of main applications of AI in games compared to performance-oriented
game-playing. One of the main purposes of play-testing a game is gathering data
on the gameplay, highlighting good and bad features of the design of the game,
providing useful insight to the game designers for improving the design. Using
AI agents has the potential of speeding the process dramatically. The purpose
of this research is to map the behavioural space (BSpace) of a game by using a
general method. Using the MAP-Elites algorithm we search the hyperparameter
space Rinascimento AI agents and map it to the BSpace defined by several
behavioural metrics. This methodology was able to highlight both exemplary and
degenerated behaviours in the original game design of Splendor and two
variations. In particular, the use of event-value functions has generally shown
a remarkable improvement in the coverage of the BSpace compared to agents based
on classic score-based reward signals.
",Rinascimento: searching the behaviour space of Splendor
"  In this paper we present a benchmark dataset generated as part of a project
for automatic identification of misogyny within online content, which focuses
in particular on memes. The benchmark here described is composed of 800 memes
collected from the most popular social media platforms, such as Facebook,
Twitter, Instagram and Reddit, and consulting websites dedicated to collection
and creation of memes. To gather misogynistic memes, specific keywords that
refer to misogynistic content have been considered as search criterion,
considering different manifestations of hatred against women, such as body
shaming, stereotyping, objectification and violence. In parallel, memes with no
misogynist content have been manually downloaded from the same web sources.
Among all the collected memes, three domain experts have selected a dataset of
800 memes equally balanced between misogynistic and non-misogynistic ones. This
dataset has been validated through a crowdsourcing platform, involving 60
subjects for the labelling process, in order to collect three evaluations for
each instance. Two further binary labels have been collected from both the
experts and the crowdsourcing platform, for memes evaluated as misogynistic,
concerning aggressiveness and irony. Finally for each meme, the text has been
manually transcribed. The dataset provided is thus composed of the 800 memes,
the labels given by the experts and those obtained by the crowdsourcing
validation, and the transcribed texts. This data can be used to approach the
problem of automatic detection of misogynistic content on the Web relying on
both textual and visual cues, facing phenomenons that are growing every day
such as cybersexism and technology-facilitated violence.
","Benchmark dataset of memes with text transcriptions for automatic
  detection of multi-modal misogynistic content"
"  The amount of information produced, whether by newspapers, blogs and social
networks, or by monitoring systems, is increasing rapidly. Processing all this
data in real-time, while taking into consideration advanced knowledge about the
problem domain, is challenging, but required in scenarios where assessing
potential risks in a timely fashion is critical. C-SPARQL, a language for
continuous queries over streams of RDF data, is one of the more prominent
approaches in stream reasoning that provides such continuous inference
capabilities over dynamic data that go beyond mere stream processing. However,
it has been shown that, in the presence of huge amounts of data, C-SPARQL may
not be able to answer queries in time, in particular when the frequency of
incoming data is higher than the time required for reasoning with that data. In
this paper, we investigate whether reasoning with C-SPARQL can be approximated
using Recurrent Neural Networks and Convolutional Neural Networks, two neural
network architectures that have been shown to be well-suited for time series
forecasting and time series classification, to leverage on their higher
processing speed once the network has been trained. We consider a variety of
different kinds of queries and obtain overall positive results with high
accuracies while improving processing time often by several orders of
magnitude.
",Deep Neural Networks for Approximating Stream Reasoning with C-SPARQL
"  With the constant increase of available data in various domains, such as the
Internet of Things, Social Networks or Smart Cities, it has become fundamental
that agents are able to process and reason with such data in real time. Whereas
reasoning over time-annotated data with background knowledge may be
challenging, due to the volume and velocity in which such data is being
produced, such complex reasoning is necessary in scenarios where agents need to
discover potential problems and this cannot be done with simple stream
processing techniques. Stream Reasoners aim at bridging this gap between
reasoning and stream processing and LASER is such a stream reasoner designed to
analyse and perform complex reasoning over streams of data. It is based on
LARS, a rule-based logical language extending Answer Set Programming, and it
has shown better runtime results than other state-of-the-art stream reasoning
systems. Nevertheless, for high levels of data throughput even LASER may be
unable to compute answers in a timely fashion. In this paper, we study whether
Convolutional and Recurrent Neural Networks, which have shown to be
particularly well-suited for time series forecasting and classification, can be
trained to approximate reasoning with LASER, so that agents can benefit from
their high processing speed.
",Faster than LASER -- Towards Stream Reasoning with Deep Neural Networks
"  Inter-agent communication can significantly increase performance in
multi-agent tasks that require co-ordination to achieve a shared goal. Prior
work has shown that it is possible to learn inter-agent communication protocols
using multi-agent reinforcement learning and message-passing network
architectures. However, these models use an unconstrained broadcast
communication model, in which an agent communicates with all other agents at
every step, even when the task does not require it. In real-world applications,
where communication may be limited by system constraints like bandwidth, power
and network capacity, one might need to reduce the number of messages that are
sent. In this work, we explore a simple method of minimizing communication
while maximizing performance in multi-task learning: simultaneously optimizing
a task-specific objective and a communication penalty. We show that the
objectives can be optimized using Reinforce and the Gumbel-Softmax
reparameterization. We introduce two techniques to stabilize training: 50%
training and message forwarding. Training with the communication penalty on
only 50% of the episodes prevents our models from turning off their outgoing
messages. Second, repeating messages received previously helps models retain
information, and further improves performance. With these techniques, we show
that we can reduce communication by 75% with no loss of performance.
","Minimizing Communication while Maximizing Performance in Multi-Agent
  Reinforcement Learning"
"  Graph transformer networks (GTN) are a variant of graph convolutional
networks (GCN) that are targeted to heterogeneous graphs in which nodes and
edges have associated type information that can be exploited to improve
inference accuracy. GTNs learn important metapaths in the graph, create
weighted edges for these metapaths, and use the resulting graph in a GCN.
Currently, the only available implementation of GTNs uses dense matrix
multiplication to find metapaths. Unfortunately, the space overhead of this
approach can be large, so in practice it is used only for small graphs. In
addition, the matrix-based implementation is not fine-grained enough to use
random-walk based methods to optimize metapath finding. In this paper, we
present a graph-based formulation and implementation of the GTN metapath
finding problem. This graph-based formulation has two advantages over the
matrix-based approach. First, it is more space efficient than the original GTN
implementation and more compute-efficient for metapath sizes of practical
interest. Second, it permits us to implement a sampling method that reduces the
number of metapaths that must be enumerated, allowing the implementation to be
used for larger graphs and larger metapath sizes. Experimental results show
that our implementation is $6.5\times$ faster than the original GTN
implementation on average for a metapath length of 4, and our sampling
implementation is $155\times$ faster on average than this implementation
without compromising on the accuracy of the GTN.
",Optimizing Graph Transformer Networks with Graph-based Techniques
"  Detecting and responding to novel situations in open-world environments is a
key capability of human cognition. Current artificial intelligence (AI)
researchers strive to develop systems that can perform in open-world
environments. Novelty detection is an important ability of such AI systems. In
an open-world, novelties appear in various forms and the difficulty to detect
them varies. Therefore, to accurately evaluate the detection capability of AI
systems, it is necessary to investigate the difficulty to detect novelties. In
this paper, we propose a qualitative physics-based method to quantify the
difficulty of novelty detection focusing on open-world physical domains. We
apply our method in a popular physics simulation game, Angry Birds. We conduct
an experiment with human players with different novelties in Angry Birds to
validate our method. Results indicate that the calculated difficulty values are
in line with the detection difficulty of the human players.
","The Difficulty of Novelty Detection in Open-World Physical Domains: An
  Application to Angry Birds"
"  Recently, Graph Convolutional Networks (GCNs) have proven to be a powerful
mean for Computer Aided Diagnosis (CADx). This approach requires building a
population graph to aggregate structural information, where the graph adjacency
matrix represents the relationship between nodes. Until now, this adjacency
matrix is usually defined manually based on phenotypic information. In this
paper, we propose an encoder that automatically selects the appropriate
phenotypic measures according to their spatial distribution, and uses the text
similarity awareness mechanism to calculate the edge weights between nodes. The
encoder can automatically construct the population graph using phenotypic
measures which have a positive impact on the final results, and further
realizes the fusion of multimodal information. In addition, a novel graph
convolution network architecture using multi-layer aggregation mechanism is
proposed. The structure can obtain deep structure information while suppressing
over-smooth, and increase the similarity between the same type of nodes.
Experimental results on two databases show that our method can significantly
improve the diagnostic accuracy for Autism spectrum disorder and breast cancer,
indicating its universality in leveraging multimodal data for disease
prediction.
","AMA-GCN: Adaptive Multi-layer Aggregation Graph Convolutional Network
  for Disease Prediction"
"  The intelligent question answering (IQA) system can accurately capture users'
search intention by understanding the natural language questions, searching
relevant content efficiently from a massive knowledge-base, and returning the
answer directly to the user. Since the IQA system can save inestimable time and
workforce in data search and reasoning, it has received more and more attention
in data science and artificial intelligence. This article introduced a domain
knowledge graph using the graph database and graph computing technologies from
massive heterogeneous data in electric power. It then proposed an IQA system
based on the electrical power knowledge graph to extract the intent and
constraints of natural interrogation based on the natural language processing
(NLP) method, to construct graph data query statements via knowledge reasoning,
and to complete the accurate knowledge search and analysis to provide users
with an intuitive visualization. This method thoroughly combined knowledge
graph and graph computing characteristics, realized high-speed multi-hop
knowledge correlation reasoning analysis in tremendous knowledge. The proposed
work can also provide a basis for the context-aware intelligent question and
answer.
",An Intelligent Question Answering System based on Power Knowledge Graph
"  Search is an important tool for computing effective policies in single- and
multi-agent environments, and has been crucial for achieving superhuman
performance in several benchmark fully and partially observable games. However,
one major limitation of prior search approaches for partially observable
environments is that the computational cost scales poorly with the amount of
hidden information. In this paper we present \emph{Learned Belief Search}
(LBS), a computationally efficient search procedure for partially observable
environments. Rather than maintaining an exact belief distribution, LBS uses an
approximate auto-regressive counterfactual belief that is learned as a
supervised task. In multi-agent settings, LBS uses a novel public-private model
architecture for underlying policies in order to efficiently evaluate these
policies during rollouts. In the benchmark domain of Hanabi, LBS can obtain 55%
~ 91% of the benefit of exact search while reducing compute requirements by
$35.8 \times$ ~ $4.6 \times$, allowing it to scale to larger settings that were
inaccessible to previous search methods.
","Learned Belief Search: Efficiently Improving Policies in Partially
  Observable Settings"
"  Adversarial images highlight how vulnerable modern image classifiers are to
perturbations outside of their training set. Human oversight might mitigate
this weakness, but depends on humans understanding the AI well enough to
predict when it is likely to make a mistake. In previous work we have found
that humans tend to assume that the AI's decision process mirrors their own.
Here we evaluate if methods from explainable AI can disrupt this assumption to
help participants predict AI classifications for adversarial and standard
images. We find that both saliency maps and examples facilitate catching AI
errors, but their effects are not additive, and saliency maps are more
effective than examples.
",Explainable AI for Natural Adversarial Images
"  The importance of building neural networks that can learn to reason has been
well recognized in the neuro-symbolic community. In this paper, we apply neural
pointer networks for conducting reasoning over symbolic knowledge bases. In
doing so, we explore the benefits and limitations of encoder-decoder
architectures in general and pointer networks in particular for developing
accurate, generalizable and robust neuro-symbolic reasoners. Based on our
experimental results, pointer networks performs remarkably well across multiple
reasoning tasks while outperforming the previously reported state of the art by
a significant margin. We observe that the Pointer Networks preserve their
performance even when challenged with knowledge graphs of the domain/vocabulary
it has never encountered before. To the best of our knowledge, this is the
first study on neuro-symbolic reasoning using Pointer Networks. We hope our
impressive results on these reasoning problems will encourage broader
exploration of pointer networks' capabilities for reasoning over more complex
logics and for other neuro-symbolic problems.
",On the Capabilities of Pointer Networks for Deep Deductive Reasoning
"  Ontologies are increasingly used for machine reasoning over the last few
years. They can provide explanations of concepts or be used for concept
classification if there exists a mapping from the desired labels to the
relevant ontology. Another advantage of using ontologies is that they do not
need a learning process, meaning that we do not need the train data or time
before using them. This paper presents a practical use of an ontology for a
classification problem from the financial domain. It first transforms a given
ontology to a graph and proceeds with generalization with the aim to find
common semantic descriptions of the input sets of financial concepts.
  We present a solution to the shared task on Learning Semantic Similarities
for the Financial Domain (FinSim-2 task). The task is to design a system that
can automatically classify concepts from the Financial domain into the most
relevant hypernym concept in an external ontology - the Financial Industry
Business Ontology. We propose a method that maps given concepts to the
mentioned ontology and performs a graph search for the most relevant hypernyms.
We also employ a word vectorization method and a machine learning classifier to
supplement the method with a ranked list of labels for each concept.
","JSI at the FinSim-2 task: Ontology-Augmented Financial Concept
  Classification"
"  This paper introduces our position on the critical issue of bias that
recently appeared in AI applications. Specifically, we discuss the combination
of current technologies used in AI applications i.e., Machine Learning and
Knowledge Graphs, and point to their involvement in (de)biased applications of
the C4I domain. Although this is a wider problem that currently emerges from
different application domains, bias appears more critical in C4I than in others
due to its security-related nature. While proposing certain actions to be taken
towards debiasing C4I applications, we acknowledge the immature aspect of this
topic within the Knowledge Graph and Semantic Web communities.
",Knowledge Graphs and Machine Learning in biased C4I applications
"  The solution to prevent maternal complications are known and preventable by
trained health professionals. But in countries like Ethiopia where the patient
to physician ratio is 1 doctor to 1000 patients, maternal mortality and
morbidity rate is high. To fill the gap of highly trained health professionals,
Ethiopia introduced health extension programs. Task shifting to health
extension workers (HEWs) contributed in decreasing mortality and morbidity rate
in Ethiopia. Knowledge-gap has been one of the major challenges to HEWs. The
reasons are trainings are not given in regular manner, there is no midwife,
gynecologists or doctors around for consultation, and all guidelines are
paper-based which are easily exposed to damage. In this paper, we describe the
design and implementation of a web-based expert system for maternal care. We
only targeted the major 10 diseases and complication of maternal health issues
seen in Sub-Saharan Africa. The expert system can be accessed through the use
of web browsers from computers as well as smart phones. Forward chaining
rule-based expert system is used in order to give suggestions and create a new
knowledge from the knowledge-base. This expert system can be used to train HEWs
in the field of maternal health.
  Keywords: expert system, maternal care, forward-chaining, rule-based expert
system, PHLIPS
",MatES: Web-based Forward Chaining Expert System for Maternal Care
"  While the computational processing of Kurdish has experienced a relative
increase, the machine translation of this language seems to be lacking a
considerable body of scientific work. This is in part due to the lack of
resources especially curated for this task. In this paper, we present the first
large scale parallel corpus of Central Kurdish-English, Awta, containing
229,222 pairs of manually aligned translations. Our corpus is collected from
different text genres and domains in an attempt to build more robust and
real-world applications of machine translation. We make a portion of this
corpus publicly available in order to foster research in this area. Further, we
build several neural machine translation models in order to benchmark the task
of Kurdish machine translation. Additionally, we perform extensive experimental
analysis of results in order to identify the major challenges that Central
Kurdish machine translation faces. These challenges include language-dependent
and-independent ones as categorized in this paper, the first group of which are
aware of Central Kurdish linguistic properties on different morphological,
syntactic and semantic levels. Our best performing systems achieve 22.72 and
16.81 in BLEU score for Ku$\rightarrow$EN and En$\rightarrow$Ku, respectively.
","Central Kurdish machine translation: First large scale parallel corpus
  and experiments"
"  There is a need for remote learning and virtual learning applications such as
virtual reality (VR) and tablet-based solutions which the current pandemic has
demonstrated. Creating complex learning scenarios by developers is highly
time-consuming and can take over a year. There is a need to provide a simple
method to enable lecturers to create their own content for their laboratory
tutorials. Research is currently being undertaken into developing generic
models to enable the semi-automatic creation of a virtual learning application.
A case study describing the creation of a virtual learning application for an
electrical laboratory tutorial is presented.
","Virtual Reality based Digital Twin System for remote laboratories and
  online practical learning"
"  The Institute of Materials and Processes, IMP, of the University of Applied
Sciences in Karlsruhe, Germany in cooperation with VDI Verein Deutscher
Ingenieure e.V, AEN Automotive Engineering Network and their cooperation
partners present their competences of AI-based solution approaches in the
production engineering field. The online congress KI 4 Industry on November 12
and 13, 2020, showed what opportunities the use of artificial intelligence
offers for medium-sized manufacturing companies, SMEs, and where potential
fields of application lie. The main purpose of KI 4 Industry is to increase the
transfer of knowledge, research and technology from universities to small and
medium-sized enterprises, to demystify the term AI and to encourage companies
to use AI-based solutions in their own value chain or in their products.
","Conference proceedings KI4Industry AI for SMEs -- The online congress
  for practical entry into AI for SMEs"
"  Class-imbalanced data, in which some classes contain far more samples than
others, is ubiquitous in real-world applications. Standard techniques for
handling class-imbalance usually work by training on a re-weighted loss or on
re-balanced data. Unfortunately, training overparameterized neural networks on
such objectives causes rapid memorization of minority class data. To avoid this
trap, we harness meta-learning, which uses both an ''outer-loop'' and an
''inner-loop'' loss, each of which may be balanced using different strategies.
We evaluate our method, MetaBalance, on image classification, credit-card fraud
detection, loan default prediction, and facial recognition tasks with severely
imbalanced data, and we find that MetaBalance outperforms a wide array of
popular re-sampling strategies.
",MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data
"  Most classical planners use grounding as a preprocessing step, reducing
planning to propositional logic. However, grounding comes with a severe cost in
memory, resulting in large encodings for SAT/QBF based planners. Despite the
optimisations in SAT/QBF encodings such as action splitting, compact encodings
and using parallel plans, the memory usage due to grounding remains a
bottleneck when actions have many parameters, such as in the Organic Synthesis
problems from the IPC 2018 planning competition (in its original non-split
form).
  In this paper, we provide a compact QBF encoding that is logarithmic in the
number of objects and avoids grounding completely by using universal
quantification for object combinations. We compare the ungrounded QBF encoding
with the simple SAT encoding and also show that we can solve some of the
Organic Synthesis problems, which could not be handled before by any SAT/QBF
based planners due to grounding.
",Classical Planning as QBF without Grounding (extended version)
"  This volume contains revised versions of the papers selected for the second
volume of the Online Handbook of Argumentation for AI (OHAAI). Previously,
formal theories of argument and argument interaction have been proposed and
studied, and this has led to the more recent study of computational models of
argument. Argumentation, as a field within artificial intelligence (AI), is
highly relevant for researchers interested in symbolic representations of
knowledge and defeasible reasoning. The purpose of this handbook is to provide
an open access and curated anthology for the argumentation research community.
OHAAI is designed to serve as a research hub to keep track of the latest and
upcoming PhD-driven research on the theory and application of argumentation in
all areas related to AI.
",Online Handbook of Argumentation for AI: Volume 2
"  One of the main goals of online competitive games is increasing player
engagement by ensuring fair matches. These games use rating systems for
creating balanced match-ups. Rating systems leverage statistical estimation to
rate players' skills and use skill ratings to predict rank before matching
players. Skill ratings of individual players can be aggregated to compute the
skill level of a team. While research often aims to improve the accuracy of
skill estimation and fairness of match-ups, less attention has been given to
how the skill level of a team is calculated from the skill level of its
members. In this paper, we propose two new aggregation methods and compare them
with a standard approach extensively used in the research literature. We
present an exhaustive analysis of the impact of these methods on the predictive
performance of rating systems. We perform our experiments using three popular
rating systems, Elo, Glicko, and TrueSkill, on three real-world datasets
including over 100,000 battle royale and head-to-head matches. Our evaluations
show the superiority of the MAX method over the other two methods in the
majority of the tested cases, implying that the overall performance of a team
is best determined by the performance of its most skilled member. The results
of this study highlight the necessity of devising more elaborated methods for
calculating a team's performance -- methods covering different aspects of
players' behavior such as skills, strategy, or goals.
",Evaluating Team Skill Aggregation in Online Competitive Games
"  We propose new width-based planning and learning algorithms inspired from a
careful analysis of the design decisions made by previous width-based planners.
The algorithms are applied over the Atari-2600 games and our best performing
algorithm, Novelty guided Critical Path Learning (N-CPL), outperforms the
previously introduced width-based planning and learning algorithms $\pi$-IW(1),
$\pi$-IW(1)+ and $\pi$-HIW(n, 1). Furthermore, we present a taxonomy of the
Atari-2600 games according to some of their defining characteristics. This
analysis of the games provides further insight into the behaviour and
performance of the algorithms introduced. Namely, for games with large
branching factors, and games with sparse meaningful rewards, N-CPL outperforms
$\pi$-IW, $\pi$-IW(1)+ and $\pi$-HIW(n, 1).
","Width-based Lookaheads with Learnt Base Policies and Heuristics Over the
  Atari-2600 Benchmark"
"  Understanding large ontologies is still an issue, and has an impact on many
ontology engineering tasks. We describe a novel method for identifying and
extracting conceptual components from domain ontologies, which are used to
understand and compare them. The method is applied to two corpora of ontologies
in the Cultural Heritage and Conference domain, respectively. The results,
which show good quality, are evaluated by manual inspection and by correlation
with datasets and tool performance from the ontology alignment evaluation
initiative.
",Extraction of common conceptual components from multiple ontologies
"  The black-box nature of machine learning models limits their use in
case-critical applications, raising faithful and ethical concerns that lead to
trust crises. One possible way to mitigate this issue is to understand how a
(mispredicted) decision is carved out from the decision boundary. This paper
presents a human-in-the-loop approach to explain machine learning models using
verbatim neighborhood manifestation. Contrary to most of the current
eXplainable Artificial Intelligence (XAI) systems, which provide hit-or-miss
approximate explanations, our approach generates the local decision boundary of
the given instance and enables human intelligence to conclude the model
behavior. Our method can be divided into three stages: 1) a neighborhood
generation stage, which generates instances based on the given sample; 2) a
classification stage, which yields classifications on the generated instances
to carve out the local decision boundary and delineate the model behavior; and
3) a human-in-the-loop stage, which involves human to refine and explore the
neighborhood of interest. In the generation stage, a generative model is used
to generate the plausible synthetic neighbors around the given instance. After
the classification stage, the classified neighbor instances provide a
multifaceted understanding of the model behavior. Three intervention points are
provided in the human-in-the-loop stage, enabling humans to leverage their own
intelligence to interpret the model behavior. Several experiments on two
datasets are conducted, and the experimental results demonstrate the potential
of our proposed approach for boosting human understanding of the complex
machine learning model.
","Human-in-the-loop model explanation via verbatim boundary identification
  in generated neighborhoods"
"  The article proposes a new type of AI system that does not give solutions
directly but rather points toward it, friendly prompting the user with
questions and adjusting messages. Models of AI human collaboration can be
deduced from the classic literary example of interaction between Mr. Holmes and
Dr. Watson from the stories by Conan Doyle, where the highly qualified expert
Mr. Holmes answers questions posed by Dr. Watson. Here Mr. Holmes, with his
rule-based calculations, logic, and memory management, apparently plays the
role of an AI system, and Dr. Watson is the user. Looking into the same
Holmes-Watson interaction, we find and promote another model in which the AI
behaves like Dr. Watson, who, by asking questions and acting in a particular
way, helps Holmes (the AI user) make the right decisions. We call the systems
based on this principle ""Dr. Watson-type systems."" The article describes the
properties of such systems and introduces two particular: Patient Management
System for intensive care physicians and Data Error Prevention System.
",Dr. Watson type Artificial Intellect (AI) Systems
"  The increasingly widespread application of AI models motivates increased
demand for explanations from a variety of stakeholders. However, this demand is
ambiguous because there are many types of 'explanation' with different
evaluative criteria. In the spirit of pluralism, I chart a taxonomy of types of
explanation and the associated XAI methods that can address them. When we look
to expose the inner mechanisms of AI models, we develop
Diagnostic-explanations. When we seek to render model output understandable, we
produce Explication-explanations. When we wish to form stable generalizations
of our models, we produce Expectation-explanations. Finally, when we want to
justify the usage of a model, we produce Role-explanations that situate models
within their social context. The motivation for such a pluralistic view stems
from a consideration of causes as manipulable relationships and the different
types of explanations as identifying the relevant points in AI systems we can
intervene upon to affect our desired changes. This paper reduces the ambiguity
in use of the word 'explanation' in the field of XAI, allowing practitioners
and stakeholders a useful template for avoiding equivocation and evaluating XAI
methods and putative explanations.
",Explanatory Pluralism in Explainable AI
"  During the last decade, entity embeddings have become ubiquitous in
Artificial Intelligence. Such embeddings essentially serve as compact but
semantically meaningful representations of the entities of interest. In most
approaches, vectors are used for representing the entities themselves, as well
as for representing their associated attributes. An important advantage of
using attribute embeddings is that (some of the) semantic dependencies between
the attributes can thus be captured. However, little is known about what kinds
of semantic dependencies can be modelled in this way. The aim of this paper is
to shed light on this question, focusing on settings where the embedding of an
entity is obtained by pooling the embeddings of its known attributes. Our
particular focus is on studying the theoretical limitations of different
embedding strategies, rather than their ability to effectively learn attribute
dependencies in practice. We first show a number of negative results, revealing
that some of the most popular embedding models are not able to capture even
basic Horn rules. However, we also find that some embedding strategies are
capable, in principle, of modelling both monotonic and non-monotonic attribute
dependencies.
","Modelling Monotonic and Non-Monotonic Attribute Dependencies with
  Embeddings: A Theoretical Analysis"
"  The automatic recognition of food on images has numerous interesting
applications, including nutritional tracking in medical cohorts. The problem
has received significant research attention, but an ongoing public benchmark to
develop open and reproducible algorithms has been missing. Here, we report on
the setup of such a benchmark using publicly available food images sourced
through the mobile MyFoodRepo app. Through four rounds, the benchmark released
the MyFoodRepo-273 dataset constituting 24,119 images and a total of 39,325
segmented polygons categorized in 273 different classes. Models were evaluated
on private tests sets from the same platform with 5,000 images and 7,865
annotations in the final round. Top-performing models on the 273 food
categories reached a mean average precision of 0.568 (round 4) and a mean
average recall of 0.885 (round 3). We present experimental validation of round
4 results, and discuss implications of the benchmark setup designed to increase
the size and diversity of the dataset for future rounds.
","The Food Recognition Benchmark: Using DeepLearning to Recognize Food on
  Images"
"  External knowledge (a.k.a. side information) plays a critical role in
zero-shot learning (ZSL) which aims to predict with unseen classes that have
never appeared in training data. Several kinds of external knowledge, such as
text and attribute, have been widely investigated, but they alone are limited
with incomplete semantics. Some very recent studies thus propose to use
Knowledge Graph (KG) due to its high expressivity and compatibility for
representing kinds of knowledge. However, the ZSL community is still in short
of standard benchmarks for studying and comparing different external knowledge
settings and different KG-based ZSL methods. In this paper, we proposed six
resources covering three tasks, i.e., zero-shot image classification (ZS-IMGC),
zero-shot relation extraction (ZS-RE), and zero-shot KG completion (ZS-KGC).
Each resource has a normal ZSL benchmark and a KG containing semantics ranging
from text to attribute, from relational knowledge to logical expressions. We
have clearly presented these resources including their construction,
statistics, data formats and usage cases w.r.t. different ZSL methods. More
importantly, we have conducted a comprehensive benchmarking study, with two
general and state-of-the-art methods, two setting-specific methods and one
interpretable method. We discussed and compared different ZSL paradigms w.r.t.
different external knowledge settings, and found that our resources have great
potential for developing more advanced ZSL methods and more solutions for
applying KGs for augmenting machine learning. All the resources are available
at https://github.com/China-UK-ZSL/Resources_for_KZSL.
",Benchmarking Knowledge-driven Zero-shot Learning
"  Maintaining the stability of the modern power grid is becoming increasingly
difficult due to fluctuating power consumption, unstable power supply coming
from renewable energies, and unpredictable accidents such as man-made and
natural disasters. As the operation on the power grid must consider its impact
on future stability, reinforcement learning (RL) has been employed to provide
sequential decision-making in power grid management. However, existing methods
have not considered the environmental constraints. As a result, the learned
policy has risk of selecting actions that violate the constraints in
emergencies, which will escalate the issue of overloaded power lines and lead
to large-scale blackouts. In this work, we propose a novel method for this
problem, which builds on top of the search-based planning algorithm. At the
planning stage, the search space is limited to the action set produced by the
policy. The selected action strictly follows the constraints by testing its
outcome with the simulation function provided by the system. At the learning
stage, to address the problem that gradients cannot be propagated to the
policy, we introduce Evolutionary Strategies (ES) with black-box policy
optimization to improve the policy directly, maximizing the returns of the long
run. In NeurIPS 2020 Learning to Run Power Network (L2RPN) competition, our
solution safely managed the power grid and ranked first in both tracks.
",Action Set Based Policy Optimization for Safe Power Grid Management
"  The explosion in the sheer magnitude and complexity of financial news data in
recent years makes it increasingly challenging for investment analysts to
extract valuable insights and perform analysis. We propose FactCheck in
finance, a web-based news aggregator with deep learning models, to provide
analysts with a holistic view of important financial events from multilingual
news sources and extract events using an unsupervised clustering method. A web
interface is provided to examine the credibility of news articles using a
transformer-based fact-checker. The performance of the fact checker is
evaluated using a dataset related to merger and acquisition (M\&A) events and
is shown to outperform several strong baselines.
",Fact Check: Analyzing Financial Events from Multilingual News Sources
"  With the wide adoption of black-box models, instance-based \emph{post hoc}
explanation tools, such as LIME and SHAP became increasingly popular. These
tools produce explanations, pinpointing contributions of key features
associated with a given prediction. However, the obtained explanations remain
at the raw feature level and are not necessarily understandable by a human
expert without extensive domain knowledge. We propose ReEx (Reasoning with
Explanations), a method applicable to explanations generated by arbitrary
instance-level explainers, such as SHAP. By using background knowledge in the
form of ontologies, ReEx generalizes instance explanations in a least general
generalization-like manner. The resulting symbolic descriptions are specific
for individual classes and offer generalizations based on the explainer's
output. The derived semantic explanations are potentially more informative, as
they describe the key attributes in the context of more general background
knowledge, e.g., at the biological process level. We showcase ReEx's
performance on nine biological data sets, showing that compact, semantic
explanations can be obtained and are more informative than generic ontology
mappings that link terms directly to feature names. ReEx is offered as a
simple-to-use Python library and is compatible with tools such as SHAP and
similar. To our knowledge, this is one of the first methods that directly
couples semantic reasoning with contemporary model explanation methods. This
paper is a preprint. Full version's doi is: 10.1109/SAMI50585.2021.9378668
",Semantic Reasoning from Model-Agnostic Explanations
"  Capturing the playing style of professional soccer coaches is a complex, and
yet barely explored, task in sports analytics. Nowadays, the availability of
digital data describing every relevant spatio-temporal aspect of soccer
matches, allows for capturing and analyzing the playing style of players,
teams, and coaches in an automatic way. In this paper, we present coach2vec, a
workflow to capture the playing style of professional coaches using match event
streams and artificial intelligence. Coach2vec extracts ball possessions from
each match, clusters them based on their similarity, and reconstructs the
typical ball possessions of coaches. Then, it uses an autoencoder, a type of
artificial neural network, to obtain a concise representation (encoding) of the
playing style of each coach. Our experiments, conducted on soccer-logs
describing the last four seasons of the Italian first division, reveal
interesting similarities between prominent coaches, paving the road to the
simulation of playing styles and the quantitative comparison of professional
coaches.
",Coach2vec: autoencoding the playing style of soccer coaches
"  Data-driven approaches have been applied to many problems in urban computing.
However, in the research community, such approaches are commonly studied under
data from limited sources, and are thus unable to characterize the complexity
of urban data coming from multiple entities and the correlations among them.
Consequently, an inclusive and multifaceted dataset is necessary to facilitate
more extensive studies on urban computing. In this paper, we present CityNet, a
multi-modal urban dataset containing data from 7 cities, each of which coming
from 3 data sources. We first present the generation process of CityNet as well
as its basic properties. In addition, to facilitate the use of CityNet, we
carry out extensive machine learning experiments, including spatio-temporal
predictions, transfer learning, and reinforcement learning. The experimental
results not only provide benchmarks for a wide range of tasks and methods, but
also uncover internal correlations among cities and tasks within CityNet that,
with adequate leverage, can improve performances on various tasks. With the
benchmarking results and the correlations uncovered, we believe that CityNet
can contribute to the field of urban computing by supporting research on many
advanced topics.
",CityNet: A Multi-city Multi-modal Dataset for Smart City Applications
"  We introduce a procedural content generation (PCG) framework at the
intersections of experience-driven PCG and PCG via reinforcement learning,
named ED(PCG)RL, EDRL in short. EDRL is able to teach RL designers to generate
endless playable levels in an online manner while respecting particular
experiences for the player as designed in the form of reward functions. The
framework is tested initially in the Super Mario Bros game. In particular, the
RL designers of Super Mario Bros generate and concatenate level segments while
considering the diversity among the segments. The correctness of the generation
is ensured by a neural net-assisted evolutionary level repairer and the
playability of the whole level is determined through AI-based testing. Our
agents in this EDRL implementation learn to maximise a quantification of
Koster's principle of fun by moderating the degree of diversity across level
segments. Moreover, we test their ability to design fun levels that are diverse
over time and playable. Our proposed framework is capable of generating
endless, playable Super Mario Bros levels with varying degrees of fun,
deviation from earlier segments, and playability. EDRL can be generalised to
any game that is built as a segment-based sequential process and features a
built-in compressed representation of its game content.
","Experience-Driven PCG via Reinforcement Learning: A Super Mario Bros
  Study"
"  Currently, Deep Learning (DL) components within a Case-Based Reasoning (CBR)
application often lack the comprehensive integration of available domain
knowledge. The trend within machine learning towards so-called Informed machine
learning can help to overcome this limitation. In this paper, we therefore
investigate the potential of integrating domain knowledge into Graph Neural
Networks (GNNs) that are used for similarity assessment between semantic graphs
within process-oriented CBR applications. We integrate knowledge in two ways:
First, a special data representation and processing method is used that encodes
structural knowledge about the semantic annotations of each graph node and
edge. Second, the message-passing component of the GNNs is constrained by
knowledge on legal node mappings. The evaluation examines the quality and
training time of the extended GNNs, compared to the stock models. The results
show that both extensions are capable of providing better quality, shorter
training times, or in some configurations both advantages at once.
","Informed Machine Learning for Improved Similarity Assessment in
  Process-Oriented Case-Based Reasoning"
"  In this PhD thesis, we explore and apply methods inspired by the free energy
principle to two important areas in machine learning and neuroscience. The free
energy principle is a general mathematical theory of the necessary
information-theoretic behaviours of systems that maintain a separation from
their environment. A core postulate of the theory is that complex systems can
be seen as performing variational Bayesian inference and minimizing an
information-theoretic quantity called the variational free energy. The thesis
is structured into three independent sections. Firstly, we focus on predictive
coding, a neurobiologically plausible process theory derived from the free
energy principle which argues that the primary function of the brain is to
minimize prediction errors, showing how predictive coding can be scaled up and
extended to be more biologically plausible, and elucidating its close links
with other methods such as Kalman Filtering. Secondly, we study active
inference, a neurobiologically grounded account of action through variational
message passing, and investigate how these methods can be scaled up to match
the performance of deep reinforcement learning methods. We additionally provide
a detailed mathematical understanding of the nature and origin of the
information-theoretic objectives that underlie exploratory behaviour. Finally,
we investigate biologically plausible methods of credit assignment in the
brain. We first demonstrate a close link between predictive coding and the
backpropagation of error algorithm. We go on to propose novel and simpler
algorithms which allow for backprop to be implemented in purely local,
biologically plausible computations.
","Applications of the Free Energy Principle to Machine Learning and
  Neuroscience"
"  Wikidata has been increasingly adopted by many communities for a wide variety
of applications, which demand high-quality knowledge to deliver successful
results. In this paper, we develop a framework to detect and analyze
low-quality statements in Wikidata by shedding light on the current practices
exercised by the community. We explore three indicators of data quality in
Wikidata, based on: 1) community consensus on the currently recorded knowledge,
assuming that statements that have been removed and not added back are
implicitly agreed to be of low quality; 2) statements that have been
deprecated; and 3) constraint violations in the data. We combine these
indicators to detect low-quality statements, revealing challenges with
duplicate entities, missing triples, violated type rules, and taxonomic
distinctions. Our findings complement ongoing efforts by the Wikidata community
to improve data quality, aiming to make it easier for users and editors to find
and correct mistakes.
",A Study of the Quality of Wikidata
"  Scoring functions, which measure the plausibility of triples, have become the
crux of knowledge graph embedding (KGE). Plenty of scoring functions, targeting
at capturing different kinds of relations in KGs, have been designed by experts
in recent years. However, as relations can exhibit intricate patterns that are
hard to infer before training, none of them can consistently perform the best
on existing benchmark tasks. AutoSF has shown the significance of using
automated machine learning (AutoML) to design KG- dependent scoring functions.
In this paper, we propose AutoSF+ as an extension of AutoSF. First, we improve
the search algorithm with the evolutionary search, which can better explore the
search space. Second, we evaluate AutoSF+ on the recently developed benchmark
OGB. Besides, we apply AutoSF+ to the new task, i.e., entity classification, to
show that it can improve the task beyond KG completion.
","AutoSF+: Towards Automatic Scoring Function Design for Knowledge Graph
  Embedding"
"  An obstacle to scientific document understanding is the extensive use of
acronyms which are shortened forms of long technical phrases. Acronym
disambiguation aims to find the correct meaning of an ambiguous acronym in a
given text. Recent efforts attempted to incorporate word embeddings and deep
learning architectures, and achieved significant effects in this task. In
general domains, kinds of fine-grained pretrained language models have sprung
up, thanks to the largescale corpora which can usually be obtained through
crowdsourcing. However, these models based on domain agnostic knowledge might
achieve insufficient performance when directly applied to the scientific
domain. Moreover, obtaining large-scale high-quality annotated data and
representing high-level semantics in the scientific domain is challenging and
expensive. In this paper, we consider both the domain agnostic and specific
knowledge, and propose a Hierarchical Dual-path BERT method coined hdBERT to
capture the general fine-grained and high-level specific representations for
acronym disambiguation. First, the context-based pretrained models, RoBERTa and
SciBERT, are elaborately involved in encoding these two kinds of knowledge
respectively. Second, multiple layer perceptron is devised to integrate the
dualpath representations simultaneously and outputs the prediction. With a
widely adopted SciAD dataset contained 62,441 sentences, we investigate the
effectiveness of hdBERT. The experimental results exhibit that the proposed
approach outperforms state-of-the-art methods among various evaluation metrics.
Specifically, its macro F1 achieves 93.73%.
","Leveraging Domain Agnostic and Specific Knowledge for Acronym
  Disambiguation"
"  This paper presents preliminary work on using deep neural networks to guide
general-purpose heuristic algorithms for performing utilitarian combinatorial
assignment. In more detail, we use deep learning in an attempt to produce
heuristics that can be used together with e.g., search algorithms to generate
feasible solutions of higher quality more quickly. Our results indicate that
our approach could be a promising future method for constructing such
heuristics.
","Towards Utilitarian Combinatorial Assignment with Deep Neural Networks
  and Heuristic Algorithms"
"  This paper applies t-SNE, a visualisation technique familiar from Deep Neural
Network research to argumentation graphs by applying it to the output of graph
embeddings generated using several different methods. It shows that such a
visualisation approach can work for argumentation and show interesting
structural properties of argumentation graphs, opening up paths for further
research in the area.
",Visualising Argumentation Graphs with Graph Embeddings and t-SNE
"  In this short note I restate and simplify the proof of the impossibility of
probabilistic induction from Popper (1992). Other proofs are possible (cf.
Popper (1985)).
",Proof of the impossibility of probabilistic induction
"  This paper considers predicting future statuses of multiple agents in an
online fashion by exploiting dynamic interactions in the system. We propose a
novel collaborative prediction unit (CoPU), which aggregates the predictions
from multiple collaborative predictors according to a collaborative graph. Each
collaborative predictor is trained to predict the status of an agent by
considering the impact of another agent. The edge weights of the collaborative
graph reflect the importance of each predictor. The collaborative graph is
adjusted online by multiplicative update, which can be motivated by minimizing
an explicit objective. With this objective, we also conduct regret analysis to
indicate that, along with training, our CoPU achieves similar performance with
the best individual collaborative predictor in hindsight. This theoretical
interpretability distinguishes our method from many other graph networks. To
progressively refine predictions, multiple CoPUs are stacked to form a
collaborative graph neural network. Extensive experiments are conducted on
three tasks: online simulated trajectory prediction, online human motion
prediction and online traffic speed prediction, and our methods outperform
state-of-the-art works on the three tasks by 28.6%, 17.4% and 21.0% on average,
respectively.
","Online Multi-Agent Forecasting with Interpretable Collaborative Graph
  Neural Network"
"  Many games often share common ideas or aspects between them, such as their
rules, controls, or playing area. However, in the context of General Game
Playing (GGP) for board games, this area remains under-explored. We propose to
formalise the notion of ""game concept"", inspired by terms generally used by
game players and designers. Through the Ludii General Game System, we describe
concepts for several levels of abstraction, such as the game itself, the moves
played, or the states reached. This new GGP feature associated with the ludeme
representation of games opens many new lines of research. The creation of a
hyper-agent selector, the transfer of AI learning between games, or explaining
AI techniques using game terms, can all be facilitated by the use of game
concepts. Other applications which can benefit from game concepts are also
discussed, such as the generation of plausible reconstructed rules for
incomplete ancient games, or the implementation of a board game recommender
system.
",General Board Game Concepts
"  Several research initiatives have been proposed for computing similarity
between two Fuzzy Sets in analysis through Fuzzy Rough Sets. These techniques
yield two measures viz. lower similarity and upper similarity. While in most
applications only one entity is useful to further analysis and for drawing
conclusions. The aim of this paper is to propose novel technique to combine
Fuzzy Rough Set based lower similarity and upper similarity using Fuzzy
Inference Engine. Further, the proposed approach is applied to the problem
computing sentence similarity and have been evaluated on SICK2014 dataset.
","Computing Fuzzy Rough Set based Similarities with Fuzzy Inference and
  Its Application to Sentence Similarity Computations"
"  Knowledge compilation (KC) languages find a growing number of practical uses,
including in Constraint Programming (CP) and in Machine Learning (ML). In most
applications, one natural question is how to explain the decisions made by
models represented by a KC language. This paper shows that for many of the best
known KC languages, well-known classes of explanations can be computed in
polynomial time. These classes include deterministic decomposable negation
normal form (d-DNNF), and so any KC language that is strictly less succinct
than d-DNNF. Furthermore, the paper also investigates the conditions under
which polynomial time computation of explanations can be extended to KC
languages more succinct than d-DNNF.
",Efficient Explanations for Knowledge Compilation Languages
"  Tree Search (TS) is crucial to some of the most influential successes in
reinforcement learning. Here, we tackle two major challenges with TS that limit
its usability: \textit{distribution shift} and \textit{scalability}. We first
discover and analyze a counter-intuitive phenomenon: action selection through
TS and a pre-trained value function often leads to lower performance compared
to the original pre-trained agent, even when having access to the exact state
and reward in future steps. We show this is due to a distribution shift to
areas where value estimates are highly inaccurate and analyze this effect using
Extreme Value theory. To overcome this problem, we introduce a novel off-policy
correction term that accounts for the mismatch between the pre-trained value
and its corresponding TS policy by penalizing under-sampled trajectories. We
prove that our correction eliminates the above mismatch and bound the
probability of sub-optimal action selection. Our correction significantly
improves pre-trained Rainbow agents without any further training, often more
than doubling their scores on Atari games. Next, we address the scalability
issue given by the computational complexity of exhaustive TS that scales
exponentially with the tree depth. We introduce Batch-BFS: a GPU breadth-first
search that advances all nodes in each depth of the tree simultaneously.
Batch-BFS reduces runtime by two orders of magnitude and, beyond inference,
enables also training with TS of depths that were not feasible before. We train
DQN agents from scratch using TS and show improvement in several Atari games
compared to both the original DQN and the more advanced Rainbow.
","Improve Agents without Retraining: Parallel Tree Search with Off-Policy
  Correction"
"  Advances in AI, and especially machine learning, are increasingly drawing
research interest and efforts towards predictive process monitoring, the
subfield of process mining (PM) that concerns predicting next events, process
outcomes and remaining execution times. Unfortunately, researchers use a
variety of datasets and ways to split them into training and test sets. The
documentation of these preprocessing steps is not always complete.
Consequently, research results are hard or even impossible to reproduce and to
compare between papers. At times, the use of non-public domain knowledge
further hampers the fair competition of ideas. Often the training and test sets
are not completely separated, a data leakage problem particular to predictive
process monitoring. Moreover, test sets usually suffer from bias in terms of
both the mix of case durations and the number of running cases. These obstacles
pose a challenge to the field's progress. The contribution of this paper is to
identify and demonstrate the importance of these obstacles and to propose
preprocessing steps to arrive at unbiased benchmark datasets in a principled
way, thus creating representative test sets without data leakage with the aim
of levelling the playing field, promoting open science and contributing to more
rapid progress in predictive process monitoring.
","Creating Unbiased Public Benchmark Datasets with Data Leakage Prevention
  for Predictive Process Monitoring"
"  In shared spaces, motorized and non-motorized road users share the same space
with equal priority. Their movements are not regulated by traffic rules, hence
they interact more frequently to negotiate priority over the shared space. To
estimate the safeness and efficiency of shared spaces, reproducing the traffic
behavior in such traffic places is important. In this paper, we consider and
combine different levels of interaction between pedestrians and cars in shared
space environments. Our proposed model consists of three layers: a layer to
plan trajectories of road users; a force-based modeling layer to reproduce free
flow movement and simple interactions; and a game-theoretic decision layer to
handle complex situations where road users need to make a decision over
different alternatives. We validate our model by simulating scenarios involving
various interactions between pedestrians and cars and also car-to-car
interaction. The results indicate that simulated behaviors match observed
behaviors well.
",Modeling Interactions of Multimodal Road Users in Shared Spaces
"  The increasing digitalization of the manufacturing domain requires adequate
knowledge modeling to capture relevant information. Ontologies and Knowledge
Graphs provide means to model and relate a wide range of concepts, problems,
and configurations. Both can be used to generate new knowledge through
deductive inference and identify missing knowledge. While digitalization
increases the amount of data available, much data is not labeled and cannot be
directly used to train supervised machine learning models. Active learning can
be used to identify the most informative data instances for which to obtain
users' feedback, reduce friction, and maximize knowledge acquisition. By
combining semantic technologies and active learning, multiple use cases in the
manufacturing domain can be addressed taking advantage of the available
knowledge and data.
",Knowledge Modelling and Active Learning in Manufacturing
"  The branching factor of a game is the average number of new states reachable
from a given state. It is a widely used metric in AI research on board games,
but less often computed or discussed for videogames. This paper provides
estimates for the branching factors of 103 Atari 2600 games, as implemented in
the Arcade Learning Environment (ALE). Depending on the game, ALE exposes
between 3 and 18 available actions per frame of gameplay, which is an upper
bound on branching factor. This paper shows, based on an enumeration of the
first 1 million distinct states reachable in each game, that the average
branching factor is usually much lower, in many games barely above 1. In
addition to reporting the branching factors, this paper aims to clarify what
constitutes a distinct state in ALE.
",Estimates for the Branching Factors of Atari Games
"  There are a range of metrics that can be applied to the artifacts produced by
procedural content generation, and several of them come with qualitative
claims. In this paper, we adapt a range of existing PCG metrics to generated
Minecraft settlements, develop a few new metrics inspired by PCG literature,
and compare the resulting measurements to existing human evaluations. The aim
is to analyze how those metrics capture human evaluation scores in different
categories, how the metrics generalize to another game domain, and how metrics
deal with more complex artifacts. We provide an exploratory look at a variety
of metrics and provide an information gain and several correlation analyses. We
found some relationships between human scores and metrics counting specific
elements, measuring the diversity of blocks and measuring the presence of
crafting materials for the present complex blocks.
","Comparing PCG metrics with Human Evaluation in Minecraft Settlement
  Generation"
"  In this work, we show how to discover a semantic web service among a
repository of web services. A new approach for web service discovery based on
calculating the functions similarity. We define the Web service functions with
Ontology Web Language (OWL). We wrote some rules for comparing two web
services` parameters. Our algorithm compares the parameters of two web
services` inputs/outputs by making a bipartite graph. We compute the similarity
rate by using the Ford-Fulkerson algorithm. The higher the similarity, the less
are the differences between their functions. At last, our algorithm chooses the
service which has the highest similarity. As a consequence, our method is
useful when we need to find a web service suitable to replace an existing one
that has failed. Especially in autonomic systems, this situation is very common
and important since we need to ensure the availability of the application which
is based on the failed web service. We use Universal Description, Discovery and
Integration (UDDI) compliant web service registry.
","How to Discover a Semantic Web Service by Knowing Its Functionality
  Parameters"
"  In this paper we discuss contrastive explanations for formal argumentation -
the question why a certain argument (the fact) can be accepted, whilst another
argument (the foil) cannot be accepted under various extension-based semantics.
The recent work on explanations for argumentation-based conclusions has mostly
focused on providing minimal explanations for the (non-)acceptance of
arguments. What is still lacking, however, is a proper argumentation-based
interpretation of contrastive explanations. We show under which conditions
contrastive explanations in abstract and structured argumentation are
meaningful, and how argumentation allows us to make implicit foils explicit.
",Contrastive Explanations for Argumentation-Based Conclusions
"  Successful and accurate modelling of level difficulty is a fundamental
component of the operationalisation of player experience as difficulty is one
of the most important and commonly used signals for content design and
adaptation. In games that feature intermediate milestones, such as completable
areas or levels, difficulty is often defined by the probability of completion
or completion rate; however, this operationalisation is limited in that it does
not describe the behaviour of the player within the area.
  In this research work, we formalise a model of level difficulty for puzzle
games that goes beyond the classical probability of success. We accomplish this
by describing the distribution of actions performed within a game level using a
parametric statistical model thus creating a richer descriptor of difficulty.
The model is fitted and evaluated on a dataset collected from the game Lily's
Garden by Tactile Games, and the results of the evaluation show that the it is
able to describe and explain difficulty in a vast majority of the levels.
",Statistical Modelling of Level Difficulty in Puzzle Games
"  Many hierarchical reinforcement learning (RL) applications have empirically
verified that incorporating prior knowledge in reward design improves
convergence speed and practical performance. We attempt to quantify the
computational benefits of hierarchical RL from a planning perspective under
assumptions about the intermediate state and intermediate rewards frequently
(but often implicitly) adopted in practice. Our approach reveals a trade-off
between computational complexity and the pursuit of the shortest path in
hierarchical planning: using intermediate rewards significantly reduces the
computational complexity in finding a successful policy but does not guarantee
to find the shortest path, whereas using sparse terminal rewards finds the
shortest path at a significantly higher computational cost. We also corroborate
our theoretical results with extensive experiments on the MiniGrid environments
using Q-learning and other popular deep RL algorithms.
",Computational Benefits of Intermediate Rewards for Hierarchical Planning
"  The delivery of Medical Countermeasures(MCMs) for mass prophylaxis in the
case of a bio-terrorist attack is an active research topic that has interested
the research community over the past decades. The objective of this study is to
design an efficient algorithm for the Receive Reload and Store Problem(RSS) in
which we aim to find feasible routes to deliver MCMs to a target population
considering time, physical, and human resources, and capacity limitations. For
doing this, we adapt the p-median problem to the POD-based emergency response
planning procedures and propose an efficient algorithm solution to perform the
p-median in reasonable computational time. We present RE-PLAN, the Response
PLan Analyzer system that contains some RSS solutions developed at The Center
for Computational Epidemiology and Response Analysis (CeCERA) at the University
of North Texas. Finally, we analyze a study case where we show how the
computational performance of the algorithm can impact the process of decision
making and emergency planning in the short and long terms.
","The Multi-phase spatial meta-heuristic algorithm for public health
  emergency transportation"
"  Creating a domain model, even for classical, domain-independent planning, is
a notoriously hard knowledge-engineering task. A natural approach to solve this
problem is to learn a domain model from observations. However, model learning
approaches frequently do not provide safety guarantees: the learned model may
assume actions are applicable when they are not, and may incorrectly capture
actions' effects. This may result in generating plans that will fail when
executed. In some domains such failures are not acceptable, due to the cost of
failure or inability to replan online after failure. In such settings, all
learning must be done offline, based on some observations collected, e.g., by
some other agents or a human. Through this learning, the task is to generate a
plan that is guaranteed to be successful. This is called the model-free
planning problem. Prior work proposed an algorithm for solving the model-free
planning problem in classical planning. However, they were limited to learning
grounded domains, and thus they could not scale. We generalize this prior work
and propose the first safe model-free planning algorithm for lifted domains. We
prove the correctness of our approach, and provide a statistical analysis
showing that the number of trajectories needed to solve future problems with
high probability is linear in the potential size of the domain model. We also
present experiments on twelve IPC domains showing that our approach is able to
learn the real action model in all cases with at most two trajectories.
",Safe Learning of Lifted Action Models
"  The game of monopoly is an adversarial multi-agent domain where there is no
fixed goal other than to be the last player solvent, There are useful subgoals
like monopolizing sets of properties, and developing them. There is also a lot
of randomness from dice rolls, card-draws, and adversaries' strategies. This
unpredictability is made worse when unknown novelties are added during
gameplay. Given these challenges, Monopoly was one of the test beds chosen for
the DARPA-SAILON program which aims to create agents that can detect and
accommodate novelties. To handle the game complexities, we developed an agent
that eschews complete plans, and adapts it's policy online as the game evolves.
In the most recent independent evaluation in the SAILON program, our agent was
the best performing agent on most measures. We herein present our approach and
results.
","Integrating Planning, Execution and Monitoring in the presence of Open
  World Novelties: Case Study of an Open World Monopoly Solver"
"  Engineering projects for railway infrastructure typically involve many
subsystems which need consistent views of the planned and built infrastructure
and its underlying topology. Consistency is typically ensured by exchanging and
verifying data between tools using XML-based data formats and UML-based
object-oriented models. A tighter alignment of these data representations via a
common topology model could decrease the development effort of railway
infrastructure engineering tools. A common semantic model is also a
prerequisite for the successful adoption of railway knowledge graphs. Based on
the RailTopoModel standard, we developed the Rail Topology Ontology as a model
to represent core features of railway infrastructures in a standard-compliant
manner. This paper describes the ontology and its development method, and
discusses its suitability for integrating data of railway engineering systems
and other sources in a knowledge graph.
  With the Rail Topology Ontology, software engineers and knowledge scientists
have a standard-based ontology for representing railway topologies to integrate
disconnected data sources. We use the Rail Topology Ontology for our rail
knowledge graph and plan to extend it by rail infrastructure ontologies derived
from existing data exchange standards, since many such standards use the same
base model as the presented ontology, viz., RailTopoModel.
",Rail Topology Ontology: A Rail Infrastructure Base Ontology
"  This demo paper presents the first system for playing the popular Angry Birds
game using a domain-independent planner. Our system models Angry Birds levels
using PDDL+, a planning language for mixed discrete/continuous domains. It uses
a domain-independent PDDL+ planner to generate plans and executes them. In this
demo paper, we present the system's PDDL+ model for this domain, identify key
design decisions that reduce the problem complexity, and compare the
performance of our system to model-specific methods for this domain. The
results show that our system's performance is on par with other domain-specific
systems for Angry Birds, suggesting the applicability of domain-independent
planning to this benchmark AI challenge.
",Playing Angry Birds with a Domain-Independent PDDL+ Planner
"  A legal knowledge graph constructed from court cases, judgments, laws and
other legal documents can enable a number of applications like question
answering, document similarity, and search. While the use of knowledge graphs
for distant supervision in NLP tasks is well researched, using knowledge graphs
for downstream graph tasks like node similarity presents challenges in
selecting node types and their features. In this demo, we describe our solution
for predicting similar nodes in a case graph derived from our legal knowledge
graph.
",Similar Cases Recommendation using Legal Knowledge Graphs
"  In this paper we discuss the relationships between conditional and
preferential logics and neural network models, based on a multi-preferential
semantics. We propose a concept-wise multipreference semantics, recently
introduced for defeasible description logics to take into account preferences
with respect to different concepts, as a tool for providing a semantic
interpretation to neural network models. This approach has been explored both
for unsupervised neural network models (Self-Organising Maps) and for
supervised ones (Multilayer Perceptrons), and we expect that the same approach
might be extended to other neural network models. It allows for logical
properties of the network to be checked (by model checking) over an
interpretation capturing the input-output behavior of the network. For
Multilayer Perceptrons, the deep network itself can be regarded as a
conditional knowledge base, in which synaptic connections correspond to
weighted conditionals. The paper describes the general approach, through the
cases of Self-Organising Maps and Multilayer Perceptrons, and discusses some
open issues and perspectives.
","From Common Sense Reasoning to Neural Network Models through Multiple
  Preferences: an overview"
"  Over the last few years, neural network derived word embeddings became
popular in the natural language processing literature. Studies conducted have
mostly focused on the quality and application of word embeddings trained on
public available corpuses such as Wikipedia or other news and social media
sources. However, these studies are limited to generic text and thus lack
technical and scientific nuances such as domain specific vocabulary,
abbreviations, or scientific formulas which are commonly used in academic
context. This research focuses on the performance of word embeddings applied to
a large scale academic corpus. More specifically, we compare quality and
efficiency of trained word embeddings to TFIDF representations in modeling
content of scientific articles. We use a word2vec skip-gram model trained on
titles and abstracts of about 70 million scientific articles. Furthermore, we
have developed a benchmark to evaluate content models in a scientific context.
The benchmark is based on a categorization task that matches articles to
journals for about 1.3 million articles published in 2017. Our results show
that content models based on word embeddings are better for titles (short text)
while TFIDF works better for abstracts (longer text). However, the slight
improvement of TFIDF for larger text comes at the expense of 3.7 times more
memory requirement as well as up to 184 times higher computation times which
may make it inefficient for online applications. In addition, we have created a
2-dimensional visualization of the journals modeled via embeddings to
qualitatively inspect embedding model. This graph shows useful insights and can
be used to find competitive journals or gaps to propose new journals.
","Document Embedding for Scientific Articles: Efficacy of Word Embeddings
  vs TFIDF"
"  The safety assessment of automated vehicles (AVs) is an important aspect of
the development cycle of AVs. A scenario-based assessment approach is accepted
by many players in the field as part of the complete safety assessment. A
scenario is a representation of a situation on the road to which the AV needs
to respond appropriately. One way to generate the required scenario-based test
descriptions is to parameterize the scenarios and to draw these parameters from
a probability density function (pdf). Because the shape of the pdf is unknown
beforehand, assuming a functional form of the pdf and fitting the parameters to
the data may lead to inaccurate fits. As an alternative, Kernel Density
Estimation (KDE) is a promising candidate for estimating the underlying pdf,
because it is flexible with the underlying distribution of the parameters.
Drawing random samples from a pdf estimated with KDE is possible without the
need of evaluating the actual pdf, which makes it suitable for drawing random
samples for, e.g., Monte Carlo methods. Sampling from a KDE while the samples
satisfy a linear equality constraint, however, has not been described in the
literature, as far as the authors know.
  In this paper, we propose a method to sample from a pdf estimated using KDE,
such that the samples satisfy a linear equality constraint. We also present an
algorithm of our method in pseudo-code. The method can be used to generating
scenarios that have, e.g., a predetermined starting speed or to generate
different types of scenarios. This paper also shows that the method for
sampling scenarios can be used in case a Singular Value Decomposition (SVD) is
used to reduce the dimension of the parameter vectors.
","Constrained Sampling from a Kernel Density Estimator to Generate
  Scenarios for the Assessment of Automated Vehicles"
"  Developing artificial intelligence based assistive systems to aid Persons
with Dementia (PwD) requires large amounts of training data. However, data
collection poses ethical, legal, economic, and logistic issues. Synthetic data
generation tools, in this regard, provide a potential solution. However, we
believe that already available such tools do not adequately reflect cognitive
deficiencies in behavior simulation. To counter these issues we propose a
simulation model (SimDem ) that primarily focuses on cognitive impairments
suffered by PwD and can be easily configured and adapted by the users to model
and evaluate assistive solutions.
","SimDem A Multi-agent Simulation Environment to Model Persons with
  Dementia and their Assistance"
"  Incorporating external knowledge to Visual Question Answering (VQA) has
become a vital practical need. Existing methods mostly adopt pipeline
approaches with different components for knowledge matching and extraction,
feature learning, etc.However, such pipeline approaches suffer when some
component does not perform well, which leads to error propagation and poor
overall performance. Furthermore, the majority of existing approaches ignore
the answer bias issue -- many answers may have never appeared during training
(i.e., unseen answers) in real-word application. To bridge these gaps, in this
paper, we propose a Zero-shot VQA algorithm using knowledge graphs and a
mask-based learning mechanism for better incorporating external knowledge, and
present new answer-based Zero-shot VQA splits for the F-VQA dataset.
Experiments show that our method can achieve state-of-the-art performance in
Zero-shot VQA with unseen answers, meanwhile dramatically augment existing
end-to-end models on the normal F-VQA task.
",Zero-shot Visual Question Answering using Knowledge Graph
"  Our paper explores the game theoretic value of the 7-in-a-row game. We reduce
the problem to solving a finite board game, which we target using Proof Number
Search. We present a number of heuristic improvements to Proof Number Search
and examine their effect within the context of this particular game. Although
our paper does not solve the 7-in-a-row game, our experiments indicate that we
have made significant progress towards it.
",Towards solving the 7-in-a-row game
"  Classical AI planners provide solutions to planning problems in the form of
long and opaque text outputs. To aid in the understanding transferability of
planning solutions, it is necessary to have a rich and comprehensible
representation for both human and computers beyond the current line-by-line
text notation. In particular, it is desirable to encode the trace of literals
throughout the plan to capture the dependencies between actions selected. The
approach of this paper is to view the actions as maps between literals and the
selected plan as a composition of those maps. The mathematical theory, called
category theory, provides the relevant structures for capturing maps, their
compositions, and maps between compositions. We employ this theory to propose
an algorithm agnostic, model-based representation for domains, problems, and
plans expressed in the commonly used planning description language, PDDL. This
category theoretic representation is accompanied by a graphical syntax in
addition to a linear notation, similar to algebraic expressions, that can be
used to infer literals used at every step of the plan. This provides the
appropriate constructive abstraction and facilitates comprehension for human
operators. In this paper, we demonstrate this on a plan within the Blocksworld
domain.
",Encoding Compositionality in Classical Planning Solutions
"  Grammatical inference consists in learning a formal grammar (as a set of
rewrite rules or a finite state machine). We are concerned with learning
Nondeterministic Finite Automata (NFA) of a given size from samples of positive
and negative words. NFA can naturally be modeled in SAT. The standard model [1]
being enormous, we also try a model based on prefixes [2] which generates
smaller instances. We also propose a new model based on suffixes and a hybrid
model based on prefixes and suffixes. We then focus on optimizing the size of
generated SAT instances issued from the hybrid models. We present two
techniques to optimize this combination, one based on Iterated Local Search
(ILS), the second one based on Genetic Algorithm (GA). Optimizing the
combination significantly reduces the SAT instances and their solving time, but
at the cost of longer generation time. We, therefore, study the balance between
generation time and solving time thanks to some experimental comparisons, and
we analyze our various model improvements.
",GA and ILS for optimizing the size of NFA models
"  As the number of Human-Centered Internet of Things (HCIoT) applications
increases, the self-adaptation of its services and devices is becoming a
fundamental requirement for addressing the uncertainties of the environment in
decision-making processes. Self-adaptation of HCIoT aims to manage run-time
changes in a dynamic environment and to adjust the functionality of IoT objects
in order to achieve desired goals during execution. SMASH is a semantic-enabled
multi-agent system for self-adaptation of HCIoT that autonomously adapts IoT
objects to uncertainties of their environment. SMASH addresses the
self-adaptation of IoT applications only according to the human values of
users, while the behavior of users is not addressed. This article presents
Q-SMASH: a multi-agent reinforcement learning-based approach for
self-adaptation of IoT objects in human-centered environments. Q-SMASH aims to
learn the behaviors of users along with respecting human values. The learning
ability of Q-SMASH allows it to adapt itself to the behavioral change of users
and make more accurate decisions in different states and situations.
","Q-SMASH: Q-Learning-based Self-Adaptation of Human-Centered Internet of
  Things"
"  Fuel optimization of diesel and petrol vehicles within industrial fleets is
critical for mitigating costs and reducing emissions. This objective is
achievable by acting on fuel-related factors, such as the driving behaviour
style.
  In this study, we developed an Explainable Boosting Machine (EBM) model to
predict fuel consumption of different types of industrial vehicles, using
real-world data collected from 2020 to 2021. This Machine Learning model also
explains the relationship between the input factors and fuel consumption,
quantifying the individual contribution of each one of them. The explanations
provided by the model are compared with domain knowledge in order to see if
they are aligned. The results show that the 70% of the categories associated to
the fuel-factors are similar to the previous literature.
  With the EBM algorithm, we estimate that optimizing driving behaviour
decreases fuel consumption between 12% and 15% in a large fleet (more than 1000
vehicles).
","Vehicle Fuel Optimization Under Real-World Driving Conditions: An
  Explainable Artificial Intelligence Approach"
"  A total of 34% of AI research and development projects fails or are
abandoned, according to a recent survey by Rackspace Technology of 1,870
companies. We propose a new strategic framework, aiSTROM, that empowers
managers to create a successful AI strategy based on a thorough literature
review. This provides a unique and integrated approach that guides managers and
lead developers through the various challenges in the implementation process.
In the aiSTROM framework, we start by identifying the top n potential projects
(typically 3-5). For each of those, seven areas of focus are thoroughly
analysed. These areas include creating a data strategy that takes into account
unique cross-departmental machine learning data requirements, security, and
legal requirements. aiSTROM then guides managers to think about how to put
together an interdisciplinary artificial intelligence (AI) implementation team
given the scarcity of AI talent. Once an AI team strategy has been established,
it needs to be positioned within the organization, either cross-departmental or
as a separate division. Other considerations include AI as a service (AIaas),
or outsourcing development. Looking at new technologies, we have to consider
challenges such as bias, legality of black-box-models, and keeping humans in
the loop. Next, like any project, we need value-based key performance
indicators (KPIs) to track and validate the progress. Depending on the
company's risk-strategy, a SWOT analysis (strengths, weaknesses, opportunities,
and threats) can help further classify the shortlisted projects. Finally, we
should make sure that our strategy includes continuous education of employees
to enable a culture of adoption. This unique and comprehensive framework offers
a valuable, literature supported, tool for managers and lead developers.
",aiSTROM -- A roadmap for developing a successful AI strategy
"  In theory, ontology-based process modelling (OBPM) bares great potential to
extend business process management. Many works have studied OBPM and are clear
on the potential amenities, such as eliminating ambiguities or enabling
advanced reasoning over company processes. However, despite this approval in
academia, a widespread industry adoption is still nowhere to be seen. This can
be mainly attributed to the fact, that it still requires high amounts of manual
labour to initially create ontologies and annotations to process models. As
long as these problems are not addressed, implementing OBPM seems unfeasible in
practice. In this work, we therefore identify requirements needed for a
successful implementation of OBPM and assess the current state of research
w.r.t. these requirements. Our results indicate that the research progress for
means to facilitate OBPM are still alarmingly low and there needs to be urgent
work on extending existing approaches.
",Ontology-Based Process Modelling -- Will we live to see it?
"  Recently, abstract argumentation-based models of case-based reasoning
($AA{\text -} CBR$ in short) have been proposed, originally inspired by the
legal domain, but also applicable as classifiers in different scenarios.
However, the formal properties of $AA{\text -} CBR$ as a reasoning system
remain largely unexplored. In this paper, we focus on analysing the
non-monotonicity properties of a regular version of $AA{\text -} CBR$ (that we
call $AA{\text -} CBR_{\succeq}$). Specifically, we prove that $AA{\text -}
CBR_{\succeq}$ is not cautiously monotonic, a property frequently considered
desirable in the literature. We then define a variation of $AA{\text -}
CBR_{\succeq}$ which is cautiously monotonic. Further, we prove that such
variation is equivalent to using $AA{\text -} CBR_{\succeq}$ with a restricted
casebase consisting of all ""surprising"" and ""sufficient"" cases in the original
casebase. As a by-product, we prove that this variation of $AA{\text -}
CBR_{\succeq}$ is cumulative, rationally monotonic, and empowers a principled
treatment of noise in ""incoherent"" casebases. Finally, we illustrate $AA{\text
-} CBR$ and cautious monotonicity questions on a case study on the U.S. Trade
Secrets domain, a legal casebase.
","Monotonicity and Noise-Tolerance in Case-Based Reasoning with Abstract
  Argumentation (with Appendix)"
"  Reinforcement learning (RL) in partially observable, fully cooperative
multi-agent settings (Dec-POMDPs) can in principle be used to address many
real-world challenges such as controlling a swarm of rescue robots or a
synchronous team of quadcopters. However, Dec-POMDPs are significantly harder
to solve than single-agent problems, with the former being NEXP-complete and
the latter, MDPs, being just P-complete. Hence, current RL algorithms for
Dec-POMDPs suffer from poor sample complexity, thereby reducing their
applicability to practical problems where environment interaction is costly.
Our key insight is that using just a polynomial number of samples, one can
learn a centralized model that generalizes across different policies. We can
then optimize the policy within the learned model instead of the true system,
reducing the number of environment interactions. We also learn a centralized
exploration policy within our model that learns to collect additional data in
state-action regions with high model uncertainty. Finally, we empirically
evaluate the proposed model-based algorithm, MARCO, in three cooperative
communication tasks, where it improves sample efficiency by up to 20x.
",Centralized Model and Exploration Policy for Multi-Agent RL
"  Biodiversity, the variation within and between species and ecosystems, is
essential for human well-being and the equilibrium of the planet. It is
critical for the sustainable development of human society and is an important
global challenge. Biodiversity research has become increasingly data-intensive
and it deals with heterogeneous and distributed data made available by global
and regional initiatives, such as GBIF, ILTER, LifeWatch, BODC, PANGAEA, and
TERN, that apply different data management practices. In particular, a variety
of metadata and semantic resources have been produced by these initiatives to
describe biodiversity observations, introducing interoperability issues across
data management systems. To address these challenges, the InteroperAble
Descriptions of Observable Property Terminology WG (I-ADOPT WG) was formed by a
group of international terminology providers and data center managers in 2019
with the aim to build a common approach to describe what is observed, measured,
calculated, or derived. Based on an extensive analysis of existing semantic
representations of variables, the WG has recently published the I-ADOPT
framework ontology to facilitate interoperability between existing semantic
resources and support the provision of machine-readable variable descriptions
whose components are mapped to FAIR vocabulary terms. The I-ADOPT framework
ontology defines a set of high level semantic components that can be used to
describe a variety of patterns commonly found in scientific observations. This
contribution will focus on how the I-ADOPT framework can be applied to
represent variables commonly used in the biodiversity domain.
","The I-ADOPT Interoperability Framework for FAIRer data descriptions of
  biodiversity"
"  Behavior trees (BTs) are a popular method for modeling NPC and enemy AI
behavior and have been widely used in commercial games. In this work, rather
than use BTs to model game playing agents, we use them for modeling game design
agents, defining behaviors as content generation tasks rather than in-game
actions. Similar to how traditional BTs enable modeling behaviors in a modular
and dynamic manner, BTs for PCG enable simple subtrees for generating parts of
levels to be combined modularly to form complex trees for generating whole
levels as well as generators that can dynamically vary the generated content.
We refer to this approach as Procedural Content Generation using Behavior
Trees, or PCGBT, and demonstrate it by using BTs to model generators for Super
Mario Bros., Mega Man and Metroid levels as well as dungeon layouts and discuss
several ways in which this paradigm could be applied and extended in the
future.
",Procedural Content Generation using Behavior Trees (PCGBT)
"  In the past few decades, artificial intelligence (AI) technology has
experienced swift developments, changing everyone's daily life and profoundly
altering the course of human society. The intention of developing AI is to
benefit humans, by reducing human labor, bringing everyday convenience to human
lives, and promoting social good. However, recent research and AI applications
show that AI can cause unintentional harm to humans, such as making unreliable
decisions in safety-critical scenarios or undermining fairness by inadvertently
discriminating against one group. Thus, trustworthy AI has attracted immense
attention recently, which requires careful consideration to avoid the adverse
effects that AI may bring to humans, so that humans can fully trust and live in
harmony with AI technologies.
  Recent years have witnessed a tremendous amount of research on trustworthy
AI. In this survey, we present a comprehensive survey of trustworthy AI from a
computational perspective, to help readers understand the latest technologies
for achieving trustworthy AI. Trustworthy AI is a large and complex area,
involving various dimensions. In this work, we focus on six of the most crucial
dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii)
Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v)
Accountability & Auditability, and (vi) Environmental Well-Being. For each
dimension, we review the recent related technologies according to a taxonomy
and summarize their applications in real-world systems. We also discuss the
accordant and conflicting interactions among different dimensions and discuss
potential aspects for trustworthy AI to investigate in the future.
",Trustworthy AI: A Computational Perspective
"  We describe several additions to the ENIGMA system that guides clause
selection in the E automated theorem prover. First, we significantly speed up
its neural guidance by adding server-based GPU evaluation. The second addition
is motivated by fast weight-based rejection filters that are currently used in
systems like E and Prover9. Such systems can be made more intelligent by
instead training fast versions of ENIGMA that implement more intelligent
pre-filtering. This results in combinations of trainable fast and slow thinking
that improves over both the fast-only and slow-only methods. The third addition
is based on ""judging the children by their parents"", i.e., possibly rejecting
an inference before it produces a clause. This is motivated by standard
evolutionary mechanisms, where there is always a cost to producing all possible
offsprings in the current population. This saves time by not evaluating all
clauses by more expensive methods and provides a complementary view of the
generated clauses. The methods are evaluated on a large benchmark coming from
the Mizar Mathematical Library, showing good improvements over the state of the
art.
",Fast and Slow Enigmas and Parental Guidance
"  We investigate the effect of using human demonstration data in the replay
buffer for Deep Reinforcement Learning. We use a policy gradient method with a
modified experience replay buffer where a human demonstration experience is
sampled with a given probability. We analyze different ratios of using
demonstration data in a task where an agent attempts to reach a goal while
avoiding obstacles. Our results suggest that while the agents trained by pure
self-exploration and pure demonstration had similar success rates, the pure
demonstration model converged faster to solutions with less number of steps.
","Mixing Human Demonstrations with Self-Exploration in Experience Replay
  for Deep Reinforcement Learning"
"  Reinforcement Learning (RL) is known to be often unsuccessful in environments
with sparse extrinsic rewards. A possible countermeasure is to endow RL agents
with an intrinsic reward function, or 'intrinsic motivation', which rewards the
agent based on certain features of the current sensor state. An intrinsic
reward function based on the principle of empowerment assigns rewards
proportional to the amount of control the agent has over its own sensors. We
implemented a variation on a recently proposed intrinsically motivated agent,
which we refer to as the 'curious' agent, and an empowerment-inspired agent.
The former leverages sensor state encoding with a variational autoencoder,
while the latter predicts the next sensor state via a variational information
bottleneck. We compared the performance of both agents to that of an advantage
actor-critic baseline in four sparse reward grid worlds. Both the empowerment
agent and its curious competitor seem to benefit to similar extents from their
intrinsic rewards. This provides some experimental support to the conjecture
that empowerment can be used to drive exploration.
","Experimental Evidence that Empowerment May Drive Exploration in
  Sparse-Reward Environments"
"  Bus timetable optimization is a key issue to reduce operational cost of bus
companies and improve the service quality. Existing methods use exact or
heuristic algorithms to optimize the timetable in an offline manner. In
practice, the passenger flow may change significantly over time. Timetables
determined in offline cannot adjust the departure interval to satisfy the
changed passenger flow. Aiming at improving the online performance of bus
timetable, we propose a Deep Reinforcement Learning based bus Timetable dynamic
Optimization method (DRL-TO). In this method, the timetable optimization is
considered as a sequential decision problem. A Deep Q-Network (DQN) is employed
as the decision model to determine whether to dispatch a bus service during
each minute of the service period. Therefore, the departure intervals of bus
services are determined in real time in accordance with passenger demand. We
identify several new and useful state features for the DQN, including the load
factor, carrying capacity utilization rate, and the number of stranding
passengers. Taking into account both the interests of the bus company and
passengers, a reward function is designed, which includes the indicators of
full load rate, empty load rate, passengers' waiting time, and the number of
stranding passengers. Building on an existing method for calculating the
carrying capacity, we develop a new technique to enhance the matching degree at
each bus station. Experiments demonstrate that compared with the timetable
generated by the state-of-the-art bus timetable optimization approach based on
a memetic algorithm (BTOA-MA), Genetic Algorithm (GA) and the manual method,
DRL-TO can dynamically determine the departure intervals based on the real-time
passenger flow, saving 8$\%$ of vehicles and reducing 17$\%$ of passengers'
waiting time on average.
",Deep Reinforcement Learning based Dynamic Optimization of Bus Timetable
"  Deep neural networks have significantly contributed to the success in
predictive accuracy for classification tasks. However, they tend to make
over-confident predictions in real-world settings, where domain shifting and
out-of-distribution (OOD) examples exist. Most research on uncertainty
estimation focuses on computer vision because it provides visual validation on
uncertainty quality. However, few have been presented in the natural language
process domain. Unlike Bayesian methods that indirectly infer uncertainty
through weight uncertainties, current evidential uncertainty-based methods
explicitly model the uncertainty of class probabilities through subjective
opinions. They further consider inherent uncertainty in data with different
root causes, vacuity (i.e., uncertainty due to a lack of evidence) and
dissonance (i.e., uncertainty due to conflicting evidence). In our paper, we
firstly apply evidential uncertainty in OOD detection for text classification
tasks. We propose an inexpensive framework that adopts both auxiliary outliers
and pseudo off-manifold samples to train the model with prior knowledge of a
certain class, which has high vacuity for OOD samples. Extensive empirical
experiments demonstrate that our model based on evidential uncertainty
outperforms other counterparts for detecting OOD examples. Our approach can be
easily deployed to traditional recurrent neural networks and fine-tuned
pre-trained transformers.
",Uncertainty-Aware Reliable Text Classification
"  In this paper, we propose a simple yet effective solution to build practical
teacher recommender systems for online one-on-one classes. Our system consists
of (1) a pseudo matching score module that provides reliable training labels;
(2) a ranking model that scores every candidate teacher; (3) a novelty boosting
module that gives additional opportunities to new teachers; and (4) a diversity
metric that guardrails the recommended results to reduce the chance of
collision. Offline experimental results show that our approach outperforms a
wide range of baselines. Furthermore, we show that our approach is able to
reduce the number of student-teacher matching attempts from 7.22 to 3.09 in a
five-month observation on a third-party online education platform.
","An Educational System for Personalized Teacher Recommendation in K-12
  Online Classrooms"
"  Mixed-integer linear programs (MILPs) are widely used in artificial
intelligence and operations research to model complex decision problems like
scheduling and routing. Designing such programs however requires both domain
and modelling expertise. In this paper, we study the problem of acquiring MILPs
from contextual examples, a novel and realistic setting in which examples
capture solutions and non-solutions within a specific context. The resulting
learning problem involves acquiring continuous parameters -- namely, a cost
vector and a feasibility polytope -- but has a distinctly combinatorial flavor.
To solve this complex problem, we also contribute MISSLE, an algorithm for
learning MILPs from contextual examples. MISSLE uses a variant of stochastic
local search that is guided by the gradient of a continuous surrogate loss
function. Our empirical evaluation on synthetic data shows that MISSLE acquires
better MILPs faster than alternatives based on stochastic local search and
gradient descent.
",Learning Mixed-Integer Linear Programs from Contextual Examples
"  The recent state-of-the-art natural language understanding (NLU) systems
often behave unpredictably, failing on simpler reasoning examples. Despite
this, there has been limited focus on quantifying progress towards systems with
more predictable behavior. We think that reasoning capability-wise behavioral
summary is a step towards bridging this gap. We create a CheckList test-suite
(184K examples) for the Natural Language Inference (NLI) task, a representative
NLU task. We benchmark state-of-the-art NLI systems on this test-suite, which
reveals fine-grained insights into the reasoning abilities of BERT and RoBERTa.
Our analysis further reveals inconsistencies of the models on examples derived
from the same template or distinct templates but pertaining to same reasoning
capability, indicating that generalizing the models' behavior through
observations made on a CheckList is non-trivial. Through an user-study, we find
that users were able to utilize behavioral information to generalize much
better for examples predicted from RoBERTa, compared to that of BERT.
","Trusting RoBERTa over BERT: Insights from CheckListing the Natural
  Language Inference Task"
"  Federated learning (FL) is a distributed model for deep learning that
integrates client-server architecture, edge computing, and real-time
intelligence. FL has the capability of revolutionizing machine learning (ML)
but lacks in the practicality of implementation due to technological
limitations, communication overhead, non-IID (independent and identically
distributed) data, and privacy concerns. Training a ML model over heterogeneous
non-IID data highly degrades the convergence rate and performance. The existing
traditional and clustered FL algorithms exhibit two main limitations, including
inefficient client training and static hyper-parameter utilization. To overcome
these limitations, we propose a novel hybrid algorithm, namely genetic
clustered FL (Genetic CFL), that clusters edge devices based on the training
hyper-parameters and genetically modifies the parameters cluster-wise. Then, we
introduce an algorithm that drastically increases the individual cluster
accuracy by integrating the density-based clustering and genetic
hyper-parameter optimization. The results are bench-marked using MNIST
handwritten digit dataset and the CIFAR-10 dataset. The proposed genetic CFL
shows significant improvements and works well with realistic cases of non-IID
and ambiguous data.
","Genetic CFL: Optimization of Hyper-Parameters in Clustered Federated
  Learning"
"  Recent E-commerce applications benefit from the growth of deep learning
techniques. However, we notice that many works attempt to maximize business
objectives by closely matching offline labels which follow the supervised
learning paradigm. This results in models obtain high offline performance in
terms of Area Under Curve (AUC) and Normalized Discounted Cumulative Gain
(NDCG), but cannot consistently increase the revenue metrics such as purchases
amount of users. Towards the issues, we build a simulated search engine AESim
that can properly give feedback by a well-trained discriminator for generated
pages, as a dynamic dataset. Different from previous simulation platforms which
lose connection with the real world, ours depends on the real data in
AliExpress Search: we use adversarial learning to generate virtual users and
use Generative Adversarial Imitation Learning (GAIL) to capture behavior
patterns of users. Our experiments also show AESim can better reflect the
online performance of ranking models than classic ranking metrics, implying
AESim can play a surrogate of AliExpress Search and evaluate models without
going online.
",Imitate TheWorld: A Search Engine Simulation Platform
"  Constraint answer set programming or CASP, for short, is a hybrid approach in
automated reasoning putting together the advances of distinct research areas
such as answer set programming, constraint processing, and satisfiability
modulo theories. Constraint answer set programming demonstrates promising
results, including the development of a multitude of solvers: acsolver,
clingcon, ezcsp, idp, inca, dingo, mingo, aspmt, clingo[l,dl], and ezsmt. It
opens new horizons for declarative programming applications such as solving
complex train scheduling problems. Systems designed to find solutions to
constraint answer set programs can be grouped according to their construction
into, what we call, integrational or translational approaches. The focus of
this paper is an overview of the key ingredients of the design of constraint
answer set solvers drawing distinctions and parallels between integrational and
translational approaches. The paper also provides a glimpse at the kind of
programs its users develop by utilizing a CASP encoding of Travelling Salesman
problem for illustration. In addition, we place the CASP technology on the map
among its automated reasoning peers as well as discuss future possibilities for
the development of CASP.
","Constraint Answer Set Programming: Integrational and Translational (or
  SMT-based) Approaches"
"  We propose an approach based on Answer Set Programming for reasoning about
actions with domain descriptions including ontological knowledge, expressed in
the lightweight description logic EL^\bot. We consider a temporal action
theory, which allows for non-deterministic actions and causal rules to deal
with ramifications, and whose extensions are defined by temporal answer sets.
We provide conditions under which action consistency can be guaranteed with
respect to an ontology, by a polynomial encoding of an action theory extended
with an EL^\bot knowledge base (in normal form) into a temporal action theory.
",Reasoning about actions with EL ontologies with temporal answer sets
"  The present preprint specifies and defines all Terms, Properties,
Relationships and Axioms of ThingFO (Thing Foundational Ontology) v1.2, which
is a slightly updated version of its predecessor, ThingFO v1.1. It is an
ontology for particular and universal Things placed at the foundational level
in the context of a four-layered ontological architecture named FCD-OntoArch
(Foundational, Core, and Domain Ontological Architecture for Sciences). This is
a five-layered ontological architecture, which considers Foundational, Core,
Domain and Instance levels. In turn, the domain level is split down in two
sub-levels, namely: Top-domain and Low-domain. Ontologies at the same level can
be related to each other, except for the foundational level where only the
ThingFO ontology is. In addition, ontologies' terms and relationships at lower
levels can be semantically enriched by ontologies' terms and relationships from
the higher levels. ThingFO and ontologies at the core level such as
SituationCO, ProcessCO, ProjectCO, among others, are domain independent.
ThingFO is made up of three main concepts, namely: Thing with the semantics of
Particular, Thing Category with the semantics of Universal, and Assertion that
represents human statements about different aspects of Particulars and
Universals. Note that annotations of updates from the previous version (v1.1)
to the current one (v1.2) can be found in Appendix A.
","ThingFO v1.2's Terms, Properties, Relationships and Axioms --
  Foundational Ontology for Things"
"  Healthcare representation learning on the Electronic Health Record (EHR) is
seen as crucial for predictive analytics in the medical field. Many natural
language processing techniques, such as word2vec, RNN and self-attention, have
been adapted for use in hierarchical and time stamped EHR data, but fail when
they lack either general or task-specific data. Hence, some recent works train
healthcare representations by incorporating medical ontology (a.k.a. knowledge
graph), by self-supervised tasks like diagnosis prediction, but (1) the
small-scale, monotonous ontology is insufficient for robust learning, and (2)
critical contexts or dependencies underlying patient journeys are never
exploited to enhance ontology learning. To address this, we propose an
end-to-end robust Transformer-based solution, Mutual Integration of patient
journey and Medical Ontology (MIMO) for healthcare representation learning and
predictive analytics. Specifically, it consists of task-specific representation
learning and graph-embedding modules to learn both patient journey and medical
ontology interactively. Consequently, this creates a mutual integration to
benefit both healthcare representation learning and medical ontology embedding.
Moreover, such integration is achieved by a joint training of both
task-specific predictive and ontology-based disease typing tasks based on fused
embeddings of the two modules. Experiments conducted on two real-world
diagnosis prediction datasets show that, our healthcare representation model
MIMO not only achieves better predictive results than previous state-of-the-art
approaches regardless of sufficient or insufficient training data, but also
derives more interpretable embeddings of diagnoses.
","MIMO: Mutual Integration of Patient Journey and Medical Ontology for
  Healthcare Representation Learning"
"  The search for binary sequences with low peak sidelobe level value represents
a formidable computational problem. To locate better sequences for this
problem, we designed a stochastic algorithm that uses two fitness functions. In
these fitness functions, the value of the autocorrelation function has a
different impact on the final fitness value. It is defined with the value of
the exponent over the autocorrelation function values. Each function is used in
the corresponding optimization phase, and the optimization process switches
between these two phases until the stopping condition is satisfied. The
proposed algorithm was implemented using the compute unified device
architecture and therefore allowed us to exploit the computational power of
graphics processing units. This algorithm was tested on sequences with lengths
$L = 2^m - 1$, for $14 \le m \le 20$. From the obtained results it is evident
that the usage of two fitness functions improved the efficiency of the
algorithm significantly, new-best known solutions were achieved, and the
achieved PSL values were significantly less than $\sqrt{L}$.
","Two-phase Optimization of Binary Sequences with Low Peak Sidelobe Level
  Value"
"  The current preprint is an update to SituationCO v1.1 (Situation Core
Ontology), which represents its new version 1.2. It specifies and defines all
the terms, properties, relationships and axioms of SituationCO v1.2, being an
ontology for particular and generic Situations placed at the core level in the
context of a four-layered ontological architecture called FCD-OntoArch
(Foundational, Core, and Domain Ontological Architecture for Sciences). This is
a four-layered ontological architecture, which considers Foundational, Core,
Domain and Instance levels. In turn, the domain level is split down in two
sub-levels, namely: Top-domain and Low-domain ontological levels. So in fact,
we can consider it to be a five-tier architecture. Ontologies at the same level
can be related to each other, except for the foundational level where only
ThingFO (Thing Foundational Ontology) is found. In addition, ontologies' terms
and relationships at lower levels can be semantically enriched by ontologies'
terms and relationships from the higher levels. Note that both ThingFO and
ontologies at the core level such as SituationCO, ProcessCO, among others, are
domain independent. SituationCO's terms and relationships are specialized
primarily from ThingFO. It also completely reuses terms primarily from
ProcessCO, ProjectCO and GoalCO ontologies. Stereotypes are the used mechanism
for enriching SituationCO terms. Note that in the end of this document, we
address the SituationCO vs. ThingFO non-taxonomic relationship verification
matrix.
","SituationCO v1.2's Terms, Properties, Relationships and Axioms -- A Core
  Ontology for Particular and Generic Situations"
"  Reinforcement Learning (RL) is a promising approach for solving various
control, optimization, and sequential decision making tasks. However, designing
reward functions for complex tasks (e.g., with multiple objectives and safety
constraints) can be challenging for most users and usually requires multiple
expensive trials (reward function hacking). In this paper we propose a
specification language (Inkling Goal Specification) for complex control and
optimization tasks, which is very close to natural language and allows a
practitioner to focus on problem specification instead of reward function
hacking. The core elements of our framework are: (i) mapping the high level
language to a predicate temporal logic tailored to control and optimization
tasks, (ii) a novel automaton-guided dense reward generation that can be used
to drive RL algorithms, and (iii) a set of performance metrics to assess the
behavior of the system. We include a set of experiments showing that the
proposed method provides great ease of use to specify a wide range of real
world tasks; and that the reward generated is able to drive the policy training
to achieve the specified goal.
",Reinforcement Learning Agent Training with Goals for Real World Tasks
"  In order to construct an ethical artificial intelligence (AI) two complex
problems must be overcome. Firstly, humans do not consistently agree on what is
or is not ethical. Second, contemporary AI and machine learning methods tend to
be blunt instruments which either search for solutions within the bounds of
predefined rules, or mimic behaviour. An ethical AI must be capable of
inferring unspoken rules, interpreting nuance and context, possess and be able
to infer intent, and explain not just its actions but its intent. Using
enactivism, semiotics, perceptual symbol systems and symbol emergence, we
specify an agent that learns not just arbitrary relations between signs but
their meaning in terms of the perceptual states of its sensorimotor system.
Subsequently it can learn what is meant by a sentence and infer the intent of
others in terms of its own experiences. It has malleable intent because the
meaning of symbols changes as it learns, and its intent is represented
symbolically as a goal. As such it may learn a concept of what is most likely
to be considered ethical by the majority within a population of humans, which
may then be used as a goal. The meaning of abstract symbols is expressed using
perceptual symbols of raw sensorimotor stimuli as the weakest (consistent with
Ockham's Razor) necessary and sufficient concept, an intensional definition
learned from an ostensive definition, from which the extensional definition or
category of all ethical decisions may be obtained. Because these abstract
symbols are the same for both situation and response, the same symbol is used
when either performing or observing an action. This is akin to mirror neurons
in the human brain. Mirror symbols may allow the agent to empathise, because
its own experiences are associated with the symbol, which is also associated
with the observation of another agent experiencing something that symbol
represents.
","Philosophical Specification of Empathetic Ethical Artificial
  Intelligence"
"  Indoor navigation systems leverage shortest path algorithms to calculate
routes. In order to define the ""shortest path"", a cost function has to be
specified based on theories and heuristics in the application domain. For the
domain of indoor routing, we survey theories and criteria identified in the
literature as essential for human path planning. We drive quantitative
definitions and integrate them into a cost function that weights each of the
criteria separately. We then apply an exhaustive grid search to find weights
that lead to an ideal cost function. ""Ideal"" here is defined as guiding the
algorithm to plan routes that are most similar to those chosen by humans. To
explore which criteria should be taken into account in an improved pathfinding
algorithm, eleven different factors whose favorable impact on route selection
has been established in past research were considered. Each factor was included
separately in the Dijkstra algorithm and the similarity of thus calculated
routes to the actual routes chosen by students at the University of Regensburg
was determined. This allows for a quantitative assessment of the factors'
impact and further constitutes a way to directly compare them. A reduction of
the number of turns, streets, revolving doors, entryways, elevators as well as
the combination of the aforementioned factors was found to have a positive
effect and generate paths that were favored over the shortest path. Turns and
the combination of criteria turned out to be most impactful.
",User Preferences and the Shortest Path
"  Exploration is critical for good results in deep reinforcement learning and
has attracted much attention. However, existing multi-agent deep reinforcement
learning algorithms still use mostly noise-based techniques. Very recently,
exploration methods that consider cooperation among multiple agents have been
developed. However, existing methods suffer from a common challenge: agents
struggle to identify states that are worth exploring, and hardly coordinate
exploration efforts toward those states. To address this shortcoming, in this
paper, we propose cooperative multi-agent exploration (CMAE): agents share a
common goal while exploring. The goal is selected from multiple projected state
spaces via a normalized entropy-based technique. Then, agents are trained to
reach this goal in a coordinated manner. We demonstrate that CMAE consistently
outperforms baselines on various tasks, including a sparse-reward version of
the multiple-particle environment (MPE) and the Starcraft multi-agent challenge
(SMAC).
",Cooperative Exploration for Multi-Agent Deep Reinforcement Learning
"  In order to design and engineer ethical and legal reasoners and responsible
systems, Benzm\""{u}ller, Parent and van der Torre introduced the LogiKEy
methodology, based on the semantical embedding of deontic logics into classic
higher-order logic. This article considerably extends the LogiKEy deontic
logics and dataset using an algebraic approach, and develops a theory of
input/output operations for normative reasoning on top of Boolean algebras.
","New Algebraic Normative Theories for Ethical and Legal Reasoning in the
  LogiKEy Framework"
"  Blame attribution is one of the key aspects of accountable decision making,
as it provides means to quantify the responsibility of an agent for a decision
making outcome. In this paper, we study blame attribution in the context of
cooperative multi-agent sequential decision making. As a particular setting of
interest, we focus on cooperative decision making formalized by Multi-Agent
Markov Decision Processes (MMDP), and we analyze different blame attribution
methods derived from or inspired by existing concepts in cooperative game
theory. We formalize desirable properties of blame attribution in the setting
of interest, and we analyze the relationship between these properties and the
studied blame attribution methods. Interestingly, we show that some of the well
known blame attribution methods, such as Shapley value, are not
performance-incentivizing, while others, such as Banzhaf index, may over-blame
agents. To mitigate these value misalignment and fairness issues, we introduce
a novel blame attribution method, unique in the set of properties it satisfies,
which trade-offs explanatory power (by under-blaming agents) for the
aforementioned properties. We further show how to account for uncertainty about
agents' decision making policies, and we experimentally: a) validate the
qualitative properties of the studied blame attribution methods, and b) analyze
their robustness to uncertainty.
","On Blame Attribution for Accountable Multi-Agent Sequential Decision
  Making"
"  Detecting rumors on social media is a very critical task with significant
implications to the economy, public health, etc. Previous works generally
capture effective features from texts and the propagation structure. However,
the uncertainty caused by unreliable relations in the propagation structure is
common and inevitable due to wily rumor producers and the limited collection of
spread data. Most approaches neglect it and may seriously limit the learning of
features. Towards this issue, this paper makes the first attempt to explore
propagation uncertainty for rumor detection. Specifically, we propose a novel
Edge-enhanced Bayesian Graph Convolutional Network (EBGCN) to capture robust
structural features. The model adaptively rethinks the reliability of latent
relations by adopting a Bayesian approach. Besides, we design a new edge-wise
consistency training framework to optimize the model by enforcing consistency
on relations. Experiments on three public benchmark datasets demonstrate that
the proposed model achieves better performance than baseline methods on both
rumor detection and early rumor detection tasks.
","Towards Propagation Uncertainty: Edge-enhanced Bayesian Graph
  Convolutional Networks for Rumor Detection"
"  Playtesting is an essential step in the game design process. Game designers
use the feedback from playtests to refine their design. Game designers may
employ procedural personas to automate the playtesting process. In this paper,
we present two approaches to improve automated playtesting. First, we propose a
goal-based persona model, which we call developing persona -- developing
persona proposes a dynamic persona model, whereas the current persona models
are static. Game designers can use the developing persona to model the changes
that a player undergoes while playing a game. Additionally, a human playtester
knows which paths she has tested before, and during the consequent tests, she
may test different paths. However, RL agents disregard the previously generated
trajectories. We propose a novel methodology that helps Reinforcement Learning
(RL) agents to generate distinct trajectories than the previous trajectories.
We refer to this methodology as Alternative Path Finder (APF). We present a
generic APF framework that can be applied to all RL agents. APF is trained with
the previous trajectories, and APF distinguishes the novel states from similar
states. We use the General Video Game Artificial Intelligence (GVG-AI) and
VizDoom frameworks to test our proposed methodologies. We use Proximal Policy
Optimization (PPO) RL agent during experiments. First, we show that the
playtest data generated by the developing persona cannot be generated using the
procedural personas. Second, we present the alternative paths found using APF.
We show that the APF penalizes the previous paths and rewards the distinct
paths.
",Playtesting: What is Beyond Personas
"  Probabilistic sentential decision diagrams are a class of
structured-decomposable probabilistic circuits especially designed to embed
logical constraints. To adapt the classical LearnSPN scheme to learn the
structure of these models, we propose a new scheme based on a partial
closed-world assumption: data implicitly provide the logical base of the
circuit. Sum nodes are thus learned by recursively clustering batches in the
initial data base, while the partitioning of the variables obeys a given input
vtree. Preliminary experiments show that the proposed approach might properly
fit training data, and generalize well to test data, provided that these remain
consistent with the underlying logical base, that is a relaxation of the
training data base.
","Structural Learning of Probabilistic Sentential Decision Diagrams under
  Partial Closed-World Assumption"
"  Rough Set based Spanning Sets were recently proposed to deal with
uncertainties arising in the problem in domain of natural language processing
problems. This paper presents a novel span measure using upper approximations.
The key contribution of this paper is to propose another uncertainty measure of
span and spanning sets. Firstly, this paper proposes a new definition of
computing span which use upper approximation instead of boundary regions. This
is useful in situations where computing upper approximations are much more
convenient that computing boundary region. Secondly, properties of novel span
and relation with earlier span measure are discussed. Thirdly, the paper
presents application areas where the proposed span measure can be utilized.
","Novel Span Measure, Spanning Sets and Applications"
"  Rough Set based concepts of Span and Spanning Sets were recently proposed to
deal with uncertainties in data. Here, this paper, presents novel concepts for
generic decision-making process using Rough Set based span for a decision
table. Majority of problems in Artificial Intelligence deal with decision
making. This paper provides real life applications of proposed Rough Set based
span for decision tables. Here, novel concept of span for a decision table is
proposed, illustrated with real life example of flood relief and rescue team
assignment. Its uses, applications and properties are explored. The key
contribution of paper is primarily to study decision making using Rough Set
based Span for a decision tables, as against an information system in prior
works. Here, the main contribution is that decision classes are automatically
learned by the technique of Rough Set based span, for a particular problem,
hence automating the decision-making process. These decision-making tools based
on span can guide an expert in taking decisions in tough and time-bound
situations.
","Decision Making Using Rough Set based Spanning Sets for a Decision
  System"
"  Reinforcement learning (RL) studies how an agent comes to achieve reward in
an environment through interactions over time. Recent advances in machine RL
have surpassed human expertise at the world's oldest board games and many
classic video games, but they require vast quantities of experience to learn
successfully -- none of today's algorithms account for the human ability to
learn so many different tasks, so quickly. Here we propose a new approach to
this challenge based on a particularly strong form of model-based RL which we
call Theory-Based Reinforcement Learning, because it uses human-like intuitive
theories -- rich, abstract, causal models of physical objects, intentional
agents, and their interactions -- to explore and model an environment, and plan
effectively to achieve task goals. We instantiate the approach in a video game
playing agent called EMPA (the Exploring, Modeling, and Planning Agent), which
performs Bayesian inference to learn probabilistic generative models expressed
as programs for a game-engine simulator, and runs internal simulations over
these models to support efficient object-based, relational exploration and
heuristic planning. EMPA closely matches human learning efficiency on a suite
of 90 challenging Atari-style video games, learning new games in just minutes
of game play and generalizing robustly to new game situations and new levels.
The model also captures fine-grained structure in people's exploration
trajectories and learning dynamics. Its design and behavior suggest a way
forward for building more general human-like AI systems.
","Human-Level Reinforcement Learning through Theory-Based Modeling,
  Exploration, and Planning"
"  Agriculture is the fundamental industry of the society, which is the basis of
food supply and an important source of employment and GDP increase. However,
the insufficient expert can not fulfill the demand of farmers. To address this
problem, we design a chatbot to answer frequently asked questions in the
Agriculture field. Template-based questions will be answered by AIML while LSA
is used for other service-based questions. This chatbot will assist farmers by
dealing with industry problems conveniently and efficiently.
",Template-based Chatbot for Agriculture Related FAQs
"  Everyday tasks are characterized by their varieties and variations, and
frequently are not clearly specified to service agents. This paper presents a
comprehensive approach to enable a service agent to deal with everyday tasks in
open, uncontrolled environments. We introduce a generic structure for
representing tasks, and another structure for representing situations. Based on
the two newly introduced structures, we present a methodology of situation
handling that avoids hard-coding domain rules while improving the scalability
of real-world task planning systems.
",Task and Situation Structures for Service Agent Planning
"  The inexpressive Description Logic (DL) $\mathcal{FL}_0$, which has
conjunction and value restriction as its only concept constructors, had fallen
into disrepute when it turned out that reasoning in $\mathcal{FL}_0$ w.r.t.
general TBoxes is ExpTime-complete, i.e., as hard as in the considerably more
expressive logic $\mathcal{ALC}$. In this paper, we rehabilitate
$\mathcal{FL}_0$ by presenting a dedicated subsumption algorithm for
$\mathcal{FL}_0$, which is much simpler than the tableau-based algorithms
employed by highly optimized DL reasoners. Our experiments show that the
performance of our novel algorithm, as prototypically implemented in our
$\mathcal{FL}_o$wer reasoner, compares very well with that of the highly
optimized reasoners. $\mathcal{FL}_o$wer can also deal with ontologies written
in the extension $\mathcal{FL}_{\bot}$ of $\mathcal{FL}_0$ with the top and the
bottom concept by employing a polynomial-time reduction, shown in this paper,
which eliminates top and bottom. We also investigate the complexity of
reasoning in DLs related to the Horn-fragments of $\mathcal{FL}_0$ and
$\mathcal{FL}_{\bot}$.
","Efficient TBox Reasoning with Value Restrictions using the
  $\mathcal{FL}_{o}$wer reasoner"
"  Current PB solvers implement many techniques inspired by the CDCL
architecture of modern SAT solvers, so as to benefit from its practical
efficiency. However, they also need to deal with the fact that many of the
properties leveraged by this architecture are no longer true when considering
PB constraints. In this paper, we focus on one of these properties, namely the
optimality of the so-called first unique implication point (1-UIP). While it is
well known that learning the first assertive clause produced during conflict
analysis ensures to perform the highest possible backjump in a SAT solver, we
show that there is no such guarantee in the presence of PB constraints. We also
introduce and evaluate different approaches designed to improve the backjump
level identified during conflict analysis by allowing to continue the analysis
after reaching the 1-UIP. Our experiments show that sub-optimal backjumps are
fairly common in PB solvers, even though their impact on the solver is not
clear.
",On Improving the Backjump Level in PB Solvers
"  We propose a novel framework that detects conflicts in IoT-based smart homes.
Conflicts may arise during interactions between the resident and IoT services
in smart homes. We propose a generic knowledge graph to represent the relations
between IoT services and environment entities. We also profile a generic
knowledge graph to a specific smart home setting based on the context
information. We propose a conflict taxonomy to capture different types of
conflicts in a single resident smart home setting. A conflict detection
algorithm is proposed to identify potential conflicts using the profiled
knowledge graph. We conduct a set of experiments on real datasets and
synthesized datasets to validate the effectiveness and efficiency of our
proposed approach.
",Conflict Detection in IoT-based Smart Homes
"  Packet routing is a fundamental problem in communication networks that
decides how the packets are directed from their source nodes to their
destination nodes through some intermediate nodes. With the increasing
complexity of network topology and highly dynamic traffic demand, conventional
model-based and rule-based routing schemes show significant limitations, due to
the simplified and unrealistic model assumptions, and lack of flexibility and
adaption. Adding intelligence to the network control is becoming a trend and
the key to achieving high-efficiency network operation. In this paper, we
develop a model-free and data-driven routing strategy by leveraging
reinforcement learning (RL), where routers interact with the network and learn
from the experience to make some good routing configurations for the future.
Considering the graph nature of the network topology, we design a multi-agent
RL framework in combination with Graph Neural Network (GNN), tailored to the
routing problem. Three deployment paradigms, centralized, federated, and
cooperated learning, are explored respectively. Simulation results demonstrate
that our algorithm outperforms some existing benchmark algorithms in terms of
packet transmission delay and affordable load.
",Packet Routing with Graph Attention Multi-agent Reinforcement Learning
"  Tables in scientific papers contain a wealth of valuable knowledge for the
scientific enterprise. To help the many of us who frequently consult this type
of knowledge, we present Tab2Know, a new end-to-end system to build a Knowledge
Base (KB) from tables in scientific papers. Tab2Know addresses the challenge of
automatically interpreting the tables in papers and of disambiguating the
entities that they contain. To solve these problems, we propose a pipeline that
employs both statistical-based classifiers and logic-based reasoning. First,
our pipeline applies weakly supervised classifiers to recognize the type of
tables and columns, with the help of a data labeling system and an ontology
specifically designed for our purpose. Then, logic-based reasoning is used to
link equivalent entities (via sameAs links) in different tables. An empirical
evaluation of our approach using a corpus of papers in the Computer Science
domain has returned satisfactory performance. This suggests that ours is a
promising step to create a large-scale KB of scientific knowledge.
",Tab2Know: Building a Knowledge Base from Tables in Scientific Papers
"  Math word problem (MWP) solving is the task of transforming a sequence of
natural language problem descriptions to executable math equations. An MWP
solver not only needs to understand complex scenarios described in the problem
texts, but also identify the key mathematical variables and associate text
descriptions with math equation logic. Although recent sequence modeling MWP
solvers have gained credits on the math-text contextual understanding,
pre-trained language models (PLM) have not been explored for solving MWP,
considering that PLM trained over free-form texts is limited in representing
text references to mathematical logic. In this work, we introduce MWP-BERT to
obtain pre-trained token representations that capture the alignment between
text description and mathematical logic. Additionally, we introduce a
keyword-based prompt matching method to address the MWPs requiring common-sense
knowledge. On a benchmark Math23K dataset and a new Ape210k dataset, we show
that MWP-BERT outperforms the strongest baseline model by 5-10% improvement on
accuracy.
",MWP-BERT: A Strong Baseline for Math Word Problems
"  Artificial intelligence (AI) in healthcare is a potentially revolutionary
tool to achieve improved healthcare outcomes while reducing overall health
costs. While many exploratory results hit the headlines in recent years there
are only few certified and even fewer clinically validated products available
in the clinical setting. This is a clear indication of failing translation due
to shortcomings of the current approach to AI in healthcare. In this work, we
highlight the major areas, where we observe current challenges for translation
in AI in healthcare, namely precision medicine, reproducible science, data
issues and algorithms, causality, and product development. For each field, we
outline possible solutions for these challenges. Our work will lead to improved
translation of AI in healthcare products into the clinical setting
",Artificial Intelligence in Healthcare: Lost In Translation?
"  Several approaches have been developed to answer specific questions that a
user may have about an AI system that can plan and act. However, the problems
of identifying which questions to ask and that of computing a
user-interpretable symbolic description of the overall capabilities of the
system have remained largely unaddressed. This paper presents an approach for
addressing these problems by learning user-interpretable symbolic descriptions
of the limits and capabilities of a black-box AI system using low-level
simulators. It uses a hierarchical active querying paradigm to generate
questions and to learn a user-interpretable model of the AI system based on its
responses. In contrast to prior work, we consider settings where imprecision of
the user's conceptual vocabulary precludes a direct expression of the agent's
capabilities. Furthermore, our approach does not require assumptions about the
internal design of the target AI system or about the methods that it may use to
compute or learn task solutions. Empirical evaluation on several game-based
simulator domains shows that this approach can efficiently learn symbolic
models of AI systems that use a deterministic black-box policy in fully
observable scenarios.
","Learning User-Interpretable Descriptions of Black-Box AI System
  Capabilities"
"  Multimodal sentiment analysis aims to extract and integrate semantic
information collected from multiple modalities to recognize the expressed
emotions and sentiment in multimodal data. This research area's major concern
lies in developing an extraordinary fusion scheme that can extract and
integrate key information from various modalities. However, one issue that may
restrict previous work to achieve a higher level is the lack of proper modeling
for the dynamics of the competition between the independence and relevance
among modalities, which could deteriorate fusion outcomes by causing the
collapse of modality-specific feature space or introducing extra noise. To
mitigate this, we propose the Bi-Bimodal Fusion Network (BBFN), a novel
end-to-end network that performs fusion (relevance increment) and separation
(difference increment) on pairwise modality representations. The two parts are
trained simultaneously such that the combat between them is simulated. The
model takes two bimodal pairs as input due to the known information imbalance
among modalities. In addition, we leverage a gated control mechanism in the
Transformer architecture to further improve the final output. Experimental
results on three datasets (CMU-MOSI, CMU-MOSEI, and UR-FUNNY) verifies that our
model significantly outperforms the SOTA. The implementation of this work is
available at https://github.com/declare-lab/multimodal-deep-learning.
","Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal
  Sentiment Analysis"
"  Knowledge graphs (KGs) have been widely used for question answering (QA)
applications, especially the entity based QA. However, searching an-swers from
an entire large-scale knowledge graph is very time-consuming and it is hard to
meet the speed need of real online QA systems. In this pa-per, we design a
sub-graph searching mechanism to solve this problem by creating sub-graph
index, and each answer generation step is restricted in the sub-graph level. We
use this mechanism into a real online QA chat system, and it can bring obvious
improvement on question coverage by well answer-ing entity based questions, and
it can be with a very high speed, which en-sures the user experience of online
QA.
",An Online Question Answering System based on Sub-graph Searching
"  We have built a novel system for the surveillance of drinking water
reservoirs using underwater sensor networks. We implement an innovative
AI-based approach to detect, classify and localize underwater events. In this
paper, we describe the technology and cognitive AI architecture of the system
based on one of the sensor networks, the hydrophone network. We discuss the
challenges of installing and using the hydrophone network in a water reservoir
where traffic, visitors, and variable water conditions create a complex,
varying environment. Our AI solution uses an autoencoder for unsupervised
learning of latent encodings for classification and anomaly detection, and time
delay estimates for sound localization. Finally, we present the results of
experiments carried out in a laboratory pool and the water reservoir and
discuss the system's potential.
","Underwater Acoustic Networks for Security Risk Assessment in Public
  Drinking Water Reservoirs"
"  With the rapid development of eXplainable Artificial Intelligence (XAI),
perturbation-based XAI algorithms have become quite popular due to their
effectiveness and ease of implementation. The vast majority of
perturbation-based XAI techniques face the challenge of Out-of-Distribution
(OoD) data -- an artifact of randomly perturbed data becoming inconsistent with
the original dataset. OoD data leads to the over-confidence problem in model
predictions, making the existing XAI approaches unreliable. To our best
knowledge, the OoD data problem in perturbation-based XAI algorithms has not
been adequately addressed in the literature. In this work, we address this OoD
data problem by designing an additional module quantifying the affinity between
the perturbed data and the original dataset distribution, which is integrated
into the process of aggregation. Our solution is shown to be compatible with
the most popular perturbation-based XAI algorithms, such as RISE, OCCLUSION,
and LIME. Experiments have confirmed that our methods demonstrate a significant
improvement in general cases using both computational and cognitive metrics.
Especially in the case of degradation, our proposed approach demonstrates
outstanding performance comparing to baselines. Besides, our solution also
resolves a fundamental problem with the faithfulness indicator, a commonly used
evaluation metric of XAI algorithms that appears to be sensitive to the OoD
issue.
",Resisting Out-of-Distribution Data Problem in Perturbation of XAI
"  Swarm optimization algorithms are widely used for feature selection before
data mining and machine learning applications. The metaheuristic
nature-inspired feature selection approaches are used for single-objective
optimization tasks, though the major problem is their frequent premature
convergence, leading to weak contribution to data mining. In this paper, we
propose a novel feature selection algorithm named Reinforced Swarm Optimization
(RSO) leveraging some of the existing problems in feature selection. This
algorithm embeds the widely used Bee Swarm Optimization (BSO) algorithm along
with Reinforcement Learning (RL) to maximize the reward of a superior search
agent and punish the inferior ones. This hybrid optimization algorithm is more
adaptive and robust with a good balance between exploitation and exploration of
the search space. The proposed method is evaluated on 25 widely known UCI
datasets containing a perfect blend of balanced and imbalanced data. The
obtained results are compared with several other popular and recent feature
selection algorithms with similar classifier configurations. The experimental
outcome shows that our proposed model outperforms BSO in 22 out of 25 instances
(88%). Moreover, experimental results also show that RSO performs the best
among all the methods compared in this paper in 19 out of 25 cases (76%),
establishing the superiority of our proposed method.
","RSO: A Novel Reinforced Swarm Optimization Algorithm for Feature
  Selection"
"  The emergence of Internet of Things technology and recent advancement in
sensor networks enabled transportation systems to a new dimension called
Intelligent Transportation System. Due to increased usage of vehicles and
communication among entities in road traffic scenarios, the amount of raw data
generation in Intelligent Transportation System is huge. This raw data are to
be processed to infer contextual information and provide new services related
to different modes of road transport such as traffic signal management,
accident prediction, object detection etc. To understand the importance of
context, this article aims to study context awareness in the Intelligent
Transportation System. We present a review on prominent applications developed
in the literature concerning context awareness in the intelligent
transportation system. The objective of this research paper is to highlight
context and its features in ITS and to address the applicability of modelling
techniques and reasoning approaches in Intelligent Transportation System. Also
to shed light on impact of Internet of Things and machine learning in
Intelligent Transportation System development.
","Modelling and Reasoning Techniques for Context Aware Computing in
  Intelligent Transportation System"
"  Autonomous driving has attracted great attention from both academics and
industries. To realise autonomous driving, Deep Imitation Learning (DIL) is
treated as one of the most promising solutions, because it improves autonomous
driving systems by automatically learning a complex mapping from human driving
data, compared to manually designing the driving policy. However, existing DIL
methods cannot generalise well across domains, that is, a network trained on
the data of source domain gives rise to poor generalisation on the data of
target domain. In the present study, we propose a novel brain-inspired deep
imitation method that builds on the evidence from human brain functions, to
improve the generalisation ability of deep neural networks so that autonomous
driving systems can perform well in various scenarios. Specifically, humans
have a strong generalisation ability which is beneficial from the structural
and functional asymmetry of the two sides of the brain. Here, we design dual
Neural Circuit Policy (NCP) architectures in deep neural networks based on the
asymmetry of human neural networks. Experimental results demonstrate that our
brain-inspired method outperforms existing methods regarding generalisation
when dealing with unseen data. Our source codes and pretrained models are
available at
https://github.com/Intenzo21/Brain-Inspired-Deep-Imitation-Learning-for-Autonomous-Driving-Systems}{https://github.com/Intenzo21/Brain-Inspired-Deep-Imitation-Learning-for-Autonomous-Driving-Systems.
",Brain-Inspired Deep Imitation Learning for Autonomous Driving Systems
"  This document provides a brief introduction to learned automated planning
problem where the state transition function is in the form of a binarized
neural network (BNN), presents a general MaxSAT encoding for this problem, and
describes the four domains, namely: Navigation, Inventory Control, System
Administrator and Cellda, that are submitted as benchmarks for MaxSAT
Evaluation 2021.
","Planning with Learned Binarized Neural Networks Benchmarks for MaxSAT
  Evaluation 2021"
"  The present preprint specifies and defines all Terms, Properties,
Relationships and Axioms of ProcessCO (Process Core Ontology). ProcessCO is an
ontology devoted mainly for Work Entities and related terms, which is placed at
the core level in the context of a multilayer ontological architecture called
FCD-OntoArch (Foundational, Core, and Domain Ontological Architecture for
Sciences). This is a five-layered ontological architecture, which considers
Foundational, Core, Domain and Instance levels, where the domain level is split
down in two sub-levels, namely: Top-domain and Low-domain. Ontologies at the
same level can be related to each other, except for the foundational level
where only ThingFO (Thing Foundational Ontology) is found. In addition,
ontologies' terms and relationships at lower levels can be semantically
enriched by ontologies' terms and relationships from the higher levels. Note
that both ThingFO and ontologies at the core level such as ProcessCO,
SituationCO, among others, are domain independent with respect to their terms.
Stereotypes are the mechanism used for enriching ProcessCO terms mainly from
the ThingFO ontology. Note that in the end of this document, we address the
ProcessCO vs. ThingFO non-taxonomic relationship verification matrix.
Additionally, note that annotations of updates from the previous version
(ProcessCO v1.2) to the current one (v1.3) can be found in Appendix A. For
instance, 6 axioms were added.
","ProcessCO v1.3's Terms, Properties, Relationships and Axioms - A Core
  Ontology for Processes"
"  Uncertain information is being taken into account in an increasing number of
application fields. In the meantime, abduction has been proved a powerful tool
for handling hypothetical reasoning and incomplete knowledge. Probabilistic
logical models are a suitable framework to handle uncertain information, and in
the last decade many probabilistic logical languages have been proposed, as
well as inference and learning systems for them. In the realm of Abductive
Logic Programming (ALP), a variety of proof procedures have been defined as
well. In this paper, we consider a richer logic language, coping with
probabilistic abduction with variables. In particular, we consider an ALP
program enriched with integrity constraints `a la IFF, possibly annotated with
a probability value. We first present the overall abductive language, and its
semantics according to the Distribution Semantics. We then introduce a proof
procedure, obtained by extending one previously presented, and prove its
soundness and completeness.
","Non-ground Abductive Logic Programming with Probabilistic Integrity
  Constraints"
"  Static analysis remains one of the most popular approaches for detecting and
correcting poor or vulnerable program code. It involves the examination of code
listings, test results, or other documentation to identify errors, violations
of development standards, or other problems, with the ultimate goal of fixing
these errors so that systems and software are as secure as possible. There
exists a plethora of static analysis tools, which makes it challenging for
businesses and programmers to select a tool to analyze their program code. It
is imperative to find ways to improve code analysis so that it can be employed
by cyber defenders to mitigate security risks. In this research, we propose a
method that employs the use of virtual assistants to work with programmers to
ensure that software are as safe as possible in order to protect
safety-critical systems from data breaches and other attacks. The proposed
method employs a recommender system that uses various metrics to help
programmers select the most appropriate code analysis tool for their project
and guides them through the analysis process. The system further tracks the
user's behavior regarding the adoption of the recommended practices.
",A Smart and Defensive Human-Machine Approach to Code Analysis
"  Solving complex real-world tasks, e.g., autonomous fleet control, often
involves a coordinated team of multiple agents which learn strategies from
visual inputs via reinforcement learning. Many existing multi-agent
reinforcement learning (MARL) algorithms however don't scale to environments
where agents operate on visual inputs. To address this issue, algorithmically,
recent works have focused on non-stationarity and exploration. In contrast, we
study whether scalability can also be achieved via a disentangled
representation. For this, we explicitly construct an object-centric
intermediate representation to characterize the states of an environment, which
we refer to as `semantic tracklets.' We evaluate `semantic tracklets' on the
visual multi-agent particle environment (VMPE) and on the challenging visual
multi-agent GFootball environment. `Semantic tracklets' consistently outperform
baselines on VMPE, and achieve a +2.4 higher score difference than baselines on
GFootball. Notably, this method is the first to successfully learn a strategy
for five players in the GFootball environment using only visual data.
","Semantic Tracklets: An Object-Centric Representation for Visual
  Multi-Agent Reinforcement Learning"
"  An increasing number and diversity of services are available, which result in
significant challenges to effective reuse service during requirement
satisfaction. There have been many service bundle recommendation studies and
achieved remarkable results. However, there is still plenty of room for
improvement in the performance of these methods. The fundamental problem with
these studies is that they ignore the evolution of services over time and the
representation gap between services and requirements. In this paper, we propose
a dynamic representation learning and aligning based model called DySR to
tackle these issues. DySR eliminates the representation gap between services
and requirements by learning a transformation function and obtains service
representations in an evolving social environment through dynamic graph
representation learning. Extensive experiments conducted on a real-world
dataset from ProgrammableWeb show that DySR outperforms existing
state-of-the-art methods in commonly used evaluation metrics, improving $F1@5$
from $36.1\%$ to $69.3\%$.
","DySR: A Dynamic Representation Learning and Aligning based Model for
  Service Bundle Recommendation"
"  In recent years, the scientific community has focused on the development of
CAD tools that could improve bone fractures' classification, mostly based on
Convolutional Neural Network (CNN). However, the discerning accuracy of
fractures' subtypes was far from optimal. This paper proposes a modified
version of a very recent and powerful deep learning technique, the Vision
Transformer (ViT), outperforming CNNs based approaches and consequently
increasing specialists' diagnosis accuracy. 4207 manually annotated images were
used and distributed, by following the AO/OTA classification, in different
fracture types, the largest labeled dataset of proximal femur fractures used in
literature. The ViT architecture was used and compared with a classic CNN and a
multistage architecture composed of successive CNNs in cascade. To demonstrate
the reliability of this approach, 1) the attention maps were used to visualize
the most relevant areas of the images, 2) the performance of a generic CNN and
ViT was compared through unsupervised learning techniques, and 3) 11
specialists were asked to evaluate and classify 150 proximal femur fractures'
images with and without the help of the ViT, then results were compared for
potential improvement. The ViT was able to correctly predict 83% of the test
images. Precision, recall and F1-score were 0.77 (CI 0.64-0.90), 0.76 (CI
0.62-0.91) and 0.77 (CI 0.64-0.89), respectively. The average specialists'
diagnostic improvement was 29% when supported by ViT's predictions,
outperforming the algorithm alone. This paper showed the potential of Vision
Transformers in bone fracture classification. For the first time, good results
were obtained in sub-fractures classification, with the largest and richest
dataset ever. Accordingly, the assisted diagnosis yielded the best results,
proving once again the effectiveness of a coordinated work between neural
networks and specialists.
",Vision Transformer for femur fracture classification
"  We present a different view for AlphaStar (AS), the program achieving
Grand-Master level in the game StarCraft II. It is considered big progress for
AI research. However, in this paper, we present problems with the AS, some of
which are the defects of it, and some of which are important details that are
neglected in its article. These problems arise two questions. One is that what
can we get from the built of AS? The other is that does the battle between it
with humans fair? After the discussion, we present the future research
directions for these problems. Our study is based on a reproduction code of the
AS, and the codes are available online.
",Rethinking of AlphaStar
"  AI-controlled characters in fighting games are expected to possess reasonably
high skills and behave in a believable, human-like manner, exhibiting a
diversity of play styles and strategies. Thus, the development of fighting game
AI requires the ability to evaluate these properties. For instance, it should
be possible to ensure that the characters created are believable and diverse.
In this paper, we show how an automated procedure can be used to compare play
styles of individual AI- and human-controlled characters, and to assess
human-likeness and diversity of game participants.
",Identification of Play Styles in Universal Fighting Engine
"  Fuzzy Cognitive Maps (FCMs) are soft computing technique that follows an
approach similar to human reasoning and human decision-making process, making
them a valuable modeling and simulation methodology. Medical Decision Systems
are complex systems consisting of many factors that may be complementary,
contradictory, and competitive; these factors influence each other and
determine the overall diagnosis with a different degree. Thus, FCMs are
suitable to model Medical Decision Support Systems. The proposed work therefore
uses FCMs arranged in hierarchical structure to classify between Diabetes,
Thyroid disorders and their subtypes. Subtypes include type 1 and type 2 for
diabetes and hyperthyroidism and hypothyroidism for thyroid.
","Symptom based Hierarchical Classification of Diabetes and Thyroid
  disorders using Fuzzy Cognitive Maps"
"  In this paper, we present our research on programming human-level artificial
intelligence (HLAI), including 1) a definition of HLAI, 2) an environment to
develop and test HLAI, and 3) a cognitive architecture for HLAI. The term AI is
used in a broad meaning, and HLAI is not clearly defined. I claim that the
essence of Human-Level Intelligence to be the capability to learn from others'
experiences via language. The key is that the event described by language has
the same effect as if the agent experiences it firsthand for the update of the
behavior policy. To develop and test models with such a capability, we are
developing a simulated environment called SEDRo. There is a 3D Home, and a
mother character takes care of the baby (the learning agent) and teaches
languages. The environment provides comparable experiences to that of a human
baby from birth to one year. Finally, I propose a cognitive architecture of
HLAI called Modulated Heterarchical Prediction Memory (mHPM). In mHPM, there
are three components: a universal module that learns to predict the next vector
given the sequence of vector signals, a heterarchical network of those modules,
and a reward-based modulation of learning. mHPM models the workings of the
neocortex but the innate auxiliary units such hippocampus, reward system,
instincts, and amygdala play critical roles, too.
",Toward Human-Level Artificial Intelligence
"  A novel method for SPECT angle interpolation based on deep learning
methodologies is presented. Projection data from software phantoms were used to
train the proposed model. For evaluation of the efficacy of the method,
phantoms based on Shepp Logan, with various noise levels added were used, and
the resulting interpolated sinograms are reconstructed using Ordered Subset
Expectation Maximization (OSEM) and compared to the reconstructions of the
original sinograms. The proposed method can quadruple the projections, and
denoise the original sinogram, in the same process. As the results show, the
proposed model significantly improves the reconstruction accuracy. Finally, to
demonstrate the efficacy and capability of the proposed method results from
real-world DAT-SPECT sinograms are presented.
",SPECT Angle Interpolation Based on Deep Learning Methodologies
"  In this paper, we present a novel method for tomographic image reconstruction
in SPECT imaging with a low number of projections. Deep convolutional neural
networks (CNN) are employed in the new reconstruction method. Projection data
from software phantoms were used to train the CNN network. For evaluation of
the efficacy of the proposed method, software phantoms and hardware phantoms
based on the FOV SPECT system were used. The resulting tomographic images are
compared to those produced by the ""Maximum Likelihood Expectation Maximisation""
(MLEM).
","Deep Convolutional Neural Network for Low Projection SPECT Imaging
  Reconstruction"
"  We propose a novel concise function representation for graphical models, a
central theoretical framework that provides the basis for many reasoning tasks.
We then show how we exploit our concise representation based on deterministic
finite state automata within Bucket Elimination (BE), a general approach based
on the concept of variable elimination that accommodates many inference and
optimisation tasks such as most probable explanation and constrained
optimisation. We denote our version of BE as FABE. By using our concise
representation within FABE, we dramatically improve the performance of BE in
terms of runtime and memory requirements. Results on standard benchmarks
obtained using an established experimental methodology show that FABE often
outperforms the best available approach (RBFAOO), leading to significant
runtime improvements (up to 2 orders of magnitude in our tests).
","A Concise Function Representation for Faster Exact MPE and Constrained
  Optimisation in Graphical Models"
"  Accurate prediction of short-term OD Matrix (i.e. the distribution of
passenger flows from various origins to destinations) is a crucial task in
metro systems. It is highly challenging due to the constantly changing nature
of many impacting factors and the real-time delayed data collection problem.
Recently, some deep learning-based models have been proposed for OD Matrix
forecasting in ride-hailing and high way traffic scenarios. However, these
models can not sufficiently capture the complex spatiotemporal correlation
between stations in metro networks due to their different prior knowledge and
contextual settings. In this paper we propose a hybrid framework Multi-view
TRGRU to address OD metro matrix prediction. In particular, it uses three
modules to model three flow change patterns: recent trend, daily trend, weekly
trend. In each module, a multi-view representation based on embedding for each
station is constructed and fed into a transformer based gated recurrent
structure so as to capture the dynamic spatial dependency in OD flows of
different stations by a global self-attention mechanism. Extensive experiments
on three large-scale, real-world metro datasets demonstrate the superiority of
our Multi-view TRGRU over other competitors.
","Multi-View TRGRU: Transformer based Spatiotemporal Model for Short-Term
  Metro Origin-Destination Matrix Prediction"
"  A novel method for sinogram denoise based on Generative Adversarial Networks
(GANs) in the field of SPECT imaging is presented. Projection data from
software phantoms were used to train the proposed model. For evaluation of the
efficacy of the method Shepp Logan based phantom, with various noise levels
added where used. The resulting denoised sinograms are reconstructed using
Ordered Subset Expectation Maximization (OSEM) and compared to the
reconstructions of the original noised sinograms. As the results show, the
proposed method significantly denoise the sinograms and significantly improves
the reconstructions. Finally, to demonstrate the efficacy and capability of the
proposed method results from real-world DAT-SPECT sinograms are presented.
",Sinogram Denoise Based on Generative Adversarial Networks
"  Artificial Intelligence has been developed for decades with the achievement
of great progress. Recently, deep learning shows its ability to solve many real
world problems, e.g. image classification and detection, natural language
processing, playing GO. Theoretically speaking, an artificial neural network
can fit any function and reinforcement learning can learn from any delayed
reward. But in solving real world tasks, we still need to spend a lot of effort
to adjust algorithms to fit task unique features. This paper proposes that the
reason of this phenomenon is the sparse feedback feature of the nature, and a
single algorithm, no matter how we improve it, can only solve dense feedback
tasks or specific sparse feedback tasks. This paper first analyses how sparse
feedback affects algorithm perfomance, and then proposes a pattern that
explains how to accumulate knowledge to solve sparse feedback problems.
",Knowledge accumulating: The general pattern of learning
"  Nowadays, artificial neural networks are widely used for users' online travel
planning. Personalized travel planning has many real applications and is
affected by various factors, such as transportation type, intention destination
estimation, budget limit and crowdness prediction. Among those factors, users'
intention destination prediction is an essential task in online travel
platforms. The reason is that, the user may be interested in the travel plan
only when the plan matches his real intention destination. Therefore, in this
paper, we focus on predicting users' intention destinations in online travel
platforms. In detail, we act as online travel platforms (such as Fliggy and
Airbnb) to recommend travel plans for users, and the plan consists of various
vacation items including hotel package, scenic packages and so on. Predicting
the actual intention destination in travel planning is challenging. Firstly,
users' intention destination is highly related to their travel status (e.g.,
planning for a trip or finishing a trip). Secondly, users' actions (e.g.
clicking, searching) over different product types (e.g. train tickets, visa
application) have different indications in destination prediction. Thirdly,
users may mostly visit the travel platforms just before public holidays, and
thus user behaviors in online travel platforms are more sparse, low-frequency
and long-period. Therefore, we propose a Deep Multi-Sequences fused neural
Networks (DMSN) to predict intention destinations from fused multi-behavior
sequences. Real datasets are used to evaluate the performance of our proposed
DMSN models. Experimental results indicate that the proposed DMSN models can
achieve high intention destination prediction accuracy.
","Spatial-Temporal Deep Intention Destination Networks for Online Travel
  Planning"
"  Human Motion Prediction is a crucial task in computer vision and robotics. It
has versatile application potentials such as in the area of human-robot
interactions, human action tracking for airport security systems, autonomous
car navigation, computer gaming to name a few. However, predicting human motion
based on past actions is an extremely challenging task due to the difficulties
in detecting spatial and temporal features correctly. To detect temporal
features in human poses, we propose an Inception Residual Block(IRB), due to
its inherent capability of processing multiple kernels to capture salient
features. Here, we propose to use multiple 1-D Convolution Neural Network (CNN)
with different kernel sizes and input sequence lengths and concatenate them to
get proper embedding. As kernels strides over different receptive fields, they
detect smaller and bigger salient features at multiple temporal scales. Our
main contribution is to propose a residual connection between input and the
output of the inception block to have a continuity between the previously
observed pose and the next predicted pose. With this proposed architecture, it
learns prior knowledge much better about human poses and we achieve much higher
prediction accuracy as detailed in the paper. Subsequently, we further propose
to feed the output of the inception residual block as an input to the Graph
Convolution Neural Network (GCN) due to its better spatial feature learning
capability. We perform a parametric analysis for better designing of our model
and subsequently, we evaluate our approach on the Human 3.6M dataset and
compare our short-term as well as long-term predictions with the state of the
art papers, where our model outperforms most of the pose results, the detailed
reasons of which have been elaborated in the paper.
","Development of Human Motion Prediction Strategy using Inception Residual
  Block"
"  Over the past decades, Answer Set Programming (ASP) has emerged as an
important paradigm for declarative problem solving. Technological progress in
this area has been stimulated by the use of common standards, such as the
ASP-Core-2 language. While ASP has its roots in non-monotonic reasoning,
efforts have also been made to reconcile ASP with classical first-order logic
(FO). This has resulted in the development of FO(.), an expressive extension of
FO, which allows ASP-like problem solving in a purely classical setting. This
language may be more accessible to domain experts already familiar with FO, and
may be easier to combine with other formalisms that are based on classical
logic. It is supported by the IDP inference system, which has successfully
competed in a number of ASP competitions. Here, however, technological progress
has been hampered by the limited number of systems that are available for
FO(.). In this paper, we aim to address this gap by means of a translation tool
that transforms an FO(.) specification into ASP-Core-2, thereby allowing
ASP-Core-2 solvers to be used as solvers for FO(.) as well. We present
experimental results to show that the resulting combination of our translation
with an off-the-shelf ASP solver is competitive with the IDP system as a way of
solving problems formulated in FO(.).
  Under consideration for acceptance in TPLP.
",FOLASP: FO(.) as Input Language for Answer Ser Solvers
"  Inspired by double q learning algorithm, the double DQN algorithm was
originally proposed in order to address the overestimation issue in the
original DQN algorithm. The double DQN has successfully shown both
theoretically and empirically the importance of decoupling in terms of action
evaluation and selection in computation of targets values; although, all the
benefits were acquired with only a simple adaption to DQN algorithm, minimal
possible change as it was mentioned by the authors. Nevertheless, there seems a
roll-back in the proposed algorithm of Double-DQN since the parameters of
policy network are emerged again in the target value function which were
initially withdrawn by DQN with the hope of tackling the serious issue of
moving targets and the instability caused by it (i.e., by moving targets) in
the process of learning. Therefore, in this paper three modifications to the
Double-DQN algorithm are proposed with the hope of maintaining the performance
in the terms of both stability and overestimation. These modifications are
focused on the logic of decoupling the best action selection and evaluation in
the target value function and the logic of tackling the moving targets issue.
Each of these modifications have their own pros and cons compared to the
others. The mentioned pros and cons mainly refer to the execution time required
for the corresponding algorithm and the stability provided by the corresponding
algorithm. Also, in terms of overestimation, none of the modifications seem to
underperform compared to the original Double-DQN if not outperform it. With the
intention of evaluating the efficacy of the proposed modifications, multiple
empirical experiments along with theoretical experiments were conducted. The
results obtained are represented and discussed in this article.
",Modified Double DQN: addressing stability
"  Modal logic S5 has attracted significant attention and has led to several
practical applications, owing to its simplified approach to dealing with
nesting modal operators. Efficient implementations for evaluating
satisfiability of S5 formulas commonly rely on Skolemisation to convert them
into propositional logic formulas, essentially by introducing copies of
propositional atoms for each set of interpretations (possible worlds). This
approach is simple, but often results into large formulas that are too
difficult to process, and therefore more parsimonious constructions are
required. In this work, we propose to use Answer Set Programming for
implementing such constructions, and in particular for identifying the
propositional atoms that are relevant in every world by means of a reachability
relation. The proposed encodings are designed to take advantage of other
properties such as entailment relations of subformulas rooted by modal
operators. An empirical assessment of the proposed encodings shows that the
reachability relation is very effective and leads to comparable performance to
a state-of-the-art S5 solver based on SAT, while entailment relations are
possibly too expensive to reason about and may result in overhead. This paper
is under consideration for acceptance in TPLP.
",Modal Logic S5 Satisfiability in Answer Set Programming
"  AI business process applications automate high-stakes business decisions
where there is an increasing demand to justify or explain the rationale behind
algorithmic decisions. Business process applications have ordering or
constraints on tasks and feature values that cause lightweight, model-agnostic,
existing explanation methods like LIME to fail. In response, we propose a local
explanation framework extending LIME for explaining AI business process
applications. Empirical evaluation of our extension underscores the advantage
of our approach in the business process setting.
",Extending LIME for Business Process Automation
"  Evolutionary neural architecture search (ENAS) has recently received
increasing attention by effectively finding high-quality neural architectures,
which however consumes high computational cost by training the architecture
encoded by each individual for complete epochs in individual evaluation.
Numerous ENAS approaches have been developed to reduce the evaluation cost, but
it is often difficult for most of these approaches to achieve high evaluation
accuracy. To address this issue, in this paper we propose an accelerated ENAS
via multifidelity evaluation termed MFENAS, where the individual evaluation
cost is significantly reduced by training the architecture encoded by each
individual for only a small number of epochs. The balance between evaluation
cost and evaluation accuracy is well maintained by suggesting a multi-fidelity
evaluation, which identifies the potentially good individuals that cannot
survive from previous generations by integrating multiple evaluations under
different numbers of training epochs. For high diversity of neural
architectures, a population initialization strategy is devised to produce
different neural architectures varying from ResNet-like architectures to
Inception-like ones. Experimental results on CIFAR-10 show that the
architecture obtained by the proposed MFENAS achieves a 2.39% test error rate
at the cost of only 0.6 GPU days on one NVIDIA 2080TI GPU, demonstrating the
superiority of the proposed MFENAS over state-of-the-art NAS approaches in
terms of both computational cost and architecture quality. The architecture
obtained by the proposed MFENAS is then transferred to CIFAR-100 and ImageNet,
which also exhibits competitive performance to the architectures obtained by
existing NAS approaches. The source code of the proposed MFENAS is available at
https://github.com/DevilYangS/MFENAS/.
","Accelerating Evolutionary Neural Architecture Search via Multi-Fidelity
  Evaluation"
"  The identification of Alzheimer's disease (AD) and its early stages using
structural magnetic resonance imaging (MRI) has been attracting the attention
of researchers. Various data-driven approaches have been introduced to capture
subtle and local morphological changes of the brain accompanied by the disease
progression. One of the typical approaches for capturing subtle changes is
patch-level feature representation. However, the predetermined regions to
extract patches can limit classification performance by interrupting the
exploration of potential biomarkers. In addition, the existing patch-level
analyses have difficulty explaining their decision-making. To address these
problems, we propose the BrainBagNet with a position-based gate
(PG-BrainBagNet), a framework for jointly learning pathological region
localization and AD diagnosis in an end-to-end manner. In advance, as all scans
are aligned to a template in image processing, the position of brain images can
be represented through the 3D Cartesian space shared by the overall MRI scans.
The proposed method represents the patch-level response from whole-brain MRI
scans and discriminative brain-region from position information. Based on the
outcomes, the patch-level class evidence is calculated, and then the
image-level prediction is inferred by a transparent aggregation. The proposed
models were evaluated on the ADNI datasets. In five-fold cross-validation, the
classification performance of the proposed method outperformed that of the
state-of-the-art methods in both AD diagnosis (AD vs. normal control) and mild
cognitive impairment (MCI) conversion prediction (progressive MCI vs. stable
MCI) tasks. In addition, changes in the identified discriminant regions and
patch-level class evidence according to the patch size used for model training
are presented and analyzed.
","Deep Joint Learning of Pathological Region Localization and Alzheimer's
  Disease Diagnosis"
"  In this study we explore the spontaneous apparition of visible intelligible
reasoning in simple artificial networks, and we connect this experimental
observation with a notion of semantic information. We start with the
reproduction of a DNN model of natural neurons in monkeys, studied by
Neromyliotis and Moschovakis in 2017 and 2018, to explain how ""motor equivalent
neurons"", coding only for the action of pointing, are supplemented by other
neurons for specifying the actor of the action, the eye E, the hand H, or the
eye and the hand together EH. There appear inner neurons performing a logical
work, making intermediary proposition, for instance E V EH. Then, we remarked
that adding a second hidden layer and choosing a symmetric metric for learning,
the activities of the neurons become almost quantized and more informative.
Using the work of Carnap and Bar-Hillel 1952, we define a measure of the
logical value for collections of such cells. The logical score growths with the
depth of the layer, i.e. the information on the output decision increases,
which confirms a kind of bottleneck principle. Then we study a bit more complex
tasks, a priori involving predicate logic. We compare the logic and the
measured weights. This shows, for groups of neurons, a neat correlation between
the logical score and the size of the weights. It exhibits a form of sparsity
between the layers. The most spectacular result concerns the triples which can
conclude for all conditions: when applying their weight matrices to their
logical matrix, we recover the classification. This shows that weights
precisely perform the proofs.
",Logical Information Cells I
"  A concept of multi-valued cognitive maps is introduced in this paper. The
concept expands the fuzzy one. However, all variables and weights are not
linearly ordered in the concept, but are only partially-ordered. Such an ap-
proach allows us to operate in cognitive maps with partially-ordered linguis-
tic variables directly, without vague fuzzification/defuzzification methods.
Hence, we may consider more subtle differences in degrees of experts' uncer-
tainty, than in the fuzzy case. We prove the convergence of such cognitive maps
and give a simple computational example which demonstrates using such a
partially-ordered uncertainty degree scale.
","Multi-Valued Cognitive Maps: Calculations with Linguistic Variables
  without Using Numbers"
"  We provide a comprehensive elaboration of the theoretical foundations of
variable instantiation, or grounding, in Answer Set Programming (ASP). Building
on the semantics of ASP's modeling language, we introduce a formal
characterization of grounding algorithms in terms of (fixed point) operators. A
major role is played by dedicated well-founded operators whose associated
models provide semantic guidance for delineating the result of grounding along
with on-the-fly simplifications. We address an expressive class of logic
programs that incorporates recursive aggregates and thus amounts to the scope
of existing ASP modeling languages. This is accompanied with a plain
algorithmic framework detailing the grounding of recursive aggregates. The
given algorithms correspond essentially to the ones used in the ASP grounder
gringo.
",On the Foundations of Grounding in Answer Set Programming
"  The frequency-based method is the most commonly used method for measuring
cable tension. However, the calculation formulas for the conventional
frequency-based method are generally based on the ideally hinged or fixed
boundary conditions without a comprehensive consideration of the inclination
angle, sag-extensibility, and flexural stiffness of cables, leading to a
significant error in cable tension identification. This study aimed to propose
a frequency-based method of cable tension identification considering the
complex boundary conditions at the two ends of cables using the particle swarm
optimization (PSO) algorithm. First, the refined stay cable model was
established considering the inclination angle, flexural stiffness, and
sag-extensibility, as well as the rotational constraint stiffness and lateral
support stiffness for the unknown boundaries of cables. The vibration mode
equation of the stay cable model was discretized and solved using the finite
difference method. Then, a multiparameter identification method based on the
PSO algorithm was proposed. This method was able to identify the tension,
flexural stiffness, axial stiffness, boundary rotational constraint stiffness,
and boundary lateral support stiffness according to the measured multiorder
frequencies in a synchronous manner. The feasibility and accuracy of this
method were validated through numerical cases. Finally, the proposed approach
was applied to the tension identification of the anchor span strands of a
suspension bridge (Jindong Bridge) in China. The results of cable tension
identification using the proposed method and the existing methods discussed in
previous studies were compared with the on-site pressure ring measurement
results. The comparison showed that the proposed approach had a high accuracy
in cable tension identification.
","Frequency-based tension assessment of an inclined cable with complex
  boundary conditions using the PSO algorithm"
"  Multimodal abstractive summarization with sentence output is to generate a
textual summary given a multimodal triad -- sentence, image and audio, which
has been proven to improve users satisfaction and convenient our life. Existing
approaches mainly focus on the enhancement of multimodal fusion, while ignoring
the unalignment among multiple inputs and the emphasis of different segments in
feature, which has resulted in the superfluity of multimodal interaction. To
alleviate these problems, we propose a Multimodal Hierarchical Selective
Transformer (mhsf) model that considers reciprocal relationships among
modalities (by low-level cross-modal interaction module) and respective
characteristics within single fusion feature (by high-level selective routing
module). In details, it firstly aligns the inputs from different sources and
then adopts a divide and conquer strategy to highlight or de-emphasize
multimodal fusion representation, which can be seen as a sparsely feed-forward
model - different groups of parameters will be activated facing different
segments in feature. We evaluate the generalism of proposed mhsf model with the
pre-trained+fine-tuning and fresh training strategies. And Further experimental
results on MSMO demonstrate that our model outperforms SOTA baselines in terms
of ROUGE, relevance scores and human evaluation.
","Abstractive Sentence Summarization with Guidance of Selective Multimodal
  Reference"
"  We study a variation of the Stable Marriage problem, where every man and
every woman express their preferences as preference lists which may be
incomplete and contain ties. This problem is called the Stable Marriage problem
with Ties and Incomplete preferences (SMTI). We consider three optimization
variants of SMTI, Max Cardinality, Sex-Equal and Egalitarian, and empirically
compare the following methods to solve them: Answer Set Programming, Constraint
Programming, Integer Linear Programming. For Max Cardinality, we compare these
methods with Local Search methods as well. We also empirically compare Answer
Set Programming with Propositional Satisfiability, for SMTI instances. This
paper is under consideration for acceptance in Theory and Practice of Logic
Programming (TPLP).
","Stable Marriage Problems with Ties and Incomplete Preferences: An
  Empirical Comparison of ASP, SAT, ILP, CP, and Local Search Methods"
"  Decision trees have long been recognized as models of choice in sensitive
applications where interpretability is of paramount importance. In this paper,
we examine the computational ability of Boolean decision trees in deriving,
minimizing, and counting sufficient reasons and contrastive explanations. We
prove that the set of all sufficient reasons of minimal size for an instance
given a decision tree can be exponentially larger than the size of the input
(the instance and the decision tree). Therefore, generating the full set of
sufficient reasons can be out of reach. In addition, computing a single
sufficient reason does not prove enough in general; indeed, two sufficient
reasons for the same instance may differ on many features. To deal with this
issue and generate synthetic views of the set of all sufficient reasons, we
introduce the notions of relevant features and of necessary features that
characterize the (possibly negated) features appearing in at least one or in
every sufficient reason, and we show that they can be computed in polynomial
time. We also introduce the notion of explanatory importance, that indicates
how frequent each (possibly negated) feature is in the set of all sufficient
reasons. We show how the explanatory importance of a feature and the number of
sufficient reasons can be obtained via a model counting operation, which turns
out to be practical in many cases. We also explain how to enumerate sufficient
reasons of minimal size. We finally show that, unlike sufficient reasons, the
set of all contrastive explanations for an instance given a decision tree can
be derived, minimized and counted in polynomial time.
",On the Explanatory Power of Decision Trees
"  Random forests have long been considered as powerful model ensembles in
machine learning. By training multiple decision trees, whose diversity is
fostered through data and feature subsampling, the resulting random forest can
lead to more stable and reliable predictions than a single decision tree. This
however comes at the cost of decreased interpretability: while decision trees
are often easily interpretable, the predictions made by random forests are much
more difficult to understand, as they involve a majority vote over hundreds of
decision trees. In this paper, we examine different types of reasons that
explain ""why"" an input instance is classified as positive or negative by a
Boolean random forest. Notably, as an alternative to sufficient reasons taking
the form of prime implicants of the random forest, we introduce majoritary
reasons which are prime implicants of a strict majority of decision trees. For
these different abductive explanations, the tractability of the generation
problem (finding one reason) and the minimization problem (finding one shortest
reason) are investigated. Experiments conducted on various datasets reveal the
existence of a trade-off between runtime complexity and sparsity. Sufficient
reasons - for which the identification problem is DP-complete - are slightly
larger than majoritary reasons that can be generated using a simple linear-
time greedy algorithm, and significantly larger than minimal majoritary reasons
that can be approached using an anytime P ARTIAL M AX SAT algorithm.
",Trading Complexity for Sparsity in Random Forest Explanations
"  While the similarity between two concept words has been evaluated and studied
for decades, much less attention has been devoted to algorithms that can
compute the similarity of nodes in very large knowledge graphs, like Wikidata.
To facilitate investigations and head-to-head comparisons of similarity
algorithms on Wikidata, we present a user-friendly interface that allows
flexible computation of similarity between Qnodes in Wikidata. At present, the
similarity interface supports four algorithms, based on: graph embeddings
(TransE, ComplEx), text embeddings (BERT), and class-based similarity. We
demonstrate the behavior of the algorithms on representative examples about
semantically similar, related, and entirely unrelated entity pairs. To support
anticipated applications that require efficient similarity computations, like
entity linking and recommendation, we also provide a REST API that can compute
most similar neighbors for any Qnode in Wikidata.
",User-friendly Comparison of Similarity Algorithms on Wikidata
"  As an open and collaborative knowledge graph created by users and bots, it is
possible that the knowledge in Wikidata is biased in regards to multiple
factors such as gender, race, and country of citizenship. Previous work has
mostly studied the representativeness of Wikidata knowledge in terms of genders
of people. In this paper, we examine the race and citizenship bias in general
and in regards to STEM representation for scientists, software developers, and
engineers. By comparing Wikidata queries to real-world datasets, we identify
the differences in representation to characterize the biases present in
Wikidata. Through this analysis, we discovered that there is an
overrepresentation of white individuals and those with citizenship in Europe
and North America; the rest of the groups are generally underrepresented. Based
on these findings, we have found and linked to Wikidata additional data about
STEM scientists from the minorities. This data is ready to be inserted into
Wikidata with a bot. Increasing representation of minority race and country of
citizenship groups can create a more accurate portrayal of individuals in STEM.
",Analyzing Race and Country of Citizenship Bias in Wikidata
"  In the context of planning and reasoning about actions and change, we call an
action reversible when its effects can be reverted by applying other actions,
returning to the original state. Renewed interest in this area has led to
several results in the context of the PDDL language, widely used for describing
planning tasks.
  In this paper, we propose several solutions to the computational problem of
deciding the reversibility of an action. In particular, we leverage an existing
translation from PDDL to Answer Set Programming (ASP), and then use several
different encodings to tackle the problem of action reversibility for the
STRIPS fragment of PDDL. For these, we use ASP, as well as Epistemic Logic
Programming (ELP), an extension of ASP with epistemic operators, and compare
and contrast their strengths and weaknesses.
  Under consideration for acceptance in TPLP.
","Determining ActionReversibility in STRIPS Using Answer Set and Epistemic
  Logic Programming"
"  In a multiagent network model consisting of nodes, each network node has an
agent and priced Friddy coins, and the agent can buy or sell Friddy coins in
the marketplace. Though every node may not effectively have an equal price
during the transaction time, the prices have to reach equilibrium by iterating
buy and sell transactions on a macro level.
",Friddy multiagent price stabilization model
"  Topology based dimensionality reduction methods such as t-SNE and UMAP have
seen increasing success and popularity in high-dimensional data. These methods
have strong mathematical foundations and are based on the intuition that the
topology in low dimensions should be close to that of high dimensions. Given
that the initial topological structure is a precursor to the success of the
algorithm, this naturally raises the question: What makes a ""good"" topological
structure for dimensionality reduction? %Insight into this will enable us to
design better algorithms which take into account both local and global
structure. In this paper which focuses on UMAP, we study the effects of node
connectivity (k-Nearest Neighbors vs \textit{mutual} k-Nearest Neighbors) and
relative neighborhood (Adjacent via Path Neighbors) on dimensionality
reduction. We explore these concepts through extensive ablation studies on 4
standard image and text datasets; MNIST, FMNIST, 20NG, AG, reducing to 2 and 64
dimensions. Our findings indicate that a more refined notion of connectivity
(\textit{mutual} k-Nearest Neighbors with minimum spanning tree) together with
a flexible method of constructing the local neighborhood (Path Neighbors), can
achieve a much better representation than default UMAP, as measured by
downstream clustering performance.
",Clustering with UMAP: Why and How Connectivity Matters
"  The recently proposed Uniswap v3 replaces the fungible liquidity provider
token (LP token) into non-fungible ones, making the design for liquidity mining
more difficult. In this paper, we propose a flexible liquidity mining scheme
that realizes the overall liquidity distribution through the fine control of
local rewards. From the liquidity provider's point of view, the liquidity
provision strategy forms a multiplayer zero-sum game. We analyze the Nash
Equilibrium and the corresponding strategy, approximately, deploying the
liquidity proportional to the reward distribution, in some special cases and
use it to guide the general situations. Based on the strategic response above,
such a scheme allows the mining rewards provider to optimize the distribution
of liquidity for the purpose such as low slippage and price stabilization.
",On Liquidity Mining for Uniswap v3
"  The recent developments in Artificial Intelligence (AI) technologies
challenge educators and educational institutions to respond with curriculum and
resources that prepare students of all ages with the foundational knowledge and
skills for success in the AI workplace. Research on AI Literacy could lead to
an effective and practical platform for developing these skills. We propose and
advocate for a pathway for developing AI Literacy as a pragmatic and useful
tool for AI education. Such a discipline requires moving beyond a conceptual
framework to a multi-level competency model with associated competency
assessments. This approach to an AI Literacy could guide future development of
instructional content as we prepare a range of groups (i.e., consumers,
co-workers, collaborators, and creators). We propose here a research matrix as
an initial step in the development of a roadmap for AI Literacy research, which
requires a systematic and coordinated effort with the support of publication
outlets and research funding, to expand the areas of competency and
assessments.
","Competency Model Approach to AI Literacy: Research-based Path from
  Initial Framework to Model"
"  Sparse rewards and long time horizons remain challenging for reinforcement
learning algorithms. Exploration bonuses can help in sparse reward settings by
encouraging agents to explore the state space, while hierarchical approaches
can assist with long-horizon tasks by decomposing lengthy tasks into shorter
subtasks. We propose HAC Explore (HACx), a new method that combines these
approaches by integrating the exploration bonus method Random Network
Distillation (RND) into the hierarchical approach Hierarchical Actor-Critic
(HAC). HACx outperforms either component method on its own, as well as an
existing approach to combining hierarchy and exploration, in a set of difficult
simulated robotics tasks. HACx is the first RL method to solve a sparse reward,
continuous-control task that requires over 1,000 actions.
","HAC Explore: Accelerating Exploration with Hierarchical Reinforcement
  Learning"
"  Target detection systems identify targets by localizing their coordinates on
the input image of interest. This is ideally achieved by labeling each pixel in
an image as a background or a potential target pixel. Deep Convolutional Neural
Network (DCNN) classifiers have proven to be successful tools for computer
vision applications. However,prior research confirms that even state of the art
classifier models are susceptible to adversarial attacks. In this paper, we
show how to generate adversarial infrared images by adding small perturbations
to the targets region to deceive a DCNN-based target detector at remarkable
levels. We demonstrate significant progress in developing visually
imperceptible adversarial infrared images where the targets are visually
recognizable by an expert but a DCNN-based target detector cannot detect the
targets in the image.
",Deep adversarial attack on target detection systems
"  We introduce OPtical ADversarial attack (OPAD). OPAD is an adversarial attack
in the physical space aiming to fool image classifiers without physically
touching the objects (e.g., moving or painting the objects). The principle of
OPAD is to use structured illumination to alter the appearance of the target
objects. The system consists of a low-cost projector, a camera, and a computer.
The challenge of the problem is the non-linearity of the radiometric response
of the projector and the spatially varying spectral response of the scene.
Attacks generated in a conventional approach do not work in this setting unless
they are calibrated to compensate for such a projector-camera model. The
proposed solution incorporates the projector-camera model into the adversarial
attack optimization, where a new attack formulation is derived. Experimental
results prove the validity of the solution. It is demonstrated that OPAD can
optically attack a real 3D object in the presence of background lighting for
white-box, black-box, targeted, and untargeted attacks. Theoretical analysis is
presented to quantify the fundamental performance limit of the system.
",Optical Adversarial Attack
"  We present a general approach to planning with incomplete information in
Answer Set Programming (ASP). More precisely, we consider the problems of
conformant and conditional planning with sensing actions and assumptions. We
represent planning problems using a simple formalism where logic programs
describe the transition function between states, the initial states and the
goal states. For solving planning problems, we use Quantified Answer Set
Programming (QASP), an extension of ASP with existential and universal
quantifiers over atoms that is analogous to Quantified Boolean Formulas (QBFs).
We define the language of quantified logic programs and use it to represent the
solutions to different variants of conformant and conditional planning. On the
practical side, we present a translation-based QASP solver that converts
quantified logic programs into QBFs and then executes a QBF solver, and we
evaluate experimentally the approach on conformant and conditional planning
benchmarks. Under consideration for acceptance in TPLP.
","Planning with Incomplete Information in Quantified Answer Set
  Programming"
"  We propose a new approach to SAT solving which solves SAT problems in vector
spaces as a cost minimization problem of a non-negative differentiable cost
function J^sat. In our approach, a solution, i.e., satisfying assignment, for a
SAT problem in n variables is represented by a binary vector u in {0,1}^n that
makes J^sat(u) zero. We search for such u in a vector space R^n by cost
minimization, i.e., starting from an initial u_0 and minimizing J to zero while
iteratively updating u by Newton's method. We implemented our approach as a
matrix-based differential SAT solver MatSat. Although existing main-stream SAT
solvers decide each bit of a solution assignment one by one, be they of
conflict driven clause learning (CDCL) type or of stochastic local search (SLS)
type, MatSat fundamentally differs from them in that it continuously approach a
solution in a vector space. We conducted an experiment to measure the
scalability of MatSat with random 3-SAT problems in which MatSat could find a
solution up to n=10^5 variables. We also compared MatSat with four
state-of-the-art SAT solvers including winners of SAT competition 2018 and SAT
Race 2019 in terms of time for finding a solution, using a random benchmark set
from SAT 2018 competition and an artificial random 3-SAT instance set. The
result shows that MatSat comes in second in both test sets and outperforms all
the CDCL type solvers.
",MatSat: a matrix-based differentiable SAT solver
"  The covid19 pandemic is a global emergency that badly impacted the economies
of various countries. Covid19 hit India when the growth rate of the country was
at the lowest in the last 10 years. To semantically analyze the impact of this
pandemic on the economy, it is curial to have an ontology. CIDO ontology is a
well standardized ontology that is specially designed to assess the impact of
coronavirus disease and utilize its results for future decision forecasting for
the government, industry experts, and professionals in the field of various
domains like research, medical advancement, technical innovative adoptions, and
so on. However, this ontology does not analyze the impact of the Covid19
pandemic on the Indian banking sector. On the other side, Covid19IBO ontology
has been developed to analyze the impact of the Covid19 pandemic on the Indian
banking sector but this ontology does not reflect complete information of
Covid19 data. Resultantly, users cannot get all the relevant information about
Covid19 and its impact on the Indian economy. This article aims to extend the
CIDO ontology to show the impact of Covid19 on the Indian economy sector by
reusing the concepts from other data sources. We also provide a simplified
schema matching approach that detects the overlapping information among the
ontologies. The experimental analysis proves that the proposed approach has
reasonable results.
","Development of the InBan_CIDO Ontology by Reusing the Concepts along
  with Detecting Overlapping Information"
"  Application developers today have three choices for exploiting the knowledge
present in Wikidata: they can download the Wikidata dumps in JSON or RDF
format, they can use the Wikidata API to get data about individual entities, or
they can use the Wikidata SPARQL endpoint. None of these methods can support
complex, yet common, query use cases, such as retrieval of large amounts of
data or aggregations over large fractions of Wikidata. This paper introduces
KGTK Kypher, a query language and processor that allows users to create
personalized variants of Wikidata on a laptop. We present several use cases
that illustrate the types of analyses that Kypher enables users to run on the
full Wikidata KG on a laptop, combining data from external resources such as
DBpedia. The Kypher queries for these use cases run much faster on a laptop
than the equivalent SPARQL queries on a Wikidata clone running on a powerful
server with 24h time-out limits.
",Creating and Querying Personalized Versions of Wikidata on a Laptop
"  Tactical Decision Games (TDGs) are military conflict scenarios presented both
textually and graphically on a map. These scenarios provide a challenging
domain for machine learning because they are open-ended, highly structured, and
typically contain many details of varying relevance. We have developed a
problem-solving component of an interactive companion system that proposes
military tasks to solve TDG scenarios using a combination of analogical
retrieval, mapping, and constraint propagation. We use this problem-solving
component to explore analogical learning.
  In this paper, we describe the problems encountered in learning for this
domain, and the methods we have developed to address these, such as partition
constraints on analogical mapping correspondences and the use of incremental
remapping to improve robustness. We present the results of learning experiments
that show improvement in performance through the simple accumulation of
examples, despite a weak domain theory.
",Analogical Learning in Tactical Decision Games
"  The representation of the personal context is complex and essential to
improve the help machines can give to humans for making sense of the world, and
the help humans can give to machines to improve their efficiency. We aim to
design a novel model representation of the personal context and design a
learning process for better integration with machine learning. We aim to
implement these elements into a modern system architecture focus in real-life
environments. Also, we show how our proposal can improve in specifically
related work papers. Finally, we are moving forward with a better personal
context representation with an improved model, the implementation of the
learning process, and the architectural design of these components.
",Streaming and Learning the Personal Context
"  In the question answering(QA) task, multi-hop reasoning framework has been
extensively studied in recent years to perform more efficient and interpretable
answer reasoning on the Knowledge Graph(KG). However, multi-hop reasoning is
inapplicable for answering n-ary fact questions due to its linear reasoning
nature. We discover that there are two feasible improvements: 1) upgrade the
basic reasoning unit from entity or relation to fact; and 2) upgrade the
reasoning structure from chain to tree. Based on these, we propose a novel
fact-tree reasoning framework, through transforming the question into a fact
tree and performing iterative fact reasoning on it to predict the correct
answer. Through a comprehensive evaluation on the n-ary fact KGQA dataset
introduced by this work, we demonstrate that the proposed fact-tree reasoning
framework has the desired advantage of high answer prediction accuracy. In
addition, we also evaluate the fact-tree reasoning framework on two binary KGQA
datasets and show that our approach also has a strong reasoning ability
compared with several excellent baselines. This work has direct implications
for exploring complex reasoning scenarios and provides a preliminary baseline
approach.
",Fact-Tree Reasoning for N-ary Question Answering over Knowledge Graphs
"  Process mining is a scientific discipline that analyzes event data, often
collected in databases called event logs. Recently, uncertain event logs have
become of interest, which contain non-deterministic and stochastic event
attributes that may represent many possible real-life scenarios. In this paper,
we present a method to reliably estimate the probability of each of such
scenarios, allowing their analysis. Experiments show that the probabilities
calculated with our method closely match the true chances of occurrence of
specific outcomes, enabling more trustworthy analyses on uncertain data.
",Probability Estimation of Uncertain Process Trace Realizations
"  Broad Explainable Artificial Intelligence moves away from interpreting
individual decisions based on a single datum and aims to provide integrated
explanations from multiple machine learning algorithms into a coherent
explanation of an agent's behaviour that is aligned to the communication needs
of the explainee. Reinforcement Learning (RL) methods, we propose, provide a
potential backbone for the cognitive model required for the development of
Broad-XAI. RL represents a suite of approaches that have had increasing success
in solving a range of sequential decision-making problems. However, these
algorithms all operate as black-box problem solvers, where they obfuscate their
decision-making policy through a complex array of values and functions.
EXplainable RL (XRL) is relatively recent field of research that aims to
develop techniques to extract concepts from the agent's: perception of the
environment; intrinsic/extrinsic motivations/beliefs; Q-values, goals and
objectives. This paper aims to introduce a conceptual framework, called the
Causal XRL Framework (CXF), that unifies the current XRL research and uses RL
as a backbone to the development of Broad-XAI. Additionally, we recognise that
RL methods have the ability to incorporate a range of technologies to allow
agents to adapt to their environment. CXF is designed for the incorporation of
many standard RL extensions and integrated with external ontologies and
communication facilities so that the agent can answer questions that explain
outcomes and justify its decisions.
","Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework
  and Survey"
"  To present the biodiversity information, a semantic model is required that
connects all kinds of data about living creatures and their habitats. The model
must be able to encode human knowledge for machines to be understood. Ontology
offers the richest machine-interpretable (rather than just machine-processable)
and explicit semantics that are being extensively used in the biodiversity
domain. Various ontologies are developed for the biodiversity domain however a
review of the current landscape shows that these ontologies are not capable to
define the Indian biodiversity information though India is one of the
megadiverse countries. To semantically analyze the Indian biodiversity
information, it is crucial to build an ontology that describes all the
essential terms of this domain from the unstructured format of the data
available on the web. Since, the curation of the ontologies heavily depends on
the domain where these are implemented hence there is no ideal methodology is
defined yet to be ready for universal use. The aim of this article is to
develop an ontology that semantically encodes all the terms of Indian
biodiversity information in all its dimensions based on the proposed
methodology. The comprehensive evaluation of the proposed ontology depicts that
ontology is well built in the specified domain.
",InBiodiv-O: An Ontology for Indian Biodiversity Knowledge Management
"  The ubiquitous availability of computing devices and the widespread use of
the internet have generated a large amount of data continuously. Therefore, the
amount of available information on any given topic is far beyond humans'
processing capacity to properly process, causing what is known as information
overload. To efficiently cope with large amounts of information and generate
content with significant value to users, we require identifying, merging and
summarising information. Data summaries can help gather related information and
collect it into a shorter format that enables answering complicated questions,
gaining new insight and discovering conceptual boundaries.
  This thesis focuses on three main challenges to alleviate information
overload using novel summarisation techniques. It further intends to facilitate
the analysis of documents to support personalised information extraction. This
thesis separates the research issues into four areas, covering (i) feature
engineering in document summarisation, (ii) traditional static and inflexible
summaries, (iii) traditional generic summarisation approaches, and (iv) the
need for reference summaries. We propose novel approaches to tackle these
challenges, by: i)enabling automatic intelligent feature engineering, ii)
enabling flexible and interactive summarisation, iii) utilising intelligent and
personalised summarisation approaches. The experimental results prove the
efficiency of the proposed approaches compared to other state-of-the-art
models. We further propose solutions to the information overload problem in
different domains through summarisation, covering network traffic data, health
data and business process data.
",Towards Personalized and Human-in-the-Loop Document Summarization
"  One of the several obstacles in the widespread use of AI systems is the lack
of requirements of interpretability that can enable a layperson to ensure the
safe and reliable behavior of such systems. We extend the analysis of an agent
assessment module that lets an AI system execute high-level instruction
sequences in simulators and answer the user queries about its execution of
sequences of actions. We show that such a primitive query-response capability
is sufficient to efficiently derive a user-interpretable causal model of the
system in stationary, fully observable, and deterministic settings. We also
introduce dynamic causal decision networks (DCDNs) that capture the causal
structure of STRIPS-like domains. A comparative analysis of different classes
of queries is also presented in terms of the computational requirements needed
to answer them and the efforts required to evaluate their responses to learn
the correct model.
",Learning Causal Models of Autonomous Agents using Interventions
"  Knowledge graph completion (KGC) has become a focus of attention across deep
learning community owing to its excellent contribution to numerous downstream
tasks. Although recently have witnessed a surge of work on KGC, they are still
insufficient to accurately capture complex relations, since they adopt the
single and static representations. In this work, we propose a novel
Disentangled Knowledge Graph Attention Network (DisenKGAT) for KGC, which
leverages both micro-disentanglement and macro-disentanglement to exploit
representations behind Knowledge graphs (KGs). To achieve
micro-disentanglement, we put forward a novel relation-aware aggregation to
learn diverse component representation. For macro-disentanglement, we leverage
mutual information as a regularization to enhance independence. With the
assistance of disentanglement, our model is able to generate adaptive
representations in terms of the given scenario. Besides, our work has strong
robustness and flexibility to adapt to various score functions. Extensive
experiments on public benchmark datasets have been conducted to validate the
superiority of DisenKGAT over existing methods in terms of both accuracy and
explainability.
","DisenKGAT: Knowledge Graph Embedding with Disentangled Graph Attention
  Network"
"  Partial MaxSAT (PMS) and Weighted Partial MaxSAT (WPMS) are both practical
generalizations to the typical combinatorial problem of MaxSAT. In this work,
we propose an effective farsighted probabilistic sampling based local search
algorithm called FPS for solving these two problems, denoted as (W)PMS. The FPS
algorithm replaces the mechanism of flipping a single variable per iteration
step, that is widely used in existing (W)PMS local search algorithms, with the
proposed farsighted local search strategy, and provides higher-quality local
optimal solutions. The farsighted strategy employs the probabilistic sampling
technique that allows the algorithm to look-ahead widely and efficiently. In
this way, FPS can provide more and better search directions and improve the
performance without reducing the efficiency. Extensive experiments on all the
benchmarks of (W)PMS problems from the incomplete track of recent four years of
MaxSAT Evaluations demonstrate that our method significantly outperforms
SATLike3.0, the state-of-the-art local search algorithm, for solving both the
PMS and WPMS problems. We furthermore do comparison with the extended solver of
SATLike, SATLike-c, which is the champion of three categories among the total
four (PMS and WPMS categories, each associated with two time limits) of the
incomplete track in the recent MaxSAT Evaluation (MSE2021). We replace the
local search component in SATLike-c with the proposed farsighted sampling local
search approach, and the resulting solver FPS-c also outperforms SATLike-c for
solving both the PMS and WPMS problems.
","Farsighted Probabilistic Sampling based Local Search for (Weighted)
  Partial MaxSAT"
"  Differentiable Architecture Search (DARTS) is an effective continuous
relaxation-based network architecture search (NAS) method with low search cost.
It has attracted significant attentions in Auto-ML research and becomes one of
the most useful paradigms in NAS. Although DARTS can produce superior
efficiency over traditional NAS approaches with better control of complex
parameters, oftentimes it suffers from stabilization issues in producing
deteriorating architectures when discretizing the continuous architecture. We
observed considerable loss of validity causing dramatic decline in performance
at this final discretization step of DARTS. To address this issue, we propose a
Mean-Shift based DARTS (MS-DARTS) to improve stability based on sampling and
perturbation. Our approach can improve bot the stability and accuracy of DARTS,
by smoothing the loss landscape and sampling architecture parameters within a
suitable bandwidth. We investigate the convergence of our mean-shift approach,
together with the effects of bandwidth selection that affects stability and
accuracy. Evaluations performed on CIFAR-10, CIFAR-100, and ImageNet show that
MS-DARTS archives higher performance over other state-of-the-art NAS methods
with reduced search cost.
",MS-DARTS: Mean-Shift Based Differentiable Architecture Search
"  As the world is rapidly moving towards digitization and money transactions
are becoming cashless, the use of credit cards has rapidly increased. The fraud
activities associated with it have also been increasing which leads to a huge
loss to the financial institutions. Therefore, we need to analyze and detect
the fraudulent transaction from the non-fraudulent ones. In this paper, we
present a comprehensive review of various methods used to detect credit card
fraud. These methodologies include Hidden Markov Model, Decision Trees,
Logistic Regression, Support Vector Machines (SVM), Genetic algorithm, Neural
Networks, Random Forests, Bayesian Belief Network. A comprehensive analysis of
various techniques is presented. We conclude the paper with the pros and cons
of the same as stated in the respective papers.
",Credit Card Fraud Detection using Machine Learning: A Study
"  Given an input dataset (i.e., a set of tuples), query definability in
Ontology-based Data Management (OBDM) amounts to find a query over the ontology
whose certain answers coincide with the tuples in the given dataset. We refer
to such a query as a characterization of the dataset with respect to the OBDM
system. Our first contribution is to propose approximations of perfect
characterizations in terms of recall (complete characterizations) and precision
(sound characterizations). A second contribution is to present a thorough
complexity analysis of three computational problems, namely verification (check
whether a given query is a perfect, or an approximated characterization of a
given dataset), existence (check whether a perfect, or a best approximated
characterization of a given dataset exists), and computation (compute a
perfect, or best approximated characterization of a given dataset).
",QDEF and Its Approximations in OBDM
"  Cricket is unarguably one of the most popular sports in the world. Predicting
the outcome of a cricket match has become a fundamental problem as we are
advancing in the field of machine learning. Multiple researchers have tried to
predict the outcome of a cricket match or a tournament, or to predict the
performance of players during a match, or to predict the players who should be
selected as per their current performance, form, morale, etc. using machine
learning and artificial intelligence techniques keeping in mind extensive
detailing, features, and parameters. We discuss some of these techniques along
with a brief comparison among these techniques.
","A study on Machine Learning Approaches for Player Performance and Match
  Results Prediction"
"  The Sparsest Permutation (SP) algorithm is accurate but limited to about 9
variables in practice; the Greedy Sparest Permutation (GSP) algorithm is faster
but less weak theoretically. A compromise can be given, the Best Order Score
Search, which gives results as accurate as SP but for much larger and denser
graphs. BOSS (Best Order Score Search) is more accurate for two reason: (a) It
assumes the ""brute faithfuness"" assumption, which is weaker than faithfulness,
and (b) it uses a different traversal of permutations than the depth first
traversal used by GSP, obtained by taking each variable in turn and moving it
to the position in the permutation that optimizes the model score. Results are
given comparing BOSS to several related papers in the literature in terms of
performance, for linear, Gaussian data. In all cases, with the proper parameter
settings, accuracy of BOSS is lifted considerably with respect to competing
approaches. In configurations tested, models with 60 variables are feasible
with large samples out to about an average degree of 12 in reasonable time,
with near-perfect accuracy, and sparse models with an average degree of 4 are
feasible out to about 300 variables on a laptop, again with near-perfect
accuracy. Mixed continuous discrete and all-discrete datasets were also tested.
The mixed data analysis showed advantage for BOSS over GES more apparent at
higher depths with the same score; the discrete data analysis showed a very
small advantage for BOSS over GES with the same score, perhaps not enough to
prefer it.
","Improving Accuracy of Permutation DAG Search using Best Order Score
  Search"
"  Today, AI technology is showing its strengths in almost every industry and
walks of life. From text generation, text summarization, chatbots, NLP is being
used widely. One such paradigm is automatic code generation. An AI could be
generating anything; hence the output space is unconstrained. A self-driving
car is driven for 100 million miles to validate its safety, but tests cannot be
written to monitor and cover an unconstrained space. One of the solutions to
validate AI-generated content is to constrain the problem and convert it from
abstract to realistic, and this can be accomplished by either validating the
unconstrained algorithm using theoretical proofs or by using Monte-Carlo
simulation methods. In this case, we use the latter approach to test/validate a
statistically significant number of samples. This hypothesis of validating the
AI-generated code is the main motive of this work and to know if AI-generated
code is reliable, a metric model CGEMs is proposed. This is an extremely
challenging task as programs can have different logic with different naming
conventions, but the metrics must capture the structure and logic of the
program. This is similar to the importance grammar carries in AI-based text
generation, Q&A, translations, etc. The various metrics that are garnered in
this work to support the evaluation of generated code are as follows:
Compilation, NL description to logic conversion, number of edits needed, some
of the commonly used static-code metrics and NLP metrics. These metrics are
applied to 80 codes generated using OpenAI's GPT-3. Post which a Neural network
is designed for binary classification (acceptable/not acceptable quality of the
generated code). The inputs to this network are the values of the features
obtained from the metrics. The model achieves a classification accuracy of
76.92% and an F1 score of 55.56%. XAI is augmented for model interpretability.
",CGEMs: A Metric Model for Automatic Code Generation using GPT-3
"  The underlying hypothesis of knowledge-based explainable artificial
intelligence is the data required for data-centric artificial intelligence
agents (e.g., neural networks) are less diverse in contents than the data
required to explain the decisions of such agents to humans. The idea is that a
classifier can attain high accuracy using data that express a phenomenon from
one perspective whereas the audience of explanations can entail multiple
stakeholders and span diverse perspectives. We hence propose to use domain
knowledge to complement the data used by agents. We formulate knowledge-based
explainable artificial intelligence as a supervised data classification problem
aligned with the CBR methodology. In this formulation, the inputs are case
problems composed of both the inputs and outputs of the data-centric agent and
case solutions, the outputs, are explanation categories obtained from domain
knowledge and subject matter experts. This formulation does not typically lead
to an accurate classification, preventing the selection of the correct
explanation category. Knowledge-based explainable artificial intelligence
extends the data in this formulation by adding features aligned with domain
knowledge that can increase accuracy when selecting explanation categories.
","Knowledge-based XAI through CBR: There is more to explanations than
  models can tell"
"  Previous research in interpretable machine learning (IML) and explainable
artificial intelligence (XAI) can be broadly categorized as either focusing on
seeking interpretability in the agent's model (i.e., IML) or focusing on the
context of the user in addition to the model (i.e., XAI). The former can be
categorized as feature or instance attribution. Example- or sample-based
methods such as those using or inspired by case-based reasoning (CBR) rely on
various approaches to select instances that are not necessarily attributing
instances responsible for an agent's decision. Furthermore, existing approaches
have focused on interpretability and explainability but fall short when it
comes to accountability. Inspired in case-based reasoning principles, this
paper introduces a pseudo-metric we call Longitudinal distance and its use to
attribute instances to a neural network agent's decision that can be
potentially used to build accountable CBR agents.
",Longitudinal Distance: Towards Accountable Instance Attribution
"  Respiratory diseases, including asthma, bronchitis, pneumonia, and upper
respiratory tract infection (RTI), are among the most common diseases in
clinics. The similarities among the symptoms of these diseases precludes prompt
diagnosis upon the patients' arrival. In pediatrics, the patients' limited
ability in expressing their situation makes precise diagnosis even harder. This
becomes worse in primary hospitals, where the lack of medical imaging devices
and the doctors' limited experience further increase the difficulty of
distinguishing among similar diseases. In this paper, a pediatric fine-grained
diagnosis-assistant system is proposed to provide prompt and precise diagnosis
using solely clinical notes upon admission, which would assist clinicians
without changing the diagnostic process. The proposed system consists of two
stages: a test result structuralization stage and a disease identification
stage. The first stage structuralizes test results by extracting relevant
numerical values from clinical notes, and the disease identification stage
provides a diagnosis based on text-form clinical notes and the structured data
obtained from the first stage. A novel deep learning algorithm was developed
for the disease identification stage, where techniques including adaptive
feature infusion and multi-modal attentive fusion were introduced to fuse
structured and text data together. Clinical notes from over 12000 patients with
respiratory diseases were used to train a deep learning model, and clinical
notes from a non-overlapping set of about 1800 patients were used to evaluate
the performance of the trained model. The average precisions (AP) for
pneumonia, RTI, bronchitis and asthma are 0.878, 0.857, 0.714, and 0.825,
respectively, achieving a mean AP (mAP) of 0.819.
","Identification of Pediatric Respiratory Diseases Using Fine-grained
  Diagnosis System"
"  Neural-symbolic and statistical relational artificial intelligence both
integrate frameworks for learning with logical reasoning. This survey
identifies several parallels across seven different dimensions between these
two fields. These cannot only be used to characterize and position
neural-symbolic artificial intelligence approaches but also to identify a
number of directions for further research.
","From Statistical Relational to Neural Symbolic Artificial Intelligence:
  a Survey"
"  Meta-learning is widely used for few-shot slot tagging in the task of
few-shot learning. The performance of existing methods is, however, seriously
affected by catastrophic forgetting. This phenomenon is common in deep learning
as the training and testing modules fail to take into account historical
information, i.e. previously trained episodes in the metric-based
meta-learning. To overcome this predicament, we propose the Memory-based
Contrastive Meta-learning (MCML) method. Specifically, we propose a
learn-from-memory mechanism that use explicit memory to keep track of the label
representations of previously trained episodes and propose a contrastive
learning method to compare the current label embedded in the few shot episode
with the historic ones stored in the memory, and an adaption-from memory
mechanism to determine the output label based on the contrast between the input
labels embedded in the test episode and the label clusters in the memory.
Experimental results show that MCML is scalable and outperforms metric-based
meta-learning and optimization-based meta-learning on all 1shot, 5-shot,
10-shot, and 20-shot scenarios of the SNIPS dataset.
","MCML: A Novel Memory-based Contrastive Meta-Learning Method for Few Shot
  Slot Tagging"
"  Due to the high efficiency and less weather dependency, autonomous
greenhouses provide an ideal solution to meet the increasing demand for fresh
food. However, managers are faced with some challenges in finding appropriate
control strategies for crop growth, since the decision space of the greenhouse
control problem is an astronomical number. Therefore, an intelligent
closed-loop control framework is highly desired to generate an automatic
control policy. As a powerful tool for optimal control, reinforcement learning
(RL) algorithms can surpass human beings' decision-making and can also be
seamlessly integrated into the closed-loop control framework. However, in
complex real-world scenarios such as agricultural automation control, where the
interaction with the environment is time-consuming and expensive, the
application of RL algorithms encounters two main challenges, i.e., sample
efficiency and safety. Although model-based RL methods can greatly mitigate the
efficiency problem of greenhouse control, the safety problem has not got too
much attention. In this paper, we present a model-based robust RL framework for
autonomous greenhouse control to meet the sample efficiency and safety
challenges. Specifically, our framework introduces an ensemble of environment
models to work as a simulator and assist in policy optimization, thereby
addressing the low sample efficiency problem. As for the safety concern, we
propose a sample dropout module to focus more on worst-case samples, which can
help improve the adaptability of the greenhouse planting policy in extreme
cases. Experimental results demonstrate that our approach can learn a more
effective greenhouse planting policy with better robustness than existing
methods.
","Robust Model-based Reinforcement Learning for Autonomous Greenhouse
  Control"
"  Utterance-level intent detection and token-level slot filling are two key
tasks for natural language understanding (NLU) in task-oriented systems. Most
existing approaches assume that only a single intent exists in an utterance.
However, there are often multiple intents within an utterance in real-life
scenarios. In this paper, we propose a multi-intent NLU framework, called SLIM,
to jointly learn multi-intent detection and slot filling based on BERT. To
fully exploit the existing annotation data and capture the interactions between
slots and intents, SLIM introduces an explicit slot-intent classifier to learn
the many-to-one mapping between slots and intents. Empirical results on three
public multi-intent datasets demonstrate (1) the superior performance of SLIM
compared to the current state-of-the-art for NLU with multiple intents and (2)
the benefits obtained from the slot-intent classifier.
","SLIM: Explicit Slot-Intent Mapping with BERT for Joint Multi-Intent
  Detection and Slot Filling"
"  Although modern object detection and classification models achieve high
accuracy, these are typically constrained in advance on a fixed train set and
are therefore not flexible to deal with novel, unseen object categories.
Moreover, these models most often operate on a single frame, which may yield
incorrect classifications in case of ambiguous viewpoints. In this paper, we
propose an active inference agent that actively gathers evidence for object
classifications, and can learn novel object categories over time. Drawing
inspiration from the human brain, we build object-centric generative models
composed of two information streams, a what- and a where-stream. The
what-stream predicts whether the observed object belongs to a specific
category, while the where-stream is responsible for representing the object in
its internal 3D reference frame. We show that our agent (i) is able to learn
representations for many object categories in an unsupervised way, (ii)
achieves state-of-the-art classification accuracies, actively resolving
ambiguity when required and (iii) identifies novel object categories.
Furthermore, we validate our system in an end-to-end fashion where the agent is
able to search for an object at a given pose from a pixel-based rendering. We
believe that this is a first step towards building modular, intelligent systems
that can be used for a wide range of tasks involving three dimensional objects.
","Disentangling What and Where for 3D Object-Centric Representations
  Through Active Inference"
"  Urban autonomous driving is an open and challenging problem to solve as the
decision-making system has to account for several dynamic factors like
multi-agent interactions, diverse scene perceptions, complex road geometries,
and other rarely occurring real-world events. On the other side, with deep
reinforcement learning (DRL) techniques, agents have learned many complex
policies. They have even achieved super-human-level performances in various
Atari Games and Deepmind's AlphaGo. However, current DRL techniques do not
generalize well on complex urban driving scenarios. This paper introduces the
DRL driven Watch and Drive (WAD) agent for end-to-end urban autonomous driving.
Motivated by recent advancements, the study aims to detect important
objects/states in high dimensional spaces of CARLA and extract the latent state
from them. Further, passing on the latent state information to WAD agents based
on TD3 and SAC methods to learn the optimal driving policy. Our novel approach
utilizing fewer resources, step-by-step learning of different driving tasks,
hard episode termination policy, and reward mechanism has led our agents to
achieve a 100% success rate on all driving tasks in the original CARLA
benchmark and set a new record of 82% on further complex NoCrash benchmark,
outperforming the state-of-the-art model by more than +30% on NoCrash
benchmark.
",WAD: A Deep Reinforcement Learning Agent for Urban Autonomous Driving
"  In this paper, we address the problem of handling inconsistent data in
Temporal Description Logic (TDL) knowledge bases. Considering the data part of
the Knowledge Base as the source of inconsistency over time, we propose an ABox
repair approach. This is the first work handling the repair in TDL Knowledge
bases. To do so, our goal is twofold: 1) detect temporal inconsistencies and 2)
propose a data temporal reparation. For the inconsistency detection, we propose
a reduction approach from TDL to DL which allows to provide a tight NP-complete
upper bound for TDL concept satisfiability and to use highly optimised DL
reasoners that can bring precise explanation (the set of inconsistent data
assertions). Thereafter, from the obtained explanation, we propose a method for
automatically computing the best repair in the temporal setting based on the
allowed rigid predicates and the time order of assertions.
","Cleaning Inconsistent Data in Temporal DL-Lite Under Best Repair
  Semantics"
"  In the context of verification of data-aware processes (DAPs), a formal
approach based on satisfiability modulo theories (SMT) has been considered to
verify parameterised safety properties of so-called artifact-centric systems.
This approach requires a combination of model-theoretic notions and algorithmic
techniques based on backward reachability. We introduce here a variant of one
of the most investigated models in this spectrum, namely simple artifact
systems (SASs), where, instead of managing a database, we operate over a
description logic (DL) ontology expressed in (a slight extension of) RDFS. This
DL, enjoying suitable model-theoretic properties, allows us to define DL-based
SASs to which backward reachability can still be applied, leading to
decidability in PSPACE of the corresponding safety problems.
","SMT-Based Safety Verification of Data-Aware Processes under Ontologies
  (Extended Version)"
"  The successes of Artificial Intelligence in recent years in areas such as
image analysis, natural language understanding and strategy games have sparked
interest from the world of finance. Specifically, there are high expectations,
and ongoing engineering projects, regarding the creation of artificial agents,
known as robotic traders, capable of juggling the financial markets with the
skill of experienced human traders. Obvious economic implications aside, this
is certainly an area of great scientific interest, due to the challenges that
such a real context poses to the use of AI techniques. Precisely for this
reason, we must be aware that artificial agents capable of operating at such
levels are not just round the corner, and that there will be no simple answers,
but rather a concurrence of various technologies and methods to the success of
the effort. In the course of this article, we review the issues inherent in the
design of effective robotic traders as well as the consequently applicable
solutions, having in view the general objective of bringing the current state
of the art of robo-trading up to the next level of intelligence, which we refer
to as Cognitive Trading. Key to our approach is the joining of two
methodological and technological directions which, although both deeply rooted
in the disciplinary field of artificial intelligence, have so far gone their
separate ways: heuristics and learning.
","Integrating Heuristics and Learning in a Computational Architecture for
  Cognitive Trading"
"  Completion through the embedding representation of the knowledge graph (KGE)
has been a research hotspot in recent years. Realistic knowledge graphs are
mostly related to time, while most of the existing KGE algorithms ignore the
time information. A few existing methods directly or indirectly encode the time
information, ignoring the balance of timestamp distribution, which greatly
limits the performance of temporal knowledge graph completion (KGC). In this
paper, a temporal KGC method is proposed based on the direct encoding time
information framework, and a given time slice is treated as the finest
granularity for balanced timestamp distribution. A large number of experiments
on temporal knowledge graph datasets extracted from the real world demonstrate
the effectiveness of our method.
","A Temporal Knowledge Graph Completion Method Based on Balanced Timestamp
  Distribution"
"  Counterfactual frameworks have grown popular in explainable and fair machine
learning, as they offer a natural notion of causation. However,
state-of-the-art models to compute counterfactuals are either unrealistic or
unfeasible. In particular, while Pearl's causal inference provides appealing
rules to calculate counterfactuals, it relies on a model that is unknown and
hard to discover in practice. We address the problem of designing realistic and
feasible counterfactuals in the absence of a causal model. We define
transport-based counterfactual models as collections of joint probability
distributions between observable distributions, and show their connection to
causal counterfactuals. More specifically, we argue that optimal transport
theory defines relevant transport-based counterfactual models, as they are
numerically feasible, statistically-faithful, and can even coincide with causal
counterfactual models. We illustrate the practicality of these models by
defining sharper fairness criteria than typical group fairness conditions.
",Transport-based Counterfactual Models
"  The Shapes Constraint Language (SHACL) is the recent W3C recommendation
language for validating RDF data, by verifying certain shapes on graphs.
Previous work has largely focused on the validation problem and the standard
decision problems of satisfiability and containment, crucial for design and
optimisation purposes, have only been investigated for simplified versions of
SHACL. Moreover, the SHACL specification does not define the semantics of
recursively-defined constraints, which led to several alternative recursive
semantics being proposed in the literature. The interaction between these
different semantics and important decision problems has not been investigated
yet. In this article we provide a comprehensive study of the different features
of SHACL, by providing a translation to a new first-order language, called SCL,
that precisely captures the semantics of SHACL. We also present MSCL, a
second-order extension of SCL, which allows us to define, in a single formal
logic framework, the main recursive semantics of SHACL. Within this language we
also provide an effective treatment of filter constraints which are often
neglected in the related literature. Using this logic we provide a detailed map
of (un)decidability and complexity results for the satisfiability and
containment decision problems for different SHACL fragments. Notably, we prove
that both problems are undecidable for the full language, but we present
decidable combinations of interesting features, even in the face of recursion.
",Satisfiability and Containment of Recursive SHACL
"  The Free-Energy-Principle (FEP) is an influential and controversial theory
which postulates a deep and powerful connection between the stochastic
thermodynamics of self-organization and learning through variational inference.
Specifically, it claims that any self-organizing system which can be
statistically separated from its environment, and which maintains itself at a
non-equilibrium steady state, can be construed as minimizing an
information-theoretic functional -- the variational free energy -- and thus
performing variational Bayesian inference to infer the hidden state of its
environment. This principle has also been applied extensively in neuroscience,
and is beginning to make inroads in machine learning by spurring the
construction of novel and powerful algorithms by which action, perception, and
learning can all be unified under a single objective. While its expansive and
often grandiose claims have spurred significant debates in both philosophy and
theoretical neuroscience, the mathematical depth and lack of accessible
introductions and tutorials for the core claims of the theory have often
precluded a deep understanding within the literature. Here, we aim to provide a
mathematically detailed, yet intuitive walk-through of the formulation and
central claims of the FEP while also providing a discussion of the assumptions
necessary and potential limitations of the theory. Additionally, since the FEP
is a still a living theory, subject to internal controversy, change, and
revision, we also present a detailed appendix highlighting and condensing
current perspectives as well as controversies about the nature, applicability,
and the mathematical assumptions and formalisms underlying the FEP.
",A Mathematical Walkthrough and Discussion of the Free Energy Principle
"  The expressiveness of propositional non-clausal (NC) formulas is
exponentially richer than that of clausal formulas. Yet, clausal efficiency
outperforms non-clausal one. Indeed, a major weakness of the latter is that,
while Horn clausal formulas, along with Horn algorithms, are crucial for the
high efficiency of clausal reasoning, no Horn-like formulas in non-clausal form
had been proposed. To overcome such weakness, we define the hybrid class
$\mathbb{H_{NC}}$ of Horn Non-Clausal (Horn-NC) formulas, by adequately lifting
the Horn pattern to NC form, and argue that $\mathbb{H_{NC}}$, along with
future Horn-NC algorithms, shall increase non-clausal efficiency just as the
Horn class has increased clausal efficiency. Secondly, we: (i) give the
compact, inductive definition of $\mathbb{H_{NC}}$; (ii) prove that
syntactically $\mathbb{H_{NC}}$ subsumes the Horn class but semantically both
classes are equivalent, and (iii) characterize the non-clausal formulas
belonging to $\mathbb{H_{NC}}$. Thirdly, we define the Non-Clausal
Unit-Resolution calculus, $UR_{NC}$, and prove that it checks the
satisfiability of $\mathbb{H_{NC}}$ in polynomial time. This fact, to our
knowledge, makes $\mathbb{H_{NC}}$ the first characterized polynomial class in
NC reasoning. Finally, we prove that $\mathbb{H_{NC}}$ is linearly
recognizable, and also that it is both strictly succincter and exponentially
richer than the Horn class. We discuss that in NC automated reasoning, e.g.
satisfiability solving, theorem proving, logic programming, etc., can directly
benefit from $\mathbb{H_{NC}}$ and $UR_{NC}$ and that, as a by-product of its
proved properties, $\mathbb{H_{NC}}$ arises as a new alternative to analyze
Horn functions and implication systems.
",The Horn Non-Clausal Class and its Polynomiality
"  Evolutionary clustering algorithms have considered as the most popular and
widely used evolutionary algorithms for minimising optimisation and practical
problems in nearly all fields. In this thesis, a new evolutionary clustering
algorithm star (ECA*) is proposed. Additionally, a number of experiments were
conducted to evaluate ECA* against five state-of-the-art approaches. For this,
32 heterogeneous and multi-featured datasets were used to examine their
performance using internal and external clustering measures, and to measure the
sensitivity of their performance towards dataset features in the form of
operational framework. The results indicate that ECA* overcomes its competitive
techniques in terms of the ability to find the right clusters. Based on its
superior performance, exploiting and adapting ECA* on the ontology learning had
a vital possibility. In the process of deriving concept hierarchies from
corpora, generating formal context may lead to a time-consuming process.
Therefore, formal context size reduction results in removing uninterested and
erroneous pairs, taking less time to extract the concept lattice and concept
hierarchies accordingly. In this premise, this work aims to propose a framework
to reduce the ambiguity of the formal context of the existing framework using
an adaptive version of ECA*. In turn, an experiment was conducted by applying
385 sample corpora from Wikipedia on the two frameworks to examine the
reduction of formal context size, which leads to yield concept lattice and
concept hierarchy. The resulting lattice of formal context was evaluated to the
original one using concept lattice-invariants. Accordingly, the homomorphic
between the two lattices preserves the quality of resulting concept hierarchies
by 89% in contrast to the basic ones, and the reduced concept lattice inherits
the structural relation of the original one.
","Artificial Intelligence Algorithms for Natural Language Processing and
  the Semantic Web Ontology Learning"
"  Abstract argumentation provides us with methods such as gradual and Dung
semantics with which to evaluate arguments after potential attacks by other
arguments. Some of these methods can take intrinsic strengths of arguments as
input, with which to modulate the effects of attacks between arguments. Coming
from abstract argumentation, these methods look only at the relations between
arguments and not at the structure of the arguments themselves. In structured
argumentation the way an argument is constructed, by chaining inference rules
starting from premises, is taken into consideration. In this paper we study
methods for assigning an argument its intrinsic strength, based on the
strengths of the premises and inference rules used to form said argument. We
first define a set of principles, which are properties that strength assigning
methods might satisfy. We then propose two such methods and analyse which
principles they satisfy. Finally, we present a generalised system for creating
novel strength assigning methods and speak to the properties of this system
regarding the proposed principles.
","Intrinsic Argument Strength in Structured Argumentation: a Principled
  Approach"
"  The human-agent team, which is a problem in which humans and autonomous
agents collaborate to achieve one task, is typical in human-AI collaboration.
For effective collaboration, humans want to have an effective plan, but in
realistic situations, they might have difficulty calculating the best plan due
to cognitive limitations. In this case, guidance from an agent that has many
computational resources may be useful. However, if an agent guides the human
behavior explicitly, the human may feel that they have lost autonomy and are
being controlled by the agent. We therefore investigated implicit guidance
offered by means of an agent's behavior. With this type of guidance, the agent
acts in a way that makes it easy for the human to find an effective plan for a
collaborative task, and the human can then improve the plan. Since the human
improves their plan voluntarily, he or she maintains autonomy. We modeled a
collaborative agent with implicit guidance by integrating the Bayesian Theory
of Mind into existing collaborative-planning algorithms and demonstrated
through a behavioral experiment that implicit guidance is effective for
enabling humans to maintain a balance between improving their plans and
retaining autonomy.
",Balancing Performance and Human Autonomy with Implicit Guidance Agent
"  This project proposes a methodology for the automatic generation of action
models from video game dynamics descriptions, as well as its integration with a
planning agent for the execution and monitoring of the plans. Planners use
these action models to get the deliberative behaviour for an agent in many
different video games and, combined with a reactive module, solve deterministic
and no-deterministic levels. Experimental results validate the methodology and
prove that the effort put by a knowledge engineer can be greatly reduced in the
definition of such complex domains. Furthermore, benchmarks of the domains has
been produced that can be of interest to the international planning community
to evaluate planners in international planning competitions.
",Planning from video game descriptions
"  We propose Dr.Aid, a logic-based AI framework for automated compliance
checking of data governance rules over data-flow graphs. The rules are modelled
using a formal language based on situation calculus and are suitable for
decentralized contexts with multi-input-multi-output (MIMO) processes. Dr.Aid
models data rules and flow rules and checks compliance by reasoning about the
propagation, combination, modification and application of data rules over the
data flow graphs. Our approach is driven and evaluated by real-world datasets
using provenance graphs from data-intensive research.
","An Automated Framework for Supporting Data-Governance Rule Compliance in
  Decentralized MIMO Contexts"
"  Current implementations of pseudo-Boolean (PB) solvers working on native PB
constraints are based on the CDCL architecture which empowers highly efficient
modern SAT solvers. In particular, such PB solvers not only implement a
(cutting-planes-based) conflict analysis procedure, but also complementary
strategies for components that are crucial for the efficiency of CDCL, namely
branching heuristics, learned constraint deletion and restarts. However, these
strategies are mostly reused by PB solvers without considering the particular
form of the PB constraints they deal with. In this paper, we present and
evaluate different ways of adapting CDCL strategies to take the specificities
of PB constraints into account while preserving the behavior they have in the
clausal setting. We implemented these strategies in two different solvers,
namely Sat4j (for which we consider three configurations) and RoundingSat. Our
experiments show that these dedicated strategies allow to improve, sometimes
significantly, the performance of these solvers, both on decision and
optimization problems.
",On Dedicated CDCL Strategies for PB Solvers
"  The OpenAI Gym project contains hundreds of control problems whose goal is to
provide a testbed for reinforcement learning algorithms. One such problem is
Freeway-ram-v0, where the observations presented to the agent are 128 bytes of
RAM. While the goals of the project are for non-expert AI agents to solve the
control problems with general training, in this work, we seek to learn more
about the problem, so that we can better evaluate solutions. In particular, we
develop on oracle to play the game, so that we may have baselines for success.
We present details of the oracle, plus optimal game-playing situations that can
be used for training and testing AI agents.
",An Oracle and Observations for the OpenAI Gym / ALE Freeway Environment
"  The following defines intent, an arbitrary task and its solutions, and then
argues that an agent which always constructs what is called an Intensional
Solution would qualify as artificial general intelligence. We then explain how
natural language may emerge and be acquired by such an agent, conferring the
ability to model the intent of other individuals labouring under similar
compulsions, because an abstract symbol system and the solution to a task are
one and the same.
",Symbol Emergence and The Solutions to Any Task
"  Scientists have long aimed to discover meaningful formulae which accurately
describe experimental data. One common approach is to manually create
mathematical models of natural phenomena using domain knowledge, then fit these
models to data. In contrast, machine-learning algorithms automate the
construction of accurate data-driven models while consuming large amounts of
data. Ensuring that such models are consistent with existing knowledge is an
open problem. We develop a method for combining logical reasoning with symbolic
regression, enabling principled derivations of models of natural phenomena. We
demonstrate these concepts for Kepler's third law of planetary motion,
Einstein's relativistic time-dilation law, and Langmuir's theory of adsorption,
automatically connecting experimental data with background theory in each case.
We show that laws can be discovered from few data points when using formal
logical reasoning to distinguish the correct formula from a set of plausible
formulas that have similar error on the data. The combination of reasoning with
machine learning provides generalizable insights into key aspects of natural
phenomena. We envision that this combination will enable derivable discovery of
fundamental laws of science. We believe that this is a crucial first step for
connecting the missing links in automating the scientific method.
","AI Descartes: Combining Data and Theory for Derivable Scientific
  Discovery"
"  Agent-based systems have the capability to fuse information from many
distributed sources and create better plans faster. This feature makes
agent-based systems naturally suitable to address the challenges in Supply
Chain Management (SCM). Although agent-based supply chains systems have been
proposed since early 2000; industrial uptake of them has been lagging. The
reasons quoted include the immaturity of the technology, a lack of
interoperability with supply chain information systems, and a lack of trust in
Artificial Intelligence (AI). In this paper, we revisit the agent-based supply
chain and review the state of the art. We find that agent-based technology has
matured, and other supporting technologies that are penetrating supply chains;
are filling in gaps, leaving the concept applicable to a wider range of
functions. For example, the ubiquity of IoT technology helps agents ""sense"" the
state of affairs in a supply chain and opens up new possibilities for
automation. Digital ledgers help securely transfer data between third parties,
making agent-based information sharing possible, without the need to integrate
Enterprise Resource Planning (ERP) systems. Learning functionality in agents
enables agents to move beyond automation and towards autonomy. We note this
convergence effect through conceptualising an agent-based supply chain
framework, reviewing its components, and highlighting research challenges that
need to be addressed in moving forward.
","Will bots take over the supply chain? Revisiting Agent-based supply
  chain automation"
"  Understanding the intent behind chat between customers and customer service
agents has become a crucial problem nowadays due to an exponential increase in
the use of the Internet by people from different cultures and educational
backgrounds. More importantly, the explosion of e-commerce has led to a
significant increase in text conversation between customers and agents. In this
paper, we propose an approach to data mining the conversation intents behind
the textual data. Using the customer service data set, we train unsupervised
text representation models, and then develop an intent mapping model which
would rank the predefined intents base on cosine similarity between sentences
and intents. Topic-modeling techniques are used to define intents and domain
experts are also involved to interpret topic modelling results. With this
approach, we can get a good understanding of the user intentions behind the
unlabelled customer service textual data.
","Effective user intent mining with unsupervised word representation
  models and topic modelling"
"  The wide application of smart devices enables the availability of multimodal
data, which can be utilized in many tasks. In the field of multimodal sentiment
analysis (MSA), most previous works focus on exploring intra- and inter-modal
interactions. However, training a network with cross-modal information
(language, visual, audio) is still challenging due to the modality gap, and
existing methods still cannot ensure to sufficiently learn intra-/inter-modal
dynamics. Besides, while learning dynamics within each sample draws great
attention, the learning of inter-class relationships is neglected. Moreover,
the size of datasets limits the generalization ability of existing methods. To
address the afore-mentioned issues, we propose a novel framework HyCon for
hybrid contrastive learning of tri-modal representation. Specifically, we
simultaneously perform intra-/inter-modal contrastive learning and
semi-contrastive learning (that is why we call it hybrid contrastive learning),
with which the model can fully explore cross-modal interactions, preserve
inter-class relationships and reduce the modality gap. Besides, a refinement
term is devised to prevent the model falling into a sub-optimal solution.
Moreover, HyCon can naturally generate a large amount of training pairs for
better generalization and reduce the negative effect of limited datasets.
Extensive experiments on public datasets demonstrate that our proposed method
outperforms existing works.
","Hybrid Contrastive Learning of Tri-Modal Representation for Multimodal
  Sentiment Analysis"
"  Hungry Geese is a n-player variation of the popular game snake. This paper
looks at state of the art Deep Reinforcement Learning Value Methods. The goal
of the paper is to aggregate research of value based methods and apply it as an
exercise to other environments. A vanilla Deep Q Network, a Double Q-network
and a Dueling Q-Network were all examined and tested with the Hungry Geese
environment. The best performing model was the vanilla Deep Q Network due to
its simple state representation and smaller network structure. Converging
towards an optimal policy was found to be difficult due to random geese
initialization and food generation. Therefore we show that Deep Q Networks may
not be the appropriate model for such a stochastic environment and lastly we
present improvements that can be made along with more suitable models for the
environment.
",An Exploration of Deep Learning Methods in Hungry Geese
"  Federated Learning (FL) bridges the gap between collaborative machine
learning and preserving data privacy. To sustain the long-term operation of an
FL ecosystem, it is important to attract high quality data owners with
appropriate incentive schemes. As an important building block of such incentive
schemes, it is essential to fairly evaluate participants' contribution to the
performance of the final FL model without exposing their private data. Shapley
Value (SV)-based techniques have been widely adopted to provide fair evaluation
of FL participant contributions. However, existing approaches incur significant
computation costs, making them difficult to apply in practice. In this paper,
we propose the Guided Truncation Gradient Shapley (GTG-Shapley) approach to
address this challenge. It reconstructs FL models from gradient updates for SV
calculation instead of repeatedly training with different combinations of FL
participants. In addition, we design a guided Monte Carlo sampling approach
combined with within-round and between-round truncation to further reduce the
number of model reconstructions and evaluations required, through extensive
experiments under diverse realistic data distribution settings. The results
demonstrate that GTG-Shapley can closely approximate actual Shapley values,
while significantly increasing computational efficiency compared to the state
of the art, especially under non-i.i.d. settings.
","GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation
  in Federated Learning"
"  Natural language instruction following tasks serve as a valuable test-bed for
grounded language and robotics research. However, data collection for these
tasks is expensive and end-to-end approaches suffer from data inefficiency. We
propose the structuring of language, acting, and visual tasks into separate
modules that can be trained independently. Using a Language, Action, and Vision
(LAV) framework removes the dependence of action and vision modules on
instruction following datasets, making them more efficient to train. We also
present a preliminary evaluation of LAV on the ALFRED task for visual and
interactive instruction following.
",Modular Framework for Visuomotor Language Grounding
"  Researchers are increasingly focusing on intelligent games as a hot research
area.The article proposes an algorithm that combines the multi-attribute
management and reinforcement learning methods, and that combined their effect
on wargaming, it solves the problem of the agent's low rate of winning against
specific rules and its inability to quickly converge during intelligent wargame
training.At the same time, this paper studied a multi-attribute decision making
and reinforcement learning algorithm in a wargame simulation environment, and
obtained data on red and blue conflict.Calculate the weight of each attribute
based on the intuitionistic fuzzy number weight calculations. Then determine
the threat posed by each opponent's chess pieces.Using the red side
reinforcement learning reward function, the AC framework is trained on the
reward function, and an algorithm combining multi-attribute decision-making
with reinforcement learning is obtained. A simulation experiment confirms that
the algorithm of multi-attribute decision-making combined with reinforcement
learning presented in this paper is significantly more intelligent than the
pure reinforcement learning algorithm.By resolving the shortcomings of the
agent's neural network, coupled with sparse rewards in large-map combat games,
this robust algorithm effectively reduces the difficulties of convergence. It
is also the first time in this field that an algorithm design for intelligent
wargaming combines multi-attribute decision making with reinforcement
learning.Attempt interdisciplinary cross-innovation in the academic field, like
designing intelligent wargames and improving reinforcement learning algorithms.
","Method for making multi-attribute decisions in wargames by combining
  intuitionistic fuzzy numbers with reinforcement learning"
"  Prompt-based knowledge probing for 1-hop relations has been used to measure
how much world knowledge is stored in pretrained language models. Existing work
uses considerable amounts of data to tune the prompts for better performance.
In this work, we compare a variety of approaches under a few-shot knowledge
probing setting, where only a small number (e.g., 10 or 20) of example triples
are available. In addition, we create a new dataset named TREx-2p, which
contains 2-hop relations. We report that few-shot examples can strongly boost
the probing performance for both 1-hop and 2-hop relations. In particular, we
find that a simple-yet-effective approach of finetuning the bias vectors in the
model outperforms existing prompt-engineering methods. Our dataset and code are
available at \url{https://github.com/cloudygoose/fewshot_lama}.
","An Empirical Study on Few-shot Knowledge Probing for Pretrained Language
  Models"
"  Job shop scheduling problem (JSP) is a widely studied NP-complete
combinatorial optimization problem. Neighborhood structures play a critical
role in solving JSP. At present, there are three state-of-the-art neighborhood
structures, i.e., N5, N6, and N7. Improving the upper bounds of some famous
benchmarks is inseparable from the role of these neighborhood structures.
However, these existing neighborhood structures only consider the movement of
critical operations within a critical block. According to our experiments, it
is also possible to improve the makespan of a scheduling scheme by moving a
critical operation outside its critical block. According to the above finding,
this paper proposes a new N8 neighborhood structure considering the movement of
critical operations within a critical block and the movement of critical
operations outside the critical block. Besides, a neighborhood clipping method
is designed to avoid invalid movement, reducing the computational time. Tabu
search (TS) is a commonly used algorithm framework combined with neighborhood
structures. This paper uses this framework to compare the N8 neighborhood
structure with N5, N6, and N7 neighborhood structures on four famous
benchmarks. The experimental results verify that the N8 neighborhood structure
is more effective and efficient in solving JSP than the other state-of-the-art
neighborhood structures.
",A new neighborhood structure for job shop scheduling problems
"  Medical students will almost inevitably encounter powerful medical AI systems
early in their careers. Yet, contemporary medical education does not adequately
equip students with the basic clinical proficiency in medical AI needed to use
these tools safely and effectively. Education reform is urgently needed, but
not easily implemented, largely due to an already jam-packed medical curricula.
In this article, we propose an education reform framework as an effective and
efficient solution, which we call the Embedded AI Ethics Education Framework.
Unlike other calls for education reform to accommodate AI teaching that are
more radical in scope, our framework is modest and incremental. It leverages
existing bioethics or medical ethics curricula to develop and deliver content
on the ethical issues associated with medical AI, especially the harms of
technology misuse, disuse, and abuse that affect the risk-benefit analyses at
the heart of healthcare. In doing so, the framework provides a simple tool for
going beyond the ""What?"" and the ""Why?"" of medical AI ethics education, to
answer the ""How?"", giving universities, course directors, and/or professors a
broad road-map for equipping their students with the necessary clinical
proficiency in medical AI.
","Readying Medical Students for Medical AI: The Need to Embed AI Ethics
  Education"
"  Driving is an intuitive task that requires skills, constant alertness and
vigilance for unexpected events. The driving task also requires long
concentration spans focusing on the entire task for prolonged periods, and
sophisticated negotiation skills with other road users, including wild animals.
These requirements are particularly important when approaching intersections,
overtaking, giving way, merging, turning and while adhering to the vast body of
road rules. Modern motor vehicles now include an array of smart assistive and
autonomous driving systems capable of subsuming some, most, or in limited
cases, all of the driving task. The UK Department of Transport's response to
the Safe Use of Automated Lane Keeping System consultation proposes that these
systems are tested for compliance with relevant traffic rules. Building these
smart automotive systems requires software developers with highly technical
software engineering skills, and now a lawyer's in-depth knowledge of traffic
legislation as well. These skills are required to ensure the systems are able
to safely perform their tasks while being observant of the law. This paper
presents an approach for deconstructing the complicated legalese of traffic law
and representing its requirements and flow. The approach (de)constructs road
rules in legal terminology and specifies them in structured English logic that
is expressed as Boolean logic for automation and Lawmaps for visualisation. We
demonstrate an example using these tools leading to the construction and
validation of a Bayesian Network model. We strongly believe these tools to be
approachable by programmers and the general public, and capable of use in
developing Artificial Intelligence to underpin motor vehicle smart systems, and
in validation to ensure these systems are considerate of the law when making
decisions.
","Smart Automotive Technology Adherence to the Law: (De)Constructing Road
  Rules for Autonomous System Development, Verification and Safety"
"  We present Fudge, an abstract argumentation solver that tightly integrates
satisfiability solving technology to solve a series of abstract argumentation
problems. While most of the encodings used by Fudge derive from standard
translation approaches, Fudge makes use of completely novel encodings to solve
the skeptical reasoning problem wrt. preferred semantics and problems wrt.
ideal semantics.
","Fudge: A light-weight solver for abstract argumentation based on SAT
  reductions"
"  The pyglaf reasoner takes advantage of circumscription to solve computational
problems of abstract argumentation frameworks. In fact, many of these problems
are reduced to circumscription by means of linear encodings, and a few others
are solved by means of a sequence of calls to an oracle for circumscription.
Within pyglaf, Python is used to build the encodings and to control the
execution of the external circumscription solver, which extends the SAT solver
glucose and implements algorithms taking advantage of unsatisfiable core
analysis and incremental computation.
",The pyglaf argumentation reasoner (ICCMA2021)
"  In this solver description we present ASPARTIX-V, in its 2021 edition, which
participates in the International Competition on Computational Models of
Argumentation (ICCMA) 2021. ASPARTIX-V is capable of solving all classical
(static) reasoning tasks part of ICCMA'21 and extends the ASPARTIX system suite
by incorporation of recent ASP language constructs (e.g. conditional literals),
domain heuristics within ASP, and multi-shot methods. In this light ASPARTIX-V
deviates from the traditional focus of ASPARTIX on monolithic approaches (i.e.,
one-shot solving via a single ASP encoding) to further enhance performance.
",Aspartix-V21
"  The recent progress in Reinforcement Learning applications to Resource
Management presents MDPs without a deeper analysis of the impacts of design
decisions on agent performance. In this paper, we compare and contrast four
different MDP variations, discussing their computational requirements and
impacts on agent performance by means of an empirical analysis. We conclude by
showing that, in our experiments, when using Multi-Layer Perceptrons as
approximation function, a compact state representation allows transfer of
agents between environments, and that transferred agents have good performance
and outperform specialized agents in 80\% of the tested scenarios, even without
retraining.
","On the impact of MDP design for Reinforcement Learning agents in
  Resource Management"
"  Visual sensation and perception refers to the process of sensing, organizing,
identifying, and interpreting visual information in environmental awareness and
understanding. Computational models inspired by visual perception have the
characteristics of complexity and diversity, as they come from many subjects
such as cognition science, information science, and artificial intelligence. In
this paper, visual perception computational models oriented deep learning are
investigated from the biological visual mechanism and computational vision
theory systematically. Then, some points of view about the prospects of the
visual perception computational models are presented. Finally, this paper also
summarizes the current challenges of visual perception and predicts its future
development trends. Through this survey, it will provide a comprehensive
reference for research in this direction.
","Visual Sensation and Perception Computational Models for Deep Learning:
  State of the art, Challenges and Prospects"
"  While artificial neural networks (ANNs) have been widely adopted in machine
learning, researchers are increasingly obsessed by the gaps between ANNs and
biological neural networks (BNNs). In this paper, we propose a framework named
as Evolutionary Plastic Recurrent Neural Networks} (EPRNN). Inspired by BNN,
EPRNN composes Evolution Strategies, Plasticity Rules, and Recursion-based
Learning all in one meta learning framework for generalization to different
tasks. More specifically, EPRNN incorporates with nested loops for meta
learning -- an outer loop searches for optimal initial parameters of the neural
network and learning rules; an inner loop adapts to specific tasks. In the
inner loop of EPRNN, we effectively attain both long term memory and short term
memory by forging plasticity with recursion-based learning mechanisms, both of
which are believed to be responsible for memristance in BNNs. The inner-loop
setting closely simulate that of BNNs, which neither query from any gradient
oracle for optimization nor require the exact forms of learning objectives. To
evaluate the performance of EPRNN, we carry out extensive experiments in two
groups of tasks: Sequence Predicting, and Wheeled Robot Navigating. The
experiment results demonstrate the unique advantage of EPRNN compared to
state-of-the-arts based on plasticity and recursion while yielding comparably
good performance against deep learning based approaches in the tasks. The
experiment results suggest the potential of EPRNN to generalize to variety of
tasks and encourage more efforts in plasticity and recursion based learning
mechanisms.
","Do What Nature Did To Us: Evolving Plastic Recurrent Neural Networks For
  Task Generalization"
"  Humans excel at learning long-horizon tasks from demonstrations augmented
with textual commentary, as evidenced by the burgeoning popularity of tutorial
videos online. Intuitively, this capability can be separated into 2 distinct
subtasks - first, dividing a long-horizon demonstration sequence into
semantically meaningful events; second, adapting such events into meaningful
behaviors in one's own environment. Here, we present Video2Skill (V2S), which
attempts to extend this capability to artificial agents by allowing a robot arm
to learn from human cooking videos. We first use sequence-to-sequence
Auto-Encoder style architectures to learn a temporal latent space for events in
long-horizon demonstrations. We then transfer these representations to the
robotic target domain, using a small amount of offline and unrelated
interaction data (sequences of state-action pairs of the robot arm controlled
by an expert) to adapt these events into actionable representations, i.e.,
skills. Through experiments, we demonstrate that our approach results in
self-supervised analogy learning, where the agent learns to draw analogies
between motions in human demonstration data and behaviors in the robotic
environment. We also demonstrate the efficacy of our approach on model learning
- demonstrating how Video2Skill utilizes prior knowledge from human
demonstration to outperform traditional model learning of long-horizon
dynamics. Finally, we demonstrate the utility of our approach for non-tabula
rasa decision-making, i.e, utilizing video demonstration for zero-shot skill
generation.
","Video2Skill: Adapting Events in Demonstration Videos to Skills in an
  Environment using Cyclic MDP Homomorphisms"
"  The widespread use of Artificial Intelligence (AI) in consequential domains,
such as healthcare and parole decision-making systems, has drawn intense
scrutiny on the fairness of these methods. However, ensuring fairness is often
insufficient as the rationale for a contentious decision needs to be audited,
understood, and defended. We propose that the attention mechanism can be used
to ensure fair outcomes while simultaneously providing feature attributions to
account for how a decision was made. Toward this goal, we design an
attention-based model that can be leveraged as an attribution framework. It can
identify features responsible for both performance and fairness of the model
through attention interventions and attention weight manipulation. Using this
attribution framework, we then design a post-processing bias mitigation
strategy and compare it with a suite of baselines. We demonstrate the
versatility of our approach by conducting experiments on two distinct data
types, tabular and textual.
",Attributing Fair Decisions with Attention Interventions
"  Modelling trajectory in general, and vessel trajectory in particular, is a
difficult task because of the multimodal and complex nature of motion data. In
this paper, we present TrAISformer-a novel deep learning architecture that can
forecast vessel positions using AIS (Automatic Identification System)
observations. We address the multimodality by introducing a discrete
representation of AIS data and re-frame the prediction, which is originally a
regression problem, as a classification problem. The model encodes complex
movement patterns in AIS data in high-dimensional vectors, then applies a
transformer to extract useful long-term correlations from sequences of those
embeddings to sample future vessel positions. Experimental results on real,
public AIS data demonstrate that TrAISformer significantly outperforms
state-of-the-art methods.
",TrAISformer-A generative transformer for AIS trajectory prediction
"  This paper quantitatively reveals the state-of-the-art and
state-of-the-practice AI systems only achieve acceptable performance on the
stringent conditions that all categories of subjects are known, which we call
closed clinical settings, but fail to work in real-world clinical settings.
Compared to the diagnosis task in the closed setting, real-world clinical
settings pose severe challenges, and we must treat them differently. We build a
clinical AI benchmark named Clinical AIBench to set up real-world clinical
settings to facilitate researches. We propose an open, dynamic machine learning
framework and develop an AI system named OpenClinicalAI to diagnose diseases in
real-world clinical settings. The first versions of Clinical AIBench and
OpenClinicalAI target Alzheimer's disease. In the real-world clinical setting,
OpenClinicalAI significantly outperforms the state-of-the-art AI system. In
addition, OpenClinicalAI develops personalized diagnosis strategies to avoid
unnecessary testing and seamlessly collaborates with clinicians. It is
promising to be embedded in the current medical systems to improve medical
services.
","OpenClinicalAI: enabling AI to diagnose diseases in real-world clinical
  settings"
"  This paper provides the first formalisation and empirical demonstration of a
particular safety concern in reinforcement learning (RL)-based news and social
media recommendation algorithms. This safety concern is what we call ""user
tampering"" -- a phenomenon whereby an RL-based recommender system may
manipulate a media user's opinions, preferences and beliefs via its
recommendations as part of a policy to increase long-term user engagement. We
provide a simulation study of a media recommendation problem constrained to the
recommendation of political content, and demonstrate that a Q-learning
algorithm consistently learns to exploit its opportunities to 'polarise'
simulated 'users' with its early recommendations in order to have more
consistent success with later recommendations catering to that polarisation.
Finally, we argue that given our findings, designing an RL-based recommender
system which cannot learn to exploit user tampering requires making the metric
for the recommender's success independent of observable signals of user
engagement, and thus that a media recommendation system built solely with RL is
necessarily either unsafe, or almost certainly commercially unviable.
",User Tampering in Reinforcement Learning Recommender Systems
"  Federated Learning, a new machine learning paradigm enhancing the use of edge
devices, is receiving a lot of attention in the pervasive community to support
the development of smart services. Nevertheless, this approach still needs to
be adapted to the specificity of the pervasive domain. In particular, issues
related to continual learning need to be addressed. In this paper, we present a
distillation-based approach dealing with catastrophic forgetting in federated
learning scenario. Specifically, Human Activity Recognition tasks are used as a
demonstration domain.
","A distillation-based approach integrating continual learning and
  federated learning for pervasive services"
"  Recently, several studies have explored the use of neural network to solve
different routing problems, which is an auspicious direction. These studies
usually design an encoder-decoder based framework that uses encoder embeddings
of nodes and the problem-specific context to produce node sequence(path), and
further optimize the produced result on top by beam search. However, existing
models can only support node coordinates as input, ignore the self-referential
property of the studied routing problems, and lack the consideration about the
low reliability in the initial stage of node selection, thus are hard to be
applied in real-world.
  In this paper, we take the orienteering problem as an example to tackle these
limitations. We propose a novel combination of a variant beam search algorithm
and a learned heuristic for solving the general orienteering problem. We
acquire the heuristic with an attention network that takes the distances among
nodes as input, and learn it via a reinforcement learning framework. The
empirical studies show that our method can surpass a wide range of baselines
and achieve results close to the optimal or highly specialized approach. Also,
our proposed framework can be easily applied to other routing problems. Our
code is publicly available.
","Boosting Graph Search with Attention Network for Solving the General
  Orienteering Problem"
"  The subject of Job Scheduling Optimisation (JSO) deals with the scheduling of
jobs in an organization, so that the single working steps are optimally
organized regarding the postulated targets. In this paper a use case is
provided which deals with a sub-aspect of JSO, the Job Shop Scheduling Problem
(JSSP or JSP). As many optimization problems JSSP is NP-complete, which means
the complexity increases with every node in the system exponentially. The goal
of the use case is to show how to create an optimized duty rooster for certain
workpieces in a flexible organized machinery, combined with an Autonomous
Ground Vehicle (AGV), using Constraint Programming (CP) and Quantum Computing
(QC) alternatively. The results of a classical solution based on CP and on a
Quantum Annealing model are presented and discussed. All presented results have
been elaborated in the research project PlanQK.
","Solving the Extended Job Shop Scheduling Problem with AGVs -- Classical
  and Quantum Approaches"
"  Constraint acquisition systems such as QuAcq and MultiAcq can assist
non-expert users to model their problems as constraint networks by classifying
(partial) examples as positive or negative. For each negative example, the
former focuses on one constraint of the target network, while the latter can
learn a maximum number of constraints. Two bottlenecks of the acquisition
process where both these algorithms encounter problems are the large number of
queries required to reach convergence, and the high cpu times needed to
generate queries, especially near convergence. In this paper we propose
algorithmic and heuristic methods to deal with both these issues. We first
describe an algorithm, called MQuAcq, that blends the main idea of MultiAcq
into QuAcq resulting in a method that learns as many constraints as MultiAcq
does after a negative example, but with a lower complexity. A detailed
theoretical analysis of the proposed algorithm is also presented. %We also
present a technique that boosts the performance of constraint acquisition by
reducing the number of queries significantly. Then we turn our attention to
query generation which is a significant but rather overlooked part of the
acquisition process. We describe %in detail how query generation in a typical
constraint acquisition system operates, and we propose heuristics for improving
its efficiency. Experiments from various domains demonstrate that our resulting
algorithm that integrates all the new techniques does not only generate
considerably fewer queries than QuAcq and MultiAcq, but it is also by far
faster than both of them, in average query generation time as well as in total
run time, and also largely alleviates the premature convergence problem.
",Efficient Multiple Constraint Acquisition
"  Most people struggle with prioritizing work. While inexact heuristics have
been developed over time, there is still no tractable principled algorithm for
deciding which of the many possible tasks one should tackle in any given day,
month, week, or year. Additionally, some people suffer from cognitive biases
such as the present bias, leading to prioritization of their immediate
experience over long-term consequences which manifests itself as
procrastination and inefficient task prioritization. Our method utilizes
optimal gamification to help people overcome these problems by incentivizing
each task by a number of points that convey how valuable it is in the long-run.
We extend the previous version of our optimal gamification method with added
services for helping people decide which tasks should and should not be done
when there is not enough time to do everything. To improve the efficiency and
scalability of the to-do list solver, we designed a hierarchical procedure that
tackles the problem from the top-level goals to fine-grained tasks. We test the
accuracy of the incentivised to-do list by comparing the performance of the
strategy with the points computed exactly using Value Iteration for a variety
of case studies. These case studies were specifically designed to cover the
corner cases to get an accurate judge of performance. Our method yielded the
same performance as the exact method for all case studies. To demonstrate its
functionality, we released an API that makes it easy to deploy our method in
Web and app services. We assessed the scalability of our method by applying it
to to-do lists with increasingly larger numbers of goals, sub-goals per goal,
hierarchically nested levels of subgoals. We found that the method provided
through our API is able to tackle fairly large to-do lists having a 576 tasks.
This indicates that our method is suitable for real-world applications.
",Optimal To-Do List Gamification for Long Term Planning
"  Homeostasis is a prevalent process by which living beings maintain their
internal milieu around optimal levels. Multiple lines of evidence suggest that
living beings learn to act to predicatively ensure homeostasis (allostasis). A
classical theory for such regulation is drive reduction, where a function of
the difference between the current and the optimal internal state. The recently
introduced homeostatic regulated reinforcement learning theory (HRRL), by
defining within the framework of reinforcement learning a reward function based
on the internal state of the agent, makes the link between the theories of
drive reduction and reinforcement learning. The HRRL makes it possible to
explain multiple eating disorders. However, the lack of continuous change in
the internal state of the agent with the discrete-time modeling has been so far
a key shortcoming of the HRRL theory. Here, we propose an extension of the
homeostatic reinforcement learning theory to a continuous environment in space
and time, while maintaining the validity of the theoretical results and the
behaviors explained by the model in discrete time. Inspired by the
self-regulating mechanisms abundantly present in biology, we also introduce a
model for the dynamics of the agent internal state, requiring the agent to
continuously take actions to maintain homeostasis. Based on the
Hamilton-Jacobi-Bellman equation and function approximation with neural
networks, we derive a numerical scheme allowing the agent to learn directly how
its internal mechanism works, and to choose appropriate action policies via
reinforcement learning and an appropriate exploration of the environment. Our
numerical experiments show that the agent does indeed learn to behave in a way
that is beneficial to its survival in the environment, making our framework
promising for modeling animal dynamics and decision-making.
","Continuous Homeostatic Reinforcement Learning for Self-Regulated
  Autonomous Agents"
"  We study the design of autonomous agents that are capable of deceiving
outside observers about their intentions while carrying out tasks in
stochastic, complex environments. By modeling the agent's behavior as a Markov
decision process, we consider a setting where the agent aims to reach one of
multiple potential goals while deceiving outside observers about its true goal.
We propose a novel approach to model observer predictions based on the
principle of maximum entropy and to efficiently generate deceptive strategies
via linear programming. The proposed approach enables the agent to exhibit a
variety of tunable deceptive behaviors while ensuring the satisfaction of
probabilistic constraints on the behavior. We evaluate the performance of the
proposed approach via comparative user studies and present a case study on the
streets of Manhattan, New York, using real travel time distributions.
",Deceptive Decision-Making Under Uncertainty
"  Recent breakthroughs in AI have shown the remarkable power of deep learning
and deep reinforcement learning. These developments, however, have been tied to
specific tasks, and progress in out-of-distribution generalization has been
limited. While it is assumed that these limitations can be overcome by
incorporating suitable inductive biases, the notion of inductive biases itself
is often left vague and does not provide meaningful guidance. In the paper, I
articulate a different learning approach where representations do not emerge
from biases in a neural architecture but are learned over a given target
language with a known semantics. The basic ideas are implicit in mainstream AI
where representations have been encoded in languages ranging from fragments of
first-order logic to probabilistic structural causal models. The challenge is
to learn from data, the representations that have traditionally been crafted by
hand. Generalization is then a result of the semantics of the language. The
goals of the paper and talk are to make these ideas explicit, to place them in
a broader context where the design of the target language is crucial, and to
illustrate them in the context of learning to act and plan. For this, after a
general discussion, I consider learning representations of actions, general
policies, and general decompositions. In these cases, learning is formulated as
a combinatorial optimization problem but nothing prevents the use deep learning
techniques instead. Indeed, learning representations over languages with a
known semantics provides an account of what is to be learned, while learning
representations with neural nets provides a complementary account of how
representations can be learned. The challenge and the opportunity is to bring
the two together.
",Target Languages (vs. Inductive Biases) for Learning to Act and Plan
"  When humans are given a policy to execute, there can be policy execution
errors and deviations in execution if there is uncertainty in identifying a
state. So an algorithm that computes a policy for a human to execute ought to
consider these effects in its computations. An optimal MDP policy that is
poorly executed (because of a human agent) maybe much worse than another policy
that is executed with fewer errors. In this paper, we consider the problems of
erroneous execution and execution delay when computing policies for a human
agent that would act in a setting modeled by a Markov Decision Process. We
present a framework to model the likelihood of policy execution errors and
likelihood of non-policy actions like inaction (delays) due to state
uncertainty. This is followed by a hill climbing algorithm to search for good
policies that account for these errors. We then use the best policy found by
hill climbing with a branch and bound algorithm to find the optimal policy. We
show experimental results in a Gridworld domain and analyze the performance of
the two algorithms. We also present human studies that verify if our
assumptions on policy execution by humans under state-aliasing are reasonable.
","Synthesizing Policies That Account For Human Execution Errors Caused By
  State-Aliasing In Markov Decision Processes"
"  The unit selection problem aims to identify a set of individuals who are most
likely to exhibit a desired mode of behavior, for example, selecting
individuals who would respond one way if encouraged and a different way if not
encouraged. Using a combination of experimental and observational data, Li and
Pearl derived tight bounds on the ""benefit function"" - the payoff/cost
associated with selecting an individual with given characteristics. This paper
shows that these bounds can be narrowed significantly (enough to change
decisions) when structural information is available in the form of a causal
model. We address the problem of estimating the benefit function using
observational and experimental data when specific graphical criteria are
assumed to hold.
",Unit Selection with Causal Diagram
"  Reinforcement Learning (RL) is emerging as tool for tackling complex control
and decision-making problems. However, in high-risk environments such as
healthcare, manufacturing, automotive or aerospace, it is often challenging to
bridge the gap between an apparently optimal policy learnt by an agent and its
real-world deployment, due to the uncertainties and risk associated with it.
Broadly speaking RL agents face two kinds of uncertainty, 1. aleatoric
uncertainty, which reflects randomness or noise in the dynamics of the world,
and 2. epistemic uncertainty, which reflects the bounded knowledge of the agent
due to model limitations and finite amount of information/data the agent has
acquired about the world. These two types of uncertainty carry fundamentally
different implications for the evaluation of performance and the level of risk
or trust. Yet these aleatoric and epistemic uncertainties are generally
confounded as standard and even distributional RL is agnostic to this
difference. Here we propose how a distributional approach (UA-DQN) can be
recast to render uncertainties by decomposing the net effects of each
uncertainty. We demonstrate the operation of this method in grid world examples
to build intuition and then show a proof of concept application for an RL agent
operating as a clinical decision support system in critical care
","Enabling risk-aware Reinforcement Learning for medical interventions
  through uncertainty decomposition"
"  An important aspect of artificial intelligence (AI) is the ability to reason
in a step-by-step ""algorithmic"" manner that can be inspected and verified for
its correctness. This is especially important in the domain of question
answering (QA). We argue that the challenge of algorithmic reasoning in QA can
be effectively tackled with a ""systems"" approach to AI which features a hybrid
use of symbolic and sub-symbolic methods including deep neural networks.
Additionally, we argue that while neural network models with end-to-end
training pipelines perform well in narrow applications such as image
classification and language modelling, they cannot, on their own, successfully
perform algorithmic reasoning, especially if the task spans multiple domains.
We discuss a few notable exceptions and point out how they are still limited
when the QA problem is widened to include other intelligence-requiring tasks.
However, deep learning, and machine learning in general, do play important
roles as components in the reasoning process. We propose an approach to
algorithm reasoning for QA, Deep Algorithmic Question Answering (DAQA), based
on three desirable properties: interpretability, generalizability, and
robustness which such an AI system should possess, and conclude that they are
best achieved with a combination of hybrid and compositional AI.
","Deep Algorithmic Question Answering: Towards a Compositionally Hybrid AI
  for Algorithmic Reasoning"
"  Anatoly Karpov's Queen sacrifices are analyzed. Stockfish 14 NNUE -- an AI
chess engine -- evaluates how efficient Karpov's sacrifices are. For
comparative purposes, we provide a dataset on Karpov's Rook and Knight
sacrifices to test whether Karpov achieves a similar level of accuracy. Our
study has implications for human-AI interaction and how humans can better
understand the strategies employed by black-box AI algorithms. Finally, we
conclude with implications for human study in. chess with computer engines.
",Karpov's Queen Sacrifices and AI
"  We propose a method for generating explainable rule sets from tree-ensemble
learners using Answer Set Programming (ASP). To this end, we adopt a
decompositional approach where the split structures of the base decision trees
are exploited in the construction of rules, which in turn are assessed using
pattern mining methods encoded in ASP to extract interesting rules. We show how
user-defined constraints and preferences can be represented declaratively in
ASP to allow for transparent and flexible rule set generation, and how rules
can be used as explanations to help the user better understand the models.
Experimental evaluation with real-world datasets and popular tree-ensemble
algorithms demonstrates that our approach is applicable to a wide range of
classification tasks.
","Generating Explainable Rule Sets from Tree-Ensemble Learning Methods by
  Answer Set Programming"
"  We present an enhancement of exp(ASP), a system that generates explanation
graphs for a literal l - an atom a or its default negation ~a - given an answer
set A of a normal logic program P, which explain why l is true (or false) given
A and P. The new system, exp(ASPc), differs from exp(ASP) in that it supports
choice rules and utilizes constraint rules to provide explanation graphs that
include information about choices and constraints.
","exp(ASPc) : Explaining ASP Programs with Choice Atoms and Constraint
  Rules"
"  The human ability to repurpose objects and processes is universal, but it is
not a well-understood aspect of human intelligence. Repurposing arises in
everyday situations such as finding substitutes for missing ingredients when
cooking, or for unavailable tools when doing DIY. It also arises in critical,
unprecedented situations needing crisis management. After natural disasters and
during wartime, people must repurpose the materials and processes available to
make shelter, distribute food, etc. Repurposing is equally important in
professional life (e.g. clinicians often repurpose medicines off-license) and
in addressing societal challenges (e.g. finding new roles for waste products,).
Despite the importance of repurposing, the topic has received little academic
attention. By considering examples from a variety of domains such as every-day
activities, drug repurposing and natural disasters, we identify some principle
characteristics of the process and describe some technical challenges that
would be involved in modelling and simulating it. We consider cases of both
substitution, i.e. finding an alternative for a missing resource, and
exploitation, i.e. identifying a new role for an existing resource. We argue
that these ideas could be developed into general formal theory of repurposing,
and that this could then lead to the development of AI methods based on
commonsense reasoning, argumentation, ontological reasoning, and various
machine learning methods, to develop tools to support repurposing in practice.
","Repurposing of Resources: from Everyday Problem Solving through to
  Crisis Management"
"  Off-policy evaluation (OPE) is the method that attempts to estimate the
performance of decision making policies using historical data generated by
different policies without conducting costly online A/B tests. Accurate OPE is
essential in domains such as healthcare, marketing or recommender systems to
avoid deploying poor performing policies, as such policies may hart human lives
or destroy the user experience. Thus, many OPE methods with theoretical
backgrounds have been proposed. One emerging challenge with this trend is that
a suitable estimator can be different for each application setting. It is often
unknown for practitioners which estimator to use for their specific
applications and purposes. To find out a suitable estimator among many
candidates, we use a data-driven estimator selection procedure for off-policy
policy performance estimators as a practical solution. As proof of concept, we
use our procedure to select the best estimator to evaluate coupon treatment
policies on a real-world online content delivery service. In the experiment, we
first observe that a suitable estimator might change with different definitions
of the outcome variable, and thus the accurate estimator selection is critical
in real-world applications of OPE. Then, we demonstrate that, by utilizing the
estimator selection procedure, we can easily find out suitable estimators for
each purpose.
","Data-Driven Off-Policy Estimator Selection: An Application in User
  Marketing on An Online Content Delivery Service"
"  Answer Set Programming (ASP) emerged in the late 1990ies as a paradigm for
Knowledge Representation and Reasoning. The attractiveness of ASP builds on an
expressive high-level modeling language along with the availability of powerful
off-the-shelf solving systems. While the utility of incorporating aggregate
expressions in the modeling language has been realized almost simultaneously
with the inception of the first ASP solving systems, a general semantics of
aggregates and its efficient implementation have been long-standing challenges.
Aggregates have been proposed and widely used in database systems, and also in
the deductive database language Datalog, which is one of the main precursors of
ASP. The use of aggregates was, however, still restricted in Datalog (by either
disallowing recursion or only allowing monotone aggregates), while several ways
to integrate unrestricted aggregates evolved in the context of ASP. In this
survey, we pick up at this point of development by presenting and comparing the
main aggregate semantics that have been proposed for propositional ASP
programs. We highlight crucial properties such as computational complexity and
expressive power, and outline the capabilities and limitations of different
approaches by illustrative examples.
",Aggregate Semantics for Propositional Answer Set Programs
"  This paper looks at solving collaborative planning problems formalized as
Decentralized POMDPs (Dec-POMDPs) by searching for Nash equilibria, i.e.,
situations where each agent's policy is a best response to the other agents'
(fixed) policies. While the Joint Equilibrium-based Search for Policies (JESP)
algorithm does this in the finite-horizon setting relying on policy trees, we
propose here to adapt it to infinite-horizon Dec-POMDPs by using finite state
controller (FSC) policy representations. In this article, we (1) explain how to
turn a Dec-POMDP with $N-1$ fixed FSCs into an infinite-horizon POMDP whose
solution is an $N^\text{th}$ agent best response; (2) propose a JESP variant,
called \infJESP, using this to solve infinite-horizon Dec-POMDPs; (3) introduce
heuristic initializations for JESP aiming at leading to good solutions; and (4)
conduct experiments on state-of-the-art benchmark problems to evaluate our
approach.
","Solving infinite-horizon Dec-POMDPs using Finite State Controllers
  within JESP"
"  Since 2015, the International Competition on Computational Models of
Argumentation (ICCMA) provides a systematic comparison of the different
algorithms for solving some classical reasoning problems in the domain of
abstract argumentation. This paper discusses the design of the Fourth
International Competition on Computational Models of Argumentation. We describe
the rules of the competition and the benchmark selection method that we used.
After a brief presentation of the competitors, we give an overview of the
results.
",Design and Results of ICCMA 2021
"  We offer a historical overview of methodologies for quantifying the notion of
risk and optimizing risk-aware autonomous systems, with emphasis on risk-averse
settings in which safety may be critical. We categorize and present
state-of-the-art approaches, and we describe connections between such
approaches and ideas from the fields of decision theory, operations research,
reinforcement learning, and stochastic control. The first part of the review
focuses on model-based risk-averse methods. The second part discusses methods
that blend model-based and model-free techniques for the purpose of designing
policies with improved adaptive capabilities. We conclude by highlighting areas
for future research.
","Risk-averse autonomous systems: A brief history and recent developments
  from the perspective of optimal control"
"  Organizations around the world face an array of risks impacting their
operations globally. It is imperative to have a robust risk identification
process to detect and evaluate the impact of potential risks before they
materialize. Given the nature of the task and the current requirements of deep
subject matter expertise, most organizations utilize a heavily manual process.
In our work, we develop an automated system that (a) continuously monitors
global news, (b) is able to autonomously identify and characterize risks, (c)
is able to determine the proximity of reaching triggers to determine the
distance from the manifestation of the risk impact and (d) identifies
organization's operational areas that may be most impacted by the risk. Other
contributions also include: (a) a knowledge graph representation of risks and
(b) relevant news matching to risks identified by the organization utilizing a
neural embedding model to match the textual description of a given risk with
multi-lingual news.
","A Framework for Institutional Risk Identification using Knowledge Graphs
  and Automated News Profiling"
"  Deep learning approaches have achieved great success in the field of Natural
Language Processing (NLP). However, deep neural models often suffer from
overfitting and data scarcity problems that are pervasive in NLP tasks. In
recent years, Multi-Task Learning (MTL), which can leverage useful information
of related tasks to achieve simultaneous performance improvement on multiple
related tasks, has been used to handle these problems. In this paper, we give
an overview of the use of MTL in NLP tasks. We first review MTL architectures
used in NLP tasks and categorize them into four classes, including the parallel
architecture, hierarchical architecture, modular architecture, and generative
adversarial architecture. Then we present optimization techniques on loss
construction, data sampling, and task scheduling to properly train a multi-task
model. After presenting applications of MTL in a variety of NLP tasks, we
introduce some benchmark datasets. Finally, we make a conclusion and discuss
several possible research directions in this field.
",Multi-Task Learning in Natural Language Processing: An Overview
"  Reference ontologies provide a shared vocabulary and knowledge resource for
their domain. Manual construction enables them to maintain a high quality,
allowing them to be widely accepted across their community. However, the manual
development process does not scale for large domains. We present a new
methodology for automatic ontology extension and apply it to the ChEBI
ontology, a prominent reference ontology for life sciences chemistry. We
trained a Transformer-based deep learning model on the leaf node structures
from the ChEBI ontology and the classes to which they belong. The model is then
capable of automatically classifying previously unseen chemical structures. The
proposed model achieved an overall F1 score of 0.80, an improvement of 6
percentage points over our previous results on the same dataset. Additionally,
we demonstrate how visualizing the model's attention weights can help to
explain the results by providing insight into how the model made its decisions.
","Automated and Explainable Ontology Extension Based on Deep Learning: A
  Case Study in the Chemical Domain"
"  Despite its rise as a prominent solution to the data inefficiency of today's
machine learning models, self-supervised learning has yet to be studied from a
purely multi-agent perspective. In this work, we propose that aligning internal
subjective representations, which naturally arise in a multi-agent setup where
agents receive partial observations of the same underlying environmental state,
can lead to more data-efficient representations. We propose that multi-agent
environments, where agents do not have access to the observations of others but
can communicate within a limited range, guarantees a common context that can be
leveraged in individual representation learning. The reason is that subjective
observations necessarily refer to the same subset of the underlying
environmental states and that communication about these states can freely offer
a supervised signal. To highlight the importance of communication, we refer to
our setting as \textit{socially supervised representation learning}. We present
a minimal architecture comprised of a population of autoencoders, where we
define loss functions, capturing different aspects of effective communication,
and examine their effect on the learned representations. We show that our
proposed architecture allows the emergence of aligned representations. The
subjectivity introduced by presenting agents with distinct perspectives of the
environment state contributes to learning abstract representations that
outperform those learned by a single autoencoder and a population of
autoencoders, presented with identical perspectives of the environment state.
Altogether, our results demonstrate how communication from subjective
perspectives can lead to the acquisition of more abstract representations in
multi-agent systems, opening promising perspectives for future research at the
intersection of representation learning and emergent communication.
","Socially Supervised Representation Learning: the Role of Subjectivity in
  Learning Efficient Representations"
"  Customer segmentation has long been a productive field in banking. However,
with new approaches to traditional problems come new opportunities.
Fine-grained customer segments are notoriously elusive and one method of
obtaining them is through feature extraction. It is possible to assign
coefficients of standard personality traits to financial transaction classes
aggregated over time. However, we have found that the clusters formed are not
sufficiently discriminatory for micro-segmentation. In a novel approach, we
extract temporal features with continuous values from the hidden states of
neural networks predicting customers' spending personality from their financial
transactions. We consider both temporal and non-sequential models, using long
short-term memory (LSTM) and feed-forward neural networks, respectively. We
found that recurrent neural networks produce micro-segments where feed-forward
networks produce only coarse segments. Finally, we show that classification
using these extracted features performs at least as well as bespoke models on
two common metrics, namely loan default rate and customer liquidity index.
","Clustering in Recurrent Neural Networks for Micro-Segmentation using
  Spending Personality"
"  Reinforcement Learning has shown success in a number of complex virtual
environments. However, many challenges still exist towards solving problems
with natural language as a core component. Interactive Fiction Games (or Text
Games) are one such problem type that offer a set of partially observable
environments where natural language is required as part of the reinforcement
learning solutions.
  Therefore, this survey's aim is to assist in the development of new Text Game
problem settings and solutions for Reinforcement Learning informed by natural
language. Specifically, this survey summarises: 1) the challenges introduced in
Text Game Reinforcement Learning problems, 2) the generation tools for
evaluating Text Games and the subsequent environments generated and, 3) the
agent architectures currently applied are compared to provide a systematic
review of benchmark methodologies and opportunities for future researchers.
","A Survey of Text Games for Reinforcement Learning informed by Natural
  Language"
"  In this paper we present a process for automatically generating manuals for
board games within the Ludii general game system. This process requires many
different sub-tasks to be addressed, such as English translation of Ludii game
descriptions, move visualisation, highlighting winning moves, strategy
explanation, among others. These aspects are then combined to create a full
manual for any given game. This manual is intended to provide a more intuitive
explanation of a game's rules and mechanics, particularly for players who are
less familiar with the Ludii game description language and grammar.
",Automatic Generation of Board Game Manuals
"  In visual semantic navigation, the robot navigates to a target object with
egocentric visual observations and the class label of the target is given. It
is a meaningful task inspiring a surge of relevant research. However, most of
the existing models are only effective for single-agent navigation, and a
single agent has low efficiency and poor fault tolerance when completing more
complicated tasks. Multi-agent collaboration can improve the efficiency and has
strong application potentials. In this paper, we propose the multi-agent visual
semantic navigation, in which multiple agents collaborate with others to find
multiple target objects. It is a challenging task that requires agents to learn
reasonable collaboration strategies to perform efficient exploration under the
restrictions of communication bandwidth. We develop a hierarchical decision
framework based on semantic mapping, scene prior knowledge, and communication
mechanism to solve this task. The results of testing experiments in unseen
scenes with both known objects and unknown objects illustrate the higher
accuracy and efficiency of the proposed model compared with the single-agent
model.
","Multi-Agent Embodied Visual Semantic Navigation with Scene Prior
  Knowledge"
"  We investigate causal inference in the asymptotic regime as the number of
variables approaches infinity using an information-theoretic framework. We
define structural entropy of a causal model in terms of its description
complexity measured by the logarithmic growth rate, measured in bits, of all
directed acyclic graphs (DAGs), parameterized by the edge density d. Structural
entropy yields non-intuitive predictions. If we randomly sample a DAG from the
space of all models, in the range d = (0, 1/8), almost surely the model is a
two-layer DAG! Semantic entropy quantifies the reduction in entropy where edges
are removed by causal intervention. Semantic causal entropy is defined as the
f-divergence between the observational distribution and the interventional
distribution P', where a subset S of edges are intervened on to determine their
causal influence. We compare the decomposability properties of semantic entropy
for different choices of f-divergences, including KL-divergence, squared
Hellinger distance, and total variation distance. We apply our framework to
generalize a recently popular bipartite experimental design for studying causal
inference on large datasets, where interventions are carried out on one set of
variables (e.g., power plants, items in an online store), but outcomes are
measured on a disjoint set of variables (residents near power plants, or
shoppers). We generalize bipartite designs to k-partite designs, and describe
an optimization framework for finding the optimal k-level DAG architecture for
any value of d \in (0, 1/2). As edge density increases, a sequence of phase
transitions occur over disjoint intervals of d, with deeper DAG architectures
emerging for larger values of d. We also give a quantitative bound on the
number of samples needed to reliably test for average causal influence for a
k-partite design.
",Asymptotic Causal Inference
"  Configuration is a successful application area of Artificial Intelligence. In
the majority of the cases, configuration systems focus on configuring one
solution (configuration) that satisfies the preferences of a single user or a
group of users. In this paper, we introduce a new configuration approach -
multi-configuration - that focuses on scenarios where the outcome of a
configuration process is a set of configurations. Example applications thereof
are the configuration of personalized exams for individual students, the
configuration of project teams, reviewer-to-paper assignment, and hotel room
assignments including individualized city trips for tourist groups. For
multi-configuration scenarios, we exemplify a constraint satisfaction problem
representation in the context of configuring exams. The paper is concluded with
a discussion of open issues for future work.
",Configuring Multiple Instances with Multi-Configuration
"  In many applications, it is important to be able to explain the decisions of
machine learning systems. An increasingly popular approach has been to seek to
provide \emph{counterfactual instance explanations}. These specify close
possible worlds in which, contrary to the facts, a person receives their
desired decision from the machine learning system. This paper will draw on
literature from the philosophy of science to argue that a satisfactory
explanation must consist of both counterfactual instances and a causal equation
(or system of equations) that support the counterfactual instances. We will
show that counterfactual instances by themselves explain little. We will
further illustrate how explainable AI methods that provide both causal
equations and counterfactual instances can successfully explain machine
learning predictions.
",Counterfactual Instances Explain Little
"  Despite the surprising power of many modern AI systems that often learn their
own representations, there is significant discontent about their inscrutability
and the attendant problems in their ability to interact with humans. While
alternatives such as neuro-symbolic approaches have been proposed, there is a
lack of consensus on what they are about. There are often two independent
motivations (i) symbols as a lingua franca for human-AI interaction and (ii)
symbols as (system-produced) abstractions use in its internal reasoning. The
jury is still out on whether AI systems will need to use symbols in their
internal reasoning to achieve general intelligence capabilities. Whatever the
answer there is, the need for (human-understandable) symbols in human-AI
interaction seems quite compelling. Symbols, like emotions, may well not be
sine qua non for intelligence per se, but they will be crucial for AI systems
to interact with us humans--as we can neither turn off our emotions nor get by
without our symbols. In particular, in many human-designed domains, humans
would be interested in providing explicit (symbolic) knowledge and advice--and
expect machine explanations in kind. This alone requires AI systems to at least
do their I/O in symbolic terms. In this blue sky paper, we argue this point of
view, and discuss research directions that need to be pursued to allow for this
type of human-AI interaction.
","Symbols as a Lingua Franca for Bridging Human-AI Chasm for Explainable
  and Advisable AI Systems"
"  Over the past years, topics ranging from climate change to human rights have
seen increasing importance for investment decisions. Hence, investors (asset
managers and asset owners) who wanted to incorporate these issues started to
assess companies based on how they handle such topics. For this assessment,
investors rely on specialized rating agencies that issue ratings along the
environmental, social and governance (ESG) dimensions. Such ratings allow them
to make investment decisions in favor of sustainability. However, rating
agencies base their analysis on subjective assessment of sustainability
reports, not provided by every company. Furthermore, due to human labor
involved, rating agencies are currently facing the challenge to scale up the
coverage in a timely manner.
  In order to alleviate these challenges and contribute to the overall goal of
supporting sustainability, we propose a heterogeneous ensemble model to predict
ESG ratings using fundamental data. This model is based on feedforward neural
network, CatBoost and XGBoost ensemble members. Given the public availability
of fundamental data, the proposed method would allow cost-efficient and
scalable creation of initial ESG ratings (also for companies without
sustainability reporting). Using our approach we are able to explain 54% of the
variation in ratings R2 using fundamental data and outperform prior work in
this area.
",Heterogeneous Ensemble for ESG Ratings Prediction
"  It has been recently shown that general policies for many classical planning
domains can be expressed and learned in terms of a pool of features defined
from the domain predicates using a description logic grammar. At the same time,
most description logics correspond to a fragment of $k$-variable counting logic
($C_k$) for $k=2$, that has been shown to provide a tight characterization of
the expressive power of graph neural networks. In this work, we make use of
these results to understand the power and limits of using graph neural networks
(GNNs) for learning optimal general policies over a number of tractable
planning domains where such policies are known to exist. For this, we train a
simple GNN in a supervised manner to approximate the optimal value function
$V^{*}(s)$ of a number of sample states $s$. As predicted by the theory, it is
observed that general optimal policies are obtained in domains where general
optimal value functions can be defined with $C_2$ features but not in those
requiring more expressive $C_3$ features. In addition, it is observed that the
features learned are in close correspondence with the features needed to
express $V^{*}$ in closed form. The theory and the analysis of the domains let
us understand the features that are actually learned as well as those that
cannot be learned in this way, and let us move in a principled manner from a
combinatorial optimization approach to learning general policies to a
potentially, more robust and scalable approach based on deep learning.
","Learning General Optimal Policies with Graph Neural Networks: Expressive
  Power, Transparency, and Limits"
"  Many approaches have been proposed for early classification of time series in
light of itssignificance in a wide range of applications including healthcare,
transportation and fi-nance. Until now, the early classification problem has
been dealt with by considering onlyirrevocable decisions. This paper introduces
a new problem calledearly and revocabletimeseries classification, where the
decision maker can revoke its earlier decisions based on thenew available
measurements. In order to formalize and tackle this problem, we propose anew
cost-based framework and derive two new approaches from it. The first approach
doesnot consider explicitly the cost of changing decision, while the second one
does. Exten-sive experiments are conducted to evaluate these approaches on a
large benchmark of realdatasets. The empirical results obtained convincingly
show (i) that the ability of revok-ing decisions significantly improves
performance over the irrevocable regime, and (ii) thattaking into account the
cost of changing decision brings even better results in
general.Keywords:revocable decisions, cost estimation, online decision making
",Early and Revocable Time Series Classification
"  Knowledge enhanced pre-trained language models (K-PLMs) are shown to be
effective for many public tasks in the literature but few of them have been
successfully applied in practice. To address this problem, we propose K-AID, a
systematic approach that includes a low-cost knowledge acquisition process for
acquiring domain knowledge, an effective knowledge infusion module for
improving model performance, and a knowledge distillation component for
reducing the model size and deploying K-PLMs on resource-restricted devices
(e.g., CPU) for real-world application. Importantly, instead of capturing
entity knowledge like the majority of existing K-PLMs, our approach captures
relational knowledge, which contributes to better-improving sentence-level text
classification and text matching tasks that play a key role in question
answering (QA). We conducted a set of experiments on five text classification
tasks and three text matching tasks from three domains, namely E-commerce,
Government, and Film&TV, and performed online A/B tests in E-commerce.
Experimental results show that our approach is able to achieve substantial
improvement on sentence-level question answering tasks and bring beneficial
business value in industrial settings.
","K-AID: Enhancing Pre-trained Language Models with Domain Knowledge for
  Question Answering"
"  Logic Production System (LPS) is a logic-based framework for modelling
reactive behaviour. Based on abductive logic programming, it combines reactive
rules with logic programs, a database and a causal theory that specifies
transitions between the states of the database. This paper proposes a
systematic mapping of the Kernel of this framework (called KELPS) into an
answer set program (ASP). For this purpose a new variant of KELPS with finite
models, called $n$-distance KELPS, is introduced. A formal definition of the
mapping from this $n$-distance KELPS to ASP is given and proven sound and
complete. The Answer Set Programming paradigm allows to capture additional
behaviours to the basic reactivity of KELPS, in particular proactive,
preemptive and prospective behaviours. These are all discussed and illustrated
with examples. Then a hybrid framework is proposed that integrates KELPS and
ASP, allowing to combine the strengths of both paradigms. Under consideration
in Theory and Practice of Logic Programming (TPLP).
",Reactive Answer Set Programming
"  With increasing world population and expanded use of forests as cohabited
regions, interactions and conflicts with wildlife are increasing, leading to
large-scale loss of lives (animal and human) and livelihoods (economic). While
community knowledge is valuable, forest officials and conservation
organisations can greatly benefit from predictive analysis of human-wildlife
conflict, leading to targeted interventions that can potentially help save
lives and livelihoods. However, the problem of prediction is a complex
socio-technical problem in the context of limited data in low-resource regions.
  Identifying the ""right"" features to make accurate predictions of conflicts at
the required spatial granularity using a sparse conflict training dataset} is
the key challenge that we address in this paper. Specifically, we do an
illustrative case study on human-wildlife conflicts in the Bramhapuri Forest
Division in Chandrapur, Maharashtra, India. Most existing work has considered
human-wildlife conflicts in protected areas and to the best of our knowledge,
this is the first effort at prediction of human-wildlife conflicts in
unprotected areas and using those predictions for deploying interventions on
the ground.
",Facilitating human-wildlife cohabitation through conflict prediction
"  In this paper we present a textual description, in terms of Description
Logics, of the BPMN Ontology, which provides a clear semantic formalisation of
the structural components of the Business Process Modelling Notation (BPMN),
based on the latest stable BPMN specifications from OMG [BPMN Version 1.1 --
January 2008]. The development of the ontology was guided by the description of
the complete set of BPMN Element Attributes and Types contained in Annex B of
the BPMN specifications.
",A formalisation of BPMN in Description Logics
"  The increasing complexity and unpredictability of many ICT scenarios let us
envision that future systems will have to dynamically learn how to act and
adapt to face evolving situations with little or no a priori knowledge, both at
the level of individual components and at the collective level. In other words,
such systems should become able to autonomously develop models of themselves
and of their environment. Autonomous development includes: learning models of
own capabilities; learning how to act purposefully towards the achievement of
specific goals; and learning how to act collectively, i.e., accounting for the
presence of others. In this paper, we introduce the vision of autonomous
development in ICT systems, by framing its key concepts and by illustrating
suitable application domains. Then, we overview the many research areas that
are contributing or can potentially contribute to the realization of the
vision, and identify some key research challenges.
",Individual and Collective Autonomous Development
"  A Qualitative Constraint Network (QCN) is a constraint graph for representing
problems under qualitative temporal and spatial relations, among others. More
formally, a QCN includes a set of entities, and a list of qualitative
constraints defining the possible scenarios between these entities. These
latter constraints are expressed as disjunctions of binary relations capturing
the (incomplete) knowledge between the involved entities. QCNs are very
effective in representing a wide variety of real-world applications, including
scheduling and planning, configuration and Geographic Information Systems
(GIS). It is however challenging to elicit, from the user, the QCN representing
a given problem. To overcome this difficulty in practice, we propose a new
algorithm for learning, through membership queries, a QCN from a non expert. In
this paper, membership queries are asked in order to elicit temporal or spatial
relationships between pairs of temporal or spatial entities. In order to
improve the time performance of our learning algorithm in practice, constraint
propagation, through transitive closure, as well as ordering heuristics, are
enforced. The goal here is to reduce the number of membership queries needed to
reach the target QCN. In order to assess the practical effect of constraint
propagation and ordering heuristics, we conducted several experiments on
randomly generated temporal and spatial constraint network instances. The
results of the experiments are very encouraging and promising.
","Exact Learning of Qualitative Constraint Networks from Membership
  Queries"
"  The Conditional Preference Network (CP-net) graphically represents user's
qualitative and conditional preference statements under the ceteris paribus
interpretation. The constrained CP-net is an extension of the CP-net, to a set
of constraints. The existing algorithms for solving the constrained CP-net
require the expensive dominance testing operation. We propose three approaches
to tackle this challenge. In our first solution, we alter the constrained
CP-net by eliciting additional relative importance statements between
variables, in order to have a total order over the outcomes. We call this new
model, the constrained Relative Importance Network (constrained CPR-net).
Consequently, We show that the Constrained CPR-net has one single optimal
outcome (assuming the constrained CPR-net is consistent) that we can obtain
without dominance testing. In our second solution, we extend the Lexicographic
Preference Tree (LP-tree) to a set of constraints. Then, we propose a recursive
backtrack search algorithm, that we call Search-LP, to find the most preferable
outcome. We prove that the first feasible outcome returned by Search-LP
(without dominance testing) is also preferable to any other feasible outcome.
Finally, in our third solution, we preserve the semantics of the CP-net and
propose a divide and conquer algorithm that compares outcomes according to
dominance testing.
",Constrained Optimization with Qualitative Preferences
"  We present a clustering- and demotion-based algorithm called Kmeans-FOLD to
induce nonmonotonic logic programs from positive and negative examples. Our
algorithm improves upon-and is inspired by-the FOLD algorithm. The FOLD
algorithm itself is an improvement over the FOIL algorithm. Our algorithm
generates a more concise logic program compared to the FOLD algorithm. Our
algorithm uses the K-means based clustering method to cluster the input
positive samples before applying the FOLD algorithm. Positive examples that are
covered by the partially learned program in intermediate steps are not
discarded as in the FOLD algorithm, rather they are demoted, i.e., their
weights are reduced in subsequent iterations of the algorithm. Our experiments
on the UCI dataset show that a combination of K-Means clustering and our
demotion strategy produces significant improvement for datasets with more than
one cluster of positive examples. The resulting induced program is also more
concise and therefore easier to understand compared to the FOLD and ALEPH
systems, two state of the art inductive logic programming (ILP) systems.
","A Clustering and Demotion Based Algorithm for Inductive Learning of
  Default Theories"
"  Recently, multiple approaches for creating agents for playing various complex
real-time computer games such as StarCraft II or Dota 2 were proposed, however,
they either embed a significant amount of expert knowledge into the agent or
use a prohibitively large for most researchers amount of computational
resources. We propose a neural network architecture for playing the full
two-player match of StarCraft II trained with general-purpose supervised and
reinforcement learning, that can be trained on a single consumer-grade PC with
a single GPU. We also show that our implementation achieves a non-trivial
performance when compared to the in-game scripted bots. We make no simplifying
assumptions about the game except for playing on a single chosen map, and we
use very little expert knowledge. In principle, our approach can be applied to
any RTS game with small modifications. While our results are far behind the
state-of-the-art large-scale approaches in terms of the final performance, we
believe our work can serve as a solid baseline for other small-scale
experiments.
","Applying supervised and reinforcement learning methods to create
  neural-network-based agents for playing StarCraft II"
"  The ability to abstract, count, and use System 2 reasoning are well-known
manifestations of intelligence and understanding. In this paper, we argue,
using the example of the ``Look and Say"" puzzle, that although deep neural
networks can exhibit high `competence' (as measured by accuracy) when trained
on large data sets (2M examples in our case), they do not show any sign on the
deeper understanding of the problem, or what D. Dennett calls `comprehension'.
We report on two sets experiments on the ``Look and Say"" puzzle data. We view
the problem as building a translator from one set of tokens to another. We
apply both standard LSTMs and Transformer/Attention -- based neural networks,
using publicly available machine translation software. We observe that despite
the amazing accuracy (on both, training and test data), the performance of the
trained programs on the actual L\&S sequence is bad. We then discuss a few
possible ramifications of this finding and connections to other work,
experimental and theoretical. First, from the cognitive science perspective, we
argue that we need better mathematical models of abstraction. Second, the
classical and more recent results on the universality of neural networks should
be re-examined for functions acting on discrete data sets. Mapping on discrete
sets usually have no natural continuous extensions. This connects the results
on a simple puzzle to more sophisticated results on modeling of mathematical
functions, where algebraic functions are more difficult to model than e.g.
differential equations. Third, we hypothesize that for problems such as ``Look
and Say"", computing the parity of bitstrings, or learning integer addition, it
might be worthwhile to introduce concepts from topology, where continuity is
defined without the reference to the concept of distance.
","Abstraction, Reasoning and Deep Learning: A Study of the ""Look and Say""
  Sequence"
"  Knowledge graphs have emerged as a widely adopted medium for storing
relational data, making methods for automatically reasoning with them highly
desirable. In this paper, we present a novel approach for inducing a hierarchy
of subject clusters, building upon our earlier work done in taxonomy induction.
Our method first constructs a tag hierarchy before assigning subjects to
clusters on this hierarchy. We quantitatively demonstrate our method's ability
to induce a coherent cluster hierarchy on three real-world datasets.
",Path Based Hierarchical Clustering on Knowledge Graphs
"  We present a unified computational theory of perception and memory. In our
model, perception, episodic memory, and semantic memory are realized by
different functional and operational modes of the oscillating interactions
between an index layer and a representation layer in a bilayer tensor network
(BTN). The memoryless semantic {representation layer} broadcasts information.
In cognitive neuroscience, it would be the ""mental canvas"", or the ""global
workspace"" and reflects the cognitive brain state. The symbolic {index layer}
represents concepts and past episodes, whose semantic embeddings are
implemented in the connection weights between both layers. In addition, we
propose a {working memory layer} as a processing center and information buffer.
Episodic and semantic memory realize memory-based reasoning, i.e., the recall
of relevant past information to enrich perception, and are personalized to an
agent's current state, as well as to an agent's unique memories. Episodic
memory stores and retrieves past observations and provides provenance and
context. Recent episodic memory enriches perception by the retrieval of
perceptual experiences, which provide the agent with a sense about the here and
now: to understand its own state, and the world's semantic state in general,
the agent needs to know what happened recently, in recent scenes, and on
recently perceived entities. Remote episodic memory retrieves relevant past
experiences, contributes to our conscious self, and, together with semantic
memory, to a large degree defines who we are as individuals.
","The Tensor Brain: A Unified Theory of Perception, Memory and Semantic
  Decoding"
"  In this work, we present a flexible method for explaining, in human readable
terms, the predictions made by decision trees used as decision support in liver
transplantation. The decision trees have been obtained through machine learning
applied on a dataset collected at the liver transplantation unit at the
Coru\~na University Hospital Center and are used to predict long term (five
years) survival after transplantation. The method we propose is based on the
representation of the decision tree as a set of rules in a logic program (LP)
that is further annotated with text messages. This logic program is then
processed using the tool xclingo (based on Answer Set Programming) that allows
building compound explanations depending on the annotation text and the rules
effectively fired when a given input is provided. We explore two alternative LP
encodings: one in which rules respect the tree structure (more convenient to
reflect the learning process) and one where each rule corresponds to a
(previously simplified) tree path (more readable for decision making).
",Explainable Machine Larning for liver transplantation
"  Enabling humans to identify potential flaws in an agent's decision making is
an important Explainable AI application. We consider identifying such flaws in
a planning-based deep reinforcement learning (RL) agent for a complex real-time
strategy game. In particular, the agent makes decisions via tree search using a
learned model and evaluation function over interpretable states and actions.
This gives the potential for humans to identify flaws at the level of reasoning
steps in the tree, even if the entire reasoning process is too complex to
understand. However, it is unclear whether humans will be able to identify such
flaws due to the size and complexity of trees. We describe a user interface and
case study, where a small group of AI experts and developers attempt to
identify reasoning flaws due to inaccurate agent learning. Overall, the
interface allowed the group to identify a number of significant flaws of
varying types, demonstrating the promise of this approach.
",Identifying Reasoning Flaws in Planning-Based RL Using Tree Explanations
"  To understand how an organisational structure relates to organisational
behaviour is an interesting fundamental challenge in the area of organisation
modelling. Specifications of organisational structure usually have a
diagrammatic form that abstracts from more detailed dynamics. Dynamic
properties of agent systems, on the other hand, are often specified in the form
of a set of logical formulae in some temporal language. This paper addresses
the question how these two perspectives can be combined in one framework. It is
shown how for different aggregation levels and other elements within an
organisation structure, sets of dynamic properties can be specified.
Organisational structure provides a structure of (interlevel) relationships
between these multiple sets of dynamic properties. Thus organisational
structure is reflected in the formalisation of the dynamics of organisational
behaviour. To illustrate the effectiveness of the approach a formal foundation
is presented for the integrated specification of both structure and behaviour
of an AGR organisation model.
",From Organisational Structure to Organisational Behaviour Formalisation
"  MatrixX is a solver for Abstract Argumentation Frameworks. Offensive and
defensive properties of an Argumentation Framework are notated in a matrix
style. Rows and columns of this matrix are systematically reduced by the
solver. This procedure is implemented through the use of hash maps in order to
accelerate calculation time. MatrixX works for stable and complete semantics
and was designed for the ICCMA 2021 competition.
",The MatrixX Solver For Argumentation Frameworks
"  Lookahead search has been a critical component of recent AI successes, such
as in the games of chess, go, and poker. However, the search methods used in
these games, and in many other settings, are tabular. Tabular search methods do
not scale well with the size of the search space, and this problem is
exacerbated by stochasticity and partial observability. In this work we replace
tabular search with online model-based fine-tuning of a policy neural network
via reinforcement learning, and show that this approach outperforms
state-of-the-art search algorithms in benchmark settings. In particular, we use
our search algorithm to achieve a new state-of-the-art result in self-play
Hanabi, and show the generality of our algorithm by also showing that it
outperforms tabular search in the Atari game Ms. Pacman.
",Scalable Online Planning via Reinforcement Learning Fine-Tuning
"  Parallel to the rising debates over sustainable energy and artificial
intelligence solutions, the world is currently discussing the ethics of
artificial intelligence and its possible negative effects on society and the
environment. In these arguments, sustainable AI is proposed, which aims at
advancing the pathway toward sustainability, such as sustainable energy. In
this paper, we offered a novel contextual topic modeling combining LDA, BERT,
and Clustering. We then combined these computational analyses with content
analysis of related scientific publications to identify the main scholarly
topics, sub-themes, and cross-topic themes within scientific research on
sustainable AI in energy. Our research identified eight dominant topics
including sustainable buildings, AI-based DSSs for urban water management,
climate artificial intelligence, Agriculture 4, the convergence of AI with IoT,
AI-based evaluation of renewable technologies, smart campus and engineering
education, and AI-based optimization. We then recommended 14 potential future
research strands based on the observed theoretical gaps. Theoretically, this
analysis contributes to the existing literature on sustainable AI and
sustainable energy, and practically, it intends to act as a general guide for
energy engineers and scientists, AI scientists, and social scientists to widen
their knowledge of sustainability in AI and energy convergence research.
","Artificial intelligence for Sustainable Energy: A Contextual Topic
  Modeling and Content Analysis"
"  In recent years, we have witnessed tremendous progress in deep reinforcement
learning (RL) for tasks such as Go, Chess, video games, and robot control.
Nevertheless, other combinatorial domains, such as AI planning, still pose
considerable challenges for RL approaches. The key difficulty in those domains
is that a positive reward signal becomes {\em exponentially rare} as the
minimal solution length increases. So, an RL approach loses its training
signal. There has been promising recent progress by using a curriculum-driven
learning approach that is designed to solve a single hard instance. We present
a novel {\em automated} curriculum approach that dynamically selects from a
pool of unlabeled training instances of varying task complexity guided by our
{\em difficulty quantum momentum} strategy. We show how the smoothness of the
task hardness impacts the final learning results. In particular, as the size of
the instance pool increases, the ``hardness gap'' decreases, which facilitates
a smoother automated curriculum based learning process. Our automated
curriculum approach dramatically improves upon the previous approaches. We show
our results on Sokoban, which is a traditional PSPACE-complete planning problem
and presents a great challenge even for specialized solvers. Our RL agent can
solve hard instances that are far out of reach for any previous
state-of-the-art Sokoban solver. In particular, our approach can uncover plans
that require hundreds of steps, while the best previous search methods would
take many years of computing time to solve such instances. In addition, we show
that we can further boost the RL performance with an intricate coupling of our
automated curriculum approach with a curiosity-driven search strategy and a
graph neural net representation.
","A Novel Automated Curriculum Strategy to Solve Hard Sokoban Planning
  Instances"
"  High-accurate machine learning (ML) image classifiers cannot guarantee that
they will not fail at operation. Thus, their deployment in safety-critical
applications such as autonomous vehicles is still an open issue. The use of
fault tolerance mechanisms such as safety monitors is a promising direction to
keep the system in a safe state despite errors of the ML classifier. As the
prediction from the ML is the core information directly impacting safety, many
works are focusing on monitoring the ML model itself. Checking the efficiency
of such monitors in the context of safety-critical applications is thus a
significant challenge. Therefore, this paper aims at establishing a baseline
framework for benchmarking monitors for ML image classifiers. Furthermore, we
propose a framework covering the entire pipeline, from data generation to
evaluation. Our approach measures monitor performance with a broader set of
metrics than usually proposed in the literature. Moreover, we benchmark three
different monitor approaches in 79 benchmark datasets containing five
categories of out-of-distribution data for image classifiers: class novelty,
noise, anomalies, distributional shifts, and adversarial attacks. Our results
indicate that these monitors are no more accurate than a random monitor. We
also release the code of all experiments for reproducibility.
",Benchmarking Safety Monitors for Image Classifiers with Machine Learning
"  Explaining predictions from Bayesian networks, for example to physicians, is
non-trivial. Various explanation methods for Bayesian network inference have
appeared in literature, focusing on different aspects of the underlying
reasoning. While there has been a lot of technical research, there is very
little known about how well humans actually understand these explanations. In
this paper, we present ongoing research in which four different explanation
approaches were compared through a survey by asking a group of human
participants to interpret the explanations.
",What is understandable in Bayesian network explanations?
"  Research in artificial intelligence (AI) is addressing a growing number of
tasks through a rapidly growing number of models and methodologies. This makes
it difficult to keep track of where novel AI methods are successfully -- or
still unsuccessfully -- applied, how progress is measured, how different
advances might synergize with each other, and how future research should be
prioritized.
  To help address these issues, we created the Intelligence Task Ontology and
Knowledge Graph (ITO), a comprehensive, richly structured and manually curated
resource on artificial intelligence tasks, benchmark results and performance
metrics. The current version of ITO contain 685,560 edges, 1,100 classes
representing AI processes and 1,995 properties representing performance
metrics.
  The goal of ITO is to enable precise and network-based analyses of the global
landscape of AI tasks and capabilities. ITO is based on technologies that allow
for easy integration and enrichment with external data, automated inference and
continuous, collaborative expert curation of underlying ontological models. We
make the ITO dataset and a collection of Jupyter notebooks utilising ITO openly
available.
","A curated, ontology-based, large-scale knowledge graph of artificial
  intelligence tasks and benchmarks"
"  In the present work, we describe a framework for modeling how models can be
built that integrates concepts and methods from a wide range of fields. The
information schism between the real-world and that which can be gathered and
considered by any individual information processing agent is characterized and
discussed, followed by the presentation of a series of the adopted requisites
while developing the modeling approach. The issue of mapping from datasets into
models is subsequently addressed, as well as some of the respectively implied
difficulties and limitations. Based on these considerations, an approach to
meta modeling how models are built is then progressively developed. First, the
reference M* meta model framework is presented, which relies critically in
associating whole datasets and respective models in terms of a strict
equivalence relation. Among the interesting features of this model are its
ability to bridge the gap between data and modeling, as well as paving the way
to an algebra of both data and models which can be employed to combine models
into hierarchical manner. After illustrating the M* model in terms of patterns
derived from regular lattices, the reported modeling approach continues by
discussing how sampling issues, error and overlooked data can be addressed,
leading to the $M^{<\epsilon>}$ variant, illustrated respectively to number
theory. The situation in which the data needs to be represented in terms of
respective probability densities is treated next, yielding the $M^{<\sigma>}$
meta model, which is then illustrated respectively to a real-world dataset
(iris flowers data). Several considerations about how the developed framework
can provide insights about data clustering, complexity, collaborative research,
deep learning, and creativity are then presented, followed by overall
conclusions.
",An Ample Approach to Data and Modeling
"  We attempt to define what is necessary to construct an Artificial Scientist,
explore and evaluate several approaches to artificial general intelligence
(AGI) which may facilitate this, conclude that a unified or hybrid approach is
necessary and explore two theories that satisfy this requirement to some
degree.
","The Artificial Scientist: Logicist, Emergentist, and Universalist
  Approaches to Artificial General Intelligence"
"  AI systems have seen dramatic advancement in recent years, bringing many
applications that pervade our everyday life. However, we are still mostly
seeing instances of narrow AI: many of these recent developments are typically
focused on a very limited set of competencies and goals, e.g., image
interpretation, natural language processing, classification, prediction, and
many others. Moreover, while these successes can be accredited to improved
algorithms and techniques, they are also tightly linked to the availability of
huge datasets and computational power. State-of-the-art AI still lacks many
capabilities that would naturally be included in a notion of (human)
intelligence.
  We argue that a better study of the mechanisms that allow humans to have
these capabilities can help us understand how to imbue AI systems with these
competencies. We focus especially on D. Kahneman's theory of thinking fast and
slow, and we propose a multi-agent AI architecture where incoming problems are
solved by either system 1 (or ""fast"") agents, that react by exploiting only
past experience, or by system 2 (or ""slow"") agents, that are deliberately
activated when there is the need to reason and search for optimal solutions
beyond what is expected from the system 1 agent. Both kinds of agents are
supported by a model of the world, containing domain knowledge about the
environment, and a model of ""self"", containing information about past actions
of the system and solvers' skills.
",Thinking Fast and Slow in AI: the Role of Metacognition
"  The following briefly discusses possible difficulties in communication with
and control of an AGI (artificial general intelligence), building upon an
explanation of The Fermi Paradox and preceding work on symbol emergence and
artificial general intelligence. The latter suggests that to infer what someone
means, an agent constructs a rationale for the observed behaviour of others.
Communication then requires two agents labour under similar compulsions and
have similar experiences (construct similar solutions to similar tasks). Any
non-human intelligence may construct solutions such that any rationale for
their behaviour (and thus the meaning of their signals) is outside the scope of
what a human is inclined to notice or comprehend. Further, the more compressed
a signal, the closer it will appear to random noise. Another intelligence may
possess the ability to compress information to the extent that, to us, their
signals would appear indistinguishable from noise (an explanation for The Fermi
Paradox). To facilitate predictive accuracy an AGI would tend to more
compressed representations of the world, making any rationale for their
behaviour more difficult to comprehend for the same reason. Communication with
and control of an AGI may subsequently necessitate not only human-like
compulsions and experiences, but imposed cognitive impairment.
","Compression, The Fermi Paradox and Artificial Super-Intelligence"
"  We present Probabilistic Decision Model and Notation (pDMN), a probabilistic
extension of Decision Model and Notation (DMN). DMN is a modeling notation for
deterministic decision logic, which intends to be user-friendly and low in
complexity. pDMN extends DMN with probabilistic reasoning, predicates,
functions, quantification, and a new hit policy. At the same time, it aims to
retain DMN's user-friendliness to allow its usage by domain experts without the
help of IT staff. pDMN models can be unambiguously translated into ProbLog
programs to answer user queries. ProbLog is a probabilistic extension of Prolog
flexibly enough to model and reason over any pDMN model.
","A Table-Based Representation for Probabilistic Logic: Preliminary
  Results"
"  We introduce SMProbLog, a generalization of the probabilistic logic
programming language ProbLog. A ProbLog program defines a distribution over
logic programs by specifying for each clause the probability that it belongs to
a randomly sampled program, and these probabilities are mutually independent.
The semantics of ProbLog is given by the success probability of a query, which
corresponds to the probability that the query succeeds in a randomly sampled
program. It is well-defined when each random sample uniquely determines the
truth values of all logical atoms. Argumentation problems, however, represent
an interesting practical application where this is not always the case.
SMProbLog generalizes the semantics of ProbLog to the setting where multiple
truth assignments are possible for a randomly sampled program, and implements
the corresponding algorithms for both inference and learning tasks. We then
show how this novel framework can be used to reason about probabilistic
argumentation problems. Therefore, the key contribution of this paper are: a
more general semantics for ProbLog programs, its implementation into a
probabilistic programming framework for both inference and parameter learning,
and a novel approach to probabilistic argumentation problems based on such
framework.
","SMProbLog: Stable Model Semantics in ProbLog and its Applications in
  Argumentation"
"  Contrastive learning (CL) has emerged as a dominant technique for
unsupervised representation learning which embeds augmented versions of the
anchor close to each other (positive samples) and pushes the embeddings of
other samples (negative samples) apart. As revealed in recent works, CL can
benefit from hard negative samples (negative samples that are difficult to
distinguish from the anchor). However, we observe minor improvement or even
performance drop when we adopt existing hard negative mining techniques in
Graph Contrastive Learning (GCL). We find that many hard negative samples
similar to anchor point are false negative ones (samples from the same class as
anchor point) in GCL, which is different from CL in computer vision and will
lead to unsatisfactory performance of existing hard negative mining techniques
in GCL. To eliminate this bias, we propose Debiased Graph Contrastive Learning
(DGCL), a novel and effective method to estimate the probability whether each
negative sample is true or not. With this probability, we devise two schemes
(i.e., DGCL-weight and DGCL-mix) to boost the performance of GCL. Empirically,
DGCL outperforms or matches previous unsupervised state-of-the-art results on
several benchmarks and even exceeds the performance of supervised ones.
",Debiased Graph Contrastive Learning
"  We introduce Scruff, a new framework for developing AI systems using
probabilistic programming. Scruff enables a variety of representations to be
included, such as code with stochastic choices, neural networks, differential
equations, and constraint systems. These representations are defined implicitly
using a set of standardized operations that can be performed on them.
General-purpose algorithms are then implemented using these operations,
enabling generalization across different representations. Zero, one, or more
operation implementations can be provided for any given representation, giving
algorithms the flexibility to use the most appropriate available
implementations for their purposes and enabling representations to be used in
ways that suit their capabilities. In this paper, we explain the general
approach of implicitly defined representations and provide a variety of
examples of representations at varying degrees of abstraction. We also show how
a relatively small set of operations can serve to unify a variety of AI
algorithms. Finally, we discuss how algorithms can use policies to choose which
operation implementations to use during execution.
","Unifying AI Algorithms with Probabilistic Programming using Implicitly
  Defined Representations"
"  Can an agent's intelligence level be negative? We extend the Legg-Hutter
agent-environment framework to include punishments and argue for an affirmative
answer to that question. We show that if the background encodings and Universal
Turing Machine (UTM) admit certain Kolmogorov complexity symmetries, then the
resulting Legg-Hutter intelligence measure is symmetric about the origin. In
particular, this implies reward-ignoring agents have Legg-Hutter intelligence 0
according to such UTMs.
",Reward-Punishment Symmetric Universal Intelligence
"  Many AI applications involve the interaction of multiple autonomous agents,
requiring those agents to reason about their own beliefs, as well as those of
other agents. However, planning involving nested beliefs is known to be
computationally challenging. In this work, we address the task of synthesizing
plans that necessitate reasoning about the beliefs of other agents. We plan
from the perspective of a single agent with the potential for goals and actions
that involve nested beliefs, non-homogeneous agents, co-present observations,
and the ability for one agent to reason as if it were another. We formally
characterize our notion of planning with nested belief, and subsequently
demonstrate how to automatically convert such problems into problems that
appeal to classical planning technology for solving efficiently. Our approach
represents an important step towards applying the well-established field of
automated planning to the challenging task of planning involving nested beliefs
of multiple agents.
","Efficient Multi-agent Epistemic Planning: Teaching Planners About Nested
  Belief"
"  Knowledge-based AI typically depends on a knowledge engineer to construct a
formal model of domain knowledge -- but what if domain experts could do this
themselves? This paper describes an extension to the Decision Model and
Notation (DMN) standard, called Constraint Decision Model and Notation (cDMN).
DMN is a user-friendly, table-based notation for decision logic, which allows
domain experts to model simple decision procedures without the help of IT
staff. cDMN aims to enlarge the expressiveness of DMN in order to model more
complex domain knowledge, while retaining DMN's goal of being understandable by
domain experts. We test cDMN by solving the most complex challenges posted on
the DM Community website. We compare our own cDMN solutions to the solutions
that have been submitted to the website and find that our approach is
competitive. Moreover, cDMN is able to solve more challenges than any other
approach.
","Tackling the DM Challenges with cDMN: A Tight Integration of DMN and
  Constraint Reasoning"
"  With the continuous improvement in various aspects in the field of artificial
intelligence, the momentum of artificial intelligence with deep learning
capabilities into the field of music is coming. The research purpose of this
paper is to design a Bach style music authoring system based on deep learning.
We use a LSTM neural network to train serialized and standardized music feature
data. By repeated experiments, we find the optimal LSTM model which can
generate imitation of Bach music. Finally the generated music is
comprehensively evaluated in the form of online audition and Turing test. The
repertoires which the music generation system constructed in this article are
very close to the style of Bach's original music, and it is relatively
difficult for ordinary people to distinguish the musics Bach authored and AI
created.
",Bach Style Music Authoring System based on Deep Learning
"  Many popular machine learning techniques in natural language processing and
data mining rely heavily on high-quality text sources. However real-world text
datasets contain a significant amount of spelling errors and improperly
punctuated variants where the performance of these models would quickly
deteriorate. Moreover, real-world, web-scale datasets contain hundreds of
millions or even billions of lines of text, where the existing text cleaning
tools are prohibitively expensive to execute over and may require an overhead
to learn the corrections. In this paper, we present FLAN, a scalable randomized
algorithm to clean and canonicalize massive text data. Our algorithm relies on
the Jaccard similarity between words to suggest correction results. We
efficiently handle the pairwise word-to-word comparisons via Locality Sensitive
Hashing (LSH). We also propose a novel stabilization process to address the
issue of hash collisions between dissimilar words, which is a consequence of
the randomized nature of LSH and is exacerbated by the massive scale of
real-world datasets. Compared with existing approaches, our method is more
efficient, both asymptotically and in empirical evaluations, and does not rely
on additional features, such as lexical/phonetic similarity or word embedding
features. In addition, FLAN does not require any annotated data or supervised
learning. We further theoretically show the robustness of our algorithm with
upper bounds on the false positive and false negative rates of corrections. Our
experimental results on real-world datasets demonstrate the efficiency and
efficacy of FLAN.
",A Fast Randomized Algorithm for Massive Text Normalization
"  Engineering design problems often involve large state and action spaces along
with highly sparse rewards. Since an exhaustive search of those spaces is not
feasible, humans utilize relevant domain knowledge to condense the search
space. Previously, deep learning agents (DLAgents) were introduced to use
visual imitation learning to model design domain knowledge. This note builds on
DLAgents and integrates them with one-step lookahead search to develop
goal-directed agents capable of enhancing learned strategies for sequentially
generating designs. Goal-directed DLAgents can employ human strategies learned
from data along with optimizing an objective function. The visual imitation
network from DLAgents is composed of a convolutional encoder-decoder network,
acting as a rough planning step that is agnostic to feedback. Meanwhile, the
lookahead search identifies the fine-tuned design action guided by an
objective. These design agents are trained on an unconstrained truss design
problem that is modeled as a sequential, action-based configuration design
problem. The agents are then evaluated on two versions of the problem: the
original version used for training and an unseen constrained version with an
obstructed construction space. The goal-directed agents outperform the human
designers used to train the network as well as the previous objective-agnostic
versions of the agent in both scenarios. This illustrates a design agent
framework that can efficiently use feedback to not only enhance learned design
strategies but also adapt to unseen design problems.
","Goal-Directed Design Agents: Integrating Visual Imitation with One-Step
  Lookahead Optimization for Generative Design"
"  Inferring the substitutable and complementary products for a given product is
an essential and fundamental concern for the recommender system. To achieve
this, existing approaches take advantage of the knowledge graphs to learn more
evidences for inference, whereas they often suffer from invalid reasoning for
lack of elegant decision making strategies. Therefore, we propose a novel
Knowledge-Aware Path Reasoning (KAPR) model which leverages the dynamic policy
network to make explicit reasoning over knowledge graphs, for inferring the
substitutable and complementary relationships. Our contributions can be
highlighted as three aspects. Firstly, we model this inference scenario as a
Markov Decision Process in order to accomplish a knowledge-aware path reasoning
over knowledge graphs. Secondly,we integrate both structured and unstructured
knowledge to provide adequate evidences for making accurate decision-making.
Thirdly, we evaluate our model on a series of real-world datasets, achieving
competitive performance compared with state-of-the-art approaches. Our code is
released on https://gitee.com/yangzijing flower/kapr/tree/master.
","Inferring Substitutable and Complementary Products with Knowledge-Aware
  Path Reasoning based on Dynamic Policy Network"
"  The last decade has seen tremendous progress in AI technology and
applications. With such widespread adoption, ensuring the reliability of the AI
models is crucial. In past, we took the first step of creating a testing
framework called AITEST for metamorphic properties such as fairness, robustness
properties for tabular, time-series, and text classification models. In this
paper, we extend the capability of the AITEST tool to include the testing
techniques for Image and Speech-to-text models along with interpretability
testing for tabular models. These novel extensions make AITEST a comprehensive
framework for testing AI models.
",Automated Testing of AI Models
"  The goal of combining the robustness of neural networks and the expressivity
of symbolic methods has rekindled the interest in neuro-symbolic AI. Recent
advancements in neuro-symbolic AI often consider specifically-tailored
architectures consisting of disjoint neural and symbolic components, and thus
do not exhibit desired gains that can be achieved by integrating them into a
unifying framework. We introduce SLASH -- a novel deep probabilistic
programming language (DPPL). At its core, SLASH consists of
Neural-Probabilistic Predicates (NPPs) and logical programs which are united
via answer set programming. The probability estimates resulting from NPPs act
as the binding element between the logical program and raw input data, thereby
allowing SLASH to answer task-dependent logical queries. This allows SLASH to
elegantly integrate the symbolic and neural components in a unified framework.
We evaluate SLASH on the benchmark data of MNIST addition as well as novel
tasks for DPPLs such as missing data prediction and set prediction with
state-of-the-art performance, thereby showing the effectiveness and generality
of our method.
","SLASH: Embracing Probabilistic Circuits into Neural Answer Set
  Programming"
"  Multi-task learning (MTL), which aims to improve performance by learning
multiple tasks simultaneously, inherently presents an optimization challenge
due to multiple objectives. Hence, multi-objective optimization (MOO)
approaches have been proposed for multitasking problems. Recent MOO methods
approximate multiple optimal solutions (Pareto front) with a single unified
model, which is collectively referred to as Pareto front learning (PFL). In
this paper, we show that PFL can be re-formulated into another MOO problem with
multiple objectives, each of which corresponds to different preference weights
for the tasks. We leverage an evolutionary algorithm (EA) to propose a method
for PFL called self-evolutionary optimization (SEO) by directly maximizing the
hypervolume. By using SEO, the neural network learns to approximate the Pareto
front conditioned on multiple hyper-parameters that drastically affect the
hypervolume. Then, by generating a population of approximations simply by
inferencing the network, the hyper-parameters of the network can be optimized
by EA. Utilizing SEO for PFL, we also introduce self-evolutionary Pareto
networks (SEPNet), enabling the unified model to approximate the entire Pareto
front set that maximizes the hypervolume. Extensive experimental results
confirm that SEPNet can find a better Pareto front than the current
state-of-the-art methods while minimizing the increase in model size and
training cost.
",Self-Evolutionary Optimization for Pareto Front Learning
"  We give a new interpretation of basic belief assignment transformation into
probability distribution, and use directed acyclic network called belief
evolution network to describe the causality between the focal elements of a
BBA. On this basis, a new probability transformations method called full
causality probability transformation is proposed, and this method is superior
to all previous method after verification from the process and the result. In
addition, using this method combined with disjunctive combination rule, we
propose a new probabilistic combination rule called disjunctive transformation
combination rule. It has an excellent ability to merge conflicts and an
interesting pseudo-Matthew effect, which offer a new idea to information fusion
besides the combination rule of Dempster.
","Belief Evolution Network: Probability Transformation of Basic Belief
  Assignment and Fusion Conflict Probability"
"  Rideshare and ride-pooling platforms use artificial intelligence-based
matching algorithms to pair riders and drivers. However, these platforms can
induce inequality either through an unequal income distribution or disparate
treatment of riders. We investigate two methods to reduce forms of inequality
in ride-pooling platforms: (1) incorporating fairness constraints into the
objective function and (2) redistributing income to drivers to reduce income
fluctuation and inequality. To evaluate our solutions, we use the New York City
taxi data set. For the first method, we find that optimizing for driver-side
fairness outperforms state-of-the-art models on the number of riders serviced,
both in the worst-off neighborhood and overall, showing that optimizing for
fairness can assist profitability in certain circumstances. For the second
method, we explore income redistribution as a way to combat income inequality
by having drivers keep an $r$ fraction of their income, and contributing the
rest to a redistribution pool. For certain values of $r$, most drivers earn
near their Shapley value, while still incentivizing drivers to maximize value,
thereby avoiding the free-rider problem and reducing income variability. The
first method can be extended to many definitions of fairness and the second
method provably improves fairness without affecting profitability.
","Data-Driven Methods for Balancing Fairness and Efficiency in
  Ride-Pooling"
"  While the availability of large datasets is perceived to be a key requirement
for training deep neural networks, it is possible to train such models with
relatively little data. However, compensating for the absence of large datasets
demands a series of actions to enhance the quality of the existing samples and
to generate new ones. This paper summarizes our winning submission to the
""Data-Centric AI"" competition. We discuss some of the challenges that arise
while training with a small dataset, offer a principled approach for systematic
data quality enhancement, and propose a GAN-based solution for synthesizing new
data points. Our evaluations indicate that the dataset generated by the
proposed pipeline offers 5% accuracy improvement while being significantly
smaller than the baseline.
",A Data-Centric Approach for Training Deep Neural Networks with Less Data
"  A fuzzy multipreference semantics has been recently proposed for weighted
conditional knowledge bases, and used to develop a logical semantics for
Multilayer Perceptrons, by regarding a deep neural network (after training) as
a weighted conditional knowledge base. This semantics, in its different
variants, suggests some gradual argumentation semantics, which are related to
the family of the gradual semantics studied by Amgoud and Doder. The
relationships between weighted conditional knowledge bases and MLPs extend to
the proposed gradual semantics to capture the stationary states of MPs, in
agreement with previous results on the relationship between argumentation
frameworks and neural networks. The paper also suggests a simple way to extend
the proposed semantics to deal attacks/supports by a boolean combination of
arguments, based on the fuzzy semantics of weighted conditionals, as well as an
approach for defeasible reasoning over a weighted argumentation graph, building
on the proposed gradual semantics.
","From Weighted Conditionals of Multilayer Perceptrons to Gradual
  Argumentation and Back"
"  Automatic Process Discovery aims at developing algorithmic methodologies for
the extraction and elicitation of process models as described in data. While
Process Discovery from event-log data is a well established area, that has
already moved from research to concrete adoption in a mature manner, Process
Discovery from text is still a research area at an early stage of development,
which rarely scales to real world documents. In this paper we analyze, in a
comparative manner, reference state-of-the-art literature, especially for what
concerns the techniques used, the process elements extracted and the
evaluations performed. As a result of the analysis we discuss important
limitations that hamper the exploitation of recent Natural Language Processing
techniques in this field and we discuss fundamental limitations and challenges
for the future concerning the datasets, the techniques, the experimental
evaluations, and the pipelines currently adopted and to be developed in the
future.
","Process Extraction from Text: state of the art and challenges for the
  future"
"  Generative design problems often encompass complex action spaces that may be
divergent over time, contain state-dependent constraints, or involve hybrid
(discrete and continuous) domains. To address those challenges, this work
introduces Design Strategy Network (DSN), a data-driven deep hierarchical
framework that can learn strategies over these arbitrary complex action spaces.
The hierarchical architecture decomposes every action decision into first
predicting a preferred spatial region in the design space and then outputting a
probability distribution over a set of possible actions from that region. This
framework comprises a convolutional encoder to work with image-based design
state representations, a multi-layer perceptron to predict a spatial region,
and a weight-sharing network to generate a probability distribution over
unordered set-based inputs of feasible actions. Applied to a truss design
study, the framework learns to predict the actions of human designers in the
study, capturing their truss generation strategies in the process. Results show
that DSNs significantly outperform non-hierarchical methods of policy
representation, demonstrating their superiority in complex action space
problems.
","Design Strategy Network: A deep hierarchical framework to represent
  generative design strategies in complex action spaces"
"  Dynamic link prediction (DLP) makes graph prediction based on historical
information. Since most DLP methods are highly dependent on the training data
to achieve satisfying prediction performance, the quality of the training data
is crucial. Backdoor attacks induce the DLP methods to make wrong prediction by
the malicious training data, i.e., generating a subgraph sequence as the
trigger and embedding it to the training data. However, the vulnerability of
DLP toward backdoor attacks has not been studied yet. To address the issue, we
propose a novel backdoor attack framework on DLP, denoted as Dyn-Backdoor.
Specifically, Dyn-Backdoor generates diverse initial-triggers by a generative
adversarial network (GAN). Then partial links of the initial-triggers are
selected to form a trigger set, according to the gradient information of the
attack discriminator in the GAN, so as to reduce the size of triggers and
improve the concealment of the attack. Experimental results show that
Dyn-Backdoor launches successful backdoor attacks on the state-of-the-art DLP
models with success rate more than 90%. Additionally, we conduct a possible
defense against Dyn-Backdoor to testify its resistance in defensive settings,
highlighting the needs of defenses for backdoor attacks on DLP.
",Dyn-Backdoor: Backdoor Attack on Dynamic Link Prediction
"  Circuit routing has been a historically challenging problem in designing
electronic systems such as very large-scale integration (VLSI) and printed
circuit boards (PCBs). The main challenge is that connecting a large number of
electronic components under specific design rules involves a very large search
space. Early solutions are typically designed with hard-coded heuristics, which
suffer from problems of non-optimal solutions and lack of flexibility for new
design needs. Although a few learning-based methods have been proposed
recently, they are typically cumbersome and hard to extend to large-scale
applications. In this work, we propose a new algorithm for circuit routing,
named Ranking Cost, which innovatively combines search-based methods (i.e., A*
algorithm) and learning-based methods (i.e., Evolution Strategies) to form an
efficient and trainable router. In our method, we introduce a new set of
variables called cost maps, which can help the A* router to find out proper
paths to achieve the global objective. We also train a ranking parameter, which
can produce the ranking order and further improve the performance of our
method. Our algorithm is trained in an end-to-end manner and does not use any
artificial data or human demonstration. In the experiments, we compare with the
sequential A* algorithm and a canonical reinforcement learning approach, and
results show that our method outperforms these baselines with higher
connectivity rates and better scalability.
","Ranking Cost: Building An Efficient and Scalable Circuit Routing Planner
  with Evolution-Based Optimization"
"  Strategic diversity is often essential in games: in multi-player games, for
example, evaluating a player against a diverse set of strategies will yield a
more accurate estimate of its performance. Furthermore, in games with
non-transitivities diversity allows a player to cover several winning
strategies. However, despite the significance of strategic diversity, training
agents that exhibit diverse behaviour remains a challenge. In this paper we
study how to construct diverse populations of agents by carefully structuring
how individuals within a population interact. Our approach is based on
interaction graphs, which control the flow of information between agents during
training and can encourage agents to specialise on different strategies,
leading to improved overall performance. We provide evidence for the importance
of diversity in multi-agent training and analyse the effect of applying
different interaction graphs on the training trajectories, diversity and
performance of populations in a range of games. This is an extended version of
the long abstract published at AAMAS.
","Pick Your Battles: Interaction Graphs as Population-Level Objectives for
  Strategic Diversity"
"  In this paper, we design and implement a generic medical knowledge based
system (MKBS) for identifying diseases from several symptoms. In this system,
some important aspects like knowledge bases system, knowledge representation,
inference engine have been addressed. The system asks users different questions
and inference engines will use the certainty factor to prune out low possible
solutions. The proposed disease diagnosis system also uses a graphical user
interface (GUI) to facilitate users to interact with the expert system. Our
expert system is generic and flexible, which can be integrated with any rule
bases system in disease diagnosis.
",A Generic Knowledge Based Medical Diagnosis Expert System
"  Deep reinforcement learning (DRL) has achieved super-human performance on
complex video games (e.g., StarCraft II and Dota II). However, current DRL
systems still suffer from challenges of multi-agent coordination, sparse
rewards, stochastic environments, etc. In seeking to address these challenges,
we employ a football video game, e.g., Google Research Football (GRF), as our
testbed and develop an end-to-end learning-based AI system (denoted as TiKick)
to complete this challenging task. In this work, we first generated a large
replay dataset from the self-playing of single-agent experts, which are
obtained from league training. We then developed a distributed learning system
and new offline algorithms to learn a powerful multi-agent AI from the fixed
single-agent dataset. To the best of our knowledge, Tikick is the first
learning-based AI system that can take over the multi-agent Google Research
Football full game, while previous work could either control a single agent or
experiment on toy academic scenarios. Extensive experiments further show that
our pre-trained model can accelerate the training process of the modern
multi-agent algorithm and our method achieves state-of-the-art performances on
various academic scenarios.
","TiKick: Towards Playing Multi-agent Football Full Games from
  Single-agent Demonstrations"
"  Reinforcement learning has been successful in many tasks ranging from robotic
control, games, energy management etc. In complex real world environments with
sparse rewards and long task horizons, sample efficiency is still a major
challenge. Most complex tasks can be easily decomposed into high-level planning
and low level control. Therefore, it is important to enable agents to leverage
the hierarchical structure and decompose bigger tasks into multiple smaller
sub-tasks. We introduce an approach where we use language to specify sub-tasks
and a high-level planner issues language commands to a low level controller.
The low-level controller executes the sub-tasks based on the language commands.
Our experiments show that this method is able to solve complex long horizon
planning tasks with limited human supervision. Using language has added benefit
of interpretability and ability for expert humans to take over the high-level
planning task and provide language commands if necessary.
",Interactive Hierarchical Guidance using Language
"  CaLiGraph is a large-scale cross-domain knowledge graph generated from
Wikipedia by exploiting the category system, list pages, and other list
structures in Wikipedia, containing more than 15 million typed entities and
around 10 million relation assertions. Other than knowledge graphs such as
DBpedia and YAGO, whose ontologies are comparably simplistic, CaLiGraph also
has a rich ontology, comprising more than 200,000 class restrictions. Those two
properties - a large A-box and a rich ontology - make it an interesting
challenge for benchmarking reasoners. In this paper, we show that a reasoning
task which is particularly relevant for CaLiGraph, i.e., the materialization of
owl:hasValue constraints into assertions between individuals and between
individuals and literals, is insufficiently supported by available reasoning
systems. We provide differently sized benchmark subsets of CaLiGraph, which can
be used for performance analysis of reasoning systems.
",The CaLiGraph Ontology as a Challenge for OWL Reasoners
"  This paper investigates the problem of bounding counterfactual queries from
an arbitrary collection of observational and experimental distributions and
qualitative knowledge about the underlying data-generating model represented in
the form of a causal diagram. We show that all counterfactual distributions in
an arbitrary structural causal model (SCM) could be generated by a canonical
family of SCMs with the same causal diagram where unobserved (exogenous)
variables are discrete with a finite domain. Utilizing the canonical SCMs, we
translate the problem of bounding counterfactuals into that of polynomial
programming whose solution provides optimal bounds for the counterfactual
query. Solving such polynomial programs is in general computationally
expensive. We therefore develop effective Monte Carlo algorithms to approximate
the optimal bounds from an arbitrary combination of observational and
experimental data. Our algorithms are validated extensively on synthetic and
real-world datasets.
","Partial Counterfactual Identification from Observational and
  Experimental Data"
"  Semantic parsing in KBQA aims to parse natural language questions into
logical forms, whose execution against a knowledge base produces answers.
Learning semantic parsers from question-answer pairs requires searching over a
huge space of logical forms for ones consistent with answers. Current methods
utilize various prior knowlege or entity-level KB constraints to reduce the
search space. In this paper, we investigate for the first time prior knowledge
from external logical form annotations and ontology-level constraints. We
design a hierarchical architecture for program transfer, and propose an
ontology-guided pruning algorithm to reduce the search space. The experiments
on ComplexWebQuestions show that our method improves the state-of-the-art F1
score from 44.0% to 58.7%, with an absolute gain of 14.7%, which demonstrates
the effectiveness of program transfer and ontology awareness.
",Program Transfer and Ontology Awareness for Semantic Parsing in KBQA
"  Reading to act is a prevalent but challenging task which requires the ability
to reason from a concise instruction. However, previous works face the semantic
mismatch between the low-level actions and the high-level language descriptions
and require the human-designed curriculum to work properly. In this paper, we
present a Feudal Reinforcement Learning (FRL) model consisting of a manager
agent and a worker agent. The manager agent is a multi-hop plan generator
dealing with high-level abstract information and generating a series of
sub-goals in a backward manner. The worker agent deals with the low-level
perceptions and actions to achieve the sub-goals one by one. In comparison, our
FRL model effectively alleviate the mismatching between text-level inference
and low-level perceptions and actions; and is general to various forms of
environments, instructions and manuals; and our multi-hop plan generator can
significantly boost for challenging tasks where multi-step reasoning form the
texts is critical to resolve the instructed goals. We showcase our approach
achieves competitive performance on two challenging tasks, Read to Fight
Monsters (RTFM) and Messenger, without human-designed curriculum learning.
",Feudal Reinforcement Learning by Reading Manuals
"  Human intelligence has the remarkable ability to adapt to new tasks and
environments quickly. Starting from a very young age, humans acquire new skills
and learn how to solve new tasks either by imitating the behavior of others or
by following provided natural language instructions. To facilitate research in
this direction, we propose IGLU: Interactive Grounded Language Understanding in
a Collaborative Environment. The primary goal of the competition is to approach
the problem of how to build interactive agents that learn to solve a task while
provided with grounded natural language instructions in a collaborative
environment. Understanding the complexity of the challenge, we split it into
sub-tasks to make it feasible for participants.
  This research challenge is naturally related, but not limited, to two fields
of study that are highly relevant to the NeurIPS community: Natural Language
Understanding and Generation (NLU/G) and Reinforcement Learning (RL).
Therefore, the suggested challenge can bring two communities together to
approach one of the important challenges in AI. Another important aspect of the
challenge is the dedication to perform a human-in-the-loop evaluation as a
final evaluation for the agents developed by contestants.
","NeurIPS 2021 Competition IGLU: Interactive Grounded Language
  Understanding in a Collaborative Environment"
"  Reified Input/Output (I/O) logic[21] has been recently proposed to model
real-world norms in terms of the logic in [11]. This is massively grounded on
the notion of reification, and it has specifically designed to model meaning of
natural language sentences, such as the ones occurring in existing legislation.
This paper presents a methodology to carry out compliance checking on reified
I/O logic formulae. These are translated in SHACL (Shapes Constraint Language)
shapes, a recent W3C recommendation to validate and reason with RDF
triplestores. Compliance checking is then enforced by validating RDF graphs
describing states of affairs with respect to these SHACL shapes.
",Compliance checking in reified IO logic via SHACL
"  One of the main issues of every business process is to be compliant with
legal rules. This work presents a methodology to check in a semi-automated way
the regulatory compliance of a business process. We analyse an e-Health
hospital service in particular: the Hospital at Home (HaH) service. The paper
shows, at first, the analysis of the hospital business using the Business
Process Management and Notation (BPMN) standard language, then, the
formalization in Defeasible Deontic Logic (DDL) of some rules of the European
General Data Protection Regulation (GDPR). The aim is to show how to combine a
set of tasks of a business with a set of rules to be compliant with, using a
tool.
",Semi-automated checking for regulatory compliance in e-Health
"  When solving a combinatorial problem using propositional satisfiability
(SAT), the encoding of the problem is of vital importance. We study encodings
of Pseudo-Boolean (PB) constraints, a common type of arithmetic constraint that
appears in a wide variety of combinatorial problems such as timetabling,
scheduling, and resource allocation. In some cases PB constraints occur
together with at-most-one (AMO) constraints over subsets of their variables
(forming PB(AMO) constraints). Recent work has shown that taking account of
AMOs when encoding PB constraints using decision diagrams can produce a
dramatic improvement in solver efficiency. In this paper we extend the approach
to other state-of-the-art encodings of PB constraints, developing several new
encodings for PB(AMO) constraints. Also, we present a more compact and
efficient version of the popular Generalized Totalizer encoding, named Reduced
Generalized Totalizer. This new encoding is also adapted for PB(AMO)
constraints for a further gain. Our experiments show that the encodings of
PB(AMO) constraints can be substantially smaller than those of PB constraints.
PB(AMO) encodings allow many more instances to be solved within a time limit,
and solving time is improved by more than one order of magnitude in some cases.
We also observed that there is no single overall winner among the considered
encodings, but efficiency of each encoding may depend on PB(AMO)
characteristics such as the magnitude of coefficient values.
","SAT Encodings for Pseudo-Boolean Constraints Together With At-Most-One
  Constraints"
"  State abstraction enables sample-efficient learning and better task transfer
in complex reinforcement learning environments. Recently, we proposed RePReL
(Kokel et al. 2021), a hierarchical framework that leverages a relational
planner to provide useful state abstractions for learning. We present a brief
overview of this framework and the use of a dynamic probabilistic logic model
to design these state abstractions. Our experiments show that RePReL not only
achieves better performance and efficient learning on the task at hand but also
demonstrates better generalization to unseen tasks.
",Dynamic probabilistic logic models for effective abstractions in RL
"  Motivated by recent innovations in biologically-inspired neuromorphic
hardware, this paper presents a novel unsupervised machine learning approach
named Hyperseed that leverages Vector Symbolic Architectures (VSA) for fast
learning a topology preserving feature map of unlabelled data. It relies on two
major capabilities of VSAs: the binding operation and computing in
superposition. In this paper, we introduce the algorithmic part of Hyperseed
expressed within Fourier Holographic Reduced Representations VSA model, which
is specifically suited for implementation on spiking neuromorphic hardware. The
two distinctive novelties of the Hyperseed algorithm are: 1) Learning from only
few input data samples and 2) A learning rule based on a single vector
operation. These properties are demonstrated on synthetic datasets as well as
on illustrative benchmark use-cases, IRIS classification and a language
identification task using n-gram statistics.
",HyperSeed: Unsupervised Learning with Vector Symbolic Architectures
"  In Mixed Integer Linear Programming (MIP), a (strong) backdoor is a ""small""
subset of an instance's integer variables with the following property: in a
branch-and-bound procedure, the instance can be solved to global optimality by
branching only on the variables in the backdoor. Constructing datasets of
pre-computed backdoors for widely used MIP benchmark sets or particular problem
families can enable new questions around novel structural properties of a MIP,
or explain why a problem that is hard in theory can be solved efficiently in
practice. Existing algorithms for finding backdoors rely on sampling candidate
variable subsets in various ways, an approach which has demonstrated the
existence of backdoors for some instances from MIPLIB2003 and MIPLIB2010.
However, these algorithms fall short of consistently succeeding at the task due
to an imbalance between exploration and exploitation. We propose BaMCTS, a
Monte Carlo Tree Search framework for finding backdoors to MIPs. Extensive
algorithmic engineering, hybridization with traditional MIP concepts, and close
integration with the CPLEX solver have enabled our method to outperform
baselines on MIPLIB2017 instances, finding backdoors more frequently and more
efficiently.
","Finding Backdoors to Integer Programs: A Monte Carlo Tree Search
  Framework"
"  Multi-agent Markov Decision Process (MMDP) has been an effective way of
modelling sequential decision making algorithms for multi-agent cooperative
environments. A number of algorithms based on centralized and decentralized
planning have been developed in this domain. However, dynamically changing
environment, coupled with exponential size of the state and joint action space,
make it difficult for these algorithms to provide both efficiency and
scalability. Recently, Centralized planning algorithm FV-MCTS-MP and
decentralized planning algorithm \textit{Alternate maximization with
Behavioural Cloning} (ABC) have achieved notable performance in solving MMDPs.
However, they are not capable of adapting to dynamically changing environments
and accounting for the lack of communication among agents, respectively.
Against this background, we introduce a simulation based online planning
algorithm, that we call SiCLOP, for multi-agent cooperative environments.
Specifically, SiCLOP tailors Monte Carlo Tree Search (MCTS) and uses
Coordination Graph (CG) and Graph Neural Network (GCN) to learn cooperation and
provides real time solution of a MMDP problem. It also improves scalability
through an effective pruning of action space. Additionally, unlike FV-MCTS-MP
and ABC, SiCLOP supports transfer learning, which enables learned agents to
operate in different environments. We also provide theoretical discussion about
the convergence property of our algorithm within the context of multi-agent
settings. Finally, our extensive empirical results show that SiCLOP
significantly outperforms the state-of-the-art online planning algorithms.
","Learning Cooperation and Online Planning Through Simulation and Graph
  Convolutional Network"
"  We have developed a framework to reliably build agents capable of UI
navigation. The state space is simplified from raw-pixels to a set of UI
elements extracted from screen understanding, such as OCR and icon detection.
The action space is restricted to the UI elements plus a few global actions.
Actions can be customized for tasks and each action is a sequence of basic
operations conditioned on status checks. With such a design, we are able to
train DQfD and BC agents with a small number of demonstration episodes. We
propose demo augmentation that significantly reduces the required number of
human demonstrations. We made a customization of DQfD to allow demos collected
on screenshots to facilitate the demo coverage of rare cases. Demos are only
collected for the failed cases during the evaluation of the previous version of
the agent. With 10s of iterations looping over evaluation, demo collection, and
training, the agent reaches a 98.7\% success rate on the search task in an
environment of 80+ apps and websites where initial states and viewing
parameters are randomized.
",Learning UI Navigation through Demonstrations composed of Macro Actions
"  The current landscape of multi-agent expert imitation is broadly dominated by
two families of algorithms - Behavioral Cloning (BC) and Adversarial Imitation
Learning (AIL). BC approaches suffer from compounding errors, as they ignore
the sequential decision-making nature of the trajectory generation problem.
Furthermore, they cannot effectively model multi-modal behaviors. While AIL
methods solve the issue of compounding errors and multi-modal policy training,
they are plagued with instability in their training dynamics. In this work, we
address this issue by introducing a novel self-supervised loss that encourages
the discriminator to approximate a richer reward function. We employ our method
to train a graph-based multi-agent actor-critic architecture that learns a
centralized policy, conditioned on a learned latent interaction graph. We show
that our method (SS-MAIL) outperforms prior state-of-the-art methods on
real-world prediction tasks, as well as on custom-designed synthetic
experiments. We prove that SS-MAIL is part of the family of AIL methods by
providing a theoretical connection to cost-regularized apprenticeship learning.
Moreover, we leverage the self-supervised formulation to introduce a novel
teacher forcing-based curriculum (Trajectory Forcing) that improves sample
efficiency by progressively increasing the length of the generated trajectory.
The SS-MAIL framework improves multi-agent imitation capabilities by
stabilizing the policy training, improving the reward shaping capabilities, as
well as providing the ability for modeling multi-modal trajectories.
",SS-MAIL: Self-Supervised Multi-Agent Imitation Learning
"  DNA-based nanonetworks have a wide range of promising use cases, especially
in the field of medicine. With a large set of agents, a partially observable
stochastic environment, and noisy observations, such nanoscale systems can be
modelled as a decentralised, partially observable, Markov decision process
(DecPOMDP). As the agent set is a dominating factor, this paper presents (i)
lifted DecPOMDPs, partitioning the agent set into sets of indistinguishable
agents, reducing the worst-case space required, and (ii) a nanoscale medical
system as an application. Future work turns to solving and implementing lifted
DecPOMDPs.
",Lifting DecPOMDPs for Nanoscale Systems -- A Work in Progress
"  Lifted inference allows to perform inference in polynomial time w.r.t. domain
sizes. For a lifted algorithm, completeness investigates model classes for
which the algorithm is guaranteed to compute a lifted solution. We contribute,
to the best of our knowledge, the first completeness and complexity analysis
for a temporal lifted algorithm, the so-called lifted dynamic junction tree
algorithm (LDJT). To treat time as a first class citizen, LDJT introduces some
constraints. Given these constraints, we analyse the classes of liftable
models. Further, we show that LDJT has many advantages from a complexity point
of view compared to a propositional temporal inference algorithm w.r.t. domain
sizes. Therefore, LDJT advances the number of models for which inference tasks
can be solved in reasonable time not only from a practically point of view, but
also from a theoretical point of view.
","On the Completeness and Complexity of the Lifted Dynamic Junction Tree
  Algorithm"
"  principles that should govern autonomous AI systems. It essentially states
that a system's goals and behaviour should be aligned with human values. But
how to ensure value alignment? In this paper we first provide a formal model to
represent values through preferences and ways to compute value aggregations;
i.e. preferences with respect to a group of agents and/or preferences with
respect to sets of values. Value alignment is then defined, and computed, for a
given norm with respect to a given value through the increase/decrease that it
results in the preferences of future states of the world. We focus on norms as
it is norms that govern behaviour, and as such, the alignment of a given system
with a given value will be dictated by the norms the system follows.
",Value alignment: a formal approach
"  We are approaching a future where social robots will progressively become
widespread in many aspects of our daily lives, including education, healthcare,
work, and personal use. All of such practical applications require that humans
and robots collaborate in human environments, where social interaction is
unavoidable. Along with verbal communication, successful social interaction is
closely coupled with the interplay between nonverbal perception and action
mechanisms, such as observation of gaze behaviour and following their
attention, coordinating the form and function of hand gestures. Humans perform
nonverbal communication in an instinctive and adaptive manner, with no effort.
For robots to be successful in our social landscape, they should therefore
engage in social interactions in a humanlike way, with increasing levels of
autonomy. In particular, nonverbal gestures are expected to endow social robots
with the capability of emphasizing their speech, or showing their intentions.
Motivated by this, our research sheds a light on modeling human behaviors in
social interactions, specifically, forecasting human nonverbal social signals
during dyadic interactions, with an overarching goal of developing robotic
interfaces that can learn to imitate human dyadic interactions. Such an
approach will ensure the messages encoded in the robot gestures could be
perceived by interacting partners in a facile and transparent manner, which
could help improve the interacting partner perception and makes the social
interaction outcomes enhanced.
","Forecasting Nonverbal Social Signals during Dyadic Interactions with
  Generative Adversarial Neural Networks"
"  We can achieve significant gains in the value of computation by metareasoning
about the nature or extent of base-level problem solving before executing a
solution. However, resources that are irrevocably committed to metareasoning
are not available for executing a solution. Thus, it is important to determine
the portion of resources we wish to apply to metareasoning and control versus
to the execution of a solution plan. Recent research on rational agency has
highlighted the importance of limiting the consumption of resources by
metareasoning machinery. We shall introduce the metareasoning-partition
problem--the problem of ideally apportioning costly reasoning resources to
planning a solution versus applying resource to executing a solution to a
problem. We exercise prototypical metareasoning-partition models to probe the
relationships between time allocated to metareasoning and to execution for
different problem classes. Finally, we examine the value of metareasoning in
the context of our functional analyses.
",Ideal Partition of Resources for Metareasoning
"  Artificial agents that support people in their daily activities (e.g.,
virtual coaches and personal assistants) are increasingly prevalent. Since many
daily activities are social in nature, support agents should understand a
user's social situation to offer comprehensive support. However, there are no
systematic approaches for developing support agents that are social situation
aware. We identify key requirements for a support agent to be social situation
aware and propose steps to realize those requirements. These steps are
presented through a conceptual architecture that centers around two key ideas:
(1) conceptualizing social situation awareness as an instantiation of `general'
situation awareness, and (2) using situation taxonomies as the key element of
such instantiation. This enables support agents to represent a user's social
situation, comprehend its meaning, and assess its impact on the user's
behavior. We discuss empirical results supporting that the proposed approach
can be effective and illustrate how the architecture can be used in support
agents through a use case.
",Towards Social Situation Awareness in Support Agents
"  A knowledge graph (KG) is a data structure which represents entities and
relations as the vertices and edges of a directed graph with edge types. KGs
are an important primitive in modern machine learning and artificial
intelligence. Embedding-based models, such as the seminal TransE [Bordes et
al., 2013] and the recent PairRE [Chao et al., 2020] are among the most popular
and successful approaches for representing KGs and inferring missing edges
(link completion). Their relative success is often credited in the literature
to their ability to learn logical rules between the relations.
  In this work, we investigate whether learning rules between relations is
indeed what drives the performance of embedding-based methods. We define motif
learning and two alternative mechanisms, network learning (based only on the
connectivity of the KG, ignoring the relation types), and unstructured
statistical learning (ignoring the connectivity of the graph). Using
experiments on synthetic KGs, we show that KG models can learn motifs and how
this ability is degraded by non-motif (noise) edges. We propose tests to
distinguish the contributions of the three mechanisms to performance, and apply
them to popular KG benchmarks. We also discuss an issue with the standard
performance testing protocol and suggest an improvement.
  To appear in the proceedings of Complex Networks 2021.
",What is Learned in Knowledge Graph Embeddings?
"  Dealing with planning problems with both discrete logical relations and
continuous numeric changes in real-world dynamic environments is challenging.
Existing numeric planning systems for the problem often discretize numeric
variables or impose convex quadratic constraints on numeric variables, which
harms the performance when solving the problem. In this paper, we propose a
novel algorithm framework to solve the numeric planning problems mixed with
discrete and continuous actions based on gradient descent. We cast the numeric
planning with discrete and continuous actions as an optimization problem by
integrating a heuristic function based on discrete effects. Specifically, we
propose a gradient-based framework to simultaneously optimize continuous
parameters and actions of candidate plans. The framework is combined with a
heuristic module to estimate the best plan candidate to transit initial state
to the goal based on relaxation. We repeatedly update numeric parameters and
compute candidate plan until it converges to a valid plan to the planning
problem. In the empirical study, we exhibit that our algorithm framework is
both effective and efficient, especially when solving non-convex planning
problems.
",Gradient-Based Mixed Planning with Discrete and Continuous Actions
"  Fact-checking on the Web has become the main mechanism through which we
detect the credibility of the news or information. Existing fact-checkers
verify the authenticity of the information (support or refute the claim) based
on secondary sources of information. However, existing approaches do not
consider the problem of model updates due to constantly increasing training
data due to user feedback. It is therefore important to conduct user studies to
correct models' inference biases and improve the model in a life-long learning
manner in the future according to the user feedback. In this paper, we present
FaxPlainAC, a tool that gathers user feedback on the output of explainable
fact-checking models. FaxPlainAC outputs both the model decision, i.e., whether
the input fact is true or not, along with the supporting/refuting evidence
considered by the model. Additionally, FaxPlainAC allows for accepting user
feedback both on the prediction and explanation. Developed in Python,
FaxPlainAC is designed as a modular and easily deployable tool. It can be
integrated with other downstream tasks and allowing for fact-checking human
annotation gathering and life-long learning.
","FaxPlainAC: A Fact-Checking Tool Based on EXPLAINable Models with HumAn
  Correction in the Loop"
"  Probabilistic programming is emerging as a popular and effective means of
probabilistic modeling and an alternative to probabilistic graphical models.
Probabilistic programs provide greater expressivity and flexibility in modeling
probabilistic systems than graphical models, but this flexibility comes at a
cost: there remains a significant disparity in performance between specialized
Bayesian network solvers and probabilistic program inference algorithms. In
this work we present a program analysis and associated optimization,
flip-hoisting, that collapses repetitious parameters in discrete probabilistic
programs to improve inference performance. flip-hoisting generalizes parameter
sharing - a well-known important optimization from discrete graphical models -
to probabilistic programs. We implement flip-hoisting in an existing
probabilistic programming language and show empirically that it significantly
improves inference performance, narrowing the gap between the performances of
probabilistic programs and probabilistic graphical models.
","flip-hoisting: Exploiting Repeated Parameters in Discrete Probabilistic
  Programs"
"  The game of 2048 is a highly addictive game. It is easy to learn the game,
but hard to master as the created game revealed that only about 1% games out of
hundreds million ever played have been won. In this paper, we would like to
explore reinforcement learning techniques to win 2048. The approaches we have
took include deep Q-learning and beam search, with beam search reaching 2048
28.5 of time.
",Playing 2048 With Reinforcement Learning
"  Route recommendation is significant in navigation service. Two major
challenges for route recommendation are route representation and user
representation. Different from items that can be identified by unique IDs in
traditional recommendation, routes are combinations of links (i.e., a road
segment and its following action like turning left) and the number of
combinations could be close to infinite. Besides, the representation of a route
changes under different scenarios. These facts result in severe sparsity of
routes, which increases the difficulty of route representation. Moreover, link
attribute deficiencies and errors affect preciseness of route representation.
Because of the sparsity of routes, the interaction data between users and
routes are also sparse. This makes it not easy to acquire user representation
from historical user-item interactions as traditional recommendations do. To
address these issues, we propose a novel learning framework R4. In R4, we
design a sparse & dense network to obtain representations of routes. The sparse
unit learns link ID embeddings and aggregates them to represent a route, which
captures implicit route characteristics and subsequently alleviates problems
caused by link attribute deficiencies and errors. The dense unit extracts
implicit local features of routes from link attributes. For user
representation, we utilize a series of historical navigation to extract user
preference. R4 achieves remarkable performance in both offline and online
experiments.
",R4: A Framework for Route Representation and Route Recommendation
"  Gray-box graph attacks aim at disrupting the performance of the victim model
by using inconspicuous attacks with limited knowledge of the victim model. The
parameters of the victim model and the labels of the test nodes are invisible
to the attacker. To obtain the gradient on the node attributes or graph
structure, the attacker constructs an imaginary surrogate model trained under
supervision. However, there is a lack of discussion on the training of
surrogate models and the robustness of provided gradient information. The
general node classification model loses the topology of the nodes on the graph,
which is, in fact, an exploitable prior for the attacker. This paper
investigates the effect of representation learning of surrogate models on the
transferability of gray-box graph adversarial attacks. To reserve the topology
in the surrogate embedding, we propose Surrogate Representation Learning with
Isometric Mapping (SRLIM). By using Isometric mapping method, our proposed
SRLIM can constrain the topological structure of nodes from the input layer to
the embedding space, that is, to maintain the similarity of nodes in the
propagation process. Experiments prove the effectiveness of our approach
through the improvement in the performance of the adversarial attacks generated
by the gradient-based attacker in untargeted poisoning gray-box setups.
","Surrogate Representation Learning with Isometric Mapping for Gray-box
  Graph Adversarial Attacks"
"  Artificial Intelligence (AI) provides practical advantages in different
applied domains. This is changing the way decision-makers reason about complex
systems. Indeed, broader visibility on greater information (re)sources, e.g.
Big Data (BD), is now available to intelligent agents. On the other hand, such
decisions are not always based on reusable, multi-purpose, and explainable
knowledge. Therefore, it is necessary to define new models to describe and
manage this new (re)source of uncertainty.
  This contribution aims to introduce a formal framework to deal with the
notion of Value in the AI-BD context, embracing both the multiplicity of Value
dimensions and the uncertainty in their visibility as the foundations for a
dynamic, relational representation of Value. The framework design is based on
abstract and highly scalable definitions to represent Value, even considering
the interaction of different agents through comparison, combination, and update
of states of knowledge. In such a model, both Big Data and different types of
intelligence are considered as resources. The information extracted from data
becomes a renewable resource if it can be transformed into knowledge, which is
reusable beyond a specific scenario and, dynamically, over time.
  The focus on reusable knowledge is exploited in the relation between Human
and Artificial intelligences, which is characterised by a ""non-classical"" form
of uncertainty related to data observability. Finally, we identify applicative
domains for future investigation, in order to address the impact of the dynamic
behaviour of Value dimensions on strategies and decision-making, enhancing the
adaptability and, hence, the sustainability of AI-BD initiatives over time.
","Aware Adoption of Artificial Intelligence and Big Data: a Value
  Framework for Reusable Knowledge"
"  Logical assessment formula (LAF) was proposed for evaluations without
accurate ground-truth labels (AGTL). In this paper, we reveal the principles of
LAF via comprehensive theoretical analyses. From the revealed principles, we
summarize the practicability of LAF: 1) LAF can be reasonably applied for
evaluations without AGTL on a more difficult task, just acting like usual
strategies for evaluations with AGTL; 2) LAF can be applied for evaluations
without AGTL from the logical perspective on an easier task, unable to be
acting like usual strategies for evaluations with AGTL. Experimental results
and analyses of LAF applied on tumour segmentation for breast cancer support
the practicability of LAF summarized from the revealed principles.
","Logical Assessment Formula and its Principles for Evaluations without
  Accurate Ground-Truth Labels"
"  Goal-directed evaluation of Answer Set Programs is gaining traction thanks to
its amenability to create AI systems that can, due to the evaluation mechanism
used, generate explanations and justifications. s(CASP) is one of these systems
and has been already used to write reasoning systems in several fields. It
provides enhanced expressiveness w.r.t. other ASP systems due to its ability to
use constraints, data structures, and unbound variables natively. However, the
performance of existing s(CASP) implementations is not on par with other ASP
systems: model consistency is checked once models have been generated, in
keeping with the generate-and-test paradigm. In this work, we present a
variation of the top-down evaluation strategy, termed Dynamic Consistency
Checking, which interleaves model generation and consistency checking. This
makes it possible to determine when a literal is not compatible with the
denials associated to the global constraints in the program, prune the current
execution branch, and choose a different alternative. This strategy is
specially (but not exclusively) relevant in problems with a high combinatorial
component. We have experimentally observed speedups of up to 90x w.r.t. the
standard versions of s(CASP).
","Towards Dynamic Consistency Checking in Goal-directed Predicate Answer
  Set Programming"
"  The fundamental goal of artificial intelligence (AI) is to mimic the core
cognitive activities of human including perception, memory, and reasoning.
Although tremendous success has been achieved in various AI research fields
(e.g., computer vision and natural language processing), the majority of
existing works only focus on acquiring single cognitive ability (e.g., image
classification, reading comprehension, or visual commonsense reasoning). To
overcome this limitation and take a solid step to artificial general
intelligence (AGI), we develop a novel foundation model pre-trained with huge
multimodal (visual and textual) data, which is able to be quickly adapted for a
broad class of downstream cognitive tasks. Such a model is fundamentally
different from the multimodal foundation models recently proposed in the
literature that typically make strong semantic correlation assumption and
expect exact alignment between image and text modalities in their pre-training
data, which is often hard to satisfy in practice thus limiting their
generalization abilities. To resolve this issue, we propose to pre-train our
foundation model by self-supervised learning with weak semantic correlation
data crawled from the Internet and show that state-of-the-art results can be
obtained on a wide range of downstream tasks (both single-modal and
cross-modal). Particularly, with novel model-interpretability tools developed
in this work, we demonstrate that strong imagination ability (even with hints
of commonsense) is now possessed by our foundation model. We believe our work
makes a transformative stride towards AGI and will have broad impact on various
AI+ fields (e.g., neuroscience and healthcare).
",WenLan 2.0: Make AI Imagine via a Multimodal Foundation Model
"  Knowledge graph embedding models learn the representations of entities and
relations in the knowledge graphs for predicting missing links (relations)
between entities. Their effectiveness are deeply affected by the ability of
modeling and inferring different relation patterns such as symmetry, asymmetry,
inversion, composition and transitivity. Although existing models are already
able to model many of these relations patterns, transitivity, a very common
relation pattern, is still not been fully supported. In this paper, we first
theoretically show that the transitive relations can be modeled with
projections. We then propose the Rot-Pro model which combines the projection
and relational rotation together. We prove that Rot-Pro can infer all the above
relation patterns. Experimental results show that the proposed Rot-Pro model
effectively learns the transitivity pattern and achieves the state-of-the-art
results on the link prediction task in the datasets containing transitive
relations.
","Rot-Pro: Modeling Transitivity by Projection in Knowledge Graph
  Embedding"
"  Cutting and Packing problems are occurring in different industries with a
direct impact on the revenue of businesses. Generally, the goal in Cutting and
Packing is to assign a set of smaller objects to a set of larger objects. To
solve Cutting and Packing problems, practitioners can resort to heuristic and
exact methodologies. Lately, machine learning is increasingly used for solving
such problems. This paper considers a 2D packing problem from the furniture
industry, where a set of wooden workpieces must be assigned to different
modules of a trolley in the most space-saving way. We present an experimental
setup to compare heuristics, constraint optimization, and deep reinforcement
learning for the given problem. The used methodologies and their results get
collated in terms of their solution quality and runtime. In the given use case
a greedy heuristic produces optimal results and outperforms the other
approaches in terms of runtime. Constraint optimization also produces optimal
results but requires more time to perform. The deep reinforcement learning
approach did not always produce optimal or even feasible solutions. While we
assume this could be remedied with more training, considering the good results
with the heuristic, deep reinforcement learning seems to be a bad fit for the
given use case.
","Comparing Heuristics, Constraint Optimization, and Reinforcement
  Learning for an Industrial 2D Packing Problem"
"  Behavior prediction remains one of the most challenging tasks in the
autonomous vehicle (AV) software stack. Forecasting the future trajectories of
nearby agents plays a critical role in ensuring road safety, as it equips AVs
with the necessary information to plan safe routes of travel. However, these
prediction models are data-driven and trained on data collected in real life
that may not represent the full range of scenarios an AV can encounter. Hence,
it is important that these prediction models are extensively tested in various
test scenarios involving interactive behaviors prior to deployment. To support
this need, we present a simulation-based testing platform which supports (1)
intuitive scenario modeling with a probabilistic programming language called
Scenic, (2) specifying a multi-objective evaluation metric with a partial
priority ordering, (3) falsification of the provided metric, and (4)
parallelization of simulations for scalable testing. As a part of the platform,
we provide a library of 25 Scenic programs that model challenging test
scenarios involving interactive traffic participant behaviors. We demonstrate
the effectiveness and the scalability of our platform by testing a trained
behavior prediction model and searching for failure scenarios.
","A Scenario-Based Platform for Testing Autonomous Vehicle Behavior
  Prediction Models in Simulation"
"  Conceptual Graphs (CGs) are a graph-based knowledge representation formalism.
In this paper we propose cgSpan a CG frequent pattern mining algorithm. It
extends the DMGM-GSM algorithm that takes taxonomy-based labeled graphs as
input; it includes three more kinds of knowledge of the CG formalism: (a) the
fixed arity of relation nodes, handling graphs of neighborhoods centered on
relations rather than graphs of nodes, (b) the signatures, avoiding patterns
with concept types more general than the maximal types specified in signatures
and (c) the inference rules, applying them during the pattern mining process.
The experimental study highlights that cgSpan is a functional CG Frequent
Pattern Mining algorithm and that including CGs specificities results in a
faster algorithm with more expressive results and less redundancy with
vocabulary.
",cgSpan: Pattern Mining in Conceptual Graphs
"  Activation-based conditional inference applies conditional reasoning to
ACT-R, a cognitive architecture developed to formalize human reasoning. The
idea of activation-based conditional inference is to determine a reasonable
subset of a conditional belief base in order to draw inductive inferences in
time. Central to activation-based conditional inference is the activation
function which assigns to the conditionals in the belief base a degree of
activation mainly based on the conditional's relevance for the current query
and its usage history. Therewith, our approach integrates several aspects of
human reasoning into expert systems such as focusing, forgetting, and
remembering.
",Conditional Inference and Activation of Knowledge Entities in ACT-R
"  Concise granule descriptions for describable granules and approaching
description methods for indescribable granules are challenging and important
issues in granular computing. The concept with only common attributes has been
frequently studied. To investigate the granules with some special needs, we
propose two new types of compound concepts in this paper: bipolar concept and
common-and-necessary concept. Based on the definitions of concept-forming
operations, the logical formulas are derived for each of the following types of
concepts: formal concept, three-way concept, object oriented concept, bipolar
concept and common-and-necessary concept. Furthermore, by utilizing the logical
relationship among various concepts, we have derived concise and unified
equivalent conditions for describable granules and approaching description
methods for indescribable granules for all five kinds of concepts.
",Granule Description based on Compound Concepts
"  As the Internet grows in size, so does the amount of text based information
that exists. For many application spaces it is paramount to isolate and
identify texts that relate to a particular topic. While one-class
classification would be ideal for such analysis, there is a relative lack of
research regarding efficient approaches with high predictive power. By noting
that the range of documents we wish to identify can be represented as positive
linear combinations of the Vector Space Model representing our text, we propose
Conical classification, an approach that allows us to identify if a document is
of a particular topic in a computationally efficient manner. We also propose
Normal Exclusion, a modified version of Bi-Normal Separation that makes it more
suitable within the one-class classification context. We show in our analysis
that our approach not only has higher predictive power on our datasets, but is
also faster to compute.
","Conical Classification For Computationally Efficient One-Class Topic
  Determination"
"  With more deep learning techniques being introduced into the knowledge
tracing domain, the interpretability issue of the knowledge tracing models has
aroused researchers' attention. Our previous study(Lu et al. 2020) on building
and interpreting the KT model mainly adopts the ASSISTment dataset(Feng,
Heffernan, and Koedinger 2009),, whose size is relatively small. In this work,
we perform the similar tasks but on a large and newly available dataset, called
EdNet(Choi et al. 2020). The preliminary experiment results show the
effectiveness of the interpreting techniques, while more questions and tasks
are worthy to be further explored and accomplished.
",Interpreting Deep Knowledge Tracing Model on EdNet Dataset
"  Graph neural networks (GNNs) have recently shown good performance in various
fields. In this paper, we propose graph tree neural networks (GTNNs) designed
to solve the problems of existing networks by analyzing the structure of human
neural networks. In GTNNs, information units are related to the form of a graph
and then they become a bigger unit of information again and have a relationship
with other information units. At this point, the unit of information is a set
of neurons, and we can express it as a vector with GTNN. Defining the starting
and ending points in a single graph is difficult, and a tree cannot express the
relationship among sibling nodes. However, a graph tree can be expressed using
leaf and root nodes as its starting and ending points and the relationship
among sibling nodes. Depth-first convolution (DFC) encodes the interaction
result from leaf nodes to the root node in a bottom-up approach, and
depth-first deconvolution (DFD) decodes the interaction result from the root
node to the leaf nodes in a top-down approach. GTNN is data-driven learning in
which the number of convolutions varies according to the depth of the tree.
Moreover, learning features of different types together is possible.
Supervised, unsupervised, and semi-supervised learning using graph tree
recursive neural network (GTR) , graph tree recursive attention networks
(GTRAs), and graph tree recursive autoencoders (GTRAEs) are introduced in this
paper. We experimented with a simple toy test with source code dataset.
",Graph Tree Neural Networks
"  While Out-of-distribution (OOD) detection has been well explored in computer
vision, there have been relatively few prior attempts in OOD detection for NLP
classification. In this paper we argue that these prior attempts do not fully
address the OOD problem and may suffer from data leakage and poor calibration
of the resulting models. We present PnPOOD, a data augmentation technique to
perform OOD detection via out-of-domain sample generation using the recently
proposed Plug and Play Language Model (Dathathri et al., 2020). Our method
generates high quality discriminative samples close to the class boundaries,
resulting in accurate OOD detection at test time. We demonstrate that our model
outperforms prior models on OOD sample detection, and exhibits lower
calibration error on the 20 newsgroup text and Stanford Sentiment Treebank
dataset (Lang, 1995; Socheret al., 2013). We further highlight an important
data leakage issue with datasets used in prior attempts at OOD detection, and
share results on a new dataset for OOD detection that does not suffer from the
same problem.
","PnPOOD : Out-Of-Distribution Detection for Text Classification via Plug
  andPlay Data Augmentation"
"  In the current era of digitization, online payment systems are attracting
considerable interest. Improving the efficiency of a payment system is
important since it has a substantial impact on revenues for businesses. A
gateway is an integral component of a payment system through which every
transaction is routed. In an online payment system, payment processors
integrate with these gateways by means of various configurations such as
pricing, methods, risk checks, etc. These configurations are called terminals.
Each gateway can have multiple terminals associated with it. Routing a payment
transaction through the best terminal is crucial to increase the probability of
a payment transaction being successful. Machine learning (ML) and artificial
intelligence (AI) techniques can be used to accurately predict the best
terminals based on their previous performance and various payment-related
attributes. We have devised a pipeline consisting of static and dynamic
modules. The static module does the initial filtering of the terminals using
static rules and a logistic regression model that predicts gateway downtimes.
Subsequently, the dynamic module computes a lot of novel features based on
success rate, payment attributes, time lag, etc. to model the terminal
behaviour accurately. These features are updated using an adaptive time decay
rate algorithm in real-time using a feedback loop and passed to a random forest
classifier to predict the success probabilities for every terminal. This
pipeline is currently in production at Razorpay routing millions of
transactions through it in real-time and has given a 4-6\% improvement in
success rate across all payment methods (credit card, debit card, UPI, net
banking). This has made our payment system more resilient to performance drops,
which has improved the user experience, instilled more trust in the merchants,
and boosted the revenue of the business.
",An AI-powered Smart Routing Solution for Payment Systems
"  Site selection determines optimal locations for new stores, which is of
crucial importance to business success. Especially, the wide application of
artificial intelligence with multi-source urban data makes intelligent site
selection promising. However, existing data-driven methods heavily rely on
feature engineering, facing the issues of business generalization and complex
relationship modeling. To get rid of the dilemma, in this work, we borrow ideas
from knowledge graph (KG), and propose a knowledge-driven model for site
selection, short for KnowSite. Specifically, motivated by distilled knowledge
and rich semantics in KG, we firstly construct an urban KG (UrbanKG) with
cities' key elements and semantic relationships captured. Based on UrbanKG, we
employ pre-training techniques for semantic representations, which are fed into
an encoder-decoder structure for site decisions. With multi-relational message
passing and relation path-based attention mechanism developed, KnowSite
successfully reveals the relationship between various businesses and site
selection criteria. Extensive experiments on two datasets demonstrate that
KnowSite outperforms representative baselines with both effectiveness and
explainability achieved.
",Knowledge-driven Site Selection via Urban Knowledge Graph
"  In this work we explain the setup for a technical, graduate-level course on
Fairness, Accountability, Confidentiality and Transparency in Artificial
Intelligence (FACT-AI) at the University of Amsterdam, which teaches FACT-AI
concepts through the lens of reproducibility. The focal point of the course is
a group project based on reproducing existing FACT-AI algorithms from top AI
conferences, and writing a report about their experiences. In the first
iteration of the course, we created an open source repository with the code
implementations from the group projects. In the second iteration, we encouraged
students to submit their group projects to the Machine Learning Reproducibility
Challenge, which resulted in 9 reports from our course being accepted to the
challenge. We reflect on our experience teaching the course over two academic
years, where one year coincided with a global pandemic, and propose guidelines
for teaching FACT-AI through reproducibility in graduate-level AI programs. We
hope this can be a useful resource for instructors to set up similar courses at
their universities in the future.
","Teaching Fairness, Accountability, Confidentiality, and Transparency in
  Artificial Intelligence through the Lens of Reproducibility"
"  Gomoku, also known as five in a row, is a classical board game, ideally
suited for quickly testing novel Artificial Intelligence (AI) techniques. With
the aim of facilitating a developer willing to write a new Gomoku player, in
this report we present an analysis of the main game concepts and strategies,
which is wider and deeper than existing ones. Moreover, after discussing the
general structure of an artificial player, we present and analyse a strong
Gomoku player, named Wine, the code of which is freely available on the
Internet and which is an excelent example of how a modern player is organised.
",Gomoku: analysis of the game and of the player Wine
"  The shipping industry is an important component of the global trade and
economy, however in order to ensure law compliance and safety it needs to be
monitored. In this paper, we present a novel Ship Type classification model
that combines vessel transmitted data from the Automatic Identification System,
with vessel imagery. The main components of our approach are the Faster R-CNN
Deep Neural Network and a Neuro-Fuzzy system with IF-THEN rules. We evaluate
our model using real world data and showcase the advantages of this combination
while also compare it with other methods. Results show that our model can
increase prediction scores by up to 15.4\% when compared with the next best
model we considered, while also maintaining a level of explainability as
opposed to common black box approaches.
","Logic Rules Meet Deep Learning: A Novel Approach for Ship Type
  Classification"
"  Autonomous 3D environment exploration is a fundamental task for various
applications such as navigation. The goal of exploration is to investigate a
new environment and build its occupancy map efficiently. In this paper, we
propose a new method which grants an agent two intertwined options of
behaviors: ""look-around"" and ""frontier navigation"". This is implemented by an
option-critic architecture and trained by reinforcement learning algorithms. In
each timestep, an agent produces an option and a corresponding action according
to the policy. We also take advantage of macro-actions by incorporating classic
path-planning techniques to increase training efficiency. We demonstrate the
effectiveness of the proposed method on two publicly available 3D environment
datasets and the results show our method achieves higher coverage than
competing techniques with better efficiency.
",Learning to Explore by Reinforcement over High-Level Options
"  The prediction of extreme greenhouse temperatures to which crops are
susceptible is essential in the field of greenhouse planting. It can help avoid
heat or freezing damage and economic losses. Therefore, it's important to
develop models that can predict them accurately. Due to the lack of extreme
temperature data in datasets, it is challenging for models to accurately
predict it. In this paper, we propose an improved loss function, which is
suitable for a variety of machine learning models. By increasing the weight of
extreme temperature samples and reducing the possibility of misjudging extreme
temperature as normal, the proposed loss function can enhance the prediction
results in extreme situations. To verify the effectiveness of the proposed
method, we implement the improved loss function in LightGBM, long short-term
memory, and artificial neural network and conduct experiments on a real-world
greenhouse dataset. The results show that the performance of models with the
improved loss function is enhanced compared to the original models in extreme
cases. The improved models can be used to guarantee the timely judgment of
extreme temperatures in agricultural greenhouses, thereby preventing
unnecessary losses caused by incorrect predictions.
","Improved Loss Function-Based Prediction Method of Extreme Temperatures
  in Greenhouses"
"  Imbalanced learning is important and challenging since the problem of the
classification of imbalanced datasets is prevalent in machine learning and data
mining fields. Sampling approaches are proposed to address this issue, and
cluster-based oversampling methods have shown great potential as they aim to
simultaneously tackle between-class and within-class imbalance issues. However,
all existing clustering methods are based on a one-time approach. Due to the
lack of a priori knowledge, improper setting of the number of clusters often
exists, which leads to poor clustering performance. Besides, the existing
methods are likely to generate noisy instances. To solve these problems, this
paper proposes a deep instance envelope network-based imbalanced learning
algorithm with the multilayer fuzzy c-means (MlFCM) and a minimum interlayer
discrepancy mechanism based on the maximum mean discrepancy (MIDMD). This
algorithm can guarantee high quality balanced instances using a deep instance
envelope network in the absence of prior knowledge. In the experimental
section, thirty-three popular public datasets are used for verification, and
over ten representative algorithms are used for comparison. The experimental
results show that the proposed approach significantly outperforms other popular
methods.
","Envelope Imbalance Learning Algorithm based on Multilayer Fuzzy C-means
  Clustering and Minimum Interlayer discrepancy"
"  In this paper, we introduce Graph Tree Deductive Networks, a network that
performs deductive reasoning. To have high-dimensional thinking, combining
various axioms and putting the results back into another axiom is necessary to
produce new relationships and results. For example, it would be given two
propositions: ""Socrates is a man."" and ""All men are mortals."" and two
propositions could be used to infer the new proposition, ""Therefore Socrates is
mortal."". To evaluate, we used MNIST Dataset, a handwritten numerical image
dataset, to apply it to the group theory and show the results of performing
deductive learning.
",Graph Tree Deductive Networks
"  State-of-the-art neural models of source code tend to be evaluated on the
generation of individual expressions and lines of code, and commonly fail on
long-horizon tasks such as the generation of entire method bodies. We propose
to address this deficiency using weak supervision from a static program
analyzer. Our neurosymbolic method allows a deep generative model to
symbolically compute, using calls to a static-analysis tool, long-distance
semantic relationships in the code that it has already generated. During
training, the model observes these relationships and learns to generate
programs conditioned on them. We apply our approach to the problem of
generating entire Java methods given the remainder of the class that contains
the method. Our experiments show that the approach substantially outperforms
state-of-the-art transformers and a model that explicitly tries to learn
program semantics on this task, both in terms of producing programs free of
basic semantic errors and in terms of syntactically matching the ground truth.
",Neural Program Generation Modulo Static Analysis
"  We propose a novel approach to explainable AI (XAI) based on the concept of
""instruction"" from neural networks. In this case study, we demonstrate how a
superhuman neural network might instruct human trainees as an alternative to
traditional approaches to XAI. Specifically, an AI examines human actions and
calculates variations on the human strategy that lead to better performance.
Experiments with a JHU/APL-developed AI player for the cooperative card game
Hanabi suggest this technique makes unique contributions to explainability
while improving human performance. One area of focus for Instructive AI is in
the significant discrepancies that can arise between a human's actual strategy
and the strategy they profess to use. This inaccurate self-assessment presents
a barrier for XAI, since explanations of an AI's strategy may not be properly
understood or implemented by human recipients. We have developed and are
testing a novel, Instructive AI approach that estimates human strategy by
observing human actions. With neural networks, this allows a direct calculation
of the changes in weights needed to improve the human strategy to better
emulate a more successful AI. Subjected to constraints (e.g. sparsity) these
weight changes can be interpreted as recommended changes to human strategy
(e.g. ""value A more, and value B less""). Instruction from AI such as this
functions both to help humans perform better at tasks, but also to better
understand, anticipate, and correct the actions of an AI. Results will be
presented on AI instruction's ability to improve human decision-making and
human-AI teaming in Hanabi.
","Instructive artificial intelligence (AI) for human training, assistance,
  and explainability"
"  Contracts underlie most modern commercial transactions defining define the
duties and obligations of the related parties in an agreement. Ensuring such
agreements are error free is crucial for modern society and their analysis of a
contract requires understanding the logical relations between clauses and
identifying potential contradictions. This analysis depends on error-prone
human effort to understand each contract clause. In this work, we develop an
approach to automate the extraction of logical relations between clauses in a
contract. We address this problem as a Natural Language Inference task to
detect the entailment type between two clauses in a contract. The resulting
approach should help contract authors detecting potential logical conflicts
between clauses.
",Detecting Logical Relation In Contract Clauses
"  In this work, we fill the gap in the Semantic Web in the context of Cultural
Symbolism. Building upon earlier work in, we introduce the Simulation Ontology,
an ontology that models the background knowledge of symbolic meanings,
developed by combining the concepts taken from the authoritative theory of
Simulacra and Simulations of Jean Baudrillard with symbolic structures and
content taken from ""Symbolism: a Comprehensive Dictionary"" by Steven Olderr. We
re-engineered the symbolic knowledge already present in heterogeneous resources
by converting it into our ontology schema to create HyperReal, the first
knowledge graph completely dedicated to cultural symbolism. A first experiment
run on the knowledge graph is presented to show the potential of quantitative
research on symbolism.
","Marriage is a Peach and a Chalice: Modelling Cultural Symbolism on the
  SemanticWeb"
"  Explainable artificial intelligence (xAI) is seen as a solution to making AI
systems less of a black box. It is essential to ensure transparency, fairness,
and accountability, which are especially paramount in the financial sector. The
aim of this study was a preliminary investigation of the perspectives of
supervisory authorities and regulated entities regarding the application of xAI
in the fi-nancial sector. Three use cases (consumer credit, credit risk, and
anti-money laundering) were examined using semi-structured interviews at three
banks and two supervisory authorities in the Netherlands. We found that for the
investigated use cases a disparity exists between supervisory authorities and
banks regarding the desired scope of explainability of AI systems. We argue
that the financial sector could benefit from clear differentiation between
technical AI (model) ex-plainability requirements and explainability
requirements of the broader AI system in relation to applicable laws and
regulations.
","Exploring Explainable AI in the Financial Sector: Perspectives of Banks
  and Supervisory Authorities"
"  We introduce Graph Tree Memory Networks that memorize and remember any data.
This neural network has two memories. One consists of a queue-structured
short-term memory to solve the class imbalance problem and long-term memory to
store the distribution of objects, introducing the contents of storing and
generating various datasets.
",Graph Tree Memory Networks
"  This paper describes three different optimised implementations of playouts,
as commonly used by game-playing algorithms such as Monte-Carlo Tree Search.
Each of the optimised implementations is applicable only to specific sets of
games, based on their rules. The Ludii general game system can automatically
infer, based on a game's description in its general game description language,
whether any optimised implementations are applicable. An empirical evaluation
demonstrates major speedups over a standard implementation, with a median
result of running playouts 5.08 times as fast, over 145 different games in
Ludii for which one of the optimised implementations is applicable.
",Optimised Playout Implementations for the Ludii General Game System
"  Even skilled fantasy football managers can be disappointed by their
mid-season rosters as some players inevitably fall short of draft day
expectations. Team managers can quickly discover that their team has a low
score ceiling even if they start their best active players. A novel and diverse
combinatorial optimization system proposes high volume and unique player trades
between complementary teams to balance trade fairness. Several algorithms
create the valuation of each fantasy football player with an ensemble of
computing models: Quantum Support Vector Classifier with Permutation Importance
(QSVC-PI), Quantum Support Vector Classifier with Accumulated Local Effects
(QSVC-ALE), Variational Quantum Circuit with Permutation Importance (VQC-PI),
Hybrid Quantum Neural Network with Permutation Importance (HQNN-PI), eXtreme
Gradient Boosting Classifier (XGB), and Subject Matter Expert (SME) rules. The
valuation of each player is personalized based on league rules, roster, and
selections. The cost of trading away a player is related to a team's roster,
such as the depth at a position, slot count, and position importance. Teams are
paired together for trading based on a cosine dissimilarity score so that teams
can offset their strengths and weaknesses. A knapsack 0-1 algorithm computes
outgoing players for each team. Postprocessors apply analytics and deep
learning models to measure 6 different objective measures about each trade.
Over the 2020 and 2021 National Football League (NFL) seasons, a group of 24
experts from IBM and ESPN evaluated trade quality through 10 Football Error
Analysis Tool (FEAT) sessions. Our system started with 76.9% of high-quality
trades and was deployed for the 2021 season with 97.3% of high-quality trades.
To increase trade quantity, our quantum, classical, and rules-based computing
have 100% trade uniqueness. We use Qiskit's quantum simulators throughout our
work.
","Large Scale Diverse Combinatorial Optimization: ESPN Fantasy Football
  Player Trades"
"  In this paper, we introduce an Imagine Network that can simulate itself
through graph tree neural networks. Among the graph tree neural networks
models, association, deduction, and memory networks are learned, and a network
is created by combining the discriminator and reinforcement learning models.
This model can learn various datasets or data samples generated in environments
and generate new data samples.
",Imagine Networks
"  This work aims to provide an engagement decision support tool for Beyond
Visual Range (BVR) air combat in the context of Defensive Counter Air (DCA)
missions. In BVR air combat, engagement decision refers to the choice of the
moment the pilot engages a target by assuming an offensive stance and executing
corresponding maneuvers. To model this decision, we use the Brazilian Air
Force's Aerospace Simulation Environment (\textit{Ambiente de Simula\c{c}\~ao
Aeroespacial - ASA} in Portuguese), which generated 3,729 constructive
simulations lasting 12 minutes each and a total of 10,316 engagements. We
analyzed all samples by an operational metric called the DCA index, which
represents, based on the experience of subject matter experts, the degree of
success in this type of mission. This metric considers the distances of the
aircraft of the same team and the opposite team, the point of Combat Air
Patrol, and the number of missiles used. By defining the engagement status
right before it starts and the average of the DCA index throughout the
engagement, we create a supervised learning model to determine the quality of a
new engagement. An algorithm based on decision trees, working with the XGBoost
library, provides a regression model to predict the DCA index with a
coefficient of determination close to 0.8 and a Root Mean Square Error of 0.05
that can furnish parameters to the BVR pilot to decide whether or not to
engage. Thus, using data obtained through simulations, this work contributes by
building a decision support system based on machine learning for BVR air
combat.
",Engagement Decision Support for Beyond Visual Range Air Combat
"  Large-scale ride-hailing systems often combine real-time routing at the
individual request level with a macroscopic Model Predictive Control (MPC)
optimization for dynamic pricing and vehicle relocation. The MPC relies on a
demand forecast and optimizes over a longer time horizon to compensate for the
myopic nature of the routing optimization. However, the longer horizon
increases computational complexity and forces the MPC to operate at coarser
spatial-temporal granularity, degrading the quality of its decisions. This
paper addresses these computational challenges by learning the MPC
optimization. The resulting machine-learning model then serves as the
optimization proxy and predicts its optimal solutions. This makes it possible
to use the MPC at higher spatial-temporal fidelity, since the optimizations can
be solved and learned offline. Experimental results show that the proposed
approach improves quality of service on challenging instances from the New York
City dataset.
","Learning Model Predictive Controllers for Real-Time Ride-Hailing Vehicle
  Relocation and Pricing Decisions"
"  Markov decision processes are typically used for sequential decision making
under uncertainty. For many aspects however, ranging from constrained or safe
specifications to various kinds of temporal (non-Markovian) dependencies in
task and reward structures, extensions are needed. To that end, in recent years
interest has grown into combinations of reinforcement learning and temporal
logic, that is, combinations of flexible behavior learning methods with robust
verification and guarantees. In this paper we describe an experimental
investigation of the recently introduced regular decision processes that
support both non-Markovian reward functions as well as transition functions. In
particular, we provide a tool chain for regular decision processes, algorithmic
extensions relating to online, incremental learning, an empirical evaluation of
model-free and model-based solution algorithms, and applications in regular,
but non-Markovian, grid worlds.
",Regular Decision Processes for Grid Worlds
"  We present a model of sense-making that greatly facilitates the collaboration
between an intelligent analyst and a knowledge-based agent. It is a general
model grounded in the science of evidence and the scientific method of
hypothesis generation and testing, where sense-making hypotheses that explain
an observation are generated, relevant evidence is then discovered, and the
hypotheses are tested based on the discovered evidence. We illustrate how the
model enables an analyst to directly instruct the agent to understand
situations involving the possible production of weapons (e.g., chemical warfare
agents) and how the agent becomes increasingly more competent in understanding
other situations from that domain (e.g., possible production of
centrifuge-enriched uranium or of stealth fighter aircraft).
",Shared Model of Sense-making for Human-Machine Collaboration
"  Collective behavior is widespread across the animal kingdom. To date,
however, the developmental and mechanistic foundations of collective behavior
have not been formally established. What learning mechanisms drive the
development of collective behavior in newborn animals? Here, we used deep
reinforcement learning and curiosity-driven learning -- two learning mechanisms
deeply rooted in psychological and neuroscientific research -- to build newborn
artificial agents that develop collective behavior. Like newborn animals, our
agents learn collective behavior from raw sensory inputs in naturalistic
environments. Our agents also learn collective behavior without external
rewards, using only intrinsic motivation (curiosity) to drive learning.
Specifically, when we raise our artificial agents in natural visual
environments with groupmates, the agents spontaneously develop ego-motion,
object recognition, and a preference for groupmates, rapidly learning all of
the core skills required for collective behavior. This work bridges the divide
between high-dimensional sensory inputs and collective action, resulting in a
pixels-to-actions model of collective animal behavior. More generally, we show
that two generic learning mechanisms -- deep reinforcement learning and
curiosity-driven learning -- are sufficient to learn collective behavior from
unsupervised natural experience.
",Development of collective behavior in newborn artificial agents
"  We present Coordinated Proximal Policy Optimization (CoPPO), an algorithm
that extends the original Proximal Policy Optimization (PPO) to the multi-agent
setting. The key idea lies in the coordinated adaptation of step size during
the policy update process among multiple agents. We prove the monotonicity of
policy improvement when optimizing a theoretically-grounded joint objective,
and derive a simplified optimization objective based on a set of
approximations. We then interpret that such an objective in CoPPO can achieve
dynamic credit assignment among agents, thereby alleviating the high variance
issue during the concurrent update of agent policies. Finally, we demonstrate
that CoPPO outperforms several strong baselines and is competitive with the
latest multi-agent PPO method (i.e. MAPPO) under typical multi-agent settings,
including cooperative matrix games and the StarCraft II micromanagement tasks.
",Coordinated Proximal Policy Optimization
"  Hesitant fuzzy linguistic preference relation (HFLPR) is of interest because
it provides an efficient way for opinion expression under uncertainty. For
enhancing the theory of decision making with HFLPR, the paper introduces an
algorithm for group decision making with HFLPRs based on the acceptable
consistency and consensus measurements, which involves (1) defining a hesitant
fuzzy linguistic geometric consistency index (HFLGCI) and proposing a procedure
for consistency checking and inconsistency improving for HFLPR; (2) measuring
the group consensus based on the similarity between the original individual
HFLPRs and the overall perfect HFLPR, then establishing a procedure for
consensus ensuring including the determination of decision-makers weights. The
convergence and monotonicity of the proposed two procedures have been proved.
Some experiments are furtherly performed to investigate the critical values of
the defined HFLGCI, and comparative analyses are conducted to show the
effectiveness of the proposed algorithm. A case concerning the performance
evaluation of venture capital guiding funds is given to illustrate the
availability of the proposed algorithm. As an application of our work, an
online decision-making portal is finally provided for decision-makers to
utilize the proposed algorithms to solve decision-making problems.
","Consistency and Consensus Driven for Hesitant Fuzzy Linguistic Decision
  Making with Pairwise Comparisons"
"  This paper presents the PlanMiner-N algorithm, a domain learning technique
based on the PlanMiner domain learning algorithm. The algorithm presented here
improves the learning capabilities of PlanMiner when using noisy data as input.
The PlanMiner algorithm is able to infer arithmetic and logical expressions to
learn numerical planning domains from the input data, but it was designed to
work under situations of incompleteness making it unreliable when facing noisy
input data. In this paper, we propose a series of enhancements to the learning
process of PlanMiner to expand its capabilities to learn from noisy data. These
methods preprocess the input data by detecting noise and filtering it and study
the learned action models learned to find erroneous preconditions/effects in
them. The methods proposed in this paper were tested using a set of domains
from the International Planning Competition (IPC). The results obtained
indicate that PlanMiner-N improves the performance of PlanMiner greatly when
facing noisy input data.
",Learning Numerical Action Models from Noisy Input Data
"  This paper presents a comprehensive framework for run-time self-checking of
logical agents, by means of temporal axioms to be dynamically checked. These
axioms are specified by using an agent-oriented interval temporal logic defined
to this purpose. We define syntax, semantics and pragmatics for this new logic,
specifically tailored for application to agents. In the resulting framework, we
encompass and extend our past work.
",Self-checking Logical Agents
"  Systems whose entities interact with each other are common. In many
interacting systems, it is difficult to observe the relations between entities
which is the key information for analyzing the system. In recent years, there
has been increasing interest in discovering the relationships between entities
using graph neural networks. However, existing approaches are difficult to
apply if the number of relations is unknown or if the relations are complex. We
propose the DiScovering Latent Relation (DSLR) model, which is flexibly
applicable even if the number of relations is unknown or many types of
relations exist. The flexibility of our DSLR model comes from the design
concept of our encoder that represents the relation between entities in a
latent space rather than a discrete variable and a decoder that can handle many
types of relations. We performed the experiments on synthetic and real-world
graph data with various relationships between entities, and compared the
qualitative and quantitative results with other approaches. The experiments
show that the proposed method is suitable for analyzing dynamic graphs with an
unknown number of complex relations.
",Discovering Latent Representations of Relations for Interacting Systems
"  Learning-based methods for training embodied agents typically require a large
number of high-quality scenes that contain realistic layouts and support
meaningful interactions. However, current simulators for Embodied AI (EAI)
challenges only provide simulated indoor scenes with a limited number of
layouts. This paper presents Luminous, the first research framework that
employs state-of-the-art indoor scene synthesis algorithms to generate
large-scale simulated scenes for Embodied AI challenges. Further, we
automatically and quantitatively evaluate the quality of generated indoor
scenes via their ability to support complex household tasks. Luminous
incorporates a novel scene generation algorithm (Constrained Stochastic Scene
Generation (CSSG)), which achieves competitive performance with human-designed
scenes. Within Luminous, the EAI task executor, task instruction generation
module, and video rendering toolkit can collectively generate a massive
multimodal dataset of new scenes for the training and evaluation of Embodied AI
agents. Extensive experimental results demonstrate the effectiveness of the
data generated by Luminous, enabling the comprehensive assessment of embodied
agents on generalization and robustness.
",LUMINOUS: Indoor Scene Generation for Embodied AI Challenges
"  Safety has become one of the main challenges of applying deep reinforcement
learning to real world systems. Currently, the incorporation of external
knowledge such as human oversight is the only means to prevent the agent from
visiting the catastrophic state. In this paper, we propose MBHI, a novel
framework for safe model-based reinforcement learning, which ensures safety in
the state-level and can effectively avoid both ""local"" and ""non-local""
catastrophes. An ensemble of supervised learners are trained in MBHI to imitate
human blocking decisions. Similar to human decision-making process, MBHI will
roll out an imagined trajectory in the dynamics model before executing actions
to the environment, and estimate its safety. When the imagination encounters a
catastrophe, MBHI will block the current action and use an efficient MPC method
to output a safety policy. We evaluate our method on several safety tasks, and
the results show that MBHI achieved better performance in terms of sample
efficiency and number of catastrophes compared to the baselines.
","Look Before You Leap: Safe Model-Based Reinforcement Learning with Human
  Intervention"
"  From the very dawn of the field, search with value functions was a
fundamental concept of computer games research. Turing's chess algorithm from
1950 was able to think two moves ahead, and Shannon's work on chess from $1950$
includes an extensive section on evaluation functions to be used within a
search. Samuel's checkers program from 1959 already combines search and value
functions that are learned through self-play and bootstrapping. TD-Gammon
improves upon those ideas and uses neural networks to learn those complex value
functions -- only to be again used within search. The combination of
decision-time search and value functions has been present in the remarkable
milestones where computers bested their human counterparts in long standing
challenging games -- DeepBlue for Chess and AlphaGo for Go. Until recently,
this powerful framework of search aided with (learned) value functions has been
limited to perfect information games. As many interesting problems do not
provide the agent perfect information of the environment, this was an
unfortunate limitation. This thesis introduces the reader to sound search for
imperfect information games.
",Search in Imperfect Information Games
"  We take up an idea from the folklore of Answer Set Programming, namely that
choices, integrity constraints along with a restricted rule format is
sufficient for Answer Set Programming. We elaborate upon the foundations of
this idea in the context of the logic of Here-and-There and show how it can be
derived from the logical principle of extension by definition. We then provide
an austere form of logic programs that may serve as a normalform for logic
programs similar to conjunctive normalform in classical logic. Finally, we take
the key ideas and propose a modeling methodology for ASP beginners and
illustrate how it can be used.
",Answer Set Programming Made Easy
"  We revisit the issue of connections between two leading formalisms in
nonmonotonic reasoning: autoepistemic logic and default logic. For each logic
we develop a comprehensive semantic framework based on the notion of a belief
pair. The set of all belief pairs together with the so called knowledge
ordering forms a complete lattice. For each logic, we introduce several
semantics by means of fixpoints of operators on the lattice of belief pairs.
Our results elucidate an underlying isomorphism of the respective semantic
constructions. In particular, we show that the interpretation of defaults as
modal formulas proposed by Konolige allows us to represent all semantics for
default logic in terms of the corresponding semantics for autoepistemic logic.
Thus, our results conclusively establish that default logic can indeed be
viewed as a fragment of autoepistemic logic. However, as we also demonstrate,
the semantics of Moore and Reiter are given by different operators and occupy
different locations in their corresponding families of semantics. This result
explains the source of the longstanding difficulty to formally relate these two
semantics. In the paper, we also discuss approximating skeptical reasoning with
autoepistemic and default logics and establish constructive principles behind
such approximations.
",Uniform semantic treatment of default and autoepistemic logics
"  Randomized algorithms for deciding satisfiability were shown to be effective
in solving problems with thousands of variables. However, these algorithms are
not complete. That is, they provide no guarantee that a satisfying assignment,
if one exists, will be found. Thus, when studying randomized algorithms, there
are two important characteristics that need to be considered: the running time
and, even more importantly, the accuracy --- a measure of likelihood that a
satisfying assignment will be found, provided one exists. In fact, we argue
that without a reference to the accuracy, the notion of the running time for
randomized algorithms is not well-defined. In this paper, we introduce a formal
notion of accuracy. We use it to define a concept of the running time. We use
both notions to study the random walk strategy GSAT algorithm. We investigate
the dependence of accuracy on properties of input formulas such as
clause-to-variable ratio and the number of satisfying assignments. We
demonstrate that the running time of GSAT grows exponentially in the number of
variables of the input formula for randomly generated 3-CNF formulas and for
the formulas encoding 3- and 4-colorability of graphs.
",On the accuracy and running time of GSAT
"  Two different types of agency are discussed based on dynamically coherent and
incoherent couplings with an environment respectively. I propose that until a
private syntax (syntactic autonomy) is discovered by dynamically coherent
agents, there are no significant or interesting types of closure or autonomy.
When syntactic autonomy is established, then, because of a process of
description-based selected self-organization, open-ended evolution is enabled.
At this stage, agents depend, in addition to dynamics, on localized, symbolic
memory, thus adding a level of dynamical incoherence to their interaction with
the environment. Furthermore, it is the appearance of syntactic autonomy which
enables much more interesting types of closures amongst agents which share the
same syntax. To investigate how we can study the emergence of syntax from
dynamical systems, experiments with cellular automata leading to emergent
computation to solve non-trivial tasks are discussed. RNA editing is also
mentioned as a process that may have been used to obtain a primordial
biological code necessary open-ended evolution.
","Syntactic Autonomy: Why There is no Autonomy without Symbols and How
  Self-Organization Might Evolve Them"
"  This paper presents a method of computing a revision of a function-free
normal logic program. If an added rule is inconsistent with a program, that is,
if it leads to a situation such that no stable model exists for a new program,
then deletion and addition of rules are performed to avoid inconsistency. We
specify a revision by translating a normal logic program into an abductive
logic program with abducibles to represent deletion and addition of rules. To
compute such deletion and addition, we propose an adaptation of our top-down
abductive proof procedure to compute a relevant abducibles to an added rule. We
compute a minimally revised program, by choosing a minimal set of abducibles
among all the sets of abducibles computed by a top-down proof procedure.
","Consistency Management of Normal Logic Program by Top-down Abductive
  Proof Procedure"
"  This is a system description for the OSCAR defeasible reasoner.
",Defeasible Reasoning in OSCAR
"  Diagnostic reasoning has been characterized logically as consistency-based
reasoning or abductive reasoning. Previous analyses in the literature have
shown, on the one hand, that choosing the (in general more restrictive)
abductive definition may be appropriate or not, depending on the content of the
knowledge base [Console&Torasso91], and, on the other hand, that, depending on
the choice of the definition the same knowledge should be expressed in
different form [Poole94].
  Since in Model-Based Diagnosis a major problem is finding the right way of
abstracting the behavior of the system to be modeled, this paper discusses the
relation between modeling, and in particular abstraction in the model, and the
notion of diagnosis.
","Abductive and Consistency-Based Diagnosis Revisited: a Modeling
  Perspective"
"  ACLP is a system which combines abductive reasoning and constraint solving by
integrating the frameworks of Abductive Logic Programming (ALP) and Constraint
Logic Programming (CLP). It forms a general high-level knowledge representation
environment for abductive problems in Artificial Intelligence and other areas.
In ACLP, the task of abduction is supported and enhanced by its non-trivial
integration with constraint solving facilitating its application to complex
problems. The ACLP system is currently implemented on top of the CLP language
of ECLiPSe as a meta-interpreter exploiting its underlying constraint solver
for finite domains. It has been applied to the problems of planning and
scheduling in order to test its computational effectiveness compared with the
direct use of the (lower level) constraint solving framework of CLP on which it
is built. These experiments provide evidence that the abductive framework of
ACLP does not compromise significantly the computational efficiency of the
solutions. Other experiments show the natural ability of ACLP to accommodate
easily and in a robust way new or changing requirements of the original
problem.
",ACLP: Integrating Abduction and Constraint Solving
"  We present a method for relevance sensitive non-monotonic inference from
belief sequences which incorporates insights pertaining to prioritized
inference and relevance sensitive, inconsistency tolerant belief revision.
  Our model uses a finite, logically open sequence of propositional formulas as
a representation for beliefs and defines a notion of inference from
maxiconsistent subsets of formulas guided by two orderings: a temporal
sequencing and an ordering based on relevance relations between the conclusion
and formulas in the sequence. The relevance relations are ternary (using
context as a parameter) as opposed to standard binary axiomatizations. The
inference operation thus defined easily handles iterated revision by
maintaining a revision history, blocks the derivation of inconsistent answers
from a possibly inconsistent sequence and maintains the distinction between
explicit and implicit beliefs. In doing so, it provides a finitely presented
formalism and a plausible model of reasoning for automated agents.
",Relevance Sensitive Non-Monotonic Inference on Belief Sequences
"  We propose a combination of probabilistic reasoning from conditional
constraints with approaches to default reasoning from conditional knowledge
bases. In detail, we generalize the notions of Pearl's entailment in system Z,
Lehmann's lexicographic entailment, and Geffner's conditional entailment to
conditional constraints. We give some examples that show that the new notions
of z-, lexicographic, and conditional entailment have similar properties like
their classical counterparts. Moreover, we show that the new notions of z-,
lexicographic, and conditional entailment are proper generalizations of both
their classical counterparts and the classical notion of logical entailment for
conditional constraints.
",Probabilistic Default Reasoning with Conditional Constraints
"  This paper describes a system, called PLP, for compiling ordered logic
programs into standard logic programs under the answer set semantics. In an
ordered logic program, rules are named by unique terms, and preferences among
rules are given by a set of dedicated atoms. An ordered logic program is
transformed into a second, regular, extended logic program wherein the
preferences are respected, in that the answer sets obtained in the transformed
theory correspond with the preferred answer sets of the original theory. Since
the result of the translation is an extended logic program, existing logic
programming systems can be used as underlying reasoning engine. In particular,
PLP is conceived as a front-end to the logic programming systems dlv and
smodels.
",A Compiler for Ordered Logic Programs
"  The SLDNFA-system results from the LP+ project at the K.U.Leuven, which
investigates logics and proof procedures for these logics for declarative
knowledge representation. Within this project inductive definition logic
(ID-logic) is used as representation logic. Different solvers are being
developed for this logic and one of these is SLDNFA. A prototype of the system
is available and used for investigating how to solve efficiently problems
represented in ID-logic.
",SLDNFA-system
"  We describe an approach for compiling preferences into logic programs under
the answer set semantics. An ordered logic program is an extended logic program
in which rules are named by unique terms, and in which preferences among rules
are given by a set of dedicated atoms. An ordered logic program is transformed
into a second, regular, extended logic program wherein the preferences are
respected, in that the answer sets obtained in the transformed theory
correspond with the preferred answer sets of the original theory. Our approach
allows both the specification of static orderings (as found in most previous
work), in which preferences are external to a logic program, as well as
orderings on sets of rules. In large part then, we are interested in describing
a general methodology for uniformly incorporating preference information in a
logic program. Since the result of our translation is an extended logic
program, we can make use of existing implementations, such as dlv and smodels.
To this end, we have developed a compiler, available on the web, as a front-end
for these programming systems.
",Logic Programs with Compiled Preferences
"  This paper proposes two kinds of fuzzy abductive inference in the framework
of fuzzy rule base. The abductive inference processes described here depend on
the semantic of the rule. We distinguish two classes of interpretation of a
fuzzy rule, certainty generation rules and possible generation rules. In this
paper we present the architecture of abductive inference in the first class of
interpretation. We give two kinds of problem that we can resolve by using the
proposed models of inference.
",Fuzzy Approaches to Abductive Inference
"  The goal of the LP+ project at the K.U.Leuven is to design an expressive
logic, suitable for declarative knowledge representation, and to develop
intelligent systems based on Logic Programming technology for solving
computational problems using the declarative specifications. The ID-logic is an
integration of typed classical logic and a definition logic. Different
abductive solvers for this language are being developed. This paper is a report
of the integration of high order aggregates into ID-logic and the consequences
on the solver SLDNFA.
",Problem solving in ID-logic with aggregates: some experiments
"  We propose a new approach to belief revision that provides a way to change
knowledge bases with a minimum of effort. We call this way of revising belief
states optimal belief revision. Our revision method gives special attention to
the fact that most belief revision processes are directed to a specific
informational objective. This approach to belief change is founded on notions
such as optimal context and accessibility. For the sentential model of belief
states we provide both a formal description of contexts as sub-theories
determined by three parameters and a method to construct contexts. Next, we
introduce an accessibility ordering for belief sets, which we then use for
selecting the best (optimal) contexts with respect to the processing effort
involved in the revision. Then, for finitely axiomatizable knowledge bases, we
characterize a finite accessibility ranking from which the accessibility
ordering for the entire base is generated and show how to determine the ranking
of an arbitrary sentence in the language. Finally, we define the adjustment of
the accessibility ranking of a revised base of a belief set.
",Optimal Belief Revision
"  High-level robot controllers in realistic domains typically deal with
processes which operate concurrently, change the world continuously, and where
the execution of actions is event-driven as in ``charge the batteries as soon
as the voltage level is low''. While non-logic-based robot control languages
are well suited to express such scenarios, they fare poorly when it comes to
projecting, in a conspicuous way, how the world evolves when actions are
executed. On the other hand, a logic-based control language like \congolog,
based on the situation calculus, is well-suited for the latter. However, it has
problems expressing event-driven behavior. In this paper, we show how these
problems can be overcome by first extending the situation calculus to support
continuous change and event-driven behavior and then presenting \ccgolog, a
variant of \congolog which is based on the extended situation calculus. One
benefit of \ccgolog is that it narrows the gap in expressiveness compared to
non-logic-based control languages while preserving a semantically well-founded
projection mechanism.
",cc-Golog: Towards More Realistic Logic-Based Robot Controllers
"  The Smodels system implements the stable model semantics for normal logic
programs. It handles a subclass of programs which contain no function symbols
and are domain-restricted but supports extensions including built-in functions
as well as cardinality and weight constraints. On top of this core engine more
involved systems can be built. As an example, we have implemented total and
partial stable model computation for disjunctive logic programs. An interesting
application method is based on answer set programming, i.e., encoding an
application problem as a set of rules so that its solutions are captured by the
stable models of the rules. Smodels has been applied to a number of areas
including planning, model checking, reachability analysis, product
configuration, dynamic constraint satisfaction, and feature interaction.
",Smodels: A System for Answer Set Programming
"  E-RES is a system that implements the Language E, a logic for reasoning about
narratives of action occurrences and observations. E's semantics is
model-theoretic, but this implementation is based on a sound and complete
reformulation of E in terms of argumentation, and uses general computational
techniques of argumentation frameworks. The system derives sceptical
non-monotonic consequences of a given reformulated theory which exactly
correspond to consequences entailed by E's model-theory. The computation relies
on a complimentary ability of the system to derive credulous non-monotonic
consequences together with a set of supporting assumptions which is sufficient
for the (credulous) conclusion to hold. E-RES allows theories to contain
general action laws, statements about action occurrences, observations and
statements of ramifications (or universal laws). It is able to derive
consequences both forward and backward in time. This paper gives a short
overview of the theoretical basis of E-RES and illustrates its use on a variety
of examples. Currently, E-RES is being extended so that the system can be used
for planning.
","E-RES: A System for Reasoning about Actions, Events and Observations"
"  In this paper, we outline the prototype of an automated inference tool,
called QUIP, which provides a uniform implementation for several nonmonotonic
reasoning formalisms. The theoretical basis of QUIP is derived from well-known
results about the computational complexity of nonmonotonic logics and exploits
a representation of the different reasoning tasks in terms of quantified
boolean formulae.
",QUIP - A Tool for Computing Nonmonotonic Reasoning Tasks
"  Over the past decade a considerable amount of research has been done to
expand logic programming languages to handle incomplete information. One such
language is the language of epistemic specifications. As is usual with logic
programming languages, the problem of answering queries is intractable in the
general case. For extended disjunctive logic programs, an idea that has proven
useful in simplifying the investigation of answer sets is the use of splitting
sets. In this paper we will present an extended definition of splitting sets
that will be applicable to epistemic specifications. Furthermore, an extension
of the splitting set theorem will be presented. Also, a characterization of
stratified epistemic specifications will be given in terms of splitting sets.
This characterization leads us to an algorithmic method of computing world
views of a subclass of epistemic logic programs.
",A Splitting Set Theorem for Epistemic Specifications
"  The US Data Encryption Standard, DES for short, is put forward as an
interesting benchmark problem for nonmonotonic reasoning systems because (i) it
provides a set of test cases of industrial relevance which shares features of
randomly generated problems and real-world problems, (ii) the representation of
DES using normal logic programs with the stable model semantics is simple and
easy to understand, and (iii) this subclass of logic programs can be seen as an
interesting special case for many other formalizations of nonmonotonic
reasoning. In this paper we present two encodings of DES as logic programs: a
direct one out of the standard specifications and an optimized one extending
the work of Massacci and Marraro. The computational properties of the encodings
are studied by using them for DES key search with the Smodels system as the
implementation of the stable model semantics. Results indicate that the
encodings and Smodels are quite competitive: they outperform state-of-the-art
SAT-checkers working with an optimized encoding of DES into SAT and are
comparable with a SAT-checker that is customized and tuned for the optimized
SAT encoding.
",DES: a Challenge Problem for Nonmonotonic Reasoning Systems
"  We generalize a theorem by Francois Fages that describes the relationship
between the completion semantics and the answer set semantics for logic
programs with negation as failure. The study of this relationship is important
in connection with the emergence of answer set programming. Whenever the two
semantics are equivalent, answer sets can be computed by a satisfiability
solver, and the use of answer set solvers such as smodels and dlv is
unnecessary. A logic programming representation of the blocks world due to
Ilkka Niemelae is discussed as an example.
",Fages' Theorem and Answer Set Programming
"  We introduced decomposable negation normal form (DNNF) recently as a
tractable form of propositional theories, and provided a number of powerful
logical operations that can be performed on it in polynomial time. We also
presented an algorithm for compiling any conjunctive normal form (CNF) into
DNNF and provided a structure-based guarantee on its space and time complexity.
We present in this paper a linear-time algorithm for converting an ordered
binary decision diagram (OBDD) representation of a propositional theory into an
equivalent DNNF, showing that DNNFs scale as well as OBDDs. We also identify a
subclass of DNNF which we call deterministic DNNF, d-DNNF, and show that the
previous complexity guarantees on compiling DNNF continue to hold for this
stricter subclass, which has stronger properties. In particular, we present a
new operation on d-DNNF which allows us to count its models under the
assertion, retraction and flipping of every literal by traversing the d-DNNF
twice. That is, after such traversal, we can test in constant-time: the
entailment of any literal by the d-DNNF, and the consistency of the d-DNNF
under the retraction or flipping of any literal. We demonstrate the
significance of these new operations by showing how they allow us to implement
linear-time, complete truth maintenance systems and linear-time, complete
belief revision systems for two important classes of propositional theories.
","On the tractable counting of theory models and its application to belief
  revision and truth maintenance"
"  The paper reports on first preliminary results and insights gained in a
project aiming at implementing the fluent calculus using methods and techniques
based on binary decision diagrams. After reporting on an initial experiment
showing promising results we discuss our findings concerning various techniques
and heuristics used to speed up the reasoning process.
",BDD-based reasoning in the fluent calculus - first results
"  Planning is a natural domain of application for frameworks of reasoning about
actions and change. In this paper we study how one such framework, the Language
E, can form the basis for planning under (possibly) incomplete information. We
define two types of plans: weak and safe plans, and propose a planner, called
the E-Planner, which is often able to extend an initial weak plan into a safe
plan even though the (explicit) information available is incomplete, e.g. for
cases where the initial state is not completely known. The E-Planner is based
upon a reformulation of the Language E in argumentation terms and a natural
proof theory resulting from the reformulation. It uses an extension of this
proof theory by means of abduction for the generation of plans and adopts
argumentation-based techniques for extending weak plans into safe plans. We
provide representative examples illustrating the behaviour of the E-Planner, in
particular for cases where the status of fluents is incompletely known.
",Planning with Incomplete Information
"  In an earlier work, we have presented operations of belief change which only
affect the relevant part of a belief base. In this paper, we propose the
application of the same strategy to the problem of model-based diangosis. We
first isolate the subset of the system description which is relevant for a
given observation and then solve the diagnosis problem for this subset.
",Local Diagnosis
"  We present a general, consistency-based framework for belief change.
Informally, in revising K by A, we begin with A and incorporate as much of K as
consistently possible. Formally, a knowledge base K and sentence A are
expressed, via renaming propositions in K, in separate languages. Using a
maximization process, we assume the languages are the same insofar as
consistently possible. Lastly, we express the resultant knowledge base in a
single language. There may be more than one way in which A can be so extended
by K: in choice revision, one such ``extension'' represents the revised state;
alternately revision consists of the intersection of all such extensions.
  The most general formulation of our approach is flexible enough to express
other approaches to revision and update, the merging of knowledge bases, and
the incorporation of static and dynamic integrity constraints. Our framework
differs from work based on ordinal conditional functions, notably with respect
to iterated revision. We argue that the approach is well-suited for
implementation: the choice revision operator gives better complexity results
than general revision; the approach can be expressed in terms of a finite
knowledge base; and the scope of a revision can be restricted to just those
propositions mentioned in the sentence for revision A.
",A Consistency-Based Model for Belief Change: Preliminary Report
"  SATEN is an object-oriented web-based extraction and belief revision engine.
It runs on any computer via a Java 1.1 enabled browser such as Netscape 4.
SATEN performs belief revision based on the AGM approach. The extraction and
belief revision reasoning engines operate on a user specified ranking of
information. One of the features of SATEN is that it can be used to integrate
mutually inconsistent commensuate rankings into a consistent ranking.
",SATEN: An Object-Oriented Web-Based Revision and Extraction Engine
"  Answer-set programming (ASP) has emerged recently as a viable programming
paradigm. We describe here an ASP system, DATALOG with constraints or DC, based
on non-monotonic logic. Informally, DC theories consist of propositional
clauses (constraints) and of Horn rules. The semantics is a simple and natural
extension of the semantics of the propositional logic. However, thanks to the
presence of Horn rules in the system, modeling of transitive closure becomes
straightforward. We describe the syntax, use and implementation of DC and
provide experimental results.
",dcs: An Implementation of DATALOG with Constraints
"  Answer-set programming (ASP) has emerged recently as a viable programming
paradigm well attuned to search problems in AI, constraint satisfaction and
combinatorics. Propositional logic is, arguably, the simplest ASP system with
an intuitive semantics supporting direct modeling of problem constraints.
However, for some applications, especially those requiring that transitive
closure be computed, it requires additional variables and results in large
theories. Consequently, it may not be a practical computational tool for such
problems. On the other hand, ASP systems based on nonmonotonic logics, such as
stable logic programming, can handle transitive closure computation efficiently
and, in general, yield very concise theories as problem representations. Their
semantics is, however, more complex. Searching for the middle ground, in this
paper we introduce a new nonmonotonic logic, DATALOG with constraints or DC.
Informally, DC theories consist of propositional clauses (constraints) and of
Horn rules. The semantics is a simple and natural extension of the semantics of
the propositional logic. However, thanks to the presence of Horn rules in the
system, modeling of transitive closure becomes straightforward. We describe the
syntax and semantics of DC, and study its properties. We discuss an
implementation of DC and present results of experimental study of the
effectiveness of DC, comparing it with CSAT, a satisfiability checker and
SMODELS implementation of stable logic programming. Our results show that DC is
competitive with the other two approaches, in case of many search problems,
often yielding much more efficient solutions.
",DATALOG with constraints - an answer-set programming system
"  We study here the well-known propagation rules for Boolean constraints. First
we propose a simple notion of completeness for sets of such rules and establish
a completeness result. Then we show an equivalence in an appropriate sense
between Boolean constraint propagation and unit propagation, a form of
resolution for propositional logic.
  Subsequently we characterize one set of such rules by means of the notion of
hyper-arc consistency introduced in (Mohr and Masini 1988). Also, we clarify
the status of a similar, though different, set of rules introduced in (Simonis
1989a) and more fully in (Codognet and Diaz 1996).
",Some Remarks on Boolean Constraint Propagation
"  A general notion of algebraic conditional plausibility measures is defined.
Probability measures, ranking functions, possibility measures, and (under the
appropriate definitions) sets of probability measures can all be viewed as
defining algebraic conditional plausibility measures. It is shown that
algebraic conditional plausibility measures can be represented using Bayesian
networks.
",Conditional Plausibility Measures and Bayesian Networks
"  In this paper we present a rule based formalism for filtering variables
domains of constraints. This formalism is well adapted for solving dynamic CSP.
We take diagnosis as an instance problem to illustrate the use of these rules.
A diagnosis problem is seen like finding all the minimal sets of constraints to
be relaxed in the constraint network that models the device to be diagnosed
","Constraint compiling into rules formalism constraint compiling into
  rules formalism for dynamic CSPs computing"
"  Despite the effort of many researchers in the area of multi-agent systems
(MAS) for designing and programming agents, a few years ago the research
community began to take into account that common features among different MAS
exists. Based on these common features, several tools have tackled the problem
of agent development on specific application domains or specific types of
agents. As a consequence, their scope is restricted to a subset of the huge
application domain of MAS. In this paper we propose a generic infrastructure
for programming agents whose name is Brainstorm/J. The infrastructure has been
implemented as an object oriented framework. As a consequence, our approach
supports a broader scope of MAS applications than previous efforts, being
flexible and reusable.
",Brainstorm/J: a Java Framework for Intelligent Agents
"  In fuzzy propositional logic, to a proposition a partial truth in [0,1] is
assigned. It is well known that under certain circumstances, fuzzy logic
collapses to classical logic. In this paper, we will show that under dual
conditions, fuzzy logic collapses to four-valued (relevance) logic, where
propositions have truth-value true, false, unknown, or contradiction. As a
consequence, fuzzy entailment may be considered as ``in between'' four-valued
(relevance) entailment and classical entailment.
",On the relationship between fuzzy logic and four-valued relevance logic
"  We propose a new definition of actual cause, using structural equations to
model counterfactuals. We show that the definition yields a plausible and
elegant account of causation that handles well examples which have caused
problems for other definitions and resolves major difficulties in the
traditional account.
","Causes and Explanations: A Structural-Model Approach, Part I: Causes"
"  Many logic programming based approaches can be used to describe and solve
combinatorial search problems. On the one hand there is constraint logic
programming which computes a solution as an answer substitution to a query
containing the variables of the constraint satisfaction problem. On the other
hand there are systems based on stable model semantics, abductive systems, and
first order logic model generators which compute solutions as models of some
theory. This paper compares these different approaches from the point of view
of knowledge representation (how declarative are the programs) and from the
point of view of performance (how good are they at solving typical problems).
","Logic Programming Approaches for Representing and Solving Constraint
  Satisfaction Problems: A Comparison"
"  In this paper, we introduce a new machine learning theory based on
multi-channel parallel adaptation for rule discovery. This theory is
distinguished from the familiar parallel-distributed adaptation theory of
neural networks in terms of channel-based convergence to the target rules. We
show how to realize this theory in a learning system named CFRule. CFRule is a
parallel weight-based model, but it departs from traditional neural computing
in that its internal knowledge is comprehensible. Furthermore, when the model
converges upon training, each channel converges to a target rule. The model
adaptation rule is derived by multi-level parallel weight optimization based on
gradient descent. Since, however, gradient descent only guarantees local
optimization, a multi-channel regression-based optimization strategy is
developed to effectively deal with this problem. Formally, we prove that the
CFRule model can explicitly and precisely encode any given rule set. Also, we
prove a property related to asynchronous parallel convergence, which is a
critical element of the multi-channel parallel adaptation theory for rule
learning. Thanks to the quantizability nature of the CFRule model, rules can be
extracted completely and soundly via a threshold-based mechanism. Finally, the
practical application of the theory is demonstrated in DNA promoter recognition
and hepatitis prognosis prediction.
",Multi-Channel Parallel Adaptation Theory for Rule Discovery
"  We present an approach for modelling the structure and coarse content of
legal documents with a view to providing automated support for the drafting of
contracts and contract database retrieval. The approach is designed to be
applicable where contract drafting is based on model-form contracts or on
existing examples of a similar type. The main features of the approach are: (1)
the representation addresses the structure and the interrelationships between
the constituent parts of contracts, but not the text of the document itself;
(2) the representation of documents is separated from the mechanisms that
manipulate it; and (3) the drafting process is subject to a collection of
explicitly stated constraints that govern the structure of the documents. We
describe the representation of document instances and of 'generic documents',
which are data structures used to drive the creation of new document instances,
and we show extracts from a sample session to illustrate the features of a
prototype system implemented in MacProlog.
",A Constraint-Driven System for Contract Assembly
"  One influential approach to assessing the ""goodness"" of arguments is offered
by the Pragma-Dialectical school (p-d) (Eemeren & Grootendorst 1992). This can
be compared with Rhetorical Structure Theory (RST) (Mann & Thompson 1988), an
approach that originates in discourse analysis. In p-d terms an argument is
good if it avoids committing a fallacy, whereas in RST terms an argument is
good if it is coherent. RST has been criticised (Snoeck Henkemans 1997) for
providing only a partially functional account of argument, and similar
criticisms have been raised in the Natural Language Generation (NLG)
community-particularly by Moore & Pollack (1992)- with regards to its account
of intentionality in text in general. Mann and Thompson themselves note that
although RST can be successfully applied to a wide range of texts from diverse
domains, it fails to characterise some types of text, most notably legal
contracts. There is ongoing research in the Artificial Intelligence and Law
community exploring the potential for providing electronic support to contract
negotiators, focusing on long-term, complex engineering agreements (see for
example Daskalopulu & Sergot 1997). This paper provides a brief introduction to
RST and illustrates its shortcomings with respect to contractual text. An
alternative approach for modelling argument structure is presented which not
only caters for contractual text, but also overcomes the aforementioned
limitations of RST.
",Modelling Contractual Arguments
"  Information Integration is a young and exciting field with enormous research
and commercial significance in the new world of the Information Society. It
stands at the crossroad of Databases and Artificial Intelligence requiring
novel techniques that bring together different methods from these fields.
Information from disparate heterogeneous sources often with no a-priori common
schema needs to be synthesized in a flexible, transparent and intelligent way
in order to respond to the demands of a query thus enabling a more informed
decision by the user or application program. The field although relatively
young has already found many practical applications particularly for
integrating information over the World Wide Web. This paper gives a brief
introduction of the field highlighting some of the main current and future
research issues and application areas. It attempts to evaluate the current and
potential role of Computational Logic in this and suggests some of the problems
where logic-based techniques could be used.
",Information Integration and Computational Logic
"  Constraint propagation is a general algorithmic approach for pruning the
search space of a CSP. In a uniform way, K. R. Apt has defined a computation as
an iteration of reduction functions over a domain. He has also demonstrated the
need for integrating static properties of reduction functions (commutativity
and semi-commutativity) to design specialized algorithms such as AC3 and DAC.
We introduce here a set of operators for modeling compositions of reduction
functions. Two of the major goals are to tackle parallel computations, and
dynamic behaviours (such as slow convergence).
",Enhancing Constraint Propagation with Composition Operators
"  We consider an approach to update nonmonotonic knowledge bases represented as
extended logic programs under answer set semantics. New information is
incorporated into the current knowledge base subject to a causal rejection
principle enforcing that, in case of conflicts, more recent rules are preferred
and older rules are overridden. Such a rejection principle is also exploited in
other approaches to update logic programs, e.g., in dynamic logic programming
by Alferes et al. We give a thorough analysis of properties of our approach, to
get a better understanding of the causal rejection principle. We review
postulates for update and revision operators from the area of theory change and
nonmonotonic reasoning, and some new properties are considered as well. We then
consider refinements of our semantics which incorporate a notion of minimality
of change. As well, we investigate the relationship to other approaches,
showing that our approach is semantically equivalent to inheritance programs by
Buccafurri et al. and that it coincides with certain classes of dynamic logic
programs, for which we provide characterizations in terms of graph conditions.
Therefore, most of our results about properties of causal rejection principle
apply to these approaches as well. Finally, we deal with computational
complexity of our approach, and outline how the update semantics and its
refinements can be implemented on top of existing logic programming engines.
",On Properties of Update Sequences Based on Causal Rejection
"  We introduce a learning method called ``gradient-based reinforcement
planning'' (GREP). Unlike traditional DP methods that improve their policy
backwards in time, GREP is a gradient-based method that plans ahead and
improves its policy before it actually acts in the environment. We derive
formulas for the exact policy gradient that maximizes the expected future
reward and confirm our ideas with numerical experiments.
",Gradient-based Reinforcement Planning in Policy-Search Methods
"  Much work in computer science has adopted competitive analysis as a tool for
decision making under uncertainty. In this work we extend competitive analysis
to the context of multi-agent systems. Unlike classical competitive analysis
where the behavior of an agent's environment is taken to be arbitrary, we
consider the case where an agent's environment consists of other agents. These
agents will usually obey some (minimal) rationality constraints. This leads to
the definition of rational competitive analysis. We introduce the concept of
rational competitive analysis, and initiate the study of competitive analysis
for multi-agent systems. We also discuss the application of rational
competitive analysis to the context of bidding games, as well as to the
classical one-way trading problem.
",Rational Competitive Analysis
"  This article aims at clarifying the language and practice of scientific
experiment, mainly by hooking observability on calculability.
",A theory of experiment
"  Many systems that exhibit nonmonotonic behavior have been described and
studied already in the literature. The general notion of nonmonotonic
reasoning, though, has almost always been described only negatively, by the
property it does not enjoy, i.e. monotonicity. We study here general patterns
of nonmonotonic reasoning and try to isolate properties that could help us map
the field of nonmonotonic reasoning by reference to positive properties. We
concentrate on a number of families of nonmonotonic consequence relations,
defined in the style of Gentzen. Both proof-theoretic and semantic points of
view are developed in parallel. The former point of view was pioneered by D.
Gabbay, while the latter has been advocated by Y. Shoham in. Five such families
are defined and characterized by representation theorems, relating the two
points of view. One of the families of interest, that of preferential
relations, turns out to have been studied by E. Adams. The ""preferential""
models proposed here are a much stronger tool than Adams' probabilistic
semantics. The basic language used in this paper is that of propositional
logic. The extension of our results to first order predicate calculi and the
study of the computational complexity of the decision problems described in
this paper will be treated in another paper.
","Nonmonotonic Reasoning, Preferential Models and Cumulative Logics"
"  This paper presents a logical approach to nonmonotonic reasoning based on the
notion of a nonmonotonic consequence relation. A conditional knowledge base,
consisting of a set of conditional assertions of the type ""if ... then ..."",
represents the explicit defeasible knowledge an agent has about the way the
world generally behaves. We look for a plausible definition of the set of all
conditional assertions entailed by a conditional knowledge base. In a previous
paper, S. Kraus and the authors defined and studied ""preferential"" consequence
relations. They noticed that not all preferential relations could be considered
as reasonable inference procedures. This paper studies a more restricted class
of consequence relations, ""rational"" relations. It is argued that any
reasonable nonmonotonic inference procedure should define a rational relation.
It is shown that the rational relations are exactly those that may be
represented by a ""ranked"" preferential model, or by a (non-standard)
probabilistic model. The rational closure of a conditional knowledge base is
defined and shown to provide an attractive answer to the question of the title.
Global properties of this closure operation are proved: it is a cumulative
operation. It is also computationally tractable. This paper assumes the
underlying language is propositional.
",What does a conditional knowledge base entail?
"  It is shown that Darwiche and Pearl's postulates imply an interesting
property, not noticed by the authors.
",A note on Darwiche and Pearl
"  A vast and interesting family of natural semantics for belief revision is
defined. Suppose one is given a distance d between any two models. One may then
define the revision of a theory K by a formula a as the theory defined by the
set of all those models of a that are closest, by d, to the set of models of K.
This family is characterized by a set of rationality postulates that extends
the AGM postulates. The new postulates describe properties of iterated
revisions.
",Distance Semantics for Belief Revision
"  We give a semantics to iterated update by a preference relation on possible
developments. An iterated update is a sequence of formulas, giving (incomplete)
information about successive states of the world. A development is a sequence
of models, describing a possible trajectory through time. We assume a principle
of inertia and prefer those developments, which are compatible with the
information, and avoid unnecessary changes. The logical properties of the
updates defined in this way are considered, and a representation result is
proved.
",Preferred History Semantics for Iterated Updates
"  A. Tarski proposed the study of infinitary consequence operations as the
central topic of mathematical logic. He considered monotonicity to be a
property of all such operations. In this paper, we weaken the monotonicity
requirement and consider more general operations, inference operations. These
operations describe the nonmonotonic logics both humans and machines seem to be
using when infering defeasible information from incomplete knowledge. We single
out a number of interesting families of inference operations. This study of
infinitary inference operations is inspired by the results of Kraus, Lehmann
and Magidor on finitary nonmonotonic operations, but this paper is
self-contained.
",Nonmonotonic inference operations
"  The Expansion property considered by researchers in Social Choice is shown to
correspond to a logical property of nonmonotonic consequence relations that is
the {\em pure}, i.e., not involving connectives, version of a previously known
weak rationality condition. The assumption that the union of two definable sets
of models is definable is needed for the soundness part of the result.
",The logical meaning of Expansion
"  The lexicographic closure of any given finite set D of normal defaults is
defined. A conditional assertion ""if a then b"" is in this lexicographic closure
if, given the defaults D and the fact a, one would conclude b. The
lexicographic closure is essentially a rational extension of D, and of its
rational closure, defined in a previous paper. It provides a logic of normal
defaults that is different from the one proposed by R. Reiter and that is rich
enough not to require the consideration of non-normal defaults. A large number
of examples are provided to show that the lexicographic closure corresponds to
the basic intuitions behind Reiter's logic of defaults.
",Another perspective on Default Reasoning
"  We provide a characterization of those nonmonotonic inference operations C
for which C(X) may be described as the set of all logical consequences of X
together with some set of additional assumptions S(X) that depends
anti-monotonically on X (i.e., X is a subset of Y implies that S(Y) is a subset
of S(X)). The operations represented are exactly characterized in terms of
properties most of which have been studied in Freund-Lehmann(cs.AI/0202031).
Similar characterizations of right-absorbing and cumulative operations are also
provided. For cumulative operations, our results fit in closely with those of
Freund. We then discuss extending finitary operations to infinitary operations
in a canonical way and discuss co-compactness properties. Our results provide a
satisfactory notion of pseudo-compactness, generalizing to deductive
nonmonotonic operations the notion of compactness for monotonic operations.
They also provide an alternative, more elegant and more general, proof of the
existence of an infinitary deductive extension for any finitary deductive
operation (Theorem 7.9 of Freund-Lehmann).
",Deductive Nonmonotonic Inference Operations: Antitonic Representations
"  Stereotypical reasoning assumes that the situation at hand is one of a kind
and that it enjoys the properties generally associated with that kind of
situation. It is one of the most basic forms of nonmonotonic reasoning. A
formal model for stereotypical reasoning is proposed and the logical properties
of this form of reasoning are studied. Stereotypical reasoning is shown to be
cumulative under weak assumptions.
",Stereotypical Reasoning: Logical Properties
"  We introduce a methodology and framework for expressing general preference
information in logic programming under the answer set semantics. An ordered
logic program is an extended logic program in which rules are named by unique
terms, and in which preferences among rules are given by a set of atoms of form
s < t where s and t are names. An ordered logic program is transformed into a
second, regular, extended logic program wherein the preferences are respected,
in that the answer sets obtained in the transformed program correspond with the
preferred answer sets of the original program. Our approach allows the
specification of dynamic orderings, in which preferences can appear arbitrarily
within a program. Static orderings (in which preferences are external to a
logic program) are a trivial restriction of the general dynamic case. First, we
develop a specific approach to reasoning with preferences, wherein the
preference ordering specifies the order in which rules are to be applied. We
then demonstrate the wide range of applicability of our framework by showing
how other approaches, among them that of Brewka and Eiter, can be captured
within our framework. Since the result of each of these transformations is an
extended logic program, we can make use of existing implementations, such as
dlv and smodels. To this end, we have developed a publicly available compiler
as a front-end for these programming systems.
",A Framework for Compiling Preferences in Logic Programs
"  Prioritized default reasoning has illustrated its rich expressiveness and
flexibility in knowledge representation and reasoning. However, many important
aspects of prioritized default reasoning have yet to be thoroughly explored. In
this paper, we investigate two properties of prioritized logic programs in the
context of answer set semantics. Specifically, we reveal a close relationship
between mutual defeasibility and uniqueness of the answer set for a prioritized
logic program. We then explore how the splitting technique for extended logic
programs can be extended to prioritized logic programs. We prove splitting
theorems that can be used to simplify the evaluation of a prioritized logic
program under certain conditions.
",Two results for proiritized logic programming
"  The (extended) AGM postulates for belief revision seem to deal with the
revision of a given theory K by an arbitrary formula, but not to constrain the
revisions of two different theories by the same formula. A new postulate is
proposed and compared with other similar postulates that have been proposed in
the literature. The AGM revisions that satisfy this new postulate stand in
one-to-one correspondence with the rational, consistency-preserving relations.
This correspondence is described explicitly. Two viewpoints on iterative
revisions are distinguished and discussed.
",Belief Revision and Rational Inference
"  We study fixpoints of operators on lattices. To this end we introduce the
notion of an approximation of an operator. We order approximations by means of
a precision ordering. We show that each lattice operator O has a unique most
precise or ultimate approximation. We demonstrate that fixpoints of this
ultimate approximation provide useful insights into fixpoints of the operator
O.
  We apply our theory to logic programming and introduce the ultimate
Kripke-Kleene, well-founded and stable semantics. We show that the ultimate
Kripke-Kleene and well-founded semantics are more precise then their standard
counterparts We argue that ultimate semantics for logic programming have
attractive epistemological properties and that, while in general they are
computationally more complex than the standard semantics, for many classes of
theories, their complexity is no worse.
",Ultimate approximations in nonmonotonic knowledge representation systems
"  Representing defeasibility is an important issue in common sense reasoning.
In reasoning about action and change, this issue becomes more difficult because
domain and action related defeasible information may conflict with general
inertia rules. Furthermore, different types of defeasible information may also
interfere with each other during the reasoning. In this paper, we develop a
prioritized logic programming approach to handle defeasibilities in reasoning
about action. In particular, we propose three action languages {\cal AT}^{0},
{\cal AT}^{1} and {\cal AT}^{2} which handle three types of defeasibilities in
action domains named defeasible constraints, defeasible observations and
actions with defeasible and abnormal effects respectively. Each language with a
higher superscript can be viewed as an extension of the language with a lower
superscript. These action languages inherit the simple syntax of {\cal A}
language but their semantics is developed in terms of transition systems where
transition functions are defined based on prioritized logic programs. By
illustrating various examples, we show that our approach eventually provides a
powerful mechanism to handle various defeasibilities in temporal prediction and
postdiction. We also investigate semantic properties of these three action
languages and characterize classes of action domains that present more
desirable solutions in reasoning about action within the underlying action
languages.
",Handling Defeasibilities in Action Domains
"  An anticipatory system for guiding plot development in interactive narratives
is described. The executable model is a finite automaton that provides the
implemented system with a look-ahead. The identification of undesirable future
states in the model is used to guide the player, in a transparent manner. In
this way, too radical twists of the plot can be avoided. Since the player
participates in the development of the plot, such guidance can have many forms,
depending on the environment of the player, on the behavior of the other
players, and on the means of player interaction. We present a design method for
interactive narratives which produces designs suitable for the implementation
of anticipatory mechanisms. Use of the method is illustrated by application to
our interactive computer game Kaktus.
",Anticipatory Guidance of Plot
"  Open logic programs and open entailment have been recently proposed as an
abstract framework for the verification of incomplete specifications based upon
normal logic programs and the stable model semantics. There are obvious
analogies between open predicates and abducible predicates. However, despite
superficial similarities, there are features of open programs that have no
immediate counterpart in the framework of abduction and viceversa. Similarly,
open programs cannot be immediately simulated with answer set programming
(ASP). In this paper we start a thorough investigation of the relationships
between open inference, abduction and ASP. We shall prove that open programs
generalize the other two frameworks. The generalized framework suggests
interesting extensions of abduction under the generalized stable model
semantics. In some cases, we will be able to reduce open inference to abduction
and ASP, thereby estimating its computational complexity. At the same time, the
aforementioned reduction opens the way to new applications of abduction and
ASP.
","Abduction, ASP and Open Logic Programs"
"  In this paper we consider three different kinds of domain-dependent control
knowledge (temporal, procedural and HTN-based) that are useful in planning. Our
approach is declarative and relies on the language of logic programming with
answer set semantics (AnsProlog*). AnsProlog* is designed to plan without
control knowledge. We show how temporal, procedural and HTN-based control
knowledge can be incorporated into AnsProlog* by the modular addition of a
small number of domain-dependent rules, without the need to modify the planner.
We formally prove the correctness of our planner, both in the absence and
presence of the control knowledge. Finally, we perform some initial
experimentation that demonstrates the potential reduction in planning time that
can be achieved when procedural domain knowledge is used to solve planning
problems with large plan length.
",Domain-Dependent Knowledge in Answer Set Planning
"  Dung's abstract framework for argumentation enables a study of the
interactions between arguments based solely on an ``attack'' binary relation on
the set of arguments. Various ways to solve conflicts between contradictory
pieces of information have been proposed in the context of argumentation,
nonmonotonic reasoning or logic programming, and can be captured by appropriate
semantics within Dung's framework. A common feature of these semantics is that
one can always maximize in some sense the set of acceptable arguments. We
propose in this paper to extend Dung's framework in order to allow for the
representation of what we call ``restricted'' arguments: these arguments should
only be used if absolutely necessary, that is, in order to support other
arguments that would otherwise be defeated. We modify Dung's preferred
semantics accordingly: a set of arguments becomes acceptable only if it
contains a minimum of restricted arguments, for a maximum of unrestricted
arguments.
","""Minimal defence"": a refinement of the preferred semantics for
  argumentation frameworks"
"  We address a general representation problem for belief change, and describe
two interrelated representations for iterative non-prioritized change: a
logical representation in terms of persistent epistemic states, and a
constructive representation in terms of flocks of bases.
",Two Representations for Iterative Non-prioritized Change
"  An extension of an abstract argumentation framework, called collective
argumentation, is introduced in which the attack relation is defined directly
among sets of arguments. The extension turns out to be suitable, in particular,
for representing semantics of disjunctive logic programs. Two special kinds of
collective argumentation are considered in which the opponents can share their
arguments.
",Collective Argumentation
"  Logic programs with ordered disjunction (LPODs) combine ideas underlying
Qualitative Choice Logic (Brewka et al. KR 2002) and answer set programming.
Logic programming under answer set semantics is extended with a new connective
called ordered disjunction. The new connective allows us to represent
alternative, ranked options for problem solutions in the heads of rules: A
\times B intuitively means: if possible A, but if A is not possible then at
least B. The semantics of logic programs with ordered disjunction is based on a
preference relation on answer sets. LPODs are useful for applications in design
and configuration and can serve as a basis for qualitative decision making.
",Logic Programming with Ordered Disjunction
"  In this paper, we investigate the extent to which knowledge compilation can
be used to improve inference from propositional weighted bases. We present a
general notion of compilation of a weighted base that is parametrized by any
equivalence--preserving compilation function. Both negative and positive
results are presented. On the one hand, complexity results are identified,
showing that the inference problem from a compiled weighted base is as
difficult as in the general case, when the prime implicates, Horn cover or
renamable Horn cover classes are targeted. On the other hand, we show that the
inference problem becomes tractable whenever DNNF-compilations are used and
clausal queries are considered. Moreover, we show that the set of all preferred
models of a DNNF-compilation of a weighted base can be computed in time
polynomial in the output size. Finally, we sketch how our results can be used
in model-based diagnosis in order to compute the most probable diagnoses of a
system.
",Compilation of Propositional Weighted Bases
"  This paper studies the problem of modeling complex domains of actions and
change within high-level action description languages. We investigate two main
issues of concern: (a) can we represent complex domains that capture together
different problems such as ramifications, non-determinism and concurrency of
actions, at a high-level, close to the given natural ontology of the problem
domain and (b) what features of such a representation can affect, and how, its
computational behaviour. The paper describes the main problems faced in this
representation task and presents the results of an empirical study, carried out
through a series of controlled experiments, to analyze the computational
performance of reasoning in these representations. The experiments compare
different representations obtained, for example, by changing the basic ontology
of the domain or by varying the degree of use of indirect effect laws through
domain constraints. This study has helped to expose the main sources of
computational difficulty in the reasoning and suggest some methodological
guidelines for representing complex domains. Although our work has been carried
out within one particular high-level description language, we believe that the
results, especially those that relate to the problems of representation, are
independent of the specific modeling language.
",Modeling Complex Domains of Actions and Change
"  This paper introduces the notion of value-based argumentation frameworks, an
extension of the standard argumentation frameworks proposed by Dung, which are
able toshow how rational decision is possible in cases where arguments derive
their force from the social values their acceptance would promote.
",Value Based Argumentation Frameworks
"  We analyze the problem of defining well-founded semantics for ordered logic
programs within a general framework based on alternating fixpoint theory. We
start by showing that generalizations of existing answer set approaches to
preference are too weak in the setting of well-founded semantics. We then
specify some informal yet intuitive criteria and propose a semantical framework
for preference handling that is more suitable for defining well-founded
semantics for ordered logic programs. The suitability of the new approach is
convinced by the fact that many attractive properties are satisfied by our
semantics. In particular, our semantics is still correct with respect to
various existing answer sets semantics while it successfully overcomes the
weakness of their generalization to well-founded semantics. Finally, we
indicate how an existing preferred well-founded semantics can be captured
within our semantical framework.
","Preferred well-founded semantics for logic programming by alternating
  fixpoints: Preliminary report"
"  In this paper we present a transformation of finite propositional default
theories into so-called propositional argumentation systems. This
transformation allows to characterize all notions of Reiter's default logic in
the framework of argumentation systems. As a consequence, computing extensions,
or determining wether a given formula belongs to one extension or all
extensions can be answered without leaving the field of classical propositional
logic. The transformation proposed is linear in the number of defaults.
",Embedding Default Logic in Propositional Argumentation Systems
"  In the present paper, the existence and multiplicity problems of extensions
are addressed. The focus is on extension of the stable type. The main result of
the paper is an elegant characterization of the existence and multiplicity of
extensions in terms of the notion of dialectical justification, a close cousin
of the notion of admissibility. The characterization is given in the context of
the particular logic for dialectical argumentation DEFLOG. The results are of
direct relevance for several well-established models of defeasible reasoning
(like default logic, logic programming and argumentation frameworks), since
elsewhere dialectical argumentation has been shown to have close formal
connections with these models.
","On the existence and multiplicity of extensions in dialectical
  argumentation"
"  Recently, it has been shown that probabilistic entailment under coherence is
weaker than model-theoretic probabilistic entailment. Moreover, probabilistic
entailment under coherence is a generalization of default entailment in System
P. In this paper, we continue this line of research by presenting probabilistic
generalizations of more sophisticated notions of classical default entailment
that lie between model-theoretic probabilistic entailment and probabilistic
entailment under coherence. That is, the new formalisms properly generalize
their counterparts in classical default reasoning, they are weaker than
model-theoretic probabilistic entailment, and they are stronger than
probabilistic entailment under coherence. The new formalisms are useful
especially for handling probabilistic inconsistencies related to conditioning
on zero events. They can also be applied for probabilistic belief revision.
More generally, in the same spirit as a similar previous paper, this paper
sheds light on exciting new formalisms for probabilistic reasoning beyond the
well-known standard ones.
","Nonmonotonic Probabilistic Logics between Model-Theoretic Probabilistic
  Logic and Probabilistic Logic under Coherence"
"  We seek to find normative criteria of adequacy for nonmonotonic logic similar
to the criterion of validity for deductive logic. Rather than stipulating that
the conclusion of an inference be true in all models in which the premises are
true, we require that the conclusion of a nonmonotonic inference be true in
``almost all'' models of a certain sort in which the premises are true. This
``certain sort'' specification picks out the models that are relevant to the
inference, taking into account factors such as specificity and vagueness, and
previous inferences. The frequencies characterizing the relevant models reflect
known frequencies in our actual world. The criteria of adequacy for a default
inference can be extended by thresholding to criteria of adequacy for an
extension. We show that this avoids the implausibilities that might otherwise
result from the chaining of default inferences. The model proportions, when
construed in terms of frequencies, provide a verifiable grounding of default
rules, and can become the basis for generating default rules from statistics.
",Evaluating Defaults
"  About ten years ago, various notions of preferential entailment have been
introduced. The main reference is a paper by Kraus, Lehmann and Magidor (KLM),
one of the main competitor being a more general version defined by Makinson
(MAK). These two versions have already been compared, but it is time to revisit
these comparisons. Here are our three main results: (1) These two notions are
equivalent, provided that we restrict our attention, as done in KLM, to the
cases where the entailment respects logical equivalence (on the left and on the
right). (2) A serious simplification of the description of the fundamental
cases in which MAK is equivalent to KLM, including a natural passage in both
ways. (3) The two previous results are given for preferential entailments more
general than considered in some of the original texts, but they apply also to
the original definitions and, for this particular case also, the models can be
simplified.
",Linking Makinson and Kraus-Lehmann-Magidor preferential entailments
"  This work analyses main features that should be present in knowledge
representation. It suggests a model for representation and a way to implement
this model in software. Representation takes care of both low-level sensor
information and high-level concepts.
",Knowledge Representation
"  We propose new definitions of (causal) explanation, using structural
equations to model counterfactuals. The definition is based on the notion of
actual cause, as defined and motivated in a companion paper. Essentially, an
explanation is a fact that is not known for certain but, if found to be true,
would constitute an actual cause of the fact to be explained, regardless of the
agent's initial uncertainty. We show that the definition handles well a number
of problematic examples from the literature.
","Causes and Explanations: A Structural-Model Approach. Part II:
  Explanations"
"  Recently, several approaches to updating knowledge bases modeled as extended
logic programs have been introduced, ranging from basic methods to incorporate
(sequences of) sets of rules into a logic program, to more elaborate methods
which use an update policy for specifying how updates must be incorporated. In
this paper, we introduce a framework for reasoning about evolving knowledge
bases, which are represented as extended logic programs and maintained by an
update policy. We first describe a formal model which captures various update
approaches, and we define a logical language for expressing properties of
evolving knowledge bases. We then investigate semantical and computational
properties of our framework, where we focus on properties of knowledge states
with respect to the canonical reasoning task of whether a given formula holds
on a given evolving knowledge base. In particular, we present finitary
characterizations of the evolution for certain classes of framework instances,
which can be exploited for obtaining decidability results. In more detail, we
characterize the complexity of reasoning for some meaningful classes of
evolving knowledge bases, ranging from polynomial to double exponential space
complexity.
",Reasoning about Evolving Nonmonotonic Knowledge Bases
"  In this thesis I present a virtual laboratory which implements five different
models for controlling animats: a rule-based system, a behaviour-based system,
a concept-based system, a neural network, and a Braitenberg architecture.
Through different experiments, I compare the performance of the models and
conclude that there is no ""best"" model, since different models are better for
different things in different contexts.
  The models I chose, although quite simple, represent different approaches for
studying cognition. Using the results as an empirical philosophical aid,
  I note that there is no ""best"" approach for studying cognition, since
different approaches have all advantages and disadvantages, because they study
different aspects of cognition from different contexts. This has implications
for current debates on ""proper"" approaches for cognition: all approaches are a
bit proper, but none will be ""proper enough"". I draw remarks on the notion of
cognition abstracting from all the approaches used to study it, and propose a
simple classification for different types of cognition.
","A Comparison of Different Cognitive Paradigms Using Simple Animats in a
  Virtual Laboratory, with Implications to the Notion of Cognition"
"  This paper deals with the revision of partially ordered beliefs. It proposes
a semantic representation of epistemic states by partial pre-orders on
interpretations and a syntactic representation by partially ordered belief
bases. Two revision operations, the revision stemming from the history of
observations and the possibilistic revision, defined when the epistemic state
is represented by a total pre-order, are generalized, at a semantic level, to
the case of a partial pre-order on interpretations, and at a syntactic level,
to the case of a partially ordered belief base. The equivalence between the two
representations is shown for the two revision operations.
",Revising Partially Ordered Beliefs
"  This is the first in a series of connected papers discussing the problem of a
dynamically reconfigurable universal learning neurocomputer that could serve as
a computational model for the whole human brain. The whole series is entitled
""The Brain Zero Project. My Brain as a Dynamically Reconfigurable Universal
Learning Neurocomputer."" (For more information visit the website
www.brain0.com.) This introductory paper is concerned with general methodology.
Its main goal is to explain why it is critically important for both neural
modeling and cognitive modeling to pay much attention to the basic requirements
of the whole brain as a complex computing system. The author argues that it can
be easier to develop an adequate computational model for the whole
""unprogrammed"" (untrained) human brain than to find adequate formal
representations of some nontrivial parts of brain's performance. (In the same
way as, for example, it is easier to describe the behavior of a complex
analytical function than the behavior of its real and/or imaginary part.) The
""curse of dimensionality"" that plagues purely phenomenological (""brainless"")
cognitive theories is a natural penalty for an attempt to represent
insufficiently large parts of brain's performance in a state space of
insufficiently high dimensionality. A ""partial"" modeler encounters ""Catch 22.""
An attempt to simplify a cognitive problem by artificially reducing its
dimensionality makes the problem more difficult.
","Can the whole brain be simpler than its ""parts""?"
"  As a part of our effort for studying the evolution and development of
cognition, we present results derived from synthetic experimentations in a
virtual laboratory where animats develop koncepts adaptively and ground their
meaning through action. We introduce the term ""koncept"" to avoid confusions and
ambiguity derived from the wide use of the word ""concept"". We present the
models which our animats use for abstracting koncepts from perceptions,
plastically adapt koncepts, and associate koncepts with actions. On a more
philosophical vein, we suggest that knowledge is a property of a cognitive
system, not an element, and therefore observer-dependent.
","Adaptive Development of Koncepts in Virtual Animats: Insights into the
  Development of Knowledge"
"  This paper presents a model for dynamic adjustment of the motivation degree,
using a reinforcement learning approach, in an action selection mechanism
previously developed by the authors. The learning takes place in the
modification of a parameter of the model of combination of internal and
external stimuli. Experiments that show the claimed properties are presented,
using a VR simulation developed for such purposes. The importance of adaptation
by learning in action selection is also discussed.
","Dynamic Adjustment of the Motivation Degree in an Action Selection
  Mechanism"
"  This article analyses the properties of the Internal Behaviour network, an
action selection mechanism previously proposed by the authors, with the aid of
a simulation developed for such ends. A brief review of the Internal Behaviour
network is followed by the explanation of the implementation of the simulation.
Then, experiments are presented and discussed analysing the properties of the
action selection in the proposed model.
",Action Selection Properties in a Software Simulated Agent
"  This paper proposes a model for combination of external and internal stimuli
for the action selection in an autonomous agent, based in an action selection
mechanism previously proposed by the authors. This combination model includes
additive and multiplicative elements, which allows to incorporate new
properties, which enhance the action selection. A given parameter a, which is
part of the proposed model, allows to regulate the degree of dependence of the
observed external behaviour from the internal states of the entity.
","A Model for Combination of External and Internal Stimuli in the Action
  Selection of an Autonomous Agent"
"  Reinforcement learning (RL) involves sequential decision making in uncertain
environments. The aim of the decision-making agent is to maximize the benefit
of acting in its environment over an extended period of time. Finding an
optimal policy in RL may be very slow. To speed up learning, one often used
solution is the integration of planning, for example, Sutton's Dyna algorithm,
or various other methods using macro-actions.
  Here we suggest to separate plannable, i.e., close to deterministic parts of
the world, and focus planning efforts in this domain. A novel reinforcement
learning method called plannable RL (pRL) is proposed here. pRL builds a simple
model, which is used to search for macro actions. The simplicity of the model
makes planning computationally inexpensive. It is shown that pRL finds an
optimal policy, and that plannable macro actions found by pRL are near-optimal.
In turn, it is unnecessary to try large numbers of macro actions, which enables
fast learning. The utility of pRL is demonstrated by computer simulations.
",Searching for Plannable Domains can Speed up Reinforcement Learning
"  Optimization of decision problems in stochastic environments is usually
concerned with maximizing the probability of achieving the goal and minimizing
the expected episode length. For interacting agents in time-critical
applications, learning of the possibility of scheduling of subtasks (events) or
the full task is an additional relevant issue. Besides, there exist highly
stochastic problems where the actual trajectories show great variety from
episode to episode, but completing the task takes almost the same amount of
time. The identification of sub-problems of this nature may promote e.g.,
planning, scheduling and segmenting Markov decision processes. In this work,
formulae for the average duration as well as the standard deviation of the
duration of events are derived. The emerging Bellman-type equation is a simple
extension of Sobel's work (1982). Methods of dynamic programming as well as
methods of reinforcement learning can be applied for our extension. Computer
demonstration on a toy problem serve to highlight the principle.
",Temporal plannability by variance of the episode length
"  Much work has been done on extending the well-founded semantics to general
disjunctive logic programs and various approaches have been proposed. However,
these semantics are different from each other and no consensus is reached about
which semantics is the most intended. In this paper we look at disjunctive
well-founded reasoning from different angles. We show that there is an
intuitive form of the well-founded reasoning in disjunctive logic programming
which can be characterized by slightly modifying some exisitng approaches to
defining disjunctive well-founded semantics, including program transformations,
argumentation, unfounded sets (and resolution-like procedure). We also provide
a bottom-up procedure for this semantics. The significance of our work is not
only in clarifying the relationship among different approaches, but also shed
some light on what is an intended well-founded semantics for disjunctive logic
programs.
","Comparisons and Computation of Well-founded Semantics for Disjunctive
  Logic Programs"
"  We provide a semantic framework for preference handling in answer set
programming. To this end, we introduce preference preserving consequence
operators. The resulting fixpoint characterizations provide us with a uniform
semantic framework for characterizing preference handling in existing
approaches. Although our approach is extensible to other semantics by means of
an alternating fixpoint theory, we focus here on the elaboration of preferences
under answer set semantics. Alternatively, we show how these approaches can be
characterized by the concept of order preservation. These uniform semantic
characterizations provide us with new insights about interrelationships and
moreover about ways of implementation.
",A semantic framework for preference handling in answer set programming
"  The work reported here introduces Defeasible Logic Programming (DeLP), a
formalism that combines results of Logic Programming and Defeasible
Argumentation. DeLP provides the possibility of representing information in the
form of weak rules in a declarative manner, and a defeasible argumentation
inference mechanism for warranting the entailed conclusions.
  In DeLP an argumentation formalism will be used for deciding between
contradictory goals. Queries will be supported by arguments that could be
defeated by other arguments. A query q will succeed when there is an argument A
for q that is warranted, ie, the argument A that supports q is found undefeated
by a warrant procedure that implements a dialectical analysis.
  The defeasible argumentation basis of DeLP allows to build applications that
deal with incomplete and contradictory information in dynamic domains. Thus,
the resulting approach is suitable for representing agent's knowledge and for
providing an argumentation based reasoning mechanism to agents.
",Defeasible Logic Programming: An Argumentative Approach
"  Cooperative constraint solving is an area of constraint programming that
studies the interaction between constraint solvers with the aim of discovering
the interaction patterns that amplify the positive qualities of individual
solvers. Automatisation and formalisation of such studies is an important issue
of cooperative constraint solving.
  In this paper we present a constraint-based analysis of composite solvers
that integrates reasoning about the individual solvers and the processed data.
The idea is to approximate this reasoning by resolution of set constraints on
the finite sets representing the predicates that express all the necessary
properties. We illustrate application of our analysis to two important
cooperation patterns: deterministic choice and loop.
",Constraint-based analysis of composite solvers
"  There is a growing interest in using Kalman-filter models for brain
modelling. In turn, it is of considerable importance to represent Kalman-filter
in connectionist forms with local Hebbian learning rules. To our best
knowledge, Kalman-filter has not been given such local representation. It seems
that the main obstacle is the dynamic adaptation of the Kalman-gain. Here, a
connectionist representation is presented, which is derived by means of the
recursive prediction error method. We show that this method gives rise to
attractive local learning rules and can adapt the Kalman-gain.
",Kalman-filtering using local interactions
"  We discuss philosophical issues concerning the notion of cognition basing
ourselves in experimental results in cognitive sciences, especially in computer
simulations of cognitive systems. There have been debates on the ""proper""
approach for studying cognition, but we have realized that all approaches can
be in theory equivalent. Different approaches model different properties of
cognitive systems from different perspectives, so we can only learn from all of
them. We also integrate ideas from several perspectives for enhancing the
notion of cognition, such that it can contain other definitions of cognition as
special cases. This allows us to propose a simple classification of different
types of cognition.
",On the Notion of Cognition
"  The paper studies an implementation methodology for partial and disjunctive
stable models where partiality and disjunctions are unfolded from a logic
program so that an implementation of stable models for normal
(disjunction-free) programs can be used as the core inference engine. The
unfolding is done in two separate steps. Firstly, it is shown that partial
stable models can be captured by total stable models using a simple linear and
modular program transformation. Hence, reasoning tasks concerning partial
stable models can be solved using an implementation of total stable models.
Disjunctive partial stable models have been lacking implementations which now
become available as the translation handles also the disjunctive case.
Secondly, it is shown how total stable models of disjunctive programs can be
determined by computing stable models for normal programs. Hence, an
implementation of stable models of normal programs can be used as a core engine
for implementing disjunctive programs. The feasibility of the approach is
demonstrated by constructing a system for computing stable models of
disjunctive programs using the smodels system as the core engine. The
performance of the resulting system is compared to that of dlv which is a
state-of-the-art special purpose system for disjunctive programs.
",Unfolding Partiality and Disjunctions in Stable Model Semantics
"  When tracking a large number of targets, it is often computationally
expensive to represent the full joint distribution over target states. In cases
where the targets move independently, each target can instead be tracked with a
separate filter. However, this leads to a model-data association problem.
Another approach to solve the problem with computational complexity is to track
only the first moment of the joint distribution, the probability hypothesis
density (PHD). The integral of this distribution over any area S is the
expected number of targets within S. Since no record of object identity is
kept, the model-data association problem is avoided.
  The contribution of this paper is a particle filter implementation of the PHD
filter mentioned above. This PHD particle filter is applied to tracking of
multiple vehicles in terrain, a non-linear tracking problem. Experiments show
that the filter can track a changing number of vehicles robustly, achieving
near-real-time performance.
",Multi-target particle filtering for the probability hypothesis density
"  Search in cyclic AND/OR graphs was traditionally known to be an unsolved
problem. In the recent past several important studies have been reported in
this domain. In this paper, we have taken a fresh look at the problem. First, a
new and comprehensive theoretical framework for cyclic AND/OR graphs has been
presented, which was found missing in the recent literature. Based on this
framework, two best-first search algorithms, S1 and S2, have been developed. S1
does uninformed search and is a simple modification of the Bottom-up algorithm
by Martelli and Montanari. S2 performs a heuristically guided search and
replicates the modification in Bottom-up's successors, namely HS and AO*. Both
S1 and S2 solve the problem of searching AND/OR graphs in presence of cycles.
We then present a detailed analysis for the correctness and complexity results
of S1 and S2, using the proposed framework. We have observed through
experiments that S1 and S2 output correct results in all cases.
",A Framework for Searching AND/OR Graphs with Cycles
"  Thomas M. Strat has developed a decision-theoretic apparatus for
Dempster-Shafer theory (Decision analysis using belief functions, Intern. J.
Approx. Reason. 4(5/6), 391-417, 1990). In this apparatus, expected utility
intervals are constructed for different choices. The choice with the highest
expected utility is preferable to others. However, to find the preferred choice
when the expected utility interval of one choice is included in that of
another, it is necessary to interpolate a discerning point in the intervals.
This is done by the parameter rho, defined as the probability that the
ambiguity about the utility of every nonsingleton focal element will turn out
as favorable as possible. If there are several different decision makers, we
might sometimes be more interested in having the highest expected utility among
the decision makers rather than only trying to maximize our own expected
utility regardless of choices made by other decision makers. The preference of
each choice is then determined by the probability of yielding the highest
expected utility. This probability is equal to the maximal interval length of
rho under which an alternative is preferred. We must here take into account not
only the choices already made by other decision makers but also the rational
choices we can assume to be made by later decision makers. In Strats apparatus,
an assumption, unwarranted by the evidence at hand, has to be made about the
value of rho. We demonstrate that no such assumption is necessary. It is
sufficient to assume a uniform probability distribution for rho to be able to
discern the most preferable choice. We discuss when this approach is
justifiable.
",On rho in a Decision-Theoretic Apparatus of Dempster-Shafer Theory
"  Currently, there is renewed interest in the problem, raised by Shafer in
1985, of updating probabilities when observations are incomplete. This is a
fundamental problem in general, and of particular interest for Bayesian
networks. Recently, Grunwald and Halpern have shown that commonly used updating
strategies fail in this case, except under very special assumptions. In this
paper we propose a new method for updating probabilities with incomplete
observations. Our approach is deliberately conservative: we make no assumptions
about the so-called incompleteness mechanism that associates complete with
incomplete observations. We model our ignorance about this mechanism by a
vacuous lower prevision, a tool from the theory of imprecise probabilities, and
we use only coherence arguments to turn prior into posterior probabilities. In
general, this new approach to updating produces lower and upper posterior
probabilities and expectations, as well as partially determinate decisions.
This is a logical consequence of the existing ignorance about the
incompleteness mechanism. We apply the new approach to the problem of
classification of new evidence in probabilistic expert systems, where it leads
to a new, so-called conservative updating rule. In the special case of Bayesian
networks constructed using expert knowledge, we provide an exact algorithm for
classification based on our updating rule, which has linear-time complexity for
a class of networks wider than polytrees. This result is then extended to the
more general framework of credal networks, where computations are often much
harder than with Bayesian nets. Using an example, we show that our rule appears
to provide a solid basis for reliable updating with incomplete observations,
when no strong assumptions about the incompleteness mechanism are justified.
",Updating beliefs with incomplete observations
"  As examples such as the Monty Hall puzzle show, applying conditioning to
update a probability distribution on a ``naive space'', which does not take
into account the protocol used, can often lead to counterintuitive results.
Here we examine why. A criterion known as CAR (``coarsening at random'') in the
statistical literature characterizes when ``naive'' conditioning in a naive
space works. We show that the CAR condition holds rather infrequently, and we
provide a procedural characterization of it, by giving a randomized algorithm
that generates all and only distributions for which CAR holds. This
substantially extends previous characterizations of CAR. We also consider more
generalized notions of update such as Jeffrey conditioning and minimizing
relative entropy (MRE). We give a generalization of the CAR condition that
characterizes when Jeffrey conditioning leads to appropriate answers, and show
that there exist some very simple settings in which MRE essentially never gives
the right results. This generalizes and interconnects previous results obtained
in the literature on CAR and MRE.
",Updating Probabilities
"  Configuring consists in simulating the realization of a complex product from
a catalog of component parts, using known relations between types, and picking
values for object attributes. This highly combinatorial problem in the field of
constraint programming has been addressed with a variety of approaches since
the foundation system R1(McDermott82). An inherent difficulty in solving
configuration problems is the existence of many isomorphisms among
interpretations. We describe a formalism independent approach to improve the
detection of isomorphisms by configurators, which does not require to adapt the
problem model. To achieve this, we exploit the properties of a characteristic
subset of configuration problems, called the structural sub-problem, which
canonical solutions can be produced or tested at a limited cost. In this paper
we present an algorithm for testing the canonicity of configurations, that can
be added as a symmetry breaking constraint to any configurator. The cost and
efficiency of this canonicity test are given.
",Pruning Isomorphic Structural Sub-problems in Configuration
"  This article introduces the idea that probabilistic reasoning (PR) may be
understood as ""information compression by multiple alignment, unification and
search"" (ICMAUS). In this context, multiple alignment has a meaning which is
similar to but distinct from its meaning in bio-informatics, while unification
means a simple merging of matching patterns, a meaning which is related to but
simpler than the meaning of that term in logic.
  A software model, SP61, has been developed for the discovery and formation of
'good' multiple alignments, evaluated in terms of information compression. The
model is described in outline.
  Using examples from the SP61 model, this article describes in outline how the
ICMAUS framework can model various kinds of PR including: PR in best-match
pattern recognition and information retrieval; one-step 'deductive' and
'abductive' PR; inheritance of attributes in a class hierarchy; chains of
reasoning (probabilistic decision networks and decision trees, and PR with
'rules'); geometric analogy problems; nonmonotonic reasoning and reasoning with
default values; modelling the function of a Bayesian network.
","Probabilistic Reasoning as Information Compression by Multiple
  Alignment, Unification and Search: An Introduction and Overview"
"  This article presents an overview of the idea that ""information compression
by multiple alignment, unification and search"" (ICMAUS) may serve as a unifying
principle in computing (including mathematics and logic) and in such aspects of
human cognition as the analysis and production of natural language, fuzzy
pattern recognition and best-match information retrieval, concept hierarchies
with inheritance of attributes, probabilistic reasoning, and unsupervised
inductive learning. The ICMAUS concepts are described together with an outline
of the SP61 software model in which the ICMAUS concepts are currently realised.
A range of examples is presented, illustrated with output from the SP61 model.
","Information Compression by Multiple Alignment, Unification and Search as
  a Unifying Principle in Computing and Cognition"
"  We propose a calculus integrating two calculi well-known in Qualitative
Spatial Reasoning (QSR): Frank's projection-based cardinal direction calculus,
and a coarser version of Freksa's relative orientation calculus. An original
constraint propagation procedure is presented, which implements the interaction
between the two integrated calculi. The importance of taking into account the
interaction is shown with a real example providing an inconsistent knowledge
base, whose inconsistency (a) cannot be detected by reasoning separately about
each of the two components of the knowledge, just because, taken separately,
each is consistent, but (b) is detected by the proposed algorithm, thanks to
the interaction knowledge propagated from each of the two compnents to the
other.
","Integrating cardinal direction relations and other orientation relations
  in Qualitative Spatial Reasoning"
"  We define a ternary Relation Algebra (RA) of relative position relations on
two-dimensional directed lines (d-lines for short). A d-line has two degrees of
freedom (DFs): a rotational DF (RDF), and a translational DF (TDF). The
representation of the RDF of a d-line will be handled by an RA of 2D
orientations, CYC_t, known in the literature. A second algebra, TA_t, which
will handle the TDF of a d-line, will be defined. The two algebras, CYC_t and
TA_t, will constitute, respectively, the translational and the rotational
components of the RA, PA_t, of relative position relations on d-lines: the PA_t
atoms will consist of those pairs <t,r> of a TA_t atom and a CYC_t atom that
are compatible. We present in detail the RA PA_t, with its converse table, its
rotation table and its composition tables. We show that a (polynomial)
constraint propagation algorithm, known in the literature, is complete for a
subset of PA_t relations including almost all of the atomic relations. We will
discuss the application scope of the RA, which includes incidence geometry, GIS
(Geographic Information Systems), shape representation, localisation in
(multi-)robot navigation, and the representation of motion prepositions in NLP
(Natural Language Processing). We then compare the RA to existing ones, such as
an algebra for reasoning about rectangles parallel to the axes of an
(orthogonal) coordinate system, a ``spatial Odyssey'' of Allen's interval
algebra, and an algebra for reasoning about 2D segments.
",A ternary Relation Algebra of directed lines
"  An intelligent agent will often be uncertain about various properties of its
environment, and when acting in that environment it will frequently need to
quantify its uncertainty. For example, if the agent wishes to employ the
expected-utility paradigm of decision theory to guide its actions, it will need
to assign degrees of belief (subjective probabilities) to various assertions.
Of course, these degrees of belief should not be arbitrary, but rather should
be based on the information available to the agent. This paper describes one
approach for inducing degrees of belief from very rich knowledge bases, that
can include information about particular individuals, statistical correlations,
physical laws, and default rules. We call our approach the random-worlds
method. The method is based on the principle of indifference: it treats all of
the worlds the agent considers possible as being equally likely. It is able to
integrate qualitative default reasoning with quantitative probabilistic
reasoning by providing a language in which both types of information can be
easily expressed. Our results show that a number of desiderata that arise in
direct inference (reasoning from statistical information to conclusions about
individuals) and default reasoning follow directly {from} the semantics of
random worlds. For example, random worlds captures important patterns of
reasoning such as specificity, inheritance, indifference to irrelevant
information, and default assumptions of independence. Furthermore, the
expressive power of the language used and the intuitive semantics of random
worlds allow the method to deal with problems that are beyond the scope of many
other non-deductive reasoning systems.
",From Statistical Knowledge Bases to Degrees of Belief
"  This paper describes an approach to the representation and processing of
ontologies in the Semantic Web, based on the ICMAUS theory of computation and
AI. This approach has strengths that complement those of languages based on the
Resource Description Framework (RDF) such as RDF Schema and DAML+OIL. The main
benefits of the ICMAUS approach are simplicity and comprehensibility in the
representation of ontologies, an ability to cope with errors and uncertainties
in knowledge, and a versatile reasoning system with capabilities in the kinds
of probabilistic reasoning that seem to be required in the Semantic Web.
","An Alternative to RDF-Based Languages for the Representation and
  Processing of Ontologies in the Semantic Web"
"  Interactions are patterns between several attributes in data that cannot be
inferred from any subset of these attributes. While mutual information is a
well-established approach to evaluating the interactions between two
attributes, we surveyed its generalizations as to quantify interactions between
several attributes. We have chosen McGill's interaction information, which has
been independently rediscovered a number of times under various names in
various disciplines, because of its many intuitively appealing properties. We
apply interaction information to visually present the most important
interactions of the data. Visualization of interactions has provided insight
into the structure of data on a number of domains, identifying redundant
attributes and opportunities for constructing new features, discovering
unexpected regularities in data, and have helped during construction of
predictive models; we illustrate the methods on numerous examples. A machine
learning method that disregards interactions may get caught in two traps:
myopia is caused by learning algorithms assuming independence in spite of
interactions, whereas fragmentation arises from assuming an interaction in
spite of independence.
",Quantifying and Visualizing Attribute Interactions
"  In this paper we develop an evidential force aggregation method intended for
classification of evidential intelligence into recognized force structures. We
assume that the intelligence has already been partitioned into clusters and use
the classification method individually in each cluster. The classification is
based on a measure of fitness between template and fused intelligence that
makes it possible to handle intelligence reports with multiple nonspecific and
uncertain propositions. With this measure we can aggregate on a level-by-level
basis, starting from general intelligence to achieve a complete force structure
with recognized units on all hierarchical levels.
",Evidential Force Aggregation
"  Article discusses the application of Kullback-Leibler divergence to the
recognition of speech signals and suggests three algorithms implementing this
divergence criterion: correlation algorithm, spectral algorithm and filter
algorithm. Discussion covers an approach to the problem of speech variability
and is illustrated with the results of experimental modeling of speech signals.
The article gives a number of recommendations on the choice of appropriate
model parameters and provides a comparison to some other methods of speech
recognition.
",Application of Kullback-Leibler Metric to Speech Recognition
"  Richard Cox [1] set the axiomatic foundations of probable inference and the
algebra of propositions. He showed that consistency within these axioms
requires certain rules for updating belief. In this paper we use the analogy
between probability and utility introduced in [2] to propose an axiomatic
foundation for utility inference and the algebra of preferences. We show that
consistency within these axioms requires certain rules for updating preference.
We discuss a class of utility functions that stems from the axioms of utility
inference and show that this class is the basic building block for any general
multiattribute utility function. We use this class of utility functions
together with the algebra of preferences to construct utility functions
represented by logical operations on the attributes.
",The Algebra of Utility Inference
"  Recent literature in the last Maximum Entropy workshop introduced an analogy
between cumulative probability distributions and normalized utility functions.
Based on this analogy, a utility density function can de defined as the
derivative of a normalized utility function. A utility density function is
non-negative and integrates to unity. These two properties form the basis of a
correspondence between utility and probability. A natural application of this
analogy is a maximum entropy principle to assign maximum entropy utility
values. Maximum entropy utility interprets many of the common utility functions
based on the preference information needed for their assignment, and helps
assign utility values based on partial preference information. This paper
reviews maximum entropy utility and introduces further results that stem from
the duality between probability and utility.
",An information theory for preferences
"  Abduction, first proposed in the setting of classical logics, has been
studied with growing interest in the logic programming area during the last
years.
  In this paper we study abduction with penalization in the logic programming
framework. This form of abductive reasoning, which has not been previously
analyzed in logic programming, turns out to represent several relevant
problems, including optimization problems, very naturally. We define a formal
model for abduction with penalization over logic programs, which extends the
abductive framework proposed by Kakas and Mancarella. We address knowledge
representation issues, encoding a number of problems in our abductive
framework. In particular, we consider some relevant problems, taken from
different domains, ranging from optimization theory to diagnosis and planning;
their encodings turn out to be simple and elegant in our formalism. We
thoroughly analyze the computational complexity of the main problems arising in
the context of abduction with penalization from logic programs. Finally, we
implement a system supporting the proposed abductive framework on top of the
DLV engine. To this end, we design a translation from abduction problems with
penalties into logic programs with weak constraints. We prove that this
approach is sound and complete.
","Abductive Logic Programs with Penalization: Semantics, Complexity and
  Implementation"
"  We study local-search satisfiability solvers for propositional logic extended
with cardinality atoms, that is, expressions that provide explicit ways to
model constraints on cardinalities of sets. Adding cardinality atoms to the
language of propositional logic facilitates modeling search problems and often
results in concise encodings. We propose two ``native'' local-search solvers
for theories in the extended language. We also describe techniques to reduce
the problem to standard propositional satisfiability and allow us to use
off-the-shelf SAT solvers. We study these methods experimentally. Our general
finding is that native solvers designed specifically for the extended language
perform better than indirect methods relying on SAT solvers.
","Local-search techniques for propositional logic extended with
  cardinality constraints"
"  We describe WSAT(cc), a local-search solver for computing models of theories
in the language of propositional logic extended by cardinality atoms. WSAT(cc)
is a processing back-end for the logic PS+, a recently proposed formalism for
answer-set programming.
",WSAT(cc) - a fast local-search ASP solver
"  This paper presents duality between probability distributions and utility
functions.
",Utility-Probability Duality
"  Disjunctive Logic Programming (\DLP) is an advanced formalism for Knowledge
Representation and Reasoning (KRR). \DLP is very expressive in a precise
mathematical sense: it allows to express every property of finite structures
that is decidable in the complexity class $\SigmaP{2}$ ($\NP^{\NP}$).
Importantly, the \DLP encodings are often simple and natural.
  In this paper, we single out some limitations of \DLP for KRR, which cannot
naturally express problems where the size of the disjunction is not known ``a
priori'' (like N-Coloring), but it is part of the input. To overcome these
limitations, we further enhance the knowledge modelling abilities of \DLP, by
extending this language by {\em Parametric Connectives (OR and AND)}. These
connectives allow us to represent compactly the disjunction/conjunction of a
set of atoms having a given property. We formally define the semantics of the
new language, named $DLP^{\bigvee,\bigwedge}$ and we show the usefulness of the
new constructs on relevant knowledge-based problems. We address implementation
issues and discuss related works.
",Parametric Connectives in Disjunctive Logic Programming
"  The research field of Agent-Oriented Software Engineering (AOSE) aims to find
abstractions, languages, methodologies and toolkits for modeling, verifying,
validating and prototyping complex applications conceptualized as Multiagent
Systems (MASs). A very lively research sub-field studies how formal methods can
be used for AOSE. This paper presents a detailed survey of six logic-based
executable agent specification languages that have been chosen for their
potential to be integrated in our ARPEGGIO project, an open framework for
specifying and prototyping a MAS. The six languages are ConGoLog, Agent-0, the
IMPACT agent programming language, DyLog, Concurrent METATEM and Ehhf. For each
executable language, the logic foundations are described and an example of use
is shown. A comparison of the six languages and a survey of similar approaches
complete the paper, together with considerations of the advantages of using
logic-based languages in MAS modeling and prototyping.
",Logic-Based Specification Languages for Intelligent Software Agents
"  We propose a generalization of expected utility that we call generalized EU
(GEU), where a decision maker's beliefs are represented by plausibility
measures, and the decision maker's tastes are represented by general (i.e.,not
necessarily real-valued) utility functions. We show that every agent,
``rational'' or not, can be modeled as a GEU maximizer. We then show that we
can customize GEU by selectively imposing just the constraints we want. In
particular, we show how each of Savage's postulates corresponds to constraints
on GEU.
","Great Expectations. Part I: On the Customizability of Generalized
  Expected Utility"
"  Many different rules for decision making have been introduced in the
literature. We show that a notion of generalized expected utility proposed in
Part I of this paper is a universal decision rule, in the sense that it can
represent essentially all other decision rules.
","Great Expectations. Part II: Generalized Expected Utility as a Universal
  Decision Rule"
"  This paper describes a novel approach to grammar induction that has been
developed within a framework designed to integrate learning with other aspects
of computing, AI, mathematics and logic. This framework, called ""information
compression by multiple alignment, unification and search"" (ICMAUS), is founded
on principles of Minimum Length Encoding pioneered by Solomonoff and others.
Most of the paper describes SP70, a computer model of the ICMAUS framework that
incorporates processes for unsupervised learning of grammars. An example is
presented to show how the model can infer a plausible grammar from appropriate
input. Limitations of the current model and how they may be overcome are
briefly discussed.
","Unsupervised Grammar Induction in a Framework of Information Compression
  by Multiple Alignment, Unification and Search"
"  We consider the integration of existing cone-shaped and projection-based
calculi of cardinal direction relations, well-known in QSR. The more general,
integrating language we consider is based on convex constraints of the
qualitative form $r(x,y)$, $r$ being a cone-shaped or projection-based cardinal
direction atomic relation, or of the quantitative form $(\alpha ,\beta)(x,y)$,
with $\alpha ,\beta\in [0,2\pi)$ and $(\beta -\alpha)\in [0,\pi ]$: the meaning
of the quantitative constraint, in particular, is that point $x$ belongs to the
(convex) cone-shaped area rooted at $y$, and bounded by angles $\alpha$ and
$\beta$. The general form of a constraint is a disjunction of the form
$[r_1\vee...\vee r_{n_1}\vee (\alpha_1,\beta_1)\vee...\vee (\alpha
_{n_2},\beta_{n_2})](x,y)$, with $r_i(x,y)$, $i=1... n_1$, and $(\alpha
_i,\beta_i)(x,y)$, $i=1... n_2$, being convex constraints as described above:
the meaning of such a general constraint is that, for some $i=1... n_1$,
$r_i(x,y)$ holds, or, for some $i=1... n_2$, $(\alpha_i,\beta_i)(x,y)$ holds. A
conjunction of such general constraints is a $\tcsp$-like CSP, which we will
refer to as an $\scsp$ (Spatial Constraint Satisfaction Problem). An effective
solution search algorithm for an $\scsp$ will be described, which uses (1)
constraint propagation, based on a composition operation to be defined, as the
filtering method during the search, and (2) the Simplex algorithm, guaranteeing
completeness, at the leaves of the search tree. The approach is particularly
suited for large-scale high-level vision, such as, e.g., satellite-like
surveillance of a geographic area.
","Integrating existing cone-shaped and projection-based cardinal direction
  relations and a TCSP-like decidable generalisation"
"  Object oriented constraint programs (OOCPs) emerge as a leading evolution of
constraint programming and artificial intelligence, first applied to a range of
industrial applications called configuration problems. The rich variety of
technical approaches to solving configuration problems (CLP(FD), CC(FD), DCSP,
Terminological systems, constraint programs with set variables ...) is a source
of difficulty. No universally accepted formal language exists for communicating
about OOCPs, which makes the comparison of systems difficult. We present here a
Z based specification of OOCPs which avoids the falltrap of hidden object
semantics. The object system is part of the specification, and captures all of
the most advanced notions from the object oriented modeling standard UML. The
paper illustrates these issues and the conciseness and precision of Z by the
specification of a working OOCP that solves an historical AI problem : parsing
a context free grammar. Being written in Z, an OOCP specification also supports
formal proofs. The whole builds the foundation of an adaptative and evolving
framework for communicating about constrained object models and programs.
",Modeling Object Oriented Constraint Programs in Z
"  In this paper we suggest an architecture for a software agent which operates
a physical device and is capable of making observations and of testing and
repairing the device's components. We present simplified definitions of the
notions of symptom, candidate diagnosis, and diagnosis which are based on the
theory of action language ${\cal AL}$. The definitions allow one to give a
simple account of the agent's behavior in which many of the agent's tasks are
reduced to computing stable models of logic programs.
",Diagnostic reasoning with A-Prolog
"  We compare two recent extensions of the answer set (stable model) semantics
of logic programs. One of them, due to Lifschitz, Tang and Turner, allows the
bodies and heads of rules to contain nested expressions. The other, due to
Niemela and Simons, uses weight constraints. We show that there is a simple,
modular translation from the language of weight constraints into the language
of nested expressions that preserves the program's answer sets. Nested
expressions can be eliminated from the result of this translation in favor of
additional atoms. The translation makes it possible to compute answer sets for
some programs with weight constraints using satisfiability solvers, and to
prove the strong equivalence of programs with weight constraints using the
logic of here-and there.
",Weight Constraints as Nested Expressions
"  (We apologize for pidgin LaTeX) Schlipf \cite{sch91} proved that Stable Logic
Programming (SLP) solves all $\mathit{NP}$ decision problems. We extend
Schlipf's result to prove that SLP solves all search problems in the class
$\mathit{NP}$. Moreover, we do this in a uniform way as defined in \cite{mt99}.
Specifically, we show that there is a single $\mathrm{DATALOG}^{\neg}$ program
$P_{\mathit{Trg}}$ such that given any Turing machine $M$, any polynomial $p$
with non-negative integer coefficients and any input $\sigma$ of size $n$ over
a fixed alphabet $\Sigma$, there is an extensional database
$\mathit{edb}_{M,p,\sigma}$ such that there is a one-to-one correspondence
between the stable models of $\mathit{edb}_{M,p,\sigma} \cup P_{\mathit{Trg}}$
and the accepting computations of the machine $M$ that reach the final state in
at most $p(n)$ steps. Moreover, $\mathit{edb}_{M,p,\sigma}$ can be computed in
polynomial time from $p$, $\sigma$ and the description of $M$ and the decoding
of such accepting computations from its corresponding stable model of
$\mathit{edb}_{M,p,\sigma} \cup P_{\mathit{Trg}}$ can be computed in linear
time. A similar statement holds for Default Logic with respect to
$\Sigma_2^\mathrm{P}$-search problems\footnote{The proof of this result
involves additional technical complications and will be a subject of another
publication.}.
",On the Expressibility of Stable Logic Programming
"  This book develops the conjecture that all kinds of information processing in
computers and in brains may usefully be understood as ""information compression
by multiple alignment, unification and search"". This ""SP theory"", which has
been under development since 1987, provides a unified view of such things as
the workings of a universal Turing machine, the nature of 'knowledge', the
interpretation and production of natural language, pattern recognition and
best-match information retrieval, several kinds of probabilistic reasoning,
planning and problem solving, unsupervised learning, and a range of concepts in
mathematics and logic. The theory also provides a basis for the design of an
'SP' computer with several potential advantages compared with traditional
digital computers.
",Unifying Computing and Cognition: The SP Theory and its Applications
"  In rule-based systems, goal-oriented computations correspond naturally to the
possible ways that an observation may be explained. In some applications, we
need to compute explanations for a series of observations with the same domain.
The question whether previously computed answers can be recycled arises. A yes
answer could result in substantial savings of repeated computations. For
systems based on classic logic, the answer is YES. For nonmonotonic systems
however, one tends to believe that the answer should be NO, since recycling is
a form of adding information. In this paper, we show that computed answers can
always be recycled, in a nontrivial way, for the class of rewrite procedures
that we proposed earlier for logic programs with negation. We present some
experimental results on an encoding of the logistics domain.
",Recycling Computed Answers in Rewrite Systems for Abduction
"  Recent advances in programming languages study and design have established a
standard way of grounding computational systems representation in category
theory. These formal results led to a better understanding of issues of control
and side-effects in functional and imperative languages. This framework can be
successfully applied to the investigation of the performance of Artificial
Intelligence (AI) inference and cognitive systems. In this paper, we delineate
a categorical formalisation of memory as a control structure driving
performance in inference systems. Abstracting away control mechanisms from
three widely used representations of memory in cognitive systems (scripts,
production rules and clusters) we explain how categorical triples capture the
interaction between learning and problem-solving.
",Memory As A Monadic Control Construct In Problem-Solving
"  The field of machine learning (ML) is concerned with the question of how to
construct algorithms that automatically improve with experience. In recent
years many successful ML applications have been developed, such as datamining
programs, information-filtering systems, etc. Although ML algorithms allow the
detection and extraction of interesting patterns of data for several kinds of
problems, most of these algorithms are based on quantitative reasoning, as they
rely on training data in order to infer so-called target functions.
  In the last years defeasible argumentation has proven to be a sound setting
to formalize common-sense qualitative reasoning. This approach can be combined
with other inference techniques, such as those provided by machine learning
theory.
  In this paper we outline different alternatives for combining defeasible
argumentation and machine learning techniques. We suggest how different aspects
of a generic argument-based framework can be integrated with other ML-based
approaches.
",Integrating Defeasible Argumentation and Machine Learning Techniques
"  Stable model semantics has become a very popular approach for the management
of negation in logic programming. This approach relies mainly on the closed
world assumption to complete the available knowledge and its formulation has
its basis in the so-called Gelfond-Lifschitz transformation.
  The primary goal of this work is to present an alternative and
epistemic-based characterization of stable model semantics, to the
Gelfond-Lifschitz transformation. In particular, we show that stable model
semantics can be defined entirely as an extension of the Kripke-Kleene
semantics. Indeed, we show that the closed world assumption can be seen as an
additional source of `falsehood' to be added cumulatively to the Kripke-Kleene
semantics. Our approach is purely algebraic and can abstract from the
particular formalism of choice as it is based on monotone operators (under the
knowledge order) over bilattices only.
",Epistemic Foundation of Stable Model Semantics
"  We address the problem of the development of representations and their
relationship to the environment. We study a software agent which develops in a
network a representation of its simple environment which captures and
integrates the relationships between agent and environment through a closure
mechanism. The inclusion of a variable behavior modifier allows better
representation development. This can be confirmed with an internal description
of the closure mechanism, and with an external description of the properties of
the representation network.
",The role of behavior modifiers in representation development
"  This document describes syntax, semantics and implementation guidelines in
order to enrich the DLV system with the possibility to make external C function
calls. This feature is realized by the introduction of parametric external
predicates, whose extension is not specified through a logic program but
implicitly computed through external code.
",Parametric external predicates for the DLV System
"  This document describes the functions as they are treated in the DLV system.
We give first the language, then specify the main implementation issues.
","Toward the Implementation of Functions in the DLV System (Preliminary
  Technical Report)"
"  We introduce Ak, an extension of the action description language A (Gelfond
and Lifschitz, 1993) to handle actions which affect knowledge. We use sensing
actions to increase an agent's knowledge of the world and non-deterministic
actions to remove knowledge. We include complex plans involving conditionals
and loops in our query language for hypothetical reasoning. We also present a
translation of Ak domain descriptions into epistemic logic programs.
",Knowledge And The Action Description Language A
"  In this paper, we examine the performance of four fuzzy rule generation
methods on Wisconsin breast cancer data. The first method generates fuzzy if
then rules using the mean and the standard deviation of attribute values. The
second approach generates fuzzy if then rules using the histogram of attributes
values. The third procedure generates fuzzy if then rules with certainty of
each attribute into homogeneous fuzzy sets. In the fourth approach, only
overlapping areas are partitioned. The first two approaches generate a single
fuzzy if then rule for each class by specifying the membership function of each
antecedent fuzzy set using the information about attribute values of training
patterns. The other two approaches are based on fuzzy grids with homogeneous
fuzzy partitions of each attribute. The performance of each approach is
evaluated on breast cancer data sets. Simulation results show that the Modified
grid approach has a high classification rate of 99.73 %.
","A Comparative Study of Fuzzy Classification Methods on Breast Cancer
  Data"
"  The integration of different learning and adaptation techniques to overcome
individual limitations and to achieve synergetic effects through the
hybridization or fusion of these techniques has, in recent years, contributed
to a large number of new intelligent system designs. Computational intelligence
is an innovative framework for constructing intelligent hybrid architectures
involving Neural Networks (NN), Fuzzy Inference Systems (FIS), Probabilistic
Reasoning (PR) and derivative free optimization techniques such as Evolutionary
Computation (EC). Most of these hybridization approaches, however, follow an ad
hoc design methodology, justified by success in certain application domains.
Due to the lack of a common framework it often remains difficult to compare the
various hybrid systems conceptually and to evaluate their performance
comparatively. This chapter introduces the different generic architectures for
integrating intelligent systems. The designing aspects and perspectives of
different hybrid archirectures like NN-FIS, EC-FIS, EC-NN, FIS-PR and NN-FIS-EC
systems are presented. Some conclusions are also provided towards the end.
",Intelligent Systems: Architectures and Perspectives
"  Neuro-fuzzy systems have attracted growing interest of researchers in various
scientific and engineering areas due to the increasing need of intelligent
systems. This paper evaluates the use of two popular soft computing techniques
and conventional statistical approach based on Box--Jenkins autoregressive
integrated moving average (ARIMA) model to predict electricity demand in the
State of Victoria, Australia. The soft computing methods considered are an
evolving fuzzy neural network (EFuNN) and an artificial neural network (ANN)
trained using scaled conjugate gradient algorithm (CGA) and backpropagation
(BP) algorithm. The forecast accuracy is compared with the forecasts used by
Victorian Power Exchange (VPX) and the actual energy demand. To evaluate, we
considered load demand patterns for 10 consecutive months taken every 30 min
for training the different prediction models. Test results show that the
neuro-fuzzy system performed better than neural networks, ARIMA model and the
VPX forecasts.
",A Neuro-Fuzzy Approach for Modelling Electricity Demand in Victoria
"  Fusion of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS)
have attracted the growing interest of researchers in various scientific and
engineering areas due to the growing need of adaptive intelligent systems to
solve the real world problems. ANN learns from scratch by adjusting the
interconnections between layers. FIS is a popular computing framework based on
the concept of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The
advantages of a combination of ANN and FIS are obvious. There are several
approaches to integrate ANN and FIS and very often it depends on the
application. We broadly classify the integration of ANN and FIS into three
categories namely concurrent model, cooperative model and fully fused model.
This paper starts with a discussion of the features of each model and
generalize the advantages and deficiencies of each model. We further focus the
review on the different types of fused neuro-fuzzy systems and citing the
advantages and disadvantages of each model.
",Neuro Fuzzy Systems: Sate-of-the-Art Modeling Techniques
"  Long-term rainfall prediction is a challenging task especially in the modern
world where we are facing the major environmental problem of global warming. In
general, climate and rainfall are highly non-linear phenomena in nature
exhibiting what is known as the butterfly effect. While some regions of the
world are noticing a systematic decrease in annual rainfall, others notice
increases in flooding and severe storms. The global nature of this phenomenon
is very complicated and requires sophisticated computer modeling and simulation
to predict accurately. In this paper, we report a performance analysis for
Multivariate Adaptive Regression Splines (MARS)and artificial neural networks
for one month ahead prediction of rainfall. To evaluate the prediction
efficiency, we made use of 87 years of rainfall data in Kerala state, the
southern part of the Indian peninsula situated at latitude -longitude pairs
(8o29'N - 76o57' E). We used an artificial neural network trained using the
scaled conjugate gradient algorithm. The neural network and MARS were trained
with 40 years of rainfall data. For performance evaluation, network predicted
outputs were compared with the actual rainfall data. Simulation results reveal
that MARS is a good forecasting tool and performed better than the considered
neural network.
",Is Neural Network a Reliable Forecaster on Earth? A MARS Query!
"  Classification of texture pattern is one of the most important problems in
pattern recognition. In this paper, we present a classification method based on
the Discrete Cosine Transform (DCT) coefficients of texture image. As DCT works
on gray level image, the color scheme of each image is transformed into gray
levels. For classifying the images using DCT we used two popular soft computing
techniques namely neurocomputing and neuro-fuzzy computing. We used a
feedforward neural network trained using the backpropagation learning and an
evolving fuzzy neural network to classify the textures. The soft computing
models were trained using 80% of the texture data and remaining was used for
testing and validation purposes. A performance comparison was made among the
soft computing models for the texture classification problem. We also analyzed
the effects of prolonged training of neural networks. It is observed that the
proposed neuro-fuzzy model performed better than neural network.
",DCT Based Texture Classification Using Soft Computing Approach
"  Sorting by reversals is an important problem in inferring the evolutionary
relationship between two genomes. The problem of sorting unsigned permutation
has been proven to be NP-hard. The best guaranteed error bounded is the 3/2-
approximation algorithm. However, the problem of sorting signed permutation can
be solved easily. Fast algorithms have been developed both for finding the
sorting sequence and finding the reversal distance of signed permutation. In
this paper, we present a way to view the problem of sorting unsigned
permutation as signed permutation. And the problem can then be seen as
searching an optimal signed permutation in all n2 corresponding signed
permutations. We use genetic algorithm to conduct the search. Our experimental
result shows that the proposed method outperform the 3/2-approximation
algorithm.
",Estimating Genome Reversal Distance by Genetic Algorithm
"  Past few years have witnessed a growing recognition of intelligent techniques
for the construction of efficient and reliable intrusion detection systems. Due
to increasing incidents of cyber attacks, building effective intrusion
detection systems (IDS) are essential for protecting information systems
security, and yet it remains an elusive goal and a great challenge. In this
paper, we report a performance analysis between Multivariate Adaptive
Regression Splines (MARS), neural networks and support vector machines. The
MARS procedure builds flexible regression models by fitting separate splines to
distinct intervals of the predictor variables. A brief comparison of different
neural network learning algorithms is also given.
",Intrusion Detection Systems Using Adaptive Regression Splines
"  The aim of our research was to apply well-known data mining techniques (such
as linear neural networks, multi-layered perceptrons, probabilistic neural
networks, classification and regression trees, support vector machines and
finally a hybrid decision tree neural network approach) to the problem of
predicting the quality of service in call centers; based on the performance
data actually collected in a call center of a large insurance company. Our aim
was two-fold. First, to compare the performance of models built using the
above-mentioned techniques and, second, to analyze the characteristics of the
input sensitivity in order to better understand the relationship between the
perform-ance evaluation process and the actual performance and in this way help
improve the performance of call centers. In this paper we summarize our
findings.
",Data Mining Approach for Analyzing Call Center Performance
"  The use of intelligent systems for stock market predictions has been widely
established. In this paper, we investigate how the seemingly chaotic behavior
of stock markets could be well represented using several connectionist
paradigms and soft computing techniques. To demonstrate the different
techniques, we considered Nasdaq-100 index of Nasdaq Stock MarketS and the S&P
CNX NIFTY stock index. We analyzed 7 year's Nasdaq 100 main index values and 4
year's NIFTY index values. This paper investigates the development of a
reliable and efficient technique to model the seemingly chaotic behavior of
stock markets. We considered an artificial neural network trained using
Levenberg-Marquardt algorithm, Support Vector Machine (SVM), Takagi-Sugeno
neuro-fuzzy model and a Difference Boosting Neural Network (DBNN). This paper
briefly explains how the different connectionist paradigms could be formulated
using different learning methods and then investigates whether they can provide
the required level of performance, which are sufficiently good and robust so as
to provide a reliable forecast model for stock market indices. Experiment
results reveal that all the connectionist paradigms considered could represent
the stock indices behavior very accurately.
",Modeling Chaotic Behavior of Stock Indices Using Intelligent Paradigms
"  The purpose of this paper is to point to the usefulness of applying a linear
mathematical formulation of fuzzy multiple criteria objective decision methods
in organising business activities. In this respect fuzzy parameters of linear
programming are modelled by preference-based membership functions. This paper
begins with an introduction and some related research followed by some
fundamentals of fuzzy set theory and technical concepts of fuzzy multiple
objective decision models. Further a real case study of a manufacturing plant
and the implementation of the proposed technique is presented. Empirical
results clearly show the superiority of the fuzzy technique in optimising
individual objective functions when compared to non-fuzzy approach.
Furthermore, for the problem considered, the optimal solution helps to infer
that by incorporating fuzziness in a linear programming model either in
constraints, or both in objective functions and constraints, provides a similar
(or even better) level of satisfaction for obtained results compared to
non-fuzzy linear programming.
","Hybrid Fuzzy-Linear Programming Approach for Multi Criteria Decision
  Making Problems"
"  In this paper, we present MLEANN (Meta-Learning Evolutionary Artificial
Neural Network), an automatic computational framework for the adaptive
optimization of artificial neural networks wherein the neural network
architecture, activation function, connection weights; learning algorithm and
its parameters are adapted according to the problem. We explored the
performance of MLEANN and conventionally designed artificial neural networks
for function approximation problems. To evaluate the comparative performance,
we used three different well-known chaotic time series. We also present the
state of the art popular neural network learning algorithms and some
experimentation results related to convergence speed and generalization
performance. We explored the performance of backpropagation algorithm;
conjugate gradient algorithm, quasi-Newton algorithm and Levenberg-Marquardt
algorithm for the three chaotic time series. Performances of the different
learning algorithms were evaluated when the activation functions and
architecture were changed. We further present the theoretical background,
algorithm, design strategy and further demonstrate how effective and inevitable
is the proposed MLEANN framework to design a neural network, which is smaller,
faster and with a better generalization performance.
",Meta-Learning Evolutionary Artificial Neural Networks
"  The phylogenetic tree construction is to infer the evolutionary relationship
between species from the experimental data. However, the experimental data are
often imperfect and conflicting each others. Therefore, it is important to
extract the motif from the imperfect data. The largest compatible subset
problem is that, given a set of experimental data, we want to discard the
minimum such that the remaining is compatible. The largest compatible subset
problem can be viewed as the vertex cover problem in the graph theory that has
been proven to be NP-hard. In this paper, we propose a hybrid Evolutionary
Computing (EC) method for this problem. The proposed method combines the EC
approach and the algorithmic approach for special structured graphs. As a
result, the complexity of the problem is dramatically reduced. Experiments were
performed on randomly generated graphs with different edge densities. The
vertex covers produced by the proposed method were then compared to the vertex
covers produced by a 2-approximation algorithm. The experimental results showed
that the proposed method consistently outperformed a classical 2- approximation
algorithm. Furthermore, a significant improvement was found when the graph
density was small.
",The Largest Compatible Subset Problem for Phylogenetic Data
"  Decision-making is a process of choosing among alternative courses of action
for solving complicated problems where multi-criteria objectives are involved.
The past few years have witnessed a growing recognition of Soft Computing
technologies that underlie the conception, design and utilization of
intelligent systems. Several works have been done where engineers and
scientists have applied intelligent techniques and heuristics to obtain optimal
decisions from imprecise information. In this paper, we present a concurrent
fuzzy-neural network approach combining unsupervised and supervised learning
techniques to develop the Tactical Air Combat Decision Support System (TACDSS).
Experiment results clearly demonstrate the efficiency of the proposed
technique.
",A Concurrent Fuzzy-Neural Network Approach for Decision Support Systems
"  In a universe with a single currency, there would be no foreign exchange
market, no foreign exchange rates, and no foreign exchange. Over the past
twenty-five years, the way the market has performed those tasks has changed
enormously. The need for intelligent monitoring systems has become a necessity
to keep track of the complex forex market. The vast currency market is a
foreign concept to the average individual. However, once it is broken down into
simple terms, the average individual can begin to understand the foreign
exchange market and use it as a financial instrument for future investing. In
this paper, we attempt to compare the performance of hybrid soft computing and
hard computing techniques to predict the average monthly forex rates one month
ahead. The soft computing models considered are a neural network trained by the
scaled conjugate gradient algorithm and a neuro-fuzzy model implementing a
Takagi-Sugeno fuzzy inference system. We also considered Multivariate Adaptive
Regression Splines (MARS), Classification and Regression Trees (CART) and a
hybrid CART-MARS technique. We considered the exchange rates of Australian
dollar with respect to US dollar, Singapore dollar, New Zealand dollar,
Japanese yen and United Kingdom pounds. The models were trained using 70% of
the data and remaining was used for testing and validation purposes. It is
observed that the proposed hybrid models could predict the forex rates more
accurately than all the techniques when applied individually. Empirical results
also reveal that the hybrid hard computing approach also improved some of our
previous work using a neuro-fuzzy approach.
","Analysis of Hybrid Soft and Hard Computing Techniques for Forex
  Monitoring Systems"
"  The rapid e-commerce growth has made both business community and customers
face a new situation. Due to intense competition on one hand and the customer's
option to choose from several alternatives business community has realized the
necessity of intelligent marketing strategies and relationship management. Web
usage mining attempts to discover useful knowledge from the secondary data
obtained from the interactions of the users with the Web. Web usage mining has
become very critical for effective Web site management, creating adaptive Web
sites, business and support services, personalization, network traffic flow
analysis and so on. In this paper, we present the important concepts of Web
usage mining and its various practical applications. We further present a novel
approach 'intelligent-miner' (i-Miner) to optimize the concurrent architecture
of a fuzzy clustering algorithm (to discover web data clusters) and a fuzzy
inference system to analyze the Web site visitor trends. A hybrid evolutionary
fuzzy clustering algorithm is proposed in this paper to optimally segregate
similar user interests. The clustered data is then used to analyze the trends
using a Takagi-Sugeno fuzzy inference system learned using a combination of
evolutionary algorithm and neural network learning. Proposed approach is
compared with self-organizing maps (to discover patterns) and several function
approximation techniques like neural networks, linear genetic programming and
Takagi-Sugeno fuzzy inference system (to analyze the clusters). The results are
graphically illustrated and the practical significance is discussed in detail.
Empirical results clearly show that the proposed Web usage-mining framework is
efficient.
",Business Intelligence from Web Usage Mining
"  Normally a decision support system is build to solve problem where
multi-criteria decisions are involved. The knowledge base is the vital part of
the decision support containing the information or data that is used in
decision-making process. This is the field where engineers and scientists have
applied several intelligent techniques and heuristics to obtain optimal
decisions from imprecise information. In this paper, we present a hybrid
neuro-genetic learning approach for the adaptation a Mamdani fuzzy inference
system for the Tactical Air Combat Decision Support System (TACDSS). Some
simulation results demonstrating the difference of the learning techniques and
are also provided.
","Adaptation of Mamdani Fuzzy Inference System Using Neuro - Genetic
  Approach for Tactical Air Combat Decision Support System"
"  Several adaptation techniques have been investigated to optimize fuzzy
inference systems. Neural network learning algorithms have been used to
determine the parameters of fuzzy inference system. Such models are often
called as integrated neuro-fuzzy models. In an integrated neuro-fuzzy model
there is no guarantee that the neural network learning algorithm converges and
the tuning of fuzzy inference system will be successful. Success of
evolutionary search procedures for optimization of fuzzy inference system is
well proven and established in many application areas. In this paper, we will
explore how the optimization of fuzzy inference systems could be further
improved using a meta-heuristic approach combining neural network learning and
evolutionary computation. The proposed technique could be considered as a
methodology to integrate neural networks, fuzzy inference systems and
evolutionary search procedures. We present the theoretical frameworks and some
experimental results to demonstrate the efficiency of the proposed technique.
","EvoNF: A Framework for Optimization of Fuzzy Inference Systems Using
  Neural Network Learning and Evolutionary Computation"
"  Evolutionary artificial neural networks (EANNs) refer to a special class of
artificial neural networks (ANNs) in which evolution is another fundamental
form of adaptation in addition to learning. Evolutionary algorithms are used to
adapt the connection weights, network architecture and learning algorithms
according to the problem environment. Even though evolutionary algorithms are
well known as efficient global search algorithms, very often they miss the best
local solutions in the complex solution space. In this paper, we propose a
hybrid meta-heuristic learning approach combining evolutionary learning and
local search methods (using 1st and 2nd order error information) to improve the
learning and faster convergence obtained using a direct evolutionary approach.
The proposed technique is tested on three different chaotic time series and the
test results are compared with some popular neuro-fuzzy systems and a recently
developed cutting angle method of global optimization. Empirical results reveal
that the proposed technique is efficient in spite of the computational
complexity.
","Optimization of Evolutionary Neural Networks Using Hybrid Learning
  Algorithms"
"  The academic literature suggests that the extent of exporting by
multinational corporation subsidiaries (MCS) depends on their product
manufactured, resources, tax protection, customers and markets, involvement
strategy, financial independence and suppliers' relationship with a
multinational corporation (MNC). The aim of this paper is to model the complex
export pattern behaviour using a Takagi-Sugeno fuzzy inference system in order
to determine the actual volume of MCS export output (sales exported). The
proposed fuzzy inference system is optimised by using neural network learning
and evolutionary computation. Empirical results clearly show that the proposed
approach could model the export behaviour reasonable well compared to a direct
neural network approach.
",Export Behaviour Modeling Using EvoNF Approach
"  The costs of fatalities and injuries due to traffic accident have a great
impact on society. This paper presents our research to model the severity of
injury resulting from traffic accidents using artificial neural networks and
decision trees. We have applied them to an actual data set obtained from the
National Automotive Sampling System (NASS) General Estimates System (GES).
Experiment results reveal that in all the cases the decision tree outperforms
the neural network. Our research analysis also shows that the three most
important factors in fatal injury are: driver's seat belt usage, light
condition of the roadway, and driver's alcohol usage.
",Traffic Accident Analysis Using Decision Trees and Neural Networks
"  This paper presents a comparative study of six soft computing models namely
multilayer perceptron networks, Elman recurrent neural network, radial basis
function network, Hopfield model, fuzzy inference system and hybrid fuzzy
neural network for the hourly electricity demand forecast of Czech Republic.
The soft computing models were trained and tested using the actual hourly load
data for seven years. A comparison of the proposed techniques is presented for
predicting 2 day ahead demands for electricity. Simulation results indicate
that hybrid fuzzy neural network and radial basis function networks are the
best candidates for the analysis and forecasting of electricity demand.
","Short Term Load Forecasting Models in Czech Republic Using Soft
  Computing Paradigms"
"  Decision-making is a process of choosing among alternative courses of action
for solving complicated problems where multi-criteria objectives are involved.
The past few years have witnessed a growing recognition of Soft Computing (SC)
technologies that underlie the conception, design and utilization of
intelligent systems. In this paper, we present different SC paradigms involving
an artificial neural network trained using the scaled conjugate gradient
algorithm, two different fuzzy inference methods optimised using neural network
learning/evolutionary algorithms and regression trees for developing
intelligent decision support systems. We demonstrate the efficiency of the
different algorithms by developing a decision support system for a Tactical Air
Combat Environment (TACE). Some empirical comparisons between the different
algorithms are also provided.
",Decision Support Systems Using Intelligent Paradigms
"  In this paper, we present a state-based regression function for planning
domains where an agent does not have complete information and may have sensing
actions. We consider binary domains and employ the 0-approximation [Son & Baral
2001] to define the regression function. In binary domains, the use of
0-approximation means using 3-valued states. Although planning using this
approach is incomplete with respect to the full semantics, we adopt it to have
a lower complexity. We prove the soundness and completeness of our regression
formulation with respect to the definition of progression. More specifically,
we show that (i) a plan obtained through regression for a planning problem is
indeed a progression solution of that planning problem, and that (ii) for each
plan found through progression, using regression one obtains that plan or an
equivalent one. We then develop a conditional planner that utilizes our
regression function. We prove the soundness and completeness of our planning
algorithm and present experimental results with respect to several well known
planning problems in the literature.
",Regression with respect to sensing actions and partial states
"  Defeasible logic is a rule-based nonmonotonic logic, with both strict and
defeasible rules, and a priority relation on rules. We show that inference in
the propositional form of the logic can be performed in linear time. This
contrasts markedly with most other propositional nonmonotonic logics, in which
inference is intractable.
",Propositional Defeasible Logic has Linear Complexity
"  Defeasible argumentation has experienced a considerable growth in AI in the
last decade. Theoretical results have been combined with development of
practical applications in AI & Law, Case-Based Reasoning and various
knowledge-based systems. However, the dialectical process associated with
inference is computationally expensive. This paper focuses on speeding up this
inference process by pruning the involved search space. Our approach is
twofold. On one hand, we identify distinguished literals for computing defeat.
On the other hand, we restrict ourselves to a subset of all possible
conflicting arguments by introducing dialectical constraints.
",Pruning Search Space in Defeasible Argumentation
"  Main purposes of the paper are followings: 1) To show examples of the
calculations in domain of QFT via ``derivative rules'' of an expert system; 2)
To consider advantages and disadvantage that technology of the calculations; 3)
To reflect about how one would develop new physical theories, what knowledge
would be useful in their investigations and how this problem can be connected
with designing an expert system.
","A proposal to design expert system for the calculations in the domain of
  QFT"
"  We will try to tackle both the theoretical and practical aspects of a very
important problem in chess programming as stated in the title of this article -
the issue of draw detection by move repetition. The standard approach that has
so far been employed in most chess programs is based on utilising positional
matrices in original and compressed format as well as on the implementation of
the so-called bitboard format.
  The new approach that we will be trying to introduce is based on using
variant strings generated by the search algorithm (searcher) during the tree
expansion in decision making. We hope to prove that this approach is more
efficient than the standard treatment of the issue, especially in positions
with few pieces (endgames). To illustrate what we have in mind a machine
language routine that implements our theoretical assumptions is attached. The
routine is part of the Axon chess program, developed by the authors. Axon, in
its current incarnation, plays chess at master strength (ca. 2400-2450 Elo,
based on both Axon vs computer programs and Axon vs human masters in over 3000
games altogether).
","A New Approach to Draw Detection by Move Repetition in Computer Chess
  Programming"
"  Learning to respond to voice-text input involves the subject's ability in
understanding the phonetic and text based contents and his/her ability to
communicate based on his/her experience. The neuro-cognitive facility of the
subject has to support two important domains in order to make the learning
process complete. In many cases, though the understanding is complete, the
response is partial. This is one valid reason why we need to support the
information from the subject with scalable techniques such as Natural Language
Processing (NLP) for abstraction of the contents from the output. This paper
explores the feasibility of using NLP modules interlaced with Neural Networks
to perform the required task in autogenic training related to medical
applications.
","Autogenic Training With Natural Language Processing Modules: A Recent
  Tool For Certain Neuro Cognitive Studies"
"  Generalized evolutionary algorithm based on Tsallis canonical distribution is
proposed. The algorithm uses Tsallis generalized canonical distribution to
weigh the configurations for `selection' instead of Gibbs-Boltzmann
distribution. Our simulation results show that for an appropriate choice of
non-extensive index that is offered by Tsallis statistics, evolutionary
algorithms based on this generalization outperform algorithms based on
Gibbs-Boltzmann distribution.
",Generalized Evolutionary Algorithm based on Tsallis Statistics
"  In this paper we present and evaluate a search strategy called Decomposition
Based Search (DBS) which is based on two steps: subproblem generation and
subproblem solution. The generation of subproblems is done through value
ranking and domain splitting. Subdomains are explored so as to generate,
according to the heuristic chosen, promising subproblems first.
  We show that two well known search strategies, Limited Discrepancy Search
(LDS) and Iterative Broadening (IB), can be seen as special cases of DBS. First
we present a tuning of DBS that visits the same search nodes as IB, but avoids
restarts. Then we compare both theoretically and computationally DBS and LDS
using the same heuristic. We prove that DBS has a higher probability of being
successful than LDS on a comparable number of nodes, under realistic
assumptions. Experiments on a constraint satisfaction problem and an
optimization problem show that DBS is indeed very effective if compared to LDS.
",Decomposition Based Search - A theoretical and experimental evaluation
"  Solution techniques for Constraint Satisfaction and Optimisation Problems
often make use of backtrack search methods, exploiting variable and value
ordering heuristics. In this paper, we propose and analyse a very simple method
to apply in case the value ordering heuristic produces ties: postponing the
branching decision. To this end, we group together values in a tie, branch on
this sub-domain, and defer the decision among them to lower levels of the
search tree. We show theoretically and experimentally that this simple
modification can dramatically improve the efficiency of the search strategy.
Although in practise similar methods may have been applied already, to our
knowledge, no empirical or theoretical study has been proposed in the
literature to identify when and to what extent this strategy should be used.
",Postponing Branching Decisions
"  In this paper, we propose an effective search procedure that interleaves two
steps: subproblem generation and subproblem solution. We mainly focus on the
first part. It consists of a variable domain value ranking based on reduced
costs. Exploiting the ranking, we generate, in a Limited Discrepancy Search
tree, the most promising subproblems first. An interesting result is that
reduced costs provide a very precise ranking that allows to almost always find
the optimal solution in the first generated subproblem, even if its dimension
is significantly smaller than that of the original problem. Concerning the
proof of optimality, we exploit a way to increase the lower bound for
subproblems at higher discrepancies. We show experimental results on the TSP
and its time constrained variant to show the effectiveness of the proposed
approach, but the technique could be generalized for other problems.
",Reduced cost-based ranking for generating promising subproblems
"  One proposes a first alternative rule of combination to WAO (Weighted Average
Operator) proposed recently by Josang, Daniel and Vannoorenberghe, called
Proportional Conflict Redistribution rule (denoted PCR1). PCR1 and WAO are
particular cases of WO (the Weighted Operator) because the conflicting mass is
redistributed with respect to some weighting factors. In this first PCR rule,
the proportionalization is done for each non-empty set with respect to the
non-zero sum of its corresponding mass matrix - instead of its mass column
average as in WAO, but the results are the same as Ph. Smets has pointed out.
Also, we extend WAO (which herein gives no solution) for the degenerate case
when all column sums of all non-empty sets are zero, and then the conflicting
mass is transferred to the non-empty disjunctive form of all non-empty sets
together; but if this disjunctive form happens to be empty, then one considers
an open world (i.e. the frame of discernment might contain new hypotheses) and
thus all conflicting mass is transferred to the empty set. In addition to WAO,
we propose a general formula for PCR1 (WAO for non-degenerate cases).
",A Simple Proportional Conflict Redistribution Rule
"  In this paper one proposes a simple algorithm of combining the fusion rules,
those rules which first use the conjunctive rule and then the transfer of
conflicting mass to the non-empty sets, in such a way that they gain the
property of associativity and fulfill the Markovian requirement for dynamic
fusion. Also, a new rule, SDL-improved, is presented.
","An Algorithm for Quasi-Associative and Quasi-Markovian Rules of
  Combination in Information Fusion"
"  FLUX is a programming method for the design of agents that reason logically
about their actions and sensor information in the presence of incomplete
knowledge. The core of FLUX is a system of Constraint Handling Rules, which
enables agents to maintain an internal model of their environment by which they
control their own behavior. The general action representation formalism of the
fluent calculus provides the formal semantics for the constraint solver. FLUX
exhibits excellent computational behavior due to both a carefully restricted
expressiveness and the inference paradigm of progression.
",FLUX: A Logic Programming Method for Reasoning Agents
"  Boltzmann selection is an important selection mechanism in evolutionary
algorithms as it has theoretical properties which help in theoretical analysis.
However, Boltzmann selection is not used in practice because a good annealing
schedule for the `inverse temperature' parameter is lacking. In this paper we
propose a Cauchy annealing schedule for Boltzmann selection scheme based on a
hypothesis that selection-strength should increase as evolutionary process goes
on and distance between two selection strengths should decrease for the process
to converge. To formalize these aspects, we develop formalism for selection
mechanisms using fitness distributions and give an appropriate measure for
selection-strength. In this paper, we prove an important result, by which we
derive an annealing schedule called Cauchy annealing schedule. We demonstrate
the novelty of proposed annealing schedule using simulations in the framework
of genetic algorithms.
","Cauchy Annealing Schedule: An Annealing Schedule for Boltzmann Selection
  Scheme in Evolutionary Algorithms"
"  In this paper we propose five versions of a Proportional Conflict
Redistribution rule (PCR) for information fusion together with several
examples. From PCR1 to PCR2, PCR3, PCR4, PCR5 one increases the complexity of
the rules and also the exactitude of the redistribution of conflicting masses.
PCR1 restricted from the hyper-power set to the power set and without
degenerate cases gives the same result as the Weighted Average Operator (WAO)
proposed recently by J{\o}sang, Daniel and Vannoorenberghe but does not satisfy
the neutrality property of vacuous belief assignment. That's why improved PCR
rules are proposed in this paper. PCR4 is an improvement of minC and Dempster's
rules. The PCR rules redistribute the conflicting mass, after the conjunctive
rule has been applied, proportionally with some functions depending on the
masses assigned to their corresponding columns in the mass matrix. There are
infinitely many ways these functions (weighting factors) can be chosen
depending on the complexity one wants to deal with in specific applications and
fusion systems. Any fusion combination rule is at some degree ad-hoc.
",Proportional Conflict Redistribution Rules for Information Fusion
"  This paper presents in detail the generalized pignistic transformation (GPT)
succinctly developed in the Dezert-Smarandache Theory (DSmT) framework as a
tool for decision process. The GPT allows to provide a subjective probability
measure from any generalized basic belief assignment given by any corpus of
evidence. We mainly focus our presentation on the 3D case and provide the
complete result obtained by the GPT and its validation drawn from the
probability theory.
",The Generalized Pignistic Transformation
"  Since no fusion theory neither rule fully satisfy all needed applications,
the author proposes a Unification of Fusion Theories and a combination of
fusion rules in solving problems/applications. For each particular application,
one selects the most appropriate model, rule(s), and algorithm of
implementation. We are working in the unification of the fusion theories and
rules, which looks like a cooking recipe, better we'd say like a logical chart
for a computer programmer, but we don't see another method to comprise/unify
all things. The unification scenario presented herein, which is now in an
incipient form, should periodically be updated incorporating new discoveries
from the fusion and engineering research.
",Unification of Fusion Theories
"  Normal forms for logic programs under stable/answer set semantics are
introduced. We argue that these forms can simplify the study of program
properties, mainly consistency. The first normal form, called the {\em kernel}
of the program, is useful for studying existence and number of answer sets. A
kernel program is composed of the atoms which are undefined in the Well-founded
semantics, which are those that directly affect the existence of answer sets.
The body of rules is composed of negative literals only. Thus, the kernel form
tends to be significantly more compact than other formulations. Also, it is
possible to check consistency of kernel programs in terms of colorings of the
Extended Dependency Graph program representation which we previously developed.
The second normal form is called {\em 3-kernel.} A 3-kernel program is composed
of the atoms which are undefined in the Well-founded semantics. Rules in
3-kernel programs have at most two conditions, and each rule either belongs to
a cycle, or defines a connection between cycles. 3-kernel programs may have
positive conditions. The 3-kernel normal form is very useful for the static
analysis of program consistency, i.e., the syntactic characterization of
existence of answer sets. This result can be obtained thanks to a novel
graph-like representation of programs, called Cycle Graph which presented in
the companion article \cite{Cos04b}.
",Normal forms for Answer Sets Programming
"  This paper may look like a glossary of the fusion rules and we also introduce
new ones presenting their formulas and examples: Conjunctive, Disjunctive,
Exclusive Disjunctive, Mixed Conjunctive-Disjunctive rules, Conditional rule,
Dempster's, Yager's, Smets' TBM rule, Dubois-Prade's, Dezert-Smarandache
classical and hybrid rules, Murphy's average rule,
Inagaki-Lefevre-Colot-Vannoorenberghe Unified Combination rules [and, as
particular cases: Iganaki's parameterized rule, Weighting Average Operator,
minC (M. Daniel), and newly Proportional Conflict Redistribution rules
(Smarandache-Dezert) among which PCR5 is the most exact way of redistribution
of the conflicting mass to non-empty sets following the path of the conjunctive
rule], Zhang's Center Combination rule, Convolutive x-Averaging, Consensus
Operator (Josang), Cautious Rule (Smets), ?-junctions rules (Smets), etc. and
three new T-norm & T-conorm rules adjusted from fuzzy and neutrosophic sets to
information fusion (Tchamova-Smarandache). Introducing the degree of union and
degree of inclusion with respect to the cardinal of sets not with the fuzzy set
point of view, besides that of intersection, many fusion rules can be improved.
There are corner cases where each rule might have difficulties working or may
not get an expected result.
","An In-Depth Look at Information Fusion Rules & the Unification of Fusion
  Theories"
"  There are many examples in the literature that suggest that
indistinguishability is intransitive, despite the fact that the
indistinguishability relation is typically taken to be an equivalence relation
(and thus transitive). It is shown that if the uncertainty perception and the
question of when an agent reports that two things are indistinguishable are
both carefully modeled, the problems disappear, and indistinguishability can
indeed be taken to be an equivalence relation. Moreover, this model also
suggests a logic of vagueness that seems to solve many of the problems related
to vagueness discussed in the philosophical literature. In particular, it is
shown here how the logic can handle the sorites paradox.
",Intransitivity and Vagueness
"  A careful analysis of conditioning in the Sleeping Beauty problem is done,
using the formal model for reasoning about knowledge and probability developed
by Halpern and Tuttle. While the Sleeping Beauty problem has been viewed as
revealing problems with conditioning in the presence of imperfect recall, the
analysis done here reveals that the problems are not so much due to imperfect
recall as to asynchrony. The implications of this analysis for van Fraassen's
Reflection Principle and Savage's Sure-Thing Principle are considered.
","Sleeping Beauty Reconsidered: Conditioning and Reflection in
  Asynchronous Systems"
"  The paper is an attempt to generalize a methodology, which is similar to the
bounded-input bounded-output method currently widely used for the system
stability studies. The presented earlier methodology allows decomposition of
input space into bounded subspaces and defining for each subspace its bounding
surface. It also defines a corresponding predefined control, which maps any
point of a bounded input into a desired bounded output subspace. This
methodology was improved by providing a mechanism for the fast defining a
bounded surface. This paper presents enhanced bounded-input
bounded-predefined-control bounded-output approach, which provides adaptability
feature to the control and allows transferring of a controlled system along a
suboptimal trajectory.
",Bounded Input Bounded Predefined Control Bounded Output
"  The number of probability distributions required to populate a conditional
probability table (CPT) in a Bayesian network, grows exponentially with the
number of parent-nodes associated with that table. If the table is to be
populated through knowledge elicited from a domain expert then the sheer
magnitude of the task forms a considerable cognitive barrier. In this paper we
devise an algorithm to populate the CPT while easing the extent of knowledge
acquisition. The input to the algorithm consists of a set of weights that
quantify the relative strengths of the influences of the parent-nodes on the
child-node, and a set of probability distributions the number of which grows
only linearly with the number of associated parent-nodes. These are elicited
from the domain expert. The set of probabilities are obtained by taking into
consideration the heuristics that experts use while arriving at probabilistic
estimations. The algorithm is used to populate the CPT by computing appropriate
weighted sums of the elicited distributions. We invoke the methods of
information geometry to demonstrate how these weighted sums capture the
expert's judgemental strategy.
","Generating Conditional Probabilities for Bayesian Networks: Easing the
  Knowledge Acquisition Problem"
"  Consider the problem of tracking a set of moving targets. Apart from the
tracking result, it is often important to know where the tracking fails, either
to steer sensors to that part of the state-space, or to inform a human operator
about the status and quality of the obtained information. An intuitive quality
measure is the correlation between two tracking results based on uncorrelated
observations. In the case of Bayesian trackers such a correlation measure could
be the Kullback-Leibler difference.
  We focus on a scenario with a large number of military units moving in some
terrain. The units are observed by several types of sensors and ""meta-sensors""
with force aggregation capabilities. The sensors register units of different
size. Two separate multi-target probability hypothesis density (PHD) particle
filters are used to track some type of units (e.g., companies) and their
sub-units (e.g., platoons), respectively, based on observations of units of
those sizes. Each observation is used in one filter only.
  Although the state-space may well be the same in both filters, the posterior
PHD distributions are not directly comparable -- one unit might correspond to
three or four spatially distributed sub-units. Therefore, we introduce a
mapping function between distributions for different unit size, based on
doctrine knowledge of unit configuration.
  The mapped distributions can now be compared -- locally or globally -- using
some measure, which gives the correlation between two PHD distributions in a
bounded volume of the state-space. To locate areas where the tracking fails, a
discretized quality map of the state-space can be generated by applying the
measure locally to different parts of the space.
",Comparing Multi-Target Trackers on Different Force Unit Levels
"  We describe the recently introduced extremal optimization algorithm and apply
it to target detection and association problems arising in pre-processing for
multi-target tracking.
  Here we consider the problem of pre-processing for multiple target tracking
when the number of sensor reports received is very large and arrives in large
bursts. In this case, it is sometimes necessary to pre-process reports before
sending them to tracking modules in the fusion system. The pre-processing step
associates reports to known tracks (or initializes new tracks for reports on
objects that have not been seen before). It could also be used as a pre-process
step before clustering, e.g., in order to test how many clusters to use.
  The pre-processing is done by solving an approximate version of the original
problem. In this approximation, not all pair-wise conflicts are calculated. The
approximation relies on knowing how many such pair-wise conflicts that are
necessary to compute. To determine this, results on phase-transitions occurring
when coloring (or clustering) large random instances of a particular graph
ensemble are used.
",Extremal optimization for sensor report pre-processing
"  The management and combination of uncertain, imprecise, fuzzy and even
paradoxical or high conflicting sources of information has always been, and
still remains today, of primal importance for the development of reliable
modern information systems involving artificial reasoning. In this chapter, we
present a survey of our recent theory of plausible and paradoxical reasoning,
known as Dezert-Smarandache Theory (DSmT) in the literature, developed for
dealing with imprecise, uncertain and paradoxical sources of information. We
focus our presentation here rather on the foundations of DSmT, and on the two
important new rules of combination, than on browsing specific applications of
DSmT available in literature. Several simple examples are given throughout the
presentation to show the efficiency and the generality of this new approach.
The last part of this chapter concerns the presentation of the neutrosophic
logic, the neutro-fuzzy inference and its connection with DSmT. Fuzzy logic and
neutrosophic logic are useful tools in decision making after fusioning the
information using the DSm hybrid rule of combination of masses.
","The Combination of Paradoxical, Uncertain, and Imprecise Sources of
  Information based on DSmT and Neutro-Fuzzy Inference"
"  In this paper, we propose a new method based on Hidden Markov Models to
interpret temporal sequences of sensor data from mobile robots to automatically
detect features. Hidden Markov Models have been used for a long time in pattern
recognition, especially in speech recognition. Their main advantages over other
methods (such as neural networks) are their ability to model noisy temporal
signals of variable length. We show in this paper that this approach is well
suited for interpretation of temporal sequences of mobile-robot sensor data. We
present two distinct experiments and results: the first one in an indoor
environment where a mobile robot learns to detect features like open doors or
T-intersections, the second one in an outdoor environment where a different
mobile robot has to identify situations like climbing a hill or crossing a
rock.
","Learning to automatically detect features for mobile robots using
  second-order Hidden Markov Models"
"  In this paper, we present a rich semantic network based on a differential
analysis. We then detail implemented measures that take into account common and
differential features between words. In a last section, we describe some
industrial applications.
",Inferring knowledge from a large semantic network
"  Answer set programming (ASP) with disjunction offers a powerful tool for
declaratively representing and solving hard problems. Many NP-complete problems
can be encoded in the answer set semantics of logic programs in a very concise
and intuitive way, where the encoding reflects the typical ""guess and check""
nature of NP problems: The property is encoded in a way such that polynomial
size certificates for it correspond to stable models of a program. However, the
problem-solving capacity of full disjunctive logic programs (DLPs) is beyond
NP, and captures a class of problems at the second level of the polynomial
hierarchy. While these problems also have a clear ""guess and check"" structure,
finding an encoding in a DLP reflecting this structure may sometimes be a
non-obvious task, in particular if the ""check"" itself is a coNP-complete
problem; usually, such problems are solved by interleaving separate guess and
check programs, where the check is expressed by inconsistency of the check
program. In this paper, we present general transformations of head-cycle free
(extended) disjunctive logic programs into stratified and positive (extended)
disjunctive logic programs based on meta-interpretation techniques. The answer
sets of the original and the transformed program are in simple correspondence,
and, moreover, inconsistency of the original program is indicated by a
designated answer set of the transformed program. Our transformations
facilitate the integration of separate ""guess"" and ""check"" programs, which are
often easy to obtain, automatically into a single disjunctive logic program.
Our results complement recent results on meta-interpretation in ASP, and extend
methods and techniques for a declarative ""guess and check"" problem solving
paradigm through ASP.
","Towards Automated Integration of Guess and Check Programs in Answer Set
  Programming: A Meta-Interpreter and Applications"
"  This paper presents an approach to enhance search engines with information
about word senses available in WordNet. The approach exploits information about
the conceptual relations within the lexical-semantic net. In the wrapper for
search engines presented, WordNet information is used to specify user's request
or to classify the results of a publicly available web search engine, like
google, yahoo, etc.
",Clever Search: A WordNet Based Wrapper for Internet Search Engines
"  This paper reports about experiments with GermaNet as a resource within
domain specific document analysis. The main question to be answered is: How is
the coverage of GermaNet in a specific domain? We report about results of a
field test of GermaNet for analyses of autopsy protocols and present a sketch
about the integration of GermaNet inside XDOC. Our remarks will contribute to a
GermaNet user's wish list.
",Issues in Exploiting GermaNet as a Resource in Real Applications
"  The aim of the project presented in this paper is to design a system for an
NLG architecture, which supports the documentation process of eBusiness models.
A major task is to enrich the formal description of an eBusiness model with
additional information needed in an NLG task.
",Transforming Business Rules Into Natural Language Text
"  Lexical semantic resources, like WordNet, are often used in real applications
of natural language document processing. For example, we integrated GermaNet in
our document suite XDOC of processing of German forensic autopsy protocols. In
addition to the hypernymy and synonymy relation, we want to adapt GermaNet's
verb frames for our analysis. In this paper we outline an approach for the
domain related enrichment of GermaNet verb frames by corpus based syntactic and
co-occurred data analyses of real documents.
",Corpus based Enrichment of GermaNet Verb Frames
"  Real applications of natural language document processing are very often
confronted with domain specific lexical gaps during the analysis of documents
of a new domain. This paper describes an approach for the derivation of domain
specific concepts for the extension of an existing ontology. As resources we
need an initial ontology and a partially processed corpus of a domain. We
exploit the specific characteristic of the sublanguage in the corpus. Our
approach is based on syntactical structures (noun phrases) and compound
analyses to extract information required for the extension of GermaNet's
lexical resources.
",Context Related Derivation of Word Senses
"  We suggest to employ techniques from Natural Language Processing (NLP) and
Knowledge Representation (KR) to transform existing documents into documents
amenable for the Semantic Web. Semantic Web documents have at least part of
their semantics and pragmatics marked up explicitly in both a machine
processable as well as human readable manner. XML and its related standards
(XSLT, RDF, Topic Maps etc.) are the unifying platform for the tools and
methodologies developed for different application scenarios.
",Transforming and Enriching Documents for the Semantic Web
"  This text introduces the twin deadlocks of strong artificial life.
Conceptualization of life is a deadlock both because of the existence of a
continuum between the inert and the living, and because we only know one
instance of life. Computationalism is a second deadlock since it remains a
matter of faith. Nevertheless, artificial life realizations quickly progress
and recent constructions embed an always growing set of the intuitive
properties of life. This growing gap between theory and realizations should
sooner or later crystallize in some kind of paradigm shift and then give clues
to break the twin deadlocks.
",Perspectives for Strong Artificial Life
"  In this chapter we describe new neural-network techniques developed for
visual mining clinical electroencephalograms (EEGs), the weak electrical
potentials invoked by brain activity. These techniques exploit fruitful ideas
of Group Method of Data Handling (GMDH). Section 2 briefly describes the
standard neural-network techniques which are able to learn well-suited
classification modes from data presented by relevant features. Section 3
introduces an evolving cascade neural network technique which adds new input
nodes as well as new neurons to the network while the training error decreases.
This algorithm is applied to recognize artifacts in the clinical EEGs. Section
4 presents the GMDH-type polynomial networks learnt from data. We applied this
technique to distinguish the EEGs recorded from an Alzheimer and a healthy
patient as well as recognize EEG artifacts. Section 5 describes the new
neural-network technique developed to induce multi-class concepts from data. We
used this technique for inducing a 16-class concept from the large-scale
clinical EEG data. Finally we discuss perspectives of applying the
neural-network techniques to clinical EEGs.
","Neural-Network Techniques for Visual Mining Clinical
  Electroencephalograms"
"  Bayesian averaging over classification models allows the uncertainty of
classification outcomes to be evaluated, which is of crucial importance for
making reliable decisions in applications such as financial in which risks have
to be estimated. The uncertainty of classification is determined by a trade-off
between the amount of data available for training, the diversity of a
classifier ensemble and the required performance. The interpretability of
classification models can also give useful information for experts responsible
for making reliable classifications. For this reason Decision Trees (DTs) seem
to be attractive classification models. The required diversity of the DT
ensemble can be achieved by using the Bayesian model averaging all possible
DTs. In practice, the Bayesian approach can be implemented on the base of a
Markov Chain Monte Carlo (MCMC) technique of random sampling from the posterior
distribution. For sampling large DTs, the MCMC method is extended by Reversible
Jump technique which allows inducing DTs under given priors. For the case when
the prior information on the DT size is unavailable, the sweeping technique
defining the prior implicitly reveals a better performance. Within this Chapter
we explore the classification uncertainty of the Bayesian MCMC techniques on
some datasets from the StatLog Repository and real financial data. The
classification uncertainty is compared within an Uncertainty Envelope technique
dealing with the class posterior distribution and a given confidence
probability. This technique provides realistic estimates of the classification
uncertainty which can be easily interpreted in statistical terms with the aim
of risk evaluation.
","Estimating Classification Uncertainty of Bayesian Decision Tree
  Technique on Financial Data"
"  Multiple Classifier Systems (MCSs) allow evaluation of the uncertainty of
classification outcomes that is of crucial importance for safety critical
applications. The uncertainty of classification is determined by a trade-off
between the amount of data available for training, the classifier diversity and
the required performance. The interpretability of MCSs can also give useful
information for experts responsible for making reliable classifications. For
this reason Decision Trees (DTs) seem to be attractive classification models
for experts. The required diversity of MCSs exploiting such classification
models can be achieved by using two techniques, the Bayesian model averaging
and the randomised DT ensemble. Both techniques have revealed promising results
when applied to real-world problems. In this paper we experimentally compare
the classification uncertainty of the Bayesian model averaging with a
restarting strategy and the randomised DT ensemble on a synthetic dataset and
some domain problems commonly used in the machine learning community. To make
the Bayesian DT averaging feasible, we use a Markov Chain Monte Carlo
technique. The classification uncertainty is evaluated within an Uncertainty
Envelope technique dealing with the class posterior distribution and a given
confidence probability. Exploring a full posterior distribution, this technique
produces realistic estimates which can be easily interpreted in statistical
terms. In our experiments we found out that the Bayesian DTs are superior to
the randomised DT ensembles within the Uncertainty Envelope technique.
","Comparison of the Bayesian and Randomised Decision Tree Ensembles within
  an Uncertainty Envelope Technique"
"  Artificial intelligence (AI) research has evolved over the last few decades
and knowledge acquisition research is at the core of AI research. PKAW-04 is
one of three international knowledge acquisition workshops held in the
Pacific-Rim, Canada and Europe over the last two decades. PKAW-04 has a strong
emphasis on incremental knowledge acquisition, machine learning, neural nets
and active mining.
  The proceedings contain 19 papers that were selected by the program committee
among 24 submitted papers. All papers were peer reviewed by at least two
reviewers. The papers in these proceedings cover the methods and tools as well
as the applications related to develop expert systems or knowledge based
systems.
",Proceedings of the Pacific Knowledge Acquisition Workshop 2004
"  In the frame of designing a knowledge discovery system, we have developed
stochastic models based on high-order hidden Markov models. These models are
capable to map sequences of data into a Markov chain in which the transitions
between the states depend on the \texttt{n} previous states according to the
order of the model. We study the process of achieving information extraction
fromspatial and temporal data by means of an unsupervised classification. We
use therefore a French national database related to the land use of a region,
named Teruti, which describes the land use both in the spatial and temporal
domain. Land-use categories (wheat, corn, forest, ...) are logged every year on
each site regularly spaced in the region. They constitute a temporal sequence
of images in which we look for spatial and temporal dependencies. The temporal
segmentation of the data is done by means of a second-order Hidden Markov Model
(\hmmd) that appears to have very good capabilities to locate stationary
segments, as shown in our previous work in speech recognition. Thespatial
classification is performed by defining a fractal scanning ofthe images with
the help of a Hilbert-Peano curve that introduces atotal order on the sites,
preserving the relation ofneighborhood between the sites. We show that the
\hmmd performs aclassification that is meaningful for the agronomists.Spatial
and temporal classification may be achieved simultaneously by means of a 2
levels \hmmd that measures the \aposteriori probability to map a temporal
sequence of images onto a set of hidden classes.
",Temporal and Spatial Data Mining with Second-Order Hidden Models
"  Our ongoing work aims at defining an ontology-centered approach for building
expertise models for the CommonKADS methodology. This approach (which we have
named ""OntoKADS"") is founded on a core problem-solving ontology which
distinguishes between two conceptualization levels: at an object level, a set
of concepts enable us to define classes of problem-solving situations, and at a
meta level, a set of meta-concepts represent modeling primitives. In this
article, our presentation of OntoKADS will focus on the core ontology and, in
particular, on roles - the primitive situated at the interface between domain
knowledge and reasoning, and whose ontological status is still much debated. We
first propose a coherent, global, ontological framework which enables us to
account for this primitive. We then show how this novel characterization of the
primitive allows definition of new rules for the construction of expertise
models.
",An ontological approach to the construction of problem-solving models
"  Automatic or assisted workflow composition is a field of intense research for
applications to the world wide web or to business process modeling. Workflow
composition is traditionally addressed in various ways, generally via theorem
proving techniques. Recent research observed that building a composite workflow
bears strong relationships with finite model search, and that some workflow
languages can be defined as constrained object metamodels . This lead to
consider the viability of applying configuration techniques to this problem,
which was proven feasible. Constrained based configuration expects a
constrained object model as input. The purpose of this document is to formally
specify the constrained object model involved in ongoing experiments and
research using the Z specification language.
",A Constrained Object Model for Configuration Based Workflow Composition
"  To the reduct problems of decision system, the paper proposes the notion of
dynamic core according to the dynamic reduct model. It describes various formal
definitions of dynamic core, and discusses some properties about dynamic core.
All of these show that dynamic core possesses the essential characters of the
feature core.
",A Study for the Feature Core of Dynamic Reduct
"  Correlated time series are time series that, by virtue of the underlying
process to which they refer, are expected to influence each other strongly. We
introduce a novel approach to handle such time series, one that models their
interaction as a two-dimensional cellular automaton and therefore allows them
to be treated as a single entity. We apply our approach to the problems of
filling gaps and predicting values in rainfall time series. Computational
results show that the new approach compares favorably to Kalman smoothing and
filtering.
","Two-dimensional cellular automata and the analysis of correlated time
  series"
"  ATNoSFERES is a Pittsburgh style Learning Classifier System (LCS) in which
the rules are represented as edges of an Augmented Transition Network.
Genotypes are strings of tokens of a stack-based language, whose execution
builds the labeled graph. The original ATNoSFERES, using a bitstring to
represent the language tokens, has been favorably compared in previous work to
several Michigan style LCSs architectures in the context of Non Markov
problems. Several modifications of ATNoSFERES are proposed here: the most
important one conceptually being a representational change: each token is now
represented by an integer, hence the genotype is a string of integers; several
other modifications of the underlying grammar language are also proposed. The
resulting ATNoSFERES-II is validated on several standard animat Non Markov
problems, on which it outperforms all previously published results in the LCS
literature. The reasons for these improvement are carefully analyzed, and some
assumptions are proposed on the underlying mechanisms in order to explain these
good results.
",ATNoSFERES revisited
"  We present a declarative language, PP, for the high-level specification of
preferences between possible solutions (or trajectories) of a planning problem.
This novel language allows users to elegantly express non-trivial,
multi-dimensional preferences and priorities over such preferences. The
semantics of PP allows the identification of most preferred trajectories for a
given goal. We also provide an answer set programming implementation of
planning problems with PP preferences.
",Planning with Preferences using Logic Programming
"  Clustering is a widely used technique in data mining applications for
discovering patterns in underlying data. Most traditional clustering algorithms
are limited to handling datasets that contain either numeric or categorical
attributes. However, datasets with mixed types of attributes are common in real
life data mining applications. In this paper, we propose a novel
divide-and-conquer technique to solve this problem. First, the original mixed
dataset is divided into two sub-datasets: the pure categorical dataset and the
pure numeric dataset. Next, existing well established clustering algorithms
designed for different types of datasets are employed to produce corresponding
clusters. Last, the clustering results on the categorical and numeric dataset
are combined as a categorical dataset, on which the categorical data clustering
algorithm is used to get the final clusters. Our contribution in this paper is
to provide an algorithm framework for the mixed attributes clustering problem,
in which existing clustering algorithms can be easily integrated, the
capabilities of different kinds of clustering algorithms and characteristics of
different types of datasets could be fully exploited. Comparisons with other
clustering algorithms on real life datasets illustrate the superiority of our
approach.
","Clustering Mixed Numeric and Categorical Data: A Cluster Ensemble
  Approach"
"  Clustering categorical data is an integral part of data mining and has
attracted much attention recently. In this paper, we present k-histogram, a new
efficient algorithm for clustering categorical data. The k-histogram algorithm
extends the k-means algorithm to categorical domain by replacing the means of
clusters with histograms, and dynamically updates histograms in the clustering
process. Experimental results on real datasets show that k-histogram algorithm
can produce better clustering results than k-modes algorithm, the one related
with our work most closely.
",K-Histograms: An Efficient Clustering Algorithm for Categorical Dataset
"  This report describes a new version of the OntoSpec methodology for ontology
building. Defined by the LaRIA Knowledge Engineering Team (University of
Picardie Jules Verne, Amiens, France), OntoSpec aims at helping builders to
model ontological knowledge (upstream of formal representation). The
methodology relies on a set of rigorously-defined modelling primitives and
principles. Its application leads to the elaboration of a semi-informal
ontology, which is independent of knowledge representation languages. We
recently enriched the OntoSpec methodology by endowing it with a new resource,
the DOLCE top-level ontology defined at the LOA (IST-CNR, Trento, Italy). The
goal of this integration is to provide modellers with additional help in
structuring application ontologies, while maintaining independence
vis-\`{a}-vis formal representation languages. In this report, we first provide
an overview of the OntoSpec methodology's general principles and then describe
the DOLCE re-engineering process. A complete version of DOLCE-OS (i.e. a
specification of DOLCE in the semi-informal OntoSpec language) is presented in
an appendix.
","Integration of the DOLCE top-level ontology into the OntoSpec
  methodology"
"  In this paper we present a new approach for marker less human motion capture
from conventional camera feeds. The aim of our study is to recover 3D positions
of key points of the body that can serve for gait analysis. Our approach is
based on foreground segmentation, an articulated body model and particle
filters. In order to be generic and simple no restrictive dynamic modelling was
used. A new modified particle filtering algorithm was introduced. It is used
efficiently to search the model configuration space. This new algorithm which
we call Interval Particle Filtering reorganizes the configurations search space
in an optimal deterministic way and proved to be efficient in tracking natural
human movement. Results for human motion capture from a single camera are
presented and compared to results obtained from a marker based system. The
system proved to be able to track motion successfully even in partial
occlusions.
","Using Interval Particle Filtering for Marker less 3D Human Motion
  Capture"
"  The aim of our study is to detect balance disorders and a tendency towards
the falls in the elderly, knowing gait parameters. In this paper we present a
new tool for gait analysis based on markerless human motion capture, from
camera feeds. The system introduced here, recovers the 3D positions of several
key points of the human body while walking. Foreground segmentation, an
articulated body model and particle filtering are basic elements of our
approach. No dynamic model is used thus this system can be described as generic
and simple to implement. A modified particle filtering algorithm, which we call
Interval Particle Filtering, is used to reorganise and search through the
model's configurations search space in a deterministic optimal way. This
algorithm was able to perform human movement tracking with success. Results
from the treatment of a single cam feeds are shown and compared to results
obtained using a marker based human motion capture system.
",Markerless Human Motion Capture for Gait Analysis
"  An agent often has a number of hypotheses, and must choose among them based
on observations, or outcomes of experiments. Each of these observations can be
viewed as providing evidence for or against various hypotheses. All the
attempts to formalize this intuition up to now have assumed that associated
with each hypothesis h there is a likelihood function \mu_h, which is a
probability measure that intuitively describes how likely each observation is,
conditional on h being the correct hypothesis. We consider an extension of this
framework where there is uncertainty as to which of a number of likelihood
functions is appropriate, and discuss how one formal approach to defining
evidence, which views evidence as a function from priors to posteriors, can be
generalized to accommodate this uncertainty.
",Evidence with Uncertain Likelihoods
"  Being able to analyze and interpret signal coming from electroencephalogram
(EEG) recording can be of high interest for many applications including medical
diagnosis and Brain-Computer Interfaces. Indeed, human experts are today able
to extract from this signal many hints related to physiological as well as
cognitive states of the recorded subject and it would be very interesting to
perform such task automatically but today no completely automatic system
exists. In previous studies, we have compared human expertise and automatic
processing tools, including artificial neural networks (ANN), to better
understand the competences of each and determine which are the difficult
aspects to integrate in a fully automatic system. In this paper, we bring more
elements to that study in reporting the main results of a practical experiment
which was carried out in an hospital for sleep pathology study. An EEG
recording was studied and labeled by a human expert and an ANN. We describe
here the characteristics of the experiment, both human and neuronal procedure
of analysis, compare their performances and point out the main limitations
which arise from this study.
","Neuronal Spectral Analysis of EEG and Expert Knowledge Integration for
  Automatic Classification of Sleep Stages"
"  Train timetabling is a difficult and very tightly constrained combinatorial
problem that deals with the construction of train schedules. We focus on the
particular problem of local reconstruction of the schedule following a small
perturbation, seeking minimisation of the total accumulated delay by adapting
times of departure and arrival for each train and allocation of resources
(tracks, routing nodes, etc.). We describe a permutation-based evolutionary
algorithm that relies on a semi-greedy heuristic to gradually reconstruct the
schedule by inserting trains one after the other following the permutation.
This algorithm can be hybridised with ILOG commercial MIP programming tool
CPLEX in a coarse-grained manner: the evolutionary part is used to quickly
obtain a good but suboptimal solution and this intermediate solution is refined
using CPLEX. Experimental results are presented on a large real-world case
involving more than one million variables and 2 million constraints. Results
are surprisingly good as the evolutionary algorithm, alone or hybridised,
produces excellent solutions much faster than CPLEX alone.
","An efficient memetic, permutation-based evolutionary algorithm for
  real-world train timetabling"
"  Evolutionary computing (EC) is an exciting development in Computer Science.
It amounts to building, applying and studying algorithms based on the Darwinian
principles of natural selection. In this paper we briefly introduce the main
concepts behind evolutionary computing. We present the main components all
evolutionary algorithms (EA), sketch the differences between different types of
EAs and survey application areas ranging from optimization, modeling and
simulation to entertainment.
",Evolutionary Computing
"  This article is taken out.
","Towards a Hierarchical Model of Consciousness, Intelligence, Mind and
  Body"
"  A fuzzy controller is usually designed by formulating the knowledge of a
human expert into a set of linguistic variables and fuzzy rules. Among the most
successful methods to automate the fuzzy controllers development process are
evolutionary algorithms. In this work, we propose the Recurrent Fuzzy Voronoi
(RFV) model, a representation for recurrent fuzzy systems. It is an extension
of the FV model proposed by Kavka and Schoenauer that extends the application
domain to include temporal problems. The FV model is a representation for fuzzy
controllers based on Voronoi diagrams that can represent fuzzy systems with
synergistic rules, fulfilling the $\epsilon$-completeness property and
providing a simple way to introduce a priory knowledge. In the proposed
representation, the temporal relations are embedded by including internal units
that provide feedback by connecting outputs to inputs. These internal units act
as memory elements. In the RFV model, the semantic of the internal units can be
specified together with the a priori rules. The geometric interpretation of the
rules allows the use of geometric variational operators during the evolution.
The representation and the algorithms are validated in two problems in the area
of system identification and evolutionary robotics.
",Evolution of Voronoi based Fuzzy Recurrent Controllers
"  When solving numerical constraints such as nonlinear equations and
inequalities, solvers often exploit pruning techniques, which remove redundant
value combinations from the domains of variables, at pruning steps. To find the
complete solution set, most of these solvers alternate the pruning steps with
branching steps, which split each problem into subproblems. This forms the
so-called branch-and-prune framework, well known among the approaches for
solving numerical constraints. The basic branch-and-prune search strategy that
uses domain bisections in place of the branching steps is called the bisection
search. In general, the bisection search works well in case (i) the solutions
are isolated, but it can be improved further in case (ii) there are continuums
of solutions (this often occurs when inequalities are involved). In this paper,
we propose a new branch-and-prune search strategy along with several variants,
which not only allow yielding better branching decisions in the latter case,
but also work as well as the bisection search does in the former case. These
new search algorithms enable us to employ various pruning techniques in the
construction of inner and outer approximations of the solution set. Our
experiments show that these algorithms speed up the solving process often by
one order of magnitude or more when solving problems with continuums of
solutions, while keeping the same performance as the bisection search when the
solutions are isolated.
",Branch-and-Prune Search Strategies for Numerical Constraint Solving
"  IS success is a complex concept, and its evaluation is complicated,
unstructured and not readily quantifiable. Numerous scientific publications
address the issue of success in the IS field as well as in other fields. But,
little efforts have been done for processing indeterminacy and uncertainty in
success research. This paper shows a formal method for mapping success using
Neutrosophic Success Map. This is an emerging tool for processing indeterminacy
and uncertainty in success research. EIS success have been analyzed using this
tool.
","Processing Uncertainty and Indeterminacy in Information Systems success
  mapping"
"  In this paper, a mathematical schema theory is developed. This theory has
three roots: brain theory schemas, grid automata, and block-shemas. In Section
2 of this paper, elements of the theory of grid automata necessary for the
mathematical schema theory are presented. In Section 3, elements of brain
theory necessary for the mathematical schema theory are presented. In Section
4, other types of schemas are considered. In Section 5, the mathematical schema
theory is developed. The achieved level of schema representation allows one to
model by mathematical tools virtually any type of schemas considered before,
including schemas in neurophisiology, psychology, computer science, Internet
technology, databases, logic, and mathematics.
",Mathematical Models in Schema Theory
"  Data-based classification is fundamental to most branches of science. While
recent years have brought enormous progress in various areas of statistical
computing and clustering, some general challenges in clustering remain: model
selection, robustness, and scalability to large datasets. We consider the
important problem of deciding on the optimal number of clusters, given an
arbitrary definition of space and clusteriness. We show how to construct a
cluster information criterion that allows objective model selection. Differing
from other approaches, our truecluster method does not require specific
assumptions about underlying distributions, dissimilarity definitions or
cluster models. Truecluster puts arbitrary clustering algorithms into a generic
unified (sampling-based) statistical framework. It is scalable to big datasets
and provides robust cluster assignments and case-wise diagnostics. Truecluster
will make clustering more objective, allows for automation, and will save time
and costs. Free R software is available.
",Truecluster: robust scalable clustering with model selection
"  An original approach, termed Divide-and-Evolve is proposed to hybridize
Evolutionary Algorithms (EAs) with Operational Research (OR) methods in the
domain of Temporal Planning Problems (TPPs). Whereas standard Memetic
Algorithms use local search methods to improve the evolutionary solutions, and
thus fail when the local method stops working on the complete problem, the
Divide-and-Evolve approach splits the problem at hand into several, hopefully
easier, sub-problems, and can thus solve globally problems that are intractable
when directly fed into deterministic OR algorithms. But the most prominent
advantage of the Divide-and-Evolve approach is that it immediately opens up an
avenue for multi-objective optimization, even though the OR method that is used
is single-objective. Proof of concept approach on the standard
(single-objective) Zeno transportation benchmark is given, and a small original
multi-objective benchmark is proposed in the same Zeno framework to assess the
multi-objective capabilities of the proposed methodology, a breakthrough in
Temporal Planning.
","Divide-and-Evolve: a New Memetic Scheme for Domain-Independent Temporal
  Planning"
"  This article considers evidence from physical and biological sciences to show
machines are deficient compared to biological systems at incorporating
intelligence. Machines fall short on two counts: firstly, unlike brains,
machines do not self-organize in a recursive manner; secondly, machines are
based on classical logic, whereas Nature's intelligence may depend on quantum
mechanics.
",Artificial and Biological Intelligence
"  Constraint Programming (CP) has proved an effective paradigm to model and
solve difficult combinatorial satisfaction and optimisation problems from
disparate domains. Many such problems arising from the commercial world are
permeated by data uncertainty. Existing CP approaches that accommodate
uncertainty are less suited to uncertainty arising due to incomplete and
erroneous data, because they do not build reliable models and solutions
guaranteed to address the user's genuine problem as she perceives it. Other
fields such as reliable computation offer combinations of models and associated
methods to handle these types of uncertain data, but lack an expressive
framework characterising the resolution methodology independently of the model.
  We present a unifying framework that extends the CP formalism in both model
and solutions, to tackle ill-defined combinatorial problems with incomplete or
erroneous data. The certainty closure framework brings together modelling and
solving methodologies from different fields into the CP paradigm to provide
reliable and efficient approches for uncertain constraint problems. We
demonstrate the applicability of the framework on a case study in network
diagnosis. We define resolution forms that give generic templates, and their
associated operational semantics, to derive practical solution methods for
reliable solutions.
","Certainty Closure: Reliable Constraint Reasoning with Incomplete or
  Erroneous Data"
"  The application of Genetic Programming to the discovery of empirical laws is
often impaired by the huge size of the search space, and consequently by the
computer resources needed. In many cases, the extreme demand for memory and CPU
is due to the massive growth of non-coding segments, the introns. The paper
presents a new program evolution framework which combines distribution-based
evolution in the PBIL spirit, with grammar-based genetic programming; the
information is stored as a probability distribution on the gra mmar rules,
rather than in a population. Experiments on a real-world like problem show that
this approach gives a practical solution to the problem of intron growth.
",Avoiding the Bloat with Stochastic Grammar-based Genetic Programming
"  This paper deals with the problem of classifying signals. The new method for
building so called local classifiers and local features is presented. The
method is a combination of the lifting scheme and the support vector machines.
Its main aim is to produce effective and yet comprehensible classifiers that
would help in understanding processes hidden behind classified signals. To
illustrate the method we present the results obtained on an artificial and a
real dataset.
",Classifying Signals with Local Classifiers
"  Open answer set programming (OASP) is an extension of answer set programming
where one may ground a program with an arbitrary superset of the program's
constants. We define a fixed point logic (FPL) extension of Clark's completion
such that open answer sets correspond to models of FPL formulas and identify a
syntactic subclass of programs, called (loosely) guarded programs. Whereas
reasoning with general programs in OASP is undecidable, the FPL translation of
(loosely) guarded programs falls in the decidable (loosely) guarded fixed point
logic (mu(L)GF). Moreover, we reduce normal closed ASP to loosely guarded OASP,
enabling for the first time, a characterization of an answer set semantics by
muLGF formulas. We further extend the open answer set semantics for programs
with generalized literals. Such generalized programs (gPs) have interesting
properties, e.g., the ability to express infinity axioms. We restrict the
syntax of gPs such that both rules and generalized literals are guarded. Via a
translation to guarded fixed point logic, we deduce 2-exptime-completeness of
satisfiability checking in such guarded gPs (GgPs). Bound GgPs are restricted
GgPs with exptime-complete satisfiability checking, but still sufficiently
expressive to optimally simulate computation tree logic (CTL). We translate
Datalog lite programs to GgPs, establishing equivalence of GgPs under an open
answer set semantics, alternation-free muGF, and Datalog lite.
",Open Answer Set Programming with Guarded Programs
"  Consistency check has been the only criterion for theory evaluation in
logic-based approaches to reasoning about actions. This work goes beyond that
and contributes to the metatheory of actions by investigating what other
properties a good domain description in reasoning about actions should have. We
state some metatheoretical postulates concerning this sore spot. When all
postulates are satisfied together we have a modular action theory. Besides
being easier to understand and more elaboration tolerant in McCarthy's sense,
modular theories have interesting properties. We point out the problems that
arise when the postulates about modularity are violated and propose algorithmic
checks that can help the designer of an action theory to overcome them.
",Metatheory of actions: beyond consistency
"  The estimation of linear causal models (also known as structural equation
models) from data is a well-known problem which has received much attention in
the past. Most previous work has, however, made an explicit or implicit
assumption of gaussianity, limiting the identifiability of the models. We have
recently shown (Shimizu et al, 2005; Hoyer et al, 2006) that for non-gaussian
distributions the full causal model can be estimated in the no hidden variables
case. In this contribution, we discuss the estimation of the model when
confounding latent variables are present. Although in this case uniqueness is
no longer guaranteed, there is at most a finite set of models which can fit the
data. We develop an algorithm for estimating this set, and describe numerical
simulations which confirm the theoretical arguments and demonstrate the
practical viability of the approach. Full Matlab code is provided for all
simulations.
","Estimation of linear, non-gaussian causal models in the presence of
  confounding latent variables"
"  Shock physics experiments are often complicated and expensive. As a result,
researchers are unable to conduct as many experiments as they would like -
leading to sparse data sets. In this paper, Support Vector Machines for
regression are applied to velocimetry data sets for shock damaged and melted
tin metal. Some success at interpolating between data sets is achieved.
Implications for future work are discussed.
","Application of Support Vector Regression to Interpolation of Sparse
  Shock Physics Data Sets"
"  In this paper, we study clustering with respect to the k-modes objective
function, a natural formulation of clustering for categorical data. One of the
main contributions of this paper is to establish the connection between k-modes
and k-median, i.e., the optimum of k-median is at most twice the optimum of
k-modes for the same categorical data clustering problem. Based on this
observation, we derive a deterministic algorithm that achieves an approximation
factor of 2. Furthermore, we prove that the distance measure in k-modes defines
a metric. Hence, we are able to extend existing approximation algorithms for
metric k-median to k-modes. Empirical results verify the superiority of our
method.
",Approximation Algorithms for K-Modes Clustering
"  A model of an organism as an autonomous intelligent system has been proposed.
This model was used to analyze learning of an organism in various environmental
conditions. Processes of learning were divided into two types: strong and weak
processes taking place in the absence and the presence of aprioristic
information about an object respectively. Weak learning is synonymous to
adaptation when aprioristic programs already available in a system (an
organism) are started. It was shown that strong learning is impossible for both
an organism and any autonomous intelligent system. It was shown also that the
knowledge base of an organism cannot be updated. Therefore, all behavior
programs of an organism are congenital. A model of a conditioned reflex as a
series of consecutive measurements of environmental parameters has been
advanced. Repeated measurements are necessary in this case to reduce the error
during decision making.
",Can an Organism Adapt Itself to Unforeseen Circumstances?
"  This paper presents two new promising rules of combination for the fusion of
uncertain and potentially highly conflicting sources of evidences in the
framework of the theory of belief functions in order to palliate the well-know
limitations of Dempster's rule and to work beyond the limits of applicability
of the Dempster-Shafer theory. We present both a new class of adaptive
combination rules (ACR) and a new efficient Proportional Conflict
Redistribution (PCR) rule allowing to deal with highly conflicting sources for
static and dynamic fusion applications.
","Adaptative combination rule and proportional conflict redistribution
  rule for information fusion"
"  Fuzzy automata, whose input alphabet is a set of numbers or symbols, are a
formal model of computing with values. Motivated by Zadeh's paradigm of
computing with words rather than numbers, Ying proposed a kind of fuzzy
automata, whose input alphabet consists of all fuzzy subsets of a set of
symbols, as a formal model of computing with all words. In this paper, we
introduce a somewhat general formal model of computing with (some special)
words. The new features of the model are that the input alphabet only comprises
some (not necessarily all) fuzzy subsets of a set of symbols and the fuzzy
transition function can be specified arbitrarily. By employing the methodology
of fuzzy control, we establish a retraction principle from computing with words
to computing with values for handling crisp inputs and a generalized extension
principle from computing with words to computing with all words for handling
fuzzy inputs. These principles show that computing with values and computing
with all words can be respectively implemented by computing with words. Some
algebraic properties of retractions and generalized extensions are addressed as
well.
",Retraction and Generalized Extension of Computing with Words
"  Through the Internet and the World-Wide Web, a vast number of information
sources has become available, which offer information on various subjects by
different providers, often in heterogeneous formats. This calls for tools and
methods for building an advanced information-processing infrastructure. One
issue in this area is the selection of suitable information sources in query
answering. In this paper, we present a knowledge-based approach to this
problem, in the setting where one among a set of information sources
(prototypically, data repositories) should be selected for evaluating a user
query. We use extended logic programs (ELPs) to represent rich descriptions of
the information sources, an underlying domain theory, and user queries in a
formal query language (here, XML-QL, but other languages can be handled as
well). Moreover, we use ELPs for declarative query analysis and generation of a
query description. Central to our approach are declarative source-selection
programs, for which we define syntax and semantics. Due to the structured
nature of the considered data items, the semantics of such programs must
carefully respect implicit context information in source-selection rules, and
furthermore combine it with possible user preferences. A prototype
implementation of our approach has been realized exploiting the DLV KR system
and its plp front-end for prioritized ELPs. We describe a representative
example involving specific movie databases, and report about experimental
results.
",A Knowledge-Based Approach for Selecting Information Sources
"  It is well known that perspective alignment plays a major role in the
planning and interpretation of spatial language. In order to understand the
role of perspective alignment and the cognitive processes involved, we have
made precise complete cognitive models of situated embodied agents that
self-organise a communication system for dialoging about the position and
movement of real world objects in their immediate surroundings. We show in a
series of robotic experiments which cognitive mechanisms are necessary and
sufficient to achieve successful spatial language and why and how perspective
alignment can take place, either implicitly or based on explicit marking.
",Perspective alignment in spatial language
"  We extend the 0-approximation of sensing actions and incomplete information
in [Son and Baral 2000] to action theories with static causal laws and prove
its soundness with respect to the possible world semantics. We also show that
the conditional planning problem with respect to this approximation is
NP-complete. We then present an answer set programming based conditional
planner, called ASCP, that is capable of generating both conformant plans and
conditional plans in the presence of sensing actions, incomplete information
about the initial state, and static causal laws. We prove the correctness of
our implementation and argue that our planner is sound and complete with
respect to the proposed approximation. Finally, we present experimental results
comparing ASCP to other planners.
","Reasoning and Planning with Sensing Actions, Incomplete Information, and
  Static Causal Laws using Answer Set Programming"
"  Computing and storing probabilities is a hard problem as soon as one has to
deal with complex distributions over multiple random variables. The problem of
efficient representation of probability distributions is central in term of
computational efficiency in the field of probabilistic reasoning. The main
problem arises when dealing with joint probability distributions over a set of
random variables: they are always represented using huge probability arrays. In
this paper, a new method based on binary-tree representation is introduced in
order to store efficiently very large joint distributions. Our approach
approximates any multidimensional joint distributions using an adaptive
discretization of the space. We make the assumption that the lower is the
probability mass of a particular region of feature space, the larger is the
discretization step. This assumption leads to a very optimized representation
in term of time and memory. The other advantages of our approach are the
ability to refine dynamically the distribution every time it is needed leading
to a more accurate representation of the probability distribution and to an
anytime representation of the distribution.
","Approximate Discrete Probability Distribution Representation using a
  Multi-Resolution Binary Tree"
"  In order to more effectively cope with the real-world problems of vagueness,
{\it fuzzy discrete event systems} (FDESs) were proposed recently, and the
supervisory control theory of FDESs was developed. In view of the importance of
failure diagnosis, in this paper, we present an approach of the failure
diagnosis in the framework of FDESs. More specifically: (1) We formalize the
definition of diagnosability for FDESs, in which the observable set and failure
set of events are {\it fuzzy}, that is, each event has certain degree to be
observable and unobservable, and, also, each event may possess different
possibility of failure occurring. (2) Through the construction of
observability-based diagnosers of FDESs, we investigate its some basic
properties. In particular, we present a necessary and sufficient condition for
diagnosability of FDESs. (3) Some examples serving to illuminate the
applications of the diagnosability of FDESs are described. To conclude, some
related issues are raised for further consideration.
",Diagnosability of Fuzzy Discrete Event Systems
"  Classification of ordinal data is one of the most important tasks of relation
learning. In this thesis a novel framework for ordered classes is proposed. The
technique reduces the problem of classifying ordered classes to the standard
two-class problem. The introduced method is then mapped into support vector
machines and neural networks. Compared with a well-known approach using
pairwise objects as training samples, the new algorithm has a reduced
complexity and training time. A second novel model, the unimodal model, is also
introduced and a parametric version is mapped into neural networks. Several
case studies are presented to assert the validity of the proposed models.
",Classification of Ordinal Data
"  Imagination is the critical point in developing of realistic artificial
intelligence (AI) systems. One way to approach imagination would be simulation
of its properties and operations. We developed two models: AI-Brain Network
Hierarchy of Languages and Semantical Holographic Calculus as well as
simulation system ScriptWriter that emulate the process of imagination through
an automatic animation of English texts. The purpose of this paper is to
demonstrate the model and to present ScriptWriter system
http://nvo.sdsc.edu/NVO/JCSG/get_SRB_mime_file2.cgi//home/tamara.sdsc/test/demo.zip?F=/home/tamara.sdsc/test/demo.zip&M=application/x-gtar
for simulation of the imagination.
",Imagination as Holographic Processor for Text Animation
"  In Dempster-Shafer belief theory, general beliefs are expressed as belief
mass distribution functions over frames of discernment. In Subjective Logic
beliefs are expressed as belief mass distribution functions over binary frames
of discernment. Belief representations in Subjective Logic, which are called
opinions, also contain a base rate parameter which express the a priori belief
in the absence of evidence. Philosophically, beliefs are quantitative
representations of evidence as perceived by humans or by other intelligent
agents. The basic operators of classical probability calculus, such as addition
and multiplication, can be applied to opinions, thereby making belief calculus
practical. Through the equivalence between opinions and Beta probability
density functions, this also provides a calculus for Beta probability density
functions. This article explains the basic elements of belief calculus.
",Belief Calculus
"  The problem of combining beliefs in the Dempster-Shafer belief theory has
attracted considerable attention over the last two decades. The classical
Dempster's Rule has often been criticised, and many alternative rules for
belief combination have been proposed in the literature. The consensus operator
for combining beliefs has nice properties and produces more intuitive results
than Dempster's rule, but has the limitation that it can only be applied to
belief distribution functions on binary state spaces. In this paper we present
a generalisation of the consensus operator that can be applied to Dirichlet
belief functions on state spaces of arbitrary size. This rule, called the
cumulative rule of belief combination, can be derived from classical
statistical theory, and corresponds well with human intuition.
",The Cumulative Rule for Belief Fusion
"  Artificial Intelligence (AI) has recently become a real formal science: the
new millennium brought the first mathematically sound, asymptotically optimal,
universal problem solvers, providing a new, rigorous foundation for the
previously largely heuristic field of General AI and embedded agents. At the
same time there has been rapid progress in practical methods for learning true
sequence-processing programs, as opposed to traditional methods limited to
stationary pattern association. Here we will briefly review some of the new
results, and speculate about future developments, pointing out that the time
intervals between the most notable events in over 40,000 years or 2^9 lifetimes
of human history have sped up exponentially, apparently converging to zero
within the next few decades. Or is this impression just a by-product of the way
humans allocate memory space to past events?
",New Millennium AI and the Convergence of History
"  In this paper we propose a new family of Belief Conditioning Rules (BCRs) for
belief revision. These rules are not directly related with the fusion of
several sources of evidence but with the revision of a belief assignment
available at a given time according to the new truth (i.e. conditioning
constraint) one has about the space of solutions of the problem.
",Belief Conditioning Rules (BCRs)
"  In this note we introduce the notion of islands for restricting local search.
We show how we can construct islands for CNF SAT problems, and how much search
space can be eliminated by restricting search to the island.
",Islands for SAT
"  Knowing the norms of a domain is crucial, but there exist no repository of
norms. We propose a method to extract them from texts: texts generally do not
describe a norm, but rather how a state-of-affairs differs from it. Answers
concerning the cause of the state-of-affairs described often reveal the
implicit norm. We apply this idea to the domain of driving, and validate it by
designing algorithms that identify, in a text, the ""basic"" norms to which it
refers implicitly.
",About Norms and Causes
"  Norms are essential to extend inference: inferences based on norms are far
richer than those based on logical implications. In the recent decades, much
effort has been devoted to reason on a domain, once its norms are represented.
How to extract and express those norms has received far less attention.
Extraction is difficult: as the readers are supposed to know them, the norms of
a domain are seldom made explicit. For one thing, extracting norms requires a
language to represent them, and this is the topic of this paper. We apply this
language to represent norms in the domain of driving, and show that it is
adequate to reason on the causes of accidents, as described by car-crash
reports.
",Representing Knowledge about Norms
"  In this paper we consider and analyze the behavior of two combinational rules
for temporal (sequential) attribute data fusion for target type estimation. Our
comparative analysis is based on Dempster's fusion rule proposed in
Dempster-Shafer Theory (DST) and on the Proportional Conflict Redistribution
rule no. 5 (PCR5) recently proposed in Dezert-Smarandache Theory (DSmT). We
show through very simple scenario and Monte-Carlo simulation, how PCR5 allows a
very efficient Target Type Tracking and reduces drastically the latency delay
for correct Target Type decision with respect to Demspter's rule. For cases
presenting some short Target Type switches, Demspter's rule is proved to be
unable to detect the switches and thus to track correctly the Target Type
changes. The approach proposed here is totally new, efficient and promising to
be incorporated in real-time Generalized Data Association - Multi Target
Tracking systems (GDA-MTT) and provides an important result on the behavior of
PCR5 with respect to Dempster's rule. The MatLab source code is provided in
","Target Type Tracking with PCR5 and Dempster's rules: A Comparative
  Analysis"
"  This paper introduces the notion of qualitative belief assignment to model
beliefs of human experts expressed in natural language (with linguistic
labels). We show how qualitative beliefs can be efficiently combined using an
extension of Dezert-Smarandache Theory (DSmT) of plausible and paradoxical
quantitative reasoning to qualitative reasoning. We propose a new arithmetic on
linguistic labels which allows a direct extension of classical DSm fusion rule
or DSm Hybrid rules. An approximate qualitative PCR5 rule is also proposed
jointly with a Qualitative Average Operator. We also show how crisp or interval
mappings can be used to deal indirectly with linguistic labels. A very simple
example is provided to illustrate our qualitative fusion rules.
",Fusion of qualitative beliefs using DSmT
"  The management and combination of uncertain, imprecise, fuzzy and even
paradoxical or high conflicting sources of information has always been, and
still remains today, of primal importance for the development of reliable
modern information systems involving artificial reasoning. In this
introduction, we present a survey of our recent theory of plausible and
paradoxical reasoning, known as Dezert-Smarandache Theory (DSmT) in the
literature, developed for dealing with imprecise, uncertain and paradoxical
sources of information. We focus our presentation here rather on the
foundations of DSmT, and on the two important new rules of combination, than on
browsing specific applications of DSmT available in literature. Several simple
examples are given throughout the presentation to show the efficiency and the
generality of this new approach.
","An Introduction to the DSm Theory for the Combination of Paradoxical,
  Uncertain, and Imprecise Sources of Information"
"  We study an alternative to the prevailing approach to modelling qualitative
spatial reasoning (QSR) problems as constraint satisfaction problems. In the
standard approach, a relation between objects is a constraint whereas in the
alternative approach it is a variable. The relation-variable approach greatly
simplifies integration and implementation of QSR. To substantiate this point,
we discuss several QSR algorithms from the literature which in the
relation-variable approach reduce to the customary constraint propagation
algorithm enforcing generalised arc-consistency.
",Relation Variables in Qualitative Spatial Reasoning
"  I explore the use of sets of probability measures as a representation of
uncertainty.
",Using Sets of Probability Measures to Represent Uncertainty
"  We present a state-based regression function for planning domains where an
agent does not have complete information and may have sensing actions. We
consider binary domains and employ a three-valued characterization of domains
with sensing actions to define the regression function. We prove the soundness
and completeness of our regression formulation with respect to the definition
of progression. More specifically, we show that (i) a plan obtained through
regression for a planning problem is indeed a progression solution of that
planning problem, and that (ii) for each plan found through progression, using
regression one obtains that plan or an equivalent one.
","A State-Based Regression Formulation for Domains with Sensing
  Actions<br> and Incomplete Information"
"  A modification of OWL-S regarding parameter description is proposed. It is
strictly based on Description Logic. In addition to class description of
parameters it also allows the modelling of relations between parameters and the
precise description of the size of data to be supplied to a service. In
particular, it solves two major issues identified within current proposals for
a Semantic Web Service annotation standard.
",Semantic Description of Parameters in Web Service Annotations
"  The paper describes the ALVIS annotation format designed for the indexing of
large collections of documents in topic-specific search engines. This paper is
exemplified on the biological domain and on MedLine abstracts, as developing a
specialized search engine for biologists is one of the ALVIS case studies. The
ALVIS principle for linguistic annotations is based on existing works and
standard propositions. We made the choice of stand-off annotations rather than
inserted mark-up. Annotations are encoded as XML elements which form the
linguistic subsection of the document record.
",The ALVIS Format for Linguistically Annotated Documents
"  The aim of this paper is to provide a sound framework for addressing a
difficult problem: the automatic construction of an autonomous agent's modular
architecture. We combine results from two apparently uncorrelated domains:
Autonomous planning through Markov Decision Processes and a General Data
Clustering Approach using a kernel-like method. Our fundamental idea is that
the former is a good framework for addressing autonomy whereas the latter
allows to tackle self-organizing problems.
",Modular self-organization
"  In this paper we elaborate on a specific application in the context of hybrid
description logic programs (hybrid DLPs), namely description logic Semantic Web
type systems (DL-types) which are used for term typing of LP rules based on a
polymorphic, order-sorted, hybrid DL-typed unification as procedural semantics
of hybrid DLPs. Using Semantic Web ontologies as type systems facilitates
interchange of domain-independent rules over domain boundaries via dynamically
typing and mapping of explicitly defined type ontologies.
","A Typed Hybrid Description Logic Programming Language with Polymorphic
  Order-Sorted DL-Typed Unification for Semantic Web Type Systems"
"  In this paper we describe an architecture of a system that answer the
question : Why did the accident happen? from the textual description of an
accident. We present briefly the different parts of the architecture and then
we describe with more detail the semantic part of the system i.e. the part in
which the norm-based reasoning is performed on the explicit knowlege extracted
from the text.
",Why did the accident happen? A norm-based reasoning approach
"  We develop a system which must be able to perform the same inferences that a
human reader of an accident report can do and more particularly to determine
the apparent causes of the accident. We describe the general framework in which
we are situated, linguistic and semantic levels of the analysis and the
inference rules used by the system.
",Une exp\'{e}rience de s\'{e}mantique inf\'{e}rentielle
"  The k-modes algorithm has become a popular technique in solving categorical
data clustering problems in different application domains. However, the
algorithm requires random selection of initial points for the clusters.
Different initial points often lead to considerable distinct clustering
results. In this paper we present an experimental study on applying a
farthest-point heuristic based initialization method to k-modes clustering to
improve its performance. Experiments show that new initialization method leads
to better clustering accuracy than random selection initialization method for
k-modes clustering.
","Farthest-Point Heuristic based Initialization Methods for K-Modes
  Clustering"
"  The opening book is an important component of a chess engine, and thus
computer chess programmers have been developing automated methods to improve
the quality of their books. For chess, which has a very rich opening theory,
large databases of high-quality games can be used as the basis of an opening
book, from which statistics relating to move choices from given positions can
be collected. In order to find out whether the opening books used by modern
chess engines in machine versus machine competitions are ``comparable'' to
those used by chess players in human versus human competitions, we carried out
analysis on 26 test positions using statistics from two opening books one
compiled from humans' games and the other from machines' games. Our analysis
using several nonparametric measures, shows that, overall, there is a strong
association between humans' and machines' choices of opening moves when using a
book to guide their choices.
",Comparing Typical Opening Move Choices Made by Humans and Chess Engines
"  We present a new local approximation algorithm for computing Maximum a
Posteriori (MAP) and log-partition function for arbitrary exponential family
distribution represented by a finite-valued pair-wise Markov random field
(MRF), say $G$. Our algorithm is based on decomposition of $G$ into {\em
appropriately} chosen small components; then computing estimates locally in
each of these components and then producing a {\em good} global solution. We
show that if the underlying graph $G$ either excludes some finite-sized graph
as its minor (e.g. Planar graph) or has low doubling dimension (e.g. any graph
with {\em geometry}), then our algorithm will produce solution for both
questions within {\em arbitrary accuracy}. We present a message-passing
implementation of our algorithm for MAP computation using self-avoiding walk of
graph. In order to evaluate the computational cost of this implementation, we
derive novel tight bounds on the size of self-avoiding walk tree for arbitrary
graph.
  As a consequence of our algorithmic result, we show that the normalized
log-partition function (also known as free-energy) for a class of {\em regular}
MRFs will converge to a limit, that is computable to an arbitrary accuracy.
",Local approximate inference algorithms
"  Creation procedure of associative patterns ensemble in terms of formal logic
with using neural net-work (NN) model is formulated. It is shown that the
associative patterns set is created by means of unique procedure of NN work
which having individual parameters of entrance stimulus transformation. It is
ascer-tained that the quantity of the selected associative patterns possesses
is a constant.
",Constant for associative patterns ensemble
"  In case-based reasoning, the adaptation step depends in general on
domain-dependent knowledge, which motivates studies on adaptation knowledge
acquisition (AKA). CABAMAKA is an AKA system based on principles of knowledge
discovery from databases. This system explores the variations within the case
base to elicit adaptation knowledge. It has been successfully tested in an
application of case-based decision support to breast cancer treatment.
",Adaptation Knowledge Discovery from a Case Base
"  Recently, the diagnosability of {\it stochastic discrete event systems}
(SDESs) was investigated in the literature, and, the failure diagnosis
considered was {\it centralized}. In this paper, we propose an approach to {\it
decentralized} failure diagnosis of SDESs, where the stochastic system uses
multiple local diagnosers to detect failures and each local diagnoser possesses
its own information. In a way, the centralized failure diagnosis of SDESs can
be viewed as a special case of the decentralized failure diagnosis presented in
this paper with only one projection. The main contributions are as follows: (1)
We formalize the notion of codiagnosability for stochastic automata, which
means that a failure can be detected by at least one local stochastic diagnoser
within a finite delay. (2) We construct a codiagnoser from a given stochastic
automaton with multiple projections, and the codiagnoser associated with the
local diagnosers is used to test codiagnosability condition of SDESs. (3) We
deal with a number of basic properties of the codiagnoser. In particular, a
necessary and sufficient condition for the codiagnosability of SDESs is
presented. (4) We give a computing method in detail to check whether
codiagnosability is violated. And (5) some examples are described to illustrate
the applications of the codiagnosability and its computing method.
",Decentralized Failure Diagnosis of Stochastic Discrete Event Systems
"  The management and combination of uncertain, imprecise, fuzzy and even
paradoxical or high conflicting sources of information has always been and
still remains of primal importance for the development of reliable information
fusion systems. In this short survey paper, we present the theory of plausible
and paradoxical reasoning, known as DSmT (Dezert-Smarandache Theory) in
literature, developed for dealing with imprecise, uncertain and potentially
highly conflicting sources of information. DSmT is a new paradigm shift for
information fusion and recent publications have shown the interest and the
potential ability of DSmT to solve fusion problems where Dempster's rule used
in Dempster-Shafer Theory (DST) provides counter-intuitive results or fails to
provide useful result at all. This paper is focused on the foundations of DSmT
and on its main rules of combination (classic, hybrid and Proportional Conflict
Redistribution rules). Shafer's model on which is based DST appears as a
particular and specific case of DSm hybrid model which can be easily handled by
DSmT as well. Several simple but illustrative examples are given throughout
this paper to show the interest and the generality of this new theory.
",DSmT: A new paradigm shift for information fusion
"  Reaction RuleML is a general, practical, compact and user-friendly
XML-serialized language for the family of reaction rules. In this white paper
we give a review of the history of event / action /state processing and
reaction rule approaches and systems in different domains, define basic
concepts and give a classification of the event, action, state processing and
reasoning space as well as a discussion of relevant / related work
","The Reaction RuleML Classification of the Event / Action / State
  Processing and Reasoning Space"
"  A fuzzy logic based classification engine has been developed for classifying
mass spectra obtained with an imaging internal source Fourier transform mass
spectrometer (I^2LD-FTMS). Traditionally, an operator uses the relative
abundance of ions with specific mass-to-charge (m/z) ratios to categorize
spectra. An operator does this by comparing the spectrum of m/z versus
abundance of an unknown sample against a library of spectra from known samples.
Automated positioning and acquisition allow I^2LD-FTMS to acquire data from
very large grids, this would require classification of up to 3600 spectrum per
hour to keep pace with the acquisition. The tedious job of classifying numerous
spectra generated in an I^2LD-FTMS imaging application can be replaced by a
fuzzy rule base if the cues an operator uses can be encapsulated. We present
the translation of linguistic rules to a fuzzy classifier for mineral phases in
basalt. This paper also describes a method for gathering statistics on ions,
which are not currently used in the rule base, but which may be candidates for
making the rule base more accurate and complete or to form new rule bases based
on data obtained from known samples. A spatial method for classifying spectra
with low membership values, based on neighboring sample classifications, is
also presented.
","Fuzzy Logic Classification of Imaging Laser Desorption Fourier Transform
  Mass Spectrometry Data"
"  Description Logics (DLs) are appropriate, widely used, logics for managing
structured knowledge. They allow reasoning about individuals and concepts, i.e.
set of individuals with common properties. Typically, DLs are limited to
dealing with crisp, well defined concepts. That is, concepts for which the
problem whether an individual is an instance of it is yes/no question. More
often than not, the concepts encountered in the real world do not have a
precisely defined criteria of membership: we may say that an individual is an
instance of a concept only to a certain degree, depending on the individual's
properties. The DLs that deal with such fuzzy concepts are called fuzzy DLs. In
order to deal with fuzzy, incomplete, indeterminate and inconsistent concepts,
we need to extend the fuzzy DLs, combining the neutrosophic logic with a
classical DL. In particular, concepts become neutrosophic (here neutrosophic
means fuzzy, incomplete, indeterminate, and inconsistent), thus reasoning about
neutrosophic concepts is supported. We'll define its syntax, its semantics, and
describe its properties.
",A Neutrosophic Description Logic
"  Support Vector Machines (SVMs) are well-established Machine Learning (ML)
algorithms. They rely on the fact that i) linear learning can be formalized as
a well-posed optimization problem; ii) non-linear learning can be brought into
linear learning thanks to the kernel trick and the mapping of the initial
search space onto a high dimensional feature space. The kernel is designed by
the ML expert and it governs the efficiency of the SVM approach. In this paper,
a new approach for the automatic design of kernels by Genetic Programming,
called the Evolutionary Kernel Machine (EKM), is presented. EKM combines a
well-founded fitness function inspired from the margin criterion, and a
co-evolution framework ensuring the computational scalability of the approach.
Empirical validation on standard ML benchmark demonstrates that EKM is
competitive using state-of-the-art SVMs with tuned hyper-parameters.
","Genetic Programming for Kernel-based Learning with Co-evolving Subsets
  Selection"
"  Functional brain imaging is a source of spatio-temporal data mining problems.
A new framework hybridizing multi-objective and multi-modal optimization is
proposed to formalize these data mining problems, and addressed through
Evolutionary Computation (EC). The merits of EC for spatio-temporal data mining
are demonstrated as the approach facilitates the modelling of the experts'
requirements, and flexibly accommodates their changing goals.
","Functional Brain Imaging with Multi-Objective Multi-Modal Evolutionary
  Optimization"
"  The paper suggests the use of Multi-Valued Decision Diagrams (MDDs) as the
supporting data structure for a generic global constraint. We give an algorithm
for maintaining generalized arc consistency (GAC) on this constraint that
amortizes the cost of the GAC computation over a root-to-terminal path in the
search tree. The technique used is an extension of the GAC algorithm for the
regular language constraint on finite length input. Our approach adds support
for skipped variables, maintains the reduced property of the MDD dynamically
and provides domain entailment detection. Finally we also show how to adapt the
approach to constraint types that are closely related to MDDs, such as AOMDDs
and Case DAGs.
",A Generic Global Constraint based on MDDs
"  Did natural consciousness and intelligent systems arise out of a path that
was co-evolutionary to evolution? Can we explain human self-consciousness as
having risen out of such an evolutionary path? If so how could it have been?
  In this first part of a two-part paper (titled IXI), we take a learning
system perspective to the problem of consciousness and intelligent systems, an
approach that may look unseasonable in this age of fMRI's and high tech
neuroscience.
  We posit conscious intelligent systems in natural environments and wonder how
natural factors influence their design paths. Such a perspective allows us to
explain seamlessly a variety of natural factors, factors ranging from the rise
and presence of the human mind, man's sense of I, his self-consciousness and
his looping thought processes to factors like reproduction, incubation,
extinction, sleep, the richness of natural behavior, etc. It even allows us to
speculate on a possible human evolution scenario and other natural phenomena.
",Conscious Intelligent Systems - Part 1 : I X I
"  This is the second part of a paper on Conscious Intelligent Systems. We use
the understanding gained in the first part (Conscious Intelligent Systems Part
1: IXI (arxiv id cs.AI/0612056)) to look at understanding. We see how the
presence of mind affects understanding and intelligent systems; we see that the
presence of mind necessitates language. The rise of language in turn has
important effects on understanding. We discuss the humanoid question and how
the question of self-consciousness (and by association mind/thought/language)
would affect humanoids too.
","Conscious Intelligent Systems - Part II - Mind, Thought, Language and
  Understanding"
"  A product configurator which is complete, backtrack free and able to compute
the valid domains at any state of the configuration can be constructed by
building a Binary Decision Diagram (BDD). Despite the fact that the size of the
BDD is exponential in the number of variables in the worst case, BDDs have
proved to work very well in practice. Current BDD-based techniques can only
handle interactive configuration with small finite domains. In this paper we
extend the approach to handle string variables constrained by regular
expressions. The user is allowed to change the strings by adding letters at the
end of the string. We show how to make a data structure that can perform fast
valid domain computations given some assignment on the set of string variables.
  We first show how to do this by using one large DFA. Since this approach is
too space consuming to be of practical use, we construct a data structure that
simulates the large DFA and in most practical cases are much more space
efficient. As an example a configuration problem on $n$ string variables with
only one solution in which each string variable is assigned to a value of
length of $k$ the former structure will use $\Omega(k^n)$ space whereas the
latter only need $O(kn)$. We also show how this framework easily can be
combined with the recent BDD techniques to allow both boolean, integer and
string variables in the configuration problem.
",Interactive Configuration by Regular String Constraints
"  Recently, M. Chertkov and V.Y. Chernyak derived an exact expression for the
partition sum (normalization constant) corresponding to a graphical model,
which is an expansion around the Belief Propagation solution. By adding
correction terms to the BP free energy, one for each ""generalized loop"" in the
factor graph, the exact partition sum is obtained. However, the usually
enormous number of generalized loops generally prohibits summation over all
correction terms. In this article we introduce Truncated Loop Series BP
(TLSBP), a particular way of truncating the loop series of M. Chertkov and V.Y.
Chernyak by considering generalized loops as compositions of simple loops. We
analyze the performance of TLSBP in different scenarios, including the Ising
model, regular random graphs and on Promedas, a large probabilistic medical
diagnostic system. We show that TLSBP often improves upon the accuracy of the
BP solution, at the expense of increased computation time. We also show that
the performance of TLSBP strongly depends on the degree of interaction between
the variables. For weak interactions, truncating the series leads to
significant improvements, whereas for strong interactions it can be
ineffective, even if a high number of terms is considered.
",Truncating the loop series expansion for Belief Propagation
"  In this paper, the traditional k-modes clustering algorithm is extended by
weighting attribute value matches in dissimilarity computation. The use of
attribute value weighting technique makes it possible to generate clusters with
stronger intra-similarities, and therefore achieve better clustering
performance. Experimental results on real life datasets show that these value
weighting based k-modes algorithms are superior to the standard k-modes
algorithm with respect to clustering accuracy.
",Attribute Value Weighting in K-Modes Clustering
"  In Verification and in (optimal) AI Planning, a successful method is to
formulate the application as boolean satisfiability (SAT), and solve it with
state-of-the-art DPLL-based procedures. There is a lack of understanding of why
this works so well. Focussing on the Planning context, we identify a form of
problem structure concerned with the symmetrical or asymmetrical nature of the
cost of achieving the individual planning goals. We quantify this sort of
structure with a simple numeric parameter called AsymRatio, ranging between 0
and 1. We run experiments in 10 benchmark domains from the International
Planning Competitions since 2000; we show that AsymRatio is a good indicator of
SAT solver performance in 8 of these domains. We then examine carefully crafted
synthetic planning domains that allow control of the amount of structure, and
that are clean enough for a rigorous analysis of the combinatorial search
space. The domains are parameterized by size, and by the amount of structure.
The CNFs we examine are unsatisfiable, encoding one planning step less than the
length of the optimal plan. We prove upper and lower bounds on the size of the
best possible DPLL refutations, under different settings of the amount of
structure, as a function of size. We also identify the best possible sets of
branching variables (backdoors). With minimum AsymRatio, we prove exponential
lower bounds, and identify minimal backdoors of size linear in the number of
variables. With maximum AsymRatio, we identify logarithmic DPLL refutations
(and backdoors), showing a doubly exponential gap between the two structural
extreme cases. The reasons for this behavior -- the proof arguments --
illuminate the prototypical patterns of structure causing the empirical
behavior observed in the competition benchmarks.
","Structure and Problem Hardness: Goal Asymmetry and DPLL Proofs in<br>
  SAT-Based Planning"
"  This short paper introduces two new fusion rules for combining quantitative
basic belief assignments. These rules although very simple have not been
proposed in literature so far and could serve as useful alternatives because of
their low computation cost with respect to the recent advanced Proportional
Conflict Redistribution rules developed in the DSmT framework.
",Uniform and Partially Uniform Redistribution Rules
"  Constraint Programming (CP) has been successfully applied to both constraint
satisfaction and constraint optimization problems. A wide variety of
specialized global constraints provide critical assistance in achieving a good
model that can take advantage of the structure of the problem in the search for
a solution. However, a key outstanding issue is the representation of 'ad-hoc'
constraints that do not have an inherent combinatorial nature, and hence are
not modeled well using narrowly specialized global constraints. We attempt to
address this issue by considering a hybrid of search and compilation.
Specifically we suggest the use of Reduced Ordered Multi-Valued Decision
Diagrams (ROMDDs) as the supporting data structure for a generic global
constraint. We give an algorithm for maintaining generalized arc consistency
(GAC) on this constraint that amortizes the cost of the GAC computation over a
root-to-leaf path in the search tree without requiring asymptotically more
space than used for the MDD. Furthermore we present an approach for
incrementally maintaining the reduced property of the MDD during the search,
and show how this can be used for providing domain entailment detection.
Finally we discuss how to apply our approach to other similar data structures
such as AOMDDs and Case DAGs. The technique used can be seen as an extension of
the GAC algorithm for the regular language constraint on finite length input.
",Generic Global Constraints based on MDDs
"  For academics and practitioners concerned with computers, business and
mathematics, one central issue is supporting decision makers. In this paper, we
propose a generalization of Decision Matrix Method (DMM), using Neutrosophic
logic. It emerges as an alternative to the existing logics and it represents a
mathematical model of uncertainty and indeterminacy. This paper proposes the
Neutrosophic Decision Matrix Method as a more realistic tool for decision
making. In addition, a de-neutrosophication process is included.
","Redesigning Decision Matrix Method with an indeterminacy-based inference
  process"
"  This paper constructs a tree structure for the music rhythm using the
L-system. It models the structure as an automata and derives its complexity. It
also solves the complexity for the L-system. This complexity can resolve the
similarity between trees. This complexity serves as a measure of psychological
complexity for rhythms. It resolves the music complexity of various
compositions including the Mozart effect K488.
  Keyword: music perception, psychological complexity, rhythm, L-system,
automata, temporal associative memory, inverse problem, rewriting rule,
bracketed string, tree similarity
",Modelling Complexity in Musical Rhythm
"  Using qualitative reasoning with geographic information, contrarily, for
instance, with robotics, looks not only fastidious (i.e.: encoding knowledge
Propositional Logics PL), but appears to be computational complex, and not
tractable at all, most of the time. However, knowledge fusion or revision, is a
common operation performed when users merge several different data sets in a
unique decision making process, without much support. Introducing logics would
be a great improvement, and we propose in this paper, means for deciding -a
priori- if one application can benefit from a complete revision, under only the
assumption of a conjecture that we name the ""containment conjecture"", which
limits the size of the minimal conflicts to revise. We demonstrate that this
conjecture brings us the interesting computational property of performing a
not-provable but global, revision, made of many local revisions, at a tractable
size. We illustrate this approach on an application.
","Space-contained conflict revision, for geographic information"
"  In case-based reasoning, the adaptation of a source case in order to solve
the target problem is at the same time crucial and difficult to implement. The
reason for this difficulty is that, in general, adaptation strongly depends on
domain-dependent knowledge. This fact motivates research on adaptation
knowledge acquisition (AKA). This paper presents an approach to AKA based on
the principles and techniques of knowledge discovery from databases and
data-mining. It is implemented in CABAMAKA, a system that explores the
variations within the case base to elicit adaptation knowledge. This system has
been successfully tested in an application of case-based reasoning to decision
support in the domain of breast cancer treatment.
",Case Base Mining for Adaptation Knowledge Acquisition
"  Because of their occasional need to return to shallow points in a search
tree, existing backtracking methods can sometimes erase meaningful progress
toward solving a search problem. In this paper, we present a method by which
backtrack points can be moved deeper in the search space, thereby avoiding this
difficulty. The technique developed is a variant of dependency-directed
backtracking that uses only polynomial space while still providing useful
control information and retaining the completeness guarantees provided by
earlier approaches.
",Dynamic Backtracking
"  Market price systems constitute a well-understood class of mechanisms that
under certain conditions provide effective decentralization of decision making
with minimal communication overhead. In a market-oriented programming approach
to distributed problem solving, we derive the activities and resource
allocations for a set of computational agents by computing the competitive
equilibrium of an artificial economy. WALRAS provides basic constructs for
defining computational market structures, and protocols for deriving their
corresponding price equilibria. In a particular realization of this approach
for a form of multicommodity flow problem, we see that careful construction of
the decision process according to economic principles can lead to efficient
distributed resource allocation, and that the behavior of the system can be
meaningfully analyzed in economic terms.
","A Market-Oriented Programming Environment and its Application to
  Distributed Multicommodity Flow Problems"
"  We describe an extensive study of search in GSAT, an approximation procedure
for propositional satisfiability. GSAT performs greedy hill-climbing on the
number of satisfied clauses in a truth assignment. Our experiments provide a
more complete picture of GSAT's search than previous accounts. We describe in
detail the two phases of search: rapid hill-climbing followed by a long plateau
search. We demonstrate that when applied to randomly generated 3SAT problems,
there is a very simple scaling with problem size for both the mean number of
satisfied clauses and the mean branching rate. Our results allow us to make
detailed numerical conjectures about the length of the hill-climbing phase, the
average gradient of this phase, and to conjecture that both the average score
and average branching rate decay exponentially during plateau search. We end by
showing how these results can be used to direct future theoretical analysis.
This work provides a case study of how computer experiments can be used to
improve understanding of the theoretical properties of algorithms.
",An Empirical Analysis of Search in GSAT
"  As real logic programmers normally use cut (!), an effective learning
procedure for logic programs should be able to deal with it. Because the cut
predicate has only a procedural meaning, clauses containing cut cannot be
learned using an extensional evaluation method, as is done in most learning
systems. On the other hand, searching a space of possible programs (instead of
a space of independent clauses) is unfeasible. An alternative solution is to
generate first a candidate base program which covers the positive examples, and
then make it consistent by inserting cut where appropriate. The problem of
learning programs with cut has not been investigated before and this seems to
be a natural and reasonable approach. We generalize this scheme and investigate
the difficulties that arise. Some of the major shortcomings are actually
caused, in general, by the need for intensional evaluation. As a conclusion,
the analysis of this paper suggests, on precise and technical grounds, that
learning cut is difficult, and current induction techniques should probably be
restricted to purely declarative logic languages.
",The Difficulties of Learning Logic Programs with Cut
"  To support the goal of allowing users to record and retrieve information,
this paper describes an interactive note-taking system for pen-based computers
with two distinctive features. First, it actively predicts what the user is
going to write. Second, it automatically constructs a custom, button-box user
interface on request. The system is an example of a learning-apprentice
software- agent. A machine learning component characterizes the syntax and
semantics of the user's information. A performance system uses this learned
information to generate completion strings and construct a user interface.
Description of Online Appendix: People like to record information. Doing this
on paper is initially efficient, but lacks flexibility. Recording information
on a computer is less efficient but more powerful. In our new note taking
softwre, the user records information directly on a computer. Behind the
interface, an agent acts for the user. To help, it provides defaults and
constructs a custom user interface. The demonstration is a QuickTime movie of
the note taking agent in action. The file is a binhexed self-extracting
archive. Macintosh utilities for binhex are available from
mac.archive.umich.edu. QuickTime is available from ftp.apple.com in the
dts/mac/sys.soft/quicktime.
",Software Agents: Completing Patterns and Constructing User Interfaces
"  Terminological knowledge representation systems (TKRSs) are tools for
designing and using knowledge bases that make use of terminological languages
(or concept languages). We analyze from a theoretical point of view a TKRS
whose capabilities go beyond the ones of presently available TKRSs. The new
features studied, often required in practical applications, can be summarized
in three main points. First, we consider a highly expressive terminological
language, called ALCNR, including general complements of concepts, number
restrictions and role conjunction. Second, we allow to express inclusion
statements between general concepts, and terminological cycles as a particular
case. Third, we prove the decidability of a number of desirable TKRS-deduction
services (like satisfiability, subsumption and instance checking) through a
sound, complete and terminating calculus for reasoning in ALCNR-knowledge
bases. Our calculus extends the general technique of constraint systems. As a
byproduct of the proof, we get also the result that inclusion statements in
ALCNR can be simulated by terminological cycles, if descriptive semantics is
adopted.
",Decidable Reasoning in Terminological Knowledge Representation Systems
"  A formalism is presented for computing and organizing actions for autonomous
agents in dynamic environments. We introduce the notion of teleo-reactive (T-R)
programs whose execution entails the construction of circuitry for the
continuous computation of the parameters and conditions on which agent action
is based. In addition to continuous feedback, T-R programs support parameter
binding and recursion. A primary difference between T-R programs and many other
circuit-based systems is that the circuitry of T-R programs is more compact; it
is constructed at run time and thus does not have to anticipate all the
contingencies that might arise over all possible runs. In addition, T-R
programs are intuitive and easy to write and are written in a form that is
compatible with automatic planning and learning methods. We briefly describe
some experimental applications of T-R programs in the control of simulated and
actual mobile robots.
",Teleo-Reactive Programs for Agent Control
"  Learning the past tense of English verbs - a seemingly minor aspect of
language acquisition - has generated heated debates since 1986, and has become
a landmark task for testing the adequacy of cognitive modeling. Several
artificial neural networks (ANNs) have been implemented, and a challenge for
better symbolic models has been posed. In this paper, we present a
general-purpose Symbolic Pattern Associator (SPA) based upon the decision-tree
learning algorithm ID3. We conduct extensive head-to-head comparisons on the
generalization ability between ANN models and the SPA under different
representations. We conclude that the SPA generalizes the past tense of unseen
verbs better than ANN models by a wide margin, and we offer insights as to why
this should be the case. We also discuss a new default strategy for
decision-tree learning algorithms.
","Learning the Past Tense of English Verbs: The Symbolic Pattern
  Associator vs. Connectionist Models"
"  The ability to identify interesting and repetitive substructures is an
essential component to discovering knowledge in structural data. We describe a
new version of our SUBDUE substructure discovery system based on the minimum
description length principle. The SUBDUE system discovers substructures that
compress the original data and represent structural concepts in the data. By
replacing previously-discovered substructures in the data, multiple passes of
SUBDUE produce a hierarchical description of the structural regularities in the
data. SUBDUE uses a computationally-bounded inexact graph match that identifies
similar, but not identical, instances of a substructure and finds an
approximate measure of closeness of two substructures when under computational
constraints. In addition to the minimum description length principle, other
background knowledge can be used by SUBDUE to guide the search towards more
appropriate substructures. Experiments in a variety of domains demonstrate
SUBDUE's ability to find substructures capable of compressing the original data
and to discover structural concepts important to the domain. Description of
Online Appendix: This is a compressed tar file containing the SUBDUE discovery
system, written in C. The program accepts as input databases represented in
graph form, and will output discovered substructures with their corresponding
value.
","Substructure Discovery Using Minimum Description Length and Background
  Knowledge"
"  The theory revision problem is the problem of how best to go about revising a
deficient domain theory using information contained in examples that expose
inaccuracies. In this paper we present our approach to the theory revision
problem for propositional domain theories. The approach described here, called
PTR, uses probabilities associated with domain theory elements to numerically
track the ``flow'' of proof through the theory. This allows us to measure the
precise role of a clause or literal in allowing or preventing a (desired or
undesired) derivation for a given example. This information is used to
efficiently locate and repair flawed elements of the theory. PTR is proved to
converge to a theory which correctly classifies all examples, and shown
experimentally to be fast and accurate even for deep theories.
",Bias-Driven Revision of Logical Domain Theories
"  We report on a series of experiments in which all decision trees consistent
with the training data are constructed. These experiments were run to gain an
understanding of the properties of the set of consistent decision trees and the
factors that affect the accuracy of individual trees. In particular, we
investigated the relationship between the size of a decision tree consistent
with some training data and the accuracy of the tree on test data. The
experiments were performed on a massively parallel Maspar computer. The results
of the experiments on several artificial and two real world problems indicate
that, for many of the problems investigated, smaller consistent decision trees
are on average less accurate than the average accuracy of slightly larger
trees.
","Exploring the Decision Forest: An Empirical Investigation of Occam's
  Razor in Decision Tree Induction"
"  This paper analyzes the correctness of the subsumption algorithm used in
CLASSIC, a description logic-based knowledge representation system that is
being used in practical applications. In order to deal efficiently with
individuals in CLASSIC descriptions, the developers have had to use an
algorithm that is incomplete with respect to the standard, model-theoretic
semantics for description logics. We provide a variant semantics for
descriptions with respect to which the current implementation is complete, and
which can be independently motivated. The soundness and completeness of the
polynomial-time subsumption algorithm is established using description graphs,
which are an abstracted version of the implementation structures used in
CLASSIC, and are of independent interest.
","A Semantics and Complete Algorithm for Subsumption in the CLASSIC
  Description Logic"
"  In this paper we describe how to modify GSAT so that it can be applied to
non-clausal formulas. The idea is to use a particular ``score'' function which
gives the number of clauses of the CNF conversion of a formula which are false
under a given truth assignment. Its value is computed in linear time, without
constructing the CNF conversion itself. The proposed methodology applies to
most of the variants of GSAT proposed so far.
",Applying GSAT to Non-Clausal Formulas
"  Given a knowledge base KB containing first-order and statistical facts, we
consider a principled method, called the random-worlds method, for computing a
degree of belief that some formula Phi holds given KB. If we are reasoning
about a world or system consisting of N individuals, then we can consider all
possible worlds, or first-order models, with domain {1,...,N} that satisfy KB,
and compute the fraction of them in which Phi is true. We define the degree of
belief to be the asymptotic value of this fraction as N grows large. We show
that when the vocabulary underlying Phi and KB uses constants and unary
predicates only, we can naturally associate an entropy with each world. As N
grows larger, there are many more worlds with higher entropy. Therefore, we can
use a maximum-entropy computation to compute the degree of belief. This result
is in a similar spirit to previous work in physics and artificial intelligence,
but is far more general. Of equal interest to the result itself are the
limitations on its scope. Most importantly, the restriction to unary predicates
seems necessary. Although the random-worlds method makes sense in general, the
connection to maximum entropy seems to disappear in the non-unary case. These
observations suggest unexpected limitations to the applicability of
maximum-entropy methods.
",Random Worlds and Maximum Entropy
"  Information extraction is the task of automatically picking up information of
interest from an unconstrained text. Information of interest is usually
extracted in two steps. First, sentence level processing locates relevant
pieces of information scattered throughout the text; second, discourse
processing merges coreferential information to generate the output. In the
first step, pieces of information are locally identified without recognizing
any relationships among them. A key word search or simple pattern search can
achieve this purpose. The second step requires deeper knowledge in order to
understand relationships among separately identified pieces of information.
Previous information extraction systems focused on the first step, partly
because they were not required to link up each piece of information with other
pieces. To link the extracted pieces of information and map them onto a
structured output format, complex discourse processing is essential. This paper
reports on a Japanese information extraction system that merges information
using a pattern matcher and discourse processor. Evaluation results show a high
level of system performance which approaches human performance.
","Pattern Matching and Discourse Processing in Information Extraction from
  Japanese Text"
"  This article describes a new system for induction of oblique decision trees.
This system, OC1, combines deterministic hill-climbing with two forms of
randomization to find a good oblique split (in the form of a hyperplane) at
each node of a decision tree. Oblique decision tree methods are tuned
especially for domains in which the attributes are numeric, although they can
be adapted to symbolic or mixed symbolic/numeric attributes. We present
extensive empirical studies, using both real and artificial data, that analyze
OC1's ability to construct oblique trees that are smaller and more accurate
than their axis-parallel counterparts. We also examine the benefits of
randomization for the construction of oblique decision trees.
",A System for Induction of Oblique Decision Trees
"  This paper introduces a framework for Planning while Learning where an agent
is given a goal to achieve in an environment whose behavior is only partially
known to the agent. We discuss the tractability of various plan-design
processes. We show that for a large natural class of Planning while Learning
systems, a plan can be presented and verified in a reasonable time. However,
coming up algorithmically with a plan, even for simple classes of systems is
apparently intractable. We emphasize the role of off-line plan-design
processes, and show that, in most natural cases, the verification (projection)
part can be carried out in an efficient algorithmic manner.
",On Planning while Learning
"  The vast amounts of on-line text now available have led to renewed interest
in information extraction (IE) systems that analyze unrestricted text,
producing a structured representation of selected information from the text.
This paper presents a novel approach that uses machine learning to acquire
knowledge for some of the higher level IE processing. Wrap-Up is a trainable IE
discourse component that makes intersentential inferences and identifies
logical relations among information extracted from the text. Previous
corpus-based approaches were limited to lower level processing such as
part-of-speech tagging, lexical disambiguation, and dictionary construction.
Wrap-Up is fully trainable, and not only automatically decides what classifiers
are needed, but even derives the feature set for each classifier automatically.
Performance equals that of a partially trainable discourse module requiring
manual customization for each domain.
",Wrap-Up: a Trainable Discourse Module for Information Extraction
"  This paper is a multidisciplinary review of empirical, statistical learning
from a graphical model perspective. Well-known examples of graphical models
include Bayesian networks, directed graphs representing a Markov chain, and
undirected networks representing a Markov field. These graphical models are
extended to model data analysis and empirical learning using the notation of
plates. Graphical operations for simplifying and manipulating a problem are
provided including decomposition, differentiation, and the manipulation of
probability models from the exponential family. Two standard algorithm schemas
for learning are reviewed in a graphical framework: Gibbs sampling and the
expectation maximization algorithm. Using these operations and schemas, some
popular algorithms can be synthesized from their graphical specification. This
includes versions of linear regression, techniques for feed-forward networks,
and learning Gaussian and discrete Bayesian networks from data. The paper
concludes by sketching some implications for data analysis and summarizing how
some popular algorithms fall within the framework presented. The main original
contributions here are the decomposition techniques and the demonstration that
graphical models provide a framework for understanding and developing complex
learning algorithms.
",Operations for Learning with Graphical Models
"  For many years, the intuitions underlying partial-order planning were largely
taken for granted. Only in the past few years has there been renewed interest
in the fundamental principles underlying this paradigm. In this paper, we
present a rigorous comparative analysis of partial-order and total-order
planning by focusing on two specific planners that can be directly compared. We
show that there are some subtle assumptions that underly the wide-spread
intuitions regarding the supposed efficiency of partial-order planning. For
instance, the superiority of partial-order planning can depend critically upon
the search strategy and the structure of the search space. Understanding the
underlying assumptions is crucial for constructing efficient planners.
",Total-Order and Partial-Order Planning: A Comparative Analysis
"  Multiclass learning problems involve finding a definition for an unknown
function f(x) whose range is a discrete set containing k &gt 2 values (i.e., k
``classes''). The definition is acquired by studying collections of training
examples of the form [x_i, f (x_i)]. Existing approaches to multiclass learning
problems include direct application of multiclass algorithms such as the
decision-tree algorithms C4.5 and CART, application of binary concept learning
algorithms to learn individual binary functions for each of the k classes, and
application of binary concept learning algorithms with distributed output
representations. This paper compares these three approaches to a new technique
in which error-correcting codes are employed as a distributed output
representation. We show that these output representations improve the
generalization performance of both C4.5 and backpropagation on a wide range of
multiclass learning tasks. We also demonstrate that this approach is robust
with respect to changes in the size of the training sample, the assignment of
distributed representations to particular classes, and the application of
overfitting avoidance techniques such as decision-tree pruning. Finally, we
show that---like the other methods---the error-correcting code technique can
provide reliable class probability estimates. Taken together, these results
demonstrate that error-correcting output codes provide a general-purpose method
for improving the performance of inductive learning programs on multiclass
problems.
",Solving Multiclass Learning Problems via Error-Correcting Output Codes
"  The paradigms of transformational planning, case-based planning, and plan
debugging all involve a process known as plan adaptation - modifying or
repairing an old plan so it solves a new problem. In this paper we provide a
domain-independent algorithm for plan adaptation, demonstrate that it is sound,
complete, and systematic, and compare it to other adaptation algorithms in the
literature. Our approach is based on a view of planning as searching a graph of
partial plans. Generative planning starts at the graph's root and moves from
node to node using plan-refinement operators. In planning by adaptation, a
library plan - an arbitrary node in the plan graph - is the starting point for
the search, and the plan-adaptation algorithm can apply both the same
refinement operators available to a generative planner and can also retract
constraints and steps from the plan. Our algorithm's completeness ensures that
the adaptation algorithm will eventually search the entire graph and its
systematicity ensures that it will do so without redundantly searching any
parts of the graph.
",A Domain-Independent Algorithm for Plan Adaptation
"  Temporal difference (TD) methods constitute a class of methods for learning
predictions in multi-step prediction problems, parameterized by a recency
factor lambda. Currently the most important application of these methods is to
temporal credit assignment in reinforcement learning. Well known reinforcement
learning algorithms, such as AHC or Q-learning, may be viewed as instances of
TD learning. This paper examines the issues of the efficient and general
implementation of TD(lambda) for arbitrary lambda, for use with reinforcement
learning algorithms optimizing the discounted sum of rewards. The traditional
approach, based on eligibility traces, is argued to suffer from both
inefficiency and lack of generality. The TTD (Truncated Temporal Differences)
procedure is proposed as an alternative, that indeed only approximates
TD(lambda), but requires very little computation per action and can be used
with arbitrary function representation methods. The idea from which it is
derived is fairly simple and not new, but probably unexplored so far.
Encouraging experimental results are presented, suggesting that using lambda
&gt 0 with the TTD procedure allows one to obtain a significant learning
speedup at essentially the same cost as usual TD(0) learning.
","Truncating Temporal Differences: On the Efficient Implementation of
  TD(lambda) for Reinforcement Learning"
"  This paper introduces ICET, a new algorithm for cost-sensitive
classification. ICET uses a genetic algorithm to evolve a population of biases
for a decision tree induction algorithm. The fitness function of the genetic
algorithm is the average cost of classification when using the decision tree,
including both the costs of tests (features, measurements) and the costs of
classification errors. ICET is compared here with three other algorithms for
cost-sensitive classification - EG2, CS-ID3, and IDX - and also with C4.5,
which classifies without regard to cost. The five algorithms are evaluated
empirically on five real-world medical datasets. Three sets of experiments are
performed. The first set examines the baseline performance of the five
algorithms on the five datasets and establishes that ICET performs
significantly better than its competitors. The second set tests the robustness
of ICET under a variety of conditions and shows that ICET maintains its
advantage. The third set looks at ICET's search in bias space and discovers a
way to improve the search.
","Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic
  Decision Tree Induction Algorithm"
"  Theory revision integrates inductive learning and background knowledge by
combining training examples with a coarse domain theory to produce a more
accurate theory. There are two challenges that theory revision and other
theory-guided systems face. First, a representation language appropriate for
the initial theory may be inappropriate for an improved theory. While the
original representation may concisely express the initial theory, a more
accurate theory forced to use that same representation may be bulky,
cumbersome, and difficult to reach. Second, a theory structure suitable for a
coarse domain theory may be insufficient for a fine-tuned theory. Systems that
produce only small, local changes to a theory have limited value for
accomplishing complex structural alterations that may be required.
Consequently, advanced theory-guided learning systems require flexible
representation and flexible structure. An analysis of various theory revision
systems and theory-guided learning systems reveals specific strengths and
weaknesses in terms of these two desired properties. Designed to capture the
underlying qualities of each system, a new system uses theory-guided
constructive induction. Experiments in three domains show improvement over
previous theory-guided systems. This leads to a study of the behavior,
limitations, and potential of theory-guided constructive induction.
","Rerepresenting and Restructuring Domain Theories: A Constructive
  Induction Approach"
"  Many studies have been carried out in order to increase the search efficiency
of constraint satisfaction problems; among them, some make use of structural
properties of the constraint network; others take into account semantic
properties of the constraints, generally assuming that all the constraints
possess the given property. In this paper, we propose a new decomposition
method benefiting from both semantic properties of functional constraints (not
bijective constraints) and structural properties of the network; furthermore,
not all the constraints need to be functional. We show that under some
conditions, the existence of solutions can be guaranteed. We first characterize
a particular subset of the variables, which we name a root set. We then
introduce pivot consistency, a new local consistency which is a weak form of
path consistency and can be achieved in O(n^2d^2) complexity (instead of
O(n^3d^3) for path consistency), and we present associated properties; in
particular, we show that any consistent instantiation of the root set can be
linearly extended to a solution, which leads to the presentation of the
aforementioned new method for solving by decomposing functional CSPs.
",Using Pivot Consistency to Decompose and Solve Functional CSPs
"  We study the process of multi-agent reinforcement learning in the context of
load balancing in a distributed system, without use of either central
coordination or explicit communication. We first define a precise framework in
which to study adaptive load balancing, important features of which are its
stochastic nature and the purely local information available to individual
agents. Given this framework, we show illuminating results on the interplay
between basic adaptive behavior parameters and their effect on system
efficiency. We then investigate the properties of adaptive load balancing in
heterogeneous populations, and address the issue of exploration vs.
exploitation in that context. Finally, we show that naive use of communication
may not improve, and might even harm system efficiency.
",Adaptive Load Balancing: A Study in Multi-Agent Learning
"  Since its inception, artificial intelligence has relied upon a theoretical
foundation centered around perfect rationality as the desired property of
intelligent systems. We argue, as others have done, that this foundation is
inadequate because it imposes fundamentally unsatisfiable requirements. As a
result, there has arisen a wide gap between theory and practice in AI,
hindering progress in the field. We propose instead a property called bounded
optimality. Roughly speaking, an agent is bounded-optimal if its program is a
solution to the constrained optimization problem presented by its architecture
and the task environment. We show how to construct agents with this property
for a simple class of machine architectures in a broad class of real-time
environments. We illustrate these results using a simple model of an automated
mail sorting facility. We also define a weaker property, asymptotic bounded
optimality (ABO), that generalizes the notion of optimality in classical
complexity theory. We then construct universal ABO programs, i.e., programs
that are ABO no matter what real-time constraints are applied. Universal ABO
programs can be used as building blocks for more complex systems. We conclude
with a discussion of the prospects for bounded optimality as a theoretical
basis for AI, and relate it to similar trends in philosophy, economics, and
game theory.
",Provably Bounded-Optimal Agents
"  We present algorithms that learn certain classes of function-free recursive
logic programs in polynomial time from equivalence queries. In particular, we
show that a single k-ary recursive constant-depth determinate clause is
learnable. Two-clause programs consisting of one learnable recursive clause and
one constant-depth determinate non-recursive clause are also learnable, if an
additional ``basecase'' oracle is assumed. These results immediately imply the
pac-learnability of these classes. Although these classes of learnable
recursive programs are very constrained, it is shown in a companion paper that
they are maximally general, in that generalizing either class in any natural
way leads to a computationally difficult learning problem. Thus, taken together
with its companion paper, this paper establishes a boundary of efficient
learnability for recursive logic programs.
",Pac-Learning Recursive Logic Programs: Efficient Algorithms
"  In a companion paper it was shown that the class of constant-depth
determinate k-ary recursive clauses is efficiently learnable. In this paper we
present negative results showing that any natural generalization of this class
is hard to learn in Valiant's model of pac-learnability. In particular, we show
that the following program classes are cryptographically hard to learn:
programs with an unbounded number of constant-depth linear recursive clauses;
programs with one constant-depth determinate clause containing an unbounded
number of recursive calls; and programs with one linear recursive clause of
constant locality. These results immediately imply the non-learnability of any
more general class of programs. We also show that learning a constant-depth
determinate program with either two linear recursive clauses or one linear
recursive clause and one non-recursive clause is as hard as learning boolean
DNF. Together with positive results from the companion paper, these negative
results establish a boundary of efficient learnability for recursive
function-free clauses.
",Pac-learning Recursive Logic Programs: Negative Results
"  There has been evidence that least-commitment planners can efficiently handle
planning problems that involve difficult goal interactions. This evidence has
led to the common belief that delayed-commitment is the ""best"" possible
planning strategy. However, we recently found evidence that eager-commitment
planners can handle a variety of planning problems more efficiently, in
particular those with difficult operator choices. Resigned to the futility of
trying to find a universally successful planning strategy, we devised a planner
that can be used to study which domains and problems are best for which
planning strategies. In this article we introduce this new planning algorithm,
FLECS, which uses a FLExible Commitment Strategy with respect to plan-step
orderings. It is able to use any strategy from delayed-commitment to
eager-commitment. The combination of delayed and eager operator-ordering
commitments allows FLECS to take advantage of the benefits of explicitly using
a simulated execution state and reasoning about planning constraints. FLECS can
vary its commitment strategy across different problems and domains, and also
during the course of a single planning problem. FLECS represents a novel
contribution to planning in that it explicitly provides the choice of which
commitment strategy to use while planning. FLECS provides a framework to
investigate the mapping from planning domains and problems to efficient
planning strategies.
",FLECS: Planning with a Flexible Commitment Strategy
"  This paper presents a method for inducing logic programs from examples that
learns a new class of concepts called first-order decision lists, defined as
ordered lists of clauses each ending in a cut. The method, called FOIDL, is
based on FOIL (Quinlan, 1990) but employs intensional background knowledge and
avoids the need for explicit negative examples. It is particularly useful for
problems that involve rules with specific exceptions, such as learning the
past-tense of English verbs, a task widely studied in the context of the
symbolic/connectionist debate. FOIDL is able to learn concise, accurate
programs for this problem from significantly fewer examples than previous
methods (both connectionist and symbolic).
","Induction of First-Order Decision Lists: Results on Learning the Past
  Tense of English Verbs"
"  ion is one of the most promising approaches to improve the performance of
problem solvers. In several domains abstraction by dropping sentences of a
domain description -- as used in most hierarchical planners -- has proven
useful. In this paper we present examples which illustrate significant
drawbacks of abstraction by dropping sentences. To overcome these drawbacks, we
propose a more general view of abstraction involving the change of
representation language. We have developed a new abstraction methodology and a
related sound and complete learning algorithm that allows the complete change
of representation language of planning cases from concrete to abstract.
However, to achieve a powerful change of the representation language, the
abstract language itself as well as rules which describe admissible ways of
abstracting states must be provided in the domain model. This new abstraction
approach is the core of Paris (Plan Abstraction and Refinement in an Integrated
System), a system in which abstract planning cases are automatically learned
from given concrete cases. An empirical study in the domain of process planning
in mechanical engineering shows significant advantages of the proposed
reasoning from abstract cases over classical hierarchical planning.
","Building and Refining Abstract Planning Cases by Change of
  Representation Language"
"  Identifying inaccurate data has long been regarded as a significant and
difficult problem in AI. In this paper, we present a new method for identifying
inaccurate data on the basis of qualitative correlations among related data.
First, we introduce the definitions of related data and qualitative
correlations among related data. Then we put forward a new concept called
support coefficient function (SCF). SCF can be used to extract, represent, and
calculate qualitative correlations among related data within a dataset. We
propose an approach to determining dynamic shift intervals of inaccurate data,
and an approach to calculating possibility of identifying inaccurate data,
respectively. Both of the approaches are based on SCF. Finally we present an
algorithm for identifying inaccurate data by using qualitative correlations
among related data as confirmatory or disconfirmatory evidence. We have
developed a practical system for interpreting infrared spectra by applying the
method, and have fully tested the system against several hundred real spectra.
The experimental results show that the method is significantly better than the
conventional methods used in many similar systems.
",Using Qualitative Hypotheses to Identify Inaccurate Data
"  Learning and reasoning are both aspects of what is considered to be
intelligence. Their studies within AI have been separated historically,
learning being the topic of machine learning and neural networks, and reasoning
falling under classical (or symbolic) AI. However, learning and reasoning are
in many ways interdependent. This paper discusses the nature of some of these
interdependencies and proposes a general framework called FLARE, that combines
inductive learning using prior knowledge together with reasoning in a
propositional setting. Several examples that test the framework are presented,
including classical induction, many important reasoning protocols and two
simple expert systems.
",An Integrated Framework for Learning and Reasoning
"  This paper studies the problem of ergodicity of transition probability
matrices in Markovian models, such as hidden Markov models (HMMs), and how it
makes very difficult the task of learning to represent long-term context for
sequential data. This phenomenon hurts the forward propagation of long-term
context information, as well as learning a hidden state representation to
represent long-term context, which depends on propagating credit information
backwards in time. Using results from Markov chain theory, we show that this
problem of diffusion of context and credit is reduced when the transition
probabilities approach 0 or 1, i.e., the transition probability matrices are
sparse and the model essentially deterministic. The results found in this paper
apply to learning approaches based on continuous optimization, such as gradient
descent and the Baum-Welch algorithm.
",Diffusion of Context and Credit Information in Markovian Models
"  Symmetric networks designed for energy minimization such as Boltzman machines
and Hopfield nets are frequently investigated for use in optimization,
constraint satisfaction and approximation of NP-hard problems. Nevertheless,
finding a global solution (i.e., a global minimum for the energy function) is
not guaranteed and even a local solution may take an exponential number of
steps. We propose an improvement to the standard local activation function used
for such networks. The improved algorithm guarantees that a global minimum is
found in linear time for tree-like subnetworks. The algorithm, called activate,
is uniform and does not assume that the network is tree-like. It can identify
tree-like subnetworks even in cyclic topologies (arbitrary networks) and avoid
local minima along these trees. For acyclic networks, the algorithm is
guaranteed to converge to a global minimum from any initial state of the system
(self-stabilization) and remains correct under various types of schedulers. On
the negative side, we show that in the presence of cycles, no uniform algorithm
exists that guarantees optimality even under a sequential asynchronous
scheduler. An asynchronous scheduler can activate only one unit at a time while
a synchronous scheduler can activate any number of units in a single time step.
In addition, no uniform algorithm exists to optimize even acyclic networks when
the scheduler is synchronous. Finally, we show how the algorithm can be
improved using the cycle-cutset scheme. The general algorithm, called
activate-with-cutset, improves over activate and has some performance
guarantees that are related to the size of the network's cycle-cutset.
",Improving Connectionist Energy Minimization
"  Functionality-based recognition systems recognize objects at the category
level by reasoning about how well the objects support the expected function.
Such systems naturally associate a ``measure of goodness'' or ``membership
value'' with a recognized object. This measure of goodness is the result of
combining individual measures, or membership values, from potentially many
primitive evaluations of different properties of the object's shape. A
membership function is used to compute the membership value when evaluating a
primitive of a particular physical property of an object. In previous versions
of a recognition system known as Gruff, the membership function for each of the
primitive evaluations was hand-crafted by the system designer. In this paper,
we provide a learning component for the Gruff system, called Omlet, that
automatically learns membership functions given a set of example objects
labeled with their desired category measure. The learning algorithm is
generally applicable to any problem in which low-level membership values are
combined through an and-or tree structure to give a final overall membership
value.
","Learning Membership Functions in a Function-Based Object Recognition
  System"
"  This paper presents an approach to learning from situated, interactive
tutorial instruction within an ongoing agent. Tutorial instruction is a
flexible (and thus powerful) paradigm for teaching tasks because it allows an
instructor to communicate whatever types of knowledge an agent might need in
whatever situations might arise. To support this flexibility, however, the
agent must be able to learn multiple kinds of knowledge from a broad range of
instructional interactions. Our approach, called situated explanation, achieves
such learning through a combination of analytic and inductive techniques. It
combines a form of explanation-based learning that is situated for each
instruction with a full suite of contextually guided responses to incomplete
explanations. The approach is implemented in an agent called Instructo-Soar
that learns hierarchies of new tasks and other domain knowledge from
interactive natural language instructions. Instructo-Soar meets three key
requirements of flexible instructability that distinguish it from previous
systems: (1) it can take known or unknown commands at any instruction point;
(2) it can handle instructions that apply to either its current situation or to
a hypothetical situation specified in language (as in, for instance,
conditional instructions); and (3) it can learn, from instructions, each class
of knowledge it uses to perform tasks.
",Flexibly Instructable Agents
"  OPUS is a branch and bound search algorithm that enables efficient admissible
search through spaces for which the order of search operator application is not
significant. The algorithm's search efficiency is demonstrated with respect to
very large machine learning search spaces. The use of admissible search is of
potential value to the machine learning community as it means that the exact
learning biases to be employed for complex learning tasks can be precisely
specified and manipulated. OPUS also has potential for application in other
areas of artificial intelligence, notably, truth maintenance.
",OPUS: An Efficient Admissible Algorithm for Unordered Search
"  The main aim of this work is the development of a vision-based road detection
system fast enough to cope with the difficult real-time constraints imposed by
moving vehicle applications. The hardware platform, a special-purpose massively
parallel system, has been chosen to minimize system production and operational
costs. This paper presents a novel approach to expectation-driven low-level
image segmentation, which can be mapped naturally onto mesh-connected massively
parallel SIMD architectures capable of handling hierarchical data structures.
The input image is assumed to contain a distorted version of a given template;
a multiresolution stretching process is used to reshape the original template
in accordance with the acquired image content, minimizing a potential function.
The distorted template is the process output.
","Vision-Based Road Detection in Automotive Systems: A Real-Time
  Expectation-Driven Approach"
"  In the area of inductive learning, generalization is a main operation, and
the usual definition of induction is based on logical implication. Recently
there has been a rising interest in clausal representation of knowledge in
machine learning. Almost all inductive learning systems that perform
generalization of clauses use the relation theta-subsumption instead of
implication. The main reason is that there is a well-known and simple technique
to compute least general generalizations under theta-subsumption, but not under
implication. However generalization under theta-subsumption is inappropriate
for learning recursive clauses, which is a crucial problem since recursion is
the basic program structure of logic programs. We note that implication between
clauses is undecidable, and we therefore introduce a stronger form of
implication, called T-implication, which is decidable between clauses. We show
that for every finite set of clauses there exists a least general
generalization under T-implication. We describe a technique to reduce
generalizations under implication of a clause to generalizations under
theta-subsumption of what we call an expansion of the original clause. Moreover
we show that for every non-tautological clause there exists a T-complete
expansion, which means that every generalization under T-implication of the
clause is reduced to a generalization under theta-subsumption of the expansion.
",Generalization of Clauses under Implication
"  We present a definition of cause and effect in terms of decision-theoretic
primitives and thereby provide a principled foundation for causal reasoning.
Our definition departs from the traditional view of causation in that causal
assertions may vary with the set of decisions available. We argue that this
approach provides added clarity to the notion of cause. Also in this paper, we
examine the encoding of causal relationships in directed acyclic graphs. We
describe a special class of influence diagrams, those in canonical form, and
show its relationship to Pearl's representation of cause and effect. Finally,
we show how canonical form facilitates counterfactual reasoning.
",Decision-Theoretic Foundations for Causal Reasoning
"  Characteristic models are an alternative, model based, representation for
Horn expressions. It has been shown that these two representations are
incomparable and each has its advantages over the other. It is therefore
natural to ask what is the cost of translating, back and forth, between these
representations. Interestingly, the same translation questions arise in
database theory, where it has applications to the design of relational
databases. This paper studies the computational complexity of these problems.
Our main result is that the two translation problems are equivalent under
polynomial reductions, and that they are equivalent to the corresponding
decision problem. Namely, translating is equivalent to deciding whether a given
set of models is the set of characteristic models for a given Horn expression.
We also relate these problems to the hypergraph transversal problem, a well
known problem which is related to other applications in AI and for which no
polynomial time algorithm is known. It is shown that in general our translation
problems are at least as hard as the hypergraph transversal problem, and in a
special case they are equivalent to it.
",Translating between Horn Representations and their Characteristic Models
"  This article describes an application of three well-known statistical methods
in the field of game-tree search: using a large number of classified Othello
positions, feature weights for evaluation functions with a
game-phase-independent meaning are estimated by means of logistic regression,
Fisher's linear discriminant, and the quadratic discriminant function for
normally distributed features. Thereafter, the playing strengths are compared
by means of tournaments between the resulting versions of a world-class Othello
program. In this application, logistic regression - which is used here for the
first time in the context of game playing - leads to better results than the
other approaches.
",Statistical Feature Combination for the Evaluation of Game Positions
"  We describe a machine learning method for predicting the value of a
real-valued function, given the values of multiple input variables. The method
induces solutions from samples in the form of ordered disjunctive normal form
(DNF) decision rules. A central objective of the method and representation is
the induction of compact, easily interpretable solutions. This rule-based
decision model can be extended to search efficiently for similar cases prior to
approximating function values. Experimental results on real-world data
demonstrate that the new techniques are competitive with existing machine
learning and statistical methods and can sometimes yield superior regression
performance.
",Rule-based Machine Learning Methods for Functional Prediction
"  Many applications -- from planning and scheduling to problems in molecular
biology -- rely heavily on a temporal reasoning component. In this paper, we
discuss the design and empirical analysis of algorithms for a temporal
reasoning system based on Allen's influential interval-based framework for
representing temporal information. At the core of the system are algorithms for
determining whether the temporal information is consistent, and, if so, finding
one or more scenarios that are consistent with the temporal information. Two
important algorithms for these tasks are a path consistency algorithm and a
backtracking algorithm. For the path consistency algorithm, we develop
techniques that can result in up to a ten-fold speedup over an already highly
optimized implementation. For the backtracking algorithm, we develop variable
and value ordering heuristics that are shown empirically to dramatically
improve the performance of the algorithm. As well, we show that a previously
suggested reformulation of the backtracking search problem can reduce the time
and space requirements of the backtracking search. Taken together, the
techniques we develop allow a temporal reasoning component to solve problems
that are of practical size.
","The Design and Experimental Analysis of Algorithms for Temporal
  Reasoning"
"  The paper describes an extension of well-founded semantics for logic programs
with two types of negation. In this extension information about preferences
between rules can be expressed in the logical language and derived dynamically.
This is achieved by using a reserved predicate symbol and a naming technique.
Conflicts among rules are resolved whenever possible on the basis of derived
preference information. The well-founded conclusions of prioritized logic
programs can be computed in polynomial time. A legal reasoning example
illustrates the usefulness of the approach.
","Well-Founded Semantics for Extended Logic Programs with Dynamic
  Preferences"
"  Traditional databases commonly support efficient query and update procedures
that operate in time which is sublinear in the size of the database. Our goal
in this paper is to take a first step toward dynamic reasoning in probabilistic
databases with comparable efficiency. We propose a dynamic data structure that
supports efficient algorithms for updating and querying singly connected
Bayesian networks. In the conventional algorithm, new evidence is absorbed in
O(1) time and queries are processed in time O(N), where N is the size of the
network. We propose an algorithm which, after a preprocessing phase, allows us
to answer queries in time O(log N) at the expense of O(log N) time per evidence
absorption. The usefulness of sub-linear processing time manifests itself in
applications requiring (near) real-time response over large probabilistic
databases. We briefly discuss a potential application of dynamic probabilistic
reasoning in computational biology.
",Logarithmic-Time Updates and Queries in Probabilistic Networks
"  We introduce an algorithm for combinatorial search on quantum computers that
is capable of significantly concentrating amplitude into solutions for some NP
search problems, on average. This is done by exploiting the same aspects of
problem structure as used by classical backtrack methods to avoid unproductive
search choices. This quantum algorithm is much more likely to find solutions
than the simple direct use of quantum parallelism. Furthermore, empirical
evaluation on small problems shows this quantum algorithm displays the same
phase transition behavior, and at the same location, as seen in many previously
studied classical search methods. Specifically, difficult problem instances are
concentrated near the abrupt change from underconstrained to overconstrained
problems.
",Quantum Computing and Phase Transitions in Combinatorial Search
"  We develop a mean field theory for sigmoid belief networks based on ideas
from statistical mechanics. Our mean field theory provides a tractable
approximation to the true probability distribution in these networks; it also
yields a lower bound on the likelihood of evidence. We demonstrate the utility
of this framework on a benchmark problem in statistical pattern
recognition---the classification of handwritten digits.
",Mean Field Theory for Sigmoid Belief Networks
"  A reported weakness of C4.5 in domains with continuous attributes is
addressed by modifying the formation and evaluation of tests on continuous
attributes. An MDL-inspired penalty is applied to such tests, eliminating some
of them from consideration and altering the relative desirability of all tests.
Empirical trials show that the modifications lead to smaller decision trees
with higher predictive accuracies. Results also confirm that a new version of
C4.5 incorporating these changes is superior to recent approaches that use
global discretization and that construct small trees with multi-interval
splits.
",Improved Use of Continuous Attributes in C4.5
"  For many types of machine learning algorithms, one can compute the
statistically `optimal' way to select training data. In this paper, we review
how optimal data selection techniques have been used with feedforward neural
networks. We then show how the same principles may be used to select data for
two alternative, statistically-based learning architectures: mixtures of
Gaussians and locally weighted regression. While the techniques for neural
networks are computationally expensive and approximate, the techniques for
mixtures of Gaussians and locally weighted regression are both efficient and
accurate. Empirically, we observe that the optimality criterion sharply
decreases the number of training examples the learner needs in order to achieve
good performance.
",Active Learning with Statistical Models
"  Inductive theorem provers often diverge. This paper describes a simple
critic, a computer program which monitors the construction of inductive proofs
attempting to identify diverging proof attempts. Divergence is recognized by
means of a ``difference matching'' procedure. The critic then proposes lemmas
and generalizations which ``ripple'' these differences away so that the proof
can go through without divergence. The critic enables the theorem prover Spike
to prove many theorems completely automatically from the definitions alone.
",A Divergence Critic for Inductive Proof
"  Termination of logic programs with negated body atoms (here called general
logic programs) is an important topic. One reason is that many computational
mechanisms used to process negated atoms, like Clark's negation as failure and
Chan's constructive negation, are based on termination conditions. This paper
introduces a methodology for proving termination of general logic programs
w.r.t. the Prolog selection rule. The idea is to distinguish parts of the
program depending on whether or not their termination depends on the selection
rule. To this end, the notions of low-, weakly up-, and up-acceptable program
are introduced. We use these notions to develop a methodology for proving
termination of general logic programs, and show how interesting problems in
non-monotonic reasoning can be formalized and implemented by means of
terminating general logic programs.
",Practical Methods for Proving Termination of General Logic Programs
"  Clustering is often used for discovering structure in data. Clustering
systems differ in the objective function used to evaluate clustering quality
and the control strategy used to search the space of clusterings. Ideally, the
search strategy should consistently construct clusterings of high quality, but
be computationally inexpensive as well. In general, we cannot have it both
ways, but we can partition the search so that a system inexpensively constructs
a `tentative' clustering for initial examination, followed by iterative
optimization, which continues to search in background for improved clusterings.
Given this motivation, we evaluate an inexpensive strategy for creating initial
clusterings, coupled with several control strategies for iterative
optimization, each of which repeatedly modifies an initial clustering in search
of a better one. One of these methods appears novel as an iterative
optimization strategy in clustering contexts. Once a clustering has been
constructed it is judged by analysts -- often according to task-specific
criteria. Several authors have abstracted these criteria and posited a generic
performance task akin to pattern completion, where the error rate over
completed patterns is used to `externally' judge clustering utility. Given this
performance task, we adapt resampling-based pruning strategies used by
supervised learning systems to the task of simplifying hierarchical
clusterings, thus promising to ease post-clustering analysis. Finally, we
propose a number of objective functions, based on attribute-selection measures
for decision-tree induction, that might perform well on the error rate and
simplicity dimensions.
",Iterative Optimization and Simplification of Hierarchical Clusterings
"  This paper presents new experimental evidence against the utility of Occam's
razor. A~systematic procedure is presented for post-processing decision trees
produced by C4.5. This procedure was derived by rejecting Occam's razor and
instead attending to the assumption that similar objects are likely to belong
to the same class. It increases a decision tree's complexity without altering
the performance of that tree on the training data from which it is inferred.
The resulting more complex decision trees are demonstrated to have, on average,
for a variety of common learning tasks, higher predictive accuracy than the
less complex original decision trees. This result raises considerable doubt
about the utility of Occam's razor as it is commonly applied in modern machine
learning.
",Further Experimental Evidence against the Utility of Occam's Razor
"  The main operations in Inductive Logic Programming (ILP) are generalization
and specialization, which only make sense in a generality order. In ILP, the
three most important generality orders are subsumption, implication and
implication relative to background knowledge. The two languages used most often
are languages of clauses and languages of only Horn clauses. This gives a total
of six different ordered languages. In this paper, we give a systematic
treatment of the existence or non-existence of least generalizations and
greatest specializations of finite sets of clauses in each of these six ordered
sets. We survey results already obtained by others and also contribute some
answers of our own. Our main new results are, firstly, the existence of a
computable least generalization under implication of every finite set of
clauses containing at least one non-tautologous function-free clause (among
other, not necessarily function-free clauses). Secondly, we show that such a
least generalization need not exist under relative implication, not even if
both the set that is to be generalized and the background knowledge are
function-free. Thirdly, we give a complete discussion of existence and
non-existence of greatest specializations in each of the six ordered languages.
",Least Generalizations and Greatest Specializations of Sets of Clauses
"  This paper surveys the field of reinforcement learning from a
computer-science perspective. It is written to be accessible to researchers
familiar with machine learning. Both the historical basis of the field and a
broad selection of current work are summarized. Reinforcement learning is the
problem faced by an agent that learns behavior through trial-and-error
interactions with a dynamic environment. The work described here has a
resemblance to work in psychology, but differs considerably in the details and
in the use of the word ``reinforcement.'' The paper discusses central issues of
reinforcement learning, including trading off exploration and exploitation,
establishing the foundations of the field via Markov decision theory, learning
from delayed reinforcement, constructing empirical models to accelerate
learning, making use of generalization and hierarchy, and coping with hidden
state. It concludes with a survey of some implemented systems and an assessment
of the practical utility of current methods for reinforcement learning.
",Reinforcement Learning: A Survey
"  Although most scheduling problems are NP-hard, domain specific techniques
perform well in practice but are quite expensive to construct. In adaptive
problem-solving solving, domain specific knowledge is acquired automatically
for a general problem solver with a flexible control architecture. In this
approach, a learning system explores a space of possible heuristic methods for
one well-suited to the eccentricities of the given domain and problem
distribution. In this article, we discuss an application of the approach to
scheduling satellite communications. Using problem distributions based on
actual mission requirements, our approach identifies strategies that not only
decrease the amount of CPU time required to produce schedules, but also
increase the percentage of problems that are solvable within computational
resource limitations.
","Adaptive Problem-solving for Large-scale Scheduling Problems: A Case
  Study"
"  Speedup learning seeks to improve the computational efficiency of problem
solving with experience. In this paper, we develop a formal framework for
learning efficient problem solving from random problems and their solutions. We
apply this framework to two different representations of learned knowledge,
namely control rules and macro-operators, and prove theorems that identify
sufficient conditions for learning in each representation. Our proofs are
constructive in that they are accompanied with learning algorithms. Our
framework captures both empirical and explanation-based speedup learning in a
unified fashion. We illustrate our framework with implementations in two
domains: symbolic integration and Eight Puzzle. This work integrates many
strands of experimental and theoretical work in machine learning, including
empirical learning of control rules, macro-operator learning, Explanation-Based
Learning (EBL), and Probably Approximately Correct (PAC) Learning.
",A Formal Framework for Speedup Learning from Problems and Solutions
"  A fundamental assumption made by classical AI planners is that there is no
uncertainty in the world: the planner has full knowledge of the conditions
under which the plan will be executed and the outcome of every action is fully
predictable. These planners cannot therefore construct contingency plans, i.e.,
plans in which different actions are performed in different circumstances. In
this paper we discuss some issues that arise in the representation and
construction of contingency plans and describe Cassandra, a partial-order
contingency planner. Cassandra uses explicit decision-steps that enable the
agent executing the plan to decide which plan branch to follow. The
decision-steps in a plan result in subgoals to acquire knowledge, which are
planned for in the same way as any other subgoals. Cassandra thus distinguishes
the process of gathering information from the process of making decisions. The
explicit representation of decisions in Cassandra allows a coherent approach to
the problems of contingent planning, and provides a solid base for extensions
such as the use of different decision-making procedures.
",2Planning for Contingencies: A Decision-based Approach
"  An important problem in geometric reasoning is to find the configuration of a
collection of geometric bodies so as to satisfy a set of given constraints.
Recently, it has been suggested that this problem can be solved efficiently by
symbolically reasoning about geometry. This approach, called degrees of freedom
analysis, employs a set of specialized routines called plan fragments that
specify how to change the configuration of a set of bodies to satisfy a new
constraint while preserving existing constraints. A potential drawback, which
limits the scalability of this approach, is concerned with the difficulty of
writing plan fragments. In this paper we address this limitation by showing how
these plan fragments can be automatically synthesized using first principles
about geometric bodies, actions, and topology.
",A Principled Approach Towards Symbolic Geometric Constraint Satisfaction
"  Motivated by the control theoretic distinction between controllable and
uncontrollable events, we distinguish between two types of agents within a
multi-agent system: controllable agents, which are directly controlled by the
system's designer, and uncontrollable agents, which are not under the
designer's direct control. We refer to such systems as partially controlled
multi-agent systems, and we investigate how one might influence the behavior of
the uncontrolled agents through appropriate design of the controlled agents. In
particular, we wish to understand which problems are naturally described in
these terms, what methods can be applied to influence the uncontrollable
agents, the effectiveness of such methods, and whether similar methods work
across different domains. Using a game-theoretic framework, this paper studies
the design of partially controlled multi-agent systems in two contexts: in one
context, the uncontrollable agents are expected utility maximizers, while in
the other they are reinforcement learners. We suggest different techniques for
controlling agents' behavior in each domain, assess their success, and examine
their relationship.
",On Partially Controlled Multi-Agent Systems
"  Visual thinking plays an important role in scientific reasoning. Based on the
research in automating diverse reasoning tasks about dynamical systems,
nonlinear controllers, kinematic mechanisms, and fluid motion, we have
identified a style of visual thinking, imagistic reasoning. Imagistic reasoning
organizes computations around image-like, analogue representations so that
perceptual and symbolic operations can be brought to bear to infer structure
and behavior. Programs incorporating imagistic reasoning have been shown to
perform at an expert level in domains that defy current analytic or numerical
methods. We have developed a computational paradigm, spatial aggregation, to
unify the description of a class of imagistic problem solvers. A program
written in this paradigm has the following properties. It takes a continuous
field and optional objective functions as input, and produces high-level
descriptions of structure, behavior, or control actions. It computes a
multi-layer of intermediate representations, called spatial aggregates, by
forming equivalence classes and adjacency relations. It employs a small set of
generic operators such as aggregation, classification, and localization to
perform bidirectional mapping between the information-rich field and
successively more abstract spatial aggregates. It uses a data structure, the
neighborhood graph, as a common interface to modularize computations. To
illustrate our theory, we describe the computational structure of three
implemented problem solvers -- KAM, MAPS, and HIPAIR --- in terms of the
spatial aggregation generic operators by mixing and matching a library of
commonly used routines.
",Spatial Aggregation: Theory and Applications
"  Finding the stable models of a knowledge base is a significant computational
problem in artificial intelligence. This task is at the computational heart of
truth maintenance systems, autoepistemic logic, and default logic.
Unfortunately, it is NP-hard. In this paper we present a hierarchy of classes
of knowledge bases, Omega_1,Omega_2,..., with the following properties: first,
Omega_1 is the class of all stratified knowledge bases; second, if a knowledge
base Pi is in Omega_k, then Pi has at most k stable models, and all of them may
be found in time O(lnk), where l is the length of the knowledge base and n the
number of atoms in Pi; third, for an arbitrary knowledge base Pi, we can find
the minimum k such that Pi belongs to Omega_k in time polynomial in the size of
Pi; and, last, where K is the class of all knowledge bases, it is the case that
union{i=1 to infty} Omega_i = K, that is, every knowledge base belongs to some
class in the hierarchy.
",A Hierarchy of Tractable Subsets for Computing Stable Models
"  We propose some domain-independent techniques for bringing well-founded
partial-order planners closer to practicality. The first two techniques are
aimed at improving search control while keeping overhead costs low. One is
based on a simple adjustment to the default A* heuristic used by UCPOP to
select plans for refinement. The other is based on preferring ``zero
commitment'' (forced) plan refinements whenever possible, and using LIFO
prioritization otherwise. A more radical technique is the use of operator
parameter domains to prune search. These domains are initially computed from
the definitions of the operators and the initial and goal conditions, using a
polynomial-time algorithm that propagates sets of constants through the
operator graph, starting in the initial conditions. During planning, parameter
domains can be used to prune nonviable operator instances and to remove
spurious clobbering threats. In experiments based on modifications of UCPOP,
our improved plan and goal selection strategies gave speedups by factors
ranging from 5 to more than 1000 for a variety of problems that are nontrivial
for the unmodified version. Crucially, the hardest problems gave the greatest
improvements. The pruning technique based on parameter domains often gave
speedups by an order of magnitude or more for difficult problems, both with the
default UCPOP search strategy and with our improved strategy. The Lisp code for
our techniques and for the test problems is provided in on-line appendices.
","Accelerating Partial-Order Planners: Some Techniques for Effective
  Search Control and Pruning"
"  Cue phrases may be used in a discourse sense to explicitly signal discourse
structure, but also in a sentential sense to convey semantic rather than
structural information. Correctly classifying cue phrases as discourse or
sentential is critical in natural language processing systems that exploit
discourse structure, e.g., for performing tasks such as anaphora resolution and
plan recognition. This paper explores the use of machine learning for
classifying cue phrases as discourse or sentential. Two machine learning
programs (Cgrendel and C4.5) are used to induce classification models from sets
of pre-classified cue phrases and their features in text and speech. Machine
learning is shown to be an effective technique for not only automating the
generation of classification models, but also for improving upon previous
results. When compared to manually derived classification models already in the
literature, the learned models often perform with higher accuracy and contain
new linguistic insights into the data. In addition, the ability to
automatically construct classification models makes it easier to comparatively
analyze the utility of alternative feature representations of the data.
Finally, the ease of retraining makes the learning approach more scalable and
flexible than manual methods.
",Cue Phrase Classification Using Machine Learning
"  This paper lays part of the groundwork for a domain theory of negotiation,
that is, a way of classifying interactions so that it is clear, given a domain,
which negotiation mechanisms and strategies are appropriate. We define State
Oriented Domains, a general category of interaction. Necessary and sufficient
conditions for cooperation are outlined. We use the notion of worth in an
altered definition of utility, thus enabling agreements in a wider class of
joint-goal reachable situations. An approach is offered for conflict
resolution, and it is shown that even in a conflict situation, partial
cooperative steps can be taken by interacting agents (that is, agents in
fundamental conflict might still agree to cooperate up to a certain point). A
Unified Negotiation Protocol (UNP) is developed that can be used in all types
of encounters. It is shown that in certain borderline cooperative situations, a
partial cooperative agreement (i.e., one that does not achieve all agents'
goals) might be preferred by all agents, even though there exists a rational
agreement that would achieve all their goals. Finally, we analyze cases where
agents have incomplete information on the goals and worth of other agents.
First we consider the case where agents' goals are private information, and we
analyze what goal declaration strategies the agents might adopt to increase
their utility. Then, we consider the situation where the agents' goals (and
therefore stand-alone costs) are common knowledge, but the worth they attach to
their goals is private information. We introduce two mechanisms, one 'strict',
the other 'tolerant', and analyze their affects on the stability and efficiency
of negotiation outcomes.
",Mechanisms for Automated Negotiation in State Oriented Domains
"  First-order learning involves finding a clause-form definition of a relation
from examples of the relation and relevant background information. In this
paper, a particular first-order learning system is modified to customize it for
finding definitions of functional relations. This restriction leads to faster
learning times and, in some cases, to definitions that have higher predictive
accuracy. Other first-order learning systems might benefit from similar
specialization.
",Learning First-Order Definitions of Functions
"  This paper describes an extension to the constraint satisfaction problem
(CSP) called MUSE CSP (MUltiply SEgmented Constraint Satisfaction Problem).
This extension is especially useful for those problems which segment into
multiple sets of partially shared variables. Such problems arise naturally in
signal processing applications including computer vision, speech processing,
and handwriting recognition. For these applications, it is often difficult to
segment the data in only one way given the low-level information utilized by
the segmentation algorithms. MUSE CSP can be used to compactly represent
several similar instances of the constraint satisfaction problem. If multiple
instances of a CSP have some common variables which have the same domains and
constraints, then they can be combined into a single instance of a MUSE CSP,
reducing the work required to apply the constraints. We introduce the concepts
of MUSE node consistency, MUSE arc consistency, and MUSE path consistency. We
then demonstrate how MUSE CSP can be used to compactly represent lexically
ambiguous sentences and the multiple sentence hypotheses that are often
generated by speech recognition algorithms so that grammar constraints can be
used to provide parses for all syntactically correct sentences. Algorithms for
MUSE arc and path consistency are provided. Finally, we discuss how to create a
MUSE CSP from a set of CSPs which are labeled to indicate when the same
variable is shared by more than a single CSP.
",MUSE CSP: An Extension to the Constraint Satisfaction Problem
"  A new method is proposed for exploiting causal independencies in exact
Bayesian network inference. A Bayesian network can be viewed as representing a
factorization of a joint probability into the multiplication of a set of
conditional probabilities. We present a notion of causal independence that
enables one to further factorize the conditional probabilities into a
combination of even smaller factors and consequently obtain a finer-grain
factorization of the joint probability. The new formulation of causal
independence lets us specify the conditional probability of a variable given
its parents in terms of an associative and commutative operator, such as
``or'', ``sum'' or ``max'', on the contribution of each parent. We start with a
simple algorithm VE for Bayesian network inference that, given evidence and a
query variable, uses the factorization to find the posterior distribution of
the query. We show how this algorithm can be extended to exploit causal
independence. Empirical studies, based on the CPCS networks for medical
diagnosis, show that this method is more efficient than previous methods and
allows for inference in larger networks than previous algorithms.
",Exploiting Causal Independence in Bayesian Network Inference
"  Efficiently entering information into a computer is key to enjoying the
benefits of computing. This paper describes three intelligent user interfaces:
handwriting recognition, adaptive menus, and predictive fillin. In the context
of adding a personUs name and address to an electronic organizer, tests show
handwriting recognition is slower than typing on an on-screen, soft keyboard,
while adaptive menus and predictive fillin can be twice as fast. This paper
also presents strategies for applying these three interfaces to other
information collection domains.
","Quantitative Results Comparing Three Intelligent Interfaces for
  Information Capture: A Case Study Adding Name Information into an Electronic
  Personal Organizer"
"  Decomposable dependency models possess a number of interesting and useful
properties. This paper presents new characterizations of decomposable models in
terms of independence relationships, which are obtained by adding a single
axiom to the well-known set characterizing dependency models that are
isomorphic to undirected graphs. We also briefly discuss a potential
application of our results to the problem of learning graphical models from
data.
",Characterizations of Decomposable Dependency Models
"  Instance-based learning techniques typically handle continuous and linear
input values well, but often do not handle nominal input attributes
appropriately. The Value Difference Metric (VDM) was designed to find
reasonable distance values between nominal attribute values, but it largely
ignores continuous attributes, requiring discretization to map continuous
values into nominal values. This paper proposes three new heterogeneous
distance functions, called the Heterogeneous Value Difference Metric (HVDM),
the Interpolated Value Difference Metric (IVDM), and the Windowed Value
Difference Metric (WVDM). These new distance functions are designed to handle
applications with nominal attributes, continuous attributes, or both. In
experiments on 48 applications the new distance metrics achieve higher
classification accuracy on average than three previous distance functions on
those datasets that have both nominal and continuous attributes.
",Improved Heterogeneous Distance Functions
"  Previous approaches of analyzing spontaneously spoken language often have
been based on encoding syntactic and semantic knowledge manually and
symbolically. While there has been some progress using statistical or
connectionist language models, many current spoken- language systems still use
a relatively brittle, hand-coded symbolic grammar or symbolic semantic
component. In contrast, we describe a so-called screening approach for learning
robust processing of spontaneously spoken language. A screening approach is a
flat analysis which uses shallow sequences of category representations for
analyzing an utterance at various syntactic, semantic and dialog levels. Rather
than using a deeply structured symbolic analysis, we use a flat connectionist
analysis. This screening approach aims at supporting speech and language
processing by using (1) data-driven learning and (2) robustness of
connectionist networks. In order to test this approach, we have developed the
SCREEN system which is based on this new robust, learned and flat analysis. In
this paper, we focus on a detailed description of SCREEN's architecture, the
flat syntactic and semantic analysis, the interaction with a speech recognizer,
and a detailed evaluation analysis of the robustness under the influence of
noisy or incomplete input. The main result of this paper is that flat
representations allow more robust processing of spontaneous spoken language
than deeply structured representations. In particular, we show how the
fault-tolerance and learning capability of connectionist networks can support a
flat analysis for providing more robust spoken-language processing within an
overall hybrid symbolic/connectionist framework.
","SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis
  Using Artificial Neural Networks"
"  Most modern formalisms used in Databases and Artificial Intelligence for
describing an application domain are based on the notions of class (or concept)
and relationship among classes. One interesting feature of such formalisms is
the possibility of defining a class, i.e., providing a set of properties that
precisely characterize the instances of the class. Many recent articles point
out that there are several ways of assigning a meaning to a class definition
containing some sort of recursion. In this paper, we argue that, instead of
choosing a single style of semantics, we achieve better results by adopting a
formalism that allows for different semantics to coexist. We demonstrate the
feasibility of our argument, by presenting a knowledge representation
formalism, the description logic muALCQ, with the above characteristics. In
addition to the constructs for conjunction, disjunction, negation, quantifiers,
and qualified number restrictions, muALCQ includes special fixpoint constructs
to express (suitably interpreted) recursive definitions. These constructs
enable the usual frame-based descriptions to be combined with definitions of
recursive data structures such as directed acyclic graphs, lists, streams, etc.
We establish several properties of muALCQ, including the decidability and the
computational complexity of reasoning, by formulating a correspondence with a
particular modal logic of programs called the modal mu-calculus.
",A Uniform Framework for Concept Definitions in Description Logics
"  We argue that the analysis of agent/environment interactions should be
extended to include the conventions and invariants maintained by agents
throughout their activity. We refer to this thicker notion of environment as a
lifeworld and present a partial set of formal tools for describing structures
of lifeworlds and the ways in which they computationally simplify activity. As
one specific example, we apply the tools to the analysis of the Toast system
and show how versions of the system with very different control structures in
fact implement a common control structure together with different conventions
for encoding task state in the positions or states of objects in the
environment.
",Lifeworld Analysis
"  We describe a new paradigm for implementing inference in belief networks,
which consists of two steps: (1) compiling a belief network into an arithmetic
expression called a Query DAG (Q-DAG); and (2) answering queries using a simple
evaluation algorithm. Each node of a Q-DAG represents a numeric operation, a
number, or a symbol for evidence. Each leaf node of a Q-DAG represents the
answer to a network query, that is, the probability of some event of interest.
It appears that Q-DAGs can be generated using any of the standard algorithms
for exact inference in belief networks (we show how they can be generated using
clustering and conditioning algorithms). The time and space complexity of a
Q-DAG generation algorithm is no worse than the time complexity of the
inference algorithm on which it is based. The complexity of a Q-DAG evaluation
algorithm is linear in the size of the Q-DAG, and such inference amounts to a
standard evaluation of the arithmetic expression it represents. The intended
value of Q-DAGs is in reducing the software and hardware resources required to
utilize belief networks in on-line, real-world applications. The proposed
framework also facilitates the development of on-line inference on different
software and hardware platforms due to the simplicity of the Q-DAG evaluation
algorithm. Interestingly enough, Q-DAGs were found to serve other purposes:
simple techniques for reducing Q-DAGs tend to subsume relatively complex
optimization techniques for belief-network inference, such as network-pruning
and computation-caching.
","Query DAGs: A Practical Paradigm for Implementing Belief-Network
  Inference"
"  An algorithm that learns from a set of examples should ideally be able to
exploit the available resources of (a) abundant computing power and (b)
domain-specific knowledge to improve its ability to generalize. Connectionist
theory-refinement systems, which use background knowledge to select a neural
network's topology and initial weights, have proven to be effective at
exploiting domain-specific knowledge; however, most do not exploit available
computing power. This weakness occurs because they lack the ability to refine
the topology of the neural networks they produce, thereby limiting
generalization, especially when given impoverished domain theories. We present
the REGENT algorithm which uses (a) domain-specific knowledge to help create an
initial population of knowledge-based neural networks and (b) genetic operators
of crossover and mutation (specifically designed for knowledge-based networks)
to continually search for better network topologies. Experiments on three
real-world domains indicate that our new algorithm is able to significantly
increase generalization compared to a standard connectionist theory-refinement
system, as well as our previous algorithm for growing knowledge-based networks.
","Connectionist Theory Refinement: Genetically Searching the Space of
  Network Topologies"
"  Several recent studies have compared the relative efficiency of alternative
flaw selection strategies for partial-order causal link (POCL) planning. We
review this literature, and present new experimental results that generalize
the earlier work and explain some of the discrepancies in it. In particular, we
describe the Least-Cost Flaw Repair (LCFR) strategy developed and analyzed by
Joslin and Pollack (1994), and compare it with other strategies, including
Gerevini and Schubert's (1996) ZLIFO strategy. LCFR and ZLIFO make very
different, and apparently conflicting claims about the most effective way to
reduce search-space size in POCL planning. We resolve this conflict, arguing
that much of the benefit that Gerevini and Schubert ascribe to the LIFO
component of their ZLIFO strategy is better attributed to other causes. We show
that for many problems, a strategy that combines least-cost flaw selection with
the delay of separable threats will be effective in reducing search-space size,
and will do so without excessive computational overhead. Although such a
strategy thus provides a good default, we also show that certain domain
characteristics may reduce its effectiveness.
",Flaw Selection Strategies for Partial-Order Planning
"  We investigate the computational properties of the spatial algebra RCC-5
which is a restricted version of the RCC framework for spatial reasoning. The
satisfiability problem for RCC-5 is known to be NP-complete but not much is
known about its approximately four billion subclasses. We provide a complete
classification of satisfiability for all these subclasses into polynomial and
NP-complete respectively. In the process, we identify all maximal tractable
subalgebras which are four in total.
",A Complete Classification of Tractability in RCC-5
"  The easy-hard-easy pattern in the difficulty of combinatorial search problems
as constraints are added has been explained as due to a competition between the
decrease in number of solutions and increased pruning. We test the generality
of this explanation by examining one of its predictions: if the number of
solutions is held fixed by the choice of problems, then increased pruning
should lead to a monotonic decrease in search cost. Instead, we find the
easy-hard-easy pattern in median search cost even when the number of solutions
is held constant, for some search methods. This generalizes previous
observations of this pattern and shows that the existing theory does not
explain the full range of the peak in search cost. In these cases the pattern
appears to be due to changes in the size of the minimal unsolvable subproblems,
rather than changing numbers of solutions.
","A New Look at the Easy-Hard-Easy Pattern of Combinatorial Search
  Difficulty"
"  This paper combines two important directions of research in temporal
resoning: that of finding maximal tractable subclasses of Allen's interval
algebra, and that of reasoning with metric temporal information. Eight new
maximal tractable subclasses of Allen's interval algebra are presented, some of
them subsuming previously reported tractable algebras. The algebras allow for
metric temporal constraints on interval starting or ending points, using the
recent framework of Horn DLRs. Two of the algebras can express the notion of
sequentiality between intervals, being the first such algebras admitting both
qualitative and metric time.
",Eight Maximal Tractable Subclasses of Allen's Algebra with Metric Time
"  Starting with a likelihood or preference order on worlds, we extend it to a
likelihood ordering on sets of worlds in a natural way, and examine the
resulting logic. Lewis earlier considered such a notion of relative likelihood
in the context of studying counterfactuals, but he assumed a total preference
order on worlds. Complications arise when examining partial orders that are not
present for total orders. There are subtleties involving the exact approach to
lifting the order on worlds to an order on sets of worlds. In addition, the
axiomatization of the logic of relative likelihood in the case of partial
orders gives insight into the connection between relative likelihood and
default reasoning.
","Defining Relative Likelihood in Partially-Ordered Preferential
  Structures"
"  Many AI researchers are today striving to build agent teams for complex,
dynamic multi-agent domains, with intended applications in arenas such as
education, training, entertainment, information integration, and collective
robotics. Unfortunately, uncertainties in these complex, dynamic domains
obstruct coherent teamwork. In particular, team members often encounter
differing, incomplete, and possibly inconsistent views of their environment.
Furthermore, team members can unexpectedly fail in fulfilling responsibilities
or discover unexpected opportunities. Highly flexible coordination and
communication is key in addressing such uncertainties. Simply fitting
individual agents with precomputed coordination plans will not do, for their
inflexibility can cause severe failures in teamwork, and their
domain-specificity hinders reusability. Our central hypothesis is that the key
to such flexibility and reusability is providing agents with general models of
teamwork. Agents exploit such models to autonomously reason about coordination
and communication, providing requisite flexibility. Furthermore, the models
enable reuse across domains, both saving implementation effort and enforcing
consistency. This article presents one general, implemented model of teamwork,
called STEAM. The basic building block of teamwork in STEAM is joint intentions
(Cohen & Levesque, 1991b); teamwork in STEAM is based on agents' building up a
(partial) hierarchy of joint intentions (this hierarchy is seen to parallel
Grosz & Kraus's partial SharedPlans, 1996). Furthermore, in STEAM, team members
monitor the team's and individual members' performance, reorganizing the team
as necessary. Finally, decision-theoretic communication selectivity in STEAM
ensures reduction in communication overheads of teamwork, with appropriate
sensitivity to the environmental conditions. This article describes STEAM's
application in three different complex domains, and presents detailed empirical
results.
",Towards Flexible Teamwork
"  SEQUITUR is an algorithm that infers a hierarchical structure from a sequence
of discrete symbols by replacing repeated phrases with a grammatical rule that
generates the phrase, and continuing this process recursively. The result is a
hierarchical representation of the original sequence, which offers insights
into its lexical structure. The algorithm is driven by two constraints that
reduce the size of the grammar, and produce structure as a by-product. SEQUITUR
breaks new ground by operating incrementally. Moreover, the method's simple
structure permits a proof that it operates in space and time that is linear in
the size of the input. Our implementation can process 50,000 symbols per second
and has been applied to an extensive range of real world sequences.
",Identifying Hierarchical Structure in Sequences: A linear-time algorithm
"  Case-Based Planning (CBP) provides a way of scaling up domain-independent
planning to solve large problems in complex domains. It replaces the detailed
and lengthy search for a solution with the retrieval and adaptation of previous
planning experiences. In general, CBP has been demonstrated to improve
performance over generative (from-scratch) planning. However, the performance
improvements it provides are dependent on adequate judgements as to problem
similarity. In particular, although CBP may substantially reduce planning
effort overall, it is subject to a mis-retrieval problem. The success of CBP
depends on these retrieval errors being relatively rare. This paper describes
the design and implementation of a replay framework for the case-based planner
DERSNLP+EBL. DERSNLP+EBL extends current CBP methodology by incorporating
explanation-based learning techniques that allow it to explain and learn from
the retrieval failures it encounters. These techniques are used to refine
judgements about case similarity in response to feedback when a wrong decision
has been made. The same failure analysis is used in building the case library,
through the addition of repairing cases. Large problems are split and stored as
single goal subproblems. Multi-goal problems are stored only when these smaller
cases fail to be merged into a full solution. An empirical evaluation of this
approach demonstrates the advantage of learning from experienced retrieval
failure.
","Storing and Indexing Plan Derivations through Explanation-based Analysis
  of Retrieval Failures"
"  Partially observable Markov decision processes (POMDPs) are a natural model
for planning problems where effects of actions are nondeterministic and the
state of the world is not completely observable. It is difficult to solve
POMDPs exactly. This paper proposes a new approximation scheme. The basic idea
is to transform a POMDP into another one where additional information is
provided by an oracle. The oracle informs the planning agent that the current
state of the world is in a certain region. The transformed POMDP is
consequently said to be region observable. It is easier to solve than the
original POMDP. We propose to solve the transformed POMDP and use its optimal
policy to construct an approximate policy for the original POMDP. By
controlling the amount of additional information that the oracle provides, it
is possible to find a proper tradeoff between computational time and
approximation quality. In terms of algorithmic contributions, we study in
details how to exploit region observability in solving the transformed POMDP.
To facilitate the study, we also propose a new exact algorithm for general
POMDPs. The algorithm is conceptually simple and yet is significantly more
efficient than all previous exact algorithms.
","A Model Approximation Scheme for Planning in Partially Observable
  Stochastic Domains"
"  The model of a non-Bayesian agent who faces a repeated game with incomplete
information against Nature is an appropriate tool for modeling general
agent-environment interactions. In such a model the environment state
(controlled by Nature) may change arbitrarily, and the feedback/reward function
is initially unknown. The agent is not Bayesian, that is he does not form a
prior probability neither on the state selection strategy of Nature, nor on his
reward function. A policy for the agent is a function which assigns an action
to every history of observations and actions. Two basic feedback structures are
considered. In one of them -- the perfect monitoring case -- the agent is able
to observe the previous environment state as part of his feedback, while in the
other -- the imperfect monitoring case -- all that is available to the agent is
the reward obtained. Both of these settings refer to partially observable
processes, where the current environment state is unknown. Our main result
refers to the competitive ratio criterion in the perfect monitoring case. We
prove the existence of an efficient stochastic policy that ensures that the
competitive ratio is obtained at almost all stages with an arbitrarily high
probability, where efficiency is measured in terms of rate of convergence. It
is further shown that such an optimal policy does not exist in the imperfect
monitoring case. Moreover, it is proved that in the perfect monitoring case
there does not exist a deterministic policy that satisfies our long run
optimality criterion. In addition, we discuss the maxmin criterion and prove
that a deterministic efficient optimal strategy does exist in the imperfect
monitoring case under this criterion. Finally we show that our approach to
long-run optimality can be viewed as qualitative, which distinguishes it from
previous work in this area.
",Dynamic Non-Bayesian Decision Making
"  Local search algorithms for combinatorial search problems frequently
encounter a sequence of states in which it is impossible to improve the value
of the objective function; moves through these regions, called plateau moves,
dominate the time spent in local search. We analyze and characterize plateaus
for three different classes of randomly generated Boolean Satisfiability
problems. We identify several interesting features of plateaus that impact the
performance of local search algorithms. We show that local minima tend to be
small but occasionally may be very large. We also show that local minima can be
escaped without unsatisfying a large number of clauses, but that systematically
searching for an escape route may be computationally expensive if the local
minimum is large. We show that plateaus with exits, called benches, tend to be
much larger than minima, and that some benches have very few exit states which
local search can use to escape. We show that the solutions (i.e., global
minima) of randomly generated problem instances form clusters, which behave
similarly to local minima. We revisit several enhancements of local search
algorithms and explain their performance in light of our results. Finally we
discuss strategies for creating the next generation of local search algorithms.
",When Gravity Fails: Local Search Topology
"  The assessment of bidirectional heuristic search has been incorrect since it
was first published more than a quarter of a century ago. For quite a long
time, this search strategy did not achieve the expected results, and there was
a major misunderstanding about the reasons behind it. Although there is still
wide-spread belief that bidirectional heuristic search is afflicted by the
problem of search frontiers passing each other, we demonstrate that this
conjecture is wrong. Based on this finding, we present both a new generic
approach to bidirectional heuristic search and a new approach to dynamically
improving heuristic values that is feasible in bidirectional search only. These
approaches are put into perspective with both the traditional and more recently
proposed approaches in order to facilitate a better overall understanding.
Empirical results of experiments with our new approaches show that
bidirectional heuristic search can be performed very efficiently and also with
limited memory. These results suggest that bidirectional heuristic search
appears to be better for solving certain difficult problems than corresponding
unidirectional search. This provides some evidence for the usefulness of a
search strategy that was long neglected. In summary, we show that bidirectional
heuristic search is viable and consequently propose that it be reconsidered.
",Bidirectional Heuristic Search Reconsidered
"  Approximating a general formula from above and below by Horn formulas (its
Horn envelope and Horn core, respectively) was proposed by Selman and Kautz
(1991, 1996) as a form of ``knowledge compilation,'' supporting rapid
approximate reasoning; on the negative side, this scheme is static in that it
supports no updates, and has certain complexity drawbacks pointed out by
Kavvadias, Papadimitriou and Sideri (1993). On the other hand, the many
frameworks and schemes proposed in the literature for theory update and
revision are plagued by serious complexity-theoretic impediments, even in the
Horn case, as was pointed out by Eiter and Gottlob (1992), and is further
demonstrated in the present paper. More fundamentally, these schemes are not
inductive, in that they may lose in a single update any positive properties of
the represented sets of formulas (small size, Horn structure, etc.). In this
paper we propose a new scheme, incremental recompilation, which combines Horn
approximation and model-based updates; this scheme is inductive and very
efficient, free of the problems facing its constituents. A set of formulas is
represented by an upper and lower Horn approximation. To update, we replace the
upper Horn formula by the Horn envelope of its minimum-change update, and
similarly the lower one by the Horn core of its update; the key fact which
enables this scheme is that Horn envelopes and cores are easy to compute when
the underlying formula is the result of a minimum-change update of a Horn
formula by a clause. We conjecture that efficient algorithms are possible for
more complex updates.
",Incremental Recompilation of Knowledge
"  An important characteristic of many logics for Artificial Intelligence is
their nonmonotonicity. This means that adding a formula to the premises can
invalidate some of the consequences. There may, however, exist formulae that
can always be safely added to the premises without destroying any of the
consequences: we say they respect monotonicity. Also, there may be formulae
that, when they are a consequence, can not be invalidated when adding any
formula to the premises: we call them conservative. We study these two classes
of formulae for preferential logics, and show that they are closely linked to
the formulae whose truth-value is preserved along the (preferential) ordering.
We will consider some preferential logics for illustration, and prove syntactic
characterization results for them. The results in this paper may improve the
efficiency of theorem provers for preferential logics.
",Monotonicity and Persistence in Preferential Logics
"  Existing plan synthesis approaches in artificial intelligence fall into two
categories -- domain independent and domain dependent. The domain independent
approaches are applicable across a variety of domains, but may not be very
efficient in any one given domain. The domain dependent approaches need to be
(re)designed for each domain separately, but can be very efficient in the
domain for which they are designed. One enticing alternative to these
approaches is to automatically synthesize domain independent planners given the
knowledge about the domain and the theory of planning. In this paper, we
investigate the feasibility of using existing automated software synthesis
tools to support such synthesis. Specifically, we describe an architecture
called CLAY in which the Kestrel Interactive Development System (KIDS) is used
to derive a domain-customized planner through a semi-automatic combination of a
declarative theory of planning, and the declarative control knowledge specific
to a given domain, to semi-automatically combine them to derive
domain-customized planners. We discuss what it means to write a declarative
theory of planning and control knowledge for KIDS, and illustrate our approach
by generating a class of domain-specific planners using state space
refinements. Our experiments show that the synthesized planners can outperform
classical refinement planners (implemented as instantiations of UCP,
Kambhampati & Srivastava, 1995), using the same control knowledge. We will
contrast the costs and benefits of the synthesis approach with conventional
methods for customizing domain independent planners.
",Synthesizing Customized Planners from Specifications
"  This paper introduces new algorithms and data structures for quick counting
for machine learning datasets. We focus on the counting task of constructing
contingency tables, but our approach is also applicable to counting the number
of records in a dataset that match conjunctive queries. Subject to certain
assumptions, the costs of these operations can be shown to be independent of
the number of records in the dataset and loglinear in the number of non-zero
entries in the contingency table. We provide a very sparse data structure, the
ADtree, to minimize memory use. We provide analytical worst-case bounds for
this structure for several models of data distribution. We empirically
demonstrate that tractably-sized data structures can be produced for large
real-world datasets by (a) using a sparse tree structure that never allocates
memory for counts of zero, (b) never allocating memory for counts that can be
deduced from other counts, and (c) not bothering to expand the tree fully near
its leaves. We show how the ADtree can be used to accelerate Bayes net
structure finding algorithms, rule learning algorithms, and feature selection
algorithms, and we provide a number of empirical results comparing ADtree
methods against traditional direct counting approaches. We also discuss the
possible uses of ADtrees in other machine learning methods, and discuss the
merits of ADtrees in comparison with alternative representations such as
kd-trees, R-trees and Frequent Sets.
","Cached Sufficient Statistics for Efficient Machine Learning with Large
  Datasets"
"  In this paper we consider the problem of `theory patching', in which we are
given a domain theory, some of whose components are indicated to be possibly
flawed, and a set of labeled training examples for the domain concept. The
theory patching problem is to revise only the indicated components of the
theory, such that the resulting theory correctly classifies all the training
examples. Theory patching is thus a type of theory revision in which revisions
are made to individual components of the theory. Our concern in this paper is
to determine for which classes of logical domain theories the theory patching
problem is tractable. We consider both propositional and first-order domain
theories, and show that the theory patching problem is equivalent to that of
determining what information contained in a theory is `stable' regardless of
what revisions might be performed to the theory. We show that determining
stability is tractable if the input theory satisfies two conditions: that
revisions to each theory component have monotonic effects on the classification
of examples, and that theory components act independently in the classification
of examples in the theory. We also show how the concepts introduced can be used
to determine the soundness and completeness of particular theory patching
algorithms.
",Tractability of Theory Patching
"  In this paper we re-investigate windowing for rule learning algorithms. We
show that, contrary to previous results for decision tree learning, windowing
can in fact achieve significant run-time gains in noise-free domains and
explain the different behavior of rule learning algorithms by the fact that
they learn each rule independently. The main contribution of this paper is
integrative windowing, a new type of algorithm that further exploits this
property by integrating good rules into the final theory right after they have
been discovered. Thus it avoids re-learning these rules in subsequent
iterations of the windowing process. Experimental evidence in a variety of
noise-free domains shows that integrative windowing can in fact achieve
substantial run-time gains. Furthermore, we discuss the problem of noise in
windowing and present an algorithm that is able to achieve run-time gains in a
set of experiments in a simple domain with artificial noise.
",Integrative Windowing
"  This paper presents a comprehensive approach for model-based diagnosis which
includes proposals for characterizing and computing preferred diagnoses,
assuming that the system description is augmented with a system structure (a
directed graph explicating the interconnections between system components).
Specifically, we first introduce the notion of a consequence, which is a
syntactically unconstrained propositional sentence that characterizes all
consistency-based diagnoses and show that standard characterizations of
diagnoses, such as minimal conflicts, correspond to syntactic variations on a
consequence. Second, we propose a new syntactic variation on the consequence
known as negation normal form (NNF) and discuss its merits compared to standard
variations. Third, we introduce a basic algorithm for computing consequences in
NNF given a structured system description. We show that if the system structure
does not contain cycles, then there is always a linear-size consequence in NNF
which can be computed in linear time. For arbitrary system structures, we show
a precise connection between the complexity of computing consequences and the
topology of the underlying system structure. Finally, we present an algorithm
that enumerates the preferred diagnoses characterized by a consequence. The
algorithm is shown to take linear time in the size of the consequence if the
preference criterion satisfies some general conditions.
",Model-Based Diagnosis using Structured System Descriptions
"  One of the most common mechanisms used for speeding up problem solvers is
macro-learning. Macros are sequences of basic operators acquired during problem
solving. Macros are used by the problem solver as if they were basic operators.
The major problem that macro-learning presents is the vast number of macros
that are available for acquisition. Macros increase the branching factor of the
search space and can severely degrade problem-solving efficiency. To make macro
learning useful, a program must be selective in acquiring and utilizing macros.
This paper describes a general method for selective acquisition of macros.
Solvable training problems are generated in increasing order of difficulty. The
only macros acquired are those that take the problem solver out of a local
minimum to a better state. The utility of the method is demonstrated in several
domains, including the domain of NxN sliding-tile puzzles. After learning on
small puzzles, the system is able to efficiently solve puzzles of any size.
","A Selective Macro-learning Algorithm and its Application to the NxN
  Sliding-Tile Puzzle"
"  We examine the computational complexity of testing and finding small plans in
probabilistic planning domains with both flat and propositional
representations. The complexity of plan evaluation and existence varies with
the plan type sought; we examine totally ordered plans, acyclic plans, and
looping plans, and partially ordered plans under three natural definitions of
plan value. We show that problems of interest are complete for a variety of
complexity classes: PL, P, NP, co-NP, PP, NP^PP, co-NP^PP, and PSPACE. In the
process of proving that certain planning problems are complete for NP^PP, we
introduce a new basic NP^PP-complete problem, E-MAJSAT, which generalizes the
standard Boolean satisfiability problem to computations involving probabilistic
quantities; our results suggest that the development of good heuristics for
E-MAJSAT could be important for the creation of efficient algorithms for a wide
variety of problems.
",The Computational Complexity of Probabilistic Planning
"  In this paper we describe SYNERGY, which is a highly parallelizable, linear
planning system that is based on the genetic programming paradigm. Rather than
reasoning about the world it is planning for, SYNERGY uses artificial
selection, recombination and fitness measure to generate linear plans that
solve conjunctive goals. We ran SYNERGY on several domains (e.g., the briefcase
problem and a few variants of the robot navigation problem), and the
experimental results show that our planner is capable of handling problem
instances that are one to two orders of magnitude larger than the ones solved
by UCPOP. In order to facilitate the search reduction and to enhance the
expressive power of SYNERGY, we also propose two major extensions to our
planning system: a formalism for using hierarchical planning operators, and a
framework for planning in dynamic environments.
",SYNERGY: A Linear Planner Based on Genetic Programming
"  We show that several constraint propagation algorithms (also called (local)
consistency, consistency enforcing, Waltz, filtering or narrowing algorithms)
are instances of algorithms that deal with chaotic iteration. To this end we
propose a simple abstract framework that allows us to classify and compare
these algorithms and to establish in a uniform way their basic properties.
",The Essence of Constraint Propagation
"  This paper examines the phenomenon of daydreaming: spontaneously recalling or
imagining personal or vicarious experiences in the past or future. The
following important roles of daydreaming in human cognition are postulated:
plan preparation and rehearsal, learning from failures and successes, support
for processes of creativity, emotion regulation, and motivation.
  A computational theory of daydreaming and its implementation as the program
DAYDREAMER are presented. DAYDREAMER consists of 1) a scenario generator based
on relaxed planning, 2) a dynamic episodic memory of experiences used by the
scenario generator, 3) a collection of personal goals and control goals which
guide the scenario generator, 4) an emotion component in which daydreams
initiate, and are initiated by, emotional states arising from goal outcomes,
and 5) domain knowledge of interpersonal relations and common everyday
occurrences.
  The role of emotions and control goals in daydreaming is discussed. Four
control goals commonly used in guiding daydreaming are presented:
rationalization, failure/success reversal, revenge, and preparation. The role
of episodic memory in daydreaming is considered, including how daydreamed
information is incorporated into memory and later used. An initial version of
DAYDREAMER which produces several daydreams (in English) is currently running.
",Towards a computational theory of human daydreaming
"  Real world combinatorial optimization problems such as scheduling are
typically too complex to solve with exact methods. Additionally, the problems
often have to observe vaguely specified constraints of different importance,
the available data may be uncertain, and compromises between antagonistic
criteria may be necessary. We present a combination of approximate reasoning
based constraints and iterative optimization based heuristics that help to
model and solve such problems in a framework of C++ software libraries called
StarFLIP++. While initially developed to schedule continuous caster units in
steel plants, we present in this paper results from reusing the library
components in a shift scheduling system for the workforce of an industrial
production plant.
","A reusable iterative optimization software library to solve
  combinatorial problems with approximate reasoning"
"  The study of belief change has been an active area in philosophy and AI. In
recent years two special cases of belief change, belief revision and belief
update, have been studied in detail. In a companion paper (Friedman & Halpern,
1997), we introduce a new framework to model belief change. This framework
combines temporal and epistemic modalities with a notion of plausibility,
allowing us to examine the change of beliefs over time. In this paper, we show
how belief revision and belief update can be captured in our framework. This
allows us to compare the assumptions made by each method, and to better
understand the principles underlying them. In particular, it shows that Katsuno
and Mendelzon's notion of belief update (Katsuno & Mendelzon, 1991a) depends on
several strong assumptions that may limit its applicability in artificial
intelligence. Finally, our analysis allow us to identify a notion of minimal
change that underlies a broad range of belief change operations including
revision and update.
","Modeling Belief in Dynamic Systems, Part II: Revision and Update"
"  How can the semantic interpretation of a formal symbol system be made
intrinsic to the system, rather than just parasitic on the meanings in our
heads? How can the meanings of the meaningless symbol tokens, manipulated
solely on the basis of their (arbitrary) shapes, be grounded in anything but
other meaningless symbols? The problem is analogous to trying to learn Chinese
from a Chinese/Chinese dictionary alone. A candidate solution is sketched:
Symbolic representations must be grounded bottom-up in nonsymbolic
representations of two kinds: (1) ""iconic representations,"" which are analogs
of the proximal sensory projections of distal objects and events, and (2)
""categorical representations,"" which are learned and innate feature-detectors
that pick out the invariant features of object and event categories from their
sensory projections. Elementary symbols are the names of these object and event
categories, assigned on the basis of their (nonsymbolic) categorical
representations. Higher-order (3) ""symbolic representations,"" grounded in these
elementary symbols, consist of symbol strings describing category membership
relations (e.g., ""An X is a Y that is Z"").
",The Symbol Grounding Problem
"  In tree search problem the best-first search algorithm needs too much of
space . To remove such drawbacks of these algorithms the IDA* was developed
which is both space and time cost efficient. But again IDA* can give an optimal
solution for real valued problems like Flow shop scheduling, Travelling
Salesman and 0/1 Knapsack due to their real valued cost estimates. Thus further
modifications are done on it and the Iterative Deepening Branch and Bound
Search Algorithms is developed which meets the requirements. We have tried
using this algorithm for the Flow Shop Scheduling Problem and have found that
it is quite effective.
",Iterative Deepening Branch and Bound
"  Agents are small programs that autonomously take actions based on changes in
their environment or ``state.'' Over the last few years, there have been an
increasing number of efforts to build agents that can interact and/or
collaborate with other agents. In one of these efforts, Eiter, Subrahmanian amd
Pick (AIJ, 108(1-2), pages 179-255) have shown how agents may be built on top
of legacy code. However, their framework assumes that agent states are
completely determined, and there is no uncertainty in an agent's state. Thus,
their framework allows an agent developer to specify how his agents will react
when the agent is 100% sure about what is true/false in the world state. In
this paper, we propose the concept of a \emph{probabilistic agent program} and
show how, given an arbitrary program written in any imperative language, we may
build a declarative ``probabilistic'' agent program on top of it which supports
decision making in the presence of uncertainty. We provide two alternative
semantics for probabilistic agent programs. We show that the second semantics,
though more epistemically appealing, is more complex to compute. We provide
sound and complete algorithms to compute the semantics of \emph{positive} agent
programs.
",Probabilistic Agent Programs
"  The assumptions needed to prove Cox's Theorem are discussed and examined.
Various sets of assumptions under which a Cox-style theorem can be proved are
provided, although all are rather strong and, arguably, not natural.
",Cox's Theorem Revisited